WO2023061914A2 - Procédés et réactifs pour le diagnostic différentiel de tumeurs utérines - Google Patents

Procédés et réactifs pour le diagnostic différentiel de tumeurs utérines Download PDF

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WO2023061914A2
WO2023061914A2 PCT/EP2022/078052 EP2022078052W WO2023061914A2 WO 2023061914 A2 WO2023061914 A2 WO 2023061914A2 EP 2022078052 W EP2022078052 W EP 2022078052W WO 2023061914 A2 WO2023061914 A2 WO 2023061914A2
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gene
uterine
leiomyosarcoma
sample
expression
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WO2023061914A3 (fr
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Aymara MÁS PERUCHO
Roberto ALONSO VALERO
Carlos Antonio SIMÓN VALLÉS
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Igenomix, S.L.
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Priority to EP22805781.6A priority Critical patent/EP4416305A2/fr
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Publication of WO2023061914A3 publication Critical patent/WO2023061914A3/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
    • C12Q2600/112—Disease subtyping, staging or classification
    • 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
    • C12Q2600/118—Prognosis of disease development
    • 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

Definitions

  • the present invention relates to the field of gynecological cancer diagnostics and, more in particular, to methods for the differential diagnosis uterine leiomyoma and uterine leiomyosarcoma in a subject as well as to reagents suitable for carrying out said methods.
  • Uterine leiomyomas are benign tumors arising in the smooth muscle cells of the uterine wall. They are the most common pelvic tumors in women, with prevalence of >80% for African American and -70% for Caucasian women before 50 years of age. Although LM are non-malignant tumors, the risk of hidden undiagnosed malignancy, such as leiomyosarcoma (LMS), occurs in one among 498 uterine tumors.
  • LMS hidden undiagnosed malignancy
  • Laparoscopic myomectomy with morcellation of the tumor is the gold standard therapeutic option for uterine tumors.
  • clinical symptoms as well as morphological features between LM and LMS are indistinguishable prior to surgery introducing the risk of potential spread of undiagnosed LMS.
  • the FDA issued a press release in 2014 discouraging use of power morcellators to treat myometrial tumors, substituting laparoscopic myomectomy for laparotomy-based procedures and thus increasing morbidity, mortality, and cost for the patient and healthcare system.
  • RNAseq whole-exome and RNA sequencing
  • the invention relates to an in vitro method for the differential diagnosis of a subject suspected of suffering from uterine leiomyoma or uterine leiomyosarcoma, the method comprising:
  • step (ii) identifying the subject as suffering from uterine leiomyosarcoma or from uterine leiomyoma by a predictive model which correlates the gene expression profile identified in step (i) with representative gene expression profiles from samples obtained from subjects previously identified as suffering from uterine leiomyosarcoma or from uterine leiomyoma, said predictive model having been generated by training a computer with a plurality of gene expression profiles from previously identified subjects suffering from uterine leiomyosarcoma or from uterine leiomyoma by machine learning on said plurality of gene expression profiles so as to obtain representative gene expression profiles associated with uterine leiomyosarcoma or with uterine leiomyoma.
  • the invention relates to an in vitro method for the differential diagnosis of a subject suspected of suffering from uterine leiomyoma or uterine leiomyosarcoma, the method comprising:
  • the invention relates to an in vitro method for differential diagnosis of a subject suspected of suffering from uterine leiomyoma or uterine leiomyosarcoma, the method comprising analyzing in a biological sample from the subject the coverage of at least one gene selected from the group consisting of the TUBB2b gene, the LRRCC1 gene, the NDRG4 gene, the HSF4 gene and the TMPRSS6 gene and wherein an increased coverage with respect to a reference sample in the HSF4 gene, in the NDRG4 gene, in the TMPRSS6 gene and/or in the TUBB2b gene and/or a decreased coverage with respect to a reference sample in the LRRCC1 gene is indicative that the patient has uterine leiomyosarcoma.
  • the invention in another aspect, relates to an in vitro method for the diagnosis of a uterine tumor selected from the group consisting of uterine leiomyoma or uterine leiomyosarcoma in a subject, the method comprising determining in the whole-exome sequence of a biological sample from the subject the value of a mutational index which correlates with the number of single nucleotide variants which are characteristic of the COSMIC mutational signature 12 and/or of the COSMIC mutational signature 20, wherein an increase in said index with respect to a reference sample is indicative that the subject is suffering from uterine leiomyosarcoma or from uterine leiomyoma.
  • the invention relates to an in vitro method for prognosis of a subject diagnosed of uterine leiomyosarcoma, comprising determining in a biological sample of the subject the presence of at least one CNVs shown in Table 5 wherein the presence of the CNV in the sample is indicative of a bad prognosis of uterine leiomyosarcoma.
  • the invention relates to an In vitro method for selecting a subject suspected of suffering from uterine leiomyoma or uterine leiomyosarcoma as a candidate to receive an adequate therapy to treat uterine leiomyosarcoma or uterine leiomyoma, the method comprising:
  • the invention in another aspect, relates to a method for the treatment of leiomyosarcoma in a subject in need thereof comprising the administration of a therapy adequate for the treatment of leiomyosarcoma, wherein the patient to be treated has been identified by any of the methods of the invention.
  • the invention relates to a kit, package and/or device comprising reagents adequate for implementing the methods according to the invention, to a method according to the invention which is computer-implemented as well as to a computer containing instructions for carrying out any of the methods according to the invention.
  • FIG. 1 Transcriptional analysis and validation of the targeted gene panel on leiomyoma (LM) and leiomyosarcoma (LMS).
  • Figure 3 Selection of the 5 predictive genes for the differential diagnosis of LMS and LM based on DNA sequencing results. Adjusted p-values were calculated using a Students t-test and the Bonferroni-Hochberg correction.
  • Figure 4 Kaplan-Meier plots showing the association between overall survival and alterations in at least 67% of the most frequent CNVs detected in LMS patients.
  • First differential diagnostic method of the invention (method based on transcriptomic analysis)
  • LM and LMS have specific transcriptomic profiles. This difference in the transcriptomic profiles allows the differential diagnosis of one disease or the other in a subject by analyzing the RNA composition in a sample from the subject and classifying the subject using artificial intelligence using an algorithm which has been trained with transcriptomic profiles from samples from known LM and LMS samples.
  • the invention relates to an in vitro method for the differential diagnosis of a subject suspected of suffering from uterine leiomyoma or uterine leiomyosarcoma (hereinafter “first method of the invention”), the method comprising: (i) measuring the expression level of gene ARHGAP11 A in a biological sample obtained from the subject thereby obtaining a gene expression profile of said sample; and
  • step (ii) identifying the subject as suffering from uterine leiomyosarcoma or from uterine leiomyoma by a predictive model which correlates the gene expression profile identified in step (i) with representative gene expression profiles from samples obtained from subjects previously identified as suffering from uterine leiomyosarcoma or from uterine leiomyoma, said predictive model having been generated by training a computer with a plurality of gene expression profiles from previously identified subjects suffering from uterine leiomyosarcoma or from uterine leiomyoma by machine learning on said plurality of gene expression profiles so as to obtain representative gene expression profiles associated with uterine leiomyosarcoma or with uterine leiomyoma.
  • the term “differential diagnosis”, as used herein, refers to the determination of which of two or more diseases with similar symptoms is likely responsible for a subject’s symptom(s), or distinguishing of a particular disease or condition from others that present similar clinical features based on an analysis of the clinical data. This determination, as it is understood by a person skilled in the art, does not claim to be correct in 100% of the analyzed samples. However, it requires that a statistically significant amount of the analyzed samples is classified correctly. The amount that is statistically significant can be established by a person skilled in the art by means of using different statistical tools; illustrative, non-limiting examples of said statistical tools include determining confidence intervals, determining the p-value, the Student’s t-test or Fisher’s discriminant functions, etc.
  • the confidence intervals are preferably at least 90%, at least 95%, at least 97%, at least 98% or at least 99%.
  • the p-value is preferably less than 0.1 , less than 0.05, less than 0.01 , less than 0.005 or less than 0.0001.
  • the teachings of the present invention preferably allow correctly diagnosing in at least 60%, in at least 70%, in at least 80%, or in at least 90% of the subjects of a determined group or population analyzed.
  • uterine leiomyoma also known as uterine fibroid, as used herein, refers to a benign tumor that appears in the smooth muscular layer of the uterus.
  • uterine leiomyosarcoma refers to a malignant tumor which originates in the smooth muscular layer of the uterus.
  • the term includes both primary tumors as well as metastasis.
  • the expression levels of ARHGAP11A are determined in a sample from the subject whose diagnosis is to be determined.
  • expression level refers to the measurable quantity of gene product produced by the gene in a sample of the subject, wherein the gene product can be a transcriptional product or a translational product.
  • the gene expression level can be quantified by measuring the messenger RNA levels of said gene or of the protein encoded by said gene.
  • the expression level of the genes used in the method according to the invention can be determined by measuring the levels of mRNA encoded by said gene, or by measuring the levels of the protein encoded by said gene, i.e. the protein or variants thereof.
  • Variants of the proteins encoded by the genes which are measured according to the method of the invention include all the physiologically relevant post-translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation, etc., provided that the functionality of the protein is maintained.
  • sample refers to biological material isolated from a subject.
  • the biological sample contains any biological material suitable for detecting DNA, RNA or protein levels.
  • the sample comprises genetic material, e.g., DNA, genomic DNA (gDNA), complementary DNA (cDNA), RNA, heterogeneous nuclear RNA (hnRNA), mRNA, etc., from the subject under study.
  • the sample can be isolated from any suitable tissue or biological fluid such as, for example blood, saliva, plasma, serum, urine, cerebrospinal liquid (CSF), feces, a surgical specimen, a specimen obtained from a biopsy, and a tissue sample embedded in paraffin. Methods for isolating samples are well known to those skilled in the art.
  • methods for obtaining a sample from a biopsy include gross apportioning of a mass, or micro-dissection or other art-known cell-separation methods. In order to simplify conservation and handling of the samples, these can be formalin-fixed and paraffin- embedded or first frozen and then embedded in a cryosolidifiable medium, such as OCT- compound, through immersion in a highly cryogenic medium that allows rapid freeze.
  • the sample from the subject according to the methods of the present invention is a biological fluid sample.
  • the sample from the subject according to the methods of the present invention is selected from the group consisting of blood, serum, plasma, and a tissue sample; more preferably from the group consisting of plasma and a tissue sample.
  • the term “gene expression profile”, as used herein, refers to a dataset generated from one or more genes listed above that make up a particular gene expression pattern that may be reflective of level of expression of each gene or set of genes in the biological sample under study.
  • the term “subject” or “patient” refers herein to a person in need of the analysis described herein.
  • the subject is a patient.
  • the subject is a human.
  • the subject is a female human (a woman).
  • the subject is a female presenting with pathology and or history consistent with uterine fibroids believed to be a benign neoplasm.
  • the subject is a female presenting with pathology and or history consistent with uterine fibroids believed to be leiomyoma (LM).
  • LM leiomyoma
  • the subject is a female presenting with pathology and or history consistent with uterine fibroids believed to be leiomyoma and desiring surgical intervention.
