[go: up one dir, main page]

WO2025078387A1 - Procédé de détermination de perte d'état d'hétérozygotie d'une tumeur - Google Patents

Procédé de détermination de perte d'état d'hétérozygotie d'une tumeur Download PDF

Info

Publication number
WO2025078387A1
WO2025078387A1 PCT/EP2024/078316 EP2024078316W WO2025078387A1 WO 2025078387 A1 WO2025078387 A1 WO 2025078387A1 EP 2024078316 W EP2024078316 W EP 2024078316W WO 2025078387 A1 WO2025078387 A1 WO 2025078387A1
Authority
WO
WIPO (PCT)
Prior art keywords
loh
genomic dna
hrd
status
positive
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/EP2024/078316
Other languages
English (en)
Inventor
Diether Lambrechts
Tom Venken
Pieter BUSSCHAERT
Liselore LOVERIX
Ignace VERGOTE
Toon VAN GORP
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Katholieke Universiteit Leuven
Vlaams Instituut voor Biotechnologie VIB
Original Assignee
Katholieke Universiteit Leuven
Vlaams Instituut voor Biotechnologie VIB
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Katholieke Universiteit Leuven, Vlaams Instituut voor Biotechnologie VIB filed Critical Katholieke Universiteit Leuven
Publication of WO2025078387A1 publication Critical patent/WO2025078387A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis

Definitions

  • This disclosure relates to methods of detecting the loss of heterozygosity (LOH) status of a tumor and to methods of detecting the homologous recombination repair deficiency (HRD) status of a tumor.
  • LOH loss of heterozygosity
  • HRD homologous recombination repair deficiency
  • Such methods further translate into ways of predicting the response of a subject having cancer to treatments or therapies comprising DNA damaging agents and/or agents inhibiting or impairing DNA repair; and into use of such treatments or therapies when the LOH or HRD status of the subject having cancer is positive.
  • One field of genetic testing in the context of predicting therapy response relates to testing of genomic scarring or homologous recombination (repair) deficiency (HRD; homologous recombination repair (HRR) is the process of normal DNA repair, especially of repair of breaks in both DNA strands) testing in the context of predicting response to therapies including poly-ADP-ribose polymerase inhibitors (PARPi), platinum-based therapies, etc.
  • HRD homologous recombination (repair) deficiency
  • PARPi poly-ADP-ribose polymerase inhibitors
  • platinum-based therapies etc.
  • HRD A number of HRD causes have been identified including mutations or lowered expression of the BRCA1 and/or BRCA2 genes and other genes such as RAD51C and PALB2. HRD is, however, not always linked to identifiable gene mutations. Across cancers, HRD occurs at a frequency of about 6%. Rates can be as high as 30% for ovarian cancer, and intermediate for breast, pancreatic and prostate cancer (12-13%).
  • LOH heterozygosity
  • percentage LOH see e.g. W02011/106541; W02011/160063; WO2013/096843; Abkevich et al. 2012, Br J Cancer 107:1776-1782
  • FLOH fractional LOH
  • TAI telomeric allelic imbalance
  • WO 2012/027224 WO2013/130347
  • Birkbak et al. 2012, Cancer Discov 2:366-375 large scale transitions
  • This disclosure relates to methods for determining the loss-of-heterozygosity (LOH) status of a (test) genomic DNA sample, comprising, or comprising the steps of:
  • the loss of heterozygosity (LOH) status of a set of N bins of the test genomic DNA sequence therewith providing a set of N LOH/bin features; wherein the N bins are of equal size of equal to or smaller than 2 Mbp; wherein the N bins were selected from a genome-wide set of bins for their highest proportional contribution or most significant contribution of the/their associated LOH/bin features to the determination of the LOH status of a set or reference genomic DNA samples, wherein the set of reference genomic DNA samples is including genomic DNA samples known of being LOH positive and/or homologous recombination repair deficient (HRD positive) and is including genomic DNA samples known of being LOH negative and/or homologous recombination repair proficient (HRD negative);
  • LOH loss of heterozygosity
  • the trained data classification algorithm or model is trained for classifying a set of reference genomic DNA samples including genomic DNA samples known of being LOH positive and/or homologous recombination repair deficient (HRD positive) and genomic DNA samples known of being LOH negative and/or homologous recombination repair proficient (HRD negative) wherein the training is based on the sets of N LOH/bin features of the reference genomic samples;
  • the LOH status of a bin is in one embodiment based on the copy number of one or more single nucleotide polymorphisms (SNPs) of interest, and is determined as the mean of the LOH positive SNPs relative to all SNPs of interest within the bin, wherein a SNP is LOH positive or has LOH positive status when the copy number of the minor allele of the SNP is zero.
  • the copy number of a SNP of interest can for instance be derived from the copy number of a segment of the genomic DNA with constant copy number comprising the SNP of interest.
  • SNPs can for example be captured by hybridization to a plurality of oligonucleotide probes.
  • the LOH status of a bin is in another embodiment based on the copy number of a nucleotide of interest in the bin, and is determined as the copy number of a segment of the genomic DNA with constant copy number comprising the nucleotide of interest in the bin, wherein a bin is LOH positive or has LOH positive status when the copy number of the minor allele of the nucleotide of interest in the bin is zero.
  • the copy number (of a SNP, of a nucleotide of interest in the bin) can be determined based on untargeted or targeted sequencing of the genomic DNA.
  • the above methods can furthermore be included in or be part of methods of predicting the response of a subject or patient having cancer or a tumor to a treatment or treatment regimen comprising a DNA damaging agent and/or an agent inhibiting or impairing DNA repair, wherein the subject or patient is likely to respond to the treatment or treatment regimen when a genomic DNA sample obtained from the tumor or from a cancer cell of the cancer or tumor is determined to have a LOH positive status , or to have a HRD positive status according to the methods as outlined hereinabove.
  • the above methods can furthermore be included in or be part of methods of determining or assessing survival probability of a subject or patient having cancer or a tumor receiving treatment or a treatment regimen comprising a DNA damaging agent or therapy and/or comprising an agent inhibiting or impairing DNA repair or therapy comprising inhibiting or impairing DNA repair, wherein the subject or patient (having the cancer or tumor) is likely to have an increased survival probability if a genomic DNA sample obtained from the tumor or from a cancer cell of the cancer or tumor is determined to have a LOH positive status, or to have a HRD positive status according to the methods as outlined hereinabove.
  • This disclosure further relates to DNA damaging agents and/or an agents inhibiting or impairing DNA repair for use in treating a tumor or cancer if a genomic DNA sample obtained from the tumor or from a cancer cell of the cancer or tumor is determined to have a LOH positive status, or to have a HRD positive status according to the methods as outlined hereinabove.
  • This disclosure further relates to computing or data processing systems or devices, machine readable media, or diagnostic kits comprising a means for carrying out or performing a method as described hereinabove, or to perform a step of a method as described hereinabove.
  • FIGURE 1 Distinguishing HRD-positive and HRD-negative samples by means of (A) fractional loss-of- heterozygosity (FLOH, calculated by the ASCAT algorithm), and by means of (B) the scarHRD-score, with indication of AUC and P-values. Analysis was performed on 192 samples of the PAOLA-1 trial for which the Myriad myChoice HRD status ("positive” or "negative") was known.
  • FLOH fractional loss-of- heterozygosity
  • FIGURE 2 ROC curves of Myriad myChoice positive versus Myriad myChoice negative samples.
  • FIGURE 3 Flowchart of the exemplary sequencing and analysis pipeline. In total, 192 FFPE samples from the PAOLA1 trial with known Myriad myChoice status were processed.
