WO2016150475A1 - Micro-arn circulants pour diagnostiquer le cancer du sein - Google Patents

Micro-arn circulants pour diagnostiquer le cancer du sein Download PDF

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WO2016150475A1
WO2016150475A1 PCT/EP2015/056028 EP2015056028W WO2016150475A1 WO 2016150475 A1 WO2016150475 A1 WO 2016150475A1 EP 2015056028 W EP2015056028 W EP 2015056028W WO 2016150475 A1 WO2016150475 A1 WO 2016150475A1
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mir
breast cancer
expression level
subject
micrornas
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PCT/EP2015/056028
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Vincent Bours
Pierre Freres
Stéphane Wenric
Claire Josse
Guy Jerusalem
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Universite De Liege
Centre Hospitalier Universitaire De Liege
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Priority to PCT/EP2015/056028 priority Critical patent/WO2016150475A1/fr
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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
    • C12Q2600/166—Oligonucleotides used as internal standards, controls or normalisation probes
    • 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

  • Circulating microRNAs for the diagnosis of breast cancer are Circulating microRNAs for the diagnosis of breast cancer.
  • the present invention relates to an in vitro method of diagnosing whether a subject has, or is at risk for developing, breast cancer.
  • the invention also relates to methods of monitoring the progress of breast cancer and of treatment of breast cancer in a subject.
  • the present invention further relates to a kit for use in said methods.
  • X-ray radiations from mammograms may be one of the factors that can actually trigger breast cancer in high-risk women, e.g. young women carrying a mutation in the BRCA genes. Moreover, these high risk women require early follow-up, beginning at 30 years, an age where mammography is less effective;
  • CA15.3 is the only validated biomarker.
  • CA15.3 lacks sensitivity in case of primary breast tumors and is only useful for the diagnostic of late stage breast cancer. Hence the accuracy of CA15.3 is directly influenced by tumor stage.
  • MicroRNAs are noncoding small RNAs that are synthesized inside the cell, and act as negative regulators of gene expression by binding to the 3'-untranslated region (3'-UTR) within target messenger RNAs (mRNAs).
  • Circulating microRNAs have the advantage of being protected from degradation by 40 to 100 nm lipoprotein vesicles, called exosomes, and their presence remains highly stable compared to other RNA molecules. Circulating microRNAs are therefore easily accessible and can be used as diagnostic markers in multiple cancers.
  • microRNAs do not reflect the abundance of microRNAs in the tumor of origin (Pigati et al, PLoS ONE 2010, 5(10): e13515; Cookson et al, Cell Oncol (Dordr) 2012 35(4): 301 -8; Zhu et al. Front Genet. 2013, 5:149-9).
  • the principle that the abundance of microRNAs in biological fluids reflects their abundance in the abnormal cell causing cancer is erroneous.
  • blood cells are also major contributors to circulating microRNA in cancer patients (Pritchard et al, Cancer Prev Res (Phila) 2012 5(3): 492-7).
  • WO20121 15885 discloses a big list of circulating microRNAs biomarkers for breast cancer diagnosis
  • WO2014202090 discloses circulating microRNAs biomarkers of breast cancer as well as combinations thereof.
  • WO201 1/1 10644 discloses the use of one endogenous miRNA, miR-16, for normalization.
  • miR-16 is predominantly derived from erythrocytes and has been shown to be prone to artificial elevation by hemolysis, as high as 30 folds (Leidner et al, PLoS ONE 2013, 8(3): e57841 ), which is a problem for accurate normalization of miRNAs expression levels.
  • WO2013107459 teaches the use of the mean expression level of the 120 most expressed microRNAs in blood samples for normalization, and Mestdagh et al (Genome Biol 2009, 10:R64) discloses the use of the mean expression value of all commonly expressed microRNAs in a given sample as normalization factor.
  • Mestdagh et al discloses the use of the mean expression value of all commonly expressed microRNAs in a given sample as normalization factor.
  • these two approaches are constraining, costly and lead to poor performance to discriminate healthy and cancer patients (Kodahl et al, Molecular Oncology 2014 Jul;8 (5):874-83).
  • the invention provides the following aspects.
  • An in vitro method of diagnosing whether a subject has, or is at risk for developing, breast cancer comprising the steps:
  • step b) comparing the expression level obtained in step a) to a reference value of a control biological fluid sample from a healthy subject;
  • an alteration in the expression level of any one or more of said microRNAs is indicative of the subject either having, or being at risk for developing, breast cancer.
  • An in vitro method of diagnosing whether a subject has, or is at risk for developing, breast cancer comprising the steps:
  • step b) comparing the expression level obtained in step a) to a reference value of a control biological fluid sample from a subject having a known diagnosis of breast cancer; wherein a similar expression level of said microRNAs is indicative of the subject either having, or being at risk for developing, breast cancer.
  • step a) the expression level of one or more additional microRNAs is further measured, wherein said additional microRNAs are selected from the group consisting of: miR-181 a, miR-107, miR-142-3p, miR- 486-5p, miR-148a, miR-20a, let-7i, miR-19a, let-7f-1 * , miR-199a-5p, miR-93, miR-451 , miR-19b, miR-30b, miR-1 , miR-26a, miR-22 * , miR- 590-5p, miR-101 , miR-22, miR-142-5p and miR-32 or combinations thereof.
  • said additional microRNAs are the following five microRNAs selected from the group consisting of: miR-107, miR-148a, let-7i, miR-19b and miR-22 * .
  • microRNAs are selected from the group comprising miR- 484, miR-652, miR-148b, miR-106a, miR-425, let-7g, miR-30b, miR- 126, miR-103, miR-146a, miR-93, miR-24, miR-18b, miR-151 -5p, miR-423-3p, miR-223, miR-15a, miR-142-3p, miR-26b, miR-15b, miR-191 , let-7d * , let-7f, miR-21 , miR-126 * , miR-125a-5p, miR-181 a, miR-23a, miR-222, miR-23b, miR-30c, miR-101 , miR-150, miR-320a, miR-26a, miR-145, miR-486-5p, miR-16, miR-199a-5p, miR-107, miR
  • the biological fluid sample is selected from the group comprising whole blood, blood serum, blood plasma, blood cells, urine, milk or saliva.
  • the biological fluid sample is blood plasma.
