WO2023130140A2

WO2023130140A2 - Personalized methods of treating cancer

Info

Publication number: WO2023130140A2
Application number: PCT/US2023/060025
Authority: WO
Inventors: Oliver D.K. Maddocks; Petros A. TYRAKIS; Hans Jonathan Gabriel LINDSTRÖM
Original assignee: Faeth Therapeutics Inc
Current assignee: Faeth Therapeutics Inc
Priority date: 2022-01-03
Filing date: 2023-01-03
Publication date: 2023-07-06
Anticipated expiration: 2024-07-03
Also published as: WO2023130140A3

Abstract

Disclosed herein are methods of analyzing genomic and transcriptomic data from cancer cells with known nutrient dependency to determine a nutrient dependency of a cancer cell with unknown nutrient dependency. The methods of the disclosure can analyze a cancer to determine a nutrient modulation therapy to treat the cancer. In some embodiments, the methods of the disclosure identify an amino acid modulation therapy to treat a cancer. In some embodiments, the methods of the disclosure identify a cholesterol modulation therapy to treat a cancer. Also disclosed herein are methods to identify therapeutic agents to be used in combination therapy with a nutrient modulation therapy.

Description

PERSONALIZED METHODS OF TREATING CANCER CROSS-REFERENCE

[0001] This application claims the benefit of U.S. Provisional Application No. 63/295,970, filed on January 3, 2022, which is incorporated herein by reference in its entirety.

BACKGROUND

[0002] Biosynthesis of proteins, nucleotides, nucleic acids, fatty acids, and other macromolecules can be essential for the malignant proliferation and survival of cancer cells. The anabolic and catabolic metabolism of cancer cells must be reprogrammed for continued cancer cell survival and proliferation. For example, protein biosynthesis can be crucial to support normal cell function and to allow cell growth and division, which can be particularly important for cancer cells given the increased rates of growth and proliferation. Protein synthesis can also support the ability of cancer cells to deposit extracellular matrix proteins, such as collagen, to shape cellular microenvironments to support tumour initiation and progression. Cancer cells can be targeted by taking advantage of the high requirements for amino acids by using amino acid starvation therapy. Personalized methods and formulations can be developed for therapy of various diseases, including cancer.

INCORPORATION BY REFERENCE

[0003] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

[0004] FIG. 1 illustrates a flow chart for using gene expression data to stratify PDAC cells into proline-dependent and proline-independent groups.

[0005] FIG. 2 shows scaled data prepared by mean-centering the raw data and dividing the mean-centered data by the standard deviation of each variable.

[0006] FIG. 3 shows a heat map of hierarchically clustered genes of pancreatic cancer cell lines based on proline-dependency.

[0007] FIG. 4 shows a refined heat map of hierarchically clustered genes of pancreatic cancer cell lines based on proline-dependency generated by incorporating prognostically relevant genes.

[0008] FIG. 5 illustrates a flow chart showing a method of stratifying cancer cells with unknown proline-dependency status into proline-dependent and proline-independent groups using hierarchical clustering data from proline-independent, proline-dependent, putatively proline-independent, and putatively proline-dependent cells.

[0009] FIG. 6A illustrates a flow chart showing a method of improving a prolinedependency signature of cells by modifying the log2 fold change between prolineindependent and proline-dependent gene expression to -0.5 > log2 > 0.6 and the CV cutoff to 25%. FIG. 6B illustrates a flow chart showing a method of improving a proline-dependency signature of cells by modifying the log2 fold change between proline-independent and proline-dependent gene expression to -0.5 > log2 > 0.6 and the CV cutoff to 10-15%.

[0010] FIG. 7 shows stratification achieved with a set of 10 genes from the list of 436 genes.

[0011] FIG. 8 shows stratification achieved with a set of 15 genes from the list of 436 genes.

[0012] FIG. 9 shows stratification achieved with a set of 30 genes from the list of 436 genes.

[0013] FIG. 10 shows stratification achieved with a set of 40 genes from the list of 436 genes.

[0014] FIG. 11 shows stratification achieved with a set of 50 genes from the list of 436 genes.

[0015] FIG. 12 shows stratification achieved with a set of 75 genes from the list of 436 genes.

[0016] FIG. 13A shows PD-L1 (CD274) expression levels in proline-dependent cancer cell lines compared to proline-independent cancer cell lines. FIG. 13B shows MR1 expression levels in proline-dependent cancer cell lines compared to proline-independent cancer cell lines.

[0017] FIG. 14A-14C show expression levels of TOP1MT, TOP2B, and TOP3A in prolineindependent cells compared to proline-dependent cells.

[0018] FIG. 15A-15D show expression levels of COQ1B, COQ3, COQ8A, and COQ10A in proline-independent cancer cell lines compared to proline-dependent cancer cell lines.

[0019] FIG. 16A-16B show expression levels of SIR1 and SIRT3 in proline-independent cancer cell lines compared to proline-dependent cell lines.

[0020] FIG. 17 shows variation in the proline depletion signature match percentage across different cell types present in samples from pancreatic ductal adenocarcinoma (PDAC) tumors and metastases.

[0021] FIG. 18 shows variation in the proline depletion signature match percentage across cancer cells from different types of PDAC or PDAC metastases. [0022] FIG. 19 shows variation in proline depletion signature match percentage across individual tumors, grouped by type.

[0023] FIG. 20 shows stratification achieved with a set of 15 genes subjected to cholesterol depletion and statin exposure.

[0024] FIG. 21 shows cancer lineage and select subtype distribution across a ranking of 9565 tumor samples from least to most putatively sensitive to cholesterol depletion during statin exposure.

[0025] FIG. 22 illustrates a computer system that can read instructions from media or a network port.

[0026] FIG. 23 illustrates a block diagram illustrating a first example architecture of a computer system that can be used in connection with the methods of the disclosure.

[0027] FIG. 24 illustrates a diagram showing a network with a plurality of computer systems, a plurality of cell phones and personal data assistances, and network attached storage (NAS).

[0028] FIG. 25 illustrates a block diagram of a multiprocessor computer system using a shared virtual address memory space in accordance with an example embodiment.

SUMMARY

[0029] In some embodiments, disclosed herein is a method of treating a subject in need thereof, comprising: (a) providing a gene expression profile of a biological sample from the subject, wherein the gene expression profile comprises an expression level of a set of genes; (b) identifying a dependence of the biological sample on a nutrient based at least on the gene expression profile; and (c) administering to the subject a nutrient modulation therapy formulated to modulate a level of the nutrient in the subject if the subject is predicted to respond to the nutrient modulation therapy.

[0030] In some embodiments, disclosed herein is a method of treating a subject in need thereof, comprising: (a) subjecting a plurality of reference cells to a plurality of drug-nutrient environments to determine a set of drug-nutrient vulnerabilities of the plurality of reference cells; (b) performing an omics method on the plurality of reference cells to generate omics data; (c) determining a set of omics signatures that correlate with the set of drug-nutrient vulnerabilities; (d) performing the omics method on a plurality of target cells of the subject to generate a set of target-specific omics signatures, wherein the plurality of target cells comprises a healthy cell and a disease cell; (e) determining a target-specific drug-nutrient vulnerability based at least on the set of omics signatures and the set of target-specific omics signatures, wherein the target-specific drug-nutrient vulnerability affects the disease cell more than the healthy cell; and (f) generating a dietary treatment configured to activate the target-specific drug-nutrient vulnerability in the subject.

DETAILED DESCRIPTION

[0031] Biosynthesis of proteins, nucleotides, nucleic acids, fatty acids, and other macromolecules are essential for the malignant proliferation and survival of cancer cells. The anabolic and catabolic metabolism of cancer cells must be reprogrammed for continued cancer cell survival and proliferation.

[0032] Protein biosynthesis is crucial to support normal cell function and to allow cell growth and division, which is particularly important for cancer cells given the increased rates of growth and proliferation of the cancer cells. Protein synthesis also supports the ability of cancer cells to deposit extracellular matrix proteins, such as collagen, to shape the cell microenvironment to support tumour initiation and progression. Protein synthesis requires an adequate supply of the 20 proteinogenic amino acids. Cancer cells can be targeted by taking advantage of the high requirements for amino acids by using amino acid starvation therapy. [0033] Cholesterol is an energy-rich, waxy hydrophobic compound synthesized by animals through the mevalonate pathway. Cholesterol functions as a source of energy, a precursor of steroid hormones and vitamin D, a structural component of cells, and is involved in multiple signaling pathways. Because the human body is able to synthesize cholesterol, cholesterol is classified as a non-essential nutrient. Cholesterol synthesis as well as cholesterol uptake can be upregulated in many cancer cells. Fast dividing cancer cells may depend on cholesterol as a source of high energy to sustain rapid proliferation. Cancer cells can be targeted by taking advantage of the cholesterol requirement by using cholesterol starvation therapy.

[0034] Lipid metabolism is frequently dysregulated in cancer. Cancer cells utilize lipids as substrates for growth, as components for biological membranes, as energy sources, and as signaling molecules involved in proliferation, survival, invasion, metastasis, and responding to the tumor microenvironment. Cancer cells can be targeted by taking advantage of the lipid dependence by using lipid starvation therapy.

[0035] Vitamins and minerals are critical for physiological functions and processes involved in cancer growth and development. Alteration of vitamin and mineral levels can target cancer cells. Carbohydrates are an important source of energy. Cancer cells have a high energy demand due to their rapid proliferation, among other processes. [0036] Disclosed herein are methods of identifying the nutrient-dependency of a cancer to select a nutrient starvation or supplementation therapy to treat the cancer. In some embodiments, the methods of the disclosure comprise identifying cells or tumors responsive to a nutrient- starvation therapy. In some embodiments, the methods disclosed herein comprise identifying cells or tumors that are dependent on exogenous supplementation of a nutrient. In some embodiments, the methods disclosed herein comprise identifying cells or tumors that are independent on exogenous supplementation of a nutrient. In some embodiments, the methods disclosed herein comprise identifying cells or tumors that are dependent on an exogenous supply of an amino acid. In some embodiments, the methods disclosed herein comprise identifying cells or tumors that are independent of an exogenous supply of an amino acid.

[0037] The methods disclosed herein comprise generating a genetic signature of a cell with a known nutrient dependency. In some embodiments, the methods comprise generating a genetic signature of a cancer cell with a known nutrient dependency. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is nutrientdependent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is amino acid-dependent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is dependent on one or more types of amino acids.

[0038] In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is sterol-dependent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is dependent on one or more types of sterols. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is lipid-dependent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is dependent on one or more types of lipids.

[0039] In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is carbohydrate-dependent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is dependent on one or more types of carbohydrates.

[0040] In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is vitamin-dependent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is dependent on one or more types of vitamins. [0041] In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is mineral-dependent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is dependent on one or more types of minerals.

[0042] In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is protein-dependent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is dependent on one or more types of proteins.

[0043] The methods disclosed herein comprise generating a genetic signature of a cell with a known nutrient independency. In some embodiments, the methods comprise generating a genetic signature of a cancer cell with a known nutrient independency. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is nutrientindependent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is amino acid-independent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is not dependent on one or more types of amino acids.

[0044] In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is sterol-independent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is not dependent on one or more types of sterols.

[0045] In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is lipid-independent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is not dependent on one or more types of lipids. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is carbohydrate-independent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is not dependent on one or more types of carbohydrates. [0046] In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is vitamin-independent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is not dependent on a one or more types of vitamins.

[0047] In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is mineral-independent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is not dependent on one or more types of minerals. [0048] In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is protein-independent. In some embodiments, the methods comprise generating a genetic signature of a cancer cell that is not dependent on one or more types of proteins.

[0049] The methods of the disclosure describe the use of a genetic signature of a cell with a known nutrient-dependency to determine the nutrient dependency of a cell without a known dependency on the nutrient. In some embodiments, the methods comprise identifying a cell without a known nutrient-dependency that the cell is dependent on the nutrient. In some embodiments, the methods comprise identifying a cell without a known nutrient-dependency that the cell is not dependent on the nutrient. In some embodiments, the methods comprise identifying a cell without a known amino acid-dependency that the cell is dependent on the amino acid. In some embodiments, the methods comprise identifying a cell without a known amino acid-dependency that the cell is not dependent on the amino acid.

[0050] In some embodiments, the methods comprise identifying a cell without a known lipiddependency that the cell is dependent on the lipid. In some embodiments, the methods comprise identifying a cell without a known lipid-dependency that the cell is not dependent on the lipid. In some embodiments, the methods comprise identifying a cell without a known sterol-dependency that the cell is dependent on the sterol. In some embodiments, the methods comprise identifying a cell without a known sterol-dependency that the cell is not dependent on the sterol. In some embodiments, the methods comprise identifying a cell without a known carbohydrate-dependency that the cell is dependent on the carbohydrate. In some embodiments, the methods comprise identifying a cell without a known carbohydrate- dependency that the cell is not dependent on the carbohydrate. In some embodiments, the methods comprise identifying a cell without a known vitamin-dependency that the cell is dependent on the vitamin. In some embodiments, the method comprise identifying a cell without a known vitamin-dependency that the cell is not dependent on the vitamin. In some embodiments, the methods comprise identifying a cell without a known mineral-dependency that the cell is dependent on the mineral. In some embodiments, the methods comprise identifying a cell without a known mineral-dependency that the cell is not dependent on the mineral. In some emboidments, the methods comprise identifying a cell without a known protein-dependency that the cell is dependent on the protein. In some embodiments, the methods comprise identifying a cell without a known protein-dependency that the cell is not dependent on the protein. [0051] Further disclosed herein are methods of identifying combination therapy agents that can be used additively or synergistically with a nutrient starvation therapy. In some embodiments, the methods comprise identifying genes that are modulated by reduction or elimination of a nutrient. In some embodiments, the genes that are modulated by nutrient starvation comprise identifying a therapeutic agent to be used in combination with the nutrient starvation therapy.

Preliminary Analysis of Cells with Known Nutrient Deficiencies

[0052] The methods disclosed herein comprise stratifying cells or tumors into nutrientdependent or nutrient-independent groups. In some embodiments, stratification of cells can identify the cell as being nutrient-dependent. In some embodiments, stratification of cells can identify the cell as being nutrient-independent. In some embodiments, stratification of cells can rank cells from nutrient-dependent to nutrient-independent. In some embodiments, stratification of cells can identify the cell as being amino acid-dependent. In some embodiments, stratification of cells can identify the cell as being nutrient dependent on one or more amino acid. In some embodiments, stratification of cells can identify the cell as being amino acid-independent. In some embodiments, stratification of cells can identify the cell as being nutrient independent of one or more amino acids. In some embodiments, stratification of cells can rank cells from amino acid-dependent to amino acid-independent. In some embodiments, stratification of cells can identify the cell as being lipid-dependent. In some embodiments, stratification of cells can identify the cell as being nutrient dependent on one or more lipid. In some embodiments, stratification of cells can identify the cell as being lipid- independent. In some embodiments, stratification of cells can identify the cell as being nutrient independent of one or more lipid. In some embodiments, stratification of cells can rank cells from lipid-dependent to lipid-independent. In some embodiments, stratification of cells can identify the cell as being sterol-dependent. In some embodiments, stratification of cells can identify the cell as being nutrient dependent on one or more sterol. In some embodiments, stratification of cells can identify the cell as being sterol-independent. In some embodiments, stratification of cells can identify the cell as being nutrient independent of one or more sterol. In some embodiments, stratification of cells can rank cells from steroldependent to sterol-independent. In some embodiments, stratification of cells can identify the cell as being cholesterol-dependent. In some embodiments, stratification of cells can identify the cells as being cholesterol-independent. In some embodiments, stratification of cells can rank cells from cholesterol-dependent to cholesterol-independent. In some embodiments, stratification of cells can identify the cell as being carbohydrate-dependent. In some embodiments, stratification of cells can identify the cell as being nutrient dependent on one or more carbohydrate. In some embodiments, stratification of cells can identify the cell as being carbohydrate-independent. In some embodiments, stratification of cells can identify the cell as being nutrient independent of one or more carbohydrate. In some embodiments, stratification of cells can rank cells from carbohydrate-dependent to carbohydrate- independent. In some embodiments, stratification of cells can identify the cell as being vitamin-dependent. In some embodiments, stratification of cells can identify the cell as being nutrient dependent on one or more vitamin. In some embodiments, stratification of cells can identify the cell as being vitamin-independent. In some embodiments, stratification of cells can identify the cell as being nutrient independent of one or more vitamin. In some embodiments, stratification of cells can rank cells from vitamin-dependent to vitamin- independent. In some embodiments, stratification of cells can identify the cell as being mineral-dependent. In some embodiments, stratification of cells can identify the cell as being nutrient dependent on one or more mineral. In some embodiments, stratification of cells can identify the cell as being mineral-independent. In some embodiments, stratification of cells can identify the cell as being nutrient independent of one or more mineral. In some embodiments, stratification of cells can rank cells from mineral-dependent to mineralindependent. In some embodiments, stratification of cells can identify the cell as being protein-dependent. In some embodiments, stratification of cells can identify the cell as being nutrient dependent on one or more protein. In some embodiments, stratification of cells can identify the cell as being protein-independent. In some embodiments, stratification of cells can identify the cell as being nutrient independent of one or more protein. In some embodiments, stratification of cells can rank cells from protein-dependent to proteinindependent.

[0053] The methods described herein can use an omics method to develop a panel of markers to determine a signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use metabolomic data to develop a metabolomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use proteomic data to develop a proteomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use genomic data to develop a genomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use DNA sequencing data to develop a genomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use Sanger Sequencing to develop a genomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use next-generation sequencing to develop a genomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use whole-genome sequencing to develop a genomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use whole-exome sequencing to develop a genomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use PacBio SMRT sequencing to develop a genomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use Oxford nanopore sequencing to develop a genomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use glycomic data to develop a glycomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use gene mutation data. In some embodiments, gene mutation data can be obtained using whole genome sequencing. In some embodiments, gene mutation data can be used to determine a mutation signature. In some embodiments, the methods described herein can use cell transcriptomic data to develop a transcriptomic signature to predict a cell’s nutrient dependency. In some embodiments, the methods described herein can use single cell transcriptomic data to develop a transcriptomic signature to predict a cell’s nutrient dependency. In some embodiments. In some embodiments, the methods described herein can use single cell RNA sequencing data to develop a transcriptomic signature to predict a cell’s nutrient dependency.

[0054] The methods of the disclosure can analyze cellular data from a cell with known nutrient sensitivity, determined experimentally or clinically, to determine a genetic signature of nutrient sensitivity. The methods of the disclosure can analyze cell transcriptomic data from a cell with known nutrient sensitivity, determined experimentally or clinically, to determine a transcriptomic genetic signature of nutrient sensitivity of the cell. The methods of the disclosure can analyze tumor transcriptomic data from a cell with known nutrient sensitivity, determined experimentally or clinically, to determine a transcriptomic genetic signature of nutrient sensitivity of the cell. In some embodiments, cell transcriptomic data from a cell with known amino acid sensitivity, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of amino acid sensitivity of the cell. In some embodiments, cancer cell transcriptomic data from a cell with known amino acid sensitivity, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of amino acid sensitivity of the cancer cell. In some embodiments, tumor transcriptomic data from a cell with known amino acid sensitivity, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of amino acid sensitivity of the cancer cell. In some embodiments, cell transcriptomic data from a cell with known sterol dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of sterol dependency of the cell. In some embodiments, cancer cell transcriptomic data from a cell with known sterol dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of sterol dependency of the cancer cell. In some embodiments, tumor transcriptomic data from a cell with known sterol dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of sterol sensitivity of the cancer cell. In some embodiments, cell transcriptomic data from a cell with known lipid dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of lipid dependency of the cell. In some embodiments, cancer cell transcriptomic data from a cell with known lipid dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of lipid dependency of the cancer cell. In some embodiments, tumor transcriptomic data from a cell with known lipid dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of lipid sensitivity of the cancer cell. In some embodiments, cell transcriptomic data from a cell with known carbohydrate dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of carbohydrate dependency of the cell. In some embodiments, cancer cell transcriptomic data from a cell with known carbohydrate dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of carbohydrate dependency of the cancer cell. In some embodiments, tumor transcriptomic data from a cell with known carbohydrate dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of carbohydrate sensitivity of the cancer cell. In some embodiments, cell transcriptomic data from a cell with known vitamin dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of vitamin dependency of the cell. In some embodiments, cancer cell transcriptomic data from a cell with known vitamin dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of vitamin dependency of the cancer cell. In some embodiments, tumor transcriptomic data from a cell with known vitamin dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of vitamin sensitivity of the cancer cell. In some embodiments, cell transcriptomic data from a cell with known mineral dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of mineral dependency of the cell. In some embodiments, cancer cell transcriptomic data from a cell with known mineral dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of mineral dependency of the cancer cell. In some embodiments, tumor transcriptomic data from a cell with known mineral dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of mineral sensitivity of the cancer cell. In some embodiments, cell transcriptomic data from a cell with known protein dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of protein dependency of the cell. In some embodiments, cancer cell transcriptomic data from a cell with known protein dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of protein dependency of the cancer cell. In some embodiments, tumor transcriptomic data from a cell with known protein dependency, determined experimentally or clinically, is analyzed to determine a transcriptomic genetic signature of protein sensitivity of the cancer cell.

[0055] In some embodiments, methods disclosed herein comprise identifying and stratifying subjects with tumors sensitive to a nutrient-starvation therapy. In some embodiments, methods disclosed herein comprise identifying and stratifying subjects with tumors sensitive to an amino acid-starvation therapy. In some embodiments, methods disclosed herein comprise identifying and stratifying subjects with tumors sensitive to a cholesterol-depletion therapy. In some embodiments, methods disclosed herein comprise identifying and stratifying subjects with tumors sensitive to cholesterol-depletion and statin treatment therapy.

[0056] The methods of the disclosure can use transcriptomic cell data on a subset of cell lines known to have a nutrient deficiency. In some embodiments, the methods use a transcriptomic cancer cell data on a subset of cancer cell lines known to have a nutrient deficiency. The methods of the disclosure can use transcriptomic cell data on a subset of cell lines known to have a nutrient dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subset of cancer cell lines known to have a nutrient dependency. In some embodiments, the methods of the disclosure can use transcriptomic cell data on a subset of cell lines known to have a nutrient independency. In some embodiments, the methods can use transcriptomic cancer cell data on a subset of cancer cell lines known to have a nutrient independency. In some embodiments, the methods can use transcriptomic cancer cell data on a subset of cancer cell lines known to have an amino acid deficiency. In some embodiments, the methods can use transcriptomic cancer cell data on a subset of cancer cell lines known to have a proline dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subset of cancer cells known to have a proline independency. In some embodiments, the methods can use transcriptomic cell data on a subset of cancer cell lines known to have a sterol dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subject of cancer cell lines known to have a sterol dependency. In some embodiments, the methods can use transcriptomic cell data on a subset of cancer cell lines known to have a cholesterol dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subject of cancer cell lines known to have a cholesterol dependency. In some embodiments, the methods can use transcriptomic cell data on a subset of cancer cell lines known to have a lipid dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subject of cancer cell lines known to have a lipid dependency. In some embodiments, the methods can use transcriptomic cell data on a subset of cancer cell lines known to have a carbohydrate dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subject of cancer cell lines known to have a carbohydrate dependency. In some embodiments, the methods can use transcriptomic cell data on a subset of cancer cell lines known to have a lipid dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subject of cancer cell lines known to have a lipid dependency. In some embodiments, the methods can use transcriptomic cell data on a subset of cancer cell lines known to have a carbohydrate dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subject of cancer cell lines known to have a carbohydrate dependency. In some embodiments, the methods can use transcriptomic cell data on a subset of cancer cell lines known to have a vitamin dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subject of cancer cell lines known to have a vitamin dependency. In some embodiments, the methods can use transcriptomic cell data on a subset of cancer cell lines known to have a mineral dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subject of cancer cell lines known to have a mineral dependency. In some embodiments, the methods can use transcriptomic cell data on a subset of cancer cell lines known to have a protein dependency. In some embodiments, the methods can use transcriptomic cancer cell data on a subject of cancer cell lines known to have a protein dependency.

[0057] In some embodiments, transcriptomic cell data are obtained from a private database. In some embodiments, transcriptomic cell data are obtained from a public database. In some embodiments, transcriptomic cell data are obtained from the Cancer Cell Line Encyclopedia (CCLE). In some embodiments, transcriptomic cell data are obtained from the Cancer Genome Atlas program (TCGA) In some embodiments, transcriptomic data are obtained from a public resource, for example, a research publication. In some embodiments, genomic cell data are obtained from a private database. In some embodiments, transcriptomic cell data are obtained from a public database. In some embodiments, genomic cell data are obtained from the CCLE. In some embodiments, genomic cell data are obtained from the TCGA. In some embodiments, genomic data are obtained from a public resource, for example, a research publication.

[0058] In some embodiments, a patient’s tumor can be analyzed for gene expression using a sequencing analysis technique. In some embodiments, the sequencing analysis technique is RNA sequencing analysis. In some embodiments, the sequencing analysis technique is microarray analysis. In some embodiments, the sequencing analysis technique is DNA sequencing analysis. In some embodiments, the DNA sequencing analysis technique is Sanger sequencing analysis. In some embodiments, the DNA sequencing analysis technique is next-generation sequencing analysis. In some embodiments, the DNA sequencing analysis technique is whole-genome sequencing analysis. In some embodiments, the DNA sequencing analysis technique is whole-exome sequencing analysis. In some embodiments, the DNA sequencing analysis technique is PacBio SMRT sequencing analysis. In some embodiments, the DNA sequencing analysis technique is Oxford nanopore sequencing analysis. In some embodiments, the tumor is microdissected to separate cancer cells from stromal cells. In some embodiments, single cell sequencing is used to separate cancer cell gene expression data from stromal cell gene expression data.

