WO2014201542A1 - Signature micro-arn pronostique pour le sarcome - Google Patents

Signature micro-arn pronostique pour le sarcome Download PDF

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WO2014201542A1
WO2014201542A1 PCT/CA2014/000501 CA2014000501W WO2014201542A1 WO 2014201542 A1 WO2014201542 A1 WO 2014201542A1 CA 2014000501 W CA2014000501 W CA 2014000501W WO 2014201542 A1 WO2014201542 A1 WO 2014201542A1
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mir
biomarker
expression
subject
therapy
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Philip Kar Fai WONG
Angela Bik Yu HUI
Fei-Fei Liu
Wei Xu
Charles CATTON
Irene L. ANDRULIS
Jay WUNDER
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University Health Network
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    • 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
    • C12N15/11—DNA or RNA fragments; Modified forms thereof; Non-coding nucleic acids having a biological activity
    • C12N15/113—Non-coding nucleic acids modulating the expression of genes, e.g. antisense oligonucleotides; Antisense DNA or RNA; Triplex- forming oligonucleotides; Catalytic nucleic acids, e.g. ribozymes; Nucleic acids used in co-suppression or gene silencing
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    • 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
    • C12N15/11—DNA or RNA fragments; Modified forms thereof; Non-coding nucleic acids having a biological activity
    • C12N15/111—General methods applicable to biologically active non-coding nucleic acids
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    • 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
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    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12N2310/00—Structure or type of the nucleic acid
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    • C12N2310/141—MicroRNAs, miRNAs
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/178—Oligonucleotides characterized by their use miRNA, siRNA or ncRNA
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    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the invention relates to a micro-RNA signature for sarcoma.
  • BACKGROUND Sarcomas are cancers of mesenchymal origin which represent 2% of human malignancies 1 .
  • One of the most common STS subtype is the undifferentiated pleomorphic sarcoma (UPS) which is amongst the most aggressive STS with a high propensity for metastasis; associated with a dismal 5-year overall survival of 30-50% 2" 4 .
  • the prognostic determinants in STS are grade, tumor size and surgical margin 5 , which are however not useful in determining who may benefit from chemotherapy 6,7 .
  • novel biomarkers which will provide both insights into the complex biology of UPS, and facilitate individualization of cancer therapy.
  • MicroRNAs are small non-coding RNA molecules of ⁇ 22-nucleotides that form one of the largest class of gene regulators by targeting up to 60% of the mRNAs to translational repression or degradation.
  • miRNA expression profiling can differentiate some STS histologies 8"10 . Hisaoka ef al.
  • a method of prognosing or classifying a subject with undifferentiated pleomorphic sarcoma comprising: (a) determining the expression of at least one biomarker in a test sample from the subject selected from Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; and (b) comparing expression of the at least one biomarker in the test sample with expression of the at least one biomarker in a control sample; wherein a difference or similarity in the expression of the at least one biomarker between the control and the test sample is used to prognose or classify the subject with UPS into a low risk group or a high risk group of developing metastasis.
  • UPS undifferentiated pleomorphic sarcoma
  • a method of selecting a therapy for a subject with UPS comprising the steps: (a) classifying the subject with UPS into a high risk group or a low risk group according to the method described herein; and (b) selecting a more aggressive therapy, preferably adjuvant chemotherapy or radiation therapy, for the high risk group or a less aggressive therapy, preferably no adjuvant chemotherapy or no radiation therapy, for the low risk group.
  • a method of selecting a therapy for a subject with UPS comprising the steps: (a) determining the expression of at least one biomarker in a test sample from the subject selected from Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; (b) comparing expression of the at least one biomarker in the test sample with expression of the at least one biomarker in a control sample; (c) classifying the subject in a high risk group or a low risk group, wherein a difference or a similarity in the expression of the at least one biomarker between the control sample and the test sample is used to classify the subject into a high risk group or a low risk group; (d) selecting a more aggressive therapy, preferably adjuvant chemotherapy or radiation therapy, for the high risk group or a less aggressive therapy, preferably no adjuvant chemotherapy or no radiation therapy, for the low risk group.
