BE1023267B1 - Method for analyzing copy number variation in the detection of cancer - Google Patents

Abstract

The present invention relates to a method for identifying the presence of cancer and / or an increased risk of cancer in a mammal, by calculating a parameter obtained from a biological sample, said threshold value being a requirement for the presence or absence of one or more aneuploidies in said target chromosome or segment of chromosome which is an indicator of the presence and / or increased risk of cancer.

Description

METHOD FOR ANALYZING COPY NUMBER VARIATION IN CANCER DETECTION

Technical area

The invention relates to the technical field of a method for determining copy number variation (CNV) of an interesting sequence in a test sample comprising a mixture of nucleic acids known or suspected to differ in the amount of one or more interesting sequences . The method includes a statistical approach that takes into account increased variability from process-related, interchromosomal and inter-sequencing variability. The method is applicable for determining CNVs that are known or suspected to be associated with various medical conditions. CNVs that can be determined according to the method include trisomies and monosomies of any one or more chromosomes 1-22, X and Y, other chromosomal null isomies and polysomies, and deletions and / or duplications and / or amplifications of segments of any one or more of the chromosomes, which can be detected by sequencing the nucleic acids of a test sample.

Background

One of the critical challenges in human medical science is the discovery of genetic abnormalities that have adverse health consequences. In many cases, specific genes and / or critical diagnostic markers have been identified in segments of the genome that are present in abnormal copy numbers. For example, in prenatal diagnosis, additional or missing copies of complete chromosomes are common genetic abnormalities. In cancer, deletion or amplification of copies of entire chromosomes or chromosomal segments or specific regions of the genome are common.

Most information about copy variation is given for cytogenetic resolution that has allowed recognition of structural abnormalities. Conventional procedures for genetic screening and biological disometry have used invasive procedures, e.g. amniocentesis or biopsy of solid tumors, to obtain cells for the analysis of karyotypes. Recognizing the need for faster test methods that do not require cell culture, fluorescence in situ hybridization (FISH), quantitative fluorescence PCR (QF-PCR), and array comparative genomic hybridization (array CGH) have been developed as molecular cytogenetic methods for the analysis of copy number variations .

The advent of technologies that allow sequencing of complete genomes in a relatively short time, and the discovery of circulating cell-free DNA (cfDNA) have offered the opportunity to compare genetic material from one chromosome to be compared to that of another without the risks associated with invasive sampling methods. US20130034546 and US20130310263 both describe a method for identifying the presence of cancer or the risk of developing cancer based on an analysis of obtained sequence readings obtained from a cell-free DNA fraction in a sample.

Vandenberghe et al., "Non-invasive detection of genomic imbalances in Hodgkin / Reed-Sternberg cells in early and advanced stage Hodgkin's lymphoma by sequencing or circulating cell-free DNA: a technical proof-of-principle study", 2015 describes a methodology for identifying genomic imbalances in a patient suffering from presymtomatic Hodgkin lymphoma cancer by massive parallel sequencing of circulating cell-free DNA.

However, the limitations of the existing methods, which include insufficient sensitivity due to the limited levels of cfDNA, and the sequencing bias of the technology due to the inherent nature of genomic information, underline the continuing need for non-invasive methods that include some or all of the specificity, sensitivity and applicability, in order to be able to make a reliable diagnosis of copy number changes in a variety of clinical settings.

Although the above methodologies are valuable, the ratio of false positives and negatives remains high in the field. Therefore, there is a constant effort to provide methodologies that can reduce the percentage of false positives, and in particular false negatives, to provide more accurate screening.

The present invention has for its object to provide a more accurate, substantially non-invasive methodology for determining whether an individual has tumor-derived cell-free DNA in his or her peripheral blood, for confirming the diagnosis of cancer, to aid in the classification of a cancer, for assessing the treatment response, for monitoring the patient.

Summary of the invention

The present invention provides a method, systems, and apparatus for determining whether there is a nucleic acid sequence imbalance (e.g., chromosome imbalance or an imbalance of a chromosome segment or region) or a genome-wide instability within a biological sample obtained from a patient or for determining of copy number variations.

The present invention particularly provides a method according to claim 1 and a computer program product according to claim 27.

Other embodiments of the invention are directed to systems and computer-readable media associated with the methods described herein.

DESCRIPTION OF THE FIGURES

Figure 1 shows a graph of secondary parameters obtained according to an embodiment of the present invention per chromosome of a sample.

Figure 2 shows scatter diagrams of chromosomes of a sample, the calculation of a parameter according to an embodiment of the present invention indicating genome-wide instability that could indicate the presence of a tumor.

DEFINITIONS

Unless defined otherwise, all terms used in the description of the invention, including technical and scientific terms, have the meaning generally understood by one skilled in the art to which this invention relates. Furthermore, definitions of the terms are included to better understand the description of the present invention.

As used herein, the following terms have the following meaning:

The term "biological sample" as used herein refers to any sample taken from a patient (e.g., a human, such as a pregnant woman) and containing one or more interesting nucleic acid molecule (s).

The term "nucleic acid" or "polynucleotide" refers to a deoxyribonucleic acid (DNA) or ribonucleic acid (RNA) and a polymer thereof in either single or double-stranded form. Unless specifically limited, the term includes nucleic acids containing well-known analogs of natural nucleotides that have similar binding properties to the reference nucleic acid and are similarly metabolized as naturally occurring nucleotides. Unless otherwise stated, a particular nucleic acid sequence also includes implicitly conversatively modified variants thereof (e.g., degenerator codon substitutions), alleles, orthologs, single-nucleotide polymorphisms (SNPs) and complementary sequences as well as the explicitly indicated sequence. Degeneration codon substitutions can in particular be obtained by generating sequences in which the third position of one or more selected (or all) codons is substituted with mixed-base and / or deoxyinosine residues. The term nucleic acid is used interchangeably with gene, DNA, cDNA, mRNA, small non-coding RNA, micro-RNA (miRNA), Piwi-interacting RNA, and short hairpin RNA (shRNA) encoded by a gene or locus.

The term "gene" means the segment of DNA that is involved in the production of a polypeptide chain. It may include regions preceding or following the coding region (head and tail) as well as intervening sequences (introns) between individual coding segments (exons).

The term "reaction" as used herein refers to any process comprising a chemical, enzymatic or physical action indicative of the presence or absence of a particular interesting polynucleotide sequence. An example of a "reaction" is an amplification reaction such as a polymerase chain reaction (PCR). Another example of a "reaction" is a sequencing reaction, either by synthesis, hybridization, or by passing DNA through a pore and measuring signals indicative of a particular nucleotide. An "informative response" is a response that indicates the presence of one or more particular interesting polynucleotide sequences, and in a case where only one interesting sequence is present. The term "well" as used herein refers to a response at a predetermined location within a limited structure, e.g., a well-shaped vial, cell, or chamber in a PCR array or e.g. the individual reaction volumes in which sequencing reactions take place (including so-called cartridge flow cells from Illumina).

The term "clinically relevant nucleic acid sequence" or "target chromosome or chromosomal segment" as used herein may refer to a polynucleotide sequence corresponding to a segment of a larger genomic sequence whose potential imbalance has been tested or to the larger genomic sequence itself. An example is the sequence of chromosome 21. Other examples include chromosome 18, 13 X and Y. Still other examples include mutated genetic sequences or genetic polymorphisms or copy number variations (CNVs) that a fetus can inherit from one or both of the parents. Still other examples include sequences that have been mutated, deleted, or amplified in a malignant tumor, e.g., sequences in which loss of heterozygosity or gene duplication occurs. In some embodiments, multiple clinically relevant nucleic acid sequences, or equivalent multiple makers of the clinically relevant nucleic acid sequences, may be used to provide data for detecting the imbalance. For example, data from five non-consecutive sequences on chromosome 21 can be used in an additional way to determine possible imbalance in chromosome 21, effectively reducing the need for sample volume to 1/5.

The term "over-represented nucleic acid sequence" as used herein refers to the nucleic acid sequence of two interesting sequences (e.g., a clinically relevant sequence and a background sequence) that is present in more abundance than the other sequence in a biological sample.

The term "based on" as used herein means "based at least in part" and refers to one value (or result) that is used in determining another value, such as occurs in connection with an input to a method and the output of that method. The term "derivation" as used herein refers to the relationship of an input of a method and the output of that method, as occurs when the derivation is the calculation of a formula.

The term "parameter" herein refers to a numerical value that characterizes a quantitative data series and / or a numerical relationship between quantitative data series. For example, a ratio (or function of a ratio) between the number of sequence readings assigned to a chromosome and the length of the chromosome to which the readings are assigned is a parameter.

The term "score" as used herein refers to a numerical value associated with or based on a specific characteristic, e.g., the number of readings or reading counts for a particular sequence present in a sample. The term "first score" is used herein to refer to a numerical value associated with the target chromosome or chromosomal segment. Another example of a score is, for example, a Z score that quantifies how much the number of readings of a particular sequence differs from the number of readings obtained from the same sequence in a series of reference samples. It is well known to a person skilled in the art how such a Z-score can be calculated. The term "threshold" or "threshold" as used herein means a numerical value whose value is used to distinguish between two or more statuses (e.g., disease and non-disease) of the classification for a biological sample. For example, if a parameter is greater than the threshold value, a first classification of the quantitative data is made (e.g., disease status); or if the parameter is lower than the threshold value, a different classification of the quantitative data is made (e.g., non-disease status).

The term "imbalance" as used herein means any significant deviation as defined by at least one threshold in an amount of the clinically relevant nucleic acid sequence of a reference amount. For example, the reference amount could be a ratio of 3/5, and an imbalance could then occur if the measured ratio is 1: 1.

The term "random sequencing" as used herein refers to sequencing, wherein the sequenced nucleic acid fragments are not specifically identified or intended before the sequencing procedure. Sequence-specific primers to target specific gene loci are not required. The groups of sequenced nucleic acids vary from sample to sample and even from analysis to analysis for the same sample. The identities of the sequenced nucleic acids are only disclosed from the generated sequencing output. In some embodiments of the present invention, random sequencing can be preceded by procedures for enriching a biological sample with certain populations of nucleic acid molecules that share certain common features. In one embodiment, each of the DNA fragments in the biological sample have an equal chance of being sequenced.

The term "fraction of the human genome" or "segment of the human genome" as used herein refers to less than 100% of the nucleotide sequences in the human genome comprising about 3 billion base pairs of nucleotides. In the context of sequencing, it refers to less than 1-fold coverage of the nucleotide sequences in the human genome. The term can be expressed as a percentage or absolute number of nucleotides / base pairs. As a user example, the term can be used to refer to the actual amount of sequencing that has been performed. Embodiments can determine the required minimum value for the sequenced fraction of the human genome to obtain an accurate diagnosis. As another use example, the term may refer to the amount of sequenced data used to derive a parameter or amount for the classification of diseases.

The term "summary statistics" as used herein is used as a statistical term, and refers to an indication of the extent of a distribution of values or scores, or an indication of the score / value present at the center of the distribution. This can be, for example, an average or median or standard deviation (StDev) or median absolute deviation (mad) or average absolute deviation of a set of scores.

The term "copy number variation" or "CNV" (copy number variation) herein refers to variation in the number of copies of a nucleic acid sequence that is a few bp kb or greater present in a test sample compared to the copy number of the nucleic acid sequence that is present in a qualified sample. A "copy number variant" refers to the few bp or larger sequence of nucleic acid in which differences in copy numbers are found by comparing an interesting sequence in the test sample with those present in a qualified sample. Copy number variants / variations include deletions, including microdeletions as well as amplifications. CNVs include chromosomal aneuploidies and partial aneuploidies.

The term "aneuploidy" herein refers to an imbalance of genetic material caused by the loss or enhancement of a complete chromosome, or part of a chromosome. Aneuploidy refers to both chromosomal and subchromosomal imbalances, such as, but not limited to, deletions, microdeletions, insertions, micro-insertions, copy number variations, duplications. Copy number variations can vary in size in the range of 1 kb to multiple Mb. Large subchromosomal aberrations that extend over dozens of MBs and / or correspond to a significant portion of a chromosome arm can also be called segmental aneuploidies.