[0059] A database used to obtain transcriptomic cell data can provide statistical, functional, or integrative analysis of metabolomics data. In some embodiments, the transcriptomic cell database can provide exploratory statistical analysis, for example, analysis of general statistics, biomarker analysis, two-factor/time series analysis, or power analysis. In some embodiments, the transcriptomic cell database can provide functional enrichment analysis, for example, metabolite set enrichment analysis, metabolic pathway analysis, or pathway activity prediction from mass spectrometry peaks. In some embodiments, the transcriptomic cell database can provide data integration and systems biology analysis, for example, biomarker meta-analysis, joint-pathway analysis, or network explorer analysis.

[0060] In some embodiments, genome-wide mRNA expression data is obtained for cell lines with a known nutrient dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known amino acid dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known amino acid dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell line with a known proline dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known sterol dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known sterol independency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known cholesterol dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known cholesterol independency. In some embodiments, genomewide mRNA expression data is obtained for cancer cell lines with a known lipid dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known lipid independency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known carbohydrate dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known carbohydrate independency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known vitamin dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known vitamin independency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known mineral dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known mineral independency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known protein dependency. In some embodiments, genome-wide mRNA expression data is obtained for cancer cell lines with a known protein independency. [0061] In some embodiments, genome-wide mRNA expression data on nutrient dependency of cells can be obtained from at least one cell line, for example, PANC0504, PANC0203, SU8686, TCCPAN2, CFPAC1, CAPAN2, HUPT3, YAPC, PANC0403, PK1, PANC0327, BXPC3, DANG, SW48, SW1990, KP2, HCC827, SW480, PSN1, NCIH1963, MCF7, T47D, JURKAT, NCIH358, ASPC1, DLD1, PC9, PANCI, PATU 8901, HCT116, LS180, MIAPACA2, CAL33, BXPC3, CAKI2, NCIH209, U266B1, KP3, A549, HEPG2, SCC25, NCIH929, SCC4, or SCC9 cells. In some embodiments, genome-wide mRNA expression data on nutrient independency of cells can be obtained from at least one cell line, for example, SUIT2, CAPAN1, PK45H, MIAPACA2, PK59, HUPT4, PATU 8901, PATU 8902, PATU 8988T, ASPC1, KP3, KP4, HP AC, QGP1, HS766T, PANCI, HCC827, SW480, PSN1, NCIH1963, MCF7, T47D, JURKAT, NCIH358, DLD1, PC9, HCT116, LS180, CFPAC1, MIAPACA2, CAL33, BXPC3, CAKI2, NCIH209, U266B1, A549, HEPG2, DANG, SCC25, NCIH929, SW48, SCC4, or SCC9 cells.

[0062] In some embodiments, gene expression data on nutrient dependency of cells can be obtained from at least one cell line, for example, PANC0504, PANC0203, SU8686, TCCPAN2, CFPAC1, CAPAN2, HUPT3, YAPC, PANC0403, PK1, PANC0327, BXPC3, DANG, SW48, SW1990, KP2, HCC827, SW480, PSN1, NCIH1963, MCF7, T47D, JURKAT, NCIH358, ASPC1, DLD1, PC9, PANCI, PATU 8901, HCT116, LS180, MIAPACA2, CAL33, BXPC3, CAKI2, NCIH209, U266B1, KP3, A549, HEPG2, SCC25, NCIH929, SCC4, or SCC9 cells. In some embodiments, genome-wide mRNA expression data on nutrient independency of cells can be obtained from at least one cell line, for example, SUIT2, CAPAN1, PK45H, MIAPACA2, PK59, HUPT4, PATU 8901, PATU 8902, PATU 8988T, ASPC1, KP3, KP4, HPAC,QGP1, HS766T, PANCI, HCC827, SW480, PSN1, NCIH1963, MCF7, T47D, JURKAT, NCIH358, DLD1, PC9, HCT116, LS180, CFPAC1, MIAPACA2, CAL33, BXPC3, CAKI2, NCIH209, U266B1, A549, HEPG2, DANG, SCC25, NCIH929, SW48, SCC4, or SCC9 cells.

[0063] The transcriptomic cell data obtained from the database can be processed, for example, by removing data points. In some embodiments, the transcriptomic cell data are filtered to remove genes at near-constant values across cell lines. In some embodiments, the transcriptomic cell data are filtered to remove with values of about 0 across all cell lines. In some embodiments, the transcriptomic cell data are processed to remove noise using a filtering technique.

[0064] In some embodiments, the transcriptomic cell data are processed to remove outlier data using a filtering technique. In some embodiments, the filtering technique removes about 25% of the outlier data. In some embodiments, the filtering technique removes about 50% of the outlier data. A filtering technique used in a method disclosed herein can be interquartile range (IQR) filtering, standard deviation (SD) filtering, median absolute deviation (MAD) filtering, relative standard deviation (RSD = SD/mean) filtering, non-parametric relative standard deviation filtering, mean intensity filtering, or median intensity filtering. In some embodiments, the filter technique is IQR filtering. In some embodiments, data are obtained on a log2 scale, and the data are filtered to remove data points for genes with log2=0 for > 50% of the cell lines.

[0065] Filtering of data, as described herein, (e.g., with a within group coefficient of variation (CV) constraint) can reduce the noise of a genetic signature. As a result, filtering can reduce overfitting by constraining genetic signatures to genes that have a significant and consistent difference. Additionally, filtering can narrow the scope of a genetic signature to one or more genes that have a prognostic value, thereby providing useful clinical information, e.g., as to whether a nutrient therapy is more likely to be effective against a less aggressive tumor or whether a nutrient therapy is more likely to be effective against a more aggressive advanced tumor as a second or third line of treatment. Thus, filtering can improve the targeted aspect of a nutrient modulation therapy described herein and combinations thereof with the various therapeutics and/or therapies described herein.

[0066] Transcriptomic cell data obtained from a data base can be auto-scaled. In some embodiments, the transcriptomic cell data are filtered using IQR filtering and auto-scaled. In some embodiments, the transcriptomic cell data are auto-scaled by mean-centering the data. In some embodiments, the transcriptomic cell data are auto-scaled by mean-centering the data and dividing the resulting data set by the standard deviation.

[0067] The transcriptomic cell data obtained from a database can be filtered, scaled, and normalized. In some embodiments, the data are normalized such that all values fall between - 1 and 1. In some embodiments, data are normalized using the equation:

Hierarchical Clustering

[0068] Hierarchical clustering, also called hierarchical cluster analysis (HCA), is a method of cluster analysis used to build a hierarchy of clusters of data. The methods disclosed herein can comprise obtaining transcriptomic cell data from a database, then filter, scale, normalize, and subject the data to hierarchical clustering. [0069] Hierarchical clustering, as described herein and utilized through the methods described elsewhere herein, can provide a unique advantage over other clustering methodologies, e.g., unrestricted decision tree(s), when identifying nutrient-dependency and/or relative nutrient-independency of a gene of a cell type. For example, allowing for one or more genes of a cell to appear more than once when clustering genes in an unrestricted decision tree can create challenges when characterizing such genes into a signature. The genes clustered using unrestricted decision tree methods may appear as both sensitive and insensitive characterizations, thereby limiting the accuracy of the gene signature. Instead, by clustering with a hierarchical clustering (i.e., a constrained decision tree) approach and/or methodology as described herein, genes can be ranked by their information content pertaining to nutrient sensitivity. This process can therefore enable subsequent filtering and processing of the gene clusters to develop accurate gene signatures that are characteristic of a nutrientdependency and/or a relative nutrient-independency. Filtering and processing methods of the gene signatures determined by hierarchical clustering may be informed by factors, e.g., clinical relevance, ease of measuring the gene signatures, etc., which are independent from generating a list of candidate gene markers by hierarchical clustering and heat map tools that are described elsewhere herein. Moreover, hierarchical clustering, as compared to unrestricted decision trees, can also provide accurate and reliable gene clustering amid sparse data with a reduced likelihood of errors.

[0070] In some embodiments, the hierarchical clustering can be agglomerative, wherein each observation starts a new cluster, and pairs of clusters are merged moving up the hierarchy. In some embodiments, the hierarchical clustering can be divisive, wherein all observations start in one cluster, and splits are performed recursively moving down the hierarchy.

[0071] The hierarchical clustering of genetic data can be visualized using a computer algorithm or database. In some embodiments, the hierarchical clustering is visualized using a tool that incorporates principal component analysis. In some embodiments, the hierarchical clustering tool uses a heatmap for visualization. In some embodiments, the hierarchical clustering cool is Morpheus (Broad Institute). In some embodiments, the hierarchical clustering tool is ClustVis. In some embodiments, the hierarchical clustering tool is Clustergrammer.

Stratification [0072] The method described herein uses hierarchical clustering and heat map visualization to determine a relative nutrient-dependency or relative nutrient-independency of a gene of a cell type. In some embodiments, the method described herein can comprise determining a relative amino acid-dependency or relative amino acid-independency of a gene of a cell type. In some embodiments, the method described herein can comprise determining a relative proline-dependency or relative proline-independency of a gene of a cell type. In some embodiments, the method described herein can comprise determining a relative steroldependency or relative sterol-independency of a gene of a cell type. In some embodiments, the method described herein can comprise determining a relative cholesterol-dependency or relative cholesterol-independency of a gene of a cell type. In some embodiments, the method described herein can comprise determining a relative lipid-dependency or relative lipid- independency of a gene of a cell type. In some embodiments, the method described herein can comprise determining a relative carbohydrate-dependency or relative carbohydrate- independency of a gene of a cell type. In some embodiments, the method described herein can comprise determining a relative protein-dependency or relative protein-independency of a gene of a cell type. In some embodiments, the method described herein can comprise determining a relative vitamin-dependency or relative vitamin-independency of a gene of a cell type. In some embodiments, the method described herein can comprise determining a relative mineral -dependency or relative mineral-independency of a gene of a cell type.

[0073] The hierarchical clustering of genes based on nutrient dependency of a cell comprises stratifying genes of a cell. In some embodiments, a gene of a cell can be stratified into at least two groups. In some embodiments, a gene of a cell can be stratified into two groups. In some embodiments, a gene of a cell can be stratified into a nutrient-dependent group and a nutrientindependent group. In some embodiments, a gene of a cell can be stratified into an amino acid-dependent group and an amino acid-independent group. In some embodiments, a gene of a cell can be stratified into one or more amino acid-sensitive groups and one or more amino acid-sensitive groups. In some embodiments, a gene of a cell can be stratified into a prolinedependent group and a proline-independent group. In some embodiments, a gene of a cell can be stratified into a sterol-dependent group and a sterol-independent group. In some embodiments, a gene of a cell can be stratified into a cholesterol-dependent group and a cholesterol-independent group. In some embodiments, a gene of a cell can be stratified into one or more cholesterol sensitive groups and one or more cholesterol insensitive groups. In some embodiments, a gene of a cell can be stratified into a lipid-dependent group and a lipid- independent group. In some embodiments, a gene of a cell can be stratified into one or more lipid sensitive groups and one or more lipid insensitive groups. In some embodiments, a gene of a cell can be stratified into a carbohydrate-dependent group and a carbohydrate- independent group. In some embodiments, a gene of a cell can be stratified into one or more carbohydrate sensitive groups and one or more carbohydrate insensitive groups. In some embodiments, a gene of a cell can be stratified into a vitamin-dependent group and a vitamin- independent group. In some embodiments, a gene of a cell can be stratified into one or more vitamin sensitive groups and one or more vitamin insensitive groups. In some embodiments, a gene of a cell can be stratified into a mineral-dependent group and a mineral-independent group. In some embodiments, a gene of a cell can be stratified into one or more mineral sensitive groups and one or more mineral insensitive groups. In some embodiments, a gene of a cell can be stratified into a protein-dependent group and a protein-independent group. In some embodiments, a gene of a cell can be stratified into one or more protein sensitive groups and one or more protein insensitive groups.

[0074] The hierarchical clustering of genes can comprise generating a genetic signature for a nutrient dependency of a cell, wherein the genetic signature reflects the nutrient-dependency status of a cell. In some embodiments, the genetic signature of a cell comprises a set of genes that are stratified into a nutrient-dependent group and a nutrient-independent group. In some embodiments, the genetic signature of a cell can comprise at least one gene in the nutrientdependent group and at least one gene in the nutrient-independent group. In some embodiments, the genetic signature of a cell can comprise at least five genes in the nutrientdependent group and at least five genes in the nutrient-independent group. In some embodiments, the genetic signature of a cell can comprise at least ten genes in the nutrientdependent group and at least ten genes in the nutrient-independent group. In some embodiments, the genetic signature of a cell can comprise at least fifteen genes in the nutrient-dependent group and at least fifteen genes in the nutrient-independent group.

Genetic Signature

[0075] The genetic signature generated for a cell based on a known nutrient dependency can comprise a set of genes. In some embodiments, the genetic signature generated for a cell based on a known nutrient dependency can comprise at least one gene.

[0076] In some embodiments, the nutrient dependency genetic signature of a cell can comprise from about 1 to about 5 genes, about 5 to about 10 genes, 10 to about 25 genes, from about 25 genes to about 50 genes, from about 50 genes to about 75 genes, from about 75 genes to about 100 genes, from about 100 genes to about 125 genes, from about 125 genes to 150 genes, from about 150 genes to about 175 genes, from about 175 genes to about 200 genes, from about 200 genes to about 225 genes, from about 250 genes to about 275 genes, from about 275 genes to about 300 genes, from about 300 genes to about 325 genes, from about 325 genes to about 350 genes, from about 350 genes to about 375 genes, from about 375 genes to about 400 genes, from about 400 genes to about 425 genes, from about 425 genes to about 450 genes. In some embodiments, the nutrient dependency genetic signature of a cell can comprise from about 25 genes to about 50 genes. In some embodiments, the nutrient dependency genetic signature of a cell can comprise from about 10 genes to about 25 genes. In some embodiments, the nutrient dependency genetic signature of a cell can comprise about 10, about 20, about 30, about 40, about 50, about 60, about 70, about 80, about 90, about 100, about 110, about 120, about 130, about 140, about 150, about 160, about 170, about 180, about 190, 200 , about 210, about 220, about 230, about 240, about 250, about 260, about 270, about 280, about 290, about 300, about 310, about 320, about 330, about 340, about 350, about 360, about 370, about 380, about 390, about 400, about 410, about 420, about 430, about 440, or about 450 genes. In some embodiments, the nutrient dependency genetic signature of a cell can comprise about 100 genes. In some embodiments, the nutrient dependency genetic signature of a cell can comprise about 50 genes. In some embodiments, the genetic signature of a cell can comprise about 25 genes. In some embodiments, the genetic signature of a cell can comprise about 15 genes. In some embodiments, the genetic signature of a cell can comprise about 10 genes. In some embodiments, the genetic signature of a cell can comprise about 5 genes. In some embodiments, the genetic signature of a cell can comprise about 3 genes.

[0077] In some embodiments, the group of genes used as a nutrient dependency genetic signature for a cell can be increased in size to a larger set of genes by testing the relationship of gene expression with nutrient sensitivity. In some embodiments, genes are included that are prognostically relevant. In some embodiments, a gene is included if the log2-fold change in expression between nutrient-dependent and nutrient-independent cells is outside a range. In some embodiments, a gene is included if the coefficient of variation (CV) of the gene expression in nutrient-dependent and nutrient-independent cells is below a threshold. In some embodiments, a gene is included if the Spearman correlation coefficient is above a threshold. In some embodiments, a gene is included if the log-odds that gene expression is different between groups is outside a range.

[0078] The group of genes used as a nutrient dependency genetic signature for a cell can be reduced in size to a smaller set of genes by changing a filtering criterion. In some embodiments, the range of log2-fold change in expression is narrowed to reduce the number of genes used as a genetic signature. In some embodiments, the coefficient of variation (CV) of the expression of each gene is reduced to reduce the number of genes used as a genetic signature. In some embodiments, genes with a CV greater than 10% and no prognostic relevance in a given type of cancer can be removed. In some embodiments, genes with a CV greater than about 25%, about 30%, about 40%, or about 50%, are removed from the data set. In some embodiments, genes with a CV greater than about 15% are removed from the data set. In some embodiments, the Spearman correlation coefficient is increased to reduce the number of genes used as a genetic signature. In some embodiments, the absolute log-odds that gene expression is different between groups can be increased to reduce the number of genes used as a genetic signature.

[0079] In some embodiments, the group of genes used as a nutrient dependency genetic signature for a cell can be reduced by from about 1% to about 5%, from about 5% to about 10%, about 10% to about 20%, from about 20% to about 30%, from about 30% to about 40%, from about 40% to about 50%, from about 50% to about 60%, from about 60% to about 70%, or from about 70% to about 80%. In some embodiments, the group of genes used as a nutrient dependency genetic signature for a cell can be reduced by from about 5% to about 10%. In some embodiments, the group of genes used as a nutrient dependency genetic signature for a cell can be reduced by from about 20% to about 30%. In some embodiments, the group of genes used as a nutrient dependency genetic signature for a cell can be reduced by from about 40% to about 50%.

[0080] In some embodiments, the group of genes used as a nutrient dependency genetic signature for a cell can be reduced by about 1%, about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, or about 80%. In some embodiments, the group of genes used as a nutrient dependency genetic signature for a cell can be reduced by about 5%. In some embodiments, the group of genes used as a nutrient dependency genetic signature for a cell can be reduced by about 20%. In some embodiments, the group of genes used as a nutrient dependency genetic signature for a cell can be reduced by about 40%. In some embodiments, the group of genes used as a nutrient dependency genetic signature for a cell can be reduced by about 60%.

[0081] A genetic signature generated for a cell based on a known nutrient dependency can comprise a set of genes. In some embodiments, the genes of a genetic signature can be upregulated in a cell that has a nutrient dependency. In some embodiments, the genes of a genetic signature can be downregulated in a cell that has a nutrient dependency. In some embodiments, the genes of a genetic signature can have increased expression in a cell that has a nutrient dependency. In some embodiments, the genes of a genetic signature can have decreased expression in a cell that has a nutrient dependency. In some embodiments, the genetic signature can comprise a set of genes with a nutrient dependency. In some embodiments, the genetic signature can comprise a set of genes without a nutrient dependency. In some embodiments, the genetic signature can comprise a first set of genes with a nutrient dependency and a second set of genes without a nutrient dependency.

[0082] In some embodiments, the genetic signature for nutrient-dependence of cell can be determined from an expression profile comprising a gene in an amino acid biosynthesis pathway, an amino acid metabolism pathway, a sterol biosynthesis pathway, a sterol metabolism pathway, a cholesterol biosynthesis pathway, a cholesterol metabolism pathway, a fatty acid biosynthesis pathway, a fatty acid metabolism pathway, the mevalonate pathway, or the sirtuin pathway. In some embodiments, the genetic signature for nutrient-dependence of a cell can be determined from an expression profile comprising a gene in an amino acid biosynthesis pathway. In some embodiments, the genetic signature for nutrient-dependence of a cell can be determined from an expression profile comprising a gene in an amino acid metabolism pathway. In some embodiments, the genetic signature for nutrient-dependence of a cell can be determined from a gene expression profile comprising a gene in a sterol biosynthesis pathway. In some embodiments, the genetic signature for nutrient-dependence of a cell can be determined from an expression profile comprising a gene in a sterol metabolism pathway. In some embodiments, the genetic signature for nutrient-dependence of a cell can be determined from an expression profile comprising a gene in a cholesterol biosynthesis pathway. In some embodiments, the genetic signature for nutrient-dependence of a cell can be determined from an expression profile comprising a gene in a cholesterol metabolism pathway. In some embodiments, the genetic signature for nutrient-dependence of a cell can be determined from an expression profile comprising a gene in a fatty acid biosynthesis pathway. In some embodiments, the genetic signature for nutrient-dependence of a cell can be determined from an expression profile comprising a gene in a fatty acid metabolism pathway. In some embodiments, the genetic signature for nutrient-dependence of a cell can be determined in an expression profile comprising a gene in the mevalonate pathway. In some embodiments, the genetic signature for nutrient-dependence of a cell can be determined from an expression profile comprising a gene in the sirtuin pathway. In some embodiments, the genetic signature for nutrient-dependence can be determined from an expression profile comprising a gene in the proline biosynthesis pathway, for example, ornithine aminotransferase (OAT), pyrroline-5-carboxylase reductase 1 (PYCR1), pyrroline-5- carboxylate reductase 2 (PYCR2), pyrroline-5-carboxylate reductase 3 (PYCR3), y-glutamyl kinase (GK), y-glutamyl phosphate reductase (GPR), pyrroline-5-carboxylate synthase 1 (P5CS1), or pyrroline-5-carboxylate synthase 1 (P5CS2).

[0083] In some embodiments, a genetic signature for nutrient-dependence of a cell can comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or more of the group of genes consisting of: CENPV, PEBP1, WASF1, TSPYL2, CITED2, MXD4, RBM3, BTD, ST6GALNAC6, ASNS, RERE, STXBP1, HACL1, NADK2, CD99L2, ARRB2, SIRT1, GCAT, POMT1, SLC25A38, COQ8A, RMCI, FECH, MTMR12, RPP40, HABP4, MYBBP1A, SLC43A2, CXXC1, PFAS, SEC11C, XPOT, PYGB, SLC35E2B, CYB5D2, DDIT3, ACAT1, TARS, C1QBP, GNE, LZTFL1, RPAIN, WDR45, TMEM43, CHCHD4, CLASP2, POLR1E, YEATS4, ACAT2, STAMBPL1, CBWD5, APPL1, WASHC2C, ZNF76, PAN2, AGFG2, TBC1D15, SLC38A2, ZCCHC14, ACADM, LTV1, RAPGEF1, NSMF, TTC33, MDN1, TATDN2, SAT2, WASHC2A, RPL14, RPS6, PIM1, PDCD4, GTF3C6, WDR81, COQ10A, TOP1MT, UHRF1BP1, RPL26, REV3L, WDR48, APBB3, PLXNA3, SREBF1, RIOK1, CCDC88A, RPL32, CTNS, ICE1, NHEJ1, MAP1LC3B, CAPN7, RPS10, WDR70, BPHL, SLC38A1, TBC1D5, DHX33, WDR6, SNX3, SUPV3L1, NUDT2, ECHDC1, LYRM2, FLCN, PYCR1, GTF2H2C, HDDC2, OGGI, ALDH3A2, GTF2H2, VSIG10, XPC, NHP2, CCT2, ACAD10, CCT5, CCDC107, GIGYF1, TACC2, RPL37, HSF2, PLCG1, BRD9, NAP1L1, SDHA, SOD2, GNL3, TRAK2, MRPS25, FAM173B, TCP1, ATP5F1A, MRPL18, PHYH, LARS, ZNF131, CEP83, EEF2, PIK3R2, ORC3, PAIP1, UBP1, PKD1, SMIM4, QSOX2, PDHB, EEF1A1, PTBP2, PSIP1, TIMM44, IP6K2, CD320, ELP2, C5orf22, NISCH, ISCA1, SNX21, BRIX1, PTMS, ZNF428, DOCK7, UHRF1BP1L, GARS, RPL15, FRA10AC1, GBA2, TSEN2, RGS10, RPSA, AHI1, PDSS2, ENDOG, KATNA1, GNPDA1, TLN1, PHPT1, TMEM120A, NARS, PON2, NR2C1, FAM102A, SEC63, CCNJ, DROSHA, NUP88, ATM, TWNK, BRPF1, ZCCHC7, IMPACT, ACTR3B, MRPS30, TRIM7, HPS1, CAMTA2, WRN, SIK3, DYNC1LI1, PHF14, C18orf21, EXOC3, T0P2B, ZNF621, UAP1, RNF44, BCKDHB, ZNF397, MAST2, ZDHHC8, RNF146, PCM1, PMPCA, UBB, MAD2L2, RAB32, KMT2A, ELK1, HINT3, MEDIO, AIMP2, PCBP4, G6PD, MCFD2, MET, MRPL52, HEXIM1, B4GALT5, COMT, LASPI, SERTAD1, CTNNBIP1, ARHGAP5, EPS8, KRT18, RAP1GDS1, PJA1, BID, GIPC1, ANAPC10, PHLDA2, SGMS2, IDS, SERPINH1, LSM6, ENCI, FURIN, PCTP, RAD51C, LRR1, BLCAP, CD276, MAPKAPK3, RAB5IF, NT5E, CYB5A, EMC10, NDFIP2, SPATS2L, TMED10, PPP3CA, MBOAT7, NET1, RHOF, MYO1B, ITPRID2, LRRC8A, SPTBN2, CD59, RBMS2, TAGLN2, STK24, EML2, SELENOW, PUDP, PRRG1, CCND1, PARP12, FAM111B, SAT1, CASP4, PSME1, DHRS7, SP100, ELF1, MESD, BCAR3, FXYD5, ACTN1, PLS3, B3GNT2, TNKS1BP1, ARHGAP18, FLNA, TPM4, NMI, WWC1, MEAK7, SMAGP, CAPN1, PTPRK, MAP4K4, HELZ2, SYNGR2, DHRS1, AJUBA, ADAMI 5, ANXA2, PDLIM7, LIF, TES, LGALS1, APOL2, OCIAD2, LY6E, ABCC3, TMOD3, FAM3C, RAB27A, ACP6, MAP2K1, PDXK, PSME2, KCNN4, ST5, TAPI, CAPN2, CD47, GALNT7, FAM111A, DTX3L, F0SL1, EPS8L2, SDC4, PRSS23, INSIG2, AK4, ITGA3, PKP3, CHMP4C, SPINT2, ARL6IP5, S100A11, BLVRB, NCEH1, IRF1, CLMN, ENDOD1, EPHX4, PLA2G12A, TNFAIP8, FERMT1, TMBIM1, ZFP36L1, SLC39A11, TSPAN14, FRMD6, API S3, FAH, DDIT4, ITGA6, C19orfi3, SLC2A1, TSKU, SCNN1A, QS0X1, DTX2, 0SBPL3, ATP1B1, TNFRSF12A, LITAF, B2M, LLGL2, RHOD, IL4R, PLSCR1, CYB561, C15orf39, C6orfl32, AGRN, LGALS3BP, RASA1, HIF1A, CLIP4, RRAS2, PML, IL15RA, MGLL, PARP9, DSG2, CAV2, PLEK2, KCNK1, FUR, ARNTL2, C16orf74, GPRC5A, TNNT1, LMO7, SFN, S100A16, SLC44A2, MYO5B, OAS1, KLF5, MALL, ITGA2, DDX60L, ZNF185, EFNB2, PTGES, ISG15, PTK6, ADAP1, PLAU, DDX60, PCDH1, MYEOV, ITGB4, EHF, KRT19, UBE2L6, TGFA, EMP1, GRHL2, TC2N, DCBLD2, B3GNT3, TGFBI, LAMB3, UNC13D, F3, LAMC2, ANXA3, ANO1, KRT7, and MAL2.