  • compositions comprising a plurality of isolated nucleic acid sequences, wherein each isolated nucleic acid sequence hybridizes to: (a) Mir- 221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; and/or (b)a nucleic acid complementary to a), wherein the composition is used to measure the level of Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132 expression.
  • an array comprising, for each of Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132, one or more polynucleotide probes complementary and hybridizable thereto.
  • a computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, the computer program product comprising a computer readable storage medium having a computer mechanism encoded thereon, wherein the computer program mechanism may be loaded into the memory of the computer and cause the computer to carry out the method described herein.
  • a computer implemented product for predicting a prognosis or classifying a subject with UPS comprising: (a) a means for receiving values corresponding to a subject expression profile in a subject sample; and (b) a database comprising a reference expression profile associated with a prognosis, wherein the subject biomarker expression profile and the biomarker reference profile each have at least one value representing the expression level of at least one biomarker selected from Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; wherein the computer implemented product selects the biomarker reference expression profile most similar to the subject biomarker expression profile, to thereby predict a prognosis or classify the subject.
  • a computer implemented product for determining therapy for a subject with UPS comprising: (a) a means for receiving values corresponding to a subject expression profile in a subject sample; and (b) a database comprising a reference expression profile associated with a prognosis, wherein the subject biomarker expression profile and the biomarker reference profile each have at least one value representing the expression level of at least one biomarker selected from Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; wherein the computer implemented product selects the biomarker reference expression profile most similar to the subject biomarker expression profile, to thereby predict the therapy.
  • a computer readable medium having stored thereon a data structure for storing the computer implemented product described herein.
  • the data structure is capable of configuring a computer to respond to queries based on records belonging to the data structure, each of the records comprising: (a) a value that identifies a biomarker reference expression profile of at least one of Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; (b) a value that identifies the probability of a prognosis associated with the biomarker reference expression profile.
  • a computer system comprising (a) a database including records comprising a biomarker reference expression profile of at least one of Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132 associated with a prognosis or therapy; (b) a user interface capable of receiving a selection of expression levels of the at least one biomarker for use in comparing to the biomarker reference expression profile in the database; (c) an output that displays a prediction of prognosis or therapy according to the biomarker reference expression profile most similar to the expression levels of the at least one biomarker.
  • kits comprising reagents for detecting the expression of any or all of Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132 in a sample.
  • Figure 1 shows application of the 6-miRNA prognostic signature on a) the "Validation Set” and analyzed for Distant Metastasis Free Survival (DMFS) based on their risk group “High” and “Low” and b) metastasis samples.
  • Figure 2 shows association of a) individual miRNAs within the 6-miR signature with distant metastasis free survival (DMFS) and disease free survival (DFS) of the combined datasets (Training + Validation) on univariate analysis (log-rank) and multivariate analysis (Cox PH regression) and b) imiRNA-138 expression with DFS in the UPS samples. Values presented as p-value and hazard ratios (HR).
  • Figure 3 shows summary of assays from different dataset and findings related to the 6- miR prognostic signature and RhoA gene expression.
  • Figure 4 shows overall survival of patients from the "Training Sef(Black) and "Validation Sef'(Red)
  • Figure 5 shows unsupervised hierarchical clustering using the Ward method of miRNA expression from 4 Normal tissues (Adipose, Carotid, Vein and Smooth Muscle), 4 primary UPS cell lines (STS48, STS93, STS109 and STS117) and 42 UPS samples from the "Training Set” (metastatic in red, non-metastatic in white).
  • Figure 6 shows that for migration and invasion assays, 1.5 * 10 5 cells were seeded inside the insert with medium containing 1% serum. High serum (20%) medium was then added to the bottom chamber of 24-well plates to serve as a chemo-attractant.
  • Invasion index is calculated as (% Invasion of Test Cell)/(% Invasion of Control Cell) and depicted in a) following transfection with 10nM of pre-miR-138 or 50nM of Locked Nucleic Acid (LNA) of miR-138 and in b) for the morphological changes of the cells on the migration and invasion chambers.