  • GATK Genome Analysis Toolkit; dedup: deduplicate.
  • FIGURE 4 ROC curves of Myriad myChoice positive versus Myriad myChoice negative samples.
  • HRD score as determined with scarHRD algorithm;
  • PAM (Partitioning Around Medoids algorithm) score classification based on mean LOH/bin, on mean LOH/chromosome and on mean genome-wide LOH features.
  • HRD+ homologous recombination deficient or HRD positive
  • HRD- homologous recombination proficient or HRD negative.
  • FIGURE 6 Survival curve analysis of olaparib arm (A) and placebo arm (B). Survival probabilities were calculated with respect to classification using the Myriad myChoice HRD status or the PAM derived score mean LOH/bin features only. HRD+: homologous recombination deficient or HRD positive; HRD-: homologous recombination proficient or HRD negative. Bin-size: 1 Mbp (1000 kbp).
  • FIGURE 7 Distinguishing HRD-positive and HRD-negative samples by means of Random Forest (RF) based classification of the LOH/bin features. Analysis was performed on 192 samples of the PAOLA-1 trial for which the Myriad myChoice HRD status ("positive” or "negative") was known.
  • RF Random Forest
  • FIGURE 8 Survival curve analysis of olaparib arm (A) and placebo arm (B). Survival probabilities were calculated with respect to classification using the Myriad myChoice HRD status or the Random Forest based classification of the LOH/bin features. HRD+: homologous recombination deficient or HRD positive; HRD-: homologous recombination proficient or HRD negative; PFS: progression free survival.
  • FIGURE 9 Survival curve analysis of olaparib arm (A,C) and placebo arm (B,D) as in Figure 6.
  • FIGURE 10 Survival curve analysis of olaparib arm (A,C,E,G) and placebo arm (B,D,F,H) as in Figure 8.
  • FIGURE 11 Survival curve analysis of olaparib arm (A, C) and placebo arm (B, D) as in Figure 6.
  • A and (B) random forest classifier model trained on/with all LOH-bins (genome-wide).
  • HRD tests assess either the cause (e.g. screening homologous recombination repair (HRR) gene panel for presence of mutations), the result of HRD (i.e., genomic scarring), or some combination of both.
  • HRR homologous recombination repair
  • genomic scarring i.e., genomic scarring
  • HRD testing has been linked with predicting outcome of some cancer therapies, such predictive results are still not optimal and in general, mutations in most HRR genes, although informative about a patient's general status, have not yet solidly been assigned such predictive power (Miller et al. 2020, Annals Oncol 31:1606-1622).
  • Most assays to identify HRD are relying on multiple different features (e.g.
  • %LOH loss of heterozygosity
  • LSTs large scale transitions
  • TAI telomeric allelic imbalance
  • An objective of earlier work was to design a relatively simple assay to detect or determine the LOH status of the DNA in a tumor as a proxy for detecting or determining the HRD status of that tumor.
  • a further objective one perceived as difficult, if not impossible to reach in view of all previous work done in the field, was that such assay should nevertheless perform at least equally well in detecting or determining HRD status as the current gold standard assay implying LOH, LST, and TAI features.
  • prior methods for detecting or determining LOH-status of the DNA in a tumor sample (i) only included regions of LOH of a size larger than 1.5 Mb (as shorter regions of LOH were reported not to correlate with HR- deficiency) and more in particular LOH-regions having a size of larger than 1.5 Mb and shorter than a full chromosome (e.g. W02011160063A2, WO2013096843A1), although supported by published data and in practice the low-end size threshold more likely is 15 Mb (Abkevich et al.
  • the bin size was equal to or smaller than 2 Mbp or equal to or smaller than 1.5 Mbp (and, optionally, equal to or larger than 0.1 Mbp, equal to or larger than 0.2 Mbp, equal to or larger than 0.3 Mbp, equal to or larger than 0.4 Mbp) as equal results were obtained with exemplary bin sizes of 1.25 Mbp (1250 kbp), 1 Mbp (1000 kbp), 0.75 Mbp (750 kbp) and 0.5 Mbp (500 kbp), this when relying on the whole genome as source of the bins (see WO2024/083971).
  • the reference to the bin size can exchangeably be expressed as number of bases (b) or as number of basepairs (bp) depending on when referring to single- or double-stranded nucleic acids.
  • this disclosure relates to methods for detecting, determining, analyzing or measuring the loss-of-heterozygosity (LOH) status of a (test) genomic DNA sample, comprising, or comprising the steps of:
  • the loss of heterozygosity (LOH) status of a set of N bins of the test genomic DNA sequence therewith providing a set of N LOH/bin features; wherein the N bins are of equal size of equal to or smaller than 2 Mbp; wherein the N bins are or were selected (such as by computer-implemented selection) from a genome-wide set of bins for their highest proportional contribution or most significant contribution of the associated LOH/bin features to the detection or determination of the LOH status of a set or reference genomic DNA samples, wherein the set of reference genomic DNA samples is including/includes genomic DNA samples known of being LOH positive and/or homologous recombination repair deficient (HRD positive) and (is including/includes) genomic DNA samples known of being LOH negative and/or homologous recombination repair proficient (HRD negative);
  • LOH loss of heterozygosity
  • the classification is with a data classification algorithm trained for classifying a set of reference genomic DNA samples including genomic DNA samples known of being LOH positive and/or homologous recombination repair deficient (HRD positive) and including genomic DNA samples known of being LOH negative and/or homologous recombination repair proficient (HRD negative) wherein the training is based on the sets of N LOH/bin features of the reference genomic samples (resulting in a trained data classification algorithm or model); or, alternatively, inferencing or scoring of the set of N LOH/bin features of a test genomic DNA relative to/with the trained data classification algorithm or model;
  • test genomic DNA sample to be LOH positive when the trained data classification algorithm or model classified, inferenced or scored the test genomic DNA sample as most closely resembling LOH positive and/or HRD positive reference genomic DNA; or detecting or determining the test genomic DNA sample to be LOH negative or to have a LOH negative status when the trained data classification algorithm or model classified, inferenced or scored the test genomic DNA sample as most closely resembling LOH negative and/or HRD negative reference genomic DNA.
  • the set of N LOH/bin features as referred to hereinabove is not referring to a set of bins covering the full genome of a subject but rather is referring to a subset of bins, more precisely to N bins wherein N is an integer/number. Such set of N bins is thus selected, or pre-selected, from a genome-wide set of bins.
  • the selection can more in particular be performed by a computer-run or computer-implemented selection test, such as a permutation or re-randomization test, to single out the N bins that contribute most to the detection or determination of the LOH status of a set or reference genomic DNA samples, wherein the set of reference genomic DNA samples is including/includes genomic DNA samples known of being LOH positive and/or homologous recombination repair deficient (HRD positive) and (is including/includes) genomic DNA samples known of being LOH negative and/or homologous recombination repair proficient (HRD negative).
  • HRD positive homologous recombination repair deficient
  • HRD negative homologous recombination repair proficient
  • a ranking of the genome-wide set of bins is constructed ranging from null or low contribution to LOH or HRD to the highest contribution to LOH or HRD. From such ranking, either the N bins with the highest proportional contribution (top N bins) or with the most significant contribution (significant N bins, e.g. based on P-value) can be selected.
  • the applied selection threshold is determining the number N, or phrased alternatively, the number or value of N can be varied by varying the applied threshold.
  • N bins are covering less than 100% of the full genome, e.g. 75% (or approximately 75%), 60% (or approximately 60%), 50% (or approximately 50%), 40% (or approximately 40%), 30% (or approximately 30%), 20% (or approximately 20%), 15% (or approximately 15%), 10% (or approximately 10%), 5% (or approximately 5%), 4% (or approximately 4%), 3% (or approximately 3%), 2% (or approximately 2%), or 1% (or approximately 1%) of the full genome.