[0084] In some embodiments, a genetic signature for nutrient-independence of a cell can comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or more of the group of genes consisting of: CENPV, PEBP1, WASF1, TSPYL2, CITED2, MXD4, RBM3, BTD, ST6GALNAC6, ASNS, RERE, STXBP1, HACL1, NADK2, CD99L2, ARRB2, SIRT1, GCAT, POMT1, SLC25A38, COQ8A, RMCI, FECH, MTMR12, RPP40, HABP4, MYBBP1A, SLC43A2, CXXC1, PFAS, SEC11C, XPOT, PYGB, SLC35E2B, CYB5D2, DDIT3, ACAT1, TARS, C1QBP, GNE, LZTFL1, RPAIN, WDR45, TMEM43, CHCHD4, CLASP2, POLR1E, YEATS4, ACAT2, STAMBPL1, CBWD5, APPL1, WASHC2C, ZNF76, PAN2, AGFG2, TBC1D15, SLC38A2, ZCCHC14, ACADM, LTV1, RAPGEF1, NSMF, TTC33, MDN1, TATDN2, SAT2, WASHC2A, RPL14, RPS6, PIM1, PDCD4, GTF3C6, WDR81, COQ10A, TOP1MT, UHRF1BP1, RPL26, REV3L, WDR48, APBB3, PLXNA3, SREBF1, RIOK1, CCDC88A, RPL32, CTNS, ICE1, NHEJ1, MAP1LC3B, CAPN7, RPS10, WDR70, BPHL, SLC38A1, TBC1D5, DHX33, WDR6, SNX3, SUPV3L1, NUDT2, ECHDC1, LYRM2, FLCN, PYCR1, GTF2H2C, HDDC2, OGGI, ALDH3A2, GTF2H2, VSIG10, XPC, NHP2, CCT2, ACAD10, CCT5, CCDC107, GIGYF1, TACC2, RPL37, HSF2, PLCG1, BRD9, NAP1L1, SDHA, SOD2, GNL3, TRAK2, MRPS25, FAM173B, TCP1, ATP5F1A, MRPL18, PHYH, LARS, ZNF131, CEP83, EEF2, PIK3R2, ORC3, PAIP1, UBP1, PKD1, SMIM4, QSOX2, PDHB, EEF1A1, PTBP2, PSIP1, TIMM44, IP6K2, CD320, ELP2, C5orf22, NISCH, ISCA1, SNX21, BRIX1, PTMS, ZNF428, DOCK7, UHRF1BP1L, GARS, RPL15, FRA10AC1, GBA2, TSEN2, RGS10, RPSA, AHI1, PDSS2, ENDOG, KATNA1, GNPDA1, TLN1, PHPT1, TMEM120A, NARS, PON2, NR2C1, FAM102A, SEC63, CCNJ, DROSHA, NUP88, ATM, TWNK, BRPF1, ZCCHC7, IMPACT, ACTR3B, MRPS30, TRIM7, HPS1, CAMTA2, WRN, SIK3, DYNC1LI1, PHF14, C18orf21, EXOC3, TOP2B, ZNF621, UAP1, RNF44, BCKDHB, ZNF397, MAST2, ZDHHC8, RNF146, PCM1, PMPCA, UBB, MAD2L2, RAB32, KMT2A, ELK1, HINT3, MEDIO, AIMP2, PCBP4, G6PD, MCFD2, MET, MRPL52, HEXIM1, B4GALT5, COMT, LASPI, SERTAD1, CTNNBIP1, ARHGAP5, EPS8, KRT18, RAP1GDS1, PJA1, BID, GIPC1, ANAPC10, PHLDA2, SGMS2, IDS, SERPINH1, LSM6, ENCI, FURIN, PCTP, RAD51C, LRR1, BLCAP, CD276, MAPKAPK3, RAB5IF, NT5E, CYB5A, EMC10, NDFIP2, SPATS2L, TMED10, PPP3CA, MBOAT7, NET1, RHOF, MYO1B, ITPRID2, LRRC8A, SPTBN2, CD59, RBMS2, TAGLN2, STK24, EML2, SELENOW, PUDP, PRRG1, CCND1, PARP12, FAM111B, SAT1, CASP4, PSME1, DHRS7, SP100, ELF1, MESD, BCAR3, FXYD5, ACTN1, PLS3, B3GNT2, TNKS1BP1, ARHGAP18, FLNA, TPM4, NMI, WWC1, MEAK7, SMAGP, CAPN1, PTPRK, MAP4K4, HELZ2, SYNGR2, DHRS1, AJUBA, ADAMI 5, ANXA2, PDLIM7, LIF, TES, LGALS1, APOL2, OCIAD2, LY6E, ABCC3, TMOD3, FAM3C, RAB27A, ACP6, MAP2K1, PDXK, PSME2, KCNN4, ST5, TAPI, CAPN2, CD47, GALNT7, FAM111A, DTX3L, F0SL1, EPS8L2, SDC4, PRSS23, INSIG2, AK4, ITGA3, PKP3, CHMP4C, SPINT2, ARL6IP5, S100A11, BLVRB, NCEH1, IRF1, CLMN, ENDOD1, EPHX4, PLA2G12A, TNFAIP8, FERMT1, TMBIM1, ZFP36L1, SLC39A11, TSPAN14, FRMD6, API S3, FAH, DDIT4, ITGA6, C19orfi3, SLC2A1, TSKU, SCNN1A, QS0X1, DTX2, OSBPL3, ATP1B1, TNFRSF12A, LITAF, B2M, LLGL2, RHOD, IL4R, PLSCR1, CYB561, C15orf39, C6orfl32, AGRN, LGALS3BP, RASA1, HIF1A, CLIP4, RRAS2, PML, IL15RA, MGLL, PARP9, DSG2, CAV2, PLEK2, KCNK1, FUR, ARNTL2, C16orf74, GPRC5A, TNNT1, LMO7, SFN, S100A16, SLC44A2, MYO5B, OAS1, KLF5, MALL, ITGA2, DDX60L, ZNF185, EFNB2, PTGES, ISG15, PTK6, ADAP1, PLAU, DDX60, PCDH1, MYEOV, ITGB4, EHF, KRT19, UBE2L6, TGFA, EMP1, GRHL2, TC2N, DCBLD2, B3GNT3, TGFBI, LAMB3, UNC13D, F3, LAMC2, ANXA3, ANO1, KRT7, and MAL2.

[0085] In some embodiments, a genetic signature for proline-independence of a cell can comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or more of the group of genes consisting of: FKBP5, REEP2, CENPV, SOX12, ZSWIM5, WASF1, KIAA1211, MXD4, BTD, HACL1, NADK2, CDK19, ATP7B, FECH, HABP4, GDF11, LZTFL1, RPAIN, WDR45, CHCHD4, WASHC2C, ULK4, TATDN2, WDR81, COQ10A, DHX33, NUP88, WRN, MAP2K1, C15orf39, FAM160A1, PML, PARP9, NRIP1, BATF2, BANK1, CATSPER1, SHANK2, TMC8, ANK3, GBP1, ISG15, CD274, NALCN, MR1, CSF2, TMC6, LRG1, IVL, GALNT9, RAB38, SAMD9L, GIMAP2, UBE2L6, APOL3, GNA15, GALNT6, TGFA, MMEL1, PTAFR, NECTIN4, TC2N, DCBLD2, SEMA7A, EPN3, ANKRD22, ADAM8, EREG, TGFBI, ITGB6, LGALS9, VAV1, TRIM22, DAPP1, TMEM92, UNCI 3D, TCN1, SLC37A2, SFTA2, MUC16, LAD1, GALNT3, ANXA3, PROM2, CRABP2, RAC2, SPRR1B, RBP1, PRSS8, ICAM2, and MAL2.

[0086] In some embodiments, a genetic signature for proline-dependence of a cell can comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or more of the group of genes consisting of: FKBP5, REEP2, CENPV, SOX12, ZSWIM5, WASF1, KIAA1211, MXD4, BTD, HACL1, NADK2, CDK19, ATP7B, FECH, HABP4, GDF11, LZTFL1, RPAIN, WDR45, CHCHD4, WASHC2C, ULK4, TATDN2, WDR81, COQ10A, DHX33, NUP88, WRN, MAP2K1, C15orf39, FAM160A1, PML, PARP9, NRIP1, BATF2, BANK1, CATSPER1, SHANK2, TMC8, ANK3, GBP1, ISG15, CD274, NALCN, MR1, CSF2, TMC6, LRG1, IVL, GALNT9, RAB38, SAMD9L, GIMAP2, UBE2L6, APOL3, GNA15, GALNT6, TGFA, MMEL1, PTAFR, NECTIN4, TC2N, DCBLD2, SEMA7A, EPN3, ANKRD22, ADAM8, EREG, TGFBI, ITGB6, LGALS9, VAV1, TRIM22, DAPP1, TMEM92, UNCI 3D, TCN1, SLC37A2, SFTA2, MUC16, LAD1, GALNT3, ANXA3, PROM2, CRABP2, RAC2, SPRR1B, RBP1, PRSS8, ICAM2, and MAL2.

[0087] In some embodiments, a genetic signature for cholesterol-dependence of a cell can comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or more of: GNG10, NDUFC2-KCTD14, UST, NDRG1, HSPG2, CIS, SEC16B, ABCB6, FCHSD2, CDKL1, TXNDC5, ALDH1A1, CAPN3, and CES1.

[0088] In some embodiments, a genetic signature for cholesterol-independence of a cell can comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or more of the group of genes consisting of: GNG10, NDUFC2-KCTD14, UST, NDRG1, HSPG2, CIS, SEC16B, ABCB6, FCHSD2, CDKL1, TXNDC5, ALDH1A1, CAPN3, and CES1.

[0089] In some embodiments, a genetic signature described herein can comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or more of the group of genes consisting of TABLE 2.

[0090] In some embodiments, a genetic signature described herein can comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or more of the group of genes consisting of TABLE 4.

[0091] In some embodiments, a genetic signature described herein can comprise at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, or more of the group of genes consisting of TABLE 7.

Identification of Nutrient Dependency in Cells of Unknown Dependency

[0092] The genetic signatures generated from cells with known nutrient dependencies comprise identifying the nutrient dependency of a cell without a known dependency on the nutrient. In some embodiments, the genetic signatures obtained from cells with a known nutrient dependency comprise stratifying genes of cells with an unknown nutrient dependency into at least two groups. In some embodiments, the genetic signatures obtained from cells with a known nutrient dependency comprise stratify genes of cells with an unknown nutrient dependency into two groups. In some embodiments, the genetic signatures obtained from cells with a known nutrient dependency comprise stratifying genes of cells with an unknown nutrient dependency into putatively nutrient-dependent and putatively nutrient-independent groups. In some embodiments, the genetic signatures obtained from cells with a known nutrient dependency comprise ranking cells from putatively nutrientdependent to putatively nutrient-independent. In some embodiments, the genetic signatures obtained from cells with a known amino acid dependency comprise stratifying genes of cells with an unknown amino acid dependency into putatively amino acid-dependent and putatively amino acid-independent groups. In some embodiments, the genetic signatures obtained from cells with a known sterol dependency comprise stratifying genes of cells with an unknown sterol dependency into putatively sterol-dependent and putatively sterol- independent groups. In some embodiments, the genetic signatures obtained from cells with a known sterol dependency comprise ranking cells from putatively sterol-dependent to putatively sterol-independent. In some embodiments, the genetic signatures obtained from cells with a known lipid dependency comprise stratifying genes of cells with an unknown lipid dependency into putatively lipid-dependent and putatively lipid-independent groups. In some embodiments, the genetic signatures obtained from cells with a known lipid dependency comprise ranking cells from putatively lipid-dependent to putatively lipid- independent. In some embodiments, the genetic signatures obtained from cells with a known carbohydrate dependency comprise stratifying genes of cells with an unknown carbohydrate dependency into putatively carbohydrate-dependent and putatively carbohydrate-independent groups. In some embodiments, the genetic signatures obtained from cells with a known carbohydrate dependency comprise ranking cells from putatively carbohydrate-dependent to putatively carbohydrate-independent. In some embodiments, the genetic signatures obtained from cells with a known vitamin dependency comprise stratifying genes of cells with an unknown vitamin dependency into putatively vitamin-dependent and putatively vitamin- independent groups. In some embodiments, the genetic signatures obtained from cells with a known vitamin dependency comprise ranking cells from putatively vitamin-dependent to putatively vitamin-independent. In some embodiments, the genetic signatures obtained from cells with a known mineral dependency comprise stratifying genes of cells with an unknown mineral dependency into putatively mineral-dependent and putatively mineral-independent groups. In some embodiments, the genetic signatures obtained from cells with a known mineral dependency comprise ranking cells from putatively mineral-dependent to putatively mineral-independent. In some embodiments, the genetic signatures obtained from cells with a known protein dependency comprise stratifying genes of cells with an unknown protein dependency into putatively protein-dependent and putatively protein-independent groups. In some embodiments, the genetic signatures obtained from cells with a known protein dependency comprise ranking cells from putatively protein-dependent to putatively proteinindependent.

[0093] The methods disclosed herein can apply a gene signature generated from cells with a known nutrient dependency comprising from about 1 gene to about 5 genes, about 5 genes to about 10 genes, about 10 to about 25 genes, from about 25 genes to about 50 genes, from about 50 genes to about 75 genes, from about 75 genes to about 100 genes, or from about 100 genes to about 125 genes to stratify genes of a cell with an unknown nutrient dependency. In some embodiments, a genetic signature generated from cells with a known nutrient dependency comprising from about 25 genes to about 50 genes comprise stratifying genes of a cell with an unknown nutrient dependency. In some embodiments, a genetic signature generated from cells with a known nutrient dependency comprising from about 10 genes to about 25 genes comprise stratifying genes of a cell with an unknown nutrient dependency. In some embodiments, a genetic signature generated from cells with a known nutrient dependency comprising from about 1 genes to about 10 genes comprise stratifying genes of a cell with an unknown nutrient dependency.

[0094] In some embodiments, the genetic signature generated from a cell with a known nutrient dependency can be applied to the cell with an unknown nutrient dependency can comprise about 1, about 5, about 10, about 20, about 30, about 40, about 50, about 60, about 70, about 80, about 90, about 100, about 110, about 120, about 130, about 140, about 150, about 160, about 170, about 180, about 190, or about 200 genes. In some embodiments, the genetic signature applied to the cell with an unknown nutrient dependency can comprise about 100 genes. In some embodiments, the genetic signature generated from a cell with a known nutrient dependency can be applied to the cell with an unknown nutrient dependency can comprise about 50 genes. In some embodiments, the genetic signature generated from a cell with a known nutrient dependency can be applied to the cell with an unknown nutrient dependency can comprise about 25 genes. In some embodiments, the genetic signature generated from a cell with a known nutrient dependency can be applied to the cell with an unknown nutrient dependency can comprise about 10 genes. In some embodiments, the genetic signature generated from a cell with a known nutrient dependency can be applied to the cell with an unknown nutrient dependency can comprise about 5 genes. In some embodiments, the genetic signature generated from a cell with a known nutrient dependency can be applied to the cell with an unknown nutrient dependency can comprise about 3 genes. [0095] The methods of the disclosure can use a scoring system to determine the nutrient dependency of a cell based on a genetic signature. In some embodiments, a cell is determined to be nutrient dependent if the cell has an about 5%, about 10%, about 20%, about 30%, about 40%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, or about 95% match to a nutrient dependency genetic signature. In some embodiments, a cell is determined to be nutrient dependent if the cell has about a 60% match to a nutrient dependency signature. In some embodiments, a cell is determined to be nutrient dependent if the cell has about a 70% match to a nutrient dependency signature. In some embodiments, a cell is determined to be nutrient dependent if the cell has about a 80% match to a nutrient dependency signature. In some embodiments, a cell is determined to be nutrient dependent if the cell has about a 90% match to a nutrient dependency signature. In some embodiments, a cell is determined to be nutrient dependent if the cell has about a 95% match to a nutrient dependency signature.

Nutrient Modulation Therapy

[0096] The methods disclosed herein comprise identifying patients with cancer type that will respond to nutrient modulation therapy. In some embodiments, the nutrient modulation therapy can be nutrient-starvation therapy. In some embodiments, the nutrient modulation therapy can be nutrient-supplementation therapy. The genetic signature generated from a cell line with a known nutrient-dependency can be applied to a cancer cell type obtained from the subject, which has an unknown nutrient dependency status. In some embodiments, the genetic signature generated from a cell line with a known nutrient-dependency can be applied to a biological sample obtained from a subject with cancer. In some embodiments, the genetic signature generated from a cell line with a known nutrient-dependency can be applied to a tumor obtained from a subject with cancer.

[0097] In some embodiments, the methods disclosed herein comprise identifying a cancer cell nutrient dependency. In some embodiments, a subject’s tumor can be analyzed for a nutrient dependency. In some embodiments, a subject’s cancer type can be determined to be nutrient-dependent, and the subject can be treated with nutrient starvation therapy for the corresponding nutrient. In some embodiments, a subject’s cancer type can be determined to be amino acid-dependent, and the subject can be treated with amino acid starvation therapy for the corresponding amino acid. In some embodiments, a subject’s cancer type can be determined to be sterol -dependent, and the subject can be treated with sterol starvation therapy for the corresponding amino acid. In some embodiments, a subject’s cancer type can be determined to be proline-dependent, and the subject can be treated with proline starvation therapy. In some embodiments, a subject’s cancer type can be determined to be cholesteroldependent, and the subject can be treated with cholesterol starvation therapy. In some embodiments, a subject’s cancer type can be determined to be lipid-dependent, and the subject can be treated with lipid starvation therapy. In some embodiments, a subject’s cancer type can be determined to be carbohydrate-dependent, and the subject can be treated with carbohydrate starvation therapy. In some embodiments, a subject’s cancer type can be determined to be vitamin-dependent, and the subject can be treated with vitamin starvation therapy. In some embodiments, a subject’s cancer type can be determined to be mineraldependent, and the subject can be treated with mineral starvation therapy. In some embodiments, a subject’s cancer type can be determined to be protein-dependent, and the subject can be treated with protein starvation therapy. A sample from a subject can be analyzed by, for example, genome sequencing, RNA sequencing, mRNA in situ hybridization, proteomics, or immunohistochemistry.

[0098] In some embodiments, methods described herein comprise identifying tumor types that are amino acid dependent. In some embodiments, methods described herein comprise identifying subjects to be treated with amino acid starvation therapy. In some embodiments, the methods of the disclosure can treat cancer using amino acid starvation therapy. In some embodiments, the methods of the disclosure can reduce cancer cell proliferation using amino acid starvation therapy. In some embodiments, the methods of the disclosure can reduce a tumor volume using amino acid starvation therapy.

[0099] In some embodiments, methods described herein comprise identifying tumor types that are sterol dependent. In some embodiments, methods described herein comprise identify subjects to be treated with sterol depletion therapy. In some embodiments, the methods of the disclosure can treat cancer using sterol depletion therapy. In some embodiments, the methods of the disclosure can reduce cancer cell proliferation using sterol depletion therapy. In some embodiments, the methods of the disclosure can reduce a tumor volume using sterol depletion therapy.

[0100] In some embodiments, methods described herein comprise identifying tumor types that are lipid dependent. In some embodiments, methods described herein comprise identifying subjects to be treated with lipid starvation therapy. In some embodiments, the methods of the disclosure can treat cancer using lipid starvation therapy. In some embodiments, the methods of the disclosure can reduce cancer cell proliferation using lipid starvation therapy. In some embodiments, the methods of the disclosure can reduce a tumor volume using lipid starvation therapy.

[0101] In some embodiments, methods described herein comprise identifying tumor types that are carbohydrate dependent. In some embodiments, methods described herein comprise identifying subjects to be treated with carbohydrate starvation therapy. In some embodiments, the methods of the disclosure can treat cancer using carbohydrate starvation therapy. In some embodiments, the methods of the disclosure can reduce cancer cell proliferation using carbohydrate starvation therapy. In some embodiments, the methods of the disclosure can reduce a tumor volume using carbohydrate starvation therapy.

[0102] In some embodiments, methods described herein comprise identifying tumor types that are vitamin dependent. In some embodiments, methods described herein comprise identify subjects to be treated with vitamin starvation therapy. In some embodiments, the methods of the disclosure can treat cancer using vitamin starvation therapy. In some embodiments, the methods of the disclosure can reduce cancer cell proliferation using vitamin starvation therapy. In some embodiments, the methods of the disclosure can reduce a tumor volume using vitamin starvation therapy.

[0103] In some embodiments, methods described herein comprise identifying tumor types that are mineral dependent. In some embodiments, methods described herein comprise identifying subjects to be treated with mineral starvation therapy. In some embodiments, the methods of the disclosure can treat cancer using mineral starvation therapy. In some embodiments, the methods of the disclosure can reduce cancer cell proliferation using mineral starvation therapy. In some embodiments, the methods of the disclosure can reduce a tumor volume using mineral starvation therapy.

[0104] In some embodiments, methods described herein comprise identifying tumor types that are protein dependent. In some embodiments, methods described herein comprise identifying subjects to be treated with protein starvation therapy. In some embodiments, the methods of the disclosure can treat cancer using protein starvation therapy. In some embodiments, the methods of the disclosure can reduce cancer cell proliferation using protein starvation therapy. In some embodiments, the methods of the disclosure can reduce a tumor volume using protein starvation therapy.

[0105] Nutrient starvation therapy can reduce the exogenous amount of at least one nutrient. In some embodiments, nutrient starvation therapy can reduce the amount of a nutrient in a cell. In some embodiments, nutrient starvation therapy can reduce the amount of a nutrient in the blood of a subject. In some embodiments, nutrient starvation therapy can reduce the amount of a nutrient in a cancer cell. In some embodiments, nutrient starvation therapy can reduce the amount of an amino acid in the blood of a subject. In some embodiments, amino acid starvation therapy can reduce the amount of an amino acid in a cancer cell. In some embodiments, sterol starvation therapy can reduce the amount of sterol in the blood of a subject. In some embodiments, sterol starvation therapy can reduce the amount of a sterol in a cancer cell. In some embodiments, lipid starvation therapy can reduce the amount of a lipid in the blood of a subject. In some embodiments, lipid starvation therapy can reduce the amount of lipid in a cancer cell. In some embodiments, carbohydrate starvation therapy can reduce the amount of a carbohydrate in a cancer cell. In some embodiments, carbohydrate starvation therapy can reduce the amount of a carbohydrate in the blood of a subject. In some embodiments, vitamin starvation therapy can reduce the amount of a vitamin in a cancer cell. In some embodiments, vitamin starvation therapy can reduce the amount of a vitamin in the blood of a subject. In some embodiments, mineral starvation therapy can reduce the amount of a mineral in a cancer cell. In some embodiments, mineral starvation therapy can reduce the amount of a mineral in the blood of a subject. In some embodiments, protein starvation therapy can reduce the amount of a protein in a cancer cell. In some embodiments, protein starvation therapy can reduce the amount of a protein in the blood of a subject.

[0106] In some embodiments, the amino acid starvation therapy reduces the exogenous amount of at least one essential amino acid administered to a subject. In some embodiments, the essential amino acid is isoleucine, leucine, valine, phenylalanine, tryptophan, histidine, lysine, threonine, or methionine. In some embodiments, the nutrient starvation therapy reduces the exogenous amount of at least one non-essential amino acid. In some embodiments, the non-essential amino acid is alanine, glycine, proline, tyrosine, aspartic acid, glutamic acid, arginine, serine, cysteine/cystine, asparagine, or glutamine. In some embodiments, the amino acid starvation therapy is glycine restriction therapy. In some embodiments, the amino acid starvation therapy is serine starvation therapy. In some embodiments, the amino acid starvation therapy is leucine starvation therapy. In some embodiments, the amino acid starvation therapy is asparagine starvation therapy. In some embodiments, the amino acid starvation therapy is methionine starvation therapy. In some embodiments, the amino acid starvation therapy is proline starvation therapy. In some embodiments, the amino acid starvation therapy is serine starvation therapy. In some embodiments, the amino acid starvation therapy is glycine starvation therapy.

