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WO2025169993A1 - Procédé pour prédire le pronostic d'un patient atteint d'un cancer colorectal - Google Patents

Procédé pour prédire le pronostic d'un patient atteint d'un cancer colorectal

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Publication number
WO2025169993A1
WO2025169993A1 PCT/JP2025/003938 JP2025003938W WO2025169993A1 WO 2025169993 A1 WO2025169993 A1 WO 2025169993A1 JP 2025003938 W JP2025003938 W JP 2025003938W WO 2025169993 A1 WO2025169993 A1 WO 2025169993A1
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Prior art keywords
gene
genes
score
prognosis
patient
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English (en)
Japanese (ja)
Inventor
誠 武藤
文彦 柿崎
弘之 三好
健二 河田
和貴 小▲浜▼
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Kyoto University NUC
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Kyoto University NUC
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6809—Methods for determination or identification of nucleic acids involving differential detection
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6869—Methods for sequencing

Definitions

  • CRC Colorectal cancer
  • CMS Consensus Molecular Subtyping
  • the present disclosure aims to provide a method and kit for predicting the prognosis of colorectal cancer patients.
  • the present disclosure provides a kit for predicting the prognosis of a colorectal cancer patient, the kit comprising: a group of genes (a) to (e): (a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16, (b) DEFA6, DEFA5, and SPINK1, (c) BST2, BATF, RAMP1, and TPM2, (d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and RBMS1.
  • the present invention provides a kit comprising a reagent for measuring the expression level of one or more genes selected from the group consisting of:
  • CRC-SC colorectal cancer stem cell
  • the nine panels on the right show the expression of marker genes in that cell population (dark grey): MKI67 and PCNA; cell proliferation markers; CD24, CD44, and BMI1; intestinal epithelial stem cell markers; EPCAM; epithelial cell marker; MUC12, PIGR, and PLA2G2A; lucky 5 gene.
  • Gene expression in CRC-SC lines compared to NCE-SC lines. (Top left) Flowchart of this study. Transcriptional activity of NCE-SC and CRC-SC spheroids was measured as mRNA expression levels using microarrays.
  • the graph on the right shows the HR and its 95% CI.
  • the graph on the right shows HR and its 95% CI.
  • Heatmap showing mRNA expression levels in 57 CRC-SC spheroid lines (55T to 83T) and 5 NCE-SC lines (3N to 73N; 3N-d in the leftmost column is a cell line differentiated in vitro from 3N by removing the medium containing Wnt ligands).
  • the graph on the right shows the HR and 95% CI for the five-signature, including the lead gene candidate CXCL14.
  • Flowchart for calculating the colorectal cancer comprehensive signature (GCS).
  • the five signatures were classified into two categories: long-term survival signatures and short-term survival signatures.
  • b and c) The mean Z-scores of the long-term and short-term survival signatures were treated as positive (+) and negative (-) scores, respectively.
  • the GCS score was calculated by summing the signature scores.
  • a retrospective observational study of the GCS score was performed using the TCGA-CRC-DSS, TCGA-CRC-PFS, or GSE39582-RFS datasets as validation cohorts.
  • the primers or probes may comprise or consist of a sequence complementary to a portion of the mRNA sequence encoded by each gene.
  • the primers or probes may be, for example, 10 to 50 nucleotides, 15 to 35 nucleotides, or 20 to 30 nucleotides.
  • the primers or probes comprise or consist of a sequence of 10 to 50 nucleotides, 15 to 35 nucleotides, or 20 to 30 nucleotides complementary to a portion of the mRNA sequence encoded by each gene.
  • the method of the present disclosure calculates a Z-score (Z) for each gene from the expression level of each gene in a patient using formula (I): (where x is the expression level of the gene in the patient, and ⁇ and ⁇ are the mean and standard deviation of the expression levels of the gene in multiple colorectal cancer patients, respectively) To seek by determining a gene expression signature score for each group of genes; and comparing the gene expression signature score to a cutoff value.
  • the gene expression signature score may be compared with multiple cutoff values. For example, for multiple colorectal cancer patients whose prognosis is known, the prognosis of each patient may be ranked into three or more levels, and two or more gene expression signature scores that can separate each rank group with a statistically significant difference may be used as cutoff values. Statistical significance can be determined, for example, by the logrank test.
  • the cutoff values may be multiple values that can separate multiple colorectal cancer patients at a certain ratio in order of their gene expression signature scores, and may be, for example, the quartiles of the gene expression signature scores of multiple colorectal cancer patients. By comparing with multiple cutoff values, colorectal cancer patients can be stratified according to prognosis.
  • the gene expression signature score of gene group (c) is compared with the cutoff value for that gene group. If the gene expression signature score of gene group (c) is higher than the cutoff value, the patient is predicted to have a poor prognosis.