  • 75% or approximately 75%)
  • 60% or approximately 60%
  • a bin size of 1 Mbp 2877 bins were delineated in the full genome (see Example 1).
  • N is an integer with a value of 1 (or 5 or 10) to 1000, 1 (or 5 or 10) to 900, 1 (or 5 or 10) to 800, 1 (or 5 or 10) to 700, 1 (or 5 or 10) to 600, 1 (or 5 or 10) to 500, 1 (or 5 or 10) to 400, 1 (or 5 or 10) to 300, 1 (or 5 or 10) to 200, 1 (or 5 or 10) to 100, or 1 (or 5 or 10) to 50; or with a value of 1, 5, 10, 20, 30, 40 ,50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 325, 350, 375, 400
  • the size of the bins is smaller than 1.5 Mb.
  • the LOH status of a bin is based on the copy number of one or more single nucleotide polymorphisms (SNPs) of interest (and located or present in the bin) and is detected or determined as the mean or average of the LOH positive SNPs relative to all SNPs of interest within the bin, wherein a SNP is LOH positive or has a LOH positive status when the copy number of the minor allele (of the SNP, or comprising the SNP) is zero.
  • SNPs single nucleotide polymorphisms
  • the (minor allele) copy number of a SNP of interest equals or is the (same as the) copy number of a segment of the (test) genomic DNA with constant (minor allele) copy number comprising the SNP of interest.
  • the LOH status of a bin is based on the (minor allele) copy number of a nucleotide position (of interest; or locus of interest) in the bin, and is detected or determined as being equal to or as being the (same as the) (minor allele) copy number of a segment of the (test) genomic DNA with constant (minor allele) copy number comprising the nucleotide position (of interest) in the bin and wherein the bin is LOH positive or has a LOH positive status when the copy number of the minor allele of the nucleotide position (of interest) in the bin is zero.
  • the nucleotide position (of interest) can be any nucleotide position in the bin (i.e. not limited to a SNP of interest).
  • the nucleotide position (of interest) is a reference position.
  • the reference position in or within each bin is the same for each bin.
  • the reference position in or within the bin can be the center position of the bin, or can be a position within a stretch of nucleotides of or spanning 5% of the bin size on either side from the center position (e.g. if the bin size is 1 Mb, then the reference position could be within 50kb from either side of the 500 kb midpoint of the bin).
  • segments of the (test) genomic DNA with constant copy number are determined or delineated based on the copy number of a plurality of SNPs or nucleotides of interest (at least 1 SNP/nucleotide of interest in or within the segment and at least 1 SNP/nucleotide of interest outside the segment).
  • the plurality of SNPs or nucleotides of interest is distributed over or across the (test) genomic DNA present in the sample.
  • the plurality of SNPs or nucleotides of interest is distributed over or across the (test) genomic DNA excluding centromeric regions.
  • such segment of genomic DNA with constant copy number is determined by the ASCAT algorithm or the asmultipcf algorithm (Van Loo et al. 2010, Proc Natl Acad Sci USA 107:16910-16915 or Ross et al. 2021, Bioinformatics 37:1909-1911, respectively). Further in particular, the plurality of SNPs or loci or nucleotides of interest is distributed over or across the (test) genomic DNA corresponding to the N selected bins.
  • each of the N bins by identifying the copy number of a nucleotide/nucleotide position (of interest) in the bin as, as being, or as being equal to/identical to/ the (same as the) copy number of a segment of the genomic DNA with constant copy number comprising the nucleotide/nucleotide position (of interest) in the bin, and wherein a bin is LOH positive or has a LOH positive status when the copy number of the minor allele of the nucleotide/nucleotide position (of interest) in the bin is zero, therewith providing a set of N LOH/bin features.
  • the LOH status of a segment of genomic DNA with constant copy number is detected or determined (such segment being LOH positive or having a LOH positive status when the copy number of the minor allele of the segment is zero), and the LOH status of any nucleotide/nucleotide position of interest in or within a bin (including SNPs, or nucleotides or loci of interest) comprised in the segment is then defined, detected, or determined as being the (same as the), identical to, or equals the LOH status of the segment.
  • the above method may comprise a further step of detecting, determining or delineating segments of constant copy number in the (test) genomic DNA based on the copy number of at least 1 SNP (or nucleotide or locus of interest) or of a plurality of SNPs (or nucleotides or loci of interest).
  • the plurality of SNPs is distributed over or across the (test) genomic DNA (more in particular across the N selected bins of (test) genomic DNA) present in the sample.
  • the plurality of SNPs (or nucleotides or loci of interest) is distributed over or across the (test) genomic DNA (more in particular across the N selected bins of (test) genomic DNA) excluding centromeric regions.
  • an outcome may be that a (test) genomic DNA cannot be classified as LOH positive or negative or as having a LOH positive or negative status in case the (test) genomic DNA does not closely resemble LOH positive reference genomic DNA and does not closely resemble LOH negative reference genomic DNA.
  • the LOH status of the (test) genomic DNA is assigned as unknown, uncertain, not defined, undefined, or not determinable; reasons for such undefined classification include e.g. poor quality of the sample itself or e.g. a technical issue occurring during sample preparation or sample processing.
  • Loss of heterozygosity can be defined as the absence of one parent's contribution to the genetic material at a specific site in the genome.
  • a locus is LOH positive, or has LOH status, when one of the parental copies of the locus is missing, i.e. when the paternal or maternal copy of the locus is missing.
  • a locus can be a SNP or nucleotide, or can comprise a SNP or nucleotide, or can be a genomic region comprising or covering a SNP or nucleotide, such as a bin referred to in the above methods.
  • the locus, SNP or nucleotide is a locus, SNP or nucleotide of interest.
  • mean or average LOH as referred to herein is meant the arithmetic or mathematical mean or average of the number of SNPs, nucleotides or loci assessed in a defined region as having LOH positive status divided by the total number of SNPs, nucleotides or loci assessed in that same defined region.
  • none of the SNPs, nucleotides or loci are LOH positive and the mean or average LOH equals zero (0), or all of the SNPs, nucleotides or loci are LOH positive and the mean or average LOH equals one (1).
  • “LOH positive” can be exchanged for e.g. having LOH status, or having LOH positive status
  • LOH negative can be exchanged for e.g. not having LOH status, or having LOH negative status.
  • LOH as referred to herein is detected or determined based on log ratios (logR) and B allele frequencies (BAFs).
  • LogR refers to log-transformed copy numbers derived from sequencing depth or SNP array data; BAF to the allelic imbalance of SNPs.
  • the LOH status of a SNP, locus or nucleotide (position) of interest or of a region of interest (e.g. bin) is derived from the (minor allele) copy number(s) of one or more segments of the genomic DNA detected or determined to have a constant copy number (within the segment), wherein the segment is comprising the SNP, locus or nucleotide (position) of interest or is comprising or in part comprising the region (e.g. bin) of interest.
  • the LOH status of a segment of the genomic DNA comprising the SNP, locus or nucleotide (position) of interest or comprising or in part comprising the region (e.g. bin) of interest, is detected or determined, and the LOH status of the SNP, locus or nucleotide (position) of interest or of a region of interest (e.g. bin) equals or is the same as or is identical to the LOH status of the segment of genomic DNA.
  • such segment of genomic DNA of/having a constant copy number is a segment as detected or determined by the ASCAT algorithm or the asmultipcf algorithm (Van Loo et al. 2010, Proc Natl Acad Sci USA 107:16910-16915 or Ross et al. 2021, Bioinformatics 37:1909-1911, respectively).