[0107] In some embodiments, the amino acid starvation therapy reduces the exogenous amount of more than one amino acid. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of two amino acids. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of three amino acids. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of four amino acids. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of five amino acids. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of six amino acids.

[0108] In some embodiments, the amino acid starvation therapy reduces the exogenous amount of at least one essential amino acid and at least one nonessential amino acid. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of proline and at least one essential amino acid. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of proline and methionine. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of proline and serine. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of proline and glycine. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of serine and glycine. In some embodiments, the amino acid starvation therapy reduces the exogenous amount of proline, serine, and glycine.

[0109] Proline is a non-essential amino acid, and proline restriction can impede tumor growth. Proline starvation can prevent clonogenic growth of cancer cells. In some embodiments, methods described herein comprise identifying tumor types that are prolinedependent. In some embodiments, methods described herein comprise identifying subjects to be treated with proline starvation therapy. In some embodiments, the methods of the disclosure comprise treating cancer using proline starvation therapy. In some embodiments, the methods of the disclosure comprise reducing cancer cell proliferation using proline starvation therapy. In some embodiments, the methods of the disclosure comprise reducing a tumor volume using proline starvation therapy.

[0110] Cholesterol is an energy-rich, waxy hydrophobic compound synthesized by animals through the mevalonate pathway. Because the human body is able to synthesize cholesterol, cholesterol is classified as a non-essential nutrient. Cholesterol synthesis as well as cholesterol uptake is shown to be upregulated in many cancer cells. Fast dividing cancer cells may depend on cholesterol as a source of high energy to sustain rapid proliferation. In some embodiments, methods described herein comprise identifying tumor types that are cholesterol-dependent. In some embodiments, the methods of the disclosure can treat cancer using cholesterol starvation therapy. In some embodiments, the methods of the disclosure can reduce a tumor volume using cholesterol starvation therapy.

[OHl] The nutrient starvation therapy disclosed herein can reduce an exogenous amount of a nutrient administered to a subject by from about 1%, about 5%, about 10% to about 20%, about 20% to about 30%, about 30% to about 40%, about 40% to about 50%, about 50% to about 60%, about 60% to about 70%, about 70% to about 80%, about 80% to about 90%, or about 90% to about 95%. In some embodiments, the exogenous amount of the nutrient administered to the subject can be reduced by from about 30% to about 40%. In some embodiments, the exogenous amount of the nutrient administered to the subject can be reduced by from about 60% to about 70%.

[0112] In some embodiments, the nutrient starvation therapy disclosed herein can reduce an exogenous amount of a nutrient administered to a subject by about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 95%. In some embodiments, the nutrient starvation therapy disclosed herein can reduce the exogenous amount of the nutrient administered to the subject by about 25%. In some embodiments, the nutrient starvation therapy disclosed herein can reduce the exogenous amount of the nutrient administered to the subject by about 50%. In some embodiments, the nutrient starvation therapy disclosed herein can reduce the exogenous amount of the nutrient administered to the subject by about 75%.

[0113] In some embodiments, the nutrient starvation therapy disclosed herein can reduce an amount of a nutrient in a subject’s tumor by about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 95%. In some embodiments, the nutrient starvation therapy disclosed herein can reduce the amount of the nutrient in the subject’s tumor by about 25%. In some embodiments, the nutrient starvation therapy disclosed herein can reduce the amount of the nutrient in the subject’s tumor by about 50%. In some embodiments, the nutrient starvation therapy disclosed herein can reduce the amount of the nutrient in the subject’s tumor by about 75%.

[0114] In some embodiments, the nutrient starvation therapy disclosed herein can reduce an amount of a nutrient in a subject’s blood by about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 95%. In some embodiments, the nutrient starvation therapy disclosed herein can reduce the amount of the nutrient in the subject’s blood by about 25%. In some embodiments, the nutrient starvation therapy disclosed herein can reduce the amount of the nutrient in the subject’s blood by about 50%. In some embodiments, the nutrient starvation therapy disclosed herein can reduce the amount of the nutrient in the subject’s blood by about 75%.

[0115] In some embodiments, the nutrient modulation therapy can be administered by administering a dietary product. In some embodiments, a dietary product used for nutrient starvation therapy can be substantially devoid of or have a limited amount of histidine, arginine, alanine, isoleucine, cysteine/cystine, aspartic acid, leucine, glutamine, asparagine, lysine, glycine, glutamic acid, methionine, proline, serine, phenylalanine, tyrosine, threonine, tryptophan, or valine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of an essential amino acid, a conditionally essential amino acid, a nonessential amino acid, or any combination thereof. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, or valine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of arginine, cysteine/cystine, glutamine, glycine, proline, or tryptophan. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of alanine, aspartic acid, asparagine, glutamic acid, or serine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of at least one of the group of amino acids consisting of: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, arginine, cysteine/cystine, glutamine, glycine, proline, tryptophan, alanine, aspartic acid, asparagine, glutamic acid, and serine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of at least two of the group of amino acids consisting of: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, arginine, cysteine/cystine, glutamine, glycine, proline, tryptophan, alanine, aspartic acid, asparagine, glutamic acid, and serine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of at least three, at least four, at least five, or more of the group of amino acids consisting of: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, arginine, cysteine/cystine, glutamine, glycine, proline, tryptophan, alanine, aspartic acid, asparagine, glutamic acid, and serine. [0116] In some embodiments, a dietary product can be substantially devoid of or have a limited amount of glycine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of serine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of proline. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of cysteine or cystine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of tyrosine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of asparagine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of glutamine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of glutamate.

[0117] In some embodiments, a dietary product can be substantially devoid of at least one of the group of amino acids consisting of: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, arginine, cysteine/cystine, glutamine, glycine, proline, tryptophan, alanine, aspartic acid, asparagine, glutamic acid, and serine. In some embodiments, a dietary product can be substantially devoid of at least two of the group of amino acids consisting of: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, arginine, cysteine/cystine, glutamine, glycine, proline, tryptophan, alanine, aspartic acid, asparagine, glutamic acid, and serine. In some embodiments, a dietary product can be substantially devoid of at least three, at least four, at least five, or more of the group of amino acids consisting of: histidine, isoleucine, leucine, lysine, methionine, phenylalanine, threonine, tryptophan, valine, arginine, cysteine/cystine, glutamine, glycine, proline, tryptophan, alanine, aspartic acid, asparagine, glutamic acid, and serine. In some embodiments, a dietary product can be substantially devoid of glycine. In some embodiments, a dietary product can be substantially devoid of serine. In some embodiments, a dietary product can be substantially devoid of proline.

[0118] In some embodiments, a dietary product can be substantially devoid of or have a limited amount of serine and glycine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of serine, glycine, and proline. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of serine, glycine, and cysteine/cystine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of serine, glycine, and tyrosine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of serine, glycine, and asparagine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of serine, glycine, proline, and cysteine/cystine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of serine, glycine, proline, and tyrosine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of serine, glycine, proline, and asparagine. In some embodiments, a dietary product can be substantially devoid of or have a limited amount of serine, glycine, glutamate, glutamine, and cysteine/cystine.

[0119] In some embodiments, a method of the disclosure can comprise administering a dietary product that is restricted in total lipid intake, e.g., a daily total lipid intake. In some embodiments, a method of the disclosure can comprise administering a dietary product that is restricted in a daily recommended dietary lipid intake. In some embodiments, a dietary product comprises no more than 80%, no more than 70%, no more than 75%, no more than 60%, no more than 50%, no more than 40%, no more than 30%, no more than 25%, no more than 20%, no more than 10%, no more than 5%, no more than 4%, no more than 3%, no more than 2%, no more than 1%, or no more than 0.5% of a subject’s average daily lipid intake prior to start of the dietary product. In some embodiments, a diet comprises less than 90%, less than 80%, less than 70%, less than 75%, less than 60%, less than 50%, less than 40%, less than 30%, less than 25%, less than 20%, less than 10%, less than 5%, less than 4%, less than 3%, less than 2%, less than 1%, or less than 0.5% of a subject’s average daily lipid intake prior to start of the dietary product. In some embodiments, a method of the disclosure can comprise administering a dietary product that does not comprise lipids. A subject’s lipid intake can be according to a dietary guideline published by a federal government agency, e.g., the United States Departments of Agriculture and Health and Human Services or the National Health and Nutrition Examination Survey (NHANES). A subject’s average daily lipid intake can be according to the Dietary Guidelines for Americans published by the United States Departments of Agriculture and Health and Human Services. A subject’s average daily recommended lipid intake can be according to the Dietary Guidelines for Americans published by the United States Departments of Agriculture and Health and Human Services. [0120] In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 100 g/day, no more than 90 g/day, no more than 80 g/day, no more than 70 g/day, no more than 60 g/day, no more than 50 g/day, no more than 40 g/day, no more than 30 g/day, no more than 20 g/day, no more than 10 g/day, no more than 5 g/day, or no more than 1 g/day of total lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising a restricted amount of lipids, e.g., less than about 40% of total calories from lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 40%, less than about 35%, less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, or less than about 5% of total daily calories from lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 30% of total daily calories from lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 25% of total daily calories from lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 20% of total daily calories from lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 15% of total daily calories from lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 10% of total daily calories from lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 5% of total daily calories from lipids, e.g., based on a 2000 kcal/day diet. The amount of lipids in the dietary product can vary in a proportionate amount for subjects who consume less than 2000 kcal/day diet or more than 2000 kcal/day diet.

[0121] In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 80 g/day, less than about 75 g/day, less than about 70 g/day, less than about 65 g/day, less than about 60 g/day, less than about 55 g/day, less than about 50 g/day, less than about 45 g/day, less than about 40 g/day, less than about 35 g/day, less than about 30 g/day, less than about 25 g/day, less than about 20 g/day, less than about 15 g/day, less than about 10 g/day, or less than about 5 g/day of lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 70 g/day of lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 60 g/day of lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 50 g/day of lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 40 g/day of lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 30 g/day of lipids, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a diet comprising less than about 20 g/day of lipids, e.g., based on a 2000 kcal/day diet. The amount of lipids in the diet can vary in a proportionate amount for subjects who consume less than 2000 kcal/day diet or more than 2000 kcal/day diet.

[0122] In some embodiments, a method of the disclosure can comprise administering a dietary product that is restricted in total dietary cholesterol intake, e.g., a daily total cholesterol intake. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 500 mg/day, no more than 450 mg/day, no more than 400 mg/day, no more than 350 mg/day, no more than 300 mg/day, no more than 250 mg/day, no more than 200 mg/day, no more than 150 mg/day, no more than 100 mg/day, no more than 75 mg/day, or no more than 50 mg/day of cholesterol, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product devoid of cholesterol. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 250 mg/day of cholesterol, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 200 mg/day of cholesterol, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 150 mg/day of cholesterol, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 100 mg/day of cholesterol, e.g., based on a 2000 kcal/day diet. Diets low in or devoid of cholesterol can exclude food products containing high levels of cholesterol, such as animal fat, egg yolks, shrimp, whole milk dairy, butter, cream, and cheese.

[0123] In some embodiments, a method of the disclosure can comprise administering a dietary product that is restricted in cholesterol intake, e.g., a daily cholesterol intake. Cholesterol dietary intake by men can average by about 350 mg/day; cholesterol dietary intake by women can average by about 240 mg/day. In some embodiments, a method of the disclosure can comprise administering a dietary product that is restricted in a daily recommended dietary cholesterol intake. In some embodiments, a dietary product comprises no more than 80%, no more than 70%, no more than 75%, no more than 60%, no more than 50%, no more than 40%, no more than 30%, no more than 25%, no more than 20%, no more than 10%, no more than 5%, no more than 4%, no more than 3%, no more than 2%, no more than 1%, or no more than 0.5% of a subject’s average daily cholesterol intake prior to start of the diet. In some embodiments, a dietary product comprises less than 90%, less than 80%, less than 70%, less than 75%, less than 60%, less than 50%, less than 40%, less than 30%, less than 25%, less than 20%, less than 10%, less than 5%, less than 4%, less than 3%, less than 2%, less than 1%, or less than 0.5% of a subject’s average daily cholesterol intake prior to start of the diet. A subject’s cholesterol intake can be according to a dietary guideline published by a federal government agency, e.g., the United States Departments of Agriculture and Health and Human Services or the National Health and Nutrition Examination Survey (NHANES). A subject’s average daily cholesterol intake can be according to the Dietary Guidelines for Americans published by the United States Departments of Agriculture and Health and Human Services. A subject’s average daily recommended cholesterol intake can be according to the Dietary Guidelines for Americans published by the United States Departments of Agriculture and Health and Human Services. For example, a daily recommended cholesterol intake is about 300 mg/day according to the 2010 Dietary Guidelines for Americans published by the United States Departments of Agriculture and Health and Human Services.

[0124] In some cases, a subject’s typical cholesterol intake exceeds a recommended cholesterol intake amount. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 170%, no more than 160%, no more than 150%, no more than 140%, no more than 130%, no more than 120%, no more than 110%, no more than 100%, no more than 90%, no more than 80%, no more than 70%, no more than 60%, no more than 50%, no more than 40%, no more than 30%, no more than 20%, no more than 10% of a daily recommended cholesterol intake amount. In some embodiments, a method of the disclosure can comprise administering a dietary product that does not comprise cholesterol. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 70% of a daily recommend daily cholesterol intake value. In some embodiments, a method of the disclosure comprise administering a dietary product comprising no more than 50% of a daily recommended cholesterol intake value. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 30% of a daily recommended cholesterol intake value. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 25% of a daily recommended cholesterol intake value.

[0125] In some embodiments, a method of the disclosure can comprise administering a dietary product that is restricted in total fat intake, e.g., a daily total fat intake. In some embodiments, a method of the disclosure can comprise administering a dietary product that is restricted in total fat intake, e.g., a daily total fat intake. In some embodiments, a method of the disclosure can comprise administering a dietary product that is restricted in a daily recommended dietary fat intake. In some embodiments, a dietary product comprises no more than 80%, no more than 70%, no more than 75%, no more than 60%, no more than 50%, no more than 40%, no more than 30%, no more than 25%, no more than 20%, no more than 10%, no more than 5%, no more than 4%, no more than 3%, no more than 2%, no more than 1%, or no more than 0.5% of a subject’s average daily fat intake prior to start of the diet. In some embodiments, a dietary product comprises less than 90%, less than 80%, less than 70%, less than 75%, less than 60%, less than 50%, less than 40%, less than 30%, less than 25%, less than 20%, less than 10%, less than 5%, less than 4%, less than 3%, less than 2%, less than 1%, or less than 0.5% of a subject’s average daily fat intake prior to start of the diet. In some embodiments, a method of the disclosure can comprise administering a dietary product that does not comprise fat. A subject’s fat intake can be according to a dietary guideline published by a federal government agency, e.g., the United States Departments of Agriculture and Health and Human Services or the National Health and Nutrition Examination Survey (NHANES). A subject’s average daily fat intake can be according to the Dietary Guidelines for Americans published by the United States Departments of Agriculture and Health and Human Services. A subject’s average daily recommended fat intake can be according to the Dietary Guidelines for Americans published by the United States Departments of Agriculture and Health and Human Services.

[0126] In some embodiments, a method of the disclosure can comprise administering a dietary product comprising no more than 100 g/day, no more than 90 g/day, no more than 80 g/day, no more than 70 g/day, no more than 60 g/day, no more than 50 g/day, no more than 40 g/day, no more than 30 g/day, no more than 20 g/day, no more than 10 g/day, no more than 5 g/day, or no more than 1 g/day of total fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising a restricted amount of fat, e.g., less than about 40% of total calories from fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 40%, less than about 35%, less than about 30%, less than about 25%, less than about 20%, less than about 15%, less than about 10%, or less than about 5% of total daily calories from fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 30% of total daily calories from fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 25% of total daily calories from fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 20% of total daily calories from fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 15% of total daily calories from fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 10% of total daily calories from fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 5% of total daily calories from fat, e.g., based on a 2000 kcal/day diet. The amount of fat in the dietary product can vary in a proportionate amount for subjects who consume less than 2000 kcal/day diet or more than 2000 kcal/day diet.

[0127] In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 80 g/day, less than about 75 g/day, less than about 70 g/day, less than about 65 g/day, less than about 60 g/day, less than about 55 g/day, less than about 50 g/day, less than about 45 g/day, less than about 40 g/day, less than about 35 g/day, less than about 30 g/day, less than about 25 g/day, less than about 20 g/day, less than about 15 g/day, less than about 10 g/day, or less than about 5 g/day of fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 70 g/day of fat, e,g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 60 g/day of fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 50 g/day of fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 40 g/day of fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 30 g/day of fat, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 20 g/day of fat, e.g., based on a 2000 kcal/day diet. The amount of fat in the dietary product can vary in a proportionate amount for subjects who consume less than 2000 kcal/day diet or more than 2000 kcal/day diet.

[0128] The amount of fat in the diet can be based on the weight of a subject, e.g., a weight in kilograms (kg). In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than about 1500 mg/kg/day, less than about 1400 mg/kg/day, less than about 1300 mg/kg/day, less than about 1200 mg/kg/day, less than about 1100 mg/kg/day, less than about 1000 mg/kg/day, less than about 900 mg/kg/day, less than about 800 mg/kg/day, less than about 700 mg/kg/day, less than about 600 mg/kg/day, less than about 500 mg/kg/day, less than about 400 mg/kg/day, less than about 300 mg/kg/day, less than about 200 mg/kg/day, or less than about 100 mg/kg/day of fat based on a subject’s weight.

[0129] In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than 110%, less than 100%, less than 90%, less than 80%, less than 70%, less than 60%, less than 50%, less than 40%, less than 30%, less than 20%, less than 10%, less than 5%, less than 4%, less than 3%, less than 2%, or less than 1% of a recommended daily fat intake, e.g., based on a 2000 kcal/day diet. A daily recommended intake for fat can be based on the United States Food and Drug Administration Rules and Regulations-Revision of the Nutrition and Supplemental Facts Label. For example, a daily recommended intake is about 78 g/day of fat, based on the United States Food and Drug Administration Rules and Regulations-Revision of the Nutrition and Supplemental Facts Label dated May 27, 2016. In some embodiments, a method of the disclosure can comprise administering dietary product comprising less than 90% of a daily recommended fat intake, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than 80% of a daily recommended fat intake, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than 70% of a daily recommended fat intake, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than 60% of a daily recommended fat intake, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than 50% of a daily recommended fat intake, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than 40% of a daily recommended fat intake, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than 30% of a daily recommended fat intake, e.g., based on a 2000 kcal/day diet. In some embodiments, a method of the disclosure can comprise administering a dietary product comprising less than 20% of a daily recommended fat intake, e.g., based on a 2000 kcal/day diet. The amount of fat in the dietary product can vary in a proportionate amount for subjects who consume less than 2000 kcal/day diet or more than 2000 kcal/day diet.

Combination Therapy

[0130] A method disclosed herein can identify at least one therapeutic agent to be used in combination with a nutrient starvation therapy. In some embodiments, the method can evaluate genes and gene stratification of putatively nutrient-dependent cells to identify a combination therapy agent. In some embodiments, the genetic information of putatively nutrient-dependent cells can be used to identify inhibitors that increase nutrient sensitivity of the cells. In some embodiments, the method can evaluate genes and gene stratification of putatively nutrient-independent cells to identify a combination therapy agent. In some embodiments, the genetic information of putatively nutrient-independent cells can be used to identify inhibitors that introduce nutrient-sensitivity to the cells. In some embodiments, the nutrient is an amino acid. In some embodiments, the nutrient is proline. In some embodiments, the nutrient is a sterol. In some embodiments, the nutrient is cholesterol. In some embodiments, the nutrient is a lipid. In some embodiments, the nutrient is a vitamin. In some embodiments, the nutrient is a mineral. In some embodiments, the nutrient is a protein. [0131] A method disclosed herein can be used to identify a therapeutic agent to be used with amino acid starvation therapy. In some embodiments, the method can be used to identify a therapeutic agent to be used with proline starvation therapy. A method disclosed herein can be used to identify a therapeutic agent to be used with sterol starvation therapy. In some embodiments, the method can be used to identify a therapeutic agent to be used with cholesterol starvation therapy. A method disclosed herein can be used to identify a therapeutic agent to be used with lipid starvation therapy. A method disclosed herein can be used to identify a therapeutic agent to be used with carbohydrate starvation therapy. A method disclosed herein can be used to identify a therapeutic agent to be used with vitamin starvation therapy. A method disclosed herein can be used to identify a therapeutic agent to be used with mineral starvation therapy. A method disclosed herein can be used to identify a therapeutic agent to be used with protein starvation therapy. In some embodiments, the therapeutic agent is a chemotherapy agent, a radiotherapeutic agent, an agent that modulates proline metabolism, proline biosynthesis, fatty acid metabolism, sterol biosynthesis, sterol metabolism, cholesterol biosynthesis, cholesterol levels, ubiquinone biosynthesis, the hypoxia pathway, or the subject’s immune system. In some embodiments, the therapeutic agent is a sirtuin modulator. In some embodiments, the therapeutic agent is an agent that modulates proline biosynthesis. In some embodiments, the therapeutic agent is an agent that modulates proline metabolism. In some embodiments, the therapeutic agent is an agent that modulates fatty acid metabolism. In some embodiments, the therapeutic agent is an agent that modulates sterol biosynthesis. In some embodiments, the therapeutic agent is an agent that modulates cholesterol biosynthesis. In some embodiments, the therapeutic agent is an agent that modulates cholesterol metabolism. In some embodiments, the therapeutic agent is an agent that modulates cholesterol levels. In some embodiments, the therapeutic agent is an agent that modulates ubiquinone biosynthesis. In some embodiments, the therapeutic agent is an agent that modulates the hypoxia pathway. In some embodiments, the therapeutic agent is an agent that modulates the subject’s immune system. In some embodiments, the therapeutic agent is a radiotherapeutic agent.

[0132] In some embodiments, the therapeutic agent is a chemotherapy agent. In some embodiments, the chemotherapy agent can be a dihydrofolate reductase (DHFR) or nicotinamide adenine dinucleotide kinase 2 (NADK2) antagonist. In some embodiments, the DHFR or NADK2 antagonist is methotrexate. In some embodiments, the chemotherapy agent can be a DNA topoisomerase 2 (TOP2) antagonist. In some embodiments, the TOP2 antagonist is etoposide. In some embodiments, the chemotherapy agent can be a DNA topoisomerase I (TOPI) antagonist. In some embodiments, the TOPI antagonist is irinotecan. [0133] In some embodiments, the therapeutic agent is an agent that modulates proline metabolism. In some embodiments, the agent that modulates proline metabolism is a lipoate- dependent dehydrogenase antagonist. In some embodiments, the lipoate-dependent dehydrogenase antagonist is 6,8-bis(benzylsulfinyl)octanoic acid (CPI-613). In some embodiments, the agent that modulates proline metabolism is a pyrroline-5-carboxylate reductase (PYCR1) antagonist. In some embodiments, the PYCR1 antagonist is N-(3- bromobenzyl)-N-methyl-2-propyn- 1 -amine.

[0134] In some embodiments, the therapeutic agent is a sirtuin modulator. In some embodiments, the agent is a sirtuin 1 (SIRT1) agonist. In some embodiments, the SIRT1 agonist is resveratrol or N-(2-(3-(l-Piperazinylmethyl)imidazo[2,l-b]thiazol-6-yl)phenyl)-2- quinolinecarboxamide (SRT1720) HC1. In some embodiments, the therapeutic agent is a SIRT1 antagonist. In some embodiments, the agent is 6-chloro-2,3,4,9-tetrahydro-lH- carb azol e-1 -carboxamide (selisistat; EX 527). In some embodiments, the SIRT1 antagonist is a racemic mixture of selisistat. In some embodiments, the agent is a sirtuin 3 (SIRT3) antagonist. In some embodiments, the agent is a SIRT1 and SIRT3 dual antagonist. In some embodiments, the SIRT1 and SIRT3 dual antagonist is 3-(lH-l,2,3-triazol-4-yl) pyridine (3- TYP).

[0135] In some embodiments, the therapeutic agent modulates fatty acid metabolism. In some embodiments, the agent that modulates fatty acid metabolism is a hydroxyacyl-CoA dehydrogenase trifunctional multienzyme complex subunit beta (HADHB) antagonist or carnitine palmitoyltransferase I (CPT1) antagonist. In some embodiments, the agent that modulates fatty acid metabolism is a peroxisome proliferator-activated receptor (PPAR)- gamma agonist or a PPAR-alpha agonist. In some embodiments, the HADHB antagonist is trimetazidine. In some embodiments, the CPT1 antagonist is 2-[6-(4-chlorophenoxy)hexyl]- oxirane-2-carboxylic acid (etomoxir) or a pharmaceutically acceptable salt thereof. In some embodiments, the CPT1 antagonist is a racemic mixture of 2-[6-(4-chlorophenoxy)hexyl]- oxirane-2-carboxylic acid (etomoxir) or a pharmaceutically acceptable salt thereof. In some embodiments, the PPAR-gamma agonist is a thiazolidinesione such as 2,4-thiazolidinedione (pioglitazone) or a pharmaceutically acceptable salt thereof. In some embodiments, the PPAR-gamma agonist is pioglitazone HC1. In some embodiments, the PPAR-alpha agonist is a fibrate such as 2-[4-(4-chlorobenzoyl)phenoxy]-2-methylpropanoic acid isopropyl ester (fenofibrate). In some embodiments, the PPAR-alpha agonist is (glucagon).