  • the subject is a female presenting with pathology and or history consistent with uterine fibroids believed to be leiomyoma, desiring surgical intervention, and requiring an evaluation of the neoplasm to evaluate the risk that the neoplasm is malignant in order to guide the selection of therapy.
  • the subject is a female presenting with pathology and or history consistent with uterine fibroids, desiring surgical intervention and requiring an evaluation of the neoplasm to evaluate the risk that the neoplasm is a leiomyosarcoma in order to guide the selection of therapy.
  • the sample wherein the expression level of the ARHGAP11A is determined can be any sample containing cells from the potential tumor.
  • the sample containing cells from the potential tumor is a potential tumor tissue or a portion thereof.
  • said potential tumor tissue sample is a uterine tissue sample from a patient in which the differential diagnosis of a subject suspected of suffering from uterine leiomyoma or uterine leiomyosarcoma is to be carried out.
  • Said sample can be obtained by conventional methods, e.g., biopsy, surgical excision, or aspiration, by using methods well known to those of ordinary skill in the related medical arts.
  • Methods for obtaining the sample from the biopsy include gross apportioning of a mass, or microdissection or other art-known cell-separation methods including and partial tumorectomy.
  • Tumor cells can additionally be obtained from fine needle aspiration cytology.
  • the sample has been obtained by hysterectomy or laparoscopic/laparotomic myomectomy.
  • these can be formalin-fixed and paraffin-embedded or first frozen and then embedded in a cryosolidifiable medium, such as OCT-compound, through immersion in a highly cryogenic medium that allows for rapid freeze.
  • a cryosolidifiable medium such as OCT-compound
  • the sample wherein the expression levels of the ARHGAP11A gene are determined is a tumor sample obtained by hysterectomy laparoscopic/laparotomic myomectomy.
  • step (i) of the method of the invention comprises the determination of the expression levels of one or more additional genes.
  • the first method of the invention comprises in step (i) measuring the expression level of CENPE gene.
  • the first method of the invention comprises in step (i) measuring the expression level of the CENPE and the COL4A5 gene.
  • the first method of the invention comprises in step (i) measuring the expression level of the CENPE gene, the COL4A5 gene and the CENPF gene.
  • the first method of the invention comprises in step (i) measuring the expression level of the CENPE gene, the COL4A5 gene, the CENPF and the MFAP5 gene.
  • step (i) of the first method of the invention may also comprise the determination of any combination of two, three, four or five of the genes shown in Table 3, which include the ARHGAP11A gene, the CENPE gene, the COL4A5 gene, the CENPF and the MFAP5 gene.
  • step (i) of the first method of the invention comprise the determination of a set of genes selected from the group consisting of ARHGAP11A and CENPE, ARHGAP11A and COL4A5, ARHGAP11A and CENPF, ARHGAP11A and MFAP5, CENPE and COL4A5, CENPE and CENPF, CENPE and MFAP5, COL4A5 and CENPF, COL4A5 and MFAP5, CENPF and MFAP5, ARHGAP11A, CENPE and COL4A5, ARHGAP11A, CENPE and CENPF, ARHGAP11A, CENPE and MFAP5, ARHGAP11A, COL4A5 and CENPF, ARHGAP11A, COL4A5 and MFAP5, ARHGAP11A, CENPF and MFAP5, ARHGAP11A, CENPF and MFAP5, ARHGAP11A, CENPF and MFAP5, ARHGAP11A, CENPF and MFAP5, CEN
  • the first step of the first diagnostic method according to the invention comprises the determination of the ARHGAP11 A gene, the CENPE gene, the COL4A5 gene, the CENPF and the MFAP5 gene together with the determination of at least one additional gene selected from the genes listed in Table 2.
  • the method comprises the determination of the expression levels of all the genes listed in Table 2.
  • Gene expression levels can be quantified by measuring the messenger RNA levels of the gene or of the protein encoded by said gene or of the protein encoded by said gene, i.e. ARHGAP11A protein or of variants thereof.
  • ARHGAP11A protein variants include all the physiologically relevant post-translational chemical modifications forms of the protein, for example, glycosylation, phosphorylation, acetylation, etc., provided that the functionality of the protein is maintained.
  • Said term encompasses the ARHGAP11A protein of any mammal species, including but not being limited to domestic and farm animals (cows, horses, pigs, sheep, goats, dogs, cats or rodents), primates and humans.
  • the ARHGAP11A protein is a human protein.
  • the biological sample may be treated to physically, mechanically or chemically disrupt tissue or cell structure, to release intracellular components into an aqueous or organic solution to prepare nucleic acids for further analysis.
  • the nucleic acids are extracted from the sample by procedures known to the skilled person and commercially available.
  • RNA is then extracted from frozen or fresh samples by any of the methods typical in the art, for example, Sambrook, J., et al., 2001. Molecular cloning: A Laboratory Manual, 3 rd ed., Cold Spring Harbor Laboratory Press, N.Y., Vol. 1-3.
  • the RNA is extracted from formalin-fixed, paraffin embedded tissues.
  • An exemplary deparaffinization method involves washing the paraffinized sample with an organic solvent, such as xylene, for example.
  • Deparaffinized samples can be rehydrated with an aqueous solution of a lower alcohol. Suitable lower alcohols, for example include, methanol, ethanol, propanols, and butanols.
  • Deparaffinized samples may be rehydrated with successive washes with lower alcoholic solutions of decreasing concentration, for example.
  • the sample is simultaneously deparaffinised and rehydrated.
  • the sample is then lysed and RNA is extracted from the sample.
  • kits may be used for RNA extraction from paraffin samples, such as PureLinkTM FFPE Total RNA Isolation Kit (Thermofisher Scientific Inc., US).
  • RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker (1987) Lab Invest. 56:A67, and De Andres et al., BioTechniques 18:42044 (1995). Preferably, care is taken to avoid degradation of the RNA during the extraction process.
  • Hybridization-based approaches typically involve incubating fluorescently labelled cDNA with custom-made microarrays or commercial high-density oligo microarrays. Specialized microarrays have also been designed; for example, arrays with probes spanning exon junctions can be used to detect and quantify distinct spliced isoforms. Genomic tiling microarrays that represent the genome at high density have been constructed and allow the mapping of transcribed regions to a very high resolution, from several base pairs to -100 bp.
  • Hybridization-based approaches are high throughput and relatively inexpensive, except for high-resolution tiling arrays that interrogate large genomes.
  • these methods have several limitations, which include: reliance upon existing knowledge about genome sequence; high background levels owing to cross-hybridization; and a limited dynamic range of detection owing to both background and saturation of signals.
  • comparing expression levels across different experiments is often difficult and can require complicated normalization methods.
  • sequence-based approaches directly determine the cDNA sequence.
  • Sanger sequencing of cDNA or EST libraries was used, but this approach is relatively low throughput, expensive and generally not quantitative.
  • Tag-based methods were developed to overcome these limitations, including serial analysis of gene expression (SAGE), cap analysis of gene expression (CAGE), and massively parallel signature sequencing (MPSS). These tag-based sequencing approaches are high throughput and can provide precise, digital gene expression levels.
  • SAGE serial analysis of gene expression
  • CAGE cap analysis of gene expression
  • MPSS massively parallel signature sequencing
  • the present methods can also involve a larger-scale analysis of mRNA levels, e.g., the detection of a plurality of biomarkers (e.g., 2-10, or 5-50, or 10-100, or 50-500 or more at one time).
  • the methods described here can also involve the step of conducting a transcriptomic analysis (i.e., the analysis of the complete set of transcripts in a cell, and their quantity, for a specific developmental stage or physiological condition). Understanding the transcriptome can be important for interpreting the functional elements of the genome and revealing the molecular constituents of cells and tissues, and also for understanding development and disease and how the biomarkers disclosed herein are indicative or predictive of a particular condition (e.g., LM or LMS).
  • LM or LMS a particular condition
  • transcriptomics The key aims of transcriptomics are: to catalogue all species of transcript, including mRNAs, non-coding RNAs and small RNAs; to determine the transcriptional structure of genes, in terms of their start sites, 5' and 3' ends, splicing patterns and other post- transcriptional modifications; and to quantify the changing expression levels of each transcript during development and under different conditions.
  • RNA-Seq RNA sequencing
  • RNAseq or"RNA-seq” is used to refer to a transcriptomic approach where the total complement of RNAs from a given sample is isolated and sequenced using high-throughput next generation sequencing (NGS) technologies (e.g., SOLiD, 454, Illumina, or ION Torrent).
  • NGS next generation sequencing
  • RNA-Seq uses deep-sequencing technologies.
  • a population of RNA (total or fractionated, such as poly(A)+) is converted to a library of cDNA fragments with adaptors attached to one or both ends.
  • Each molecule, with or without amplification, is then sequenced in a high-throughput manner to obtain short sequences from one end (single-end sequencing) or both ends (pair-end sequencing).
  • the reads are typically 30- 400 bp, depending on the DNA-sequencing technology used.
  • any high- throughput sequencing technology can be used for RNA-Seq, e.g., the Illumina IG18, Applied Biosystems SOUD22 and Roche 454 Life Science systems have already been applied for this purpose.
  • the Helicos Biosciences tSMS system is also appropriate and has the added advantage of avoiding amplification of target cDNA.
  • the resulting reads are either aligned to a reference genome or reference transcripts, or assembled de novo without the genomic sequence to produce a genomescale transcription map that consists of both the transcriptional structure and/or level of expression for each gene.
  • RNA-seq Transcriptome analysis by next-generation sequencing (RNA-seq) allows investigation of a transcriptome at unsurpassed resolution.
  • RNA-seq is independent of a priori knowledge on the sequence under investigation.
  • the transcriptome can be profiled by high throughput techniques including SAGE, microarray, and sequencing of clones from cDNA libraries.
  • high throughput techniques including SAGE, microarray, and sequencing of clones from cDNA libraries.
  • oligo nucleotide microarrays have been the method of choice providing high throughput and affordable costs.
  • microarray technology suffers from well- known limitations including insufficient sensitivity for quantifying lower abundant transcripts, narrow dynamic range and biases arising from non-specific hybridizations. Additionally, microarrays are limited to only measuring known/annotated transcripts and often suffer from inaccurate annotations. Sequencing -based methods such as SAGE rely upon cloning and sequencing cDNA fragments.
  • Sequencingbased approaches have a number of significant technical advantages over hybridizationbased microarray methods.
  • the output from sequence-based protocols is digital, rather than analog, obviating the need for complex algorithms for data normalization and summarization while allowing for more precise quantification and greater ease of comparison between results obtained from different samples. Consequently, the dynamic range is essentially infinite, if one accumulates enough sequence tags.
  • Sequence-based approaches do not require prior knowledge of the transcriptome and are therefore useful for discovery and annotation of novel transcripts as well as for analysis of poorly annotated genomes.
  • the application of sequencing technology in transcriptome profiling has been limited by high cost, by the need to amplify DNA through bacterial cloning, and by the traditional Sanger approach of sequencing by chain termination.
  • next-generation sequencing (NGS) technology eliminates some of these barriers, enabling massive parallel sequencing at a high but reasonable cost for small studies.
  • the technology essentially reduces the transcriptome to a series of randomly fragmented segments of a few hundred nucleotides in length. These molecules are amplified by a process that retains spatial clustering of the PGR products, and individual clusters are sequenced in parallel by one of several technologies.