  • LNA Locked Nucleic Acid
  • Figure 7 shows clonogenic assay of STS117 cells following transfection with 10nM or 50nM of Locked Nucleic Acid (Control-Scrambled and mir-138).
  • Figure 8 shows selection of genes affected by the transfection of STS 1 17 cells by LNA-antimiR-138, LNA-antimiR-224 and pre-miR-375 while excluding genes modulated by LNA-antimiR-130a.
  • Global mRNA profiling of STS117 cells 24-hours post-transfection were done using the Affymetrix Human Genome U133 plus 2.0 array that was processed with Affymetrix's WT Express protocol and 100 ng of starting material. The arrays were hybridized for 17 hrs at 45oC and washed and stained in fluidic station P450. Images were acquired with GeneChip scanner 3000 and preliminary analysis was carried out with Affymetrix gene expression console.
  • Figure 10 shows miR-138-Rho-ROCK pathway schema (a), (b) proposed changes secondary to increased expression of miR-138 in combination with reduced RhoA and (c) in the potential convergence of targets from the other miRNA within the prognostic signature.
  • Figure 11 shows distant metastasis free survival of 1056 breast cancer patients from 7 datasets dichotomized by the median expression of RhoA. Median follow-up was 238 months.
  • DMFS distant metastasis-free survival
  • a method of prognosing or classifying a subject with undifferentiated pleomorphic sarcoma comprising: (a) determining the expression of at least one biomarker in a test sample from the subject selected from Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; and (b) comparing expression of the at least one biomarker in the test sample with expression of the at least one biomarker in a control sample; wherein a difference or similarity in the expression of the at least one biomarker between the control and the test sample is used to prognose or classify the subject with UPS into a low risk group or a high risk group of developing metastasis.
  • UPS undifferentiated pleomorphic sarcoma
  • level of expression or “expression level” as used herein refers to a measurable level of expression of the products of biomarkers, such as, without limitation, the level of micro-RNA, messenger RNA transcript expressed or of a specific exon or other portion of a transcript, the level of proteins or portions thereof expressed of the biomarkers, the number or presence of DNA polymorphisms of the biomarkers, the enzymatic or other activities of the biomarkers, and the level of specific metabolites.
  • control refers to a specific value or dataset that can be used to prognose or classify the value e.g. expression level or reference expression profile obtained from the test sample associated with an outcome class.
  • control refers to a specific value or dataset that can be used to prognose or classify the value e.g. expression level or reference expression profile obtained from the test sample associated with an outcome class.
  • differential expression refers to a difference in the level of expression of the biomarkers that can be assayed by measuring the level of expression of the products of the biomarkers, such as the difference in level of micro-RNA or a portion thereof expressed . In a preferred embodiment, the difference is statistically significant.
  • difference in the level of expression refers to an increase or decrease in the measurable expression level of a given biomarker, for example as measured by the amount of micro-RNA as compared with the measurable expression level of a given biomarker in a control.
  • low risk refers to a lower risk of UPS r as compared to a general or control population.
  • sample refers to any fluid, cell or tissue sample from a subject that can be assayed for biomarker expression products and/or a reference expression profile, e.g. genes differentially expressed in subjects.
  • the level of gene expression is determined and compared.
  • RNA was quantified as follows in the disclosed exampels: Training Set: Total RNA from all tumor samples were extracted using the RNeasy kits (Qiagen). Global profiling of miRNA expression on the "Training Set” was performed using the TaqMan® Human Micro-RNA Array A (Applied Biosystems, Inc. CA, USA).
  • RNA 300ng was first reverse-transcribed with the Multiplex RT pool set, then quantitated using an Applied Biosystems 7900 HT Real-Time PCR system as previously describedl . Data were normalized using endogenous controls (RNU6B, RNU44 and RNU48) that were simultaneously quantified.