[0136] In some embodiments, the therapeutic agent modulates ubiquinone biosynthesis. In some embodiments, the therapeutic agent that modulates ubiquinone biosynthesis is a para- hydroxybenzoate-polyprenyltransferase (COQ2) antagonist, a 3 -hydroxy-3 -methylglutaryl- CoA reductase (HMGCR) antagonist Type I, an HMGCR antagonist Type II, or a Coenzyme 8QA (COQ8A) antagonist. In some embodiments, the COQ2 antagonist is 4-nitrobenzoate (4-nitrobenzoic acid). In some embodiments, the HMGCR Type I antagonist is (lS,3R,7S,8S,8aR)-8-{2-[(2R,4R)-4-hydroxy-6-oxotetrahydro-2H-pyran-2-yl]ethyl}-3,7- dimethyl-l,2,3,7,8,8a-hexahydronaphthalen-l-yl 2,2-dimethylbutanoate (simvastatin). In some embodiments, the HMGCR antagonist Type II is (3R,5R)-7-[2-(4-fluorophenyl)-3- phenyl-4-(phenylcarbamoyl)-5-(propan-2-yl)-lH-pyrrol-l-yl]-3,5-dihydroxyheptanoic acid (atorvastatin). In some embodiments, the COQ8A antagonist is 4-((3,4,5- trimethoxyphenyl)amino)quinoline-7-carbonitrile (UNC-CA157).

[0137] In some embodiments, the therapeutic agent modulates cholesterol biosynthesis. In some embodiments the therapeutic agent that modulates cholesterol biosynthesis is a 3- hydroxy-3-methylglutaryl-CoA reductase (HMGCR) antagonist. In some embodiments, the HMGCR antagonist is a statin. In some embodiments, the statin is atorvastatin or a pharmaceutically acceptable salt thereof. In some embodiments, the statin is atorvastatin calcium. In some embodiments, the statin is fluvastatin or a pharmaceutically acceptable salt thereof. In some embodiments, the statin is fluvastatin sodium. In some embodiments, the statin is pravastatin or a pharmaceutically acceptable salt thereof. In some embodiments, the statin is pravastatin sodium. In some embodiments, the statin is rosuvastatin or a pharmaceutically acceptable salt thereof. In some embodiments, the statin is rosuvastatin calcium. In some embodiments, the statin is simvastatin or a pharmaceutically acceptable salt thereof. In some embodiments, the statin is simvastatin sodium. In some embodiments, the statin is lovastatin or a pharmaceutically acceptable salt thereof. In some embodiments, the statin is lovastatin sodium. In some embodiments, the statin is pitavastatin or a pharmaceutically acceptable salt thereof. In some embodiments, the statin is pitavastatin calcium. In some embodiments, the statin is pitavastatin magnesium.

[0138] In some embodiments, the therapeutic agent modulates cholesterol levels. In some embodiments, the therapeutic agent that modulates cholesterol levels is a cholesterol lowering agent. In some embodiments, the cholesterol lowering agent is a cholesteryl ester transfer protein (CETP) inhibitor. In some embodiments, the cholesterol lowering agent is a lecithin- cholesterol acyltransferase (LCAT) inhibitor. In some embodiments, the cholesterol lowering agent is a bile acid sequestrant. In some embodiments, the cholesterol lowering agent is a cholesterol absorption inhibitor.

[0139] In some embodiments, the therapeutic agent modulates a hypoxia pathway. In some embodiments, the therapeutic agent that modulates a hypoxia pathway is prolyl hydroxylase 1 (PHD1), prolyl hydroxylase 2 (PHD2), and prolyl hydroxylase 3 (PHD3) antagonist. In some embodiments, the therapeutic agent that modulates a hypoxia pathway is a PHD2 antagonist. In some embodiments, the therapeutic agent that modulates a hypoxia pathway is a hypoxiainducible factor 1 -alpha (HIFla) antagonist. In some embodiments, the therapeutic agent that modulates a hypoxia pathway is a pyruvate dehydrogenase kinase isoform 2 (PDK2) and pyruvate dehydrogenase kinase isoform 4 (PDK4) antagonist. In some embodiments, the therapeutic agent that modulates a hypoxia pathway is a pyruvate dehydrogenase kinase isoform 1 (PDK1) and pyruvate dehydrogenase kinase isoform 2 (PDK2) antagonist. In some embodiments, the PHD1, PHD2, and PHD3 antagonist is N-[(4-hydroxy-l-methyl-7- phenoxy-3-isoquinolinyl)carbonyl]glycine (roxadustat). In some embodiments, the PHD1, PHD2, and PHD3 antagonist is N-[bis(4-methoxyphenyl)methyl]-4-hydroxy-2-(pyridazin-3- yl)pyrimidine-5-carboxamide (MK-8617). In some embodiments, the PHD2 antagonist is N- [[l,2-dihydro-4-hydroxy-2-oxo-l-(phenylmethyl)-3-quinolinyl]carbonyl]-glycine (IOX2). In some embodiments, the PHD2 antagonist is tert-butyl 6-[3-oxo-4-(triazol-l-yl)-lH-pyrazol-2- yl]pyridine-3 -carboxylate (IOX4). In some embodiments, the HIFla antagonist is (S)-4-(2- amino-2-carboxyethyl)-N,N-bis(2-chloroethyl)aniline oxide (PX-478) or a pharmaceutically acceptable salt thereof. In some embodiments, the PDK2 and PKD4 antagonist is di chloroacetate (DCA). In some embodiments, the PDK1 and PKD2 antagonist is 4-[3- chloro-4-[[(2R)-3,3,3-trifluoro-2-hydroxy-2-methylpropanoyl]amino]phenyl]sulfonyl-N,N- dimethylbenzamide (AZD7545).

[0140] In some embodiments, the therapeutic agent is an immune system modulator. In some embodiments, the immune system modulator is an immune checkpoint inhibitor. In some embodiments, the immune system modulator is interferon alpha, interferon beta, or interferon gamma. In some embodiments, the immune system modulator is a cyclic GMP-AMP synthase (cGAS) agonist. In some embodiments, the cGAS agonist is a stimulator of interferon genes (STING) agonist. In some embodiments, the immune system modulator is a programmed cell death protein (PD1) antagonist. In some embodiments, the PD1 antagonist is ipilimumab or pembrolizumab. In some embodiments, the immune system modulator is a PD-L1 antagonist. In some embodiments, the PD-L1 antagonist is avelumab, atezolizumab, or durvalumab. In some embodiments, the immune system modulator is a integrin-associated protein (CD47) antagonist. In some embodiments, the CD47 antagonist is magrolimab, TTI- 622, TTI-621, or ALX148. In some embodiments, the immune system modulator is a therapy that depletes MDSCs, for example, otilimab. In some embodiments, the therapeutic agent is an indoleamine 2,3 -dioxygenase 1 (IDO1) antagonist. In some embodiments, the IDO1 antagonist is epacadostat, indoximod or linrodostat. In some embodiments, the therapeutic agent is an antagonist of tryptophan 2,3-dioxygenase (TDO). In some embodiments the antagonist is HTI-1090, LM 10 or 680C91.

Methods of Treating Cancer

[0141] The methods disclosed herein can be used to treat cancer in a subject. Non-limiting examples of cancer that can be treated by a method of the disclosure include: acute lymphoblastic leukemia, acute myeloid leukemia, adrenocortical carcinoma, AIDS-related cancers, AIDS-related lymphoma, anal cancer, appendix cancer, astrocytomas, basal cell carcinoma, bile duct cancer, bladder cancer, bone cancers, brain tumors, such as cerebellar astrocytoma, cerebral astrocytoma/malignant glioma, ependymoma, medulloblastoma, supratentorial primitive neuroectodermal tumors, visual pathway and hypothalamic glioma, breast cancer, bronchial adenomas, Burkitt lymphoma, carcinoma of unknown primary origin, central nervous system lymphoma, cerebellar astrocytoma, cervical cancer, childhood cancers, chronic lymphocytic leukemia, chronic myelogenous leukemia, chronic myeloproliferative disorders, colon cancer, cutaneous T-cell lymphoma, desmoplastic small round cell tumor, endometrial cancer, ependymoma, esophageal cancer, Ewing's sarcoma, germ cell tumors, gallbladder cancer, gastric cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal tumor, gliomas, hairy cell leukemia, head and neck cancer, heart cancer, hepatocellular (liver) cancer, Hodgkin lymphoma, Hypopharyngeal cancer, intraocular melanoma, islet cell carcinoma, Kaposi sarcoma, kidney cancer, laryngeal cancer, lip and oral cavity cancer, liposarcoma, liver cancer, lung cancers, such as non-small cell and small cell lung cancer, lymphomas, leukemias, macroglobulinemia, malignant fibrous histiocytoma of bone/osteosarcoma, medulloblastoma, melanomas, mesothelioma, metastatic squamous neck cancer with occult primary, mouth cancer, multiple endocrine neoplasia syndrome, myelodysplastic syndromes, myeloid leukemia, nasal cavity and paranasal sinus cancer, nasopharyngeal carcinoma, neuroblastoma, non-Hodgkin lymphoma, non-small cell lung cancer, oral cancer, oropharyngeal cancer, osteosarcoma/ malignant fibrous histiocytoma of bone, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumor, pancreatic cancer, pancreatic cancer islet cell, paranasal sinus and nasal cavity cancer, parathyroid cancer, penile cancer, pharyngeal cancer, pheochromocytoma, pineal astrocytoma, pineal germinoma, pituitary adenoma, pleuropulmonary blastoma, plasma cell neoplasia, primary central nervous system lymphoma, prostate cancer, rectal cancer, renal cell carcinoma, renal pelvis and ureter transitional cell cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcomas, skin cancers, skin carcinoma merkel cell, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, stomach cancer, T- cell lymphoma, throat cancer, thymoma, thymic carcinoma, thyroid cancer, trophoblastic tumor (gestational), cancers of unknown primary site, urethral cancer, uterine sarcoma, vaginal cancer, vulvar cancer, Waldenstrom macroglobulinemia, and Wilms tumor.

[0142] In some embodiments, a method of the disclosure can be used to treat pancreatic cancer. In some embodiments, the method of the disclosure can be used to treat lung cancer. In some embodiments, the method of the disclosure can be used to treat breast cancer. In some embodiments, the method of the disclosure can be used to treat ovarian cancer. In some embodiments, the method of the disclosure can be used to treat stomach cancer. In some embodiments, the method of the disclosure can be used to treat skin cancer. In some embodiments, the method of the disclosure can be used to treat cervical cancer. In some embodiments, a method of the disclosure can be used to treat colorectal cancer. In some embodiments, a method of the disclosure can be used to treat renal cancer. In some embodiments, a method of the disclosure can be used to treat head and neck cancer. In some embodiments, a method of the disclosure can be used to treat leukemia. In some embodiments, a method of the disclosure can be used to treat a blood cancer.

Computer Systems

[0143] In various aspects, any of the systems described herein are operably linked to a computer and are optionally automated through a computer either locally or remotely. In various instances, the methods and systems of the invention further comprise software programs on computer systems and use thereof.

[0144] The computer system 2200 illustrated in FIG. 22 may be understood as a logical apparatus that can read instructions from media 2211 and/or a network port 2205, which can optionally be connected to server 2209 having fixed media 2212. The system can include a CPU 2201, disk drives 2203, optional input devices such as keyboard 2215 and/or mouse 2216 and optional monitor 2207. Data communication can be achieved through the indicated communication medium to a server at a local or a remote location. The communication medium can include any means of transmitting and/or receiving data. For example, the communication medium can be a network connection, a wireless connection or an internet connection. Such a connection can provide for communication over the World Wide Web. Data relating to the present disclosure can be transmitted over such networks or connections for reception and/or review by a party 2222.

[0145] FIG. 23 is a block diagram illustrating a first example architecture of a computer system that can be used in connection with example instances of the present disclosure. As depicted in FIG. 23, the example computer system can include a processor 2302 for processing instructions. Non-limiting examples of processors include: Intel XeonTM processor, AMD OpteronTM processor, Samsung 32-bit RISC ARM 1176JZ(F)-S vl.OTM processor, ARM Cortex- A8 Samsung S5PC100TM processor, ARM Cortex- A8 Apple A4TM processor, Marvell PXA 930TM processor, or a functionally-equivalent processor. Multiple threads of execution can be used for parallel processing. In some instances, multiple processors or processors with multiple cores can also be used, whether in a single computer system, in a cluster, or distributed across systems over a network comprising a plurality of computers, cell phones, and/or personal data assistant devices.

[0146] As illustrated in FIG. 23, a high speed cache 2304 can be connected to, or incorporated in, the processor 2302 to provide a high speed memory for instructions or data that have been recently, or are frequently, used by processor 2302. The processor 2302 is connected to a north bridge 2306 by a processor bus 2308. The north bridge 2306 is connected to random access memory (RAM) 2310 by a memory bus 2312 and manages access to the RAM 2310 by the processor 2302. The north bridge 2306 is also connected to a south bridge 2314 by a chipset bus 2316. The south bridge 2314 is, in turn, connected to a peripheral bus 2318. The peripheral bus can be, for example, PCI, PCI-X, PCI Express, or other peripheral bus. The north bridge and south bridge are often referred to as a processor chipset and manage data transfer between the processor, RAM, and peripheral components on the peripheral bus 2318. In some alternative architectures, the functionality of the north bridge can be incorporated into the processor instead of using a separate north bridge chip. [0147] In some instances, a system 2300 can include an accelerator card 2322 attached to the peripheral bus 2318. The accelerator can include field programmable gate arrays (FPGAs) or other hardware for accelerating certain processing. For example, an accelerator can be used for adaptive data restructuring or to evaluate algebraic expressions used in extended set processing.

[0148] Software and data are stored in external storage 2324 and can be loaded into RAM 2310 and/or cache 2304 for use by the processor. The system 1800 includes an operating system for managing system resources; non-limiting examples of operating systems include: Linux, WindowsTM, MACOSTM, BlackBerry OSTM, iOSTM, and other functionally- equivalent operating systems, as well as application software running on top of the operating system for managing data storage and optimization in accordance with example embodiments of the present disclosure.

[0149] In this example, system 2300 also includes network interface cards (NICs) 2320 and 2321 connected to the peripheral bus for providing network interfaces to external storage, such as Network Attached Storage (NAS) and other computer systems that can be used for distributed parallel processing.

[0150] FIG. 24 is a diagram showing a network 2400 with a plurality of computer systems 2402a, and 2402b, a plurality of cell phones and personal data assistants 1902c, and Network Attached Storage (NAS) 2404a, and 2404b. In example embodiments, systems 2402a, 2402b, and 2402c can manage data storage and optimize data access for data stored in Network Attached Storage (NAS) 2404a and 2404b. A mathematical model can be used for the data and be evaluated using distributed parallel processing across computer systems 2402a, and 2402b, and cell phone and personal data assistant systems 2402c. Computer systems 2402a, and 2402b, and cell phone and personal data assistant systems 2402c can also provide parallel processing for adaptive data restructuring of the data stored in Network Attached Storage (NAS) 2404a and 2404b. FIG. 24 illustrates an example only, and a wide variety of other computer architectures and systems can be used in conjunction with the various embodiments of the present disclosure. For example, a blade server can be used to provide parallel processing. Processor blades can be connected through a back plane to provide parallel processing. Storage can also be connected to the back plane or as Network Attached Storage (NAS) through a separate network interface.

[0151] In some example embodiments, processors can maintain separate memory spaces and transmit data through network interfaces, back plane or other connectors for parallel processing by other processors. In other embodiments, some or all of the processors can use a shared virtual address memory space.

[0152] FIG. 25 is a block diagram of a multiprocessor computer system 2500 using a shared virtual address memory space in accordance with an example embodiment. The system includes a plurality of processors 2502a-f that can access a shared memory subsystem 2504. The system incorporates a plurality of programmable hardware memory algorithm processors (MAPs) 2506a-f in the memory subsystem 2504. Each MAP 2506a-f can comprise a memory 2508a-f and one or more field programmable gate arrays (FPGAs) 2510a-f. The MAP provides a configurable functional unit, and particular algorithms or portions of algorithms can be provided to the FPGAs 2510a-f for processing in close coordination with a respective processor. For example, the MAPs can be used to evaluate algebraic expressions regarding the data model and to perform adaptive data restructuring in example embodiments. In this example, each MAP is globally accessible by all of the processors for these purposes. In one configuration, each MAP can use Direct Memory Access (DMA) to access an associated memory 2508a-f, allowing it to execute tasks independently of, and asynchronously from, the respective microprocessor 2502a-f. In this configuration, a MAP can feed results directly to another MAP for pipelining and parallel execution of algorithms.

[0153] The above computer architectures and systems are examples only, and a wide variety of other computer, cell phone, and personal data assistant architectures and systems can be used in connection with example embodiments, including systems using any combination of general processors, co-processors, FPGAs and other programmable logic devices, system on chips (SOCs), application specific integrated circuits (ASICs), and other processing and logic elements. In some embodiments, all or part of the computer system can be implemented in software or hardware. Any variety of data storage media can be used in connection with example embodiments, including random access memory, hard drives, flash memory, tape drives, disk arrays, Network Attached Storage (NAS) and other local or distributed data storage devices and systems.

[0154] In example embodiments, the computer system can be implemented using software modules executing on any of the above or other computer architectures and systems. In other embodiments, the functions of the system can be implemented partially or completely in firmware, programmable logic devices such as field programmable gate arrays (FPGAs), system on chips (SOCs), application specific integrated circuits (ASICs), or other processing and logic elements. For example, the Set Processor and Optimizer can be implemented with hardware acceleration through the use of a hardware accelerator card.

EXAMPLES

EXAMPLE 1: Hierarchical clustering and stratification based on known prolinedependency of pancreatic cancer cell lines.

[0155] A genetic signature for proline-dependency was determined and used to stratify pancreatic cancer cell lines to determine cancer cell sensitivity to proline restriction therapy. [0156] Genome-wide mRNA expression data was obtained from the Cancer Cell Line Encyclopedia (CCLE) for 29 pancreatic cancer cell lines. The proline-dependency status for the 29 pancreatic cancer cell lines were analyzed, and gene signatures were created as indicators of how a cell line would survive.

[0157] Pancreatic ductal adenocarcinoma (PDAC) cell lines with known proline dependencies (proline-independent cell lines = EPI; proline-dependent cell lines = EPD) were subjected to hierarchical clustering using -16500 genes. 91 genes that produced clear stratification were identified, and the signature of the 91 genes were applied to PDAC cells of unknown proline dependency (EPU). The 91 genes identified that produced clear stratification were used to stratify the EPU cells into two groups: putatively prolinedependent cells (pEPD) and putatively proline-independent cells (pEPI). FIG. 1 illustrates a flow chart of how gene expression data was used to stratify PDAC cells according to proline dependency.

[0158] Scaling of data. Data were first filtered to remove data points deemed not useful for modeling proline dependency. Data points of near-constant values across the 29 pancreatic cancer cell lines were filtered out using the interquartile range, which yielded 5000 genes for downstream analysis. Data were then scaled by mean centering and dividing the values by the standard deviation of each variable. The effect of scaling the data are illustrated in FIG. 2. FIG. 2 shows scaled data prepared by mean-centering the raw data and dividing the resulting data by the standard deviation of each variable.

[0159] Hierarchical clustering'. 91 genes led to stratification of proline-dependent and proline-independent cancer cell lines. FIG. 3 shows hierarchical clustering of pancreatic cancer cell lines based on proline-dependency. TABLE 1 shows the expression of the 91 identified genes in proline-independent cell lines identified by the hierarchical clustering process. TABLE 2 shows the expression of the 91 genes in proline-dependent cell lines identified by the hierarchical clustering process.

TABLE 1

TABLE 2

[0160] Further relevant genes were identified by incorporating prognostic data. The difference in expression of each gene was calculated between proline-dependent and prolineindependent cell lines. A gene was considered relevant if the gene had a log2-fold change in expression of 0.6 < log2 < -0.5. The coefficient of variation (CV) of the expression of each gene in proline-dependent or proline-independent cell lines was calculated. Genes with known prognostic relevance in PDAC were identified from the Human Protein Atlas. If a gene was not prognostic in PDAC, then the gene was considered relevant if the gene expression had a CV < 25% in both proline-dependent and proline-independent cell lines. If a gene was prognostic in PDAC, then the gene was considered relevant if the gene expression had a CV < 25% in either proline-dependent or proline-independent cell lines. TABLE 3 and TABLE 4 show the 436 genes that were identified using the described approach.

[0161] 100 genes from the 436 gene set were stratified based on proline-dependency. FIG. 4 shows the hierarchical clustering of pancreatic cancer cell lines based on proline dependency by incorporating prognostically relevant genes. The data show stratification of 100 genes from the list of 436 genes identified. TABLE 3 shows expression of 436 identified genes in proline-independent cell lines. TABLE 4 shows expression of 436 identified genes in proline-dependent cell lines.

TABLE 3

TABLE 4

[0162] Further subsets of the 436 genes were used to stratify the cell lines based on prolinedependency. Genes were considered based on more stringent CV criteria such that if a gene was not prognostic in PDAC, then the gene was considered relevant if the gene expression had a CV < 10% in both proline-dependent and proline-independent cell lines. If a gene was prognostic in PDAC, then the gene was considered relevant if the gene expression had a CV < 15% in either proline-dependent or proline-independent cell lines. 162 genes met these criteria and were stratified based on proline-dependency. Increasingly more stringent CV criteria were used to prepare smaller subsets of genes that were stratified based on prolinedependencies.

[0163] FIG. 5 illustrates a flow chart of stratifying cells of unknown proline dependency into proline-dependent and proline-independent groups using hierarchical clustering data from proline-independent, proline-dependent, putatively proline-independent, and putatively proline-dependent cells. FIG. 6A illustrates a flow chart used to improve the prolinedependency signature of cells using a fold change between proline-independent and prolinedependency of 0.6 < log2 or log 2 < -0.5 and a CV cutoff of 25%. FIG. 6B illustrates a flow chart used to further improve the proline-dependency signature of cells using a fold change between proline-independent and proline-dependency of 0.6 < log2 < -0.5 and a CV cutoff of 10-15%.

[0164] FIG. 7 shows stratification achieved with a set of 10 genes from the list of 436 genes. FIG. 8 shows stratification achieved with a set of 15 genes from the list of 436 genes. FIG. 9 shows stratification achieved with a set of 30 genes from the list of 436 genes. FIG. 10 shows stratification achieved with a set of 40 genes from the list of 436 genes. FIG. 11 shows stratification achieved with a set of 50 genes from the list of 436 genes. FIG. 12 shows stratification achieved with a set of 75 genes from the list of 436 genes.

EXAMPLE 2: Identification of combination therapy agents.

[0165] A series of experiments were designed to examine the expression of genes from a proline-dependency signature to identify potential therapeutic agents for use in combination with proline restriction therapy.

[0166] Genes stratified into proline-dependent and proline-independent groups were examined for differences in expression between the proline-dependent and prolineindependent groups. Proline-dependent cell lines were enriched for interferon-stimulated genes (ISGs). Programmed death-ligand 1 (PD-L1, CD274) had significantly increased expression levels in proline-dependent cancer cell lines compared to proline-independent cancer cell lines. Major histocompatibility complex class 1-related gene protein (MR1) also had significantly increased expression levels in proline-dependent cancer cell lines compared to proline-independent cancer cell lines. FIG. 13A shows that PD-L1 (CD274) had significantly higher expression level in proline-dependent cancer cell lines compared to proline-independent cancer cell lines. FIG. 13B shows that MR1 had significantly higher expression level in proline-dependent cancer cell lines compared to proline-independent cancer cell lines. The data show mRNA expression levels represented as log2(TPM+l) with two-sided T-tests. The increased levels of PD-L1 and MR1 expression in proline-dependent cancer cell lines indicated that proline starvation therapy could be used synergistically with immunotherapies such as cell therapies (for example, T-cell therapy) and immune checkpoint modulators (for example, PD-1 and PD-L1 inhibitors). [0167] Topoisomerase genes were expressed at significantly higher levels in prolineindependent cancer cell lines compared to proline-dependent cancer cell lines. FIG. 14A-14C show that expression levels of TOP1MT, TOP2B, and TOP3A expression levels were significantly higher in proline-independent cancer cell lines compared to proline-dependent cancer cell lines. The data show mRNA expression levels represented as log2(TPM+l) with two-sided T-tests. The increased levels of topoisomerase genes in proline-independent cancer cell lines indicated that proline starvation therapy could be used synergistically with topoisomerase inhibitors to further sensitize cancer cells to proline starvation.

[0168] Genes involved in ubiquinone biosynthesis were also expressed at higher levels in proline-independent cancer cell lines compared to proline-dependent cancer cell lines. Genes of the mevalonate pathway (e.g., ACAT1, ACAT2, HMGCR, PMVK, FDPS, GGPS1) that generate prenyl moieties of ubiquinone were significantly correlated with proline biosynthetic genes (e.g., PYCR1, PYCR2, and PYCR3). FIG. 15A-15D show that the ubiquinone biosynthesis and mevalonate pathways were associated with proline dependency. Expression levels of COQ1B, COQ3, COQ8A, and COQ10A were significantly higher in prolineindependent cells compared to proline-dependent cells. The data show mRNA expression levels represented as log2(TPM+l) with two-sided T-tests. TABLE 5 shows that the expression of genes of the mevalonate pathway were strongly correlated with proline biosynthesis genes, such as PYCR1, PYCR2, and PYCR3. The data suggest that that proline starvation therapy could be effective when used synergistically with agents that modulate the ubiquinone biosynthetic pathway, such as statins, by sensitizing cancer cells to proline starvation.