  • Current NGS platforms include the Roche 454 Genome Sequencer, Illumina's Genome Analyzer, and Applied Biosystems' SOLiD. These platforms can analyze tens to hundreds of millions of DNA fragments simultaneously, generate giga-bases of sequence information from a single run, and have revolutionized SAGE and cDNA sequencing technology.
  • the 3' tag Digital Gene Expression uses oligo-dT priming for first strand cDNA synthesis, generates libraries that are enriched in the 3' untranslated regions of polyadenylated mRNAs, and produces base cDNA tags.
  • DGE Digital Gene Expression
  • sequencing methods contemplated herein requires the preparation of sequencing libraries.
  • sequencing library preparation is described in U.S. Patent Application Publication No. US 2013/0203606, which is incorporated by reference in its entirety.
  • this preparation may take the coagulated portion of the sample from the droplet actuator as an assay input.
  • the library preparation process is a ligation-based process, which includes four main operations: (a) blunt-ending, (b) phosphorylating, (c) A-tailing, and (d) ligating adaptors. DNA fragments in a droplet are provided to process the sequencing library.
  • nucleic acid fragments with 5'- and/or 3 '-overhangs are blunt-ended using T4 DNA polymerase that has both a 3 '-5' exonuclease activity and a 5'-3' polymerase activity, removing overhangs and yielding complementary bases at both ends on DNA fragments.
  • the T4 DNA polymerase may be provided as a droplet.
  • T4 polynucleotide kinase may be used to attach a phosphate to the 5'-hydroxyl terminus of the blunt-ended nucleic acid.
  • the T4 polynucleotide kinase may be provided as a droplet.
  • the 3' hydroxyl end of a dATP is attached to the phosphate on the 5 '-hydroxyl terminus of a blunt-ended fragment catalyzed by exo-Klenow polymerase.
  • sequencing adaptors are ligated to the A-tail.
  • T4 DNA ligase is used to catalyze the formation of a phosphate bond between the A-tail and the adaptor sequence.
  • end-repairing including blunt-ending and phosphorylation
  • sequencing library preparation can involve the production of a random collection of adapter-modified DNA fragments (e.g., polynucleotides) that are ready to be sequenced.
  • Sequencing libraries of polynucleotides can be prepared from DNA or RNA, including equivalents, analogs of either DNA or cDNA, for example, DNA or cDNA that is complementary or copy DNA produced from an RNA template, by the action of reverse transcriptase.
  • the polynucleotides may originate in double-stranded form (e.g., dsDNA such as genomic DNA fragments, cDNA, PCR amplification products, and the like) or, in certain embodiments, the polynucleotides may originated in singlestranded form (e.g., ssDNA, RNA, etc.) and have been converted to dsDNA form.
  • single stranded mRNA molecules may be copied into double-stranded cDNAs suitable for use in preparing a sequencing library.
  • the precise sequence of the primary polynucleotide molecules is generally not material to the method of library preparation, and may be known or unknown.
  • the polynucleotide molecules are DNA molecules.
  • the polynucleotide molecules represent the entire genetic complement of an organism or substantially the entire genetic complement of an organism, and are genomic DNA molecules (e.g., cellular DNA, cell free DNA (cfDNA), etc.), that typically include both intron sequence and exon sequence (coding sequence), as well as non-coding regulatory sequences such as promoter and enhancer sequences.
  • genomic DNA molecules e.g., cellular DNA, cell free DNA (cfDNA), etc.
  • coding sequence typically include both intron sequence and exon sequence (coding sequence)
  • non-coding regulatory sequences such as promoter and enhancer sequences.
  • the primary polynucleotide molecules comprise human genomic DNA molecules, e.g., cfDNA molecules present in peripheral blood of a subject.
  • Preparation of sequencing libraries for some NGS sequencing platforms is facilitated by the use of polynucleotides comprising a specific range of fragment sizes.
  • Preparation of such libraries typically involves the fragmentation of large polynucleotides (e.g. cellular genomic DNA) to obtain polynucleotides in the desired size range.
  • the expression level can be determined using mRNA obtained from a formalin- fixed, paraffin-embedded tissue sample.
  • mRNA may be isolated from an archival pathological sample or biopsy sample which is first deparaffinized.
  • An exemplary deparaffinization method involves washing the paraffinized sample with an organic solvent, such as xylene.
  • Deparaffinized samples can be rehydrated with an aqueous solution of a lower alcohol. Suitable lower alcohols, for example, include methanol, ethanol, propanols and butanols.
  • Deparaffinized samples may be rehydrated with successive washes with lower alcoholic solutions of decreasing concentration, for example. Alternatively, the sample is simultaneously deparaffinized and rehydrated. The sample is then lysed and RNA is extracted from the sample.
  • Samples can be also obtained from fresh tumor tissue such as a resected tumor.
  • samples can be obtained from fresh tumor tissue or from OCT embedded frozen tissue.
  • samples can be obtained by laparoscopic myomectomy and then paraffin-embedded.
  • control RNA relates to RNA whose expression levels do not change or change only in limited amounts in tumor cells with respect to non-tumorigenic cells.
  • the control RNA is mRNA derived from housekeeping genes and which code for proteins which are constitutively expressed and carry out essential cellular functions.
  • housekeeping genes for use in the present invention include p-2-microglobulin, ubiquitin, 18-S ribosomal protein, cyclophilin, IPO8, HPRT, GAPDH, PSMB4, tubulin and p-actin.
  • the relative gene expression quantification is calculated according to the comparative threshold cycle (Ct) method using GAPDH, IPO8, HPRT, P-actin or PSMB4 as an endogenous control and commercial RNA controls as calibrators.
  • Ct comparative threshold cycle
  • Final results are determined according to the formula 2 _(ACt sam P
  • Suitable methods to determine gene expression levels at the mRNA level include, without limitation, standard assays for determining mRNA expression levels such as qPCR, RT-PCR, RNA protection analysis, Northern blot, RNA dot blot, in situ hybridization, microarray technology, tag based methods such as serial analysis of gene expression (SAGE) including variants such as LongSAGE and SuperSAGE, microarrays, fluorescence in situ hybridization (FISH), including variants such as Flow- FISH, qFiSH and double fusion FISH (D-FISH), and the like.
  • SAGE serial analysis of gene expression
  • FISH fluorescence in situ hybridization
  • D-FISH double fusion FISH
  • the determination of the expression levels of the genes or genes is carried out by exome-wide gene expression from RNAseq.
  • the biological sample is a sample containing myometrial cells or RNA derived from myometrial cells.
  • the sample containing myometrial cells is a myometrial biopsy.
  • the first method of the invention comprises identifying the subject as suffering from uterine leiomyosarcoma or from uterine leiomyoma by a predictive model which correlates the gene expression profile identified in step (i) with representative gene expression profiles from samples obtained from subjects previously identified as suffering from uterine leiomyosarcoma or from uterine leiomyoma, said predictive model having been generated by training a computer with a plurality of gene expression profiles from previously identified subjects suffering from uterine leiomyosarcoma or from uterine leiomyoma by machine learning on said plurality of gene expression profiles so as to obtain representative gene expression profiles associated with uterine leiomyosarcoma or with uterine leiomyoma.
  • the representative data sets use at least 10, and more preferably 20, 25, 30 or more gene expression profiles from samples obtained from subjects suffering uterine leiomyoma or uterine leiomyosarcoma.
  • the data sets may derive from subjects with multiple different parameters such as gender, age, weight, national origin, etc.
  • the second step of the first method of the invention is performed by a machine learning method selected from a regression method, a classification method or a combination thereof.
  • machine learning generally refers to algorithms that give a computer the ability to learn without being explicitly programmed, including algorithms that learn from and make predictions about data.
  • Machine learning algorithms employed by the embodiments disclosed herein may include, but are not limited to, random forest (“RF”), least absolute shrinkage and selection operator (“LASSO”) logistic regression, regularized logistic regression, XGBoost, decision tree learning, artificial neural networks (“ANN”), deep neural networks (“DNN”), support vector machines, rule-based machine learning, and/or others.
  • RF random forest
  • LASSO least absolute shrinkage and selection operator
  • XGBoost decision tree learning
  • ANN artificial neural networks
  • DNN deep neural networks
  • support vector machines rule-based machine learning, and/or others.
  • linear regression For clarity, algorithms such as linear regression or logistic regression can be used as part of a machine learning process. However, it will be understood that using linear regression or another algorithm as part of a machine learning process is distinct from performing a statistical analysis such as regression with a spreadsheet program. Whereas statistical modeling relies on finding relationships between variables (e.g., mathematical equations) to predict an outcome, a machine learning process may continually update model parameters and adjust a classifier as new data becomes available, without relying on explicit or rules-based programming.
  • variables e.g., mathematical equations
  • the second step of the first method of the invention is performed by a classification method, which results in identifying the subject as suffering from uterine leiomyoma or uterine leiomyosarcoma.
  • step (b) is carried out by a classification method; preferably selected from logistic regression, random forest, gradient boosting (GB), adaptive boosting (AB), extreme Gradient Boosting (XGB) k-nearest neighbors (kNN), artificial neural network (ANN), support vector machine (SVM), and combinations thereof.
  • a classification method preferably selected from logistic regression, random forest, gradient boosting (GB), adaptive boosting (AB), extreme Gradient Boosting (XGB) k-nearest neighbors (kNN), artificial neural network (ANN), support vector machine (SVM), and combinations thereof.
  • the predictive model is generated by training the computer with a plurality of gene expression profiles from previously identified samples from subjects suffering from uterine leiomyoma or uterine leiomyosarcoma by machine learning on said plurality of gene expression profiles so as to obtain representative multivariable data sets associated with uterine leiomyoma or uterine leiomyosarcoma; wherein the training comprises the following steps: (i) training data, from a plurality of gene expression profiles, is randomly stratified into:
  • the predictive model is seeded on the calibration dataset (particularly is developed by applying a machine learning method selected from a regression method, a classification method or a combination thereof on the calibration dataset);
  • the predictive model is optimized by an internal cross validation; preferably by a k-fold cross validation, wherein each of the k cases of the k-fold cross validation is used for testing only once and one at a time; and
  • the predictive model is further validated by predicting new samples using the validation dataset.
  • the second step is performed by a classification method wherein the patients are assigned a probability of belonging to given category such as patients suffering from leiomyoma or patients suffering from leiomyosarcoma.
  • the classification method is carried out by a method selected from gradient boosting, support vector machine (SVM), decision trees, K nearest neighbors, Naive Bayes or neural networks.
  • the classification method is carried out by a Gradient Boosting.
  • Gradient Boosting is a machine learning algorithm that uses a gradient boosting framework.
  • Gradient Boosting trees, a decision-tree-based ensemble model differ fundamentally from conventional statistical techniques that aim to fit a single model using the entire dataset.
  • Such ensemble approach improves performance by combining strengths of models that learn the data by recursive binary splits, such as trees, and of "boosting", an adaptive method for combining several simple (base) models.
  • a subsample of the training data is selected at random (without replacement) from the entire training data set, and then a simple base learner is fitted on each subsample.
  • the final boosted trees model is an additive tree model, constructed by sequentially fitting such base learners on different subsamples. This procedure incorporates randomization, which is known to substantially improve the predictor accuracy and also increase robustness.