  • Validation Set Single well quantification of miRNA expressions was assessed by initially reverse-transcribing 200ng of total RNA with multiscribe reverse transcriptase and miR-specific primers (50nM), followed by qRT-PCR analysis using TaqMan microRNA Assays (Applied Biosystems). [Hui AB, Shi W, Boutros PC, et al: Robust global micro-RNA profiling with formalin-fixed paraffin-embedded breast cancer tissues. Lab Invest 89:597-606, 2009].
  • the at least one biomarkers is two biomarkers, three biomarkers, four biomarkers, five biomarkers, or six biomarkers.
  • overexpression of Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132 and underexpression of Mir-221 is indicative of high risk.
  • determining the biomarker expression level comprises use of quantitative PCR or an array, preferably sequencing technologies or nanostring.
  • the sample comprises a tissue sample.
  • a method of selecting a therapy for a subject with UPS comprising the steps: (a) classifying the subject with UPS into a high risk group or a low risk group according to the method described herein; and (b) selecting a more aggressive therapy, preferably adjuvant chemotherapy or radiation therapy, for the high risk group or a less aggressive therapy, preferably no adjuvant chemotherapy or no radiation therapy, for the low risk group.
  • a method of selecting a therapy for a subject with UPS comprising the steps: (a) determining the expression of at least one biomarker in a test sample from the subject selected from Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; (b) comparing expression of the at least one biomarker in the test sample with expression of the at least one biomarker in a control sample; (c) classifying the subject in a high risk group or a low risk group, wherein a difference or a similarity in the expression of the at least one biomarker between the control sample and the test sample is used to classify the subject into a high risk group or a low risk group; (d) selecting a more aggressive therapy, preferably adjuvant chemotherapy or radiation therapy, for the high risk group or a less aggressive therapy, preferably no adjuvant chemotherapy or no radiation therapy, for the low risk group.
  • compositions comprising a plurality of isolated nucleic acid sequences, wherein each isolated nucleic acid sequence hybridizes to: (a) Mir- 221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; and/or (b)a nucleic acid complementary to a), wherein the composition is used to measure the level of Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132 expression.
  • nucleic acid includes DNA and RNA and can be either double stranded or single stranded.
  • hybridize or “hybridizable” refers to the sequence specific non-covalent binding interaction with a complementary nucleic acid.
  • the hybridization is under high stringency conditions. Appropriate stringency conditions which promote hybridization are known to those skilled in the art, or can be found in Current Protocols in Molecular Biology, John Wiley & Sons, N.Y. (1989), 6.3.1 6.3.6. For example, 6.0 x sodium chloride/sodium citrate (SSC) at about 45°C, followed by a wash of 2.0 x SSC at 50°C may be employed.
  • SSC sodium chloride/sodium citrate
  • an array comprising, for each of Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132, one or more polynucleotide probes complementary and hybridizable thereto.
  • probe refers to a nucleic acid sequence that will hybridize to a nucleic acid target sequence.
  • the probe hybridizes to the RNA biomarker or a nucleic acid sequence complementary thereof.
  • the length of probe depends on the hybridization conditions and the sequences of the probe and nucleic acid target sequence. In one embodiment, the probe is at least 8, 10, 15, 20, 25, 50, 75, 100, 150, 200, 250, 400, 500 or more nucleotides in length.
  • primer refers to a nucleic acid sequence, whether occurring naturally as in a purified restriction digest or produced synthetically, which is capable of acting as a point of synthesis when placed under conditions in which synthesis of a primer extension product, which is complementary to a nucleic acid strand is induced (e.g. in the presence of nucleotides and an inducing agent such as DNA polymerase and at a suitable temperature and pH).
  • the primer must be sufficiently long to prime the synthesis of the desired extension product in the presence of the inducing agent.
  • the exact length of the primer will depend upon factors, including temperature, sequences of the primer and the methods used.
  • a primer typically contains 15-25 or more nucleotides, although it can contain less or more. The factors involved in determining the appropriate length of primer are readily known to one of ordinary skill in the art.