  • An in vitro method of monitoring the progress of breast cancer in a subject comprising the steps of:
  • step a) the expression level of the following five microRNAs selected from the group consisting of: miR-107, miR-148a, let-7i, miR-19b and miR-22 * is further measured.
  • a method of treatment of breast cancer in a subject comprising treating the subject, diagnosed as being in need of breast cancer treatment according to the method of any one of aspects 1 to 15, with a treatment selected from the group consisting of: surgery, radiotherapy, chemotherapy, hormonal therapy or biological treatment.
  • a method of treatment of breast cancer in a subject comprising the steps of: a) determining whether the subject is in need of receiving breast cancer treatment comprising performing the method according to any one of aspects 1 to 15;
  • step b) treating the subject diagnosed in step a) as being in need of breast cancer treatment with a treatment selected from the group consisting of: surgery, radiotherapy, chemotherapy, hormonal therapy or biological treatment.
  • kits for use in an in vitro method of diagnosing whether a subject has, or is at risk for developing, breast cancer, or in a method of treatment of breast cancer in a subject, or in monitoring the progress of breast cancer in a subject comprising at least or consists of three oligonucleotide probes specific for the detection of the following three microRNAs selected from the group consisting of: miR-16, let-7d and miR-103.
  • kit according to aspects 20 or 21 wherein said kit consists of eight oligonucleotide probes specific for the detection of the following eight microRNAs selected from the group consisting of: miR-16, let- 7d, miR-103, miR-107, miR-148a, let-7i, miR-19b and miR-22 * .
  • kit according to any one of aspects 20 to 23, adapted for performance of an assay selected from a quantitative reverse- transcription real-time polymerase chain reaction (qRT-PCR), a locked nucleic acid (LNA) real-time PCR, a northern blotting or a micro-array assay.
  • qRT-PCR quantitative reverse- transcription real-time polymerase chain reaction
  • LNA locked nucleic acid
  • kits according to any one of aspects 20 to 24 for diagnosing whether a subject has, or is at risk for developing, breast cancer, preferably by performing the method according to any one of aspects
  • the invention relates to an in vitro method of diagnosing whether a subject has, or is at risk for developing, breast cancer, comprising the steps:
  • step b) comparing the expression level obtained in step a) to a reference value of a control biological fluid sample from a healthy subject;
  • the diagnostic model based on these three miRNAs was designed in a profiling cohort (41 primary breast cancers, 26 healthy women and 19 benign mammary lesions). The miRNAs-based diagnostic tool was then validated on an independent cohort (108 primary breast cancer, 46 healthy women, 42 benign mammary lesions, 35 breast cancer in complete remission, 31 metastatic breast cancers and 30 gynecologic tumors). Receiver-operating characteristics curve derived from this three miRNAs Random Forests based diagnostic tool exhibited an area under the curve of 0.76 and 0.71 , respectively, in the primary breast cancer patients profiling- and validation cohort.
  • miR-16 and miR-103 used in the art as endogenous controls genes, are differentially expressed in plasma from healthy and cancer patients a hence can be used for breast cancer diagnostic.
  • a biological fluid sample refers to one or more than one biological fluid samples.
  • the term "subject” can be any mammal.
  • the term “mammal” refers to all mammals, including, but not limited to, humans, dogs, cats, rabbits, ferrets, guinea pigs, mice, rats, hamsters, gerbils, horses, cows and hedgehogs.
  • Preferred mammals are humans. Most preferred mammals are women. Even most preferred mammals are young women, preferably at high risk for breast cancer.
  • the expression "subject at risk for developing breast cancer” refers to subject exhibiting risk factors for breast cancer.
  • Risk factors for breast cancer include, but are not limited to, age, sex, heredity (such as, but not limited to, BRCA1 and BRCA2 mutations), alcohol, fat intake, deregulation in some hormone production, environmental factors, etc.
  • age, sex, heredity such as, but not limited to, BRCA1 and BRCA2 mutations
  • alcohol fat intake
  • deregulation in some hormone production environmental factors, etc.
  • the skilled person is aware of the fact that several different risk factors for breast cancer are disclosed in the art.
  • the terms microRNA, miRNA, hsa-miR or miR are used herein interchangeably, and refer to 19-25 nucleotides mature non-coding RNAs or precursors thereof, or fragments thereof, derived from endogenous genes of living organisms such as animals.
  • Mature microRNAs are processed from longer hairpin-like precursors termed pre-microRNAs (pre-miRs) having a length of approximately 75 nucleotides.
  • pre-miRs pre-microRNAs
  • MicroRNAs are known from the scientific literature and public databases such as the miRBase database (http://www.mirbase.org).
  • a further property of microRNAs is their presence, in a stable, resistant form, in blood (whole blood, blood serum, blood plasma or blood cells) and in various other biological fluids.
  • microRNAs of interest in the present application are incorporated in table 1 below as an example.
  • the skilled person is well aware that microRNAs may be referred to by different names, or synonyms.
  • Table 1 microRNAs of interest in the present application.
  • miR-103a- miR-103 AGCAGCAUUGUACAGGGCUAUGA MIMAT0000101
  • miR-106a- miR-106a AAAAGUGCUUACAGUGCAGGUAG MIMAT0000103
  • miR-148b- miR-148b UCAGUGCAUCACAGAACUUUGU MIMAT0000759
  • miR-15a- miR-15a UAGCAGCACAUAAUGGUUUGUG MIMAT0000068
  • miR-15b- miR-15b UAGCAGCACAUCAUGGUUUACA MIMAT0000417
  • miR-16 miR-16-5p UAGCAGCACGUAAAUAUUGGCG MIMAT0000069 miR-181a- miR-181a AACAUUCAACGCUGUCGGUGAGU MIMAT0000256
  • miR-18b- miR-18b UAAGGUGCAUCUAGUGCAGUUAG MIMAT0001412
  • miR-20a- miR-20a UAAAGUGCUUAUAGUGCAGGUAG MIMAT0000075
  • miR-21 miR-21-5p UAGCUUAUCAGACUGAUGUUGA MIMAT0000076 miR-22 miR-22-3p AAGCUGCCAGUUGAAGAACUGU MIMAT0000077 miR-22* miR-22-5p AGUUCUUCAGUGGCAAGCUUUA MIMAT0004495 miR-221- miR-221 AGCUACAUUGUCUGCUGGGUUUC MIMAT0000278
  • miR-222- miR-222 AGCUACAUCUGGCUACUGGGU MIMAT0000279
  • miR-223- miR-223 UGUCAGUUUGUCAAAUACCCCA MIMAT0000280
  • miR-23a- miR-23a AUCACAUUGCCAGGGAUUUCC MIMAT0000078
  • miR-23b- miR-23b AUCACAUUGCCAGGGAUUACC MIMAT0000418
  • miR-24 miR-24-3p UGGCUCAGUUCAGCAGGAACAG MIMAT0000080 miR-26a- miR-26a UUCAAGUAAUCCAGGAUAGGCU MIMAT0000082
  • miR-26b- miR-26b UUCAAGUAAUUCAGGAUAGGU MIMAT0000083
  • miR-30b- miR-30b UGUAAACAUCCUACACUCAGCU MIMAT0000420
  • the term "expression level” is any measure for the degree to which the microRNA is produced.