The term "chromosomal aneuploidy" herein refers to an imbalance of genetic material caused by the loss or enhancement of a complete chromosome, and includes germline anuploidy and mosaic anuploidy.

The term "partial aneuploidy" herein refers to an imbalance of genetic material caused by a loss or enhancement of a portion of a chromosome, e.g., partial monosomy and partial trisomy, and includes imbalances resulting from translocations, deletions, and insertions.

The term "polymorphism, polymorphic target nucleic acid", "polymorphic sequence", "polymorphic target nucleic acid sequence" and "polymorphic nucleic acid" are used interchangeably to refer to a nucleic acid sequence containing one or more polymorphic sites.

The term "polymorphic site" here refers to a single nucleotide polymorphism (SNP, a single-nucleotide polymorphism), a small-scale multi-base deletion or insertion, a Multi-Nucleotide Polymorphism (MNP) or a Short Tandem Repeat (STR, short tandem repeat) or a CNV (copy number variation).

The term "multiple" is used herein with reference to a number of nucleic acid molecules or sequence tags or readings sufficient to identify significant differences in copy number variations (e.g., chromosome doses) in test sample and qualified samples using the methods of the invention. In some embodiments, at least about 3x106 sequence tags, at least about 5x106 sequence tags, at least about 8x106 sequence tags, at least about 10x106 sequence tags, at least about 15x106 sequence tags, at least about 20x106 sequence tags, at least about 30x106 sequence tags, at least about 40x06 sequence tags sequence tags, or at least about 50x106 sequence tags obtained for each test sample. Each sequence tag can be a single sequence reading from 20 to 400 bp, or a pair of 2 end-read sequence readings with each 20 to 400 bp.

The terms "polynucleotide", "nucleic acid" and "nucleic acid molecules" are used interchangeably and refer to a covalently linked sequence of nucleotides (ie ribonucleotides for RNA and deoxyribonucleotides for DNA) in which the 3 'position of the pentose of one nucleotide is bound by a phosphodiester group to the 5 'position of the pentose of the following, include sequences in any form of nucleic acid, but not limited to RNA and DNA molecules. The term "polynucleotide" includes, but is not limited to, single and double stranded polynucleotide.

The term "portion" when used with reference to the amount of sequence information of nucleic acid molecules in a biological sample herein refers to the amount of sequence information of nucleic acid molecules in a biological sample that together are lower in number than the sequence information of <1 human genome.

The term "test sample" herein refers to a sample comprising a mixture of nucleic acids comprising at least one nucleic acid sequence suspected of having undergone copy number variation or at least one nucleic acid sequence for which it is desirable to determine whether a copy number variation exists. Nucleic acids present in a test sample are called test nucleic acids, or target nucleic acids or target chromosomes or target chromosomal segments.

The term "reference sample" herein refers to a sample comprising a mixture of nucleic acids for which the sequencing data is used together with the sequencing data of the test sample for calculating scores and parameters as described in claim 1. Although it is not necessary, a reference sample is at preferred normally ie not aneuploid for the sequence of interest. Thus, a reference sample is preferably a qualified sample that does not carry a trisomy 21 and that can be used to identify the presence of a trisomy 21 in a test sample.

The term "reference set" includes several "reference samples".

The term "enrichment" herein refers to the process of specifically amplifying certain target nucleic acids that are included in a segment of a sample. The amplified product is then often combined with the rest of the sample from which the segment was removed.

The term "interesting sequence" herein refers to a nudeic acid sequence that is associated with a difference in sequence representation in healthy versus sick individuals. An interesting sequence may be a sequence on a chromosome that is misrepresented, i.e., over- or under-represented, in a disease or genetic disorder. An interesting sequence can also be a segment of a chromosome, or a chromosome. For example, an interesting sequence may be a chromosome that is over-represented in an aneuploidy disorder, or a gene that encodes a tumor suppressor that is under-represented in a cancer. Interesting sequences comprising sequences that are over- or under-represented in the total population, or a sub-population of cells from a patient.

The term "multiple polymorphic target nucleic acids" herein refers to a number of nucleic acid sequences each comprising at least one polymorphic site e.g. one SNP or CNV, such that at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40 or more different polymorphic sites are amplified.

The term "group of chromosomes" herein refers to two or more chromosomes. The term "set" refers to a set of chromosomes or chromosomal segments, but can also refer to a set of values or scores derived from a corresponding set of chromosomes or chromosomal segments.

The term "reading" refers to an experimentally obtained DNA sequence that is sufficiently long (e.g., at least about 20 bp) that can be used to identify a larger sequence or region, e.g., that can be aligned and in particular assigned to a chromosome location or genomic region or gene.

The term "reading count" refers to the number of readings retrieved from a sample assigned to a reference genome or a segment of said reference genome (piece).

The term "piece" (bin) of a genome is to be understood as a segment of the genome. A genome can be subdivided into different pieces, with either a fixed or a predetermined size or a variable size. A possible piece size can be, for example, 10 kB, 20 kB, 30 kB, 40 kB, 50 kB, 60 kB, 70 kB, etc.

The term "window" is to be understood as multiple pieces.

The terms "aligned", "alignment", "assigned" or "alignment", "assignment" refers to one or more sequences identified as a match in terms of the order of their nucleic acid molecules with a known sequence of a reference genome. Such alignment can be done manually or by a computer algorithm, examples of which include the Efficient Local Alignment or Nucleotide Data (ELAND) computer program as part of the Illumina Genomics Analysts pipeline. The correspondence of a sequence reading in the alignment may be a sequence match of 100% or less than 100% (non-perfect match).

The term "reference genome" as used herein refers to a digital nucleic acid sequence database, assembled as a representative example of a kind of DNA. Since it is assembled from the sequencing of multiple DNA, a reference genome does not accurately represent the DNA of a single person. It is used to allow the assignment of sequencing readings from a sample to specific chromosomal positions.

The term "clinically relevant sequence" herein refers to a nucleic acid sequence that is well known and suspected of being associated or associated with a genetic or disease condition. Determining the absence or presence of a clinically relevant sequence may be useful in determining a diagnosis or confirming a diagnosis of a medical condition, or making a prognosis for the development of a disease.

The term "derived" when used in the context of a nucleic acid or a mixture of nucleic acids, refers herein to the means by which it or the nucleic acids are obtained from the source from which they originate. In one embodiment, a mixture of nucleic acids derived from two different genomes means, for example, that the nucleic acids, e.g., cell-free DNA, were naturally released by cells by naturally occurring processes such as necrosis or apoptosis, or by lysis of the cells due to improper storage. or transportation conditions.

The term "biological fluid" herein refers to a fluid taken from a biological source and includes, for example, blood, serum, plasma, sputum, wash fluid, cerebrospinal fluid, urine, sperm, sweat, tears, saliva, blastocoel fluid, and the like. It also refers to the medium in which biological samples can be grown, such as in vitro culture medium in which cells, tissue or embryo can be grown. As used herein, the terms "blood", "plasma" and "serum" explicitly include fractions or processed segments thereof. Similarly, when a sample is taken from a biopsy, smear, smear, etc., the "sample" explicitly includes a processed fraction or segment derived from the biopsy, smear, smear, etc.

The term "corresponding" herein refers to a nucleic acid sequence, e.g., a gene or a chromosome, that is present in the genome of different patients, and that does not necessarily have the same sequence in all genomes, but serves to provide identity rather than the genetic information of an interesting sequence, e.g. a gene or chromosome.

The term "substantially cell-free" herein refers to preparations of the desired sample from which components that are normally associated with them have been removed. For example, a plasma sample can be made substantially cell-free by removing blood cells, e.g., white blood cells, which are normally associated with it. In some embodiments, essentially free samples are processed to remove cells that would otherwise contribute to the genetic material to be tested for aneuploidy.

As used herein, the term "chromosome" refers to the hereditary gene carrier of a living cell that is derived from chromatin and which includes DNA and protein components (in particular histones). The conventionally internationally recognized individual human genome chromosome numbering system is used herein. The term "chromosomal segments" is to be understood as a part of a chromosome. Said segments may refer to a part, window, or specific region within a chromosome, e.g., known to include, for example, deletions or insertions or copy number variations.

As used herein, the term "polynucleotide length" refers to the absolute number of nucleic acid molecules (nucleotides) in a sequence or in a region of a reference genome. The term "chromosome length" refers to the known length of the chromosome given in base pairs.

The term "patient" herein refers to a human patient as well as a non-human patient such as a mammal, an invertebrate animal, a fungus, a yeast, a bacterium and a virus. Although the examples herein refer to human cells and the description is primarily directed to humans, the concept of the present invention applies to genomes of any plant or animal, and can be used in the field of veterinary medicine, animal sciences, research laboratories and the like.

The term "condition" here refers to "medical condition" as a broad term that covers all diseases and conditions, but which may include injuries and normal healthy situations, such as pregnancy, which may affect a person's health, benefit from have medical assistance or implications for medical treatments. The said condition is preferably linked to the presence of a tumor.

Detailed description of the invention

The present invention relates to a method for determining whether a patient has tumor-derived cell-free DNA in his or her peripheral blood, for confirming a cancer diagnosis, for assisting in the classification of a cancer, for assessing the treatment response, for monitoring the patient, to identify the presence of a cancer and / or an increased risk of cancer in a patient, said patient preferably being a mammal. This identification can be performed by calculating a parameter associated with chromosomal and / or subchromosomal data obtained from a biological sample. A computer-readable medium is also provided which is coded with multiple instructions for controlling a computer system for performing the methods.

In one aspect, an amount of reading counts linked to a chromosome or chromosomal segment is determined based on a sequencing of nucleic acid molecules in a biological sample, such as urine, plasma, serum, blastocoel fluid, and other suitable biological samples.

Nucleic acid molecules from the biological sample are randomly sequenced so that a fraction of the genome is sequenced. One or more threshold values are selected to determine whether there is a change compared to a reference amount (i.e., imbalance), for example, with respect to the ratio of amounts of two chromosomal regions (or arrays of regions).

The chromosomal target region (also referred to as a clinically relevant nudeic acid sequence) and the background nucleic acid sequence may be from a first type of cells or from one or more second types of cells. 1. General method for evaluating aneuploidy

The present invention describes a methodology for determining whether a patient has tumor-derived cell-free DNA in his or her peripheral blood, for confirming a cancer diagnosis, for assisting in the classification of a cancer, for assessing the treatment response, for monitoring of the patient, to identify the presence of a cancer and / or an increased risk of cancer in a patient, said patient preferably being a mammal.

In a first aspect, the method for determining whether a patient has tumor-derived cell-free DNA in his or her peripheral blood, for confirming a cancer diagnosis, for assisting in classifying a cancer, for assessing the treatment response, for patient monitoring, to identify the presence of a cancer and / or an increased risk of cancer in a patient, based on the determination of a parameter of the nucleic acid content of a biological sample. The biological sample can be plasma, urine, serum, blastocoel fluid or any other suitable sample. The nucleic acid molecules may, for example, be fragments of chromosomes. At least a portion of several of the nucleic acid molecules included in the biological sample are randomly sequenced to obtain a number of sequences. The sequenced portion represents a fraction of the human genome and can be isolated from the sample by conventional means (e.g., cell-free DNA extraction means and preparation of an NGS library). In one embodiment, the nucleic acid molecules are fragments of respective chromosomes. One end (e.g., 50 base pairs (bp)), both ends, or the entire fragment can be sequenced. A subset of the nucleic acid molecules in the sample can be sequenced, and this subset is randomly selected, as will be described in more detail below.

In one embodiment, the random sequencing is done using solid parallel sequencing. Solid parallel sequencing, such as this achieved on the HiSeq2500, HiSeq3000, HiSeq4000, HiSeq X, MiSeq, MiSeqDx, NextSeq500, NextSeq550 flowcell, the 454 platform (Roche), Illumina Genome Analyzer (or Solexa platform) or PGMorTorrent (PGM) Proton or GeneRead (Qiagen) or SOLiD System (Applied Biosystems) or the Hélicos True Single Molecule DNA sequencing technology, single molecule, real-time (SMRT ™) technology from Pacific Biosciences, and nanopore sequencing as in MinlON, PromethION, GridlON (Oxford) Nanopore technologies), allow the sequencing of many nucleic acid molecules isolated in parallel from a specimen at higher orders of multiplexing. Each of these platforms sequencially clonally expanded or even non-amplified single molecules of nucleic acid fragments. Clonal expansion can be achieved by bridge amplification, emulsion PCR or Wildfire technology.