  • the gene expression signature score for gene group (e) is compared with the cutoff value for that gene group. If the gene expression signature score for gene group (e) is higher than the cutoff value, the patient is predicted to have a poor prognosis.
  • the method of the present disclosure includes measuring the expression levels of genes in two gene groups selected from gene groups (a) to (e). In one embodiment, the method of the present disclosure includes measuring the expression levels of genes in three gene groups selected from gene groups (a) to (e). In one embodiment, the method of the present disclosure includes measuring the expression levels of genes in four gene groups selected from gene groups (a) to (e). In one embodiment, the method of the present disclosure includes measuring the expression levels of genes in five gene groups selected from gene groups (a) to (e).
  • the GCS score is compared with a cutoff value.
  • the cutoff value is a GCS score value that can statistically significantly divide multiple colorectal cancer patients with known prognosis into good prognosis and poor prognosis groups.
  • the cutoff value can be, for example, the value that minimizes the P value when multiple colorectal cancer patients are divided into good prognosis and poor prognosis groups using the Kaplan-Meier survival curve.
  • the statistical significance between the good prognosis and poor prognosis groups can be determined, for example, using the logrank test. If the colorectal cancer composite score is higher than the cutoff value, the patient is predicted to have a good prognosis.
  • the present disclosure provides a method for treating colorectal cancer patients, which comprises predicting the prognosis of a colorectal cancer patient using the prognosis prediction method of the present disclosure, and administering to the patient a therapeutic agent or therapy selected based on the prediction results.
  • the therapeutic agent may be an anticancer drug such as 5-fluorouracil (5-FU), tegafur, tegafur-uracil (UFT), doxifluridine (5'-DFUR), carmofur, tegafur-gimeracil-oteracil potassium (S-1), capecitabine (Cape), regorafenib, trifluridine-tipiracil hydrochloride (FTD/TPI), mitomycin C, irinotecan (IRI), oxaliplatin (OX), or vascular endothelial growth factor (VGF).
  • an anticancer drug such as 5-fluorouracil (5-FU), tegafur, tegafur-uracil (UFT), doxifluridine (5'-DFUR), carmofur, tegafur-gimeracil-oteracil potassium (S-1), capecitabine (Cape), regorafenib, trifluridine-tipiracil hydrochloride (FTD
  • a method for predicting the prognosis of a colorectal cancer patient comprising: (a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16, (b) DEFA6, DEFA5, and SPINK1, (c) BST2, BATF, RAMP1, and TPM2, (d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and SPINK1.
  • a kit for predicting the prognosis of a colorectal cancer patient comprising: (a) MUC12, PIGR, PLA2G2A, SLC4A4, and ZG16, (b) DEFA6, DEFA5, and SPINK1, (c) BST2, BATF, RAMP1, and TPM2, (d) MAGEA6, MAGEA3, MAGEA12, and CSAG1, and (e) IGF2, NELL2, and RBMS1.
  • a kit comprising a reagent for measuring the expression level of one or more genes in a group selected from the group consisting of: [14] 14.
  • the kit according to 13 above, wherein the one or more gene groups are gene groups (a) to (e).
  • the reagent is a primer or a probe.
  • a method for treating a patient with colorectal cancer comprising: 13.
  • a method comprising: predicting the prognosis of a colorectal cancer patient by the method according to any one of 1 to 12; and administering to the patient a therapeutic agent or treatment selected based on the result of the prediction.
  • RNA from each specimen was linearly amplified and labeled with Cy3-dCTP.
  • Labeled cRNA was purified using the RNAeasy Mini Kit (Qiagen). The concentration and specific activity of the labeled cRNA preparation (pmol Cy3/ ⁇ g cRNA) were measured using a NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA).
  • Each labeled cRNA sample 600 ng was fragmented with 5 ⁇ l 10x blocking agent and 1 ⁇ l 25x fragmentation buffer, then heated at 60°C for 30 min. Finally, 25 ⁇ l of 2x GE hybridization buffer was added to dilute the labeled cRNA.
  • Lead genes and their associated genes We identified genes with extremely variable mRNA expression (SD > 6.5) in CRC-SC lines. Among these genes, we selected five lead genes encoded on different chromosomes and independently regulated; their expression levels were not correlated with each other (Spearman correlation coefficient
  • Gene expression signature scores were calculated as previously reported ⁇ Jackstadt et al., 2019 ⁇ .
  • Z-score Zij for the jth specific gene in the ith patient in the dataset was calculated using the following formula:
  • Xij (uppercase letters) indicates the logarithmic value of the mRNA expression level after log2 transformation for each patient's tumor.
  • the GSE39582 data was measured using microarrays and was originally displayed as log2 values, whereas the TCGA-CRC data used in this study was RNA-seq data, so values were displayed without logarithmic transformation.