  • the “expression level” may be determined by measuring the amount of a microRNA present in the biological fluid sample.
  • the expression level of the microRNA can be determined, for example, with an assay for global gene expression in a biological fluid sample (e.g. using a microarray assay for microRNA expression profiling analysis, or a ready-to-use microRNA qPCR plate), or by specific detection assays, for example, but not limited to, quantitative PCR, quantitative reverse-transcription (real-time) PCR (qRT- PCR), locked nucleic acid (LNA) real-time PCR, or northern blotting. All such assays are well known to those skilled in the art.
  • the measurement of the expression level of a microRNA in a biological fluid sample may be carried out with an oligonucleotide probe specific for the detection of said microRNA.
  • Said oligonucleotide probe may bind directly and specifically to the microRNA, or may specifically reverse transcribe said microRNA.
  • said oligonucleotide probe may bind a cDNA obtained from said microRNA.
  • Said oligonucleotide probe may also amplify a cDNA obtained from said microRNA.
  • biological fluid sample refers to a whole blood sample, a urine sample, a milk sample or a saliva sample. It can also refer to a sample derived from whole blood, such as, but not limited to, blood serum, blood plasma or blood cells.
  • biological fluid sample does not refer solely to a liquid sample, but can also refer to a liquid sample that has been dried. Hence, for example, biological fluid sample can refer to dried blood.
  • Blood samples may be obtained from a subject by various techniques, for example, by using a needle to aspirate a blood sample.
  • the sample is obtained in a non-invasive or minimally invasive manner.
  • blood components for example blood plasma, blood serum or blood cells.
  • a "control biological fluid sample from a healthy subject” is for example a biological fluid sample from a subject of the same species not affected by breast cancer, and preferably with no reported history of breast cancer.
  • An alteration in the expression level of a microRNA in a test biological fluid sample generally occurs if a difference of the expression level of the microRNA to the reference value of a control biological fluid sample from a healthy subject is statistically significant.
  • an alteration can refer to a down-regulation of the expression level or an up-regulation of the expression level of a microRNA. If the difference is not considered statistically significant, the expression level is considered unchanged. The difference may be considered to be statistically significant if its absolute value exceeds a predetermined threshold value. This threshold value can, for example, be the standard deviation of the expression level found in biological fluid samples from a population of healthy subjects as indicated in the table below:
  • the invention further provides an in vitro method of diagnosing whether a subject has, or is at risk for developing, breast cancer, comprising the steps: a) measuring the expression level of the following three microRNAs selected from the group consisting of: miR-16, let-7d and miR-103 in a test biological fluid sample from said subject;
  • step b) comparing the expression level obtained in step a) to a reference value of a control biological fluid sample from a subject having a known diagnosis of breast cancer;
  • control biological fluid sample from a subject having a known diagnosis of breast cancer refers to a sample of biological fluid from a subject of the same species that has been diagnosed with breast cancer, such as primary or metastatic breast cancer.
  • a "similar" or “unchanged” expression level of a microRNA in a test biological fluid sample generally occurs if a difference of the expression level of the microRNA to the reference value of a control biological fluid sample from a subject having a known diagnosis of breast cancer is not statistically significant.
  • the reference value is the expression level of the same respective microRNAs of a control biological fluid sample from a healthy subject or of a control biological fluid sample from a subject having a known diagnosis of breast cancer.
  • the reference value may be a previous value for the expression level of a microRNA obtained from a specific subject. This kind of reference value may be used if the method is to be used to monitor the progress of breast cancer, or to monitor the response of a subject to a particular treatment.
  • the reference value is the average expression level of the same microRNA found in biological fluid samples from a population of subjects of the same species not affected by breast cancer or with known diagnosis of breast cancer.
  • said average expression level found in biological fluid samples from a population of subjects of the same species is determined once and then stored in a database for reference.
  • the reference value is measured in biological fluid samples obtained from one or more subjects of the same species and the same sex and age group as the subject, in which breast cancer is to be diagnosed.
  • breast cancer is primary or metastatic breast cancer.
  • a primary breast cancer refers to a breast tumor growing at the anatomical site where tumor progression began, namely the breast, and proceeded to yield a cancerous mass.
  • Metastatic breast cancer may be a complication of primary breast cancer. It refers to a stage of breast cancer where the disease has spread to distant sites. It is also referred to as, but not limited to, metastases, advanced breast cancer, secondary tumors, secondary or stage 4 breast cancer. Metastatic breast cancer may occur several years after the primary breast cancer, or sometimes may be diagnosed at the same time as the primary breast cancer, or before the primary breast cancer has been diagnosed.
  • an up-regulation of the expression level of miR-16 and a down-regulation of the expression level of let-7d and miR-103 as compared to the expression level of said respective microRNAs in a control biological fluid sample from a healthy subject is indicative of the subject either having, or being at risk for developing, primary breast cancer.
  • an up-regulation of the expression level of miR-16, a down- regulation of the expression level of let-7d and no change of the expression level of miR-103 as compared to the expression level of said respective microRNAs in a control biological fluid sample from a healthy subject is indicative of the subject either having, or being at risk for developing, metastatic breast cancer.
  • step a) the expression level of one or more additional microRNAs is further measured, wherein said additional microRNAs are selected from the group consisting of: miR-181 a, miR-107, miR-142-3p, miR- 486-5p, miR-148a, miR-20a, let-7i, miR-19a, let-7f-1 * , miR-199a-5p, miR-93, miR-451 , miR-19b, miR-30b, miR-1 , miR-26a, miR-22 * , miR-590-5p, miR- 101 , miR-22, miR-142-5p and miR-32 or combinations thereof.