  • Such segments can be delineated e.g. based on the copy number detection or determination of a plurality of SNPs, such as a plurality of SNPs distributed across the genome. In particular the distribution of the SNPs is such that the N selected bins are covered.
  • An advantage of reducing the size of the regions of interest is that the copy number of such region of interest (e.g. bins) can be reliably detected or determined based on the copy number as detected or determined for the segments of genomic DNA with constant copy number: the smaller the size of the region of interest (e.g. bin), the larger the chance that there will be no switch of copy number within the region of interest (and the larger the chance LOH of e.g. a short genomic DNA region, such as a bin as defined herein, will be either 0 or 1). As such, the copy number of a small enough region of interest (e.g.
  • bin will with very few exceptions be identical to, be equal to, or be the same as the copy number of a segment of genomic DNA with constant copy number. This then logically extrapolates to all SNPs, loci or nucleotides (nucleotide positions) of interest present within the region of interest (e.g. bin). Deriving copy number of an individual SNP, locus or nucleotide (position) of interest from a segment of genomic DNA with constant copy number comprising the individual SNP, locus or nucleotide (position) of interest may additionally reduce possible noise-induced errors occurring by individual detection or determination of the copy number of the SNP, locus or nucleotide (position) of interest.
  • any of the above methods may comprise a step of detecting, determining or delineating segments of constant copy number in the (test) genomic DNA based on the copy number of a plurality of SNPs (or nucleotides or loci of interest; located or present in the segment).
  • the set of N LOH/bin features is maintained during classification or during computer-implemented data classification, meaning that individual (LOH/bin) features of the set are not combined, merged, added or collapsed into a larger bin.
  • centromere regions of chromosome(s) are excluded from the set of N LOH/bin features.
  • sex chromosomes are excluded from the set of N LOH/bin features.
  • the copy number of a SNP, nucleotide, bin, segment or locus of interest is detected or determined based on, or starting from, sequencing of the (test or reference) genomic DNA comprising the SNP, nucleotide, bin, segment or locus of interest.
  • the sequencing is targeted (e.g. involving linear, non-linear or PCR amplification and/or capture by hybridization of a genomic DNA region comprising a SNP, bin, segment or locus of interest of interest) or untargeted (e.g. whole genome, exome, transcriptome) sequencing.
  • Nucleotide sequencing is possible by any known means including classical Sanger sequencing, massive parallel sequencing and (nano)pore sequencing.
  • the sequencing is at low coverage, at high coverage, at lx to lOx coverage, at lx to 500x coverage, at lOx coverage, at 20x coverage, at 30x coverage, at 40x coverage, is at 50x to 500x coverage, is at 50x coverage, at 60x coverage, at 70x coverage, at 80x coverage, at 90x coverage, at lOOx coverage, at llOx coverage, at 120x coverage, at 130x coverage, at 140x coverage, at 150x coverage, at 160x coverage, at 170x coverage, at 180x coverage, at 190x coverage, or at 200x coverage; or wherein the sequencing is at 50x to lOOOx coverage, at 300x coverage, at 400x coverage, at 500x coverage, at 600x coverage, at 700x coverage, at 800x coverage, at 900x coverage, or at lOOOx coverage.
  • the sequencing is shallow sequencing, deep sequencing, ultradeep sequencing, or maximum-depth sequencing.
  • the sequencing is paired-end sequencing such as 2x50 bp reads to 2x200 bp reads (e.g. 2x50 bp reads, 2x60 bp reads, 2x70 bp reads, 2x80 bp reads, 2x90 bp reads, 2x100 bp reads, 2x110 bp reads, 2x120 bp reads, 2x130 bp reads, 2x140 bp reads, 2x150 bp reads, 2x151 bp reads, 2x160 bp reads, 2x170 bp reads, 2x180 bp reads, 2x190 bp reads, 2x200 bp reads).
  • the sequenced stretch of the genomic DNA is comprising one or more of the plurality of SNPs, nucleotides or loci of interest.
  • the sequencing reads are aligned to a reference genome (such as the human hgl8, hgl9, NCBI/hgl8, GRCh37/hgl9, or GRCh38.pl4 reference genome).
  • a reference genome such as the human hgl8, hgl9, NCBI/hgl8, GRCh37/hgl9, or GRCh38.pl4 reference genome.
  • the copy number of a SNP, nucleotide, bin, segment or locus of interest of interest is detected or determined based on, or starting from, minimum quality sequencing data (e.g. minimum base quality and/or minimum mapping quality).
  • minimum quality sequencing data e.g. minimum base quality and/or minimum mapping quality
  • detection or determination of the copy number of a SNP, nucleotide, bin, segment or locus of interest of interest is involving an algorithm or bio-informatic algorithm such as run or executed on a computer.
  • algorithm can e.g. be based on a hidden Markov algorithm or on circular binary segmentation.
  • detection or determination of the copy number of a SNP, nucleotide, bin, segment or locus of interest of interest is involving an analytical method compensating for contamination present in the DNA sample (e.g. if the DNA sample is obtained from a tumor sample, DNA from non-tumor cells is a "contaminant" expected to be present in the DNA sample).
  • the SNP, nucleotide, bin, segment or locus of interest of interest, or the genomic DNA comprising a SNP, nucleotide, bin, segment or locus of interest of interest is captured from the genomic DNA sample by means of (hybridization to) an oligonucleotide probe.
  • a plurality of SNPs, nucleotides, bins, segments or loci of interest of interest, or a plurality of SNPs, nucleotides, bins, segments or loci of interest of interest-comprising genomic DNA molecules is captured by or captured by means of (hybridization to) a library of oligonucleotide probes (capture library / nucleic acid capture), or captured by or captured by means of (hybridization to) a plurality of oligonucleotide probes.
  • This process is also known as hybridization capture or target enrichment.
  • the process may include prior shearing, such as mechanical shearing, of the input material (e.g.
  • the genomic DNA sample from a tumor or cancer cell may in principle be any type of biological sample comprising tumor genomic DNA including fresh or processed (e.g. FFPE, fresh frozen) tumor biopsy samples, circulating tumor DNA, genomic DNA from circulating tumor cells etc.
  • FFPE formalin fixed paraffin embedded
  • the number of SNPs, nucleotides, bins, loci, or segments to be analyzed can be limited as genome-wide coverage is not required. This renders these methods applicable to analysis or assessment of smaller amounts of input genomic DNA, thus in particular also applicable to analysis or assessment of ctDNA or cfDNA.
  • the tumor or cancer can be of primary origin or of metastatic origin.
  • the tumor or cancer may in particular be a tumor or cancer known to be prone or susceptible to (genetic scarring by means of) any defect or impairment in the homologous recombination repair pathway; or known to be prone or susceptible to losing homologous recombination repair proficiency (or HRD negative status); or known to be prone or susceptible to become homologous recombination repair deficient (or HRD positive).
  • the tumor or cancer may in particular be any of e.g. ovarian, breast, colon, prostate, pancreatic, lung, renal or esophagal origin.
  • a plurality of SNPs, nucleotides, bins, segments or loci is meant to include more than 1 SNP, nucleotide, bin, segment or locus, thus 2 or more SNPs, nucleotides, bins, segments or loci.
  • SNPs nucleotides, bins, segments or loci are envisaged, or between 2 and 150k, between 2 and 100k , between 2 and 75k, between 2 and 50k, or between 2 and 25k SNPs, nucleotides, bins, segments or loci; or between 10k and 200k SNPs, nucleotides, bins, segments or loci, or between 10k and 150k, or between 10k and 100k, or between 10k and 75k, or between 10k and 50k, or between 10k and 25k SNPs, nucleotides, bins, segments or loci.