  • the gene expression signature score was the average Z score calculated using the following formula: That is, the lucky 5 gene expression signature score was calculated by adding the Z scores of the five jth genes, and that of the lead gene was calculated by adding the Z scores of the lead gene and the jth gene whose expression is correlated within that subgroup, and then dividing by the number of genes (m) (i.e., 5 for the lucky 5, 3-4 for the lead gene signature).
  • OS overall survival
  • DSS disease-specific survival
  • PFS progression-free survival
  • RFS recurrence-free survival
  • CRC specimens were divided into four equal subgroups according to GCS score, and analyzed using the Kaplan-Meier method followed by the log-rank test.
  • the 95% CI and HR for the poorest quartile group were calculated using the Mantel-Haenszel method in the GraphPad Prism software package.
  • Matrigel-enclosed spheroids were suspended in cell recovery solution (Corning) and collected in a 1.5 mL tube. Matrigel was dissolved at 4°C for 30 minutes with rotation and mixing. Spheroids were centrifuged at 4°C for 5 minutes and washed twice with PBS. Genomic DNA from the spheroids was purified using the DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. Hotspot mutations in 50 or 409 cancer-related genes in CRC-SC lines were detected by Macrogen Inc. (Seoul, Republic of Korea).
  • k-means analysis Among the genes highly expressed in CRC-SC lines compared to NCE-SC lines, the 100 most differentially expressed genes were subjected to gene cluster analysis using Integrated Differential Expression and Pathway analysis v0.951 (iDEP.951) ⁇ Ge, Son et al. 2018 ⁇ .
  • scRNA-seq Single-cell RNA-seq library preparation, data processing, and quality check.
  • scRNA-seq library preparation was performed by Rhelixa Inc. (Tokyo, Japan) as previously described ⁇ Joanito, Wirapati et al. 2022 ⁇ . Briefly, fresh 70N-NCE-SC were cultured in single suspension and loaded into a Chromium system (10X Genomics) at 5,000 cells per well. Barcoded sequencing libraries were generated using the Chromium Single Cell 3' v3.1 reagent Kit Dual (10X Genomics). The libraries were sequenced on an Illumina NovaSeq 6000 until full saturation was achieved.
  • MUC12 and PIGR are expressed in colonocytes
  • PLA2G2A and ZG16 are expressed in goblet cells
  • SLC4A4 is expressed in CA1-positive colonocytes.
  • NCE-SCs cultured as spheroids in our medium appear to be partially differentiated toward fetal colon and small intestine, as suggested by the Metascape analysis data above (Fig. 2C).
  • EMT epi-mesenchymal transition
  • a novel prognostic signature candidate represented by high, non-intersecting mRNA expression of DEFA6, CXCL14, BST2, MAGEA6, and IGF2 in human colorectal cancer stem cell spheroid cell lines.
  • We found five lead gene candidates representing novel molecular subgroups (Table 2 and Figure 5A). These gene candidates are DEFA6, CXCL14, BST2, MAGEA6, and IGF2.
  • These lead gene candidates are only moderately expressed in NCE-SC, and largely do not intersect with each other or with the lucky 5-gene signature expression group (Figure 2A, Figure 5A).
  • k-means method a representative non-hierarchical statistical method, to classify the data into five subgroups.
  • This signature is the sum of the individual standardized signatures, with + indicating a good prognosis and - indicating a poor prognosis, and is called the General Colorectal Cancer Signature (GCS).
  • GCS General Colorectal Cancer Signature
  • the colorectal cancer stem cell spheroid lines whose total mRNA we analyzed as our discovery cohort were a set that included additional stage III and IV lines in consideration of clinical importance. However, since the number of lines was limited to 57, we first analyzed the correlation with overall survival (OS) using the TCGA-CRC cancer tissue mRNA database of nearly 600 cases as a validation cohort.
  • OS overall survival
  • GCS can be a very useful prognostic signature.
  • a unified prognosis prediction may be possible, and relatively mild chemotherapy may be sufficient for patients with high GCS values, while more intensive chemotherapy may be recommended for patients with low GCS values, if possible.
  • PDSOX patient-derived spheroid orthotopic xenografts
  • mice transplanted with the favorable-prognosis Q4 quartile CRC-SC line survived throughout the observation period ( Figure 6F, upper left), and consistent with this, no metastatic lesions were detected in the liver or lungs at autopsy.
  • Two CRC-SC lines from each of the Q2 and Q3 quartiles died within 26 weeks after transplantation ( Figure 6F, lower left).
  • many of the Q4 CRC-SC lines had high levels of the Lucky 5 signature, a signature associated with a favorable prognosis (Figure 6G).
  • the GCS score may play an important role in the outcome of colorectal cancer patients, reflecting the unique growth and metastatic characteristics of individual CRC-SCs.
  • each gene signature and the GCS can be very useful signatures for predicting the prognosis of colorectal cancer patients.
  • the GCS is a value that reflects the integrated analysis of each gene signature, and can be an extremely practical prognostic prediction tool.
  • the GCS score is expected to facilitate understanding of the condition of colorectal cancer patients and the physicians involved in their treatment, and to be useful in determining the most appropriate treatment plan.