Since a large number of sequencing readings, in the order of hundreds of thousands to millions or even possibly one hundred million or billions, are generated from each sample in each run, the resulting sequenced readings form a representative profile of the mix of nucleic acid species in the original specimen. For example, the halotype, transcriptome and methylation profiles of the sequenced readings resemble those of the original specimen. Because of the large sampling of sequences from each specimen, the number of identical sequences, such as those generated from the sequencing of a nucleic acid group at different multiples of coverage or high redundancy, is also a good quantitative representation of the count of a particular nucleic acid species or locus in the original sample.

A first score of a target chromosome or chromosomal segment is determined based on the sequencing (e.g., data from the sequencing). The first score is determined based on sequences identified as coming from (i.e., aligning with) the target chromosome or segment. A bio-information procedure can then be used, for example, to locate any of these DNA sequences for the human genome or a reference genome. It is possible that some of such sequences will be removed from later analysis because they are present in the repeat regions of the human genome, or in regions that are subjected to inter-individual variations, e.g., copy number variations. A score of the target chromosome or chromosomal segment and of one or more other chromosomes can thus be determined.

Based on the sequencing, a set of scores from one or more chromosomes or chromosomal segments is determined from sequences identified as coming from (i.e., aligning with) a series of one or more chromosomes. In one embodiment, said set contains all other chromosomes in addition to the first (i.e., the first tested). In another embodiment, said set contains a single different chromosome. In a most preferred embodiment, said set contains chromosomes or chromosomal segments and comprises the target chromosome or chromosomal segment.

There are a number of ways to determine a score. Said score is preferably based on the reading counts obtained from sequencing. Said reading counts may include counting the number of readings, the number of sequenced nucleotides (base pairs) or the accumulated lengths of sequenced nucleotides (base pairs) originating from a certain chromosomal (s) or chromosomal segments such as pieces or windows or clinical relevant chromosome parts.

Rules can be imposed on the results of the sequencing to determine what is counted. In one aspect, a reading theorem can be obtained based on a part of the sequenced output. For example, sequencing output corresponding to nucleic acid fragments with a specified size range could be selected.

In one embodiment, said score is the raw reading count for a particular chromosome or chromosomal segment.

In a preferred embodiment, said reading counts are subjected to mathematical functions or operations to derive said score from said reading count. Such operations include, but are not limited to, statistical operations, regression models, standard calculations (addition, subtraction, multiplication, and division), said standard calculations being preferably based on one or more reading counts obtained.

In a preferred embodiment, said first score is a normalized value derived from the reading counts or mathematically modified reading counts. In another preferred embodiment, said score is a Z score or standard score with respect to the reading counts of a particular chromosome, chromosomal segment or mathematically modified counts, the Z score quantifying how much the number of readings of a given sequence differs from the number of readings obtained from the same sequence in a series of reference samples. It is well known to a person skilled in the art how such a Z-score can be calculated.

In a preferred embodiment, a parameter is determined based on a first score (corresponding to the target chromosome or chromosomal segment) and a set of scores. The parameter preferably represents a relative score between the first score and a summary statistic of the set of scores. The parameter may, for example, represent a simple ratio of the first score to a summary statistic of the set of scores. In one aspect, each score could be an argument of a function or individual functions, whereby a ratio can then be taken of these individual functions. The parameter may, for example, represent a simple ratio of the first score to a summary statistic of scores in the set. In one aspect, each score could be an argument of a function or individual functions, whereby a ratio can then be taken of these individual functions.

In a preferred embodiment, the parameter can be obtained by a ratio between: - a first function where the first score and the set of scores are the arguments; - a second function where the set of scores is the argument.

In a preferred embodiment, said first function is defined as a difference, preferably the difference between the first score and a summary statistic of the set of scores, said summary statistic preferably being selected from the mean, the median, the standard deviation or median absolute deviation (mad) or average absolute deviation.

In another preferred embodiment, said second function is defined as a variability summary statistic of the set of scores, said summary statistic preferably being selected from the mean, median, standard deviation or median absolute deviation (mad) or average absolute deviation .

A suitable embodiment of the present invention usually comprises the following steps (after obtaining DNA sequences from a random, low-coverage sequencing process on a biological sample). - aligning sequences with a reference genome; - obtaining the reading counts per chromosome or chromosomal segment; - normalizing the number of readings or a derivative thereof to a normalized number of readings; - obtaining a first score derived from said normalized number of readings and a set of scores derived from said normalized reading counts for a target chromosome or chromosomal segment, and wherein said set of scores is a set of scores derived from the normalized number of readings obtained from a series of chromosomes or chromosome segments comprising the chromosomal target segment or chromosome; - calculating a parameter of said scores, wherein said parameter represents a ratio between said first score and a summary statistic of said set of scores, the first function of said ratio being defined as a difference between the first score and a summary statistic of said set of scores; and wherein the second function of said ratio is defined as a summary statistic of said set of scores. Said sequences are preferably obtained by low-coverage sequencing.

Said normalization preferably takes place on the basis of a series of reference samples, said reference samples preferably being, but not necessarily, euploid or substantially euploid for the chromosome or chromosomal segment corresponding to the target chromosome or chromosomal segment (ie, the major part of the chromosome or chromosomal segment in the reference samples corresponding to the target chromosome or chromosomal segment in the test sample are euploid). Such a reference series has different sample sizes. A possible sample size can be, for example, 100 samples, such as 50 male and 50 female samples. It will be clear to a person skilled in the art that the reference series can be freely chosen by the user.

Said number of readings is preferably recalibrated to correct for GC content and / or total number of readings obtained from said sample.

By taking into account a set of scores derived from readings from chromosomes or chromosomal segments that contain the target chromosome or chromosomal segment for calculating the set of scores, a more sensitive and reliable parameter can be obtained in comparison with prior art methods. Unlike in the methods known in the art, no assumption has to be made about the ploid status of any of the chromosomes in the test sample. Even if multiple aneuploidies were present in the test sample or there was a high level of technical or biological noise (e.g. from the presence of cancer or CNVs), the current parameter p still provides a valuable tool, while the methods known in the state of the art can fail in these situations (Vandenberghe et al., "Non-invasive detection or genomic imbalances in Hodgkin / Reed-Sternberg cells in early and advanced stage Hodgkin's lymphoma by sequencing or circulating cell-free DNA: a technical proof- of-principle study ", 2015). Namely, by defining a parameter according to the present invention, the parameter for the chromosome or region to be analyzed is clear (i.e., it is greatly increased / decreased) and does not disappear in the noise (i.e., only moderately or not increased / decreased). Sensitivity is also essential for screening, since it is important to have a reliable and reliable result, minimizing the number of false negatives. For screening it may be more important to have high sensitivity compared to specificity.

The parameter according to the present invention allows robust detection and automatic classification of chromosomes, even in data with noise. By taking into account a set of chromosomes or segments, including the target chromosome or segment, i.e., the majority of information present in the data set, the majority of the available information is used, resulting in a more adequate analysis. If, for example, chromosome 1 (the largest chromosome, 7.9% of the genome) were to be deleted, a large amount of data would be deleted which would not be taken into account, which would cause a disruption in the analysis.

The present invention is particularly useful in situations where a low number of readings or data with noise is obtained. The inventors have found that, in the latter situations, the parameter of the present invention was superior to other methodologies.

In a preferred embodiment, said scores are obtained based on the genomic representation of the target chromosome or chromosomal segment (or a region thereof) and the genomic representation of all autosomes or other chromosomes, thereby recording the target chromosome or chromosomal segment.

The parameter is compared with one or more threshold values. The threshold values can be determined in any number of suitable ways. Such ways include Bayesian-type probability method, sequential probability test (SPRT), discovery of false results, confidence interval, receiver-operated characteristic (ROC, receiver operating characteristic). In a more preferred embodiment, said threshold value is based on statistical considerations or is determined empirically by testing biological samples. The threshold value can be validated by means of test data or a validation series and, if necessary, can be changed whenever more data is available.

Based on the comparison, a classification is determined as to whether chromosomal aneuploidy exists for the target chromosome. In one embodiment, the classification is a definitive yes or no. In another embodiment, the classification may not be classifiable or uncertain. In yet another embodiment, the classification may be a score to be interpreted at a later date, for example, by a physician. In another embodiment, the classification can be done at a genome-wide level. In yet another embodiment, the classification may be a score that determines the probability of the presence of genome-wide instability or the presence of a predefined CNV signature (i.e., a defined combination of CNVs or subchromosomal or chromosomal copy number abnormalities).

In a further preferred embodiment, secondary parameters of the reading counts are calculated, which serve as an additional internal check for the usability of the parameter, the extent of the aneuploidy (if identified) and / or an indication for the reliability of the parameter, the biological sample or the sequences obtained therefrom and therefore the final assessment. Said secondary parameters may be a requirement for the presence of said aneuploidy and / or a measure of the quality of the samples as well as a measure of genome-wide instability.

In one embodiment, said secondary parameter is calculated as the median of the Z distribution of the reading counts or a derivative thereof, for a target chromosome or chromosomal target segment measured per piece or a set of pieces (i.e., windows). The last secondary parameters allow assessment if the majority (more than 50%) of the windows in a chromosome have increased or decreased. The latter allows the detection of chromosomal and large subchromosomal aneuploids. If less than 50% of the windows are affected, the secondary parameters will not be affected (eg smaller CNVs).

In another embodiment, said secondary parameters can be calculated as the median of the absolute value of the Z-scores for the reading counts or a derivative thereof, of the remaining chromosomes (that is a set of chromosomes or segments that exclude the target chromosome or segment ).

The latter secondary parameters allow the detection of, for example, the presence of technical or biological instabilities (cf. malignancies, cancer) and the distinction thereof from CNVs of the mother. If fewer than the windows of the other or all chromosomes are affected, these secondary parameters will not be affected. If more than 50% of the windows are affected, this can be deduced from the aforementioned secondary parameters.

In another embodiment, the present invention also provides a quality score (QS). QS makes it possible to assess the general variation within the genome. A low QS is an indication of good sample processing and a low level of technical and biological noise. An increase in the QS can have two possible reasons. Either an error occurred during the processing of the sample. In general, the user will be asked to take and test a new biological sample. This is typical of moderately increased QS scores. A greatly increased QS could be an indication of a strong aneuploid sample and the user will be encouraged to do a confirmatory test. Said QS is preferably determined by calculating the standard deviations of all Z scores for chromosomes or chromosomal segments and optionally by removing the outliers thereof (i.e. the highest and lowest Z scores in this set).

In an embodiment of the present invention, the parameter p will be sufficient to distinguish between the presence and / or absence of an aneuploidy. In a more preferred embodiment of the present invention, both the parameter and the secondary parameters will be used to make a decision regarding the presence or absence of an aneuploidy. The said secondary parameters will also preferably be compared with predefined threshold values.