  • a computer program product for use in conjunction with a computer having a processor and a memory connected to the processor, the computer program product comprising a computer readable storage medium having a computer mechanism encoded thereon, wherein the computer program mechanism may be loaded into the memory of the computer and cause the computer to carry out the method described herein.
  • a computer implemented product for predicting a prognosis or classifying a subject with UPS comprising: (a) a means for receiving values corresponding to a subject expression profile in a subject sample; and (b) a database comprising a reference expression profile associated with a prognosis, wherein the subject biomarker expression profile and the biomarker reference profile each have at least one value representing the expression level of at least one biomarker selected from Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; wherein the computer implemented product selects the biomarker reference expression profile most similar to the subject biomarker expression profile, to thereby predict a prognosis or classify the subject.
  • computer implemented product is for use with the method described herein.
  • a computer implemented product for determining therapy for a subject with UPS comprising: (a) a means for receiving values corresponding to a subject expression profile in a subject sample; and (b) a database comprising a reference expression profile associated with a prognosis, wherein the subject biomarker expression profile and the biomarker reference profile each have at least one value representing the expression level of at least one biomarker selected from Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; wherein the computer implemented product selects the biomarker reference expression profile most similar to the subject biomarker expression profile, to thereby predict the therapy.
  • computer implemented product is for use with the method described herein.
  • a computer readable medium having stored thereon a data structure for storing the computer implemented product described herein.
  • the data structure is capable of configuring a computer to respond to queries based on records belonging to the data structure, each of the records comprising: (a) a value that identifies a biomarker reference expression profile of at least one of Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132; (b) a value that identifies the probability of a prognosis associated with the biomarker reference expression profile.
  • a computer system comprising (a) a database including records comprising a biomarker reference expression profile of at least one of Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132 associated with a prognosis or therapy; (b) a user interface capable of receiving a selection of expression levels of the at least one biomarker for use in comparing to the biomarker reference expression profile in the database; (c) an output that displays a prediction of prognosis or therapy according to the biomarker reference expression profile most similar to the expression levels of the at least one biomarker.
  • kits comprising reagents for detecting the expression of any or all of Mir-221 , Mir-491-5p, Mir-224, Mir-138, Mir-143 and Mir-132 in a sample.
  • RNA samples originating from non-cancerous human tissues of mesenchymal origin were purchased from Clontech Laboratories, Inc. (Smooth muscle), Applied Biosystem, Inc. (Adipose tissue) and Agilent Technologies, Inc. (Carotid Artery and Vein).
  • RNA from the tumors were extracted using the RNeasy kits (Qiagen). Samples were assayed randomly, with clinical outcome unknown, to avoid experimental bias. Cell lines and reagents
  • RNA concentrations and quality were measured using the Nanodrop 1000A spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA).
  • the cytotoxic effects of transfections were investigated in cells using clonogenic assays 11 .
  • Cell-cycle analysis of pre-miRNA-138 and LNA-antimiRNA-138 transfected cells were done using propidium iodide staining as previously described 11 .
  • Analyses were made using the BD FACScalibur using the FL-2 channel.
  • the flow cytometry data were analyzed using FlowJo software (Tree Star, Inc.). Invasion and migration of cells were assayed using the BD BioCoat Matrigel Invasion Chambers and Control Inserts (BD Bioscience) as per the manufacturer's instructions.
  • RNA 300ng was first reverse-transcribed with the Multiplex RT pool set, then quantitated using an Applied Biosystems 7900 HT Real-Time PCR system as previously described 12 . Data was normalized using endogenous controls (RNU6B, RNU44 and RNU48) that were simultaneously quantified. The resulting ACt values were used for hierarchical clustering and signature derivation. Clustering was done using JMP 10 (SAS institute, Cary, NC, USA).