  • such oligonucleotide probe(s) are attached to a solid support such as a sheet, bead, or plate well (of any suitable size or dimension).
  • a spacer is introduced between the solid support and the oligonucleotide probe(s) to support efficient capture of the DNA of interest from the sample such as biological sample.
  • the individual oligonucleotide probes can have a length of 50 to 150bp, or an average length of 50 bp, 60 bp, 70bp, 80 bp, 90 bp, of 100 bp, of 110 bp, of 120 bp, of 130 bp, of 140 bp, or of 150 bp.
  • the capture library is optionally comprising or further comprising one or a set of gene-specific oligonucleotides such as oligonucleotide probes.
  • the capture oligonucleotides comprise molecular barcodes, molecular indexes, or unique molecular identifiers (UMIs).
  • the size of the size-defined bins is equal to or lower than/smaller than 2000 kb(p) (2 Mb(p)), or equal to or lower than/smaller than 1500 kb(p) (1.5 Mb(p)), e.g. about 50 kb(p) to 2000 kb(p), about 50 kb(p) to 1500 kb(p), about 50 kb(p) to 1400 kb(p), e.g. approximately 50 kb(p), e.g. approximately 100 kb(p), e.g. approximately 200 kb(p), e.g. approximately 300 kb(p), e.g.
  • kb(p) approximately 400 kb(p), e.g. approximately 500 kb(p), approximately 550 kb(p), approximately 600 kb(p), approximately 650 kb(p), approximately 700 kb(p), approximately 750 kb(p), approximately 800 kb(p), approximately 850 kb(p), approximately 900 kb(p), approximately 950 kb(p), approximately 1000 kb(p), approximately 1050 kb(p), approximately 1100 kb(p), approximately 1150 kb(p), approximately 1200 kb(p), approximately 1250 kb(p), approximately 1300 kb(p), approximately 1350 kb(p), approximately 1400 kb(p), approximately 1450 kb(p); and wherein the size of one or more bins may deviate from the size of the majority of the bins depending on chromosome size (e.g.
  • a size-defined bin can cover either no SNP, nucleotide or locus of interest, one SNP, nucleotide or locus of interest, two SNPs, nucleotides or loci of interest, or three or more SNPs, nucleotides or loci of interest.
  • detection or determination of the LOH status of a test genomic DNA sample can involve an algorithm or bio-informatic algorithm such as run or executed on a computer; in particular the algorithm is a machine learning algorithm or model, more in particular the algorithm is a data classification algorithm or data classifier algorithm.
  • Random Forest Random Forest
  • SVM Support Vector Machines
  • KNN K-Nearest Neighbors
  • LDA Linear Discriminant Analysis
  • MLP multi layer perceptron
  • DT decision trees
  • Ada adaboost (Ada) (Turgut et al. 2018 doi: 10.1109/EBBT.2018.8391468), (nearest) shrunken centroid classifier algorithms, etc.
  • the machine learning model or algorithm Prior to detecting or determining the status of interest of a test item (alternatively: inferencing or scoring a test item) with the help of a computer-implemented data classification algorithm or model, the machine learning model or algorithm must be trained with a set of training items of which the status of interest is known, or must be trained with a set of reference items.
  • a set of reference genomic DNA samples was used. More in particular, a set of reference genomic DNA samples of an exemplary clinical trial study was used, wherein the exemplary clinical trial studied the effect of adding the PARP inhibitor olaparib to bevacizumab therapy of ovarian cancer, the beneficial effect of such addition being more pronounced/substantial in patients with a HRD-positive tumor.
  • the clinical trial is the PAOLA-1 trial (Ray-Coquard et al. 2019, N Engl J Med 381: 2416-2428; ClinicalTrials.gov identifier NCT02477644). More in particular, the data classification model or algorithm is trained using (as input features) sets of N LOH/bin features as described above and as detected or determined for (each of) the reference genomic DNA samples (comprising genomic DNA samples known of being LOH positive and/or HRD positive and genomic DNA samples known of being LOH negative and/or HRD negative).
  • a reference genomic DNA sample is assigned as being LOH or HRD positive, or as being LOH or HRD negative, based on a method, kit or assay known or available in the art.
  • LOH or HRD status of a reference genomic DNA sample can have been detected or determined by means of a HRD-LOH score.
  • HRD-LOH status HRD-LOH status
  • LST large scale transition
  • TAI telomeric allelic imbalance
  • HRD-LOH score is based on the number of LOH regions of intermediate size (longer than 15 Mb but shorter than the whole chromosome: Abkevich et al. 2012, Br J Cancer 107:1776-1782) occurring in the DNA of a tumor sample.
  • LST Large-scale state transitions
  • TAI Telomeric-allelic imbalance
  • TAI is allelic imbalance extending to the subtelomere but not crossing the centromere and is enriched in cancers with BRCA1/2 deficiency and response to platinum chemotherapy (Birkbak, et al. 2012, Cancer Discov 2:366-375).
  • fractional LOH or percentage LOH are parameters that are less reliable in detecting or determining LOH or HRD status of a (test or) reference genomic DNA sample; this is in line with previous observations reported by Timms et al.
  • an LOH positive and -negative reference genomic DNA profile can be made/is made by means of classification (such as by means of machine learning assisted classification) as is subject of this disclosure.
  • a test genomic DNA sample in general will have (or will be inferenced or scored as having) an LOH status profile more or most closely resembling the LOH positive and/or HRD positive reference genomic DNA profile or more or most closely resembling the LOH negative and/or HRD negative reference genomic DNA profile and can that way be classified as having either LOH positive status or LOH negative status.
  • An alternative outcome may be that a (test) genomic DNA cannot be classified/inferenced/scored as LOH positive or negative or as having a LOH positive or negative status in case the (test) genomic DNA does not closely resemble LOH positive reference genomic DNA and does not closely resemble LOH negative reference genomic DNA. In such cases, the LOH status of the (test) genomic DNA is assigned as unknown, uncertain, not defined, undefined, or not determinable.
  • any of the above-mentioned methods are methods to detect or determine the homologous recombination deficiency (HRD) status of a tumor.
  • any of the above-mentioned methods are methods for detecting or determining the homologous recombination deficiency (HRD) status of the (test) genomic DNA sample, wherein a genomic DNA sample classified/inferenced/scored as being LOH positive or as having a LOH positive status is HRD positive or is having a HRD positive status; or, wherein a genomic DNA sample classified/inferenced/scored as being LOH negative or as having a LOH negative status is HRD negative or is having a HRD negative status, respectively.
  • the overall LOH status of tumor-associated DNA is a footprint linked to defects in a cell's homologous recombination repair machinery, such as defects in the homologous recombination repair genes BRCA1 and/or BRCA2.
  • one of the initial HRD scores was build based on the number of LOH regions of intermediate size (longer than 15 Mb but shorter than the whole chromosome: Abkevich et al. 2012, Br J Cancer 107:1776-1782) occurring in the DNA of a tumor sample, and is sometimes referred to as the "HRD-LOH" score. It is thus accepted in the field that detecting or determining LOH status is a proxy or alternative for detecting or determining HRD-status.
  • any of the above-mentioned methods are alternatively methods to detect or determine defects in the homologous recombination repair genes BRCA1 and/or BRCA2 wherein a LOH positive status or HRD positive status of the analyzed genomic DNA is indicative of the presence of a defect in the BRCA1 and/or BRCA2 gene, or is indicative of a BRCA positive status (defects present - as opposed to a BRCA negative status when no defects are present); or, wherein a (test) genomic DNA sample classified/inferenced/scored as being LOH or HRD negative or as having a LOH or HRD negative status is BRCA negative or is having a BRCA negative status.