In a preferred embodiment, said target chromosome or chromosomal segment includes complete chromosome amplifications and / or deletions that are known to be associated with a cancer (e.g., as described herein). In certain embodiments, said target chromosome or chromosome segments include chromosome segment amplifications or deletions that are known to be associated with one or more cancers. In certain embodiments, the chromosome segments comprise substantially complete chromosome arms (e.g., as described herein). In certain embodiments, the chromosome segments comprise complete chromosome anuploidies. In certain embodiments, the complete chromosome anuploidies include a loss, while in some other embodiments, the complete chromosome anuploids include a gain (e.g., a gain or a loss as shown in Table 1). In certain embodiments, the interesting chromosome segments are essentially arm-level segments comprising a p arm or a q arm of one or more chromosomes 1-22, X, and Y. In certain embodiments, the aneuploidies include an amplification of a substantial arm-level segment of a chromosome or a deletion of a substantial arm-level segment of a chromosome. In certain embodiments, the interesting chromosomal segments essentially comprise one or more arms selected from the group consisting of 1q, 3q, 4p, 4q, 5p, 5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q, 16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q and / or 22q. In certain embodiments, the aneuploidies include an amplification of one or more arms selected from the group consisting of 1q, 3q, 4p, 4q, 5p, 5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q , 12p, 12q, 13q, 14q, 16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q, 22q. In certain embodiments, the aneuploidies include a deletion of one or more arms selected from the group consisting of 1q, 3q, 4p, 4q, 5q, 6q, 8p, 8q, 9p, 9q, 10p, 10q, lip, 11q, 13q, 14q , 15q, 16q, 17p, 17q, 18p, 18q, 19p, 19q, 22q. In certain embodiments, the interesting chromosomal segments are segments comprising an area and / or a gene shown in Table 3 and / or Table 5 and / or Table 4 and / or Table 6. In certain embodiments, the aneuploidies include an amplification of an area and / or a gene shown in Table 3 and / or Table 5. In certain embodiments, the aneuploidies include a deletion of a region and / or a gene shown in Table 4 and / or Table 6. In certain embodiments, the interesting chromosome segments are segments known to be is that they comprise one or more oncogenes and / or one or more tumor-suppressing genes. In certain embodiments, the aneuploidies include an amplification of one or more regions selected from the group consisting of 20Q13, 19q12, Iq21-lq23, 8pl1-p12, and the ErbB2. In certain embodiments, the aneuploidies include an amplification of one or more regions comprising a gene selected from the group consisting of MYC, ERBB2 (EGFR), CCND1 (Cyclin D1), FGFR1, FGFR2, HRAS, KRAS, MYB, MDM2, CCNE, NRAS , WITH, ERBB1, CDK4, MYCB, ERBB2, AKT2, MDM2, BRAF, ARAF, CRAF, PIK3CA, AKT1, PTEN, STK11, MAP2K1, ALK, ROS1, CTNNB1, TP53, SMAD4, FBX7, FGFR3, NOTCH1, CD, and such. In certain embodiments, the cancer is a cancer selected from the group consisting of leukemia, ALL, brain cancer, breast cancer, colorectal cancer, differentiated liposarcoma, esophageal adenocarcinoma, esophageal squamous cell cancer, GIST, glioma, HCC, hepatocellular cancer, lung cancer, lung NSC, lung SC, medullobastoma, melanoma, MPD, myeloproliferative disorder, cervical cancer, ovarian cancer, prostate cancer and kidney cancer.

In certain embodiments, the biological sample comprises a sample selected from the group consisting of whole blood, a blood fraction, saliva / oral fluid, urine, a tissue biopsy, pleural fluid, pericardial fluid, cerebrospinal fluid, and peritonal fluid.

In certain embodiments, the detection of aneuploidies or genome-wide instability or CNV signatures or microsatellite instability (MSI) indicates a positive result, and said method further comprises prescribing, initiating and / or changing a treatment to a human patient whose test sample was decreased. In certain embodiments, prescribing, initiating and / or changing a treatment to a human patient from whom the test sample was taken includes prescribing and / or performing further diagnosis to determine the presence and / or severity of a cancer. In certain embodiments, the further diagnostic procedures include screening a sample of said patient for a cancer biomarker, and / or imaging said patient for a cancer. In certain embodiments, when said method indicates the presence of neoplastic cells in said mammal, treating said mammal or causing said mammal to be treated includes removing and / or inhibiting the growth or proliferation of said mammal. said neoplastic cells. In certain embodiments, treating the mammal comprises surgically removing the neoplastic (e.g., tumor) cells. In certain embodiments, treating the mammal includes performing radiotherapy or causing radiotherapy to be performed on said mammal to kill the neoplastic cells. In certain embodiments, treating said mammal includes administering or ensuring that said mammal is administered anti-cancer drugs such as Receptor Tyrosine Kinase (RTK) inhibitors, kinase inhibitors, CTLA4 inhibitors, PD1 inhibitors, PDL1 inhibitors, immunotherapy , tumor targeting T cell therapies, chimeric antigen receptor (CAR) T cell therapy, cancer vaccines (e.g., matuzumab, erbitux, vectibix, nimotuzumab, matuzumab, panitumumab, fluorouracil, capecitabine, 5-trifluoromethyl-2'-deoxyurate, methotrex, methotre raltitrexed, pemetrexed, cytosine arabinoside, 6-mercaptopurine, azathioprine, 6-thioguanine, pentostatin, fludarabine, cladribine, floxuridine, cyclophosphamide, neosar, ifosfamide, thiotepa, 1,3-bis (2-chloroethyl) -1-nitosour - (2-chloroethyl) -3-cyclohexyl-nitrosourea, hexamethylmelamine, busulfan, procarbazine, dacarbazine, chlorambucil, melphalan, cisplatin, carboplatin, oxaliplatin, bendamustine, carmustine, chloromethine, dacarbazine, fotemustine, lomustine, mannosulfan, nedaplatin, nimustine, prednimustine, ranimustine, satraplatin, semustine, streptozocin, temozolomide, treosulfan, triaziquon, triethylene melamine, thiotepa, triplatin tranitx, trofustic, doramid, urofosin, doofamorin, doofuborin, doofubutin, doofu, trio irinotecan, camptosar, camptothecin, belotecan, rubitecan, vincristine, vinblastine, vinorelbine, vindesine, paclitaxel, docetaxel, abraxane, ixabepilone, larotaxel, ortataxel, tesetaxel, vinflunine, imateibibitibibibitibibitibilibitibilibitibilibate , vandetanib, vatalanib, vemurafenib, dabrafenib, trametinib, ipilimumab, pembrolizumab, nivolumab, retinoic acid, a retinoic acid derivative, and the like).

Methods for monitoring a patient's cancer treatment are also provided. In various embodiments, the methods include performing a method for determining whether a patient has tumor-derived cell-free DNA in his or her peripheral blood, for confirming the diagnosis of cancer, for assisting in the classification of a cancer, for assessing the treatment response, monitoring the patient, identifying the presence of a cancer and / or an increased risk of cancer in a mammal as described herein on a sample from the patient or receiving the results of such a method performed on the sample before or during treatment; and performing the method again on a second sample from the patient or receiving the results of such a method performed on the second sample at a later time during or after the treatment; wherein a reduced number or a reduced severity of aneuploidy (e.g. a reduced aneuploidy frequency and / or a decrease or absence of certain of certain aneuploidies) or a change in the CNV signature in the second measurement (e.g. compared to the first measurement ) can be an indicator of a positive course of treatment and the same or an increased number or severity of aneuploidy or no or an unfavorable change in the CNV signature in the second measurement (e.g. compared to the first measurement) can be an indicator being a negative course of treatment and, if said indicator is negative, adjusting the treatment regime to a more aggressive treatment regime and / or a palliative treatment regime.

Table 1 Illustrative specific, recurring chromosome gains and losses in human cancer (see, e.g., Gordon et al. (2012) Nature Rev. Genetics. 13: 189-203).

In various embodiments, the method described herein can be used to detect and / or quantify complete chromosome anuploidies associated with cancer in general and / or associated with certain cancers. Therefore, in certain embodiments, detection and / or quantification of complete chromosome anuploidies characterized by gains or losses shown in Table 1 are expected.

Several studies have reported patterns of copy number variations at arm level within large numbers of cancer specimens (Lin et al. Cancer Res 68, 664-673 (2008); George et al. PLoS ONE 2, e255 (2007); Demichelis et al. Genes Chromosomes Cancer 48 : 366-380 (2009); Beroukhim et al. Nature. 463 (7283): 899-905 [2010]). In addition, it has been established that the frequency of copy number variations at arm level decreases with the length of chromosome arms. Adapted to this trend, most chromosome arms show strong evidence of preferential gains or losses, but rarely both, within multiple cancer genera (see, e.g., Beroukhim et al. Nature. 463 (7283): 899-905 [2010]).

In one embodiment, the methods described herein are accordingly used to determine arm level CNVs (CNVs comprising one chromosomal arm or substantially one chromosomal arm) in a sample. The CNVs can be determined in a test sample comprising a constitutional (germline) nucleic acid and the arm-level CNVs can be identified in these constitutional nucleic acids. In certain embodiments, arm-level CNVs are identified (if present) in a sample comprising a mixture of nucleic acids (e.g., nucleic acids derived from normal and nucleic acids derived from neoplastic cells). In certain embodiments, the sample is derived from a patient suspected or known to have cancer e.g. carcinoma, sarcoma, lymphoma, leukemia, germ cell tumors, blastoma and the like. In one embodiment, the samples is a plasma sample derived (processed) from peripheral blood that comprises a mixture of cfDNA derived from normal and cancer cells. In another embodiment, the biological sample used to determine whether a CNV is present is derived from cells which, if a cancer is present, comprises a mixture of cancer and non-cancer cells from other biological tissues including, but not limited to to, biological fluids such as serum, sweat, tears, sputum, urine, sputum, earwax, lymph, saliva, cerebropinal fluid, havoc, bone marrow suspension, vaginal fluid, transcervical flush, cerebrospinal fluid, ascites, milk, respiratory, intestinal secretions and urogenital channels, and leukophoresis samples, or in tissue biopsies, smears, or spreads. In other embodiments, the biological sample is a stool sample (fecal sample).

In various embodiments, the CNVs identified as indicative of the presence of a cancer or an increased risk of cancer include, but are not limited to, arm-level CNVs listed in Table 2. As illustrated in Table 2, certain CNVs that include a substantial gain at arm level are indicative of the presence of a cancer or an increased risk of cancer for a particular cancer. A gain of lq is therefore, for example, indicative of the presence or increased risk of acute lymphoblastic leukemia (ALL), breast cancer, GIST, HCC, lung NSC, medulloblastoma, melanoma, MPD, ovarian cancer and / or prostate cancer. A gain of 3q is indicative of the presence or an increased risk of esophageal squamous cancer, lung SC and / or PMD. A gain of 7q is indicative of the presence or increased risk of colorectal cancer, glioma, HCC, lung NSC, medulloblastoma, melanoma, prostate cancer and / or kidney cancer. A gain of 7p is indicative of the presence or increased risk of breast cancer, colorectal cancer, esophageal adenocarcinoma, glioma, HCC, Long NSC, medulloblastoma, melanoma and / or kidney cancer. A gain of 20q is indicative of the presence or increased risk of breast cancer, colorectal cancer, differentiated liposarcoma, esophageal adenocarcinoma, esophageal squamous, glioma cancer, HCC, lung NSC, melanoma, ovarian cancer and / or kidney cancer and so on.

Similarly, as illustrated in Table 2, certain CNVs that include a substantial gain at arm level are indicative of the presence and / or an increased risk for certain cancers. A loss of lp is therefore, for example, indicative of the presence or an increased risk of gastrointestinal stromal tumor. A loss of 4q is indicative of the presence or an increased risk of colorectal cancer, esophageal adenocarcinoma, lung SC, melanoma, ovarian cancer and / or kidney cancer. A gain of 17p is indicative of the presence or an increased risk of breast cancer, colorectal cancer, esophageal adenocarcinoma, HCC, Long NSC, Long SC, and / or ovarian cancer.

Table 2 Significant copy number changes of the chromosomal segment at arm level for each of 16 cancer subtypes

The examples of associations between copy number variations at arm level are intended to be illustrative and not limitative. Other copy number variations at arm level and their cancer associations are well known to those skilled in the art.

Other copy number variations that do not cover a significant part of a chromosome or chromosome arm, such as CNVs from 1 kb to 1 Mb, or 1 kb to 10 Mb, or 100 kb to 10 Mb, or 1 kb to 50 Mb, or 2 bp to 10 Mb or 2 bp to 50 Mb could be equally informative for the detection or confirmation of the presence of tumor-derived cell-free DNA.

As indicated above, the method described herein can be used to determine the presence or absence of a chromosomal amplification. In some embodiments, the chromosomal amplification is the gain of one or more complete chromosomes. In other embodiments, the chromosomal amplification is the gain of one or more segments of a chromosome. In still other embodiments, the chromosomal amplification is the gain of two or more segments of two or more chromosomes. In various embodiments, the chromosomal amplification can include the gain of one or more oncogenes.