  • additional microRNAs are selected from the group consisting of: miR-181 a, miR-107, miR-142-3p, miR- 486-5p, miR-148a, miR-20a, let-7i, miR-19a, let-7f-1 * , miR-199a-5p, miR-93, miR-451
  • microRNAs are the following five microRNAs selected from the group consisting of: miR-107, miR-148a, let-7i, miR-19b and miR-22 * .
  • an up-regulation of the expression level of miR- 148a and miR-19b, a down-regulation of the expression level of miR-107 and let-7i and no change of the expression level of miR-22 * as compared to the expression level of said respective microRNAs in a control biological fluid sample from a healthy subject is indicative of the subject either having, or being at risk for developing, primary breast cancer.
  • an up-regulation of the expression level of miR-148a, let-7i and miR-22 * and a down-regulation of the expression level of miR-107 and miR-19b as compared to the expression level of said respective microRNAs in a control biological fluid sample from a healthy subject is indicative of the subject either having, or being at risk for developing, metastatic breast cancer.
  • Receiver-operating characteristics curve derived from this 8-miRNAs Random Forests based diagnostic signature (namely miR-16, let-7d, miR- 103, miR-107, miR-148a, let-7i, miR-19b and miR-22 * ) exhibited an area under the curve of 0.84 and 0.81 , respectively, in the primary breast cancer patients profiling- and validation cohort.
  • the accuracy of the diagnostic tool remained unchanged in the presence of benign mammary lesion(s) and according to the age and tumor stage.
  • the 8-miRNA signature correctly identified metastatic breast cancer patients.
  • the use of the classification model on cohorts of gynecologic cancers and breast cancers in complete remission yielded prediction distributions similar to that of the control group.
  • this 8 miRNAs-based diagnostic model shows interesting characteristics for clinical application:
  • the diagnostic test is not affected by age and could be useful for monitoring young women at high risk for breast cancer, in which mammography is less effective but also harmful because of the exposure to radiations;
  • the model can also detect metastatic breast cancers and classify patients in complete remission as controls, offering potential utility for monitoring patients.
  • said additional microRNAs are selected from the group comprising:
  • microRNAs selected from the group consisting of: miR- 181 a, miR-107, miR-142-3p, miR-148a, let-7-1 * , miR-199a-5p, miR-590-5p and miR-32;
  • microRNAs selected from the group consisting of: miR- 181 a, miR-107, miR-142-3p, miR-486-5p, miR-148a, miR-20a, let-7i, miR- 19a, let-7-1 * , miR-199a-5p, miR-22 and miR-32;
  • miR- 181 a miR-107, miR-142-3p, miR-486-5p, miR-148a, let-7i, let-7-1 * , miR- 199a-5p, miR-30b, miR-22, miR-142-5p and miR-32; the following thirteen microRNAs selected from the group consisting of: miR- 181 a, miR-107, miR-142-3p, miR-486-5p, miR-148a, miR-20a, let-7i, let-7-1 * , miR-451 , miR-1 , miR-590-5p, miR-22 and miR-142-5p.
  • microRNAs selected from the group consisting of: miR- 148a, miR-19a, miR-199a-5p and miR-22.
  • the expression level of said additional microRNAs is compared to a reference expression level of said respective microRNAs, using a method selected from a decision tree-based ensemble classification method such as Random Forest, a bagging method, an extra-trees method, a boosting method, a support vector machine supervised learning model, a logistic regression method or another appropriate systems biology or statistical method.
  • a decision tree-based ensemble classification method such as Random Forest, a bagging method, an extra-trees method, a boosting method, a support vector machine supervised learning model, a logistic regression method or another appropriate systems biology or statistical method.
  • the preferred method is the Random Forest method.
  • the expression level from an unknown test biological fluid sample can be compared to and clustered together with the best fitting reference expression level representative for either healthy subjects or subjects having breast cancer such as primary or metastatic breast cancer, without needing to know the exact up-and down regulation of each individual microRNA.
  • Random Forest method refers to an ensemble tree-based supervised learning method that operates by building a large number of decision trees on bootstrap samples from the training data where the chosen features are randomly selected.
  • logistical regression refers to a type of regression analysis used for predicting the outcome of a categorical criterion variable (a variable that can take on a limited number of categories) based on one or more predictor variables. The probabilities describing the possible outcome of a single trial are modeled as a function of explanatory variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and usually a continuous independent variable (or several), by converting the dependent variable to probability scores.
  • the expression level in the test or control biological fluid sample is normalized with a mean expression level of the fifty most expressed microRNAs in said test or control biological fluid sample.
  • the term "normalized or “normalization” refers to the comparison of the expression level to one or several control(s) to remove as much variation as possible between biological fluid samples except for that difference that is a consequence of the breast cancer itself.
  • the expression level is normalized with a mean expression level of the fifty most expressed microRNAs in biological fluid samples.
  • Many assays are available in the art to determine the most expressed microRNAs in a biological fluid samples, for example, but non-limited to, using a microarray assay for microRNA expression profiling analysis, or ready-to-use microRNA qPCR plates.
  • the fifty most expressed microRNAs used to normalize the microRNA expression level in both the test and control biological fluid samples, are the same. More preferably the most expressed microRNAs are selected from the group comprising miR-484, miR-652, miR-148b, miR-106a, miR-425, let-7g, miR- 30b, miR-126, miR-103, miR-146a, miR-93, miR-24, miR-18b, miR-151 -5p, miR-423-3p, miR-223, miR-15a, miR-142-3p, miR-26b, miR-15b, miR-191 , let-7d * , let-7f, miR-21 , miR-126 * , miR-125a-5p, miR-181 a, miR-23a, miR- 222, miR-23b, miR-30c, miR-101 , miR-150, miR-320a, miR-26a, miR-145, miR
  • Blood plasma has the advantage of being a cell-free sample, so its microRNA content is not artificially elevated by hemolysis of blood cells, the number of which may be furthermore modified in breast cancer patients.