  • the second step is performed by a regression method; preferably selected from multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLSR), artificial neural network (ANN), support vector machine (SVM), random forest (RF), lassor regression, ridge regression and combinations thereof.
  • MLR linear regression
  • PCR principal component regression
  • PLSR partial least squares regression
  • ANN artificial neural network
  • SVM support vector machine
  • RF random forest
  • the second step is performed by a classification method, more in particular, by gradient boosting, which includes the value of one or more variables of the gene expression profile collected in step (i) and which contribute to the identification of the subject as suffering from uterine leiomyoma or uterine leiomyosarcoma.
  • the second step is performed by a regression method which includes the value of one or more variables of the gene expression profile collected in step (i) and which contribute to the identification of the subject as suffering from uterine leiomyoma or uterine leiomyosarcoma.
  • the second method according to the invention is carried out in a patient that has been previously identified as suffering an uterine myometrial tumor, being either leiomyosarcoma or uterine leiomyoma by imaging examination, preferably by ultrasonography.
  • Second differential diagnostic method of the invention (method based on transcriptomic analysis)
  • the invention relates to an in vitro method (hereinafter second method of the invention) for the differential diagnosis of a subject suspected of suffering from uterine leiomyoma or uterine leiomyosarcoma, the method comprising:
  • the second method of the invention comprises the determination of the level of expression of at least one gene selected from the list shown in Table 1 in a biological sample obtained from the subject. In some embodiments, this step comprises the determination. In some embodiments, the first step of the method of the invention comprises the determination of the expression levels of at least 5, at least 10, at least 20, at least 30, at least 40, at least 50, at least 60, at least 80, at least 90, at least 100, at least 110, at least 120, at least 130, at least 140, at least 150, at least 160, at least
  • At least 180 at least 190, at least 200, at least 210, at least 220, at least 230, at least 240, at least 250, at least 260, at least 270, at least 280, at least 290, at least 300, at least 310, at least 320, at least 330, at least 340, at least 350, at least 360, at least
  • the first step of the second method of the invention comprises the determination of the expression levels of the genes mentioned in Table 3.
  • the first step of the second method of the invention comprises the determination of the expression levels of the genes mentioned in Table 2.
  • the first step of the second method of the invention comprises the determination of the expression levels of the genes mentioned in Table 1.
  • the biological sample is a sample containing myometrial cells, or RNA derived from myometrial cells.
  • the sample containing myometrial cells is a myometrial biopsy.
  • the second method of the invention comprises comparing said level of expression with a reference value.
  • Reference value refers to a laboratory value used as a reference for values/data obtained by laboratory examinations of subjects or samples collected from subjects.
  • the reference value or reference level can be an absolute value; a relative value; a value that has an upper and/or lower limit; a range of values; an average value; a median value, a mean value, or a value as compared to a particular control or baseline value.
  • a reference value can be based on an individual sample value, such as for example, a value obtained from a sample from the subject being tested, but at an earlier point in time or from a non-cancerous tissue.
  • the reference value can be based on a large number of samples, such as from population of subjects of the chronological age matched group, or based on a pool of samples including or excluding the sample to be tested.
  • Various considerations are taken into account when determining the reference value of the marker. Among such considerations are the age, weight, sex, general physical condition of the patient and the like. For example, equal amounts of a group of at least 2, at least 10, at least 100 to preferably more than 1000 subjects, preferably classified according to the foregoing considerations, for example according to various age categories, are taken as the reference group.
  • the quantity of the biomarker in a sample from a tested subject may be determined directly relative to the reference value (e.g., in terms of increase or decrease, or fold-increase or fold-decrease).
  • this may allow to compare the quantity of the biomarker in the sample from the subject with the reference value (in other words to measure the relative quantity of any one or more biomarkers in the sample from the subject vis-a-vis the reference value) without the need to first determine the respective absolute quantities of said biomarker.
  • reference values are the expression level of the gene being compared in a reference sample.
  • the “reference sample” may vary depending on whether diagnosis of uterine leiomyosarcoma or of uterine leiomyoma is desired. If the diagnosis of uterine leiomyosarcoma is desired, then the reference sample means a sample obtained from a pool of subjects suffering uterine leiomyoma or which do not have a history of leiomyoma. Thus, in an embodiment, the reference value is the mean level of expression of the gene or genes in a pool of samples from leiomyoma patients.
  • the reference sample means a sample obtained from a pool of subjects suffering uterine leiomyosarcoma or which do not have a history of leiomyosarcoma.
  • the reference value is the mean level of expression of the gene or genes in a pool of samples from leiomyosarcoma patients
  • the reference value for the expression level of the gene or genes of interest is the mean level of expression of said gene or genes in a pool of samples from primary tumours, preferably obtained from subjects suffering from the same type of cancer as the patient object of the study.
  • the reference value is the expression levels of the gene of interest in a pool obtained from primary tumor tissue obtained from patients.
  • the expression profile of the genes in the reference sample can preferably, be generated from a population of two or more individuals. The population, for example, can comprise 3, 4, 5, 10, 15, 20, 30, 40, 50 or more individuals.
  • the expression profile of the genes in the reference sample and in the sample of the individual that is going to be diagnosed according to the methods of the present invention can be generated from the same individual, provided that the profiles to be assayed and the reference profile are generated from biological samples taken at different times and are compared to one another. For example, a sample of an individual can be obtained at the beginning of a study period. A reference biomarker profile from this sample can then be compared with the biomarker profiles generated from subsequent samples of the same individual.
  • the reference sample is a pool of samples from several individuals and corresponds to portions of tissue that are far from the tumor area and which have preferably been obtained in the same biopsy but which do not have any anatomopathological characteristic of tumor tissue.
  • the level of this marker expressed in tumor tissue from subjects can be compared with this reference value, and thus be assigned a level of deviation with respect to a reference value.
  • the “deviation” can be either an increase or a decrease in the expression levels. For example, an increase in expression level above the reference value of at least 1.1 -fold, 1.5-fold, 2-fold, 5-fold, 10- fold, 20-fold, 30-fold, 40-fold, 50-fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold or even more compared with the reference value is considered as “increased” expression level.
  • the expression of a gene is considered increased in a sample of the subject under study when the levels increase with respect to the reference sample by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more.
  • a decrease in expression levels below the reference value of at least 0.9-fold, 0.75- fold, 0.2-fold, 0.1-fold, 0.05-fold, 0.025-fold, 0.02-fold, 0.01-fold, 0.005-fold or even less compared with reference value is considered as “decreased” expression level.
  • the expression of a gene is considered decreased when its levels decrease with respect to the reference sample by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100% (i.e., absent).
  • the comparison of the expression levels of the gene or genes of interest with the reference value allows differential diagnosis between uterine leiomyoma or uterine leiomyosarcoma.
  • the patient is detected as having leiomyosarcoma if the expression level of the gene or genes under examination is/are increased with respect to the expression levels found in leiomyoma samples, which is defined in Table 1 as genes having a logFC higher than 0.
  • the patient is detected as having leiomyosarcoma if the expression level of the gene or genes under examination is/are decreased with respect to the expression levels found in leiomyoma samples, which is defined in Table 1 as genes having a logFC lower than 0.
  • the second method according to the invention allows the differential diagnoses of uterine leiomyosarcoma when the deviation in the level of expression of the gene or genes is/are of at least four fold with respect to the reference value or values for said genes, said reference value being the expression level of the same gene or genes determined in sample from a patient with uterine leiomyoma.
  • the second method according to the invention allows the differential diagnoses of uterine leiomyoma when the deviation in the level of expression of the gene or genes is/are of at least four fold with respect to the reference value or values for said genes, said reference value being the expression level of the same gene or genes determined in sample from a patient with uterine leiomyosarcoma.
  • the second method according to the invention is carried out in a patient that has been previously identified as suffering an uterine myometrial tumor, being either leiomyosarcoma or uterine leiomyoma by imaging examination, preferably by ultrasonography.
  • the invention relates to an in vitro method for differential diagnosis (hereinafter “the third method of the invention) of a subject suspected of suffering from uterine leiomyoma or uterine leiomyosarcoma, the method comprising analyzing in a biological sample from the subject the coverage in at least one gene selected from the group consisting of the TUBB2b gene, the LRRCC1 gene, the NDRG4 gene, the HSF4 gene and the TMPRSS6 gene, wherein an increased coverage in the HSF4 gene, in the NDRG4 gene, in the TMPRSS6 gene and/or in the TUBB2b gene and/or a decreased coverage in the LRRCC1 gene is indicative that the patient has uterine leiomyosarcoma.
  • coverage refers to the abundance of sequence tags mapped to a defined sequence which is obtained after the sequence has been determined.
  • a given genomic region is read, which may comprise the whole genome or regions thereof, and the number of reads which contain a given sequence tag within the genomic region is defined as the coverage for this specific sequence tag.
  • sequence tag is herein used interchangeably with the term "mapped sequence tag” to refer to a sequence read that has been specifically assigned, i.e., mapped, to a larger sequence, e.g., a reference genome, by alignment.
  • Mapped sequence tags are uniquely mapped to a reference genome, i.e., they are assigned to a single location to the reference genome. Unless otherwise specified, tags that map to the same sequence on a reference sequence are counted once. Tags may be provided as data structures or other assemblages of data.
  • a tag contains a read sequence and associated information for that read such as the location of the sequence in the genome, e.g., the position on a chromosome.
  • the location is specified for a positive strand orientation.
  • a tag may be defined to allow a limited amount of mismatch in aligning to a reference genome.
  • tags that can be mapped to more than one location on a reference genome, i.e., tags that do not map uniquely, may not be included in the analysis.
  • Coverage can be quantitatively indicated by sequence tag density (or count of sequence tags), sequence tag density ratio, normalized coverage amount, adjusted coverage values, etc.
  • the levels can be displayed graphically on a display as numeric values or proportional bars (i.e., a bar graph) or any other display method known to those skilled in the art.
  • the graphic display can provide a visual representation of the amount of copy number variation in the biological sample being evaluated.
  • coverage quantity refers to a modification of raw coverage and often represents the relative quantity of sequence tags (sometimes called counts) in a region of a genome such as a bin.
  • a coverage quantity may be obtained by normalizing, adjusting and/or correcting the raw coverage or count for a region of the genome.
  • a normalized coverage quantity for a region may be obtained by dividing the sequence tag count mapped to the region by the total number sequence tags mapped to the entire genome. Normalized coverage quantity allows comparison of coverage of a bin across different samples, which may have different depths of sequencing. It differs from sequence dose in that the latter is typically obtained by dividing by the tag count mapped to a subset of the entire genome. The subset is one or more normalizing segments or chromosomes. Coverage quantities, whether or not normalized, may be corrected for global profile variation from region to region on the genome, G-C fraction variations, outliers in robust chromosomes, etc.
  • step (a) described above can comprise sequencing at least a portion of the nucleic acid molecules of a test sample to obtain said sequence information for the nucleic acid molecules of the test sample.
  • step (c) comprises calculating a single gene dose for each of the gene of interest as the ratio of the number of sequence tags or the other parameter identified for each of the genes of interest and the number of sequence tags or the other parameter identified for the genes chromosome sequence(s).
  • gene dose is based on processed sequence coverage quantities derived from the number of sequence tags or another parameter.