Dominant genes associated with solid human tumors usually exert their effect through overexpression or altered expression. Gene amplification is a common mechanism that leads to the upregulation of gene expression. Evidence from cytogenetic studies indicates that significant amplification occurs in more than 50% of human breast cancers. The amplification of the proto-oncogene human epidermal growth factor receptor 2 (HER2) located on chromosome 17 (17 (17q21-q22)) usually results in overexpression of HER2 receptors on the cell surface leading to excessive and disregulated signaling in breast cancer and other malignant cancers (Park et al., Clinical Breast Cancer 8: 392-401 [2008]). Several oncogenes have been found to be amplified in other human malignant cancers. Examples of the amplification of cellular oncogenes in human tumors including amplification of: c-myc in promyelocytic leukemia cell line HL60, and in small cell lung carcinoma cell lines, N-myc in primary neuroblastomas (stages III and IV), neuroblastoma cell lines, retinoblastoma cell line and primary tumors, and small cell lung cancer. and tumors, L-myc in small cell lung carcinoma cell lines and tumors, c-myb in acute myeloid leukemia and in colon carcinoma cell lines, c-erbb in epidermoid carcinoma cell, and primary gliomas, cK-ras-2 or KRAS in primary carcinomas of the lung, large intestine , bladder and rectum, N-ras or NRAS in mammalian carcinoma cell lines (Varmus H., Ann Rev Genetics 18: 553-612 (1984) [cited in Watson et al., Molecular Biology of the Gene (4th ed .; Benjamin / Cummings Publishing Co. 1987)].

Amplifications of oncogenes are a common cause of many types of cancer, as is the case with P70-S6 Kinase 1 amplification and breast cancer. In such cases, the genetic amplification takes place in a somatic cell and only affects the genome of the cancer cells themselves, not the entire organism, let alone some of the offspring. Other examples of oncogenes amplified in human cancers include MYC, ERBB2 (EFGR), CCND1 (Cyclin D1), FGFR1 and FGFR2 in breast cancer, MYC and ERBB2 in cervical cancer, HRAS, KRAS, NRAS, and MYB in colorectal cancer, MYC, CCND1 and MDM2 in esophageal cancer, CCNE, KRAS and MET in gastric cancer, ERBB1, and CDK4 in glioblastomas, CCND1, ERBB1, and MYC in head and neck cancer, CCND1 in hépatocellular cancer, MYCB in neuroblastomas, MYC, ERBB2 and AKT2 in ovarian cancer , MDM2 and CDK4 in sarcomas, NRAS in melanomas and MYC in small cell lung cancer. In one embodiment, the present method can be used to determine the presence or absence of amplification of an oncogene associated with a cancer. In some embodiments, the amplified oncogene is associated with breast cancer, cervical cancer, colorectal cancer, esophageal cancer, stomach cancer, glioblastoma, head and neck cancer, hepatocellular cancer, neuroblastoma, ovarian cancer, melanoma, prostate cancer, sarcoma, and small cell lung cancer.

In one embodiment, the present method can be used to determine the presence or absence of a chromosomal deletion. In some embodiments, the chromosomal deletion is the loss of one or more complete chromosomes. In other embodiments, the chromosomal deletion is the loss of one or more segments of a chromosome. In still other embodiments, the chromosomal deletion is the loss of two or more segments of two or more chromosomes. The chromosomal deletion may include the loss of one or more tumor-suppressing genes.

Chromosomal deletions comprising tumor suppressing genes are believed to play an important role in the development and progression of said tumors. The retinoblastoma tumor suppressor gene (Rb-1), which is located in chromosome 13q14, is the most extensively characterized tumor suppressor gene. The Rb-1 gene product, a 105 kDa nuclear phosphoprotein, apparently plays an important role in cell cycle regulation (Howe et al., Proc Natl Acad Sei (USA) 87: 5883-5887 [1990]). Altered or lost expression of the Rb protein is caused by inactivation of both geneallels either by a point mutation or by a chromosomal deletion. It has been found that Rb-i gene alterations are present not only in retinoblastomas, but also in other malignant tumors such as osteosarcomas, small cell lung cancer (Rygaard et al., Cancer Res 50: 5312-5317 [1990)]) and breast cancer. Restriction fragment length polymorphism (RFLP) studies have indicated that such tumor types have frequently lost heterozygosity at 13q, suggesting that one of the Rb-1 gene alleles has been lost due to a large chromosomal deletion (Bowcock et al., Am J Hum Genet, 46: 12) [1990]). Chromosome 1 abnormalities including duplications, deletions and unbalanced translocations including chromosome 6 and other partner chromosomes indicate that regions of chromosome 1, in particular Iq21-lq32 and lpll-13, may contain pathogenetically relevant for both chronic and chronic genes advanced phases of myeloproliferative neoplasms (Caramazza et al., Eur J Hematol 84: 191-200 [2010]). Myeloproliferative neoplasms are also associated with chromosome 5 deletions. Complete loss or interstitial chromosome 5 deletions are the most common karyotypic abnormality in myelodysplastic syndromes (MDSs). Isolated del (5q) / 5q MDS patients have a more favorable prognosis than those with additional karyotypic abnormalities, which tend to develop myeloproliferative neoplasms (MPNs) and acute myeloid leukemia. The frequency of unbalanced chromosome 5 deletions has led to the idea that 5q has one or more tumor-suppressing genes that have fundamental roles in the growth control of hematopoietic stem / progenitor cells (HSCs / HPCs). Cytogenetic mapping of commonly deleted regions (commonly deleted regions) centered on 5q31 and 5q32 identified candidate tumor suppressing genes, including the ribosomal subunit RPS14, the transcription factor Egrl / Krox20 and the cytoskeletal remodeling protein, alpha-catenin (Eisenmann et al. , Oncogene 28: 3429-3441 [2009]). Cytogenetic and allelotyping studies of fresh tumors and tumor cell lines have shown that allele loss from different distinct regions on chromosome 3p, including 3p25, 3p21-22, 3p21.3, 3pl2-13 and 3pl4, are the earliest and most frequent genomic abnormalities involved in a broad spectrum of major epithelial cancers of the lungs, breast, kidney, head and neck, ovary, cervix, colon, pancreas, esophagus, bladder and other organs. Several tumor suppressing genes have been assigned to the chromosome 3p region, and interstitial deletions or promoter hypermethylation is believed to precede the loss of the 3p or the complete chromosome 3 in carcinoma development (Angeloni D., Briefings Functional Genomics 6: 19-39 [2007]).

Newborns and children with Down syndrome (DS) often have congenital transient leukemia and have an increased risk of acute myeloid leukemia and acute lymphoblastic leukemia. Chromosome 21, with approximately 300 genes, may be involved in many structural abnormalities, e.g., translocations, deletions and amplifications, in leukemia, lymphomas and solid tumors. In addition, it has been found that genes located on chromosome 21 play an important role in tumorigenesis. Somatic numerical as well as structural chromosome 21 abnormalities are associated with leukemia, and specific genes including RUNX1, TMPRSS2, and TFF, which are located in 21q, play a role in tumorigenesis (Fonatsch C Gene Chromosomes Cancer 49: 497-508 [2010] ).

In view of the foregoing, the method described herein can be used, in various embodiments, to determine the segment CNVs that are known to contain one or more oncogenes or tumor-suppressing genes, and / or that are known to be associated with a cancer or an increased risk of cancer. In certain embodiments, the CNVs can be determined in a test sample comprising a constitutional (germline) nucleic acid and the segment can be identified in these constitutional nucleic acids. In certain embodiments, segment CNVs are identified (if present) in a sample comprising a mixture of nucleic acids (e.g., nucleic acids derived from normal and nucleic acids derived from neoplastic cells). In certain embodiments, the sample is derived from a patient suspected or known to have cancer e.g. carcinoma, sarcoma, lymphoma, leukemia, germ cell tumors, blastoma and the like. In one embodiment, the samples is a plasma sample derived (processed) from peripheral blood that comprises a mixture of cfDNA derived from normal and cancer cells. In another embodiment, the biological sample used to determine whether a CNV is present is derived from cells which, if a cancer is present, comprises a mixture of cancer and non-cancer cells from other biological tissues including, but not limited to to, biological fluids such as serum, sweat, tears, sputum, urine, sputum, earwax, lymph, saliva, cerebropinal fluid, havoc, bone marrow suspension, vaginal fluid, transcervical flush, cerebrospinal fluid, ascites, milk, respiratory, intestinal secretions and urogenital channels, and leukophoresis samples, or in tissue biopsies, smears, or spreads. In other embodiments, the biological sample is a stool sample (fecal sample).

The CNVs used to determine the presence of a cancer and / or an increased risk of cancer may include amplification or deletions.

In various embodiments, the CNVs identified as indicative of the presence of a cancer or an increased risk of cancer include one or more of the amplifications shown in Table 3.

Table 3 Illustrative, but non-limiting, chromosomal segments characterized by amplifications associated with cancers.

In certain embodiments, in combination with the above-described amplifications (herein), or separately, the CNVs identified as indicative of the presence of a cancer or an increased risk of cancer include one or more of the deletions shown in Table 4.

Table 4 Illustrative, but not limitative, chromosomal segments characterized by deletions associated with cancers.

The aneuploidies identified as being characteristic of various cancers (e.g., the aneuploidies identified in Tables 3 and 4) may contain genes known to be involved in cancer etiologies (e.g., tumor-suppressing genes, oncogenes, etc.). It can also be investigated that these aneupoids identify relevant but previously unknown genes.

Table 5 illustrates target genes that are known to fall within the identified amplified segment and predicted genes, and Table 6 illustrates target genes that are known to fall within the identified deleted segment and predicted genes.

Table 5 Illustrative but non-limitative chromosomal segments and genes known or predicted to be present in regions characterized by amplification in different cancers

Table 6 Illustrative, but non-limiting, chromosomal segments and genes known or predicted to be present in regions characterized by amplification in different cancers

Although the examples herein refer to human genomes and the description is primarily directed to humans, the concept of the present invention applies to genomes of any plant or animal.

In various embodiments, it is expected to use the method identified here for identifying CNVs of segments comprising the amplified regions or genes identified in Table 5 and / or using the methods identified here for identifying CNVs of segments comprising deleted regions or genes identified in Table 6. In other embodiments, it is expected to use the method identified here for screening for the presence of CNVs from segments that were not previously linked to cancer or not described in Tables 5 or 6.

In one embodiment, the methods described herein provide a means for assessing the association between gene amplification and the degree of tumor evolution. Correlation between amplification and / or deletion and the stage or degree of a cancer can be important for the prognosis because such information can contribute to the definition of a genetically based tumor degree that will determine the future course of a disease with more advanced tumors with the worst prognosis. would predict better. In addition, information about early amplification and / or deletion events may be useful in associating these events as predictors of the later disease progression.

Gene amplification and deletions as identified by the method can be associated with other known parameters such as tumor grade, histology, Brd / Urd labeling index, hormonal status, nodal involvement, tumor size, life expectancy and other tumor properties available from epidemiological and biostatistical studies. For example, tumor DNA to be tested by the method could include atypical hyperplasia, ductal carcinoma in situ, stage I-III cancer, and metastatic lymph nodes to allow the identification of associations between amplifications and deletions and stage. The associations made make an effective therapeutic intervention possible. For example, consistently amplified regions may contain an overly expressed gene, the product of which can be therapeutically attacked (e.g., the growth factor receptor tyrosine kinase, p185HER2).

In various embodiments, the method described herein can be used to identify amplification and / or deletion events associated with drug resistance by determining the copy number variations of nudeic acid sequence of primary cancers relative to those metastasized to other sites. If gene amplification and / or deletion is a manifestation of karyotypic instability that allows rapid development of drug resistance, more amplification and / or deletion in primary tumors of chemoresistant patients would be expected than in tumors in chemosensitive patients. For example, if amplification of specific genes is responsible for drug resistance development, regions surrounded by these genes would be expected to be consistently amplified in tumor cells from pleural effusions of chemoresistence patients, but not in primary tumors. The discovery of associations between gene amplification and / or deletion and the development of drug resistance may allow the identification of patients who may or may not benefit from adjuvant therapy.