  • the invention further relates to an in vitro method of monitoring the progress of breast cancer in a subject comprising the steps of: a) measuring the expression level of the following three microRNAs selected from the group consisting of: miR-16, let-7d and miR-103 in two or more test biological fluid samples from said subject, taken at different time intervals;
  • step a) the expression level of the following five microRNAs selected from the group consisting of: miR-107, miR-148a, let-7i, miR-19b and miR-22 * is further measured.
  • an alteration in the expression level of any one or more of said microRNAs over time is indicative of a favorable breast cancer progression.
  • a similar expression level of said microRNAs over time is indicative of an unfavorable breast cancer progression.
  • the invention relates to a method of treatment of breast cancer in a subject, comprising treating the subject, diagnosed as being in need of breast cancer treatment according to the method of the first or second aspects of the present invention, with a treatment selected from the group consisting of: surgery, radiotherapy, chemotherapy, hormonal therapy or biological treatment.
  • a treatment selected from the group consisting of: surgery, radiotherapy, chemotherapy, hormonal therapy or biological treatment is selected from the group consisting of: surgery, radiotherapy, chemotherapy, hormonal therapy or biological treatment.
  • the invention further relates to a method of treatment of breast cancer in a subject, comprising the steps of:
  • a) determining whether the subject is in need of receiving breast cancer treatment comprising performing the method of the first or second aspects of the present invention
  • step b) treating the subject diagnosed in step a) as being in need of breast cancer treatment with a treatment selected from the group consisting of: surgery, radiotherapy, chemotherapy, hormonal therapy or biological treatment.
  • treatment encompasses both the therapeutic treatment of an already developed breast cancer, as well as prophylactic or preventative measures, wherein the aim is to prevent or lessen the chances of incidence of breast cancer.
  • Beneficial or desired clinical results may include, without limitation, alleviation of one or more symptoms or one or more biological markers, such as, but not limited to, the microRNAs according to the present invention, diminishment of extent of disease, stabilized (i.e., not worsening) state of disease, delay or slowing of disease progression, amelioration or palliation of the disease state, and the like.
  • Treatment or “treating” can also mean prolonging survival as compared to expected survival if not receiving treatment.
  • hormone therapy refers to the therapeutic use of hormones, for example, but not limited to, the administration of hormones to increase diminished levels in the body, also referred as hormone replacement therapy, or therapy involving the use of drugs or surgical procedures or any other suitable composition or procedure to suppress the production of or inhibit the effects of a hormone.
  • hormone replacement therapy include, but are not limited to, the use of selective estrogen receptor modulators (such as tamoxifen), aromatase inhibitors or luteinizing hormone releasing hormone analogues (such as goserelin).
  • biological treatment refers to the use of living organisms, or substances derived from living organisms, or synthetic versions of such substances to treat breast cancer.
  • biological treatments include, but are not limited to, antibodies, such as anti-HER2 antibodies, antibody-drug conjugates, antibody fragments, cytokines, therapeutic vaccines, cancer-killing viruses, gene therapy or adoptive T-cell transfer.
  • the invention relates to a kit for use in an in vitro method of diagnosing whether a subject has, or is at risk for developing, breast cancer, or in a method of treatment of breast cancer in a subject, or in monitoring the progress of breast cancer in a subject, wherein the kit comprises at least or consists of three oligonucleotide probes specific for the detection of the following three microRNAs selected from the group consisting of: miR-16, let- 7d and miR-103.
  • kit refers to any combination of reagents or apparatus that can be used to perform a method of the invention.
  • the kit may also comprise instructions for use in an in vitro method of diagnosing whether a subject has, or is at risk for developing, breast cancer, or in a method of treatment of breast cancer in a subject, or in monitoring the progress of breast cancer in a subject.
  • oligonucleotide probe refers to a short, non-naturally occurring, synthetically obtained sequence of nucleotides that match a specific region of a microRNA or a cDNA obtained from said microRNA, or fragments thereof, and then used as a molecular probe to detect said microRNA or cDNA sequence.
  • an oligonucleotide probe "specific for the detection of a microRNA” refers to an oligonucleotide probe that bind directly and specifically to a microRNA or a fragment thereof, or specifically reverse transcribe said microRNA.
  • said oligonucleotide probe may bind specifically to a cDNA obtained from said microRNA.
  • Said oligonucleotide probe may also specifically amplify a cDNA obtained from said microRNA.
  • the kit further comprises one or more oligonucleotide probes specific for the detection of one or more additional microRNAs selected from the group consisting of: miR-181 a, miR-107, miR- 142-3p, miR-486-5p, miR-148a, miR-20a, let-7i, miR-19a, let-7f-1 * , miR- 199a-5p, miR-93, miR-451 , miR-19b, miR-30b, miR-1 , miR-26a, miR-22 * , miR-590-5p, miR-101 , miR-22, miR-142-5p and miR-32 or combinations thereof.
  • additional microRNAs selected from the group consisting of: miR-181 a, miR-107, miR- 142-3p, miR-486-5p, miR-148a, miR-20a, let-7i, miR-19a, let-7f-1 * , miR- 199a-5p, miR-93, miR
  • the kit consists of eight oligonucleotide probes specific for the detection of the following eight microRNAs selected from the group consisting of: miR-16, let-7d, miR-103, miR-107, miR-148a, let-7i, miR-19b and miR-22 * .
  • the kit consists of the following oligonucleotides probes selected from the group comprising:
  • oligonucleotide probes specific for the detection of the following eleven microRNAs selected from the group consisting of: miR-16, let-7d, miR-103, miR-181 a, miR-107, miR-142-3p, miR-148a, let-7-1 * , miR-199a-5p, miR-590-5p and miR-32;
  • oligonucleotide probes specific for the detection of the following fifteen microRNAs selected from the group consisting of: miR-16, let-7d, miR-103, miR-181 a, miR-107, miR-142-3p, miR-486-5p, miR-148a, miR-20a, let-7i, miR-19a, let-7-1 * , miR-199a-5p, miR-22 and miR-32;
  • oligonucleotide probes specific for the detection of the following fifteen microRNAs selected from the group consisting of: miR-16, let-7d, miR-103, miR-181 a, miR-107, miR-142-3p, miR-486-5p, miR-148a, let-7i, let-7-1 * , miR-199a-5p, miR-30b, miR-22, miR-142-5p and miR-32;
  • sixteen oligonucleotide probes specific for the detection of the following sixteen microRNAs selected from the group consisting of: miR-16, let-7d, miR-103, miR-181 a, miR-107, miR-142-3p, miR-486-5p, miR-148a, miR-20a, let-7i, let-7-1 * , miR-451 , miR-1 , miR-590-5p, miR-22 and miR-142-5p;
  • oligonucleotide probes specific for the detection the following seven microRNAs selected from the group consisting of: miR-16, let-7d, miR-103, miR-148a, miR-19a, miR-199a-5p and miR-22.