  • an outcome may be that a (test) genomic DNA cannot be classified/inferenced/scored as LOH positive or negative or as having a LOH positive or negative status in case the (test) genomic DNA does not closely resemble LOH positive reference genomic DNA and does not closely resemble LOH negative reference genomic DNA.
  • the HRD or BRCA status of the (test) genomic DNA is assigned as unknown, uncertain, not defined, undefined, or not determinable.
  • the presence of HRD in a tumor cell furthermore is at the basis of identifying cancer patients that are expected to respond well to therapies (further) inhibiting or impairing DNA repair or otherwise damaging DNA, therewith exacerbating the defects in HRD leading to LOH, such as with inhibitors of poly ADP ribose polymerase (PARP)(e.g. olaparib, rucaparib, niraparib, talazoparib, veliparib, pamiparib), of type I topoisomerases (e.g.
  • PARP poly ADP ribose polymerase
  • camptothecins such as topotecan, irinotecan and belotecan; and non- camptothecins such as indenoisoquinoline, phenthridines and indolocarbazoles), of type II topoisomerases (e.g. doxorubicin, daunorubicin, and other anthracycline antibiotics (also acting as DNA damaging agent by intercalating in DNA)), of dihydrofolate reductase (DHFR; e.g. methotrexate) or with DNA damaging agents or treatments.
  • DNA damaging treatments include ionizing radiation, irradiation, radiotherapy, or UV radiation.
  • DNA damaging agents further include platins or platinum-containing chemotherapeutics/coordination complexes of platinum, such as cisplatin, carboplatin, oxaliplatin, nedaplatin, triplatin tetranitrate, phenanthriplatin, picoplatin, satraplatin (overall also referred to as platinum-based agents).
  • DNA damaging agents yet further non-exhaustively include alkylating agents (e.g. cyclophosphamide), antimetabolites (e.g.
  • Prediction of response to therapy with one member of a class of therapeutic agents will likewise be able to predict response to therapy with another member of the same class of therapeutic agents.
  • Prediction of response to therapy with an agent inhibiting DNA repair e.g. an inhibitor of PARP
  • a DNA damaging agent e.g. a platinum-based or platinum-containing agent
  • any of the above methods may optionally comprise a clinician or a medical professional (e.g. nurse or doctor) taking or obtaining a tumor sample or sample comprising a tumor or cancer cell from a subject or patient; and/or a clinician, medical professional, laboratory technician or laboratory professional obtaining or isolating the (test) genomic DNA from a tumor sample or sample comprising a tumor or cancer cell; and/or a clinician, medical professional, laboratory technician or laboratory professional detecting, determining or assessing the LOH, HRD and/or BRCA status of the (test) genomic DNA.
  • GDPRs General Data Protection Regulations
  • any of the above methods may optionally comprise a clinician or a medical professional (e.g. nurse or doctor), or laboratory technician or laboratory professional, providing a tumor sample or sample comprising a tumor or cancer cell (obtained from a subject or patient) directly or indirectly to a test laboratory (such as independent accredited test laboratory); and/or a clinician or a medical professional, or laboratory technician or laboratory professional, providing the (test) genomic DNA from a tumor sample or sample comprising a tumor or cancer cell (obtained from a subject or patient) directly or indirectly to a test laboratory (such as independent accredited test laboratory); and/or a clinician or a medical professional (e.g.
  • test laboratory such as independent accredited test laboratory
  • test laboratory such as independent accredited test laboratory
  • test laboratory such as independent accredited test laboratory; or one or its technicians or professionals
  • GDPRs are complied with.
  • Another aspect of the invention relates to methods of predicting the response of a subject or patient having cancer or a tumor to a treatment or treatment regimen comprising a DNA damaging agent or therapy and/or comprising an agent inhibiting or impairing DNA repair or therapy comprising inhibiting or impairing DNA repair, wherein the subject or patient (having the cancer or tumor) is likely to respond to the treatment or treatment regimen if a genomic DNA sample obtained from the tumor or from a cancer cell of the cancer or tumor is detected or determined to have a LOH positive, HRD positive and/or BRCA positive status according to any of the relevant methods described hereinabove.
  • such methods are methods for detecting, determining or assessing if a subject or patient is likely to respond to such treatment or treatment regimen; or are methods for detecting, determining or assessing the likelihood of response of a subject or patient to such treatment or treatment regimen; or are methods predicting a likely response of a subject or patient to such treatment or treatment regimen.
  • such methods comprise detecting/determining/inferencing/scoring the LOH, HRD, or BRCA-status of a test genomic DNA sample obtained from the subject or patient according to, with, via, or applying any of the above methods, and predicting the subject or patient to respond, or likely to respond, to the treatment, treatment regimen, or therapy when the test genomic DNA is detected/determined/inferenced/scored to be LOH, HRD, or BRCA positive or to have a positive LOH, HRD, or BRCA status according to/using/with a method as described herein.
  • test genomic DNA is detected/determined/inferenced/scored to be LOH, HRD, or BRCA negative or to have a negative LOH, HRD, or BRCA status
  • the methods are then predicting the subject or patient not to respond, or likely not to respond, to the treatment, treatment regimen, or therapy.
  • Such methods may optionally comprise a clinician or a medical professional (e.g. nurse or doctor) making such prediction or assessment after having detected/determined/inferenced/scored the LOH, HRD and/or BRCA status of the (test) genomic DNA or after having received (in tangible or intangible form) the LOH, HRD and/or BRCA status of the (test) genomic DNA as detected or determined by a test laboratory (cf. supra).
  • Such methods may optionally comprise a test laboratory (cf. supra) making such prediction or assessment after having detected or determined the LOH, HRD and/or BRCA status of the (test) genomic DNA, or after having received (in tangible or intangible form) the LOH, HRD and/or BRCA status of the (test) genomic DNA as detected or determined by an individual or entity not linked to the test laboratory.
  • a test laboratory cf. supra
  • the invention in a further aspect relates to a DNA damaging agent or therapy comprising a DNA damaging agent, and/or to an agent inhibiting or impairing DNA repair or therapy comprising an agent inhibiting or impairing DNA repair - or to a treatment or treatment regimen comprising a DNA damaging agent and/or comprising an agent inhibiting or impairing DNA repair - for use in (the manufacture of a medicament for) treating a tumor or cancer if a genomic DNA sample obtained from the tumor or from a cancer cell of the cancer or tumor is detected/determined/inferenced/scored to have a LOH positive, HRD positive and/or BRCA positive status according to any of the relevant methods described hereinabove.
  • the DNA damaging agent or therapy comprising the DNA damaging agent, and/or the agent inhibiting or impairing DNA repair or therapy comprising the agent inhibiting or impairing DNA repair, or the treatment or treatment regimen comprising a DNA damaging agent and/or comprising an agent inhibiting or impairing DNA repair are for use in (the manufacture of a medicament for) treating a (subject having a) tumor or cancer, comprising
  • the invention relates to methods for treating a subject or patient having cancer or a tumor, such methods comprising administering a DNA damaging agent or therapy comprising a DNA damaging agent to the subject or patient having the cancer or tumor, and/or administering an agent inhibiting or impairing DNA repair or therapy comprising an agent inhibiting or impairing DNA repair to the subject or patient having the cancer or tumor, if a genomic DNA sample obtained from the tumor or from a cancer cell of the cancer or tumor is detected/determined/inferenced/scored to have a LOH positive, HRD positive and/or BRCA positive status according to any of the relevant methods described hereinabove.