In other embodiments, the method described herein can be used to identify the presence of genome-wide instability and / or microsatellite instability and / or specific combinations of copy number variations (which could cover entire chromosomes, chromosome arms or smaller DNA segments).

In yet another embodiment, the method described herein can be used to identify the origin of the tumor, i.e., the primary tissue or organ from which the tumor originates before becoming metastatic.

Both full and partial chromosomal aneuploidies as well as smaller copy number variations of DNA segments that could be associated with the formation, and progression of cancer can be determined according to the present invention. II Seauencina. uitliinina and correction

As mentioned above, only a fraction of the genome is sequenced. In one aspect, even when a group of nucleic acids in a specimen is sequenced at <100% genomic coverage rather than with multiple multiples of coverage, and from the ratio of sequenced nucleic acid molecules, most of each nucleic acid species is not sequenced or only sequenced once.

This contrasts with situations where targeted enrichment is performed on a subset of the genome prior to the sequencing reaction, followed by high-coverage sequencing of that subset.

In one embodiment, massive parallel short reading sequencing is used. Short sequence tags or readings are generated, e.g. from a certain length between 20 bp and 400 bp. Paired end sequencing could also be performed.

In one embodiment, a pre-processing step is available for pre-processing the obtained readings. Such prior processing option allows filtering of low quality readings, thereby preventing them from being assigned. Assignment of low-quality readings may require long-term computer processing capacity, may be incorrect, and risk increasing the technical noise in the data, resulting in a less accurate parameter. Such prior processing is particularly valuable when next-generation sequencing data is used that has a generally lower quality or any other condition associated with a generally lower quality of the readings.

The generated readings can be later aligned with one or more human reference genome sequences. The number of aligned readings are preferably counted and / or sorted according to their chromosomal location.

An additional cleaning protocol can be performed, where deduplication is performed, e.g. with Picard instruments, where only uniquely assigned readings are retained. Readings with mismatches and gaps can be removed. Readings that divide the blacklisted areas can be excluded. Such blacklisted areas can be taken from a predefined list of, for example, common CNVs, collapsed repeats, DAC blacklisted areas as identified in the ENCODE project (ie a set of areas in the human genome that have abnormal, unstructured, high has signal / reading counts in NGS experiments independent of cell line and type of experiment) and the undefined segment of the reference genome. In one embodiment, areas on the blacklist are given to the user. In another embodiment, the user can use or define his or her own set of areas on the blacklist.

In another embodiment, chromosomes are subdivided into regions with a predefined length, generally referred to as pieces. In one embodiment, the piece size is a predefined size given to the user. In another embodiment, said piece size may be defined by a user, it may be uniform for all chromosomes, or it may be a specific piece size per chromosome, or it may vary according to the obtained sequence data. Changing the piece size can have an effect on the final parameter to be defined, either by improving the sensitivity (usually obtained by reducing the piece size, often at the expense of specificity) or by improving specificity (in generally by increasing the piece size, often at the expense of sensitivity). A possible piece size that provides acceptable specificity and sensitivity is 50 kb.

In a further step, the aligned and filtered readings are counted within one piece to obtain reading counts.

The obtained reading counts can be corrected for the GC count for the piece. GC bias is known to exacerbate genome assembly. Various GC corrections are well known in the art (e.g., Benjamini et al., Nucleic Acid Research 2012). In a preferred embodiment, said GC correction will be a LOESS regression. In one embodiment, a user of the methodology of the present invention may be provided with the choice of various possible GC corrections.

In a later step, the genomic representation (GR, genomic reproduction) of reading counts is calculated. Such representation is preferably defined as a ratio between the GC-corrected reading counts for a specific piece and the sum of all GC-corrected reading counts.

In one embodiment, said GR is defined as follows:

with k over all chromosomal pieces (where the factor 107 in the above formula is arbitrarily defined, and can be any constant value)

In a final step, the obtained GC are joined piece by piece over an area, said area being a sub-area (window) of a chromosome or the entire chromosome. Said window may have a predefined or variable size, which may be optionally selected by the user. A possible window could have a size of 5 MB or 100 adjacent pieces with a size of 50 kb.

The GR pooled for a chromosome can be defined by

In another embodiment, the genomic representation of a series of reference samples must be calculated. Said set of reference samples (or also referred to as reference series) can be predefined or selected by a user (e.g. selected from his / her own reference samples). By allowing the user to use his own reference set, a user will be able to better capture the recurring technical variation of his / her environment and its variables (eg different wet lab reagents or protocol, different NGS instrument or platform, etc.) . In a preferred embodiment, said reference set comprises genomic information of 'healthy' samples that are expected or known to contain (relevant) aneuploidies. The genomic representation (GR) of the reference series can be defined either at the level of the genome and / or at a sub-area (chromosome, chromosomal segment, window or bin).

Other single molecule sequencing strategies such as the Roche 454 platform, the Applied Biosystems SOLiD platform, the Hélicos True Single Molecule DNA sequencing technology, the single molecule, real-time (SMRT ™) technology from Pacific Biosciences, and nanopore sequencing technologies such as MinlON, GridlON or PromethION from Oxford Nanopore Technologies could also be used in this application. III Determination of scores, parameter and secondary parameters

Based on the alignments and the obtained reading counts or a derivative thereof, optionally corrected for GC content and / or total number of readings obtained from said samples, scores are calculated that ultimately lead to a parameter that allows the presence of an aneuploidy in a determine the sample. The scores mentioned are normalized values derived from the counts of the readings or mathematically modified counts of the readings, with normalization taking place with a view to the reference series. Consequently, each score is obtained by comparison with the reference series. The term first score is used to refer to the score associated with the count of the readings for a target chromosome or a chromosomal segment. A set of scores is a set of scores derived from a set of normalized number of readings that may include the normalized number of readings from said chromosomal target segment or target chromosome.

Said first score preferably represents a Z score or standard score for a target chromosome or chromosomal segment. Said set is preferably derived from a set of Z scores obtained from a corresponding set of chromosomes or chromosomal segments comprising said chromosomal target segment or target chromosome.

Such scores can be calculated as follows:

With i a window or a chromosome or a chromosome segment.

A summary statistic of the said set of scores can be calculated, for example, as the average or median value of the individual scores.

Another summary statistic of the said set of scores can be calculated as the standard deviation or median absolute deviation or average absolute deviation of the individual scores.

Optionally, but not necessarily, the same set of scores is used for both types of calculations.

The said parameter p will be calculated as a function of the first score and a derivative (e.g. summary statistics) of the set of scores. In a preferred embodiment, said parameter p will be a ratio between the first score corrected by the set of scores (or a derivative thereof) and a derivative of the said set of scores.

In another embodiment, said parameter will be a ratio between the first score corrected by a summary statistic of a first set of scores and a summary statistic of another, second set of scores, both sets of scores comprising the first score.

In a specific preferred embodiment, said parameter p is a ratio between the first score corrected by a summary statistic of said set of scores, and a summary statistic of said set of scores. The summary statistic is preferably selected from the mean, the median, the standard deviation, the median absolute deviation or the average absolute deviation. In one embodiment, the said two used summary statistics in the function are the same. In another more preferred embodiment, said summary statistics differ from the set of scores in the numerator and denominator.

A suitable embodiment of the present invention usually comprises the following steps (after obtaining DNA sequences from a random, sequencing process on a biological sample). aligning said obtained sequences with a reference genome; - counting the number of readings on a series of chromosomal segments and / or chromosomes, whereby counts of readings are obtained; - normalizing said counts of readings or a derivative thereof to a normalized number of readings; - obtaining a first score and a set of scores derived from said normalized reading counts for a target chromosome or chromosomal segment, and wherein said set of scores is a set of scores derived from a corresponding set of chromosomes or chromosome segments that comprise the chromosomal target segment or chromosome; - calculating a parameter p on the basis of said first score and said set of scores, wherein said parameter represents a ratio between * said first score, corrected by a summary statistic of said set of scores, and * a summary statistic of the aforementioned set of scores.

A possible parameter p can be calculated as follows:

wherein Zi represents the first score and Z j represents the set of scores and where i represents the target chromosome or chromosomal section, and where j represents a set of chromosomes or chromosomal segments i, a, b, ... representing said chromosomal segment or chromosome i contain. In another embodiment, the said parameter p is calculated as

wherein Zi represents the first score and Z j represents the set of scores and where i represents the target chromosome or chromosomal section, and where j represents a set of chromosomes or chromosomal segments i, a, b, ... representing said chromosomal segment or chromosome i contain.

In yet another most preferred embodiment, said parameter p is calculated as

Where Zi represents the first score and Z j represents the set of scores and where i represents the target chromosome or chromosomal section, and where j represents a set of chromosomes or chromosomal segments i, a, b, ... representing said chromosomal segment or chromosome i introduce.

In addition to the parameter p that allows the identification of the presence of aneuploidies, secondary parameters can be calculated that can serve as a quality check or provide additional information regarding one or more aneuploidies present in the samples.

A first secondary parameter that can be calculated allows defining whether chromosomal and large subchromosomal aneuploidies are present in the sample (compared to, for example, smaller aneuploidies). In a preferred embodiment, such a parameter is defined by a median of Z scores measured per sub-area (e.g., 5 Mb windows) in a target chromosome. If more than 50% of these subareas are affected, this will be noticeable in the secondary parameters.

In another embodiment, a secondary parameter can be calculated as the median of the absolute value of the Z scores calculated over the remaining chromosomes (that is, all chromosomes except the target chromosome or chromosomal segment) per sub-area (e.g., 5 Mb windows). The last secondary parameters allow the detection from at. the presence of technical or biological instabilities (cf. malignancy, cancer). If less than half the windows of the other or all autosomes or chromosomes are affected, this secondary parameter will not be affected. If more than 50% of the windows are affected, this can be deduced from the aforementioned secondary parameters.

In another embodiment, the present invention also provides a quality score (QS). QS makes it possible to assess the general variation within the genome. A low QS is an indication of good sample processing and a low level of technical and biological noise. An increase in the QS can have two possible reasons. Either an error occurred during the processing of the sample. In general, the user will be asked to take a new biological sample and to sequence it. This is typical of moderately increased QS scores. A greatly increased QS is an indication of a highly aneuploid or genome-wide unstable sample and the user will be encouraged to do an affirmative test to further assess whether the patient is developing cancer. Said QS is preferably determined by calculating the standard deviations of all Z distributions for the chromosomes and by removing the highest and lowest scoring chromosome.

Samples with a QS higher than 2 are, for example, considered to be of poor quality, or as samples with an increased risk of developing cancer, and a QS between 1.5 and 2 is of intermediate quality. IV. Comparison of threshold value

The parameter p as calculated in the above embodiments will then be compared to a threshold value to determine if there is a change compared to a reference amount (ie imbalance), for example with regard to the ratio of amounts of two chromosomal regions (or series of regions) ). The presence of aneuploidy and / or an increased number of said aneuploidy is an indicator of the presence and / or an increased risk of cancer. In one embodiment, the user will be able to define his / her own threshold value, either empirically based on experience or previous experiments, or for example based on standard statistical considerations. If a user wants to increase the sensitivity of the test, the user can lower the thresholds (i.e., bring them closer to 0). If a user would like to increase the specificity of the test, the user can raise the thresholds (i.e., move them further from 0). A user will often have to find a balance between sensitivity and specificity, and this balance is often lab and application specific, so it is easy if a user can change the threshold values themselves.

Based on the comparison with the threshold value, aneuploidy can be found present or absent. Such presence is indicative of the presence and / or an increased risk of cancer.

In an embodiment of the present invention, comparison of parameter p with a threshold value is sufficient to determine the presence or absence of an aneuploidy. In another embodiment, the said aneuploidy is determined on the basis of a comparison of parameter p with a threshold value and a comparison of at least one of the secondary parameters, quality score and / or first score with a threshold value, with a corresponding threshold value for each score. defined or set.

In a preferred embodiment, the said presence / absence of an aneuploidy is defined by comparing a parameter p with a predefined threshold value, as well as comparing all secondary parameters and first scores as described above with their corresponding threshold values.

