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WO2011137288A2 - Réseaux d'arnmi dans des cancers et leucémies et leurs utilisations - Google Patents

Réseaux d'arnmi dans des cancers et leucémies et leurs utilisations Download PDF

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WO2011137288A2
WO2011137288A2 PCT/US2011/034451 US2011034451W WO2011137288A2 WO 2011137288 A2 WO2011137288 A2 WO 2011137288A2 US 2011034451 W US2011034451 W US 2011034451W WO 2011137288 A2 WO2011137288 A2 WO 2011137288A2
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mir
hsa
network
mirna
cancer
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WO2011137288A3 (fr
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Carlo M. Croce
Stefano Volinia
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Ohio State University
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Ohio State University
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    • 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
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • 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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • 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
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA

Definitions

  • This invention relates generally to the field of molecular biology. More particularly, it concerns miRNA networks in cancers and leukemias and uses thereof.
  • Certain aspects of the invention include application in diagnostics, therapeutics, and prognostics of cancers and leukemias associated disorders.
  • miRNAs have been studied by using the gene profiling approach. Each miRNA has been studied for its single contribution to differential expression or to a compact predictive signature.
  • the effect of miRNAs on cell pathology and physiology is likely to be complex for two reasons: ( 1) their activity is exerted in a one-to-many fashion, such that each miRNA can control translation of tens or even hundreds of different coding messengers and (2) a single messenger can be controlled by more than one miRNA.
  • RNA coordinated microRNA
  • an miRNA network for different solid tumors and leukemias and uses thereof.
  • the nonmalignant tissues and cancer networks display a change in hubs, the most connected miRNAs.
  • a method for determining miRNAs with comprehensive roles in cancer comprising combining one or more of: differential expression, genetic networks, and DNA copy number alterations.
  • a miRNA networks comprised from miRNA expression data that includes a least one miRNA network from normal tissues, and/or at least one miRNA network for coupled cancerous and noncancerous tissues.
  • a method for identification of cancer variations in miRNA net works comprising comparing normal miR networks to cancer miR networks.
  • a method for diagnosing, adenocarcinoma in a subject by identifying a set of miRs in a sample from the subject, and determining whether the set of miRs fall within the normal miRNA network substantially as shown in Figure 4A, or within the cancer miRNA network substantially as shown in Figure 4B.
  • At least one miRNA network comprising the miRNA network substantially as shown in Figure 4B, for lung adenocarcinoma.
  • At least one miRNA network comprising the miRNA network substantially as shown in Figure 6, for acute myeloid leukemia (AML).
  • AML acute myeloid leukemia
  • At least one miRNA network comprising the miRNA network substantially as shown in Figure 7, for chronic lymphocytic leukemia (CLL).
  • CLL chronic lymphocytic leukemia
  • At least one miRNA network comprising the miRNA network substantially as shown in Figure 8, for leukemias.
  • At least one miRNA network comprising the miRNA network substantially as shown in Figure 16, for colon adenocarcinomas.
  • At least one miRNA network comprising the miRNA network substantially as shown in Figure 17, for breast cancer.
  • At least one miRNA network comprising the miRNA network substantially as shown in Figure 18, for prostate cancer.
  • a method for diagnosing a subject suspected of having a solid cancer comprising screening a sample from the subject for the set of differentially up-regulated miRNAs listed in Supplemental Table IV.
  • a method for diagnosing a subject suspected of having a solid cancer comprising screening a sample from the subject for the set of differentially down-regulated miRNAs listed in Supplemental Table V.
  • a method for diagnosing a subject suspected of having a solid cancer comprising screening a sample from the subject for one or more of the differentially regulated miRNAs listed in Supplemental Table VI.
  • a method for diagnosing a subject suspected of having a solid cancer comprising screening a sample from the subject for one or more of the differentially regulated miRNAs listed in Supplemental Table VIII.
  • a method for diagnosing a subject suspected of having a solid cancer comprising screening a sample from the subject for one or more of the differentially regulated miRNAs listed in Supplemental Table IX.
  • a change in miRNA expression in a subject is indicative of the subject being predisposed to, or having, a solid cancer and/or hematological cancer.
  • the cancer is one or more of lung, breast, colon, prostate, pancreas, and nasopharyngeal carcinomas, glioblastoma, melanoma, Ewing sarcoma, osteosarcoma, T-cell acute lymphoblastic leukemia (T-ALL), AML, CLL, myelodysplasia, various lymphomas, and mucosa-associated lymphoid tissue (MALT).
  • lung, breast, colon, prostate, pancreas, and nasopharyngeal carcinomas glioblastoma, melanoma, Ewing sarcoma, osteosarcoma, T-cell acute lymphoblastic leukemia (T-ALL), AML, CLL, myelodysplasia, various lymphomas, and mucosa-associated lymphoid tissue (MALT).
  • a method for detecting cancer differentiation in a subject comprising screening for at least one of hsa-miR-221 and hsa-miR-218.
  • a method for compiling and/or analyzing a network of biological functions in a subject comprising: i) obtaining information on one or more rruR present in the subject, wherein at least one miR reflects a biological function; and ii) subjecting the obtained information to analysis of coherent groups of nodes by using clustering algorithms to calculate a relationship between the functional reporters to generate a network relationship of the miRs to the biological function.
  • the subject is human.
  • FIG. 1 The miRNA network in normal tissues ( 1 107 samples, 50 tissues, 1 15 miRNAs). The network was inferred for all expressed and varying miRNAs, without preselecting for differential expression. Standard Banjo parameters were adopted with a q6 discretization policy. The consensus graph depicted here was obtained from the best 100 nets (after searching through 8.3 3 109 networks). MCL graph-based clustering algorithm was applied to clusters extraction (miRNAs with highly related expression pattern have edges of the same color); thus, different clusters in the network are linked by different color edges. yEd graph editor (yFiles software) was employed for graphs visualization.
  • Figure 2 Cellular pathways regulated by differentially expressed miRNAs in cancer.
  • the KEGG pie-chart shows the functional effect of differentially expressed miRNAs on cellular pathways in cancer.
  • the large majority of the affected pathways is related to cancer or signal transduction (i.e., Wnt, VEGF, TGF-beta, insulin, and phosphatidylinositol signaling,
  • Target genes selection was performed with DIANA-miRpath, microT-V4.0. The union of the target mRNAs with a score above 3 was used as an input to ClueGO. Right-sided hypergeometric test yielded the enrichment for GO terms. Benjamini-Hochberg correction for multiple testing controlled the P-values. GO term fusion was appli ed for redundancy reduction.
  • FIG. 3 The miRNA network in solid cancers (2532 samples. 31 cancer types. 1 20 miRNAs). The network was inferred for all expressed and varying miRNAs, without preselecting for differential expression. Standard Banjo parameters were adopted with a q6 discretization policy. The consensus graph depicted here was obtained from the best 100 nets (after searching through 8.4 3 109 networks). MCL expression clusters are linked by different color edges. yEd graph editor (yFiles software) was employed for graphs visualization. The miRNAs expressed differentially in the tumors are color-coded and in the graph miRNA neighbors are of the same color code: Closely clustered miRNAs are either red (overexpressed) or green (down-regulated).
  • node labels for which expression and physical alteration (CGH, see “miRNA copy number variations in cancer and leukemia” section, infra) were concordant (i.e., overexpression and amplification), were emboldened and visually reinforced with a hexagonally shaped border.
  • CGH expression and physical alteration
  • FIGS 4A-4B Comparison of miRNA networks in normal lung and adenocarcinoma.
  • FIG. A - top
  • Normal lung 71 samples
  • Fig. 4B Lung adenocarcinoma (125 samples).
  • hsa-miR- lOa/b has-miR-29a b
  • hsa-miR-107 hsa-miR-107
  • hsa-miR-103 are in minor independent sub-networks disjoint from the main one.