  • RNA expressions Single well quantification of miRNA expressions was assessed by initially reverse- transcribing 200ng of total RNA with multiscribe reverse transcriptase and miRNA- specific primers (50nM), followed by qRT-PCR analysis using TaqMan microRNA Assays (Applied Biosystems) 12 . Quantitative RT-PCR was also utilized to analyze mRNA expression of: DICER, RHOA, RHOC, ROCK1 , ROCK2, LEPR, LIMK1 and GAPDH. Following reverse transcription of 200ng of total RNA using Superscript II I Reverse Transcriptase (Invitrogen), qRT-PCR was done using SYBR Green PCR Master Mix (Applied Biosystems). Primers for PCR amplifications were designed using Primer 3 Input (version 0.4.0) (Table 1 ). Relative mRNA levels were calculated using the 2 ⁇ ACt method 13 .
  • Total protein extracts were harvested from cell lines 48 hours post-transfection and prepared for immunoblotting as previously described 14 .
  • Membranes were probed with anti-RhoA, anti-RhoC, anti-ROCK1 , anti-ROCK2, anti-LIMK1 , anti-LIMK2, anti- phosphoLIMKI , anti-phosphoLIMK2 (Cell Signaling Technology, Inc.) and anti-GAPDH (glyceraldehyde-3-phosphate dehydrogenase) mAbs (1 :15,000 dilution; Abeam, Inc.), followed by secondary antibodies conjugated to horseradish peroxidase (1 :2,000 dilution; Abeam, Inc.) GAPDH protein levels were used as loading controls.
  • Western blots were quantified with the Adobe Photoshop Pixel Quantification Plug-In (Richard Rosenman Advertising & Design.
  • the signature score was based on the weighted combination of the miRNAs with the estimated regression coefficient of the Cox PH regression model as the weight 15,16 .
  • Statistics were performed using SAS version 9.1 (SAS institute, Cary, NC, USA) and the R package (http://CRAN.R- project.org, R Foundation, Vienna, Austria). All in-vitro experiments were conducted at least 3 independent times, with the data presented as the mean ⁇ SEM. The statistical differences between treatment groups were determined using a Student f-test when comparing 2 treatment groups.
  • Breast cancer dataset - TCGA breast cancer (BRCA) dataset for the 856 samples in which corresponding miRNA and mRNA were profiled, miRNA counts-per-million- reads and mRNA RPKM from the dataset were converted into z-scores. The sarcoma signature scoring was then applied onto the z-scores to dichotomize patients into risk groups (High and Low). MRNA expressions were dichotomized by the median Z-score for univariate and multivariate analysis.
  • Table 2 Patient, disease and treatment characteristics of the UPS “Training Set” and “Validation Set”
  • the patients were stratified into "Low risk” and "High risk” categories according to their signature score value: low risk (score ⁇ median); high risk (risk score >median).
  • the hazard ratio (HR) for DMFS was 16.0 in the training set (p ⁇ 0.0001).
  • Table 3 List of miRNAs associated with clinical outcomes of patients in the "Training Set”. MiRNAs within the final signature prognostic of Distant Metastasis Free survival are in red.
  • DM Distant Metastasis
  • LR Local Recurrence
  • DFS Disease Free Survival
  • OS Overall Survival
  • OS OS hsa-miR-10a hsa-let-7g hsa-miR-125b hsa-miR-224 hsa-miR-93 hsa-miR-139-5p hsa-miR-128 hsa-miR-491-5p hsa-miR-106b hsa-miR-211 hsa-miR-130a hsa-miR-128 hsa-miR-449a hsa-miR-138 hsa-miR-130a hsa-miR-512-3p hsa-miR-139-5p hsa-miR-132 hsa-miR-181a hsa-miR-143 hsa-m
  • Table 4a Multivariate analysis of the 6-miRNA signature score for its ability to predict distant metastasis free survival in the combined UPS cohort of "Training Set” and "Validation Set”.
  • MiRNA-138 promotes invasion in sarcoma cells
  • Three primary cell lines (STS48, STS93, and STS117) derived from patients diagnosed with UPS were used to study the biologic functions of miRNAs in UPS.