The final decision tree can thus depend on parameter p alone, or combined with one of the secondary parameters and / or quality score or first score as described above.

In a preferred embodiment, said methodology according to the present invention comprises the following steps: - multiplex sequencing of 50 bp single-end readings (performed by end user) - uploading sequence readings - assignment of readings to a reference genome - count number of readings per piece ( a piece has a size of 50 kb) - calculate GC content per piece and correct for GC content - calculate genomic representation (GR) score per item. For item i this is equal to

combining the GR values per window (a window consists of 100 consecutive windows) calculating a Z score per window or per chromosome, the Z score being based on the GR score per chromosome, compared to the GR score scores in a series of reference samples.

with i a chromosome or a window, μ Ref, i the mean or median GR score for the corresponding pieces in the set of reference samples and σ Ref; i the standard deviation of the GR scores for the corresponding pieces in the set of reference samples - calculation of a ZofZ score, where the ZofZ score is based on the Z score, corrected by the median (or average) of the Z scores of a set of chromosomes comprising target chromosome i and divided by a factor that affects the variability of the Z measures scores of a set of chromosomes comprising the target chromosome i (standard deviation of a more robust version thereof, such as, for example, the median absolute deviation or mad). - comparison of the Z score with a threshold value, and the ZofZ score with a threshold value, for predicting the presence or absence of aneuploidy.

In another preferred embodiment, said prediction of the presence or absence of an aneuploidy takes place via a decision tree based on a parameter p and secondary parameters. V Toolbox and kit

The methodologies as described above are preferably all implemented by a computer. Therefore, the present invention also relates to a computer program product comprising a computer-readable medium encoded with multiple instructions for controlling a computer system to perform an operation for performing analysis of a chromosomal or subchromosomal aneuploidy in a biological sample that is obtained from a patient, wherein the biological sample comprises nucleic acid molecules.

With regard to determining the presence or absence of an aneuploidy or genome-wide instability in a sample, the processing comprises the steps of: - receiving the sequences of at least a portion of the nucleic acid molecules contained in a biological sample that is obtained from said pregnant woman; aligning said obtained sequences with a reference genome; - counting the number of readings on a series of chromosomal segments and / or chromosomes, whereby counts of readings are obtained; - normalizing said counts of readings or a derivative thereof to a normalized number of readings; - obtaining a first score of said normalized readings and a set of scores derived from said normalized readings for a target chromosome or chromosomal segment, and wherein said set of scores is a set of scores derived from the normalized number of readings for a series of chromosomes or chromosome segments comprising the chromosomal target segment or chromosome; - calculating a parameter p based on the said first score and the said set of scores.

In a preferred embodiment, said parameter represents a ratio between: * a first score, corrected by a summary statistic of said set of scores, and * a summary statistic of the set of scores.

Said operations can be performed by a user or professional in an environment away from the location where the sample was taken and / or the wet lab procedure, which is the extraction of the nucleic acids from the biological sample and the sequencing.

The aforementioned operations can be delivered to the user through custom software that must be installed on a computer, and can be stored in the cloud.

After having performed the required or desired operation, the practitioner or user will receive a report or score, the said report or score giving information about the characteristic being analyzed. A report preferably includes a link to a patient or sample ID that has been analyzed. Said report or score provides information about the presence or absence of aneuploidy in a sample, said information being obtained based on a parameter calculated by the above methodology. The report can also provide information about the nature of the aneuploidy (if detected, eg major or minor chromosomal abnormalities) and / or the quality of the sample that has been analyzed.

It will be clear to a person skilled in the art that the above information can be presented to a practitioner in one report.

The above operations are preferably part of a digital platform that permits the molecular analysis of a sample through various computer-implemented operations.

In particular, the present invention also includes a visualization tool that allows the user or practitioner to visualize the results obtained as well as the raw data entered into the system. In one embodiment, said visualizations include a window per chromosome, showing the chromosome that has been analyzed, that readings per area and the scores and / or parameters that have been calculated. By showing the calculated scores or parameter to the practitioner or user together with the visual representation of the counts of readings, a user can perform an additional check or assessment of the results obtained. By allowing the user to view the data, users will be able to define improved decision rules and thresholds.

In addition, an additional check is added, since the visual data per chromosome allows the user to evaluate for each chromosome whether the automated classification is correct. This adds an additional safety parameter.

In a preferred embodiment, said platform and said visualization instrument are provided with algorithms that take into account the fact that certain areas yield more readings (due to a recurring technical deviation that always makes some areas of the genome over- or under-represented). Correction measurements can be given for this overrepresentation by making a comparison with a reference series (which is ideally processed using the same or a similar protocol) and plotting of, for example, Z-scores or alternative scores that reduce the probability of certain observations under the assumption of propose aneuploidy. Standard visualization tools only show counts of readings, and do not allow to correct the recurring technical deviation.

Finally, based on the link between the obtained scores and / or parameters and the visual data per chromosome, a user or practitioner may decide to change the threshold value used to define the presence of aneuploidy. The user may therefore decide to aim for a higher sensitivity (e.g., to be less stringent about the rise / fall of the parameter or scores) or higher specificity (e.g., to be more stringent about the rise / fall of the parameter or scores) .

The platform may be provided with other features that provide a more accurate analysis of the molecular data obtained from the biological sample.

The platform of the present invention is inherently compatible with many different types of NGS library preparation kits and protocols and NGS sequencing platform. This is an advantage since a user will not have to invest in special NGS sequencing platform or NGS library preparation kits specific to a specific application, but the user can use the preferred platform and preferred kit instead. Moreover, it offers a user a certain degree of flexibility with regard to the material to be used. If newer or cheaper instruments or kits become available, a user can easily change.

As mentioned above, the present methodology is compatible with cell-free DNA extracted from various types of biological samples, including blood, saliva and urine. The use of urine or saliva instead of blood would provide a truly non-invasive sample type and allow, for example, testing at home and dispatch of the sample to the test lab. This is clearly an additional benefit compared to other methods of obtaining samples such as blood collection.

The invention is further described by the following non-limitative examples which further illustrate the invention, and which are not intended, and should not be construed as limiting the scope of the invention.

Examples

Preparation and seauencina of the sample

1. Blood collection, plasma separation and cell-free DNA extraction One test tube (10 ml) of peripheral blood is collected in Streck test tubes and stored at 4 ° C. The blood is collected via a standard phlebotomy procedure.

The plasma (+/- 5 ml) is separated for a maximum of 72 hours after the blood has been collected by the standard double centrifugation method: • The blood sample is centrifuged at 2000xg for 20 minutes (this can take place at room temperature), without the use of the brake . The plasma is then transferred to either three 1.5 ml low binding test tubes or a single 5 ml low binding test tube. A second centrifugation at 13000xg takes place for 2 minutes (this can take place at room temperature). • The plasma is transferred to sterile 1.5 ml or 5 ml low binding test tubes for storage at -20 ° C prior to cell-free DNA (cfDNA) extraction.

The pale yellow coating can optionally be saved for later testing. Genomic DNA from the pale yellow coating layer can be examined to confirm or rule out germline abnormalities.

The cell-free DNA is extracted from the plasma using the QIAam Circulating Nucleic Acid Kit (Qiagen) according to the manufacturer's recommendations, with a final elution volume of 60 µl. The DNA samples are stored at -20 ° C if they are not immediately used for library preparation. 2. cfDNA quantifiers

The extracted cfDNA is quantified using a Qubit fluorometer. The concentration of the cell-free DNA is usually 0.1-1 ng / µl. 3. Library preparation 25 µl of the extracted cfDNA is used as starting material for library preparation.

During library preparation, the DNA samples are adapted for next-generation sequencing. Adapters are added to the ends of the DNA fragments.

The sequencing libraries are prepared using the TruSeq ChlP library preparation kit (Illumina) with certain modifications to the manufacturer's protocol by lowering the reagent volumes to allow the generation of sequencing libraries using low starting amounts of DNA.

The library preparation protocol can be summarized as follows: (Note: The grain-based size selection for removing large DNA fragments and removing small DNA fragments described in the protocol is NOT used.)

Final repair of the DNA fractions: 1. Add 5 μl Resuspension buffer and 20 μΙ final repair mix to the 25 μΙ starting material (total = 50 μΙ)

The grain-based size selection for removing large DNA fragments and removing small DNA fragments described in the protocol is NOT used.

2. Incubate for 30 minutes at 30 ° C. 3. Add 80 μΙ undiluted AMPure beads to the 50 μΙ sample mixture after final repair. 4. Wash the pellets twice with 190 μΙ 80% EtOH. 5. Re-suspend the dried pellet with 10 µl resuspension buffer. Transfer 9 μΙ of the supernatant layer to a new test tube.

Adenvlation of the 3 'ends 1. Add 6.25 μΙ A-tail mix

2. Heat for 30 minutes at 37 ° C + 5 minutes at 70 ° C

Liaation of the indexed horse-tail end adapters with the DNA 1. Adapters are diluted 1 / 2nd with resuspension buffer => add 2.5 μΙ to the sample

2. Add 1.25 μΙ ligation mix (no resuspension buffer). Incubate for 30 minutes at 30 ° C 3. Add 2.5 μΙ stop ligation buffer 4. Add 21 μΙ AMPure for cleaning 5. Wash the beads twice with 190 μΙ 80% EtOH. 6. Re-suspend the dried pellet in 27 μΙ resuspension buffer. Transfer 25 μΙ of the supernatant layer to a new test tube. 7. Add 25 μΙ AMPure for cleaning. 8. Wash the pellets twice with 190 μΙ 80% EtOH. 9. Re-suspend the dried pellet in 12.5 μΙ resuspension buffer. Transfer 10 μΙ of the supernatant layer to a new test tube.

Enriching DNA fractions 1. Prepare the PCR mix by mixing 2.5 μΙ PCR Primer Cocktail and 12.5 μΙ PCR Master Mix for each sample. 2. PCR conditions: 98 ° C for 30 seconds 15 cycles of:

98 ° C for 10 seconds 60 ° C for 30 seconds 72 ° C for 30 seconds Keep 72 ° C for 5 seconds at 4 ° C 3. Add 25 μΙ AMPure for cleaning 4. Wash the pellets twice with 190 μΙ 80% EtOH . 5. Re-suspend the dried pellet in 32.5 μΙ resuspension buffer. Transfer 30 μΙ of the supernatant layer to a new test tube. 6. Use 2 μΙ of the sample for Qubit quantification and 2 μΙ for fragment analysis (see next section). 4. Quality control of library

Good cell-free DNA isolation and NGS library preparation are tested by analyzing each library on the fragment analyzer (Advanced Analytical Technologies Ine., Germany) prior to sequencing, to assess: • size distribution (confirm appropriate size profile using concentration, peak ratio, peak height, ...), • the quality of the library. High molecular weight fragments containing samples will be classified as samples that are eligible for sequencing (indicates contamination with genomic DNA).

Typical libraries exhibit a narrow size distribution with a peak at about 300-350 bp.

In addition, a Qubit quantization step is performed so that the enrichment reaction will take place with the appropriate amount of inserted DNA material. The concentration of DNA is usually 15-30 ng / µl. 5. Normalize and group libraries

Samples are indexed during library preparation and up to 24 samples are normalized and grouped in equal volumes for multiplex sequencing across both lanes of an Illumina HiSeq2500 flow cell. 6. NGS run

Sequencing is performed on the HiSeq 2500 (Illumina) in Fast Run mode where 50 bp single-end readings are produced.