  • FIG. 5 The KEGG functional analysis of eight disjointed minor miRNA networks in lung adenocarcinoma.
  • the miRNA present in the unconnected cliques target genes are involved in many cancer-related terms, such as focal adhesion, small cell lung cancer, and calcium signaling.
  • the detailed list of significant GO terms is shown in Figure 15.
  • FIG. 6 The miRNA network in acute myeloid leukemia (589 samples, two subnetworks). Standard Banjo parameters were adopted with a q6 discretization policy. The consensus graph depicted here was obtained from the best 100 nets (after searching through 8.5 3 109 networks).
  • the miRNA network in AML has disjointed cliques. hsa-miR- 155 and hsa-miR- 181 , two miRNAs with clinical relevance are in two separated sub-networks, as expected from their prognostic independence. hsa-miR-181 is associated with hsa-miR- 146a in a detached yellow miniclique.
  • mir- 155 belongs to the main sub-network, in the same red MCL clique of hsa-miR-223, hsa-miR-92a, hsa-miR-25, and hsa-miR-32. Finally, hsa-miR-29b has a key role in AML and acts as a hub in the AML net.
  • FIG. 7 The miRNA network in chronic lymphocytic leukemia (254 samples, three sub-networks). Standard Banjo parameters were adopted with a q6 discretization policy.
  • the consensus graph depicted here was obtained from the best 100 nets (after searching through >1 x 10'° networks).
  • the network graph shows a major net and two separated minicliques: hsa-miR- 23a/b and the hsa-miR- 15/ 16 cluster.
  • hsa-miR-15 and hsa-miR-16 two miRNAs frequently deleted in CLL, have been showed to regulate apoptosis via BCL2.
  • hsa-rruR-29b acting as a hub in AML, is only a branch in CLL.
  • AML prognostic hsa-miR- 181 i s disjoi nted in AML but not in CLL, while the reverse happens in CLL for prognostic hsa-miR-15/16 genes.
  • FIG. 8 Deregulated miRNAs in leukemia from Mirl 55 transgenic mice are preferentially located close to hsa-miR- 155 in the cancer network.
  • the inventors compared miRNA profiles of three leukemia samples from Mirl 55 transgenes to controls from wild-type mice.
  • the deregulated miRNAs (Supplemental Table X) were mapped onto the cancer network and highlighted in yellow. Most of the other miRNAs are concentrated around hsa-miR- 155 node (black). When a diagonal is drawn and the two sides compared the difference between yellow nodes is significant (Fisher's exact test, two-tail P-value ⁇ 0.009).
  • FIG. 9 miRNA specificity in 50 normal tissues grouped by system. The tissue specificity was calculated by using the information content (IC), value expressed on y-axis; each color represents a system. hsa-miR-302 cluster is the most representative for embryo tissues.
  • FIG. 10 The miRNA specificity during ES cell differentiation. miRNA specificity in 7 different types of embryonic tissues (embryonic stem cells, 7 days and 14 days embryonic bodies, trophoblasts, endoderm, induced pluripotent stem cells (iPS), spontaneously differentiating monolayers). The specificity was calculated by using the information content (IC).
  • IC information content
  • FIGs 11A-11B The node degree distribution of the normal tissues and solid cancers miRNA networks.
  • the figure illustrates the apparent scale-freeness of the graphs for normal tissues (Fig. 11A) and solid cancers (Fig. 11B).
  • the blue curve represents the absolute frequency of node degrees and the green curve the inverse cumulative frequency.
  • the exponential decrease of both curves shows that there are a lot more poorly connected nodes than highly connected (hubs). Both miRNA graphs thus present a scale free behavior.
  • FIG. 12 miRNA specificity in 31 solid tumors and 20 leukemia types. The specificity was calculated by using the information content (IC), value expressed on y-axis; each color represents a cancer type.
  • IC information content
  • Figure 13 Functions repressed in cancer by up-regulated miRNAs.
  • KEGG analysis by ClueGO Bodea et al. 2009) of terms (p-value ⁇ l x l0 3 ) targeted by up-regulated miRNAs (listed in Table I and Supplemental Table IV).
  • Target genes selection was performed with DIANA- miRpath, microT-V4.0 (Papadopoulos et al. 2009).
  • the union of the target mRNAs with a score above 3 was used as an input to ClueGO.
  • Right-sided hyper-geometric test yielded the enrichment for GO-terms. Benjamini-Hochberg correction for multiple testing controlled the p-values.
  • GO term fusion was applied for redundancy reduction.
  • Figure 14 Functions activated in cancer by down-regulated miRNAs.
  • Target genes selection was performed with DIANA-miRpath, microT- V4.0.
  • the union of the target mRNAs with a score above 3 was used as an input to ClueGO.
  • Right- sided hyper-geometric test yielded the enrichment for GO-terms. Benjamini-Hochberg correction for multiple testing controlled the p-values.
  • GO term fusion was applied for redundancy reduction.
  • Figure 15 Functions controlled in lung adenocarcinoma by miRNAs from disjointed minor clusters.
  • the colored groups are assigned the name of the most prominent GO group.
  • the chart presents the specific terms and information related to the miRNA targets differentially expressed in cancer.
  • the bars represent the number of the genes from the analyzed cluster found to be associated with the term, and the label displayed on the bars is the percentage of found genes compared to all the genes associated with the term. Term significance information is included in the chart.
  • FIG. 16 MicroRNA genetic network in colon adenocarcinoma (245 samples, 10 subnets). The network was inferred for all expressed and varying miRNAs, without preselecting for differential expression. Standard Banjo parameters were adopted with a q6 discretization policy. The consensus graph depicted here was obtained from the best 100 nets (after searching through >lx l0 10 networks). MCL graph-based clustering algorithm was applied to clusters extraction (miRNAs with highly related expression pattern have edges of the same color); thus, different clusters in the network are linked by different color edges. yEd graph editor (yFiles software, Tubingen, Germany) was employed for graphs visualization.
  • FIG. MicroRNA genetic network in prostate cancer ( 170 samples and 5 subnets). Same procedure described in legend of Figure 14.
  • the inventors built our miRNA networks exclusively from miRNA expression data. Here, the inventors report the first miRNA network from normal tissues. In parallel, the inventors built miRNA networks for coupled cancerous and noncancerous tissues. By comparing normal to cancer networks the inventors attained a second goal: the identification of cancer variations in miRNA networks. Also, inventors superimposed DNA variations onto expression data to generate a comprehensive miRNA alteration map in cancer.
  • Described herein is the use of a miRNA networks comprised from miRNA expression data that includes a least one miRNA network from normal tissues, and/or at least one miRNA network for coupled cancerous and noncancerous tissues.
  • the method for identification of cancer variations in miRNA netw orks includes comparing normal to cancer networks by generating a comprehensive miRNA alteration map in cancer, and superimposing DNA variations onto expression data.
  • at least one miRNA network comprising the miRNA network substantially as shown in one or more of Figure 4B, Figure 6, Figure 7; Figure 8, Figure 16, Figure 17, and Figure 18.
  • a method for diagnosing a subject suspected of having a solid cancer as also described herein which includes screening a sample from the subject for one or more of the differentially regulated miRNAs listed in Supplemental Table IV, Supplemental Table V, Supplemental Table VIII and/or Supplemental Table DC.
  • the methods are useful to show where a change in miRNA expression in a subject is indicative of the subject being predisposed to, or having, a solid cancer and/or hematological cancer.
  • the cancer is one or more of lung, breast, colon, prostate, pancreas, and nasopharyngeal carcinomas, glioblastoma, melanoma, Ewing sarcoma, osteosarcoma, T-cell acute lymphoblastic leukemia (T-ALL), AML, CLL, myelodysplasia, various lymphomas, and mucosa- associated lymphoid tissue MALT.