  • said oligonucleotide probes are specific for the detection of cDNAs obtained from said microRNAs.
  • a “cDNA” or “complement DNA” refers to a complementary DNA produced by reverse transcription of an RNA template, such as a microRNA, using a reverse transcriptase enzyme.
  • reverse transcriptase are reverse transcriptases derived from moloney murine leukemia virus (M-MuLV) reverse transcriptase, avian myeloblastosis virus (AMV) reverse transcriptase, bovine leukemia virus (BLV) reverse transcriptase, Rous sarcoma virus (RSV) reverse transcriptase or human immunodeficiency virus (HIV) reverse transcriptase.
  • M-MuLV moloney murine leukemia virus
  • AMV avian myeloblastosis virus
  • BLV bovine leukemia virus
  • RSV Rous sarcoma virus
  • HAV human immunodeficiency virus
  • the kit is adapted for performance of an assay selected from a quantitative reverse-transcription real-time polymerase chain reaction (qRT-PCR), a locked nucleic acid (LNA) real-time PCR, a northern blotting or a micro-array assay.
  • an assay selected from a quantitative reverse-transcription real-time polymerase chain reaction (qRT-PCR), a locked nucleic acid (LNA) real-time PCR, a northern blotting or a micro-array assay.
  • qRT-PCR quantitative reverse-transcription real-time polymerase chain reaction
  • LNA locked nucleic acid
  • the invention relates to the use of a kit according to the sixth aspect of the invention for diagnosing whether a subject has, or is at risk for developing, breast cancer, preferably by performing the method according to the first or second aspect of the invention, or for monitoring the progress of breast cancer in a subject, preferably by performing the method according to the third aspect of the invention, or for treating breast cancer in a subject, preferably by performing the method according to the fourth or fifth aspect of the invention.
  • Figure 1 represents the Random forests based methodology.
  • Figure 2 represents 8 miRNAs present in the best-performing signature for breast cancer diagnosis.
  • Figure 3 represents the best performing 8 miRNAs based diagnostic tool performance in the validating cohort.
  • A Receiver-operator characteristics (ROC) curve of the diagnostic miRNAs model applied to the validating cohort. The area under the curve (AUC) obtained equals to 0.81 .
  • B Model outcome distributions for the primary breast cancers, controls, metastatic breast cancers, breast cancers in complete remission, and gynecologic cancers. The x-axis corresponds to the model predictions. The dashed line represents the chosen threshold used to compute finite values for sensitivity and specificity for each cohort.
  • the table below reports the AUC, sensitivity and specificity on the independent cohort, the sensitivity and specificity on the other cancers cohort.
  • the true positive count for the metastatic breast cancers amounts to 25.
  • the true negative count amounts to 14 for breast cancers in remission and gynecologic cancers.
  • Figure 4 represents a comparison of the accuracy between the best performing diagnostic 8 miRNA signature, the mammography and CA15.3.
  • A While the diagnostic performance of screening mammography (white histograms) is weaker in women under 50 years, the area under the curve (AUC) of the 8 miRNAs based-diagnostic model (black histograms) was as stable for women under and over 50 years.
  • B The CA15.3 is not useful for the diagnosis of early breast cancer. While the AUC of CA15.3 (white histograms) increases proportionally to the tumor stage, our 8 miRNAs based-diagnostic model performance (black histograms) was stable regardless of the tumor stage.
  • Table 2 represents patients and primary breast tumors characteristics.
  • NA not accessed
  • ER estrogen receptor
  • PR progesterone receptor
  • HER2 human epidermal growth factor 2
  • IDC invasive ductal carcinoma
  • ILC invasive lobular carcinoma.
  • Plasma samples were withdrawn in 9 ml EDTA tubes. Plasma was prepared within 1 hour by retaining supernatant after double centrifugation at 4°C (10 min at 81 5 g and 1 0 min at 2500 g) and was stored at -80°C. Absorbance at 414 nm (ABS 4 i 4 ) was measured with NanoDrop in all samples to evaluate the degree of hemolysis.
  • Circulating miRNAs were purified from 100 ⁇ of plasma with the miRNeasy mini kit (Qiagen, Germany) according to the manufacturer's instructions. The standard protocol was adapted on the basis of Kroh's recommendations (Kroh et al, Methods. Elsevier Inc; 2010 Apr 1 ;50(4): 298-301 ). MS2 (Roche, Belgium) was added to the samples as a carrier, cel-miR-39 and cel-miR-238 were added as spike-ins. RNA was eluted in 50 ⁇ of RNase-free water at the end of the procedure.
  • Reverse transcription was performed using the miRCURY LNATM Universal RT microRNA PCR, polyadenylation and cDNA synthesis kit (Exiqon, Denmark). Quantitative PCR was performed according to the manufacturer's instructions on custom panels of 188 selected miRNAs (Pick-&-Mix microRNA PCR Panels, Exiqon). Controls included the reference genes described in the text, inter-plate calibrators in triplicate (Sp3) and negative controls.
  • a gene expression normalization factor can be calculated based on the geometric mean of a user- defined number of reference genes.
  • the M-value threshold for stability of a gene according to GeNorm is 1 .5.
  • GeNorm is an algorithm that determines the most stable reference genes from a set of tested candidate reference genes in a given sample panel. The mean Ct of the 50 most highly expressed miRNAs was used for normalization because it was the most stable reference gene according to the GeNorm software.