  • only unique, non-redundant sequence tags are used to calculate the processed sequence coverage quantities or another parameter.
  • the processed sequence coverage quantity is a sequence tag density ratio, which is the number of sequence tag standardized by sequence length.
  • the processed sequence coverage quantity or the other parameter is a normalized sequence tag or another normalized parameter, which is the number of sequence tags or the other parameter of a sequence of interest divided by that of all or a substantial portion of the genome.
  • the processed sequence coverage quantity or the other parameter such as a fragment size parameter is adjusted according to a global profile of the sequence of interest.
  • the processed sequence coverage quantity or the other parameter is adjusted according to the within-sample correlation between the GC content and the sequence coverage for the sample being tested.
  • the processed sequence coverage quantity or the other parameter results from combinations of these processes, which are further described elsewhere herein.
  • a gene dose is calculated as the ratio of the processed sequence coverage or the other parameter for each of the genes of interest and that for the normalizing gene sequence(s).
  • the biological sample is a sample containing myometrial cells or a sample containing DNA from myometrial cells.
  • the value is compared to the coverage of the same gene in a reference sample.
  • the coverage in the sample of the patient which is to be differentially diagnosed is compared to the coverage in a reference sample variation in the genomic DNA in a uterine leiomyoma and/or to the coverage in the genomic DNA of a uterine leiomyosarcoma, thereby determining the coverage for the specific gene between the genomic DNA of uterine leiomyoma and the genomic DNA in uterine leiomyosarcoma.
  • the term “increased coverage” for a given gene in the context of the third method of the invention is understood as that, when the genome containing the gene is sequenced, the number of reads which contain the sequence of the gene or of sequence tags associated with said gene is increased with respect to the number of reads containing the sequence of the gene or of sequence tags associated with said gene in a reference sample, wherein the reference sample is either a sample of a patient suffering from uterine leiomyosarcoma or a sample from a patient suffering uterine leiomyoma.
  • the coverage of the gene under consideration is at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%, at least 100% higher than the coverage of the gene in the reference sample.
  • the term “decreased coverage” in the context of the fourth diagnostic method of the invention is understood for a given gene, in the context of the third method of the invention is understood as that, when the genome containing the gene is sequenced, the number of reads which contain the sequence of the gene or of sequence tags associated with said gene is decrease with respect to the number of reads containing the sequence of the gene or of sequence tags associated with said gene in a reference sample, wherein the reference sample is either a sample of a patient suffering from uterine leiomyosarcoma or a sample from a patient suffering uterine leiomyoma.
  • the coverage of the gene under consideration is less than 90%, less than 80%, less than 70%, less than 60%, less than 50%, less than 40%, less than 30%, less then 20%, less than 10% or lower than the coverage of the the gene in the reference sample
  • a subject can be diagnosed as having uterine leiomyosarcoma if the subject shows an increased coverage in the HSF4 gene, in the NDRG4 gene, in the TMPRSS6 gene and/or in the TUBB2b gene with respect to a reference sample.
  • the subject can be diagnosed as having uterine leiomyosarcoma if the subject shows a decreased coverage in the LRRCC1 gene is indicative that the patient has uterine leiomyosarcoma with respect to a reference sample.
  • a subject can be diagnosed as having uterine leiomyosarcoma if the subject shows an increased coverage in the HSF4 gene, in the NDRG4 gene, in the TMPRSS6 gene and in the TUBB2b gene and a decreased coverage in the LRRCC1 gene with respect to a reference sample.
  • the second method according to the invention is carried out in a patient that has been previously identified as suffering an uterine myometrial tumor, being either leiomyosarcoma or uterine leiomyoma by imaging examination, preferably by ultrasonography.
  • First diagnostic method of the invention (method based on genomic mutational analysis)
  • RNAseq whole-exome and RNA sequencing
  • the invention relates to an in vitro method (hereinafter referred to indistinctly as “fourth method of the invention” or “diagnostic method of the invention”) for the diagnosis of a uterine tumor selected from the group consisting of uterine leiomyoma or uterine leiomyosarcoma in a subject, the method comprising determining in the whole-exome sequence of a biological sample from the subject the value of an index which correlates with the number of single nucleotide variants which are characteristic of the COSMIC mutational signature 12 and/or of the COSMIC mutational signature 20, wherein an increase in said index with respect to a reference sample is indicative that the subject is suffering from uterine leiomyosarcoma or from uterine leiomyoma.
  • diagnosis refers both to the process of attempting to determine and/or identify a possible disease in a subject, i.e. the diagnostic procedure, and to the opinion reached by this process, i.e. the diagnostic opinion. As such, it can also be regarded as an attempt at classification of an individual's condition into separate and distinct categories that allow medical decisions about treatment and prognosis to be made.
  • diagnosis of the uterine tumor relates to the capacity to identify or detect the presence of a tumor in a subject. This detection, as it is understood by a person skilled in the art, does not claim to be correct in 100% of the analyzed samples. However, it requires that a statistically significant amount of the analyzed samples are classified correctly.
  • the amount that is statistically significant can be established by a person skilled in the art by means of using different statistical tools; illustrative, non-limiting examples of said statistical tools include determining confidence intervals, determining the p-value, the Student’s t-test or Fisher’s discriminant functions, etc. (see, for example, Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York 1983).
  • the confidence intervals are preferably at least 90%, at least 95%, at least 97%, at least 98% or at least 99%.
  • the p-value is preferably less than 0.1 , less than 0.05, less than 0.01 , less than 0.005 or less than 0.0001.
  • the teachings of the present invention preferably allow correctly diagnosing in at least 60%, in at least 70%, in at least 80%, or in at least 90% of the subjects of a determined group or population analyzed.
  • uterine leiomyoma and “uterine leiomyosarcoma” have been defined above and are equally applicable to the fourth method according to the invention.
  • the fourth method of the invention comprises determining in a whole-exome sequence of a biological sample from a subject the value of an index which correlates with the number of single nucleotide variants which are characteristic of the COSMIC mutational signature 12 and/or of the COSMIC mutational signature 20.
  • whole exome sequence generally means the sequence that results from the sequencing of all the protein-coding genes in a genome (known as the exome). It consists of first selecting only the subset of DNA that encodes proteins (known as exons) and then sequencing this DNA using any high-throughput DNA sequencing technology. Humans have about 180,000 exons, constituting about 1.5 percent of the human genome, or approximately million base pairs. In particular, the exome sequencing may be carried out by next-generation sequencing.
  • the fourth method according to the invention comprises determining within the whole-exome sequence data the value of an index which correlates with the number of single nucleotide variants which are characteristic of the COSMIC mutational signature 12 and/or of the COSMIC mutational signature 20.
  • mutantational index which correlates with the number of single nucleotide variants” as used herein, refers to a numeric value which defines the number of one or more type or types of predetermined single nucleotide variants within a given sequence dataset. In some embodiments, the index is the number of all the mutations present in the sequence dataset.
  • the mutational index corresponds to the mutational profile similarity as described in Blokzijl et al. (Genome Med., 2018, 10(1):33, doi: 10.1186/s13073-018-0539-0) which measures the similarity between two mutational profiles .
  • COSMIC mutational signature refers to a list of somatic single nucleotide variants that appear in cancer cells as a result of the mutational processes that have been operative in said cells as described by Alexandrov et al. (Nature, 2013, 500(7463) :415-21, doi: 10.1038/nature12477). COSMIC is the acronym of "Catalogue of Somatic Mutations in Cancer”.
  • COSMIC mutational signature 12 is characterized by a transcriptional strand bias for T>C substitutions with more mutations of A than T on the untranscribed strands of genes consistent with damage to adenine and repair by transcription-coupled nucleotide excision repair.
  • COSMIC mutational signature 20 results from concurrent POLD1 (polymerase delta 1) mutations and defective DNA mismatch repair, leading to microsatellite instability. Mutational profiles characterized by microsatellilte instability are well known as well as methods for their identification once a genomic sequence is available (see e.g. Shah et al., Cancer Res. 2010 Jan 15; 70(2): 431-435).
  • step (ii) of the fourth method of the invention comprises the determination of the mutational index for COSMIC signatures 12 and 20,
  • the diagnostic method of the invention comprises diagnosing the presence of uterine leiomyosarcoma or uterine leiomyoma if said index is increased with respect to a reference sample.
  • the term “reference value” has been defined above in the context of the first, second or third method of the invention and is equally applicable to the fourth method of the invention, with the exception that, in this case, the reference sample which is used for the determination of the reference value is sample obtained from a subject or from a pool of subjects which do not suffer uterine cancer or which do not suffer leiomyoma or leiomyosarcoma.
  • the reference value is the mean mutational index in a pool of samples from patients which do not suffer uterine cancer or which do not suffer leiomyoma or leiomyosarcoma.
  • the reference value for the mutational index is the mean mutational index in a pool of samples preferably obtained from subjects suffering from the same type of cancer as the patient object of the study.
  • the reference value is the mutational index in a pool from primary tumor tissues obtained from patients.
  • the values can be compared with the reference value, and thus be assigned a level of increase with respect to a reference value.
  • the “increase” can be of at least 1.1 -fold, 1.5-fold, 2-fold, 5-fold, 10-fold, 20-fold, 30-fold, 40-fold, 50- fold, 60-fold, 70-fold, 80-fold, 90-fold, 100-fold or even more compared with the reference value is considered as “increased” expression level.
  • value is considered increased in a sample of the subject under study when the value increases with respect to the reference sample by at least 5%, by at least 10%, by at least 15%, by at least 20%, by at least 25%, by at least 30%, by at least 35%, by at least 40%, by at least 45%, by at least 50%, by at least 55%, by at least 60%, by at least 65%, by at least 70%, by at least 75%, by at least 80%, by at least 85%, by at least 90%, by at least 95%, by at least 100%, by at least 110%, by at least 120%, by at least 130%, by at least 140%, by at least 150%, or more.
  • the comparison of the mutational values for each of COSMIC signatures or for both COSMIC signatures with the reference value for each of the signatures allows the diagnosis of an uterine tumor selected from uterine leiomyoma or uterine leiomyosarcoma.
  • the biological sample is a sample containing myometrial cells or DNA derived from myometrial cells.
  • the sample containing myometrial cells is a myometrial biopsy.
  • the fourth method according to the invention is carried out in a patient that has been previously identified as suffering uterine leiomyosarcoma or uterine leiomyoma by imaging examination, preferably by ultrasonography.
  • Prognostic method of the invention (method based on genomic mutational data)
  • the invention relates to a method (hereinafter referred indistinctl as “fifth method of the invention” or “prognostic method of the invention”) for the prognosis of a patient diagnosed of uterine leiomyosarcoma, comprising
  • step (ii) comparing the number of CNVs obtained in step (i) with a reference value wherein the presence of an increased number of CNVs with respect to the reference value, is indicative of a bad prognosis of uterine leiomyosarcoma.
  • the prognostic or fifth method according to the invention allows the determination of the prognosis of a patient suffering from uterine leiomyosarcoma.
  • prognosis refers to the prediction in a subject having a leiomyosarcoma of the likelihood of cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of a leiomyosarcoma.
  • Prognosis may also be referred to in terms of "aggressiveness” or “severity”: an aggressive cancer is determined to have a high risk of negative outcome (i.e. , negative or poor prognosis) and a non-aggressive cancer has a low risk of negative outcome (i.e., positive or favorable prognosis).