  • the method for detecting cancer differentiation in a subject can include screening for at least one of hsa-miR-221 and hsa-miR-218.
  • a method for compiling and/or analyzing a network of biological functions in a subject includes:
  • the subject is human.
  • the method includes detecting miRNA networks in cancer cells that are independently regulated miRNAs.
  • the method includes detecting target genes of uncoordinated miRs that are involved in specific cancer-related pathways.
  • the method can include wherein the miRs are up-regulated microRNAs found in solid cancers, the set of miRs comprising one or more of: the miRs in Table IV, namely: hsa-miR-21 , hsa-miR-25, hsa-miR-20a, hsa-miR-17, hsa-miR- 106a, hsa-miR- 106b, hsa-rruR- 146a, hsa-miR-92a, hsa-miR- 103, hsa-miR- 130b,hsa-miR-30c, hsa-rruR-93, hsa-miR- 107, hsa-miR-30e, hsa-rruR-15a, hsa-miR-181 b, hsa-miR-15b, hsa-miR
  • the method can include wherein the miRs are down -regulated microRNAs found in solid cancers, the set of miRs comprising one or more of: the miRs in Table V, namely: miR-203, hsa-rruR- 145, hsa-miR-205, hsa-miR-206, hsa-miR-33b, hsa-miR- 193a, hsa-miR-204, hsa-miR-143, hsa-miR-326, hsa-miR-338, hsa-miR-9, hsa-miR-95, hsa-miR- 138, hsa-miR- 183, hsa- miR-202, hsa-miR-128a, hsa-miR-214, hsa-miR-132, h
  • the cancer based on at least a subset of the miRNAs selected from: the network shown in Figure 4B; the network shown in Figure 6; the network shown in Figure 7; the network shown in Figure 8; the network shown in Figure 16; the network shown in Figure 17; or, the network shown in Figure 18.
  • the characterizing is with increased sensitivity or specificity as compared to characterizing by detecting an expression level of less than each of the plurality of miRNAs selected from: the network shown in Figure 4B; the network shown in Figure 6; the network shown in Figure 7; the network shown in Figure 8; the network shown in Figure 16; the network shown in Figure 17; or the network shown in Figure 18.
  • the plurality comprises at least 10 miRNAs.
  • the, wherein at least a subset of the plurality of miRNAs is selected from Supplemental Table V.
  • the method can include classifying a cell as non-cancerous, by: i) determining an expression level of one or more miRNAs in a biological sample of a subject; and
  • ii) classifying the cell as non-cancerous when less than about 3000 copies per microliter of at least a subset of the miRNAs is detected in the sample.
  • the method can include classifying a cell as non-cancerous, by: i) determining an expression level of one or more miRNAs in a biological sample of a subject; and
  • ii) classifying the cell as cancerous when greater than about 9000 copies per microliter of at least a subset of the miRNAs is detected in the sample.
  • the method can further include selecting a therapy or treatment regimen based on the classification.
  • the biological sample is selected from the group consisting of: a heterogeneous cell sample, sputum, blood, blood cells, serum, biopsy, urine, peritoneal fluid, and pleural fluid.
  • a detection system configured to assess miRNAs selected from the group consisting of: the network shown in Figure 4B; the network shown in Figure 6; the network shown in Figure 7; the network shown in Figure 8; the network shown in Figure 16; the network shown in Figure 17; the network shown in Figure 18; miRs in Supplemental Table IV; or miRs in Supplemental Table V.
  • the system comprises a set of probes that selectively hybridizes to the two or more miRNAs.
  • kits comprising a set of probes that selectively hybridizes to two or more miRNAs selected from the group consisting of: the network shown in Figure 4B; the network shown in Figure 6; the network shown in Figure 7; the network shown in Figure 8; the network shown in Figure 16; the network shown in Figure 17; the network shown in Figure 18; miRs in Supplemental Table IV; or miRs in Supplemental Table V.
  • each of the probes can be coupled to a substrate.
  • Also described herein is a method for analyzing a network of biological functions in a biological entity, comprising:
  • the biological entity is a cell.
  • the network can be used for a use selected from the group consisting of identification of a biomarker, analysis of a drug target, analysis of a side effect, diagnosis of a cellular function, analysis of a cellular pathway, evaluation of a biological effect of a compound, and diagnosis of an infectious disease.
  • the system can further include means for analyzing the generated network by conducting an actual biological experiment.
  • the means for analyzing can comprise a regulation agent specific to the function.
  • Also described herein is a computer program for implementing in a computer, a method for analyzing a network of biological functions in a biological entity, comprising the steps of: i) obtaining information on at least two miRs in the biological entity, wherein the miRs reflect a biological function; and
  • storage medium comprising a computer program for implementing in a computer, a method for analyzing a network of biological functions in a biological entity, comprising the steps of:
  • a transmission medium comprising a computer program for implementing in a computer, a method for analyzing a network of biological functions in a biological entity, comprising the steps of:
  • an assay kit for detecting a risk that a cell will become malignant comprising reagents for determining a cell signature, wherein the signature comprises the presence and/or level of two or more miRs selected from: the network shown in Figure 4B; the network shown in Figure 6; the network shown in Figure 7; the network shown in Figure 8; the network shown in Figure 16; the network shown in Figure 17; or, the network shown in Figure 18.
  • the miR is differentially expressed in the cells shows that the cell is at risk of becoming malignant.
  • Also described herein is method of determining a risk of developing cancer in a subject, comprising:
  • the signature comprises a collection of measurements of at least the presence and/or level of two or more miRs selected from: the network shown in Figure 4B; the network shown in Figure 6; the network shown in Figure 7; the network shown in Figure 8; the network shown in Figure 16; the network shown in Figure 17; or the network shown in Figure 18;
  • the biological sample is selected from the group consisting of a living cell, a dead cell, any non-cellular liquid samples comprising nipple aspirate fluid, urine, blood, serum, plasma and a lavage sample, and any combinations thereof.
  • the cell signature of the control sample is obtained from a database.
  • the automated means comprises a computer-based system configured to carry out at least one of the following: measuring the presence of absence of a substance in the sample; recording data obtained from the measurement; analyzing the data; determining the risk of cancer; and generating a report.
  • the computer-based system can comprise suitable any hardware, software, firmware, processor, and any combinations thereof.
  • the computer-based system is configured to access and/or use a database comprising a cell signature of a pre-cancerous cell and/or a control epithelial cells via a network system.
  • Also described herein is a method of making a medical report related to the risk of developing cancer in a subject, comprising:
  • the signature comprises a collection of measurements of at least two characteristics of the sample selected from one or more of following: presence and/or level of at least one miRNA;
  • the automated means comprises a computer-based system configured to carry out at least one of the following: measuring the cell signature; recording data obtained from the measurement; analyzing the data; determining the risk of developing cancer; and generating a report.
  • the computer-based system can comprise any suitable hardware, software, firmware, processor, and any combinations thereof.
  • the computer-based system is configured to access and/or use a database comprising a cell signature of a pre-cancerous cell and/or a control cell via a network system.
  • Also described herein is a method of making a medical report related to the risk of developing cancer in a subject, comprising:
  • the signature comprises a collection of measurements of at least two characteristics of the cell, the at least two miRs selected from: the network shown in Figure 4B; the network shown in Figure 6; the network shown in Figure 7; the network shown in Figure 8; the network shown in Figure 16; the network shown in Figure 17; or the network shown in Figure 18;
  • Also described herein is a method for compiling and/or analyzing an miRNA database, comprising one or more of:
  • ClueGO visualizes the selected terms in a functionally grouped annotation network that reflects the relationships between the terms based on the similarity of their associated genes, the size of the nodes reflects the statistical significance of the terms; and the degree of connectivity between terms (edges) is calculated using kappa statistics;
  • the expression values are preprocessed to only filter out nonvarying miRNAs, according to the following parameters:
  • #filter.flag filter (variation filter and thresholding flag);
  • #mindelta 512 (minimum delta for filter);
  • #num.excl 2% of total chips (number of experiments to exclude (max and min) before applying variation filter);
  • threshold 1 % of total chips (remove row if n columns not > than given threshold above.threshold);
  • Also described herein is a system for analyzing a an miRNA database, comprising one or more means for:
  • ClueGO visualizes the selected terms in a functionally grouped annotation network that reflects the relationships between the terms based on the similarity of their associated genes, the size of the nodes reflects the statistical significance of the terms; and the degree of connectivity between terms (edges) is calculated using kappa statistics;
  • the subject is a human.