  • miR-146a 1 041 miR-191 1,072 miR-15a 1,099 miR-125a-5p 1,113 let-7f 1,127 miR-26a 1,128 miR-30c 1,130 miR-21 1,133 miR-148b 1,144 miR-145 1,172 miR-23b 1,180 miR-27a 1,215 miR-16 1,229 miR-486-5p 1,232 miR-181a 1,258 miR-221 1,258 miR-92a 1,265 let-7b 1,272 miR-20a 1,302 miR-19b 1,316 miR-23a 1,365 miR-151-5p 1,370 miR-126 1,383 miR-320a 1,385 miR-199a-5p 1,422 miR-24 1,460 miR-27b 1,539 miR-451 1,626 miR-150 1,754 miR-107 1,892 miR-199a-3p 1,909 miR-19a 2,006 miR-106a 2,090 Futhermore, the delta Cq (miR-23a
  • Random forests are an ensemble tree- based supervised learning method that operates by building a large number of decision trees on bootstrap samples from the training data where the chosen features are randomly selected (hence the name, Random forests).
  • R implementation of Breiman's original Random forests algorithm has been used, provided in the R package randomForest (Liaw et al, R news. 2002;2(3):18-22.).
  • a methodology somewhat similar to the algorithmic solution proposed by Geurts et al. has been used (Geurts et al, Bioinformatics. 2005 Jul 14;21 (14):3138-45.) and is represented in Fig. 1 . The different steps are described in detail below.
  • a Random forests model was built on the profiling cohort (86 samples: 41 individuals with primary breast cancers, 26 healthy women and 19 individuals with benign mammary lesions) with the normalized expression values all 188 miRNAs as features.
  • a conservative value of 3000 has been selected for the number of trees used in the model, as the rankings of the miRNAs in terms of both importance metrics, the Mean Decrease in Accuracy (MDA) and the Mean Decrease in Gini (MDG), were stable at such value.
  • MDA Mean Decrease in Accuracy
  • MDG Mean Decrease in Gini
  • a subset of m miRNAs was determined based on both importance metrics (the m top ranked miRNAs based on the mean of their rankings in terms of MDA and MDG were selected).
  • a threshold related to the numerical prediction of the algorithm was chosen to be able to compute finite values for sensitivity and specificity.
  • the Random forests algorithm's output is a numerical value representing the probability for a sample to be part of a specific class (case or control). To obtain finite values, a specific threshold had to be picked to separate the 2 classes.
  • the classification tool was then validated on an independent cohort with a similar ratio of primary breast cancers, benign mammary lesions, and healthy women as the profiling cohort and a total number of samples 2.3 times greater (198 samples: 108 individuals with primary breast cancers, 46 healthy women and 42 individuals with benign mammary lesions).
  • the classification tool was also tested on a separate cohort consisting of 35 individuals in breast cancer complete remission, 31 metastatic breast cancer patients and 30 gynecologic cancers.
  • miRNAs composing the signatures genes predicted by at least 4 algorithms among 5 used (miRanda, miRDB, miRWalk, TargetScan and RNA22) were retained. As miR-22 * was not present in TargetScan, DIANAmT was used. The miRWalk 2.0 database was employed for this purpose. For each of the miRNA composing the signatures, their experimentally validated target genes curated in miRTarBase (release 4.5) and DIANA- TarBase v7.0 were retained.
  • ABS 4 i 4 Absorbance at 414 nm (ABS 4 i 4 ), the maximum absorbance of hemoglobin, correlated with the degree of hemolysis. ABS 4 i 4 was measured with NanoDrop for all samples. The median ABS 4 i 4 level was 0.19 ⁇ 0.1 , with a hemolysis cut-off fixed at 0.2.
  • the level of a miRNA highly expressed in red blood cells was compared with the level of a miRNA unaffected by hemolysis (miR-23a), considering that a ACq (miR-23a - miR-451 ) of more than 5 is an indicator of possible erythrocyte miRNA contamination.
  • the miRNA expression is modified in benign breast lesions.
  • the expression of the circulating miRNAs was compared between women with a normal mammogram and women carrying benign mammary lesion(s). We did not find any miRNA significantly deregulated after multiple comparisons testing correction. As benign breast lesions did not interfere with our circulating miRNAs profile, these individuals were included in the control group with healthy women.
  • miRNA profile of newly diagnosed primary breast cancers 1 12 miRNAs were founded significantly deregulated, 107 after adjusted p-value for multiple testing (table 4). miR-16 and let-7d were respectively the most up- and down-regulated miRNA. A global upregulation of miRNAs was observed in primary breast cancer patients compared to controls (1 .7 fold change).
  • miRNA profiles from plasma of patients with metastatic breast cancer were compared to those of controls. 84 miRNAs were found significantly deregulated, 53 after adjusted p-value for multiple testing (table 4). The most significantly upregulated miRNA was miR-148a and the most significantly downregulated miRNA was miR-15b. As seen in primary breast cancer samples, a global upregulation of miRNAs was observed when compared to healthy subjects (1 .1 fold-change).
  • let-7a 4.45E-04 1.27E-03 0.85 1.68E-04 1.43E-03 1.27 3.16E-02 4.44E-02 let-7a* 5.89E-06 3.36E-05 2.26 9.05E-02 1.63E-01 1.25 1.97E-01 2.22E-01 let-7b 6.43E-01 7.03E-01 1.04 4.03E-01 4.77E-01 0.96 3.00E-08 3.90E-07 let-7c 1.69E-04 5.79E-04 0.81 7.88E-01 8.14E-01 0.98 3.63E-02 4.98E-02 let-7d 4.01 E-13 3.77E-11 0.72 2.50E-04 2.04E-03 0.78 1.15E-03 2.66E-03 let-7d* 1.75E-02 3.20E-02 1.09 1.10E-01 1.81 E-01 1.11 7.37E-03 1.32E-02 let-7f 2.54E-01 3.41 E-01 0.98 3.42E-03 1.89E-02 1.25 2.08E-02 3.08E-02 let-7f-1* 1.11 E-07 1.89E
  • MDA Mean Decrease in Accuracy
  • MDG Mean Decrease in Gini
  • the list of the 25 top ranked miRNAs is available in table 5 below.
  • Table 5 list of the 25 top ranked miRNAs.
  • the first top ranked 25 miRNAs were chosen 0 to be used to generate the combinations tested. It corresponds to conservative values of MDG greater than or equal to 1 and MDA greater than or equal to 0.001 .
  • the total number of miRNAs combinations tested amounts to 33554431 .
  • the best performing model based on a ten-fold cross-validation on the profiling cohort, makes use of the following miRNAs : miR-16, let-7d, miR- 103, miR-107, miR-148a, let-7i, miR-19b, miR-22 * .