  • tumour is a cell-proliferation disorder that has the biological capability to rapidly spread outside of its primary location or organ.
  • Indicators of tumour aggressiveness include, without limitation, tumour stage, tumour grade, Gleason grade, Gleason score, nodal status, and survival.
  • the term “survival” is not limited to mean survival until mortality (wherein said mortality may be either irrespective of cause or related to a cellproliferation disorder), but may also used in combination with other terms to define clinical outcomes (e.g., "recurrence-free survival”, in which the term “recurrence” includes both localized and distant recurrence; “metastasis-free survival”; “disease-free survival”, in which the term “disease” includes cancer and diseases associated therewith).
  • the length of the survival may be calculated by reference to a defined starting point (e.g., time of diagnosis or start of treatment) and a defined end point. Accordingly, a negative or poor prognosis is defined by a lower post-treatment survival term or survival rate.
  • a positive or good prognosis is defined by an elevated post-treatment survival term or survival rate.
  • prognosis is provided as the time of progression free survival or overall survival.
  • said determination is not usually correct for all (i.e., 100%) of the patients to be identified.
  • the term requires being able to identify a significant part of the subjects.
  • One skilled in the art can readily determine if a part is statistically significant using several well-known statistical evaluation tools, for example, the determination of confidence intervals, the determination of p-values, Student’s t-test, Mann-Whitney test, etc. Details can be found in Dowdy and Wearden, Statistics for Research, John Wiley and Sons, New York 1983.
  • Preferred confidence intervals are at least 90%, at least 95%, at least 97%, at least 98%, or at least 99%.
  • the p-values are preferably 0.1 , 0.05, 0.01 , 0.005, or 0.0001. More preferably, at least 60%, at least 70%, at least 80%, or at least 90% of the subjects of a population can be suitably identified by the method of the present invention.
  • the prognostic method according to the invention comprises determining in a biological sample of the subject for the presence of at least one CNVs shown in Table 5.
  • CNV or “copy number variations refers to variation in the number of copies of a nucleic acid sequence present in a test sample in comparison with the copy number of the nucleic acid sequence present in a reference sample.
  • the nucleic acid sequence is 1 kb or larger.
  • the nucleic acid sequence is a whole chromosome or significant portion thereof.
  • a "copy number variant” refers to the sequence of nucleic acid in which copy-number differences are found by comparison of a nucleic acid sequence of interest in test sample with an expected level of the nucleic acid sequence of interest. For example, the level of the nucleic acid sequence of interest in the test sample is compared to that present in a qualified sample.
  • Copy number variants/variations include deletions, including microdeletions, insertions, including microinsertions, duplications, multiplications, and translocations.
  • CNVs encompass chromosomal aneuploidies and partial aneuploidies.
  • Methods for determining CNV of a gene of interest are well-known in the art such as, for instance, the methods disclosed in, U.S. patent application Ser. No. 16/913,965; Hastings et al., Nat Rev Genet; 10(8):551-64 (2009); and Shishido et al., Psychiatry Clin Neurosci, 68(2):85-95 (2014), the disclosures of which are incorporated by reference herein.
  • Existing methods to determine CNVs typically include cytogenetic methods such as fluorescent in situ hybridization, comparative genomic hybridization, and/or virtual karyotyping with SNP arrays.
  • qPCR next-generation sequencing and quantitative PCR
  • PRT paralog- ratio testing
  • MCC molecular copy number counting
  • qPCR compares threshold cycles (Ct) between the target gene and a reference sequence with normal copy numbers, to generate ACt values which are used for CNV calculation.
  • This method has been used in large-scale CNV analysis in detecting disease associations, for example, psoriasis and Crohn's disease. With the development of genome-wide CNV screening, qPCR is often used as a confirmation method for computationally identified loci.
  • multiplex PCR-based approaches such as multiplex amplifiable probe hybridization, multiplex ligation- dependent probe amplification, multiplex PCR-based real-time invader assay, quantitative multiplex PCR of short fluorescent fragments, and multiplex amplicon quantification, have also been used for targeted screening and validation of CNVs.
  • CNV variation is determined by whole genome sequencing. In some embodiments, CNV variation is determined by whole exome sequencing. Both the whole genome sequence and the whole exome sequence can be carried out by Next Generation Sequencing.
  • next Generation Sequencing refers to sequencing technologies having high-throughput sequencing as compared to traditional Sanger- and capillary electrophoresis-based approaches, wherein the sequencing process is performed in parallel, for example producing thousands or millions of relatively small sequences reads at a time.
  • determining copy number variation includes the steps of: a. providing at least two sets of first polynucleotides, wherein each set maps to a different reference sequence in a genome, and, for each set of first polynucleotides; i. amplifying the polynucleotides to produce a set of amplified polynucleotides; ii. sequencing a subset of the set of amplified polynucleotides, to produce a set of sequencing reads; iii. grouping sequences reads sequenced from amplified polynucleotides into families, each family amplified from the same first polynucleotide in the set; iv. inferring a quantitative measure of families in the set; v. determining copy number variation by comparing the quantitative measure of families in each set.
  • the method for determining the presence or absence of any CNV in a sample comprises (a) obtaining sequence information for nucleic acids in the sample; (b) using the sequence information and the method described above to identify a number of sequence tags, sequence coverage quantity, a fragment size parameter, or another parameter for each of the genes of interest and to identify a number of sequence tags or another parameter for one or more normalizing gene sequences; (c) using the number of sequence tags or the other parameter identified for each of the genes of interest and the number of sequence tags or the other parameter identified for each of the normalizing genes to calculate a single gene dose for each of the genes of interests; and (d) comparing each gene dose to a threshold value, and thereby determining the presence or absence of any complete CNVs in the sample.
  • sequence tag has been defined above in the context of the third method according to the invention and is herein used interchangeably with the term “mapped sequence tag” to refer to a sequence read that has been specifically assigned,
  • Mapped sequence tags are uniquely mapped to a reference genome, i.e., they are assigned to a single location to the reference genome. Unless otherwise specified, tags that map to the same sequence on a reference sequence are counted once.
  • Tags may be provided as data structures or other assemblages of data.
  • a tag contains a read sequence and associated information for that read such as the location of the sequence in the genome, e.g., the position on a chromosome. In certain embodiments, the location is specified for a positive strand orientation.
  • a tag may be defined to allow a limited amount of mismatch in aligning to a reference genome.
  • tags that can be mapped to more than one location on a reference genome, i.e., tags that do not map uniquely may not be included in the analysis.
  • the CNV is a deletion, in some embodiments, the CNV is a duplication. In some embodiments, the first step comprises the determination of at least
  • the term “increased copy number for a gene” in the context of the third differential diagnostic method of the invention is understood as that at least one additional copy of the gene is present in the sample from the patient which is to be differentially diagnosed with respect to a reference sample, wherein the reference sample is either a sample of a patient suffering from uterine leiomyosarcoma or a sample from a patient suffering uterine leiomyoma.
  • the copy number of the gene under consideration is at least 1 , at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10 or more copies of the gene in the reference sample.
  • the term “decreased copy number” in the context of the fourth diagnostic method of the invention is understood that at least one less copy of the gene is present in the subject which is to be differentially diagnosed as compared to the copy number of the same gene in the reference sample, wherein the reference sample is a sample from the patient which is to be differentially diagnosed and the reference sample is either a sample of a patient suffering from uterine leiomyosarcoma or from a patient suffering uterine leiomyoma.
  • the biological sample is a sample containing myometrial cells, DNA derived from myometrial cells or RNA derived from myometrial cells.
  • the sample containing myometrial cells is a myometrial biopsy.
  • the biological sample is a biofluid.
  • the biofluid is plasma, blood, serum, urine or uterine fluid.
  • the prognostic or fifth method of the invention comprises comparing the determining that the patient shows a bad prognosis if one or more of the CNVs listed in Table 5 are present in the sample from the subject.
  • bad prognosis denotes a significantly less favorable probability of survival after patient treatment in the group of patients defined as “bad prognosis” compared with the group of patients defined as “good prognosis”. According to the invention, the term “bad prognosis” also denotes a significantly less favorable probability of not needing treatment to survive in the group of patients defined as “bad prognosis” compared with the group of patients defined as “good prognosis”. In one embodiment, the prognosis of the patient is measured as survival, as disease-free progression or using any other parameter which is reflective of the outcome of the patient.
  • the invention relates to an in vitro method for selecting a subject suspected of suffering from uterine leiomyoma or uterine leiomyosarcoma as a candidate to receive an adequate therapy to treat uterine leiomyosarcoma or uterine leiomyoma, the method comprising:
  • the in vitro method for selecting a subject suspected of suffering from uterine leiomyoma or uterine leiomyosarcoma as a candidate to receive an adequate therapy to treat uterine leiomyosarcoma or uterine leiomyoma comprises determining whether the patient is suffering from uterine leiomyoma or leiomyosarcoma by using any of the diagnostic or differential diagnostic methods according to the invention.
  • the method comprises selecting a patient to be treated with either a therapy adequate for the treatment of uterine leiomyosarcoma if the patient is diagnosed as suffering from uterine leiomyosarcoma or with a therapy adequate for the treatment of uterine leiomyoma if the patient is diagnosed as suffering from uterine leiomyoma.
  • the patient when the patient is being diagnosed with leiomyosarcoma, the patient is selected to be treated by a therapy selected from the group consisting of surgery, radiation therapy, chemotherapy, hormonal therapy, or targeted therapy.
  • the surgery is a simple hysterectomy, radical hysterectomy, or bilateral salpingo-oophorectomy
  • the chemotherapy includes one or more drugs selected from the group consisting of dacarbazine (DTIC), docetaxel, doxorubicin, epirubicin, gemcitabine, ifosfamide, Paclitaxel, temozolomide, trabectedin and vinorelbine.
  • DTIC dacarbazine
  • docetaxel docetaxel
  • doxorubicin epirubicin
  • gemcitabine gemcitabine
  • ifosfamide Paclitaxel
  • temozolomide temozolomide
  • trabectedin trabectedin
  • vinorelbine vinorelbine
  • the hormonal therapy comprises:
  • Gonadotropin-releasing hormone such as goserelin or leuprolide, leuprorelin acetate, leuprorelin acetate sustained release depot (ATRIGEL), triptorelin pamoate, buserelin, naferelin, histrelin, goserelin, deslorelin, degarelix, ozarelix, ABT-620 (elagolix), TAK-385 (relugolix), EP- 100, KLH-2109 or triptorelinand goserelin acetate, or
  • An aromatase inhibitor which is defined as a compound which inhibits estrogen production, for instance, the conversion of the substrates androstenedione and testosterone to estrone and estradiol, respectively, and that includes, but is not limited to steroids, especially atamestane, exemestane and formestane and, in particular, non-steroids, especially aminoglutethimide, roglethimide, pyridoglutethimide, trilostane, testolactone, ketokonazole, vorozole, fadrozole, anastrozole and letrozole
  • therapy is a targeted therapy.
  • targeted therapy refers to drugs which attack specific genetic mutations within cancer cells, such as leiomyosarcoma while minimizing harm to healthy cells.
  • the targeted therapy comprises the use of pazopanib.