  • Also described herein is a computer program for implementing in a computer, a method for compiling and/or analyzing an miRNA database, comprising one or more of:
  • ClueGO visualizes the selected terms in a functionally grouped annotation network that reflects the relationships between the terms based on the similarity of their associated genes, the size of the nodes reflects the statistical significance of the terms; and the degree of connectivity between terms (edges) is calculated using kappa statistics;
  • a storage medium comprising a computer program for implementing in a computer, a method for compiling and/or analyzing an miRNA database, comprising one or more of:
  • ClueGO visualizes the selected terms in a functionally grouped annotation network that reflects the relationships between the terms based on the similarity of their associated genes, the size of the nodes reflects the statistical significance of the terms; and the degree of connectivity between terms (edges) is calculated using kappa statistics; ⁇
  • a transmission medium comprising a computer program for implementing in a computer, a method for compiling and/or analyzing an miRNA database, comprising one or more of:
  • ClueGO visualizes the selected terms in a functionally grouped annotation network that reflects the relationships between the terms based on the similarity of their associated genes, the size of the nodes reflects the statistical significance of the terms; and the degree of connectivity between terms (edges) is calculated using kappa statistics;
  • an assay kit for analyzing an miRNA database comprising reagents one or more of:
  • ClueGO visualizes the selected terms in a functionally grouped annotation network that reflects the relationships between the terms based on the similarity of their associated genes, the size of the nodes reflects the statistical significance of the terms; and the degree of connectivity between terms (edges) is calculated using kappa statistics;
  • the substance detected is differentially expressed in a sample from a subject at risk of becoming malignant.
  • the assay kit detects wherein one or more miRNAs are differentially transcribed in the sample at risk of becoming malignant.
  • the assay kit detects wherein one or more of the miRNAs may have an expression level and/or copy number in a sample from a subject at risk of becoming malignant compared to a normal cell.
  • the assay kit detects wherein the miRNAs is selected from the group consisting rruRs shown in: the network shown in Figure 4B; the network shown in Figure 6; the network shown in Figure 7; the network shown in Figure 8; the network shown in Figure 16; the network shown in Figure 17; or the network shown in Figure 18.
  • the assay kit detects wherein the miRNAs is selected from the group consisting miRs shown in: Supplemental Table IV or Supplemental Table V.
  • the sample is selected from one or more of: a living cell, a dead cell, any non-cellular liquid samples comprising aspirate fluid, urine, blood, serum, plasma and a lavage sample, and any combinations thereof.
  • GSE20099, GSE3467, GSE7828, GSE7055, GSE6857, GSE16654, GSE8126, and GSE 14936, and the microarray data from this study have been submitted to ArrayExpress (ebi.ac.uk/microarray- as/ae) under accession nos. E-TABM-866, E-TABM-762, E-TABM-508, E-TABM-429, E-TABM- 434, E-TABM-405, E-TABM-343, E-TABM-42, E-TABM-48, E-TABM-49, E-TABM-50, E- MEXP- 1796, and E-GEOD-3292.
  • ArrayExpress ebi.ac.uk/microarray- as/ae
  • the miRNA network in normal tissues [001 10] The inventors assayed mature miRNAs in 17 groups of normal human tissues, from a total of 1 107 chips. Tissue specificity was calculated by using the information content (IC) (Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, Aravin A, Pfeffer S, Rice A, Kamphorst AO, Landthaler M, et al. 2007.
  • IC information content
  • a mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129: 1401-1414, by measuring expression levels by sequencing cloned miRNAs.
  • the most tissue-specific miRNAs are the members of the hsa-miR-302 cluster, as shown in Figure 9 and Supplemental Tables I and III.
  • [001 1 1 ] hsa-miR-302a/b/c were expressed in embryonic samples. Complexity of genetic regulatory mechanisms in higher organisms is thought to also be achieved through controlled and coordinated networks of miRNAs. The inventors developed and used a microarray database to generate miRNA networks based exclusively on expression data.
  • Figure 1 displays the miRNA network of normal tissues, obtained from over 1000 samples and 50 cell types/tissues. The inventors used all of the expressed miRNAs to build the Bayesian networks (rather than only the differentially expressed ones). The MCL clusters with high coexpression patterns throughout normal tissues are linked by specific colored edges. miRNAs are generally connected as expected from the published literature.
  • hsa-miR-133a/b was in a cluster with hsa-miR- 1 (light orange) and all were involved in skeletal muscle proliferation and differentiation.
  • a close cluster is hsa-miR-1 Oa/b and hsa-miR-214 (green).
  • hsa-miR-214 is expressed during early segmentation stages in somites and can modulate the expression of genes regulated by Hedgehog. Inhibition of hsa-miR-214 results in a reduction or loss of slow-muscle cell types.
  • These muscle/differentiation clusters are linked to hsa-miR- 143/145, a miRNA capable of pushing ES cells to differentiate.
  • the top, right proliferation cluster hsa-miR- 106a/b/93 to hsa-miR-20a/17 and to hsa-miR-25/92a includes hsa-miR-223 and is involved in cell cycle progression.
  • the has-miR-145 node links the proliferation clusters described above to the muscle differentiation clusters.
  • hsa- miR-29 that targets the anti-apoptotic protein MCL1 and plays a role in the TP53 pathway is linked to hsa-rruR-30 and to hsa-miR- 15/16, miRNAs that target the anti-apoptotic protein BCL2.
  • EMT epithelial-to-mesenchymal transition
  • hsa-miR-221 -222 regulators of the cell cycle, together with hsa-miR-206, hsa-miR-155 (pre-B cell proliferation), and hsa-miR- 130a/b are in a yellow cluster.
  • hsa-miR- 194 and hsa-miR- 192 connect to hsa-miR- 215, within a purple cluster associated with TP53 activation.
  • hsa-rruR-203 and hsa-miR-205 are directly involved in TGF-beta-mediated EMT and differentiation.
  • hsa-miR-181 family members are involved in B- and T-lineage and myoblast differentiation.
  • hsa-miR-181, hsa-miR-200, hsa-miR-205, and hsa-miR-215 are all linked to hsa-miR- 145.
  • hsa-miR-26 is a key gene in hepatocellular carcinoma and its expression is associated with survival and response to adjuvant therapy with interferon alpha.
  • Let-7 regulates Ras and hsa-miR-302 are expressed in ES cells and other early embryonic tissues.
  • the EGG pie chart in Figure 2 and the corresponding network graphs (Figure 13, Figure 14) show the functional effect of differentially expressed miRNAs on cellular pathways.
  • the large majority of the affected pathways is related to cancer or signal transduction (i.e., Wnt, VEGF, TGF-beta, insulin, phosphatidylinositol signaling, focal adhesion, and colorectal cancer).
  • the inventors also applied the IC measure to identify cancer-specific miRNAs. The ICs were almost as high as those measured for the normal tissues, indicating that there were miRNAs with high cancer-type specificity (Figure 12; Supplemental Table VI).
  • the inventors generated a global miRNA expression network for solid cancers ( Figures 3A-3B). It is important to note that, to build the Bayesian net, the inventors used all the expressed and varying miRNAs as input, rather than only using the differentially expressed ones (miRNAs with low variation were excluded from the analysis).