  • Table 6 summarizes the Mann-Whitney U p-values, MDG and MDA values.
  • Fig. 2 summarizes relative expression changes for the best-performing 8 miRNAs included in the signature.
  • Table 6 Results of statistical analyses comparing the 8 miRNAs expression present in the diagnostic signature between different groups. No significant difference was observed between healthy women and benign mammary lesions patients. Healthy women and benign mammary lesions patients were thus determined as controls. The 8 diagnostic miRNAs were then compared between primary breast cancer patients, breast cancer patients in complete remission, metastatic breast cancer patients, gynecologic cancer patients and controls. P-values and Benjamini-Hochberg adjusted p-values were obtained through a Mann-Whitney U test. The Mean Decrease in Accuracy (MDA) and the Mean Decrease in Gini (MDG) were obtained through the Random Forests model construction step on the profiling cohort.
  • MDA Mean Decrease in Accuracy
  • MDG Mean Decrease in Gini
  • a threshold value of 0.68 was chosen to allow the computation of finite sensitivity and specificity values.
  • the value of 0.68 corresponded to an acceptable trade-off between a high sensitivity (>0.9) and a satisfactory specificity (>0.5).
  • Fig. 3A represents the ROC curve obtained by validating the model on the independent cohort.
  • Fig. 3B The test of the classification model on the other cancer groups yielded slightly lower values for sensitivity (0.80 ⁇ 0.05 for metastatic breast cancer patients) and specificity (0.40 ⁇ 0.08 for breast cancer patients in remission and 0.41 ⁇ 0.06 for gynecologic cancer patients) (Fig. 3B). As shown on Fig. 3B, the breast cancer patients in complete remission show the same classification outcome distribution as the control group.
  • Table 7 performances of 6 alternative miRNAs combinations.
  • the performances of 6 different other miRNAs combinations were also tested on the validation cohort. AUC for each of these combinations are calculated as well as their sensitivity and specificity at the specified threshold.
  • Specificities on independent cohorts of gynecological cancers, benign mammary lesions, healthy women and breast cancer in remission were also tested, as well as sensitivity on metastatic breast cancers. Performances on other cohorts
  • miR-181a miR-107, miR- 142-3p, miR-148a, let-7-1*, 0.80 0.66 0.90 0.46 0.79 0.40 0.57 0.35 0.45 miR-199a-5p, miR-590-5p,
  • miR-181a miR-107, miR- 142-3p, miR-486-5p, miR-
  • miR-22 miR-32
  • miR-181a miR-107, miR- 142-3p, miR-486-5p, miR-
  • miR-142-5p miR-32
  • miR-181a miR-107, miR- 142-3p, miR-486-5p, miR-
  • miR-148a miR-19a
  • miR- 0.81 0.69 0.91 0.50 0.81 0.46 0.58 0.42 0.43 199a-5p miR-22 Comparison between the 8-miRNA signature and the established diagnostic methods
  • CA15.3 is the only validated biomarker in breast cancer and its accuracy is directly influenced by tumor stage, with AUC ranging from 0.56 in stage I to 0.80 in stage III breast cancers. Thereby, CA15.3 is only useful for the diagnostic of late stage and metastatic breast cancers. Interestingly, tumor stage does not seem to affect the signature miRNAs performance, remaining stable at 0.81 through breast cancer stage I to III (Fig. 4B).
  • miRNAs can be related to genomic rearrangement occurring in the tumor.
  • Table 8 the 8 miRNAs were widely dispersed on different chromosomes, thereby eliminating this hypothesis.
  • Table 8 the best performing 8 miRNA signature: miRNAs names, accession number, and cluster as curated in the miRBase release 21 . Genomic position as curated in HNGC. miRNAs families as curated in TargetScan 5.2.
  • AJCC-UICC American Joint Committee on Cancer and the International Union for
  • DNA deoxyribonucleic acid
  • HER2 human epidermal growth factor 2
  • LNA locked nucleic acid
  • miRNAs microRNAs
  • mRNAs messenger RNAs
  • RNA ribonucleic acid

Abstract

Cette invention concerne un procédé in vitro pour diagnostiquer si un sujet a, ou présente un risque de développer, un cancer du sein, comprenant a) la mesure du niveau d'expression des trois micro-ARN suivants choisis dans le groupe constitué par : miR-16, let-7d et miR-103 dans un échantillon de fluide biologique d'essai provenant du sujet ; b) la comparaison du niveau d'expression obtenu à l'étape a) à une valeur de référence d'un échantillon de fluide biologique témoin provenant d'un sujet sain ; toute altération dans le niveau d'expression de l'un quelconque ou de plusieurs desdits micro-ARN indiquant un sujet présentant, ou étant à risque de développer, un cancer du sein. L'invention concerne également des méthodes de surveillance de l'évolution du cancer du sein et de traitement du cancer du sein chez un sujet. Un kit destiné à être utilisé dans lesdites méthodes qui comprend au moins trois sondes d'oligonucléotides spécifiques pour la détection des trois micro-ARN suivants choisis dans le groupe constitué par : miR-16, let-7d and miR-103 est en outre décrit.
PCT/EP2015/056028 2015-03-22 2015-03-22 Micro-arn circulants pour diagnostiquer le cancer du sein WO2016150475A1 (fr)

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CN111850047A (zh) * 2020-07-28 2020-10-30 枣庄学院 一种miR-16与miR-30c联合表达载体及其构建方法与应用
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210139984A1 (en) * 2018-01-22 2021-05-13 Sistemic Scotland Ltd Cell contamination assay
CN108330183A (zh) * 2018-03-26 2018-07-27 深圳市展行生物有限公司 一种血浆miRNA的qRT-PCR检测方法
CN109321656A (zh) * 2018-10-22 2019-02-12 上海交通大学医学院附属仁济医院 蛋白depdc1作为诊断三阴乳腺癌的标记物的用途
CN109321656B (zh) * 2018-10-22 2021-10-01 上海交通大学医学院附属仁济医院 蛋白depdc1作为诊断三阴乳腺癌的标记物的用途
CN111850047A (zh) * 2020-07-28 2020-10-30 枣庄学院 一种miR-16与miR-30c联合表达载体及其构建方法与应用
CN111850047B (zh) * 2020-07-28 2022-08-12 枣庄学院 一种miR-16与miR-30c联合表达载体及其构建方法与应用

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