TABLE 5

[0169] Sirtuin genes were also found to have significantly higher expression levels in proline-independent cells compared to proline-dependent cells. FIG. 16A and 16B show that SIRT1 and SIRT3 had higher expression levels proline-independent cancer cell lines than proline-dependent cells. The data show mRNA expression levels represented as log2(TPM+l) with two-sided T-tests. The data suggest that proline-starvation therapy could be used synergistically with agents that modulate SIRT1 or SIRT3.

EXAMPLE 3: Refining the proline sensitivity prediction gene expression/gene mutation signature.

[0170] Using the determined gene expression and gene mutation signatures, predictions are made about the sensitivity of various cancer cell lines to proline starvation. Predictions for proline dependency are made for human cancer cell lines, mouse cancer cell lines, immortalized cell lines, human or mouse derived organoids or spheroids, cells grown in 2- dimensional surfaces, or cells grown in 3-dimensional spaces (e.g., Matrigel or in attachment- free conditions). The relative sensitivity of a cell line to proline starvation is tested by growing the cancer cells in tissue culture plates in media in the presence or absence of proline. A range of cell densities (e.g., 100%, 75%, and 25% confluent) are used, and the cancer cells are incubated in the presence or absence of proline for 24 h, 48 h, 96 h, and 120 h.

[0171] The cell number and markers of cell survival and cell death are used to determine the effect of the proline starvation treatment on the cancer cells. DAPI staining of nuclei is detected using an automated microscope, such as an Operetta, and biochemical assays to determine adenosine triphosphate (ATP) levels are used. The results of cell sensitivity of the cancer cell lines are used to validate and refine the gene expression and mutation signatures, and the sensitivity of tumors to dietary proline restriction therapy are predicted.

EXAMPLE 4: Testing and refining predicated combinations of proline starvation and drug treatment.

[0172] Using the gene expression signatures and drug sensitivity predictions, the ability of proline starvation therapy to sensitize cancer cells to therapeutic agents is assessed. Human cancer cell lines, mouse cancer cell lines, immortalized cell lines, human or mouse derived organoids or spheroids, cells grown in 2-dimensional surfaces, or cells grown in 3- dimensional spaces (e.g., Matrigel or in attachment-free conditions) are used to test the efficacy of proline starvation as a combination therapy. The relative sensitivity of a range of cancer cell lines to the drugs are tested, including the drugs listed in TABLE 6. Drug sensitivity is tested by growing cells in tissue culture plates in media in the presence or absence of proline. A range of cell densities (i.e., 100%, 75%, and 25% confluent) are tested, and the cells are incubated in the presence or absence of proline for 24 h, 48 h, 96 h, or 120 h. Cancer cells may be cultured in the presence of other cell types, for example, immune cells and/or fibroblasts. Cells are also treated with therapeutic agents alone, or in combination with proline starvation therapy. A range of concentrations (i.e., pM to mM) of the therapeutic agents are used, and cell numbers and markers of cell survival and cell death are determined. Cell numbers are quantified using DAPI staining of nuclei visualized with an automated microscope (e.g., Operetta). Biochemical assays to quantify ATP levels are used to determine cell survival and cell death. The results are used to validate and refine proline-free diet and therapeutic agent combinations.

TABLE 6

EXAMPLE 5: Applying proline signature to identify particular proline-sensitive cell subpopulations using pancreatic ductal adenocarcinoma (PDAC) and PDAC metastasis single cell gene expression.

[0173] An experiment was designed to assess whether a genetic signature for proline dependency can be used to determine tumor sensitivity to proline restriction therapy.

[0174] Single cell RNAseq data from pancreatic ductal adenocarcinoma (PDAC) tumors and metastases in several different organs was retrieved from public datasets (CRA001160, GSE155698, GSE156405, and SCP1644). The data were filtered by removing cells that had more than 20% mitochondrial gene reads, or that were in the top 5% or bottom 5% for the number of genes detected. To remove batch effects, study and patient sample ID were used as categorical covariates. Within the data, cell types were inferred by transferring labels from one already labeled dataset to nearby clusters.

[0175] The 436 gene proline sensitivity signature from EXAMPLE 1 was applied to the PDAC and metastases data. Out of the 436 genes, only 365 were present in the single cell data. Using the 365 genes present, cells were scored by weighting together the normalized expression for the 365 genes. FIG. 17 shows variation in the match in proline depletion signature across different cell types. The signature score had a much wider distribution in cancer cells than in normal cells, consistent with the expected increased heterogeneity in cancer. Among the cell types assessed, cancer cells had the highest proline dependency score, supporting proline deprivation as a treatment option for cancer. Among the cancer cells and metastases, liver, omentum, and adrenal gland PDAC metastases had some of the highest dependency scores (FIG. 18). On a sample-by-sample basis, some samples within the sensitive groups had a lower match with the proline dependency signature, while some samples within the less sensitive groups matched well with the proline dependency signature. For example, one lung metastasis was observed to have a high degree of match with the proline dependency signature, while another was observed to have a very low degree of match with the proline dependency signature (FIG. 19). Overall, a proline dependency gene signature may be necessary to identify cases where proline depletion would be an effective treatment strategy.

EXAMPLE 6: Identifying a gene expression signature for sensitivity to cholesterol depletion during statin exposure.

[0176] Cholesterol is an essential component of cell membranes. Since cells are capable of synthesizing cholesterol, a combination of depletion with cholesterol synthesis inhibitors may be an effective anti-cancer combination therapy by depriving cancer cells of both internal and exogenous sources of cholesterol. To identify such cases, dose response relationships for simvastatin in complete and cholesterol free medium were experimentally determined in 32 cell lines.

[0177] A panel of 32 cell lines (HCC827, SW480, PSN1, NCIH1963, NCIH292, MCF7, T47D, JURKAT, NCIH358, ASPC1, DLD1, PC9, PANCI, PATU8902, HCT116, LSI 80, CFPAC1, MIAPACA2, CAL33, BXPC3, CAKI2, NCIH209, U266B1, KP3, A549, HEPG2, DANG, SCC25, NCH4929, SW48, SCC4, and SCC9) was assessed for sensitivity to cholesterol synthesis inhibition by simvastatin treatment in complete and cholesterol-free media. The resulting dose-response data was fit with four-parameter log-logistic function (LL.4) models, and the IC50 values for simvastatin in complete and cholesterol-free medium determined for each cell line. The log-fold change in the IC50 value between complete and cholesterol-free media was calculated and used to rank the cell lines from most to least sensitive to cholesterol depletion during statin treatment.

[0178] Gene expression data for 1376 cell line were retrieved from the CCLE. Cell lineage effects were regressed out by subtracting the mean expression of each gene in each category to prevent uneven lineage distribution of the 32 cells lines from biasing the result.

Subsequently, all data from cells other than the 32 with known sensitivity to cholesterol depletion during statin treatment were discarded, and gene expression was further filtered through the removal of genes with a low interquartile-range (IQR) of expression. Following this filtering, a total of 5,000 genes remained. The data from the 5,000 remaining genes was then normalized by mean centering and dividing by the standard deviation of each variable. Following normalization, hierarchical clustering was applied to the 5,000 genes, resulting in 0 genes that could perfectly stratify the 32 cell lines into the previously determined order from most to least sensitive to cholesterol depletion during statin exposure, with perfect stratification for ranked data defined as having a Spearman correlation coefficient of -1 or 1. [0179] To identify relevant genes in a genetic signature for cholesterol depletion during statin treatment, the perfect stratification of the data requirement was relaxed. Accordingly, relevant genes in the gene expression signature were identified by considering: 1) the Spearman correlation of normalized gene expression with the previously determined sensitivity ranking; and 2) the ability for a gene to approximately stratify the ranking of the cells at a dividing point, which was measured by the absolute log-odds that the gene expression in the more sensitive group was lower than the expression of the same gene in the less sensitive group, and the coefficient of variation (CV) was above a given threshold in at least one of the groups, with the dividing point constrained such that each resulting group contained at least 8 cells. To be considered relevant for the gene expression signature, a gene had to be both correlated and an efficient stratifier. This resulted in narrowing of the thresholds for correlation and absolute log-odds that minimized the size of the gene expression signature while preserving the predictive ability. The final selection criteria were as follows: (Spearman’s p| > 0.4, |log-odds| > 1.0, and CV > 0.25. A total of 15 genes were identified that comprise the gene expression signature for sensitivity to cholesterol depletion during statin exposure. The list of the 15 genes and their expression in the 32 cell lines is included in TABLE 7. FIG. 20 shows a hierarchical clustering of the 15 genes in the 32 cell lines. The stratification demonstrate that a genetic signature can be used to stratify sensitive cell lines with a known response.

TABLE 7

EXAMPLE 7: Stratifying patient samples using the gene expression for sensitivity to cholesterol depletion during statin exposure.

[0180] An experiment was designed to apply the gene expression for sensitivity to cholesterol depletion during statin exposure to patient samples to identify cancer types that could potentially be treated by the combination of statin treatment and cholesterol depletion. [0181] Gene expression data from 9565 tumor samples were obtained from the Cancer Genome Atlas and compared to the gene expression signature from EXAMPLE 6. For each gene in the 15 gene expression signature, the gene expression rank of each sample in each of the genes was calculated. The gene expression ranks for UST, NDUFC2-KCTD14, and GNG10 were reversed as those genes were correlated with cholesterol sensitivity rather than anti correlated. Tumor samples were sorted by the sum of ranks across the 15 genes of the signature. To evaluate the tendency of each type and subtype of cancer to have a higher or lower than average cholesterol depletion sensitivity, Mann-Whitney U tests were applied. FIG. 21 is a visualization of the Mann-Whitney U statistical test ordered by cancer type and subtype, showing cancer lineage and subtype distribution from least to most putatively sensitive to cholesterol depletion during statin exposure. Among the cancer types evaluated, liver and kidney cancer had a high probability for an individual tumor to be sensitive to cholesterol depletion and statin treatment. CIS, ABCB6, NDRG1, and TXNDC5 were found to be prognostic markers in renal cancer. ABCB6, NDRG1, TXNDC5 were found to be prognostic markers in liver cancer. This suggests that sensitivity to cholesterol depletion may be tied to severity. Of note, tumors that matched the signature were found in most cancer types, not just liver and renal cancer.

EMBODIMENTS

[0182] Embodiment 1. A method of treating a subject in need thereof, comprising: (a) providing a gene expression profile of a biological sample from the subject, wherein the gene expression profile comprises an expression level of a set of genes; (b) identifying a dependence of the biological sample on a nutrient based at least on the gene expression profile; and (c) administering to the subject a nutrient modulation therapy formulated to modulate a level of the nutrient in the subject if the subject is predicted to respond to the nutrient modulation therapy.

[0183] Embodiment 2. The method of embodiment 1, wherein the identifying comprises hierarchical clustering.

[0184] Embodiment 3. The method of embodiment 1 or 2, wherein the identifying comprises using an expression heatmap.

[0185] Embodiment 4. The method of any one of embodiments 1-3, wherein the identifying comprises comparing the gene expression profile with a reference gene expression profile associated with a known dependence on the nutrient.

[0186] Embodiment 5. The method of any one of embodiments 1-4, wherein the identifying comprises determining a coefficient of variation (CV) of an expression level of a gene in the set of genes.

[0187] Embodiment 6. The method of any one of embodiments 1-5, wherein the identifying comprises determining a Spearman’s rank correlation coefficient of an expression level of a gene in the set of genes.

[0188] Embodiment 7. The method of any one of embodiments 1-6, further comprising stratifyting the biological sample into a nutrient-dependent or nutrient-independent-group. [0189] Embodiment 8. The method of any one of embodiments 1-7, further comprising ranking the biological sample based on the dependence of the biological sample on the nutrient.

[0190] Embodiment 9. The method of any one of embodiments 1-8, further comprising filtering out a subset of genes from the gene expression profile.

[0191] Embodiment 10. The method of any one of embodiments 1-9, wherein the biological sample is dependent on exogenous supplementation of the nutrient.

[0192] Embodiment 11. The method of any one of embodiments 1-9, wherein the biological sample is independent of exogenous supplementation of the nutrient.

[0193] Embodiment 12. The method of any one of embodiments 1-11, wherein the therapy modulates the level of the nutrient in the subject.

[0194] Embodiment 13. The method of any one of embodiments 1-12, wherein the therapy reduces the level of the nutrient in the subject.

[0195] Embodiment 14. The method of any one of embodiments 1-12, wherein the therapy elevates the level of the nutrient in the subject. [0196] Embodiment 15. The method of any one of embodiments 1-12, wherein the therapy is a nutrient starvation therapy.

[0197] Embodiment 16. The method of any one of embodiments 1-12, wherein the therapy is a nutrient supplementation therapy.

[0198] Embodiment 17. The method of any one of embodiments 1-16, wherein the gene expression profile comprises mRNA expression data.

[0199] Embodiment 18. The method of any one of embodiments 1-16, wherein the gene expression profile comprises DNA expression data.

[0200] Embodiment 19. The method of any one of embodiments 1-18, further comprising determining the gene expression profile of the biological sample.

[0201] Embodiment 20. The method of embodiment 19, wherein the determining the gene expression profile comprises using a sequencing analysis technique.

[0202] Embodiment 21. The method of embodiment 20, wherein the sequencing analysis technique comprises single cell sequencing.

[0203] Embodiment 22. The method of embodiment 20, wherein the sequencing analysis technique comprises RNA sequencing.

[0204] Embodiment 23. The method of embodiment 20, wherein the sequencing analysis technique comprises DNA sequencing.

[0205] Embodiment 24. The method of any one of embodiments 1-23, further comprising identifying a gene in the biological sample that is indicative of sensitivity of the biological sample to the therapy.

[0206] Embodiment 25. The method of any one of embodiments 1-24, further comprising determining based at least on the dependence of the biological sample on the nutrient whether the subject is predicted to respond to the nutrient modulation therapy.

[0207] Embodiment 26. The method of any one of embodiments 1-25, wherein the biological sample is a cancer cell.

[0208] Embodiment 27. The method of any one of embodiments 1-26, wherein the therapy reduces viability of the cancer cell.

[0209] Embodiment 28. The method of any one of embodiments 1-27, wherein the subject has a cancer.

[0210] Embodiment 29. The method of embodiment 28, wherein the cancer is ovarian cancer. [0211] Embodiment 30. The method of embodiment 28, wherein the cancer is endometrial cancer.

[0212] Embodiment 31. The method of embodiment 28, wherein the cancer is colorectal cancer.

[0213] Embodiment 32. The method of embodiment 28, wherein the cancer is pancreatic cancer.

[0214] Embodiment 33. The method of embodiment 28, wherein the cancer is renal cancer.

[0215] Embodiment 34. The method of embodiment 28, wherein the cancer is renal cancer.

[0216] Embodiment 35. The method of embodiment 28, wherein the cancer is liver cancer.

[0217] Embodiment 36. The method of embodiment 28, wherein the cancer is kidney cancer.

[0218] Embodiment 37. The method of any one of embodiments 1-36, wherein the therapy modulates biosynthesis of the nutrient in the subject.

[0219] Embodiment 38. The method of any one of embodiments 1-37, wherein the therapy modulates proline biosynthesis in the subject.

[0220] Embodiment 39. The method of any one of embodiments 1-37, wherein the therapy modulates cholesterol biosynthesis in the subject.

[0221] Embodiment 40. The method of any one of embodiments 1-39, wherein the therapy comprises a dietary product.

[0222] Embodiment 41. The method of embodiment 40, wherein the dietary product is devoid of proline.

[0223] Embodiment 42. The method of embodiment 40 or 41, wherein the dietary product is devoid of serine.

[0224] Embodiment 43. The method of any one of embodiments 40-42, wherein the dietary product is devoid of glycine.

[0225] Embodiment 44. The method of embodiment 40, wherein the dietary product is devoid of cholesterol.

[0226] Embodiment 45. The method of embodiment 40 or 44, wherein the dietary product is devoid of fats.

[0227] Embodiment 46. The method of any one of embodiments 1-45, wherein the therapy comprises a cholesterol lowering agent.

[0228] Embodiment 47. The method of embodiment 46, wherein the cholesterol lowering agent is a statin. [0229] Embodiment 48. The method of embodiment 46, wherein the cholesterol lowering agent is a cholesteryl ester transfer protein (CETP) inhibitor.

[0230] Embodiment 49. The method of embodiment 46, wherein the cholesterol lowering agent is a lecithin-cholesterol acyltransferase (LCAT) inhibitor.

[0231] Embodiment 50. The method of embodiment 46, wherein the cholesterol lowering agent is a bile acid sequestrant.

[0232] Embodiment 51. The method of embodiment 46, wherein the cholesterol lowering agent is a cholesterol absorption inhibitor.

[0233] Embodiment 52. The method of any one of embodiments 1-51, wherein the nutrient is an amino acid.

[0234] Embodiment 53. The method of embodiment 52, wherein the amino acid is proline.

[0235] Embodiment 54. The method of any one of embodiments 1-51, wherein the nutrient is a sterol.

[0236] Embodiment 55. The method of embodiment 54, wherein the sterol is cholesterol.

[0237] Embodiment 56. The method of any one of embodiments 1-55, wherein the gene expression profile comprises a gene in the proline biosynthesis pathway.

[0238] Embodiment 57. The method of embodiment 56, wherein the gene expression profile comprises at least one of: PYCR1, PYCR2, and PYCR3.

[0239] Embodiment 58. The method of any one of embodiments 1-55, wherein the gene expression profile comprises a gene in the cholesterol biosynthesis pathway.

[0240] Embodiment 59. The method of any one of embodiments 1-55, wherein the gene expression profile comprises a gene in the fatty acid biosynthesis pathway.

[0241] Embodiment 60. The method of any one of embodiments 1-55, wherein the gene expression profile comprises a gene in the mevalonate pathway.

[0242] Embodiment 61. The method of any one of embodiments 1-55, wherein the gene expression profile comprises a gene in the sirtuin pathway.

[0243] Embodiment 62. The method of any one of embodiments 1-55, wherein the gene expression profile comprises a gene listed in TABLE 2.

[0244] Embodiment 63. The method of any one of embodiments 1-55, wherein the gene expression profile comprises at least one of: FKBP5, REEP2, CENPV, SOX12, ZSWIM5, WASFI, KIAA1211, MXD4, BTD, HACL1, NADK2, CDK19, ATP7B, FECH, HABP4, GDF11, LZTFL1, RPAIN, WDR45, CHCHD4, WASHC2C, ULK4, TATDN2, WDR81, COQ10A, DHX33, NUP88, WRN, MAP2K1, C15orfi9, FAM160A1, PML, PARP9, NR1P1, BATF2, BANK1, CATSPER1, SHANK2, TMC8, ANK3, GBP1, ISG15, CD274, NALCN, MR1, CSF2, TMC6, LRG1, IVL, GALNT9, RAB38, SAMD9L, GIMAP2, UBE2L6, APOL3, GNA15, GALNT6, TGFA, MMEL1, PTAFR, NECTIN4, TC2N, DCBLD2, SEMA7A, EPN3, ANKRD22, ADAM8, EREG, TGFBI, ITGB6, LGALS9, VAV1, TRIM22, DAPP1, TMEM92, UNC13D, TCN1, SLC37A2, SFTA2, MUC16, LAD1, GALNT3, ANXA3, PROM2, CRABP2, RAC2, SPRR1B, RBP1, PRSS8, ICAM2, and MAL2.

[0245] Embodiment 64. The method of any one of embodiments 1-55, wherein the gene expression profile comprises a gene listed in TABLE 4.

[0246] Embodiment 65. The method of any one of embodiments 1-55, wherein the gene expression profile comprises at least one of: CENPV, PEBP1, WASF1, TSPYL2, CITED2, MXD4, RBM3, BTD, ST6GALNAC6, ASNS, RERE, STXBP1, HACL1, NADK2, CD99L2, ARRB2, SIRT1, GCAT, POMT1, SLC25A38, COQ8A, RMCI, FECH, MTMR12, RPP40, HABP4, MYBBP1A, SLC43A2, CXXC1, PF AS, SEC11C, XPOT, PYGB, SLC35E2B, CYB5D2, DDIT3, ACAT1, TARS, C1QBP, GNE, LZTFL1, RPAIN, WDR45, TMEM43, CHCHD4, CLASP2, POLR1E, YEATS4, ACAT2, STAMBPL1, CBWD5, APPL1, WASHC2C, ZNF76, PAN2, AGFG2, TBC1D15, SLC38A2, ZCCHC14, ACADM, LTV1, RAPGEF1, NSMF, TTC33, MDN1, TATDN2, SAT2, WASHC2A, RPL14, RPS6, PIM1, PDCD4, GTF3C6, WDR81, COQ10A, TOP1MT, UHRF1BP1, RPL26, REV3L, WDR48, APBB3, PLXNA3, SREBF1, RIOK1, CCDC88A, RPL32, CTNS, ICE1, NHEJ1, MAP1LC3B, CAPN7, RPS10, WDR70, BPHL, SLC38A1, TBC1D5, DHX33, WDR6, SNX3, SUPV3L1, NUDT2, ECHDC1, LYRM2, FLCN, PYCR1, GTF2H2C, HDDC2, OGGI, ALDH3A2, GTF2H2, VSIG10, XPC, NHP2, CCT2, ACAD10, CCT5, CCDC107, GIGYF1, TACC2, RPL37, HSF2, PLCG1, BRD9, NAP1L1, SDHA, SOD2, GNL3, TRAK2, MRPS25, FAM173B, TCP1, ATP5F1A, MRPL18, PHYH, LARS, ZNF131, CEP83, EEF2, PIK3R2, ORC3, PAIP1, UBP1, PKD1, SMIM4, QSOX2, PDHB, EEF1A1, PTBP2, PSIP1, TIMM44, IP6K2, CD320, ELP2, C5orf22, NISCH, ISCA1, SNX21, BRIX1, PTMS, ZNF428, DOCK7, UHRF1BP1L, GARS, RPL15, FRA10AC1, GBA2, TSEN2, RGS10, RPSA, AHI1, PDSS2, ENDOG, KATNA1, GNPDA1, TLN1, PHPT1, TMEM120A, NARS, PON2, NR2C1, FAM102A, SEC63, CCNJ, DROSHA, NUP88, ATM, TWNK, BRPF1, ZCCHC7, IMPACT, ACTR3B, MRPS30, TRIM7, HPS1, CAMTA2, WRN, SIK3, DYNC1LI1, PHF14, C18orf21, EXOC3, TOP2B, ZNF621, UAP1, RNF44, BCKDHB, ZNF397, MAST2, ZDHHC8, RNF146, PCM1, PMPCA, UBB, MAD2L2, RAB32, KMT2A, ELK1, HINT3, MEDIO, AIMP2, PCBP4, G6PD, MCFD2, MET, MRPL52, HEXIM1, B4GALT5, COMT, LASPI, SERTAD1, CTNNBIP1, ARHGAP5, EPS8, KRT18, RAP1GDS1, PJA1, BID, GIPC1, ANAPC10, PHLDA2, SGMS2, IDS, SERPINH1, LSM6, ENCI, FURIN, PCTP, RAD51C, LRR1, BLCAP, CD276, MAPKAPK3, RAB5IF, NT5E, CYB5A, EMC10, NDFIP2, SPATS2L, TMED10, PPP3CA, MBOAT7, NET1, RHOF, MYO1B, ITPRID2, LRRC8A, SPTBN2, CD59, RBMS2, TAGLN2, STK24, EML2, SELENOW, PUDP, PRRG1, CCND1, PARP12, FAM111B, SAT1, CASP4, PSME1, DHRS7, SP100, ELF1, MESD, BCAR3, FXYD5, ACTN1, PLS3, B3GNT2, TNKS1BP1, ARHGAP18, FLNA, TPM4, NMI, WWC1, MEAK7, SMAGP, CAPN1, PTPRK, MAP4K4, HELZ2, SYNGR2, DHRS1, AJUBA, ADAMI 5, ANXA2, PDLIM7, LIF, TES, LGALS1, APOL2, OCIAD2, LY6E, ABCC3, TMOD3, FAM3C, RAB27A, ACP6, MAP2K1, PDXK, PSME2, KCNN4, ST5, TAPI, CAPN2, CD47, GALNT7, FAM111A, DTX3L, F0SL1, EPS8L2, SDC4, PRSS23, INSIG2, AK4, ITGA3, PKP3, CHMP4C, SPINT2, ARL6IP5, S100A11, BLVRB, NCEH1, IRF1, CLMN, ENDOD1, EPHX4, PLA2G12A, TNFAIP8, FERMT1, TMBIM1, ZFP36L1, SLC39A11, TSPAN14, FRMD6, API S3, FAH, DDIT4, ITGA6, C19orf33, SLC2A1, TSKU, SCNN1A, QS0X1, DTX2, 0SBPL3, ATP1B1, TNFRSF12A, LITAF, B2M, LLGL2, RHOD, IL4R, PLSCR1, CYB561, C15orf39, C6orfl32, AGRN, LGALS3BP, RASA1, HIF1A, CLIP4, RRAS2, PML, IL15RA, MGLL, PARP9, DSG2, CAV2, PLEK2, KCNK1, FUR, ARNTL2, C16orf74, GPRC5A, TNNT1, LMO7, SFN, S100A16, SLC44A2, MYO5B, OAS1, KLF5, MALL, ITGA2, DDX60L, ZNF185, EFNB2, PTGES, ISG15, PTK6, ADAP1, PLAU, DDX60, PCDH1, MYEOV, ITGB4, EHF, KRT19, UBE2L6, TGFA, EMP1, GRHL2, TC2N, DCBLD2, B3GNT3, TGFBI, LAMB3, UNC13D, F3, LAMC2, ANXA3, ANO1, KRT7, and MAL2.