  • FIG. 3B The node degree distribution of the solid cancer miRNA network is illustrated in Figure 3B. Like the normal tissues miRNA graph, the solid cancer net also presented a scale-free behavior. In cancer, the most connected hub was hsa-miR-30c (degree 10), followed by has-miR-16 (degree 6). Whereas, in nonmalignant tissues, hsa-miR- 16 was the most connected node (degree 8) and hsa-miR- 30c had only a low degree of 3. Opposite behavior had TP53 regulated hsa-miR-215 (degree 6 in normal tissues and degree 3 in cancer) and has-rruR- 103/ 106a (degree 5 in normal tissues and only degree 1 in cancer). The exchanges of hubs between nonmalignant and cancer tissues were the first notable sign of divergences in their respective miRNA programs.
  • the MCL clustering algorithm was employed to map the sub-networks with high coexpression patterns (these MCL clusters, or cliques, are linked by specific colored edges).
  • the inventors color-coded the miRNA nodes according to their differential expression in tumors (red, overexpressed; green, down-regulated). Neighbors preferentially appeared with the same trend, such that clustered miRNAs were either overexpressed or down-regulated. For example, hsa-miR- 17/20a (chr 13q31 .3), hsa-miR- 106a/b (chr Xq26.2 and chr 7q22.1 ), and hsa- miR-93 (chr 7q22.1) were all up-regulated in cancers.
  • hsa-miR- 143/ 145 (chr 5q32), hsa-miR- 133a/b (chr 18ql 1.2, chr 20ql 3.3 and chr 6pl2.2), hsa-miR-214 (chr lq24.3), and hsa-rruR- 138 (chr 3p21.33 and chr 16ql3), all in the same coexpression clique, were down-regulated.
  • has-miR- lOa/b was identified in lung, colon, and breast cancers; miR 26a/b in colon and prostate cancers; hsa-miR-29a/b in breast, colon, and lung cancers; hsa-miR-181 family members in colon and breast cancers; and hsa-miR-107/ hsa-miR-103 in breast, prostate, lung, and colon cancers.
  • the let-7c/a miRNAs were prominent in colon, lung, and prostate cancers, and hsa-miR-106a/b, linked to hsa-miR-17 and hsa-miR-20, in colon and lung cancers.
  • Other miRNA cliques included hsa-miR200c, linked to TP53-associated hsa-miR-192/215. in a colon sub-network.
  • miRNA networks were reprogrammed in solid cancer and the expression of few notable miRNAs was independent from the major network. While not wishing to be bound by theory, the inventors herein now believe that the single graph in the overall solid cancer net can be explained only by the same miRNAs having variable roles in a range of cancers, such that a miRNA regulates different targets in different cell types.
  • Leukemias confirm that miRNA networks are aberrant in neoplasia
  • AML acute myeloid leukemia
  • CLL chronic lymphocytic leukemia
  • hsa-miR-155 and hsa-miR-181 two miRNAs with clinical relevance, were positioned in two separated sub-networks, as expected from their prognostic independence.
  • hsa-miR-181 was associated to hsa-miR-146a in a detached yellow mini- clique, while hsa-miR-155 belonged to the main sub-network, in the same red MCL clique as hsa- miR-223, hsa-miR-92a, hsa-miR-25, and hsa-miR-32.
  • hsa-miR-29b has a key role in AML and, in accordance, it acts as a hub in the AML net.
  • CLL chronic lymphocytic leukemia
  • two small cliques were separated from the main net: hsa-rruR-23a/b and a second one embracing the hsa-miR- 15/16 pair.
  • hsa-miR- 15 and hsa-rruR- 16 two miRNAs frequently deleted in CLL, have been showed to regulate apoptosis via BCL2.
  • the network topologies for these two leukemias could recapitulate their respective molecular pathology, with the key AML hsa-miR-29b acting as a hub in AML, but only a branch in CLL.
  • AML prognostic hsa-miR-181 was disjointed in AML, but not in CLL, with the reverse being true for the CLL prognostic hsa-miR-15/16 pair.
  • miRNAs are differentially expressed in human cancer, but little is known about their chromosomal alterations, such as amplifications (hsa-miR- 17/92) and deletions (hsa-miR-15a/16-l ).
  • amplifications hsa-miR- 17/92
  • deletions hsa-miR-15a/16-l .
  • the inventors investigated 744 samples (solid cancers and leukemia), at medium resolution ( 150 kb). The inventors used data from array comparative genomic hybridization (aCGH) and calculated, for each of 20,000 different chromosomal locations, two P-values, one for deletion and one for amplification.
  • aCGH array comparative genomic hybridization
  • the inventors used their respective host genes or, when unavailable, their two flanking genes.
  • miRNA families have similar targeting properties and thus their members are expected to have similar impacts on oncogenesis.
  • the inventors worked on aCGH samples from lung, pancreas, breast, colon, and nasopharyngeal carcinomas, glioblastoma, melanoma, Ewing sarcoma, osteosarcoma, T-cell acute lymphoblastic leukemia (T-ALL), AML, CLL, myelodysplasia, various lymphomas, and mucosa- associated lymphoid tissue MALT (Supplemental Table VII).
  • CD N2A and CD N2B were identified as the most deleted genes in cancers, followed by other tumor suppressors PTEN, ATM, and TP53.
  • Oncogenes like EGFR, MYC, LYN, ⁇ , and MOS, were amplified. Supplemental Tables VIII and IX list amplified and deleted miRNA families. The detection of amplified hsa-miR- 17- 5p/20/93/106 family was a successful validation of our approach. It is also noteworthy that the MIR17HG host gene for the hsa-miR- 17/92 cluster was not present in the arrays, but its flanking genes successfully compensated for its absence.
  • the top deleted miRNA family was hsa-miR- 204/21 1, followed by other families including hsa-miR-200b/c/429, hsa-miR-141/200a, hsa-miR- 125/351, and hsa-miR-218.
  • Down-regulation of hsa-miR-200a/b/c/429 and 141 have been linked to breast cancer stem cells by targeting BMI1 , a stem cell self-renewal regulator.
  • hsa-miR- 21 1 is involved in stem cells as it shows the highest Information content in an ES cell differentiation series (Figure 9; Supplemental Table III).
  • Deregulated miRNAs in a MMSS-induced leukemia are preferentially located around hsa- miR- 155 in the miRNA network
  • the inventors generated two cases of leukemias in Em/VH Mirl55 transgenic mice. These leukemias were positive for CD43 and T-cell markers (CD3, CD8) and negative for B220. Both cases exhibited VDJ and TCR oligoclonal rearrangement.
  • This T-cell immunophenotype might be caused by the proliferation of lymphoid progenitors that atypically differentiated into T cells. The disease started early, at 2 and 4 mo of age, respectively, and had a rapid course with the mice dying 2 wk later.
  • the inventors compared the miRNA profiles of three leukemia samples from these Mirl55 trangenes to controls from wild-type mice. Then the inventors located the positions in the network for the miRNAs regulated in the transgene's leukemias (Supplemental Table X). The inventors did not have an acute lymphocytic leukemia miRNA network as reference, therefore the inventors mapped the deregulated miRNAs onto the generic cancer network and highlighted the nodes in yellow (Figure 8). The yellow nodes appeared concentrated around the has-miR-155 node (black). When a diagonal, separating the hsa-miR-155 half from the other one, was drawn and the two sides compared the difference in yellow nodes concentrations was significant (14 vs.
  • the inventors have presented a thorough analysis of miRNA tissue specificity in 50 different normal tissues grouped by 17 systems, corresponding to 1 107 human samples. A small set of miRNAs were tissue-specific, while many others were broadly expressed. The inventors also studied 51 oncologic or hemato-oncologic disorders and identified cancer-type-specific miRNAs. Then the inventors inferred genetic networks for miRNAs in normal tissues and in their pathological counterparts. Normal tissues were represented by single complete miRNA networks. Cancers instead were portrayed by separate and un-linked miRNA subnets.