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Abstract

La présente divulgation concerne un procédé de prédiction du pronostic d'un patient atteint d'un cancer colorectal, le procédé comprenant la mesure des niveaux d'expression de gènes d'un ou de plusieurs groupes de gènes choisis parmi les groupes de gènes (a) à (e) : (a) MUC12, PIGR, PLA2G2A, SLC4A4, et ZG16, (b) DEFA6, DEFA5, et SPINK1, (c) BST2, BATF, RAMP1, et TPM2, (d) MAGEA6, MAGEA3, MAGEA12, et CSAG1, et (e) IGF2, NELL2, et RBMAS1 dans un échantillon de cancer d'un patient ; et un kit pour prédire le pronostic d'un patient atteint d'un cancer colorectal, le kit comprenant un réactif pour mesurer les niveaux d'expression des gènes d'un ou plusieurs groupes de gènes sélectionnés parmi les groupes de gènes (a) à (e) ; etc.
PCT/JP2025/003938 2024-02-07 2025-02-06 Procédé pour prédire le pronostic d'un patient atteint d'un cancer colorectal Pending WO2025169993A1 (fr)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011015681A (ja) * 2003-08-08 2011-01-27 Perseus Proteomics Inc 癌高発現遺伝子
US20110097423A1 (en) * 2009-10-22 2011-04-28 Vanderbilt University Gene Prognosis Predictor Signature for Colorectal Carcinoma
US20150354009A1 (en) * 2012-11-26 2015-12-10 Ecole Polytechnique Federale De Lausanne (Epfl) Colorectal cancer classification with differential prognosis and personalized therapeutic responses

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2011015681A (ja) * 2003-08-08 2011-01-27 Perseus Proteomics Inc 癌高発現遺伝子
US20110097423A1 (en) * 2009-10-22 2011-04-28 Vanderbilt University Gene Prognosis Predictor Signature for Colorectal Carcinoma
US20150354009A1 (en) * 2012-11-26 2015-12-10 Ecole Polytechnique Federale De Lausanne (Epfl) Colorectal cancer classification with differential prognosis and personalized therapeutic responses

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