[0247] Embodiment 66. The method of any one of embodiments 1-55, wherein the gene expression profile comprises a gene listed in TABLE 7.

[0248] Embodiment 67. The method of any one of embodiments 1-55, wherein the gene expression profile comprises at least one of: GNG10, NDUFC2-KCTD14, UST, NDRG1, HSPG2, CIS, SEC16B, ABCB6, FCHSD2, CDKL1, TXNDC5, ALDH1A1, CAPN3, or CES1.

[0249] Embodiment 68. The method of any one of embodiments 1-67, further comprising identifying sensitivity of the biological sample to a combination therapy based at least on the gene expression profile. [0250] Embodiment 69. The method of embodiment 68, wherein the combination therapy targets a gene or expression product from the gene expression profile.

[0251] Embodiment 70. The method of embodiment 68 or 69, wherein the combination therapy modulates biosynthesis of the nutrient in the subject.

[0252] Embodiment 71. The method of embodiment 68 or 69, wherein the combination therapy modulates proline biosynthesis in the subject.

[0253] Embodiment 72. The method of embodiment 68 or 69, wherein the combination therapy modulates cholesterol biosynthesis in the subject.

[0254] Embodiment 73. The method of embodiment 68 or 69, wherein the combination therapy modulates fatty acid metabolsim in the subject.

[0255] Embodiment 74. The method of embodiment 68 or 69, wherein the combination therapy modulates a sirtuin pathway in the subject.

[0256] Embodiment 75. The method of embodiment 68 or 69, wherein the combination therapy modulates ubiquinone biosynthesis in the subject.

[0257] Embodiment 76. The method of embodiment 68 or 69, wherein the combination therapy modulates a hypoxia pathway in the subject.

[0258] Embodiment 77. The method of any one of embodiments 68-76, wherein the combination therapy comprises a chemotherapy.

[0259] Embodiment 78. The method of any one of embodiments 68-77, wherein the combination therapy comprises a radiotherapy.

[0260] Embodiment 79. The method of any one of embodiments 68-78, wherein the combination therapy comprises an immunotherapy.

[0261] Embodiment 80. The method of any one of embodiments 68-79, further comprising administering to the subject the combination therapy.

[0262] Embodiment 81. The method of any one of embodiments 68-80, wherein the nutrient modulation therapy and the combination therapy has a synergistic therapeutic effect in the subject.

[0263] Embodiment 82. The method of any one of embodiments 1-81, further comprising administering to the subject a chemotherapy.

[0264] Embodiment 83. The method of any one of embodiments 1-82, further comprising administering to the subject a radiotherapy.

[0265] Embodiment 84. The method of any one of embodiments 1-83, further comprising administering to the subject an immunotherapy. [0266] Embodiment 85. A method of treating a subject in need thereof, comprising: (a) subjecting a plurality of reference cells to a plurality of drug-nutrient environments to determine a set of drug-nutrient vulnerabilities of the plurality of reference cells; (b) performing an omics method on the plurality of reference cells to generate omics data; (c) determining a set of omics signatures that correlate with the set of drug-nutrient vulnerabilities; (d) performing the omics method on a plurality of target cells of the subject to generate a set of target-specific omics signatures, wherein the plurality of target cells comprises a healthy cell and a disease cell; (e) determining a target-specific drug-nutrient vulnerability based at least on the set of omics signatures and the set of target-specific omics signatures, wherein the target-specific drug-nutrient vulnerability affects the disease cell more than the healthy cell; and (f) generating a dietary treatment configured to activate the target-specific drug-nutrient vulnerability in the subject.

[0267] Embodiment 86. The method of embodiment 85, wherein the plurality of target cells comprises a plurality of healthy cells and a plurality of disease cells.

[0268] Embodiment 87. The method of embodiment 86, wherein the determining the targetspecific drug-nutrient vulnerability comprises: (a) determining a plurality of drug-nutrient vulnerabilities of the plurality of target cells; and (b) clustering the plurality of drug-nutrient vulnerabilities to associate target cells with similar drug-nutrient vulnerabilities.

[0269] Embodiment 88. The method of any one of embodiments 85-87, further comprising administering to the subject the dietary treatment.

[0270] Embodiment 89. The method of any one of embodiments 85-88, wherein the plurality of drug-nutrient environments comprises different levels of a nutrient, different levels of a drug, or both.

[0271] Embodiment 90. The method of any one of embodiments 85-89, wherein the set of drug-nutrient vulnerabilities and the target-specific drug-nutrient vulnerability each comprise different levels of a nutrient, different levels of a drug, or both.

[0272] Embodiment 91. The method of any one of embodiments 85-90, wherein the omics method comprises genomics, transcriptomics, proteomics, or a combination thereof.

[0273] Embodiment 92. The method of any one of embodiments 85-91, wherein the set of omics signatures and the set of target-specific omics signatures comprise DNA, mRNA, or protein.

[0274] Embodiment 93. The method of any one of embodiments 85-92, wherein the omics method comprises single cell sequencing. [0275] Embodiment 94. The method of any one of embodiments 85-92, wherein the omics method comprises RNA sequencing.

[0276] Embodiment 95. The method of any one of embodiments 85-92, wherein the omics method comprises DNA sequencing.

[0277] Embodiment 96. The method of any one of embodiments 85-95, wherein the dietary treatment modulates the level of a nutrient in the subject.

[0278] Embodiment 97. The method of any one of embodiments 85-96, wherein the dietary treatment reduces the level of a nutrient in the subject.

[0279] Embodiment 98. The method of any one of embodiments 85-96, wherein the dietary treatment elevates the level of a nutrient in the subject.

[0280] Embodiment 99. The method of any one of embodiments 85-96, wherein the dietary treatment is a nutrient starvation therapy.

[0281] Embodiment 100. The method of any one of embodiments 85-96, wherein the dietary treatment is a nutrient supplementation therapy.

[0282] Embodiment 101. The method of any one of embodiments 85-100, wherein disease cell is a cancer cell.

[0283] Embodiment 102. The method of embodiment 101, wherein the dietary treatment reduces viability of the cancer cell.

[0284] Embodiment 103. The method of any one of embodiments 85-102, wherein the subject has a cancer.

[0285] Embodiment 104. The method of embodiment 103, wherein the cancer is ovarian cancer.

[0286] Embodiment 105. The method of embodiment 103, wherein the cancer is endometrial cancer.

[0287] Embodiment 106. The method of embodiment 103, wherein the cancer is colorectal cancer.

[0288] Embodiment 107. The method of embodiment 103, wherein the cancer is pancreatic cancer.

[0289] Embodiment 108. The method of embodiment 103, wherein the cancer is renal cancer.

[0290] Embodiment 109. The method of embodiment 103, wherein the cancer is liver cancer.

[0291] Embodiment 110. The method of embodiment 103, wherein the cancer is kidney cancer. [0292] Embodiment 111. The method of any one of embodiments 85-110, wherein the dietary treatment modulates biosynthesis of a nutrient in the subject.

[0293] Embodiment 112. The method of any one of embodiments 85-111, wherein the dietary treatment modulates proline biosynthesis in the subject.

[0294] Embodiment 113. The method of any one of embodiments 85-111, wherein the dietary treatment modulates cholesterol biosynthesis in the subject.

[0295] Embodiment 114. The method of any one of embodiments 85-113, wherein the dietary treatment comprises a dietary product.

[0296] Embodiment 115. The method of embodiment 114, wherein the dietary product is devoid of proline.

[0297] Embodiment 116. The method of embodiment 114 or 115, wherein the dietary product is devoid of serine.

[0298] Embodiment 117. The method of any one of embodiments 114-116, wherein the dietary product is devoid of glycine.

[0299] Embodiment 118. The method of embodiment 114, wherein the dietary product is devoid of cholesterol.

[0300] Embodiment 119. The method of embodiment 114 or 118, wherein the dietary product is devoid of fats.

[0301] Embodiment 120. The method of any one of embodiments 85-119, wherein the dietary treatment comprises a cholesterol lowering agent.

[0302] Embodiment 121. The method of embodiment 120, wherein the cholesterol lowering agent is a statin.

[0303] Embodiment 122. The method of embodiment 120, wherein the cholesterol lowering agent is a cholesteryl ester transfer protein (CETP) inhibitor.

[0304] Embodiment 123. The method of embodiment 120, wherein the cholesterol lowering agent is a lecithin-cholesterol acyltransferase (LCAT) inhibitor.

[0305] Embodiment 124. The method of embodiment 120, wherein the cholesterol lowering agent is a bile acid sequestrant.

[0306] Embodiment 125. The method of embodiment 120, wherein the cholesterol lowering agent is a cholesterol absorption inhibitor.

[0307] Embodiment 126. The method of any one of embodiments 85-125, wherein the set of target-specific omics signature comprises a gene expression profile. [0308] Embodiment 127. The method of embodiment 126, wherein the gene expression profile comprises a gene in the proline biosynthesis pathway.

[0309] Embodiment 128. The method of embodiment 127, wherein the gene expression profile comprises at least one of: PYCR1, PYCR2, and PYCR3.

[0310] Embodiment 129. The method of embodiment 126, wherein the gene expression profile comprises a gene in the cholesterol biosynthesis pathway.

[0311] Embodiment 130. The method of embodiment 126, wherein the gene expression profile comprises a gene in the fatty acid biosynthesis pathway.

[0312] Embodiment 131. The method of embodiment 126, wherein the gene expression profile comprises a gene in the mevalonate pathway.

[0313] Embodiment 132. The method of embodiment 126, wherein the gene expression profile comprises a gene in the sirtuin pathway.

[0314] Embodiment 133. The method of embodiment 126, wherein the gene expression profile comprises a gene listed in TABLE 2.

[0315] Embodiment 134. The method of embodiment 126, wherein the gene expression profile comprises at least one of: FKBP5, REEP2, CENPV, SOX12, ZSWIM5, WASFI, KIAA1211, MXD4, BTD, HACL1, NADK2, CDK19, ATP7B, FECH, HABP4, GDF11, LZTFL1, RPAIN, WDR45, CHCHD4, WASHC2C, ULK4, TATDN2, WDR81, COQ10A, DHX33, NUP88, WRN, MAP2K1, C15orf39, FAM160A1, PML, PARP9, NR1P1, BATF2, BANK1, CATSPER1, SHANK2, TMC8, ANK3, GBP1, ISG15, CD274, NALCN, MR1, CSF2, TMC6, LRG1, IVL, GALNT9, RAB38, SAMD9L, GIMAP2, UBE2L6, APOL3, GNA15, GALNT6, TGFA, MMEL1, PTAFR, NECTIN4, TC2N, DCBLD2, SEMA7A, EPN3, ANKRD22, ADAM8, EREG, TGFBI, ITGB6, LGALS9, VAV1, TRIM22, DAPP1, TMEM92, UNC13D, TCN1, SLC37A2, SFTA2, MUC16, LAD1, GALNT3, ANXA3, PROM2, CRABP2, RAC2, SPRR1B, RBP1, PRSS8, ICAM2, and MAL2.

[0316] Embodiment 135. The method of embodiment 126, wherein the gene expression profile comprises a gene listed in TABLE 4.

[0317] Embodiment 136. The method of embodiment 126, wherein the gene expression profile comprises at least one of: CENPV, PEBP1, WASFI, TSPYL2, CITED2, MXD4, RBM3, BTD, ST6GALNAC6, ASNS, RERE, STXBP1, HACL1, NADK2, CD99L2, ARRB2, SIRT1, GCAT, POMT1, SLC25A38, COQ8A, RMCI, FECH, MTMR12, RPP40, HABP4, MYBBP1A, SLC43A2, CXXC1, PF AS, SEC11C, XPOT, PYGB, SLC35E2B, CYB5D2, DDIT3, ACAT1, TARS, C1QBP, GNE, LZTFL1, RPAIN, WDR45, TMEM43, CHCHD4, CLASP2, POLR1E, YEATS4, ACAT2, STAMBPL1, CBWD5, APPL1, WASHC2C, ZNF76, PAN2, AGFG2, TBC1D15, SLC38A2, ZCCHC14, ACADM, LTV1, RAPGEF1, NSMF, TTC33, MDN1, TATDN2, SAT2, WASHC2A, RPL14, RPS6, PIM1, PDCD4, GTF3C6, WDR81, COQIOA, TOP1MT, UHRF1BP1, RPL26, REV3L, WDR48, APBB3, PLXNA3, SREBF1, RIOK1, CCDC88A, RPL32, CTNS, ICE1, NHEJ1, MAP1LC3B, CAPN7, RPS10, WDR70, BPHL, SLC38A1, TBC1D5, DHX33, WDR6, SNX3, SUPV3L1, NUDT2, ECHDC1, LYRM2, FLCN, PYCR1, GTF2H2C, HDDC2, OGGI, ALDH3A2, GTF2H2, VSIG10, XPC, NHP2, CCT2, ACAD10, CCT5, CCDC107, GIGYF1, TACC2, RPL37, HSF2, PLCG1, BRD9, NAP1L1, SDHA, SOD2, GNL3, TRAK2, MRPS25, FAM173B, TCP1, ATP5F1A, MRPL18, PHYH, LARS, ZNF131, CEP83, EEF2, PIK3R2, ORC3, PAIP1, UBP1, PKD1, SMIM4, QS0X2, PDHB, EEF1A1, PTBP2, PSIP1, TIMM44, IP6K2, CD320, ELP2, C5orf22, NISCH, ISCA1, SNX21, BRIX1, PTMS, ZNF428, DOCK7, UHRF1BP1L, GARS, RPL15, FRA10AC1, GBA2, TSEN2, RGS10, RPSA, AHI1, PDSS2, ENDOG, KATNA1, GNPDA1, TLN1, PHPT1, TMEM120A, NARS, PON2, NR2C1, FAM102A, SEC63, CCNJ, DROSHA, NUP88, ATM, TWNK, BRPF1, ZCCHC7, IMPACT, ACTR3B, MRPS30, TRIM7, HPS1, CAMTA2, WRN, SIK3, DYNC1LI1, PHF14, C18orf21, EXOC3, TOP2B, ZNF621, UAP1, RNF44, BCKDHB, ZNF397, MAST2, ZDHHC8, RNF146, PCM1, PMPCA, UBB, MAD2L2, RAB32, KMT2A, ELK1, HINT3, MEDIO, AIMP2, PCBP4, G6PD, MCFD2, MET, MRPL52, HEXIM1, B4GALT5, COMT, LASPI, SERTAD1, CTNNBIP1, ARHGAP5, EPS8, KRT18, RAP1GDS1, PJA1, BID, GIPC1, ANAPC10, PHLDA2, SGMS2, IDS, SERPINH1, LSM6, ENCI, FURIN, PCTP, RAD51C, LRR1, BLCAP, CD276, MAPKAPK3, RAB5IF, NT5E, CYB5A, EMC10, NDFIP2, SPATS2L, TMED10, PPP3CA, MBOAT7, NET1, RHOF, MYO1B, ITPRID2, LRRC8A, SPTBN2, CD59, RBMS2, TAGLN2, STK24, EML2, SELENOW, PUDP, PRRG1, CCND1, PARP12, FAM111B, SAT1, CASP4, PSME1, DHRS7, SP100, ELF1, MESD, BCAR3, FXYD5, ACTN1, PLS3, B3GNT2, TNKS1BP1, ARHGAP18, FLNA, TPM4, NMI, WWC1, MEAK7, SMAGP, CAPN1, PTPRK, MAP4K4, HELZ2, SYNGR2, DHRS1, AJUBA, ADAMI 5, ANXA2, PDLIM7, LIF, TES, LGALS1, APOL2, OCIAD2, LY6E, ABCC3, TMOD3, FAM3C, RAB27A, ACP6, MAP2K1, PDXK, PSME2, KCNN4, ST5, TAPI, CAPN2, CD47, GALNT7, FAM111A, DTX3L, F0SL1, EPS8L2, SDC4, PRSS23, INSIG2, AK4, ITGA3, PKP3, CHMP4C, SPINT2, ARL6IP5, S100A11, BLVRB, NCEH1, IRF1, CLMN, ENDOD1, EPHX4, PLA2G12A, TNFAIP8, FERMT1, TMBIM1, ZFP36L1, SLC39A11, TSPAN14, FRMD6, API S3, FAH, DDIT4, ITGA6, C19orf33, SLC2A1, TSKU, SCNN1A, QS0X1, DTX2, OSBPL3, ATP1B1, TNFRSF12A, LITAF, B2M, LLGL2, RHOD, IL4R, PLSCR1, CYB561, C15orf39, C6orfl32, AGRN, LGALS3BP, RASA1, HIF1A, CLIP4, RRAS2, PML, IL15RA, MGLL, PARP9, DSG2, CAV2, PLEK2, KCNK1, FUR, ARNTL2, C16orf74, GPRC5A, TNNT1, LMO7, SFN, S100A16, SLC44A2, MYO5B, OAS1, KLF5, MALL, ITGA2, DDX60L, ZNF185, EFNB2, PTGES, ISG15, PTK6, ADAP1, PLAU, DDX60, PCDH1, MYEOV, ITGB4, EHF, KRT19, UBE2L6, TGFA, EMP1, GRHL2, TC2N, DCBLD2, B3GNT3, TGFBI, LAMB3, UNC13D, F3, LAMC2, ANXA3, ANO1, KRT7, and MAL2.

[0318] Embodiment 137. The method of embodiment 126, wherein the gene expression profile comprises a gene listed in TABLE 7.

[0319] Embodiment 138. The method of embodiment 126, wherein the gene expression profile comprises at least one of: GNG10, NDUFC2-KCTD14, UST, NDRG1, HSPG2, CIS, SEC16B, ABCB6, FCHSD2, CDKL1, TXNDC5, ALDH1A1, CAPN3, and CES1.

[0320] Embodiment 139. The method of any one of embodiments 85-138, further comprising generating a combination therapy configured to activate the target-specific drug-nutrient vulnerability in the subject.

[0321] Embodiment 140. The method of embodiment 139, wherein the combination therapy targets a gene or an expression product thereof from the set of target-specific omics signature. [0322] Embodiment 141. The method of embodiment 139, wherein the combination therapy modulates biosynthesis of a nutrient in the subject.

[0323] Embodiment 142. The method of embodiment 139, wherein the combination therapy modulates proline biosynthesis in the subject.

[0324] Embodiment 143. The method of embodiment 141, wherein the combination therapy modulates cholesterol biosynthesis in the subject.

[0325] Embodiment 144. The method of embodiment 139, wherein the combination therapy modulates proline biosynthesis in the subject.

[0326] Embodiment 145. The method of embodiment 139, wherein the combination therapy modulates cholesterol biosynthesis in the subject.

[0327] Embodiment 146. The method of embodiment 139, wherein the combination therapy modulates fatty acid metabolism in the subject.

[0328] Embodiment 147. The method of embodiment 139, wherein the combination therapy modulates a sirtuin pathway in the subject. [0329] Embodiment 148. The method of embodiment 139, wherein the combination therapy modulates proline biosynthesis in the subject.

[0330] Embodiment 149. The method of embodiment 139, wherein the combination therapy modulates ubiquinone biosynthesis in the subject.

[0331] Embodiment 150. The method of embodiment 139, wherein the combination therapy modulates a hypoxia pathway in the subject.

[0332] Embodiment 151. The method of embodiment 139, wherein the combination therapy comprises a chemotherapy.

[0333] Embodiment 152. The method of embodiment 139, wherein the combination therapy comprises a radiotherapy.

[0334] Embodiment 153. The method of embodiment 139, wherein the combination therapy comprises an immunotherapy.

[0335] Embodiment 154. The method of any one of embodiments 139-153, further comprising administering to the subject the combination therapy.

[0336] Embodiment 155. The method of any one of embodiments 139-154, wherein the dietary treatment and the combination therapy has a synergistic therapeutic effect in the subject.

[0337] Embodiment 156. The method of any one of embodiments 85-155, further comprising administering to the subject a chemotherapy.

[0338] Embodiment 157. The method of any one of embodiments 85-156, further comprising administering to the subject a radiotherapy.

[0339] Embodiment 158. The method of any one of embodiments 85-157, further comprising administering to the subject an immunotherapy.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A method of treating a subject in need thereof, comprising:

(a) providing a gene expression profile of a biological sample from the subject, wherein the gene expression profile comprises an expression level of a set of genes;

(b) identifying a dependence of the biological sample on a nutrient based at least on the gene expression profile; and

(c) administering to the subject a nutrient modulation therapy formulated to modulate a level of the nutrient in the subject if the subject is predicted to respond to the nutrient modulation therapy.

2. The method of claim 1, wherein the identifying comprises hierarchical clustering.

3. The method of claim 1 or 2, wherein the identifying comprises using an expression heatmap.

4. The method of any one of claims 1-3, wherein the identifying comprises comparing the gene expression profile with a reference gene expression profile associated with a known dependence on the nutrient.

5. The method of any one of claims 1-4, wherein the identifying comprises determining a coefficient of variation (CV) of an expression level of a gene in the set of genes.

6. The method of any one of claims 1-5, wherein the identifying comprises determining a Spearman’s rank correlation coefficient of an expression level of a gene in the set of genes.

7. The method of any one of claims 1-6, further comprising stratifyting the biological sample into a nutrient-dependent or nutrient-independent-group.

8. The method of any one of claims 1-7, further comprising ranking the biological sample based on the dependence of the biological sample on the nutrient.

9. The method of any one of claims 1-8, further comprising filtering out a subset of genes from the gene expression profile.

10. The method of any one of claims 1-9, wherein the biological sample is dependent on exogenous supplementation of the nutrient.

11. The method of any one of claims 1-9, wherein the biological sample is independent of exogenous supplementation of the nutrient.

12. The method of any one of claims 1-11, wherein the therapy modulates the level of the nutrient in the subject. The method of any one of claims 1-12, wherein the therapy reduces the level of the nutrient in the subject. The method of any one of claims 1-12, wherein the therapy elevates the level of the nutrient in the subject. The method of any one of claims 1-12, wherein the therapy is a nutrient starvation therapy. The method of any one of claims 1-12, wherein the therapy is a nutrient supplementation therapy. The method of any one of claims 1-16, wherein the gene expression profile comprises mRNA expression data. The method of any one of claims 1-16, wherein the gene expression profile comprises DNA expression data. The method of any one of claims 1-18, further comprising determining the gene expression profile of the biological sample. The method of claim 19, wherein the determining the gene expression profile comprises using a sequencing analysis technique. The method of claim 20, wherein the sequencing analysis technique comprises single cell sequencing. The method of claim 20, wherein the sequencing analysis technique comprises RNA sequencing. The method of claim 20, wherein the sequencing analysis technique comprises DNA sequencing. The method of any one of claims 1-23, further comprising identifying a gene in the biological sample that is indicative of sensitivity of the biological sample to the therapy. The method of any one of claims 1-24, further comprising determining based at least on the dependence of the biological sample on the nutrient whether the subject is predicted to respond to the nutrient modulation therapy. The method of any one of claims 1-25, wherein the biological sample is a cancer cell. The method of any one of claims 1-26, wherein the therapy reduces viability of the cancer cell. The method of any one of claims 1-27, wherein the subject has a cancer. The method of claim 28, wherein the cancer is ovarian cancer. The method of claim 28, wherein the cancer is endometrial cancer. The method of claim 28, wherein the cancer is colorectal cancer. The method of claim 28, wherein the cancer is pancreatic cancer. The method of claim 28, wherein the cancer is renal cancer. The method of claim 28, wherein the cancer is renal cancer. The method of claim 28, wherein the cancer is liver cancer. The method of claim 28, wherein the cancer is kidney cancer. The method of any one of claims 1-36, wherein the therapy modulates biosynthesis of the nutrient in the subject. The method of any one of claims 1-37, wherein the therapy modulates proline biosynthesis in the subject. The method of any one of claims 1-37, wherein the therapy modulates cholesterol biosynthesis in the subject. The method of any one of claims 1-39, wherein the therapy comprises a dietary product. The method of claim 40, wherein the dietary product is devoid of proline. The method of claim 40 or 41, wherein the dietary product is devoid of serine. The method of any one of claims 40-42, wherein the dietary product is devoid of glycine. The method of claim 40, wherein the dietary product is devoid of cholesterol. The method of claim 40 or 44, wherein the dietary product is devoid of fats. The method of any one of claims 1-45, wherein the therapy comprises a cholesterol lowering agent. The method of claim 46, wherein the cholesterol lowering agent is a statin. The method of claim 46, wherein the cholesterol lowering agent is a cholesteryl ester transfer protein (CETP) inhibitor. The method of claim 46, wherein the cholesterol lowering agent is a lecithin- cholesterol acyltransferase (LCAT) inhibitor. The method of claim 46, wherein the cholesterol lowering agent is a bile acid sequestrant. The method of claim 46, wherein the cholesterol lowering agent is a cholesterol absorption inhibitor. The method of any one of claims 1-51, wherein the nutrient is an amino acid.