  • miRNAs independent from the general transcriptional program were often known as cancer-related. While not wishing to be bound by theory, the inventors herein now believe that this "egocentric" behavior of cancer miRNAs is the result of positive selection during cancer establishment and progression, as supported by aCGH. Leukemias were also rewired, but to a much lower extent. Nevertheless, miRNAs related to AML and CLL pathogenesis, such as hsa-miR-155, hsa- miR- 181 , and hsamiR-15/16, were still removed from coordinated control.
  • Microarray analysis was performed as previously described (Volinia, S., Calin, G.A., Liu, C.G., Ambs, S., Cimmino, A., Petrocca, F., Visone, R., Iorio, M., Roldo, C, Ferracin, M. et al. 2006.
  • a microRNA expression signature of human solid tumors defines cancer gene targets.
  • RNA microarray chips contain gene-specific oligonucleotide probes, spotted by contacting technologies and covalently attached to a polymeric matrix.
  • microarrays were hybridized in 63 SSPE (0.9 M NaCl, 60 mM NaH2P04 ⁇ H2Q, 8 mM EDTA at pH 7.4), 30% formamide at 25°C for 18 h, washed in 0.753 TNT (Tris-HCl, NaCl, Tween 20) at 37°C for 40 min, and processed by using a method of detection of the biotin-containing transcripts by streptavidin-Alexa647 conjugate. Processed slides were scanned using a microarray scanner (Axon), with the laser set to 635 nm, at fixed PMT setting, and a scan resolution of 10 mm. Microarray images were analyzed by using GenePix Pro and post-processing was performed essentially as described in Volinia et al. (2006). Briefly, average values of the replicate spots of each miRNA were background-subtracted and subject to further analysis.
  • miRNAs were retained, when present, in at least 20% of samples and when at least 20% of the miRNA had a fold change of more than 1.5 from the gene median. Absent calls were thresholded prior to normalization and statistical analysis. Normalization was performed by using the quantiles method. MiRNA nomenclature was according to the miRNA database at Sanger Center.
  • Target genes selection was performed by DIANA-miRpath, microT-V4. The union of the target mRNAs with a score >3 was used as an input to ClueGO.
  • ClueGO was used to relate differential expression in cancer to functional pathways (KEGG).
  • ClueGO visualizes the selected terms in a functionally grouped annotation network that reflects the relationships between the terms based on the similarity of their associated genes.
  • the size of the nodes reflects the statistical significance of the terms.
  • the degree of connectivity between terms (edges) is calculated using kappa statistics.
  • the calculated kappa score is also used for defining functional groups.
  • a term can be included in several groups. The reoccurrence of the term is shown by adding "n.”
  • the not grouped terms are shown in white color.
  • the group leading term is the most significant term of the group.
  • the network integrates only the positive kappa score term associations and is automatically laid out using Organic layout algorithm supported by Cytoscape. Right-sided hypergeometnc test yielded the enrichment for GO-terms. Benjamini-Hochberg correction for multiple testing controlled the P-values.
  • Chips were retained only if less than 25% of miRNAs were absent (expression value ⁇ 64). Similarly, miRNAs were retained only when less than 25% of samples had absent expression (value ⁇ 64).
  • the static Bayesian network inference algorithm was run on the miRNA expression matrix by using standard parameters, with a discretization policy of q6. Consensus graphs, based on top 100 networks, were obtained from at least 8 3 109 searched networks.
  • the inventors applied the CL graph-based clustering algorithm to extraction of clusters (i.e., groups of densely connected nodes) from miRNA networks.
  • MCL Neat
  • yEd graph editor yFiles software, Tubingen, Germany
  • TargetScan The threshold P-value for a miRNA family was set at 0.05 to the number of family members, n (0.05"). To control for multiple testing, the inventors performed 100 bootstrapping cycles and used the results to calculate the false discovery rate (FDR).
  • FDR false discovery rate
  • the resampling analysis was executed by randomly assigning the original P-values to the miRNA loci, while all family structures and chromosomal locations were kept unchanged.
  • the FDR was defined as the percentage of families in the simulation evaluating better (lower P-values) than in the original test. Since the number of family member was variable (from a minimum of 2 to 7), FDRs were computed for each family according to its size (n, number of miRNA members).
  • GSE8126 and GSE7055 (Ambs et al. 2008Ambs, S., Prueitt, R.L., Yi, M., Hudson,
  • GSE17155 (Fassan, M., Baffa, R., Palazzo, J.P., Lloyd, J., Crosariol, M, Liu, C.G.,
  • GSE3467 He, H., Jazdzewski, K., Li, W., Liyanarachchi, S., Nagy, R., Volinia, S.,
  • GSE7828 (Schetter, A.J., Leung, S.Y., Sohn, J.J., Zanetti, K.A., Bowman, E.D.,
  • GSE6857 (Budhu, A., Jia, H.L., Forgues, M., Liu, C.G., Goldstein, D., Lam, A., Zanetti, K.A., Ye, Q.H., Qin, L.X., Croce, CM. et al. 2008. Identification of metastasis-related microRNAs in hepatocellular carcinoma.
  • GSE16654 Cholinia, S., Singer, M., Peterson, C, Ambartsumyan, G., Aimiuwu, O., Richter, L., Zhang, J. et al. 2009. Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures.
  • Cell stem cell 5 1 1 1 - 123), and
  • GSE14936 (Seike, M., Goto, A., Okano, T., Bowman, E.D., Schetter, A.J.. Horikawa, I.. Mathe, E.A., Jen, J., Yang, P., Sugimura, H. et al. 2009.
  • MiR-21 is an EGFR-regulated anti- apoptotic factor in lung cancer in never-smokers. Proceedings of the National Academy of Sciences of the United States of America 12085-12090),
  • E-TABM-866 (Pineau, P., Volinia, S., McJunkin, K., Marchio, A., Battiston, C, Terns, B., Mazzaferro, V., Lowe, S.W., Croce, CM., and Dejean, A. miR-221 overexpression contributes to liver tumorigenesis. Proceedings of the National Academy of Sciences of the United States of America 107: 264-269),
  • E-TABM-664 (Bloomston, M., Frankel, W.L., Petrocca, F., Volinia, S., Alder, H., Hagan, J.P., Liu, C.G., Bhatt, D., Taccioli, C, and Croce, CM. 2007. MicroRNA expression patterns to differentiate pancreatic adenocarcinoma from normal pancreas and chronic pancreatitis. Jama 297: 1901-1908),
  • E-TABM-762 and E-TABM-763 (Visone, R., Rassenti, L.Z., Veronese, A., Taccioli, C, Costinean, S., Aguda, B.D., Volinia, S., Ferracin, M., Palatini, J., Balatti, V. et al. 2009. Karyotype- specific microRNA signature in chronic lymphocytic leukemia. Blood 1 14: 3872-3879),
  • E-TABM-508 (Pichiorri, F., Suh, S.S., Ladetto, M., uehl, M., Palumbo, T., Drandi, D., Taccioli, C, Zanesi, N., Alder, H., Hagan, J.P. et al. 2008. MicroRNAs regulate critical genes associated with multiple myeloma pathogenesis. Proceedings of the National Academy of Sciences of the United States of America, 12885-12890) ,
  • E-TABM-429 (Garzon, R., Garofalo, M., Martelli, M.P., Briesewitz, R., Wang, L., Fernandez-Cymering, C, Volinia, S., Liu, C.G., Thomasger, S., Haferlach, T. et al. 2008a.