  • the patient when the patient is being diagnosed with leiomyoma, the patient is selected to be treated by a morcellation procedure or other surgical methods for removing leiomyomas after they have been determined not to comprise any leiomyosarcomas.
  • large tissue masses such as fibroid tissue masses (leiomyomas)
  • leiomyomas are traditionally excised during a surgical procedure and removed intact from the patient through the surgical incision. These tissue masses can easily be several centimeters in diameter or larger.
  • the surgery is typically conducted using incisions of less than 1 centimeter, and often 5 millimeters or less.
  • Morcellation medical devices are well-known in the art.
  • the instruments described m U.S. Pat Nos. 5,037,379; 5,403,276; 5,520,634; 5,327,896 and 5,443,472 can be used herein (each patent is incorporated herein by reference).
  • excised tissue is morcellated (i.e. debulked), collected and removed from the patient's body through, for example, a surgical trocar or directly through one of the surgical incisions.
  • Mechanical morcellators cut tissue using, for example, sharp end-effectors such as rotating blades.
  • Electrosurgical and ultrasonic morcellators use energy to morcellate tissue.
  • a system for fragmenting tissue utilizing an ultrasonic surgical instrument is described in "Physics of Ultrasonic Surgery Using Tissue Fragmentation", 1995 IEEE Ultrasonics Symposium Proceedings, pages 1597-1600.
  • the excised tissue is can be transferred to a specimen bag prior to being morcellated.
  • some morcellators are used without specimen bags. Specimen bags are, therefore, designed to hold excised tissue without spilling tissue, or tissue components, into the abdominal cavity during morcellation.
  • Ultrasonic morcellation instruments may be particularly advantageous for use in certain surgical procedures and for debulking certain types of tissue.
  • a blunt or rounded ultrasonic morcellator tip may reduce the possibility of unintended cutting or tearing of a specimen bag while the ultrasonic energy morcellates the tissue.
  • the invention also provides methods for the treatment of subjects which have been identified as suffering leiomyosarcomas by any of the differential diagnostic or diagnostic method according to the invention, wherein if the subject has been diagnosed as suffering from a leiomyosarcoma, the subject is treated with a therapy adequate for the treatment of leiomyosarcoma.
  • the therapy is selected from the group consisting of surgery, radiation therapy, chemotherapy, hormonal therapy or a replacement therapy.
  • treatment comprises any type of therapy, which aims at terminating, preventing, ameliorating and/or reducing the susceptibility to a clinical condition as described herein.
  • the term treatment relates to prophylactic treatment (i.e. a therapy to reduce the susceptibility of a clinical condition, a disorder or condition as defined herein).
  • prophylactic treatment i.e. a therapy to reduce the susceptibility of a clinical condition, a disorder or condition as defined herein.
  • treatment “treating,” and the like, as used herein, refer to obtaining a desired pharmacologic and/or physiologic effect, covering any treatment of a pathological condition or disorder in a mammal, including a human.
  • treatment includes (1) preventing the disorder from occurring or recurring in a subject, (2) inhibiting the disorder, such as arresting its development, (3) stopping or terminating the disorder or at least symptoms associated therewith, so that the host no longer suffers from the disorder or its symptoms, such as causing regression of the disorder or its symptoms, for example, by restoring or repairing a lost, missing or defective function, or stimulating an inefficient process, or (4) relieving, alleviating, or ameliorating the disorder, or symptoms associated therewith, where ameliorating is used in a broad sense to refer to at least a reduction in the magnitude of a parameter, such as inflammation, pain, and/or immune deficiency.
  • a parameter such as inflammation, pain, and/or immune deficiency
  • the therapeutic method according to the invention are applied to patients which have been diagnosed as suffering leiomyosarcoma by using any of the diagnostic method for leiomyosarcoma or the differential diagnostic methods according to the invention.
  • the method comprises a first step in which the diagnostic method for leiomyosarcoma or the differential diagnostic methods is applied to the patient and, a second step in which patients diagnosed as suffering leiomyosarcoma are selected and a third step in which the patients are treated with a therapy adequate for the treatment of leiomyosarcoma.
  • Suitable surgical therapies, radiation therapies, chemotherapies, hormonal therapies or targeted therapies have been described in the context of the methods for selecting a therapy for a subject based on the diagnostic or differential diagnostic methods according to the invention and are equally applicable to the therapeutic methods according to the invention.
  • the invention relates to a kit, package or device that contains reagents adequate for implementing any of the methods of the invention. It will be understood that, depending on the nature of the method, the reagents adequate for its implementation will vary.
  • kit is understood as a product containing the different reagents required for carrying out the methods of the invention packaged such that it allows being transported and stored.
  • the materials suitable for the packaging of the components of the kit include glass, plastic (polyethylene, polypropylene, polycarbonate, and the like), bottles, vials, paper, sachets, and the like. Where there are more than one component in a kit they may be packaged together if suitable or the kit will generally contain a second, third or other additional container into which the additional components may be separately placed. However, in some embodiments, certain combinations of components may be packaged together comprised in one container means.
  • a kit can also include a means for containing any reagent containers in close confinement for commercial sale.
  • Such containers may include injection or blow- molded plastic containers into which the desired vials are retained.
  • One or more compositions of a kit can be lyophilized. In some embodiments, all compositions of a kit of the disclosure will be lyophilized. In some embodiments, a kit of the disclosure with one or more lyophilized agents will be supplied with a re-constitution buffer. Reagents and components of kits may be comprised in one or more suitable container means.
  • a container means may generally comprise at least one vial, test tube, flask, bottle, syringe or other container means, into which a component may be placed, and preferably, suitably aliquoted.
  • kits according to the invention can also comprise one or more reagents for preparing crude cell lysates and/or reagents for extracting, isolating and/or purification of nucleic acids from a sample.
  • Additional components can comprise particles with affinity for nucleic acids and/or solid supports with affinity for nucleic acids, one or more wash buffers, binding enhancers, binding solutions, polar solvents, alcohols, elution buffers, filter membranes and/or columns for isolation of DNA/RNA.
  • a kit may further comprise reagents for downstream processing of an isolated nucleic acid and may include without limitation at least one RNase inhibitor; at least one cDNA construction reagents (such as reverse transcriptase); one or more reagents for amplification of RNA, one or more reagents for amplification of DNA including primers, reagents for purification of DNA, probes for detection of specific nucleic acids.
  • the kits of the invention can contain instructions for the simultaneous, sequential, or separate use of the different components that are in the kit.
  • Said instructions can be in the form of printed material or in the form of an electronic medium capable of storing instructions such that they can be read by a subject, such as electronic storage media (magnetic disks, tapes, and the like), optical media (CD-ROM, DVD), and the like.
  • the media may additionally or alternatively contain Internet addresses providing said instructions.
  • the kit comprises
  • kits when the diagnostic method or the differential diagnostic method according to the invention is based on the determination of the expression levels of one or more genes, the kits contain primers or probes adequate for the detection of the expression levels of said one or more genes.
  • primer refers to oligonucleotides that can specifically hybridize to a target polynucleotide sequence, due to the sequence complementarity of at least part of the primer within a sequence of the target polynucleotide sequence.
  • a primer can have a length of at least 8 nucleotides, typically 8 to 70 nucleotides, usually of 18 to 26 nucleotides.
  • a primer can have at least 75 percent, at least 80 percent, at least 85 percent, at least 90 percent, or at least 95 percent sequence complementarity to the hybridized portion of the target polynucleotide sequence.
  • Oligonucleotides useful as primers may be chemically synthesized according to the solid phase phosphoramidite triester method first described by Beaucage and Caruthers, Tetrahedron Letts. (1981) 22: 1859-1862, using an automated synthesizer, as described in Needham-Van Devanter et al, Nucleic Acids Res. (1984) 12: 6159-6168. Primers are useful in nucleic acid amplification reactions in which the primer is extended to produce a new strand of the polynucleotide.
  • Primers can be readily designed by a skilled artisan using common knowledge known in the art, such that they can specifically anneal to the nucleotide sequence of the target nucleotide sequence of the at least one biomarker provided herein.
  • the 3' nucleotide of the primer is designed to be complementary to the target sequence at the corresponding nucleotide position, to provide optimal primer extension by a polymerase.
  • probe refers to oligonucleotides or analogs thereof that can specifically hybridize to a target polynucleotide sequence, due to the sequence complementarity of at least part of the probe within a sequence of the target polynucleotide sequence.
  • exemplary probes can be, for example DNA probes, RNA probes, or protein nucleic acid (PNA) probes.
  • a probe can have a length of at least 8 nucleotides, typically 8 to 70 nucleotides, usually of 18 to 26 nucleotides.
  • a probe can have at least 75 percent, at least 80 percent, at least 85 percent, at least 90 percent, or at least 95 percent sequence complementarity to hybridized portion of the target polynucleotide sequence. Probes can also be chemically synthesized according to the solid phase phosphoramidite triester method as described above. Methods for preparation of DNA and RNA probes, and the conditions for hybridization thereof to target nucleotide sequences, are described in Molecular Cloning: A Laboratory Manual, J. Sambrook et al., eds., 2nd edition. Cold Spring Harbor Laboratory Press, 1989, Chapters 10 and 11.
  • the reagents adequate for the determination of the expression levels of one or more genes comprise at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% of the total amount of reagents adequate for the determination of the expression levels of genes forming the kit.
  • the kit comprises reagents suitable for determining the presence of the CNV such as at least a pair of target gene specific primers, a background sequence, at least a pair of primers specific to the background sequence, optionally, a pair of primers specific to a control or reference sequence, a DNA polymerase, dNTP's, MgCI2 and one or more buffers.
  • the kit can also comprise one or more probes, wherein the probe comprises a nucleic acid sequence operable to selectively hybridize to: a target nucleic acid sequence, a reference/control nucleic acid sequence and/or to an amplicon or a fragment of an amplicon, including a target gene amplicon or a fragment thereof, a reference/control amplicon or fragment, and in some embodiments, and optionally to hybridize selectively to a background sequence, an amplicon or a fragment of an amplicon of a background sequence.
  • Probes in a kit of the disclosure can include probes to perform a 5' nuclease assay and/or one or more probes to detect the products of amplification.
  • one or more probe of a kit of the disclosure is labeled. In some embodiments, one or more probes of a kit of the disclosure is a dual labeled probe. In some embodiments, one or more of the probes of a kit of the disclosure is labeled with a fluor and a quencher. In some embodiments, each probe of a kit is dually labeled with a different fluor and a different quencher. In some embodiments, each probe of a kit is dually labeled with a different fluor and the same quencher.
  • the reagents adequate for the determination of one or more of the CNVs as defined in the present invention comprise at least 10%, at least 20%, at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90% or at least 100% of the total amount of reagents adequate for the determination of the CNVs forming the kit.
  • Methods of the invention can be performed using software, hardware, firmware, hardwiring, or combinations of any of these.
  • the invention relates to a computer-implemented method, wherein the method is any of the diagnostic, differential diagnostic or prognostic method according to the invention.
  • the invention relates to a computer containing instructions for carrying any of said methods.