  • the invention relates to methods for treating a subject or patient having cancer or a tumor, such methods comprising: detecting/determining/inferencing/scoring a genomic DNA sample obtained from the tumor or from a cancer cell of the cancer or tumor to have a LOH positive or HRD positive status according to any of the relevant methods described hereinabove; and administering a DNA damaging agent or therapy comprising a DNA damaging agent to the subject or patient having the cancer or tumor, and/or administering an agent inhibiting or impairing DNA repair to the subject or patient having the cancer or tumor, and/or administering a therapy comprising an agent inhibiting or impairing DNA repair to the subject or patient having the cancer or tumor.
  • the subject or patient is being treated.
  • a therapeutically effective amount or dose of the DNA damaging agent and/or agent inhibiting or impairing DNA repair is administered to the subject or patient as (part of a) treatment or as part of a treatment regimen.
  • the DNA damaging agent and/or agent inhibiting or impairing DNA repair is combined with a further anticancer agent, then the amount or dose of the DNA damaging agent and/or agent inhibiting or impairing DNA repair may in itself not be sufficient to result in treatment, but then is sufficient in combination with the further anticancer agent.
  • Such combinations of treatment modalities may e.g. decrease possible adverse effects or side effects of (one of) the individual modalities.
  • Treatment refers to any rate of reduction, delaying or retardation of the progress of the disease or disorder, or a single symptom thereof, compared to the progress or expected progress of the disease or disorder, or single symptom thereof, when left untreated. This implies that a therapeutic modality on its own may not result in a complete or partial response (or may even not result in any response), but may, in particular when combined with other therapeutic modalities (such as, but not limited thereto: surgery, radiation, etc.), contribute to a complete or partial response (e.g. by rendering the disease or disorder more sensitive to therapy). More desirable, the treatment results in no/zero progress of the disease or disorder, or single symptom thereof (i.e.
  • Treatment/treating also refers to achieving a significant amelioration of one or more clinical symptoms associated with a disease or disorder, or of any single symptom thereof. Depending on the situation, the significant amelioration may be scored quantitatively or qualitatively. Qualitative criteria may e.g. by patient well-being.
  • the significant amelioration is typically a 10% or more, a 20% or more, a 25% or more, a 30% or more, a 40% or more, a 50% or more, a 60% or more, a 70% or more, a 75% or more, a 80% or more, a 95% or more, or a 100% improvement over the situation prior to treatment.
  • the time-frame over which the improvement is evaluated will depend on the type of criteria/disease observed and can be determined by the person skilled in the art.
  • a “therapeutically effective amount or dose” refers to an amount of a therapeutic agent to treat, inhibit or prevent a disease or disorder in a subject (such as a mammal).
  • the therapeutically effective amount of the therapeutic agent may reduce the number of cancer cells; reduce the primary tumor size; inhibit (i.e., slow down to some extent and preferably stop) cancer cell infiltration into peripheral organs; inhibit (i.e., slow down to some extent and preferably stop) tumor metastasis; inhibit, to some extent, tumor growth; and/or relieve to some extent one or more of the symptoms associated with the disorder.
  • the drug may prevent growth and/or kill existing cancer cells, it may be cytostatic and/or cytotoxic.
  • efficacy in vivo can, e.g., be measured by assessing the duration of survival (e.g. overall survival), time to disease progression (TTP), response rates (e.g., complete response and partial response, stable disease), length of progression-free survival (PFS), duration of response, and/or quality of life.
  • duration of survival e.g. overall survival
  • time to disease progression TTP
  • response rates e.g., complete response and partial response, stable disease
  • PFS length of progression-free survival
  • duration of response e.g., duration of response, and/or quality of life.
  • an effective amount (or dose) or “therapeutically effective amount (or dose)” may depend on the dosing regimen of the agent/therapeutic agent or composition comprising the agent/therapeutic agent (e.g. medicament or pharmaceutical composition).
  • the effective amount will generally depend on and/or will need adjustment to the mode of contacting or administration.
  • the effective amount of the agent or composition comprising the agent is the amount required to obtain the desired clinical outcome or therapeutic effect without causing significant or unnecessary toxic effects (often expressed as maximum tolerable dose, MTD).
  • MTD maximum tolerable dose
  • the agent or composition comprising the agent may be administered as a single dose or in multiple doses.
  • the effective amount may further vary depending on the severity of the condition that needs to be treated; this may depend on the overall health and physical condition of the subject or patient and usually the treating doctor's or physician's assessment will be required to establish what is the effective amount.
  • the effective amount may further be obtained by a combination of different types of contacting or administration.
  • the aspects and embodiments described above in general may comprise the administration of one or more therapeutic compounds to a subject (such as a mammal) in need thereof, i.e., harboring a tumor, cancer or neoplasm in need of treatment.
  • a subject such as a mammal
  • a (therapeutically) effective amount of (a) therapeutic compound(s) is administered to the mammal in need thereof in order to obtain the described clinical response(s).
  • administering means any mode of contacting that results in interaction between an agent (e.g. a therapeutic compound or immunotherapeutic compound or agent) or composition comprising the agent (such as a medicament or pharmaceutical composition) and an object (e.g. cell, tissue, organ, body lumen) with which said agent or composition is contacted.
  • agent e.g. a therapeutic compound or immunotherapeutic compound or agent
  • an object e.g. cell, tissue, organ, body lumen
  • the interaction between the agent or composition and the object can occur starting immediately or nearly immediately with the administration of the agent or composition, can occur over an extended time period (starting immediately or nearly immediately with the administration of the agent or composition), or can be delayed relative to the time of administration of the agent or composition. More specifically the "contacting" results in delivering an effective amount of the agent or composition comprising the agent to the object.
  • Another aspect of the invention relates to methods of prognosing survival or determining or assessing survival probability of a subject or patient having cancer or a tumor upon receiving treatment or a treatment regimen comprising a DNA damaging agent or therapy and/or comprising an agent inhibiting or impairing DNA repair or therapy comprising inhibiting or impairing DNA repair, wherein the subject or patient (having the cancer or tumor) is likely to have an increased survival or increased survival probability if a genomic DNA sample obtained from the tumor or from a cancer cell of the subject is detected/determined/inferenced/scored to have a LOH positive, HRD positive and/or BRCA positive status according to any of the relevant methods described hereinabove; or, wherein the subject or patient (having the cancer or tumor) is likely to have an decreased survival or decreased survival probability if a genomic DNA sample obtained from the tumor or from a cancer cell of the subject is detected/determined/inferenced/scored to have a LOH negative, HRD negative and/or BRCA negative status according to any of the relevant methods described hereinabo
  • such methods comprise detecting/determining/inferencing/scoring the LOH-, HRD-, or BRCA-status of a test genomic DNA sample obtained from the subject or patient according to, with, via, or applying any of the above methods, and prognosing, determining or assessing the subject or patient receiving treatment or a treatment regimen comprising a DNA damaging agent or therapy and/or comprising an agent inhibiting or impairing DNA repair or therapy comprising inhibiting or impairing DNA likely to have an increased survival or increased survival probability when or if the test genomic DNA is detected/determined/inferenced/scored to be LOH, HRD, or BRCA positive or to have a positive LOH, HRD, or BRCA status according to/using/with a method as described herein.
  • test genomic DNA is detected/determined/inferenced/scored to be LOH, HRD, or BRCA negative or to have a negative LOH, HRD, or BRCA status
  • the methods are then prognosing, determining or assessing the subject or patient receiving treatment or a treatment regimen comprising a DNA damaging agent or therapy and/or comprising an agent inhibiting or impairing DNA repair or therapy comprising inhibiting or impairing DNA likely not to have an increased survival or increased survival probability upon receiving treatment or a treatment regimen.