  • E-TABM-434 (Petrocca, F., Visone, R., Qnelli, M.R., Shah, M.H., Nicoloso, M.S., de Martino, I., Iliopoulos, D., Pilozzi, E., Liu, C.G., Negrini, M. et al. 2008. E2F1 -regulated microRNAs impair TGFbeta-dependent cellcycle arrest and apoptosis in gastric cancer. Cancer cell 13: 272-286),
  • E-TABM-405 Garzon, R., Volinia, S., Liu, C.G., Fernandez-Cymering, C, Palumbo, T., Pichiorri, F., Fabbri, M., Coombes, ., Alder, H., Nakamura, T. et al. 2008b. MicroRNA signatures associated with cytogenetics and prognosis in acute myeloid leukemia. Blood 1 1 1 : 3183- 3189,
  • E-TABM-343 (Iorio, M.V., Visone, R., Di Leva, G., Donati, V., Petrocca, F., Casalini,
  • E-TABM-41 and E-TABM-42 (Calin, G.A., Ferracin, M., Cimmino, A., Di Leva, G.,
  • MicroRNA signature associated with prognosis and progression in chronic lymphocytic leukemia is associated with prognosis and progression in chronic lymphocytic leukemia.
  • E-TABM-48 (Roldo, C, Missiaglia, E., Hagan, J.P., Falconi, M., Capelli, P., Bersani,
  • E-TABM-22 (Yanaihara, N., Caplen, N., Bowman, E., Seike, M., Kumamoto, K., Yi,
  • E-TABM-23 (Iorio, M.V., Ferracin, M., Liu, C.G., Veronese, A., Spizzo, R., Sabbioni,
  • a microRNA expression signature of human solid tumors defines cancer gene targets.
  • E-MEXP-1796 (Godlewski, J., Nowicki, M.O., Bronisz, A., Williams, S., Otsuki, A., Nuovo, G., Raychaudhury, A., Newton, H.B., Chiocca, E.A., and Lawler, S. 2008. Targeting of the Bmi-1 oncogene/stem cell renewal factor by microRNA- 128 inhibits glioma proliferation and self- renewal. Cancer research, 68: 9125-913),
  • E-TABM-37 (Ciafre, S.A., Galardi, S., Mangiola, A., Ferracin, M., Liu, C.G., Sabatino, G., Negrini, M., Maira, G., Croce, CM., and Farace, M.G. 2005. Extensive modulation of a set of microRNAs in primary glioblastoma. Biochemical and biophysical research communications 334: 1351 -1358),
  • E-TABM-341 (Ueda, T., Volinia, S., Okumura, H., Shimizu, M., Taccioli, C, Rossi, S., Alder, H., Liu, C.G., Oue, N., Yasui, W. et al. Relation between microRNA expression and progression and prognosis of gastric cancer: a microRNA expression analysis. Lancet Oncol 1 1 : 136-146).
  • Embryonic cell types such as embryonic bodies, trophoblasts, endoderm, or other stem cells including induced pluripotent stem cells (iPS) also harbored high levels of hsa-miR-302. Since hsa-miR-302 expression only partially decreased throughout these stages of ES differentiation, its information content (IC) in embryonic tissues was low (0.5).
  • iPS induced pluripotent stem cells
  • the miRNAs which had highest tissue specificity in embryos were: hsa-miR-21 1 in 14 day embryoid bodies (EBs), hsa-miR-lOb, hsa-miR- 218, hsa-miR-122, and hsa-miR-148a in spontaneous differentiated monolayers, hsa-miR-138 and hsa-miR-338-3p in 7 day EBs, and hsa-miR-99a in trophoblast ( Figure 10 and Supplemental Table III).
  • hsa-miR-302 genes The information content (IC) of hsa-miR-302 genes is in the datasets even higher than that found by Landgraf, who considered a lower number of tissues/organs/systems: After hsa-miR- 302, the most specific miRNAs are hsa-miR-338-5p, hsa-miR-323-3p, hsa-miR-335, with highest expression in epidermis/nervous system, nervous system and breast respectively ( Figure 9 and Supplemental Table I).
  • hsa-miR- 122 is the miRNA with the highest IC difference between ours and the sequencing dataset.
  • hsa-miR-371-5p is here well represented in embryonic cell types, since we have high hsa-miR-371 -5p expression in embryoid bodies, not assayed in the clones' dataset.
  • hsa-miR- 129-3p is specific for nervous system in both datasets.
  • hsa-miR-9, hsa-miR-128 and hsa-miR-138 were nervous system specific in both datasets.
  • the inventors now report twice as many different tissue groups that in the clones' dataset, thus the inventors expected less tissue specific miRNAs and lower information contents.
  • Tissue specificity First, all samples were classified according to their organ-, tissue- and cell-type; then the normal samples were grouped in specific systems and the disease samples in specific pathological states. To assess the specificity of miRNA expression across groups, the inventors estimated what fraction of the total, for a given miRNA belonged to each single group. Therefore, the inventors used the procedure described in the first miRNA expression atlas (Landgraf et al. 2007), but modified by what the inventors called Em,t the value of miRNA m in the group t referred to as "mean expression value (subtracted of the background value, 100). From here onwards, the inventors proceeded as the reference stated.
  • the specificity score which varies between 0, when the expression level of the miRNA m is the same across all tissues, and log2 (number of tissue types), when only one tissue expresses the miRNA.
  • log2 number of tissue types
  • the inventors considered only miRNAs with a total expression value above 10 times the number of normal tissues and above 100 times the number of cancer types; with a minimal expression value (after background subtraction) of 100.
  • Each cancer sample was compared to a healthy control on a two channel normalized log2 ratio (cancer over control). Different probes related to the same gene were averaged (gene symbols were used as keys). Data were normalized according to the providers. As a pre-processing step the inventors retained only those genes with high variability (standard deviation> 0.2). For each gene the inventors computed the 5th and 95th percentiles (only for genes measured in at least 300 samples). A gene harboring recurrent deletions in tumors would result in a low 5th percentile log2 ratio (negative), while one with amplifications would display a high 95th percentile (positive).
  • the inventors followed the following procedure (illustrated here for amplifications): the inventors selected all miRNA families where at least 1 member was significantly amplified (p ⁇ 0.05).
  • the inventors defined the family p-value for amplification, as the product of the amplification p-values for each family member (including also the non significant miRNAs), with the following exceptions : i) replicated identical mature miRNAs were considered only when mapping on different loci (i.e. represented by different host genes or flanking genes); ii) physically clustered family members were scored only once.
  • the distinct human genes we assayed in the aCGH dataset were 19,654 in total.
  • the inventors studied 530 distinct miRNA precursors in 308 chromosomal loci, corresponding to 47 1 different mature miRNAs and 356 distinct miRNA families.
  • the average distance between the miRNAs and their flanking genes was of 188 Kb for the 5' and of 240 Kb for the 3' gene.
  • the number of miRNA loci deleted/amplified in cancer was not significantly different from expectation, when compared to the whole coding genome (in both cases p»0.05).
  • the inventors took in consideration two phenomena, associated to aCGH, but not linked to cancer: sex chromosomes and polymorphic copy number variations (CNV). Since the control sample was more frequently from male, while roughly half of the tumors were of female origin, the Y-chromosome genes were incorrectly expected to appear as deleted. Conversely, the inventors expected the X chromosome genes, except for those belonging to the pseudo-autosomal region, to incorrectly appear as amplified. Genes located in the sex chromosomes were indeed behaving exactly as expected (data not shown). Polymorphic CNVs could also display large fold-changes, resulting in high 95th or low 5th percentiles.