  • FFPE paraffin-embedded
  • DNA and RNA from 5-10-pm-thick FFPE tumor sections were isolated using GeneRead DNA FFPE kit (Qiagen GmbH, Hilden, Germany) and miRNAeasy FFPE kit (Qiagen GmbH, Hilden, Germany) following manufacturer instructions. Quality control and quantification analysis were performed using Qubit 2.0 Fluorometer (Thermo Scientific, Waltham, MA, USA) and 2100 Bioanalyzer system (Agilent, Santa Clara, CA, USA). Additionally, we measured amplification potential of nucleic acids by assessing ACq value through quantitative PCR, after normalization for a fixed input mass. Only DNA samples with ACq ⁇ 5 and 1 pg total DNA were included.
  • RNA samples with 100 ng total RNA and DV200 >30% were selected for further experiments. Accordingly, five out of 34 LMS cases in the experimental cohort were excluded for RNAseq due to insufficient quantity and/or quality of RNA.
  • DNA sequencing libraries from 44 LM and 34 LMS were constructed using the KAPA Hyper Prep kit (KK8514 and KK8515; Roche, Basel, Switzerland). Next, all exons were captured by a custom-designed SeqCap EZ MedExome kit (NimbleGen; Roche), which is targeted and enriched for exons and neighboring introns (within 50 bp) of 571 hematological-associated genes. Lastly, each quantified library was loaded in a HiSeqXTen platform (Illumina, San Diego, CA, USA), and paired-end sequencing was performed according to manufacturer instructions.
  • DNA sequence data were demultiplexed and formatted in FASTQ files (FASTQ 1 .9 files with Phred+33) containing at least 32 million DNA reads for each sample. Reads were aligned to the human hg19 genome (CRGCh37) using the Burrows-Wheeler Alignment tool software (vO.7.17; https://github.com/lh3/bwa), with -M and -R options to mark short and split alignments as secondary and add read group information, respectively.
  • SAM files were then converted to coordinate sorted BAM files using samtools v1.9, and Picard Tools v2.20.1 (https://github.com/broadinstitute/picard) was used to mark and remove duplicates.
  • Interval reference files (design files) for the SeqCap EZ MedExome were also provided. Specifically, ⁇ 100 bases were added to the capture bed file for calling with a mean depth higher than 150X, with >80% of target regions covered. Coverage data were obtained using the bedtools software (https://github.com/arq5x/bedtools/) and normalized to counts per million (cpm) as follows:
  • SnpEff version 4.3t, https://github.com/pcingola/SnpEff was used for variant annotation. Variants were visualized and analyzed using R(v3.6.1), tidyverse (v1.3; https://github.com/tidyverse/tidyverse), and vroom (v1.2; https://github.com/VROOM-Project/vroom).
  • CNV detection we used the CNVkit Python library (v.0.9.6; https://github.com/etal/cnvkit) with default parameters for tumor analysis. Specifically, sample read depths were normalized and individually compared with the reference, using the circular binary segmentation algorithm to infer copy number segments, which were then annotated to genes. Lastly, to evaluate whether CNV data could be used to differentiate LMS and LM, we performed unsupervised hierarchical clustering based on Euclidean distance calculation of Iog2 values and using heatmap3 (https://github.com/slzhao/heatmap3).
  • RNA sequencing libraries were prepared using Truseq RNA exome (Illumina), normalized to 10 nM, and clustered into a single pool with final concentration of ⁇ 2 pM. Paired-end sequencing (2x75 bp) was carried out in a NextSeq 500 instrument (Illumina).
  • RNAseq data were demultiplexed to generate intermediate analysis FASTQ format files containing at least 20 million RNA reads per sample. Reads were aligned to the human hg19 genome (GRCh37) using STAR Alignment software (v2.7.0f). After quality filtering, we obtained an average of 47 million uniquely mapped reads to the human transcriptome per sample. Finally, gene transcript abundance was estimated using the HTseq Python package.
  • RNAseq data could be used to differentiate LMS from LM
  • we built a classification model using the caret R package (v6.0-86; https://github.com/topepo/caret). For this purpose, our sample cohort was randomly split, keeping balanced class distributions into a training set (75% of samples) and validation set (25% of samples).
  • AdaBoost models were trained with 10-fold stratified cross-validation to obtain robust estimates of tumor classification capabilities, following a two-step approach. In the first step, five subsets of samples were created from the training data. From them, four subsets were used for fitting the model, and the other subset was used for feature pruning. Since model composition varied each time due to the probabilistic nature of classification models, fitting and feature pruning were repeated 10 times for each feature pruning subset, generating a total of 50 models.
  • RNA extracted from 96 FFPE tissue sections was normalized to 100 ng total and used to prepare libraries with a PCR/amplicon-based workflow (AmpliSeq Library Plus, Illumina) following manufacturer instructions. Data from the Illumina NextSeq500 sequencer were demultiplexed and aligned to the CanTRAN hg19 reference genome using BWA mem. Coverage for each of the 20 target genes was calculated using bedtools and normalized based on total reads per sample.
  • Table 1 Differentially expressed genes between LMS and LM samples.
  • unsupervised hierarchical clustering grouped LMS samples in a homogeneous cluster of 29 samples, while 30 LM samples were detected in a separate cluster.
  • another cluster included the remaining LM with some LMS (LMS03, LMS11 , LMS26, LMS35, and LMS62).
  • LMS03, LMS11 , LMS26, LMS35, and LMS62 LMS03, LMS11 , LMS26, LMS35, and LMS62.
  • Table 2 List of 20 differentially expressed genes identified from RNAseq data using a machine learning approach
  • the total 96 samples were randomly split into a training set to build the machine learning model and a test set to validate the model (75% and 25% class- balanced samples for training and test sets, respectively).
  • the gradient boosting algorithm was used to build a new model, which achieved optimal values of sensitivity and specificity, since it was able to correctly classify all test and training samples.
  • the model was used to calculate class probabilities for all samples, allowing for a more fine-tuned classification of samples, where we defined a “warning range” for those tumors where the model was not confident enough, defined as probabilities of ⁇ 75% for each group (Fig. 1 B).
  • this model could correctly classify all samples with high class probabilities, even for sample LMS39 with the lowest LMS probability.
  • Table 3 Sensitivity and specificity of predictive models for the differential diagnosis of LMS and LM based on gene expression (RNAseq).
  • Example 3 Identification of differential somatic single nucleotide variants and insertions/deletions
  • SNVs single nucleotide variants
  • Indels insertions/deletions
  • signature 1 results from an endogenous mutational process initiated by spontaneous deamination of 5- methylcytosine, while signature 5 exhibits transcriptional strand-bias for T>C substitutions at ApTpN context as additional mutational features.
  • signature 20 is associated with defective DNA mismatch repair due to high numbers of small indels at mono/polynucleotide repeats.
  • signature 12 represents a novel mutational signature only present in these uterine tumors, showing similarities to liver cancer and exhibiting a strong transcriptional strand-bias for T>C substitutions as additional mutational features.
  • coverage values which can be extrapolated to copy number states (where a higher coverage is interpreted as a duplication or amplification, while lower coverage is interpreted as a deletion) were calculated and normalized for all genes.
  • DNA coverage data was used to build a classification model using the xgboost algorithm. Of these, the top 5 genes with highest importance were selected to build new classification models of each individual gene and combinations of them ( Figure 3 and Table 4).
  • the coverage of the TUBB2B, LRRCC1 , NDRG4, HSF4 and the TMPRSS6 genes were identified as predictive for the differential diagnosis of LMS and LM (Fig. 3).
  • Kaplan-Meier survival curves were generated to assess the association between LMS-specific CNVs and clinical prognosis based on overall survival.

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  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
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Abstract

La présente invention concerne des procédés pour le diagnostic différentiel du léiomyosarcome et du léiomyome, ainsi que des procédés pour le pronostic de patients atteints de léiomyosarcome. L'invention concerne également des kits et des dispositifs pour mettre en oeuvre l'un quelconque des procédés de pronostic et de diagnostic différentiel de l'invention.
PCT/EP2022/078052 2021-10-11 2022-10-10 Procédés et réactifs pour le diagnostic différentiel de tumeurs utérines WO2023061914A2 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5037379A (en) 1990-06-22 1991-08-06 Vance Products Incorporated Surgical tissue bag and method for percutaneously debulking tissue
US5327896A (en) 1993-06-30 1994-07-12 Arthrex, Inc. Suction downbiter
US5403276A (en) 1993-02-16 1995-04-04 Danek Medical, Inc. Apparatus for minimally invasive tissue removal
US5443472A (en) 1993-10-08 1995-08-22 Li Medical Technologies, Inc. Morcellator system
US5520634A (en) 1993-04-23 1996-05-28 Ethicon, Inc. Mechanical morcellator
US20130203606A1 (en) 2010-02-25 2013-08-08 Advanced Liquid Logic Inc Method of Preparing a Nucleic Acid Library

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220220564A1 (en) * 2019-04-17 2022-07-14 Igenomix, S.L. Improved methods for the early diagnosis of uterine leiomyomas and leiomyosarcomas

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5037379A (en) 1990-06-22 1991-08-06 Vance Products Incorporated Surgical tissue bag and method for percutaneously debulking tissue
US5403276A (en) 1993-02-16 1995-04-04 Danek Medical, Inc. Apparatus for minimally invasive tissue removal
US5520634A (en) 1993-04-23 1996-05-28 Ethicon, Inc. Mechanical morcellator
US5327896A (en) 1993-06-30 1994-07-12 Arthrex, Inc. Suction downbiter
US5443472A (en) 1993-10-08 1995-08-22 Li Medical Technologies, Inc. Morcellator system
US20130203606A1 (en) 2010-02-25 2013-08-08 Advanced Liquid Logic Inc Method of Preparing a Nucleic Acid Library

Non-Patent Citations (14)

* Cited by examiner, † Cited by third party
Title
"Molecular Cloning: A Laboratory Manual", 1989, COLD SPRING HARBOR LABORATORY PRESS
"Physics of Ultrasonic Surgery Using Tissue Fragmentation", 1995, IEEE ULTRASONICS SYMPOSIUM PROCEEDINGS, pages: 1597 - 1600
ALEXANDROV, NATURE, vol. 500, no. 7463, 2013, pages 415 - 21
BEAUCAGECARUTHERS, TETRAHEDRON LETTS., vol. 22, 1981, pages 1859 - 1862
BLOKZIJL ET AL., GENOME MED., vol. 10, no. 1, 2018, pages 33
DE ANDRES ET AL., BIOTECHNIQUES, vol. 18, 1995, pages 42044
DOWDYWEARDEN: "Statistics for Research", 1983, JOHN WILEY AND SONS
FORBES, S. ET AL., NUCLEIC ACIDS RESEARCH, vol. 45, 2017
HASTINGS ET AL., NAT REV GENET, vol. 10, no. 8, 2009, pages 551 - 64
NEEDHAM-VAN DEVANTER ET AL., NUCLEIC ACIDS RES., vol. 12, 1984, pages 6159 - 6168
RUPPLOCKER: "Lab Invest.", vol. 56, 1987, pages: A67
SAMBROOK, J. ET AL.: "Molecular cloning: A Laboratory Manual", vol. 1-3, 2001, COLD SPRING HARBOR LABORATORY PRESS
SHAH ET AL., CANCER RES., vol. 70, no. 2, 15 January 2010 (2010-01-15), pages 431 - 435
SHISHIDO ET AL., PSYCHIATRY CLIN NEUROSCI, vol. 68, no. 2, 2014, pages 85 - 95

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