-108- The method of claim 52, wherein the amino acid is proline. The method of any one of claims 1-51, wherein the nutrient is a sterol. The method of claim 54, wherein the sterol is cholesterol. The method of any one of claims 1-55, wherein the gene expression profile comprises a gene in the proline biosynthesis pathway. The method of claim 56, wherein the gene expression profile comprises at least one of: PYCR1, PYCR2, and PYCR3. The method of any one of claims 1-55, wherein the gene expression profile comprises a gene in the cholesterol biosynthesis pathway. The method of any one of claims 1-55, wherein the gene expression profile comprises a gene in the fatty acid biosynthesis pathway. The method of any one of claims 1-55, wherein the gene expression profile comprises a gene in the mevalonate pathway. The method of any one of claims 1-55, wherein the gene expression profile comprises a gene in the sirtuin pathway. The method of any one of claims 1-55, wherein the gene expression profile comprises a gene listed in TABLE 2. The method of any one of claims 1-55, wherein the gene expression profile comprises at least one of: FKBP5, REEP2, CENPV, SOX12, ZSWIM5, WASFI, KIAA1211, MXD4, BTD, HACL1, NADK2, CDK19, ATP7B, FECH, HABP4, GDF11, LZTFL1, RPAIN, WDR45, CHCHD4, WASHC2C, ULK4, TATDN2, WDR81, COQ10A, DHX33, NUP88, WRN, MAP2K1, C15orf39, FAM160A1, PML, PARP9, NR1P1, BATF2, BANK1, CATSPER1, SHANK2, TMC8, ANK3, GBP1, ISG15, CD274, NALCN, MR1, CSF2, TMC6, LRG1, IVL, GALNT9, RAB38, SAMD9L, GIMAP2, UBE2L6, APOL3, GNA15, GALNT6, TGFA, MMEL1, PTAFR, NECTIN4, TC2N, DCBLD2, SEMA7A, EPN3, ANKRD22, ADAM8, EREG, TGFBI, ITGB6, LGALS9, VAV1, TRIM22, DAPP1, TMEM92, UNC13D, TCN1, SLC37A2, SFTA2, MUC16, LAD1, GALNT3, ANXA3, PR0M2, CRABP2, RAC2, SPRR1B, RBP1, PRSS8, ICAM2, and MAL2. The method of any one of claims 1-55, wherein the gene expression profile comprises a gene listed in TABLE 4. The method of any one of claims 1-55, wherein the gene expression profile comprises at least one of: CENPV, PEBP1, WASFI, TSPYL2, CITED2, MXD4, RBM3, BTD,

-109- ST6GALNAC6, ASNS, RERE, STXBP1, HACL1, NADK2, CD99L2, ARRB2, SIRT1, GCAT, POMT1, SLC25A38, COQ8A, RMCI, FECH, MTMR12, RPP40, HABP4, MYBBP1A, SLC43A2, CXXC1, PF AS, SEC11C, XPOT, PYGB, SLC35E2B, CYB5D2, DDIT3, ACAT1, TARS, C1QBP, GNE, LZTFL1, RPAIN, WDR45, TMEM43, CHCHD4, CLASP2, POLR1E, YEATS4, ACAT2, STAMBPL1, CBWD5, APPL1, WASHC2C, ZNF76, PAN2, AGFG2, TBC1D15, SLC38A2, ZCCHC14, ACADM, LTV1, RAPGEF1, NSMF, TTC33, MDN1, TATDN2, SAT2, WASHC2A, RPL14, RPS6, PIM1, PDCD4, GTF3C6, WDR81, COQ10A, TOP1MT, UHRF1BP1, RPL26, REV3L, WDR48, APBB3, PLXNA3, SREBF1, RIOK1, CCDC88A, RPL32, CTNS, ICE1, NHEJ1, MAP1LC3B, CAPN7, RPS10, WDR70, BPHL, SLC38A1, TBC1D5, DHX33, WDR6, SNX3, SUPV3L1, NUDT2, ECHDC1, LYRM2, FLCN, PYCR1, GTF2H2C, HDDC2, OGGI, ALDH3A2, GTF2H2, VSIG10, XPC, NHP2, CCT2, ACAD 10, CCT5, CCDC107, GIGYF1, TACC2, RPL37, HSF2, PLCG1, BRD9, NAP1L1, SDHA, SOD2, GNL3, TRAK2, MRPS25, FAM173B, TCP1, ATP5F1A, MRPL18, PHYH, LARS, ZNF131, CEP83, EEF2, PIK3R2, ORC3, PAIP1, UBP1, PKD1, SMIM4, QSOX2, PDHB, EEF1A1, PTBP2, PSIP1, TIMM44, IP6K2, CD320, ELP2, C5orf22, NISCH, ISCA1, SNX21, BRIX1, PTMS, ZNF428, DOCK7, UHRF1BP1L, GARS, RPL15, FRA10AC1, GBA2, TSEN2, RGS10, RPSA, AHI1, PDSS2, ENDOG, KATNA1, GNPDA1, TLN1, PHPT1, TMEM120A, NARS, PON2, NR2C1, FAM102A, SEC63, CCNJ, DROSHA, NUP88, ATM, TWNK, BRPF1, ZCCHC7, IMPACT, ACTR3B, MRPS30, TRIM7, HPS1, CAMTA2, WRN, SIK3, DYNC1LI1, PHF14, C18orf21, EXOC3, TOP2B, ZNF621, UAP1, RNF44, BCKDHB, ZNF397, MAST2, ZDHHC8, RNF146, PCM1, PMPCA, UBB, MAD2L2, RAB32, KMT2A, ELK1, HINT3, MEDIO, AIMP2, PCBP4, G6PD, MCFD2, MET, MRPL52, HEXIM1, B4GALT5, COMT, LASPI, SERTAD1, CTNNBIP1, ARHGAP5, EPS8, KRT18, RAP1GDS1, PJA1, BID, GIPC1, ANAPC10, PHLDA2, SGMS2, IDS, SERPINH1, LSM6, ENCI, FURIN, PCTP, RAD51C, LRR1, BLCAP, CD276, MAPKAPK3, RAB5IF, NT5E, CYB5A, EMC10, NDFIP2, SPATS2L, TMED10, PPP3CA, MBOAT7, NET1, RHOF, MYO1B, ITPRID2, LRRC8A, SPTBN2, CD59, RBMS2, TAGLN2, STK24, EML2, SELENOW, PUDP, PRRG1, CCND1, PARP12, FAM111B, SAT1, CASP4, PSME1, DHRS7, SP100, ELF1, MESD, BCAR3, FXYD5, ACTN1, PLS3, B3GNT2, TNKS1BP1, ARHGAP18, FLNA, TPM4, NMI, WWC1, MEAK7, SMAGP, CAPN1,

-110- PTPRK, MAP4K4, HELZ2, SYNGR2, DHRS1, AJUBA, ADAMI 5, ANXA2, PDLIM7, LIF, TES, LGALS1, APOL2, OCIAD2, LY6E, ABCC3, TMOD3, FAM3C, RAB27A, ACP6, MAP2K1, PDXK, PSME2, KCNN4, ST5, TAPI, CAPN2, CD47, GALNT7, FAM111A, DTX3L, F0SL1, EPS8L2, SDC4, PRSS23, INSIG2, AK4, ITGA3, PKP3, CHMP4C, SPINT2, ARL6IP5, S100A11, BLVRB, NCEH1, IRF1, CLMN, ENDOD1, EPHX4, PLA2G12A, TNFAIP8, FERMT1, TMBIM1, ZFP36L1, SLC39A11, TSPAN14, FRMD6, API S3, FAH, DDIT4, ITGA6, C19orfi3, SLC2A1, TSKU, SCNN1A, QSOX1, DTX2, OSBPL3, ATP1B1, TNFRSF12A, LITAF, B2M, LLGL2, RHOD, IL4R, PLSCR1, CYB561, C15orfi9, C6orfl32, AGRN, LGALS3BP, RASA1, HIF1A, CLIP4, RRAS2, PML, IL15RA, MGLL, PARP9, DSG2, CAV2, PLEK2, KCNK1, FUR, ARNTL2, C16orf74, GPRC5A, TNNT1, LMO7, SFN, S100A16, SLC44A2, MYO5B, OAS1, KLF5, MALL, ITGA2, DDX60L, ZNF185, EFNB2, PTGES, ISG15, PTK6, ADAP1, PLAU, DDX60, PCDH1, MYEOV, ITGB4, EHF, KRT19, UBE2L6, TGFA, EMP1, GRHL2, TC2N, DCBLD2, B3GNT3, TGFBI, LAMB3, UNC13D, F3, LAMC2, ANXA3, ANO1, KRT7, and MAL2. The method of any one of claims 1-55, wherein the gene expression profile comprises a gene listed in TABLE 7. The method of any one of claims 1-55, wherein the gene expression profile comprises at least one of: GNG10, NDUFC2-KCTD14, UST, NDRG1, HSPG2, CIS, SEC16B, ABCB6, FCHSD2, CDKL1, TXNDC5, ALDH1A1, CAPN3, or CES1. The method of any one of claims 1-67, further comprising identifying sensitivity of the biological sample to a combination therapy based at least on the gene expression profile. The method of claim 68, wherein the combination therapy targets a gene or expression product from the gene expression profile. The method of claim 68 or 69, wherein the combination therapy modulates biosynthesis of the nutrient in the subject. The method of claim 68 or 69, wherein the combination therapy modulates proline biosynthesis in the subject. The method of claim 68 or 69, wherein the combination therapy modulates cholesterol biosynthesis in the subject.

-111- The method of claim 68 or 69, wherein the combination therapy modulates fatty acid metabolsim in the subject. The method of claim 68 or 69, wherein the combination therapy modulates a sirtuin pathway in the subject. The method of claim 68 or 69, wherein the combination therapy modulates ubiquinone biosynthesis in the subject. The method of claim 68 or 69, wherein the combination therapy modulates a hypoxia pathway in the subject. The method of any one of claims 68-76, wherein the combination therapy comprises a chemotherapy. The method of any one of claims 68-77, wherein the combination therapy comprises a radiotherapy. The method of any one of claims 68-78, wherein the combination therapy comprises an immunotherapy. The method of any one of claims 68-79, further comprising administering to the subject the combination therapy. The method of any one of claims 68-80, wherein the nutrient modulation therapy and the combination therapy has a synergistic therapeutic effect in the subject. The method of any one of claims 1-81, further comprising administering to the subject a chemotherapy. The method of any one of claims 1-82, further comprising administering to the subject a radiotherapy. The method of any one of claims 1-83, further comprising administering to the subject an immunotherapy. A method of treating a subject in need thereof, comprising:

(a) subjecting a plurality of reference cells to a plurality of drug-nutrient environments to determine a set of drug-nutrient vulnerabilities of the plurality of reference cells;

(b) performing an omics method on the plurality of reference cells to generate omics data;

(c) determining a set of omics signatures that correlate with the set of drugnutrient vulnerabilities;

-112- (d) performing the omics method on a plurality of target cells of the subject to generate a set of target-specific omics signatures, wherein the plurality of target cells comprises a healthy cell and a disease cell;

(e) determining a target-specific drug-nutrient vulnerability based at least on the set of omics signatures and the set of target-specific omics signatures, wherein the target-specific drug-nutrient vulnerability affects the disease cell more than the healthy cell; and

(f) generating a dietary treatment configured to activate the target-specific drugnutrient vulnerability in the subject. The method of claim 85, wherein the plurality of target cells comprises a plurality of healthy cells and a plurality of disease cells. The method of claim 86, wherein the determining the target-specific drug-nutrient vulnerability comprises:

(a) determining a plurality of drug-nutrient vulnerabilities of the plurality of target cells; and

(b) clustering the plurality of drug-nutrient vulnerabilities to associate target cells with similar drug-nutrient vulnerabilities. The method of any one of claims 85-87, further comprising administering to the subject the dietary treatment. The method of any one of claims 85-88, wherein the plurality of drug-nutrient environments comprises different levels of a nutrient, different levels of a drug, or both. The method of any one of claims 85-89, wherein the set of drug-nutrient vulnerabilities and the target-specific drug-nutrient vulnerability each comprise different levels of a nutrient, different levels of a drug, or both. The method of any one of claims 85-90, wherein the omics method comprises genomics, transcriptomics, proteomics, or a combination thereof. The method of any one of claims 85-91, wherein the set of omics signatures and the set of target-specific omics signatures comprise DNA, mRNA, or protein. The method of any one of claims 85-92, wherein the omics method comprises single cell sequencing. The method of any one of claims 85-92, wherein the omics method comprises RNA sequencing.

-113- The method of any one of claims 85-92, wherein the omics method comprises DNA sequencing. The method of any one of claims 85-95, wherein the dietary treatment modulates the level of a nutrient in the subject. The method of any one of claims 85-96, wherein the dietary treatment reduces the level of a nutrient in the subject. The method of any one of claims 85-96, wherein the dietary treatment elevates the level of a nutrient in the subject. The method of any one of claims 85-96, wherein the dietary treatment is a nutrient starvation therapy. The method of any one of claims 85-96, wherein the dietary treatment is a nutrient supplementation therapy. The method of any one of claims 85-100, wherein disease cell is a cancer cell. The method of claim 101, wherein the dietary treatment reduces viability of the cancer cell. The method of any one of claims 85-102, wherein the subject has a cancer. The method of claim 103, wherein the cancer is ovarian cancer. The method of claim 103, wherein the cancer is endometrial cancer. The method of claim 103, wherein the cancer is colorectal cancer. The method of claim 103, wherein the cancer is pancreatic cancer. The method of claim 103, wherein the cancer is renal cancer. The method of claim 103, wherein the cancer is liver cancer. The method of claim 103, wherein the cancer is kidney cancer. The method of any one of claims 85-110, wherein the dietary treatment modulates biosynthesis of a nutrient in the subject. The method of any one of claims 85-111, wherein the dietary treatment modulates proline biosynthesis in the subject. The method of any one of claims 85-111, wherein the dietary treatment modulates cholesterol biosynthesis in the subject. The method of any one of claims 85-113, wherein the dietary treatment comprises a dietary product. The method of claim 114, wherein the dietary product is devoid of proline. The method of claim 114 or 115, wherein the dietary product is devoid of serine.

-114- The method of any one of claims 114-116, wherein the dietary product is devoid of glycine. The method of claim 114, wherein the dietary product is devoid of cholesterol. The method of claim 114 or 118, wherein the dietary product is devoid of fats. The method of any one of claims 85-119, wherein the dietary treatment comprises a cholesterol lowering agent. The method of claim 120, wherein the cholesterol lowering agent is a statin. The method of claim 120, wherein the cholesterol lowering agent is a cholesteryl ester transfer protein (CETP) inhibitor. The method of claim 120, wherein the cholesterol lowering agent is a lecithin- cholesterol acyltransferase (LCAT) inhibitor. The method of claim 120, wherein the cholesterol lowering agent is a bile acid sequestrant. The method of claim 120, wherein the cholesterol lowering agent is a cholesterol absorption inhibitor. The method of any one of claims 85-125, wherein the set of target-specific omics signature comprises a gene expression profile. The method of claim 126, wherein the gene expression profile comprises a gene in the proline biosynthesis pathway. The method of claim 127, wherein the gene expression profile comprises at least one of: PYCR1, PYCR2, and PYCR3. The method of claim 126, wherein the gene expression profile comprises a gene in the cholesterol biosynthesis pathway. The method of claim 126, wherein the gene expression profile comprises a gene in the fatty acid biosynthesis pathway. The method of claim 126, wherein the gene expression profile comprises a gene in the mevalonate pathway. The method of claim 126, wherein the gene expression profile comprises a gene in the sirtuin pathway. The method of claim 126, wherein the gene expression profile comprises a gene listed in TABLE 2 The method of claim 126, wherein the gene expression profile comprises at least one of: FKBP5, REEP2, CENPV, SOX12, ZSWIM5, WASFI, KIAA1211, MXD4, BTD,

-115- HACL1, NADK2, CDK19, ATP7B, FECH, HABP4, GDF11, LZTFL1, RPAIN, WDR45, CHCHD4, WASHC2C, ULK4, TATDN2, WDR81, COQIOA, DHX33, NUP88, WRN, MAP2K1, C15orf39, FAM160A1, PML, PARP9, NR1P1, BATF2, BANK1, CATSPER1, SHANK2, TMC8, ANK3, GBP1, ISG15, CD274, NALCN, MR1, CSF2, TMC6, LRG1, IVL, GALNT9, RAB38, SAMD9L, GIMAP2, UBE2L6, APOL3, GNA15, GALNT6, TGFA, MMEL1, PTAFR, NECTIN4, TC2N, DCBLD2, SEMA7A, EPN3, ANKRD22, ADAM8, EREG, TGFBI, ITGB6, LGALS9, VAV1, TRIM22, DAPP1, TMEM92, UNC13D, TCN1, SLC37A2, SFTA2, MUC16, LAD1, GALNT3, ANXA3, PROM2, CRABP2, RAC2, SPRR1B, RBP1, PRSS8, ICAM2, and MAL2. The method of claim 126, wherein the gene expression profile comprises a gene listed in TABLE 4 The method of claim 126, wherein the gene expression profile comprises at least one of: CENPV, PEBP1, WASF1, TSPYL2, CITED2, MXD4, RBM3, BTD, ST6GALNAC6, ASNS, RERE, STXBP1, HACL1, NADK2, CD99L2, ARRB2, SIRT1, GCAT, POMT1, SLC25A38, COQ8A, RMCI, FECH, MTMR12, RPP40, HABP4, MYBBP1A, SLC43A2, CXXC1, PF AS, SEC11C, XPOT, PYGB, SLC35E2B, CYB5D2, DDIT3, ACAT1, TARS, C1QBP, GNE, LZTFL1, RPAIN, WDR45, TMEM43, CHCHD4, CLASP2, POLR1E, YEATS4, ACAT2, STAMBPL1, CBWD5, APPL1, WASHC2C, ZNF76, PAN2, AGFG2, TBC1D15, SLC38A2, ZCCHC14, ACADM, LTV1, RAPGEF1, NSMF, TTC33, MDN1, TATDN2, SAT2, WASHC2A, RPL14, RPS6, PIM1, PDCD4, GTF3C6, WDR81, COQIOA, TOP1MT, UHRF1BP1, RPL26, REV3L, WDR48, APBB3, PLXNA3, SREBF1, RIOK1, CCDC88A, RPL32, CTNS, ICE1, NHEJ1, MAP1LC3B, CAPN7, RPS10, WDR70, BPHL, SLC38A1, TBC1D5, DHX33, WDR6, SNX3, SUPV3L1, NUDT2, ECHDC1, LYRM2, FLCN, PYCR1, GTF2H2C, HDDC2, OGGI, ALDH3A2, GTF2H2, VSIG10, XPC, NHP2, CCT2, ACAD 10, CCT5, CCDC107, GIGYF1, TACC2, RPL37, HSF2, PLCG1, BRD9, NAP1L1, SDHA, SOD2, GNL3, TRAK2, MRPS25, FAM173B, TCP1, ATP5F1A, MRPL18, PHYH, LARS, ZNF131, CEP83, EEF2, PIK3R2, ORC3, PAIP1, UBP1, PKD1, SMIM4, QSOX2, PDHB, EEF1A1, PTBP2, PSIP1, TIMM44, IP6K2, CD320, ELP2, C5orf22, NISCH, ISCA1, SNX21, BRIX1, PTMS, ZNF428, DOCK7, UHRF1BP1L, GARS, RPL15, FRA10AC1, GBA2, TSEN2, RGS10, RPSA, AHI1, PDSS2, ENDOG, KATNA1, GNPDA1, TLN1, PHPT1, TMEM120A, NARS, PON2, NR2C1, FAM102A, SEC63, CCNJ, DROSHA, NUP88, ATM, TWNK, BRPF1, ZCCHC7, IMPACT, ACTR3B, MRPS30, TRIM7, HPS1, CAMTA2, WRN, SIK3, DYNC1LI1, PHF14, C18orf21, EXOC3, TOP2B, ZNF621, UAP1, RNF44, BCKDHB, ZNF397, MAST2, ZDHHC8, RNF146, PCM1, PMPCA, UBB, MAD2L2, RAB32, KMT2A, ELK1, HINT3, MEDIO, AIMP2, PCBP4, G6PD, MCFD2, MET, MRPL52, HEXIM1, B4GALT5, COMT, LASPI, SERTAD1, CTNNBIP1, ARHGAP5, EPS8, KRT18, RAP1GDS1, PJA1, BID, GIPC1, ANAPC10, PHLDA2, SGMS2, IDS, SERPINH1, LSM6, ENCI, FURIN, PCTP, RAD51C, LRR1, BLCAP, CD276, MAPKAPK3, RAB5IF, NT5E, CYB5A, EMC10, NDFIP2, SPATS2L, TMED10, PPP3CA, MBOAT7, NET1, RHOF, MYO1B, ITPRID2, LRRC8A, SPTBN2, CD59, RBMS2, TAGLN2, STK24, EML2, SELENOW, PUDP, PRRG1, CCND1, PARP12, FAM111B, SAT1, CASP4, PSME1, DHRS7, SP100, ELF1, MESD, BCAR3, FXYD5, ACTN1, PLS3, B3GNT2, TNKS1BP1, ARHGAP18, FLNA, TPM4, NMI, WWC1, MEAK7, SMAGP, CAPN1, PTPRK, MAP4K4, HELZ2, SYNGR2, DHRS1, AJUBA, ADAMI 5, ANXA2, PDLIM7, LIF, TES, LGALS1, APOL2, OCIAD2, LY6E, ABCC3, TMOD3, FAM3C, RAB27A, ACP6, MAP2K1, PDXK, PSME2, KCNN4, ST5, TAPI, CAPN2, CD47, GALNT7, FAM111A, DTX3L, F0SL1, EPS8L2, SDC4, PRSS23, INSIG2, AK4, ITGA3, PKP3, CHMP4C, SPINT2, ARL6IP5, S100A11, BLVRB, NCEH1, IRF1, CLMN, ENDOD1, EPHX4, PLA2G12A, TNFAIP8, FERMT1, TMBIM1, ZFP36L1, SLC39A11, TSPAN14, FRMD6, API S3, FAH, DDIT4, ITGA6, C19orfi3, SLC2A1, TSKU, SCNN1A, QS0X1, DTX2, 0SBPL3, ATP1B1, TNFRSF12A, LITAF, B2M, LLGL2, RHOD, IL4R, PLSCR1, CYB561, C15orfi9, C6orfl32, AGRN, LGALS3BP, RASA1, HIF1A, CLIP4, RRAS2, PML, IL15RA, MGLL, PARP9, DSG2, CAV2, PLEK2, KCNK1, FUR, ARNTL2, C16orf74, GPRC5A, TNNT1, LMO7, SFN, S100A16, SLC44A2, MYO5B, OAS1, KLF5, MALL, ITGA2, DDX60L, ZNF185, EFNB2, PTGES, ISG15, PTK6, ADAP1, PLAU, DDX60, PCDH1, MYEOV, ITGB4, EHF, KRT19, UBE2L6, TGFA, EMP1, GRHL2, TC2N, DCBLD2, B3GNT3, TGFBI, LAMB3, UNC13D, F3, LAMC2, ANXA3, ANO1, KRT7, and MAL2. The method of claim 126, wherein the gene expression profile comprises a gene listed in TABLE 7 The method of claim 126, wherein the gene expression profile comprises at least one of: GNG10, NDUFC2-KCTD14, UST, NDRG1, HSPG2, CIS, SEC16B, ABCB6, FCHSD2, CDKL1, TXNDC5, ALDH1A1, CAPN3, and CES1. The method of any one of claims 85-138, further comprising generating a combination therapy configured to activate the target-specific drug-nutrient vulnerability in the subject. The method of claim 139, wherein the combination therapy targets a gene or an expression product thereof from the set of target-specific omics signature. The method of claim 139, wherein the combination therapy modulates biosynthesis of a nutrient in the subject. The method of claim 139, wherein the combination therapy modulates proline biosynthesis in the subject. The method of claim 141, wherein the combination therapy modulates cholesterol biosynthesis in the subject. The method of claim 139, wherein the combination therapy modulates proline biosynthesis in the subject. The method of claim 139, wherein the combination therapy modulates cholesterol biosynthesis in the subject. The method of claim 139, wherein the combination therapy modulates fatty acid metabolism in the subject. The method of claim 139, wherein the combination therapy modulates a sirtuin pathway in the subject. The method of claim 139, wherein the combination therapy modulates proline biosynthesis in the subject. The method of claim 139, wherein the combination therapy modulates ubiquinone biosynthesis in the subject. The method of claim 139, wherein the combination therapy modulates a hypoxia pathway in the subject. The method of claim 139, wherein the combination therapy comprises a chemotherapy. The method of claim 139, wherein the combination therapy comprises a radiotherapy. The method of claim 139, wherein the combination therapy comprises an immunotherapy.

-118- The method of any one of claims 139-153, further comprising administering to the subject the combination therapy. The method of any one of claims 139-154, wherein the dietary treatment and the combination therapy has a synergistic therapeutic effect in the subject. The method of any one of claims 85-155, further comprising administering to the subject a chemotherapy. The method of any one of claims 85-156, further comprising administering to the subject a radiotherapy. The method of any one of claims 85-157, further comprising administering to the subject an immunotherapy.

-119-