  • CNV polymorphic copy number variations
  • CNVs not associated to cancer
  • Supplemental Table III Expression levels of tissue specific microRNAs during differentiation of embryonic stem cells (ES) . Sorted by information content (IC)
  • Embryonic Bodies EB
  • Spontaneous Differentiated Monolayer Monolayer
  • Burkitt's Lymphoma BL; Thyroid Papillary Cancer: TPC; Multiple Myeloma: MM; Squamous Cell Carcinoma: SCC; Basal Cell Carcinoma: BCC; Chronic Myelogenous Leukemia: CML; Acute Promyelocytic Leukemia: APL; High Grade Squamous Intraepithelial Lesion: HGSIL; Non-Small Cell Lung Cancer: NSCLC; Acute Monocytic Leukemia: AmoL; Cervix Carcinoma: CC. ]
  • microRNAs are short RNA strands approximately 21 -23 nucleotides in length. mRNAs are encoded by genes that are transcribed from DNA but not translated into protein (non-coding RNA). MiRs are processed from primary transcripts known as pri-miRNA to short stem-loop structures called pre-miRNA and finally to functional miRNA, as the precursors typically form structures that fold back on each other in self-complementary regions. The miRs are then processed by the nuclease Dicer in animals or DCL 1 in plants. Mature miRNA molecules are partially complementary to one or more messenger RNA (mRNA) molecules. The sequences of miRNA can be accessed at publicly available databases, such as "microRNA.org.”
  • miRNAs are involved in gene regulation, and miRNAs are part of a growing class of non-coding RNAs that is now recognized as a major tier of gene control.
  • miRNAs can interrupt translation by binding to regulatory sites embedded in the 3'-UTRs of their target mRNAs, leading to the repression of translation.
  • Target recognition involves complementary base pairing of the target site with the miRNA's seed region (positions 2-8 at the miRNA's 5' end), although the exact extent of seed complementarity is not precisely determined and can be modified by 3' pairing.
  • miRNAs function like small interfering RNAs (siRNA) and bind to perfectly complementary mRNA sequences to destroy the target transcript.
  • a single miRNA is assessed to characterize a cancer.
  • at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 1000, or more miRNAs are assessed.
  • a change in the expression level, such as absence, presence, underexpression or overexpression of the miRNA as compared to a reference level, such as a level determined for a subject without the cancer, can be used to characterize a cancer for the subject.
  • a reference level for classifying a cell as benign or malignant can include obtaining a sample from a subject, determining an amount of a miRNA in the subject's sample, and comparing the amount of the miRNA to one or more controls.
  • the step of comparing the amount of the miRNA to one or more controls may include the steps of obtaining a range of the miRNA found in the sample for a plurality of subjects having a benign condition, or normal cells to arrive at a first control range, obtaining a range of the miRNA found in the sample for a plurality of subjects having malignant cancer cells to arrive at a second control range, and comparing the amount of the miRNA in the subject's sample with the first and second control ranges to determine if the subject's sample is classified as benign, or normal, or is a cancer
  • a detection system configured to determine one or more RNAs for characterizing a cancer.
  • the detection system can be configured to assess 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 1000, 2500, 5000, 7500, 10,000, 100,000, 150,000, 200,000, 250,000, 300,000, 350,000, 400,000, 450,000, 500,000, 750,000, 1 ,000,000 or more miRNAs, wherein one or more of the miRNAs are selected from the data described herein.
  • the detection system can be a low density detection system or a high density detection system.
  • a low density detection system can detect up to about 100, 200, 300, 400, 500, or 1000 RNA
  • a high density detection system can detect at least about 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9,000, 10,000, 15,000, 20,000; 25,000, 50,000, or 100,000 miRNAs.
  • the detection system can comprise a set of probes that selectively hybridizes to the one or more of the miRNAs.
  • the detection system can comprise a set of probes that selectively hybridizes to at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 1000, 2500, 5000, 7500, 10,000, 100,000, 150,000, 200,000, 250,000, 300,000, 350,000, 400,000, 450,000, 500,000, 750,000, or 1 ,000,000 miRNAs.
  • the set of probes can selectively hybridize to or more miRNAs selected from the data described herein [00227]
  • the probes may be attached to a solid substrate, such as an array or bead. Alternatively, the probes are not attached.
  • the detection system may be an array based system, a sequencing system, a PCR-based system, or a bead-based system.
  • the detection system may be part of a kit.
  • the kit may comprise the one or more probe sets described herein.
  • the kit may comprise probes for detecting one or more of the miRNAs selected from data described herein.
  • the computer system can include a logic device through which a phenotype profile and report may be generated.
  • the computer system (or digital device) can be configured to receive the expression level data from a biological sample, analyze the expression levels, determine a characteristic for a cancer (such as, but not limited to, classifying a cancer, determining whether a second sample should be obtained, providing a diagnosis, providing a prognosis, selecting a treatment, determining a drug efficacy), and produce the results, such as an output on the screen, printed out as a report, or transmitted to another computer system.
  • a characteristic for a cancer such as, but not limited to, classifying a cancer, determining whether a second sample should be obtained, providing a diagnosis, providing a prognosis, selecting a treatment, determining a drug efficacy
  • the computer system may be understood as a logical apparatus that can read
  • Data communication can be achieved through a 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.
  • 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 invention such as the expression levels of the one or more miRNAs, the results of the analysis of the expression levels (such as the characterizing or classifying of the cancer), can be transmitted over such networks or connections for reception and/or review by a party.
  • the receiving party can be, but is not limited, to a subject, a health care provider or a health care manager.
  • the information is stored on a computer-readable medium.

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Abstract

La présente invention concerne des procédés pour l'identification de variations du cancer dans des réseaux d'ARNmi, comprenant la comparaison des réseaux normaux à des réseaux cancéreux par la génération d'une carte complète d'altérations d'ARNmi dans le cancer, et la superposition de variations de l'ADN sur des données d'expression, des bases de données contenant lesdites altérations ainsi que leurs utilisations.
PCT/US2011/034451 2010-04-30 2011-04-29 Réseaux d'arnmi dans des cancers et leucémies et leurs utilisations Ceased WO2011137288A2 (fr)

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EP3796002B1 (fr) * 2006-07-14 2023-11-22 The Regents of The University of California Biomarqueurs du cancer et leurs procédés d'utilisation

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US8945829B2 (en) 2011-03-22 2015-02-03 Cornell University Distinguishing benign and malignant indeterminate thyroid lesions
EP2785872A4 (fr) * 2011-12-01 2015-12-09 Univ Ohio State Matériels et méthodes se rapportant à une chimio-prévention par nsaid dans un cancer colorectal
US9334498B2 (en) 2012-05-10 2016-05-10 Uab Research Foundation Methods and compositions for modulating MIR-204 activity
CN107076747A (zh) * 2014-06-04 2017-08-18 阿托萨遗传学公司 分子乳房摄影术
EP3152579A4 (fr) * 2014-06-04 2018-01-17 Atossa Genetics Inc. Mammographie moléculaire
US10782301B2 (en) 2015-02-05 2020-09-22 Queen Mary University Of London Biomarkers for pancreatic cancer
US11977077B2 (en) 2015-02-05 2024-05-07 Queen Mary University Of London Biomarkers for pancreatic cancer
CN105154541A (zh) * 2015-08-31 2015-12-16 北京泱深生物信息技术有限公司 miRNA在急性髓系白血病诊断和治疗中的应用
WO2017051203A1 (fr) * 2015-09-25 2017-03-30 Queen Mary University Of London Nouveaux biomarqueurs de maladies pancréatiques
CN109841281A (zh) * 2017-11-29 2019-06-04 郑州大学第一附属医院 基于共表达相似性识别肺腺癌早期诊断标识及风险预测模型的构建方法
CN109841281B (zh) * 2017-11-29 2023-09-29 郑州大学第一附属医院 基于共表达相似性搭建肺腺癌早期诊断模型
CN109712670A (zh) * 2018-12-25 2019-05-03 湖南城市学院 一种miRNA功能模块的识别方法及系统
CN117198401A (zh) * 2023-09-18 2023-12-08 西安电子科技大学 一种基于信息熵的基因表达模式发现系统及方法

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