  • Global miRNA profiling of the "Training Set” 4 primary UPS cell lines (STS48, STS93, STS109 and STS117) and 4 RNA samples from normal mesenchymal tissues (Adipose, Carotid, Vein and Smooth Muscle) demonstrated that the expression of miRNAs differed between normal and sarcoma samples (Figure 5).
  • Screen migration/invasion and clonogenic assays were performed following modulation of cellular miRNA levels (Table 5).
  • STS117 cells were transfected with 50nM of Locked Nucleic Acid antimir-128, 130a, 138, 139-5p and 224, and pre-miR-375 to evaluate the effect of miRNA modulation on the migration, invasion and clonogenic survival of the cells.
  • RhoA mRNA profiling of the "Training Set” identified the under-expression of RhoA to be associated with increased odds of developing metastasis.
  • RhoA mRNA expression was measured in 28 samples from the "Validation Set” (14 patients who developed metastasis and 14 patients without metastasis) and the 10 metastatic samples. The association between metastasis and reduced expression of RhoA mRNA was validated in both the "Validation Set" and the metastatic samples (p ⁇ ).006) ( Figure 9a).
  • Table 4b Cox-regression proportional hazard analysis of the 6-miR signature adjusting for clinical factors in Melanoma TCGA database
  • Table 4c Cox-regression proportional hazard analysis of the 6-miR signature adjusting for clinical factors in Breast cancers from the TCGA database.
  • N 856; 105 deaths Indeed, they were all significantly (p ⁇ 0.042) associated with overall survival on univariate analysis.
  • the associations of the 3 genes (RhoA, LIMK1 and DICER) with breast cancer patient DMFS were further tested using 1056 previously profiled breast samples from 7 datasets.
  • the 6-miR signature may be prognostic in breast cancer and explored the signature's value using the TCGA breast cancer dataset.
  • the 6-miR signature along with 3 genes from the cofilin pathway (RhoA and LIMK1) and miRNA machinery (DICER) were prognostic for breast cancer patient OS after adjusting for patient age, disease stage and ER status (Table 4c).
  • the association between the 3 genes and breast cancer patient outcome was further tested using 1056 publically available mRNA profiles from breast cancers annotated with patient DMFS from 7 studies 17,19"23 .
  • RhoA and RhoC were thought to be prometastatic and involved in epithelial-mesenchymal transition (EMT) ⁇ , the current results derived from sarcoma and breast cancer clinical and in-vitro data suggest that reduced expression of RhoA and RhoC is associated with higher metastatic rates.
  • Livak KJ, Schmittgen TD Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25:402-8, 2001 14.
  • Hui AB, Bruce JP, Alajez NM, et al Significance of dysregulated metadherin and microRNA-375 in head and neck cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 17:7539-50, 2011
  • Verdine GL, Walensky LD The challenge of drugging undruggable targets in cancer: lessons learned from targeting BCL-2 family members. Clin Cancer Res 13:7264-70, 2007

Abstract

L'invention concerne une signature micro-ARN pronostique pour le sarcome, comprenant Mir-221, Mir-491 -5 p, Mir-224, Mir-138, Mir-143 et/ou Mir-132.
PCT/CA2014/000501 2013-06-17 2014-06-16 Signature micro-arn pronostique pour le sarcome WO2014201542A1 (fr)

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WO2017073862A1 (fr) * 2015-10-30 2017-05-04 가톨릭대학교 산학협력단 Microarn biomarqueur pour la prédiction du pronostic d'un cancer de la tête et du cou
TWI614629B (zh) * 2016-08-31 2018-02-11 National Central University 預測癌症放射線治療之預後的分析器及方法
US10738363B2 (en) 2016-08-31 2020-08-11 National Central University Analyzer and analytical method for predicting prognosis of cancer radiotherapy
RU2625035C1 (ru) * 2016-09-06 2017-07-11 Федеральное государственное бюджетное учреждение "Ростовский научно-исследовательский онкологический институт" Министерства здравоохранения Российской Федерации Способ прогнозирования рецидивирования сарком мягких тканей

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