Detection of aneuploidy in a biological sample: validation of the methodology * Mapping and filtering of the assigned readings

The 50 bp single-end sequence readings from a test sample are assigned to the reference genome GRCh37.75 with BWA backtrack. With Picard tools, duplicated readings can be deleted and based on mapping quality, readings that refer to multiple locations can be ignored. Readings that refer sub-optimally to multiple locations are also deleted. To reduce the variability of samples, we only retain the readings that perfectly match the reference genome (i.e., no mismatches and no openings are allowed), although this is rather an optional step. Finally, readings that are blacklisted in an internally compiled list of areas can also be deleted. These blacklisted areas include common polymorphic CNVs, collapsed repeats, DAC blacklisted areas generated for the ENCODE project and the undefined segment of the reference genome (i.e., the eNs). * Calculation of genomic representation

The reference genome is subdivided into 50 kb pieces and the number of readings from the test sample is counted per piece. These counts of readings are corrected according to the GC levels of the pieces with locally weighted scatter plot smoothing (Loess regression). These GC-corrected reading counts are then divided by the total sum of all autosomal GC-corrected reading counts and multiplied by 107. This is defined as the genomic representations (GR) per piece. A sliding window is applied to these per-piece GR values and the sum of these GR values is determined for all consecutive 100 pieces. The windows are each shifted in time by 1 piece (i.e., 50 kb). In this way a GR value is obtained per window of 5 Mb. Similarly, for each autosome, the sum of the piece GR values is calculated to obtain a GR value for each autosome in the test sample. * Comparison with a reference series

In a reference series of 100 reference samples (a smaller or larger number is also possible), the GR values are calculated for all autosomes and for all 50 Mb windows as described above. For each autosome and window of 5 Mb, the mean μ and the standard deviation σ of the GR scores are calculated over all reference samples. In this way, a Z score can be calculated for each window and every autosome i in a test sample, defined as

wherein Gfy is the GR value in the test sample for window or autosome i and μί; σ; the average and standard deviation, respectively, of the GR scores measured in the reference samples for window or autosome i.

Based on the 22 Z-scores of the car steams in a test sample, a ZZ2 score was calculated for each autosome as

wherein the Z score zt of autosome i in the test sample is compared with the median and the standard deviation (sd) of the 22 Z scores obtained for all 22 autosomes in the test sample.

Alternatively, a ZofZ score is calculated as

wherein the Z score zt of chromosome i in the test sample is compared with the median and median absolute deviation (mad) of the 22 Z scores obtained for all 22 autosomes in the test sample. The ZZ2 and ZofZ scores quantify the deviation of the Z score of the target autosome from? all Z scores observed in the test sample. This robust version of the Z-or-Z scores makes no assumptions about the aneuploid status of the autosome and the other autosomes.

Based on the Z scores calculated for all 5 Mb windows in the test sample, the BM score of each car steam / is calculated as the median of the Z scores for all windows in the target autosome:

where the median of the Z-scores is calculated over all windows j on autosome / '.

This BM score reflects the magnitude of the deviation: aneuploidies will result in higher BM values, while smaller, segmental CNVs will have less influence on the median of the Z scores and result in lower BM scores. To distinguish between abnormal BM scores and elevated BM values due to noise in the data set, or the presence of genome-wide instability that could be indicative of cancer, the OM score for an autosome / is calculated as the median of the Z-scores of all windows of the other autosomes:

the median being calculated over all, absolute Z-scores for 5 Mb windows j that are not on autosome.

Finally, for each test sample, a quality score (QS) is calculated as

with j over all autosomes except for the 2 car steams with the highest and lowest Z score. This score will identify test samples with poor quality that result in unreliable aneuploidy calling. A greatly increased QS score can also refer to DNA samples containing at least a fraction of DNA from a tumor.

A threshold value can be defined for each of the parameters calculated above. A threshold value of 2, 2.5 or 3 can be chosen based on standard statistical considerations. In the context of the Z score, this means that the probability that the test result is normal (i.e. the obtained GR score is similar to the GR scores for the same area in the reference series) is very small. To make a test more specific, the threshold value could be increased. To make a test more sensitive, the threshold value could be lowered. These threshold values can be determined for each of the parameters, and can differ for each of the parameters. For example, it is conceivable that threshold values for BM and OM are set to 1 while they are set to 3 for the Z score and ZZ score. Negative thresholds can also be used.

Sample G analyzed showed a QS score of 27.526. The graph of the OM values or the autosomes showed extreme values (Figure 1). Visual inspection of the graphs of individual autosomes confirmed that this sample behaved abnormally (Figure 2). This type of generally widely varying patterns in the Z scores is indicative of genome-wide instability.

It is believed that the present invention is not limited to any embodiment described above and that certain changes may be added to the present example without revaluing the appended claims.

Claims (30)

Conclusions A method for identifying the presence of tumor-derived cell-free DNA in a mammal, said method comprising: - providing the sequences of at least a segment of the nucleic acid molecules contained in a biological sample obtained from a patient, wherein said biological sample comprises cell-free DNA; - aligning said obtained sequences with a reference genome; - counting the number of readings on a series of chromosomal segments and / or chromosomes, whereby counts of readings are obtained; - normalizing said counts of readings or a derivative thereof to a normalized number of readings; - obtaining a first score of said normalized readings and obtaining a set of scores of said normalized readings, wherein said first score is derived from said normalized readings for a target chromosome or chromosomal segment, and wherein said set of scores is a set of scores derived from the normalized number of readings for a set of chromosomes or chromosome segments containing the chromosomal target segment or chromosome; - calculating a parameter p on the basis of said first score and said set of scores, wherein said parameter represents a ratio between * said first score, corrected by a summary statistic of said set of scores, and * a summary statistic of the said set of scores; and - comparing said parameter with a threshold value, wherein said threshold value is a requirement for the presence or absence of one or more aneuploidies of said target chromosome or chromosomal segment that is an indicator of the presence of tumor-derived cell-free DNA. The method according to claim 1, characterized in that said number of readings is recalibrated to correct for GC content and / or total number of readings obtained from said sample. The method according to claim 1 or 2, characterized in that said normalization occurs via comparison with data obtained from corresponding chromosomal segments or chromosomes from a reference series. The method according to any one of claims 1 to 3, characterized in that said summary statistic is the average, the median, the standard deviation, the average absolute deviation or the median absolute deviation. The method of any one of claims 1 to 4, wherein the sequencing is randomly performed on a segment of the nucleic acid molecules contained in the biological sample. The method of any one of the preceding claims, wherein the biological sample is blood, plasma, serum, urine, transcervical fluid or saliva. The method according to any one of the preceding claims, wherein said threshold value is determined using standard statistical considerations, or empirically determined using biological samples. The method of any one of the preceding claims, wherein said first score is calculated as: wherein i is a chromosome or chromosomal segment or the target chromosome or chromosomal target segment. The method according to any one of the preceding claims, characterized in that said parameter p is calculated as: wherein (Zj) represents a set of scores derived from chromosomes or chromosomal segments i, a, b, ... where i corresponds to the chromosomal target segment or chromosome. The method of any one of the preceding claims, comprising the calculation of secondary parameters, wherein said secondary parameters are a requirement for the amount of said aneuploidy, if it is found to be present, and / or a measure of the quality of the monster. The method of claim 10, wherein said secondary parameters are compared to a threshold value. The method of any one of claims 10 or 11, wherein said presence or absence of an aneuploidy is determined by comparing said parameter with a threshold value and comparing one or more secondary parameters and corresponding threshold values. The method according to any of the preceding claims 1 to 12, wherein said aneuploidy comprises aneuploidy of the entire chromosome, a loss, a gain, an amplification or a deletion of a substantial segment of an arm-level chromosome. The method of claim 13, wherein said aneuploidy of the entire chromosome comprises a gain or a loss as shown in Table 1. The method of claim 14, wherein said chromosomal target segments are substantially arm-level segments comprising a p-arm or a q-arm of one or more of the chromosomes 1-22, X and Y. The method of claim 15, wherein said chromosomal target segment comprises one or more arms selected from the group consisting of 1q, 3q, 4p, 4q, 5p, 5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q, 16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q and / or 22q. The method of claims 1 to 14, wherein said aneuploidy comprises an amplification or deletion of one or more arms selected from the group consisting of 1q, 3q, 4p, 4q, 5p, 5q, 6p, 6q, 7p, 7q, 8p, 8q, 9p, 9q, 10p, 10q, 12p, 12q, 13q, 14q, 16p, 17p, 17q, 18p, 18q, 19p, 19q, 20p, 20q, 21q, 22q. The method of claims 1 to 17, wherein said chromosomal segments are segments comprising an area and / or a gene shown in Table 3 and / or Table 5 and / or Table 4 and / or Table 6. The method of claims 1 to 17, wherein said aneuploidy comprises an amplification of a region and / or a gene shown in Table 3 and / or Table 5. The method of claims 1 to 17, wherein said aneuploidy comprises a deletion of a region and / or a gene shown in Table 4 and / or Table 6. The method of claims 1 to 20, wherein said chromosome segments are segments that are known to contain one or more oncogenes and / or one or more tumor-suppressing genes. The method of claims 1 to 17, wherein said aneuploidy comprises an amplification of one or more regions selected from the group consisting of 20Q13, 19ql2, Iq21-lq23, 8pll-p12, MYC, ERBB2 (EGFR), CCIMD1 (Cyclin D1), FGFR1, FGFR2, HRAS, KRAS, MYB, MDM2, CCNE, IMRAS, WITH, ERBB1, CDK4, MYCB, ERBB2, AKT2, MDM2, BRAF, ARAF, CRAF, PIK3CA, AKT1, PTEIM, STK11, MAP2K1, ALK , ROSI, CTIMIMB1, TP53, SMAD4, FBX7, FGFR3, NOTCH1, ERBB4 and CDK4 and the like. The method of any one of claims 1 to 22, wherein said cancer is a cancer selected from the group consisting of leukemia, ALL, brain cancer, breast cancer, colorectal cancer, differentiated liposarcoma, esophageal adenocarcinoma, esophageal squamous cell cancer, GIST, glioma, HCC, hepatocellular cancer, lung cancer, lung NSC, lung SC, medullobastoma, melanoma, MPD, myeloproliferative disorder, cervical cancer, ovarian cancer, prostate cancer and kidney cancer. The method of any one of claims 1 to 23, wherein the detection of aneuploidies indicates a positive result and said method further comprises prescribing, initiating and / or changing a treatment of a human patient from whom the test sample was taken. The method of claim 24, wherein said prescribing, initiating and / or changing a treatment of a human patient from whom the test sample was taken comprises prescribing and / or performing further diagnosis for determining the presence and / or or severity of a cancer. The method of claim 25, wherein the further diagnostics comprises screening a sample of said patient for a cancer biomarker, and / or imaging said patient for a cancer. A computer program product comprising a computer readable medium encoded with a plurality of instructions for controlling a computer system for performing an operation for performing the analysis of the presence of a cancer and / or an increased risk of a cancer in a mammal in a biological sample obtained from a patient, the biological sample containing nucleic acid molecules, the processing comprising the steps of: - receiving the sequences of at least one segment of the nucleic acid molecules contained in a biological sample that is obtained from a patient, wherein said biological sample comprises cell-free DNA; - aligning said obtained sequences with a reference genome; - counting the number of readings on a series of chromosomal segments and / or chromosomes, whereby counts of readings are obtained; - normalizing said counts of readings or a derivative thereof to a normalized number of readings; - obtaining a first score of said normalized readings and obtaining a set of scores of said normalized readings, wherein said first score is derived from the normalized readings for a target chromosome or chromosomal segment, and wherein said set of scores is a set of scores derived from the normalized readings for a set of chromosomes or chromosomal segments that contain the chromosomal target segment or chromosome; - calculating a parameter p on the basis of said first score and said set of scores, wherein said parameter represents a ratio between * said first score, corrected by a summary statistic of said set of scores, and * a summary statistic of the said set of scores; and - comparing said parameter with a threshold value, said threshold value being a requirement for the presence or absence of one or more aneuploidies in said target chromosome or chromosome segment that is an indicator of the presence and / or an increased risk of cancer . The computer program product of claim 27, further comprising operations for calculating one or more secondary parameters, said secondary parameters being a requirement for the intensity of said aneuploidy, if found to be present, and / or a measure of the quality of the sample. A kit comprising a computer program product according to any of claims 27 or 28 and a protocol for obtaining the sequences of at least a portion of the nucleic acid molecules contained in a biological sample, said biological sample comprising cell-free DNA. A report comprising an estimate of the presence or absence of a chromosomal aneuploidy in a patient, said report comprising the parameter p, one or more secondary parameters and a comparison with a threshold value as defined in any one of claims 1 to 27 and a visualization of said readings per chromosome.