  • Such increased survival or increased survival probability is a consequence of the increased likelihood of response of the subject or patient to the treatment or treatment regimen.
  • Such increased survival or increased survival probability is in comparison with survival or survival probability of a reference subject or patient, such as the average survival or survival probability of subjects or patients with the same tumor or cancer having a LOH positive, HRD positive and/or BRCA positive status but not receiving the treatment or treatment regimen, or such as the average survival or survival probability of subjects or patients with the same tumor or cancer having a LOH negative, HRD negative and/or BRCA negative status either receiving or not receiving the treatment or treatment regimen.
  • Survival can be expressed as progression-free survival, relapse-free survival, overall survival, etc., and can be determined e.g. by the Kaplan-Meier method or analysis.
  • Any such methods may optionally comprise a clinician or a medical professional (e.g. nurse or doctor), or a test laboratory (cf. supra) determining the survival probability.
  • any of the above methods in one particular embodiment are in vitro methods, or methods on biologicals samples having been obtained from a subject or patient.
  • the subject or patient is a treatment-naive subject or patient.
  • a subject or patient in general is a mammalian species having cancer or a tumor or diagnosed with cancer.
  • the mammalian species in general is a higher species including primates, cattle (e.g. cows, sheep, goats, pigs), horses, and pets (e.g. dogs, cats).
  • the subject or patient is a human subject or patient.
  • such methods optionally comprise a step of detecting or determining a further or additional genomic DNA feature described in the art as being useful in the detection or determination of the LOH, HRD or BRCA-status of a genomic DNA sample.
  • Such further or additional genomic DNA feature for instance can be a TAI-score, a LST-score, a BRCA mutation (germline and/or somatic/tumor), a mutation in a HRR gene, BRCA methylation (e.g. BRCA1 methylation), RAD51 methylation (e.g.
  • the invention further relates to systems, such as computer systems, data-processing systems, computer or machine readable media, computer programs, or diagnostic kits for use in performing any of the above-mentioned methods.
  • such (computer/data-processing) systems or kits are comprising a (trained) data classification algorithm, or a computer or machine readable medium comprising a (trained) data classification algorithm, wherein the data classification algorithm is or was trained to classify a test genomic DNA sample as being LOH, HRD, and/or BRCA positive, or as being LOH, HRD and/or BRCA negative.
  • the data classification algorithm is or was trained with input features or based on input features comprising sets of N LOH/bin features as described hereinabove.
  • Such set of N bins are or were selected, or pre-selected, from a genome-wide set of bins as described hereinabove.
  • the data classification algorithm is/was, using the input features, trained on a set of reference genomic DNA samples including genomic DNA samples known of being LOH positive and/or HRD positive and genomic DNA samples known of being LOH negative and/or HRD negative as described hereinabove.
  • kits are comprising a set of immobilized oligonucleotides suitable for capturing from a genomic DNA sample the genomic DNA required for the detection or determination of the N LOH/bin features as described hereinabove; in particular, the number of immobilized oligonucleotides is ranging from lk to 200k, or is (between) lk to 200k.
  • this disclosure covers any computer program that in particular is having the instructions which when executed cause a computer system or device, or a computing system or device, to perform any of the methods (or (at least) a step of such methods) subject of this disclosure. More in particular to perform any herein disclosed method (or (at least) a step of such method) of/for detecting or determining the LOH status of a test genomic DNA sample, any herein disclosed method (or (at least) a step of such method) of/for predicting response to therapy or treatment including a DNA damaging agent or including an agent inhibiting DNA repair, or any herein disclosed method (or (at least) a step of such method) of/for prognosing survival or determining or assessing survival probability of a subject or patient having cancer or a tumor upon receiving treatment, treatment regimen or therapy comprising a DNA damaging agent or including an agent impairing DNA repair.
  • the at least one step of any of the methods performed by the computer program is the step of computer- implemented scoring of the set of N LOH/bin features of the test genomic DNA with a trained data classification model.
  • the at least one step of any of the methods performed by the computer program is the step of detecting or determining the test genomic DNA sample to be LOH-, HRD-, or BRCA-positive or -negative.
  • this disclosure covers any such system comprising a means for carrying out or performing any herein disclosed method (or (at least) a step of such method) of/for detecting or determining the LOH status of a test genomic DNA sample, any herein disclosed method (or (at least) a step of such method) of/for predicting response to therapy or treatment including a DNA damaging agent or including an agent inhibiting DNA repair, or any herein disclosed method (or (at least) a step of such method) of/for prognosing survival or determining or assessing survival probability of a subject or patient having cancer or a tumor upon receiving treatment, treatment regimen or therapy comprising a DNA damaging agent or including an agent impairing DNA repair.
  • the at least one step of any of the methods performed by the computer, computing system or data-processing system is the step of computer-implemented scoring of the set of N LOH/bin features of the test genomic DNA with a trained data classification model.
  • the at least one step of any of the methods performed by the computer, computing system or data-processing system is the step of detecting or determining the test genomic DNA sample to be LOH-, HRD-, or BRCA-positive or -negative.
  • a computer, computer or computing system, or data-processing system as mentioned herein may utilize one or more subsystems.
  • a computer or computer or computing system, or data-processing system may be a single apparatus comprising the one or more subsystems (e.g. internal components), or may be multiple apparatuses each being a subsystem, and optionally, each comprising one or more own subsystems.
  • Desktops, laptops, mainframe servers, tablets, mobile phones etc. all are eligible as computer, computer or computing system, or data-processing system.
  • the subsystems are usually interconnected and include a (central) processor (single-core processor, multi-core processor on a same integrated chip, or multiple processing units on a single circuit board or networked) capable of executing instructions, an input/output (I/O) controller, and a storage device (external, internal, peripheral, cloud, any medium readable by a computer or computer system).
  • Input devices include keyboards, scanners, a computer mouse, camera, microphone, etc.
  • the input device is a data collection or data generating device (which by itself may comprise a computer or computer or computing system, or data- processing system), such as a polynucleotide sequencing device (whether automated or not).
  • Collected or generated data are fed to a computer, computer or computing system, or data-processing system designed to analyze the collected or generated data; this may be an ordinary computer system on which data analyzing software is installed (on a storage device) or which is capable of accessing data analyzing software (e.g. installed in or transmitted from a network) and whereby the processor of the computer/computing/data-processing system is instructed by the data analysis software on how to process the collected or generated data fed to the computer/computing/data-processing system, and how to display these via a display adapter to an output device.
  • Output devices are further subsystems and comprise printers, monitors, computer readable medium.
  • Input and output devices are usually connected to a computer/computing/data-processing system via input/output ports to one another or via a network.
  • the specific combination of hardware and software allows implementation of e.g. analysis of data generated by a polynucleotide sequencing device, or of e.g. detection or determination of the copy number of a SNP, or of e.g. detecting/determining/inferencing/scoring the LOH or HRD status of a genomic DNA sample such as by a trained or machine learned data classification algorithm.
  • Different software packages proprietary or open source
  • Output of one computerized data analysis can be the input of a subsequent computerized data analysis step, hence creating an analysis pipeline.
  • Software components can be written in different codes (e.g. Java, C, C++, Perl, Python) as long as the computer processor is able to execute the functions of the software component.
  • the methods of this disclosure may be computer-implemented methods, or methods that are assisted or supported (in part) by a computer or by a computer/computing/data-processing system.
  • information reflecting the DNA sequencing analysis can be provided in user readable format by at least one/another processor.
  • the same or a further processor may be calculating/inferencing/scoring, as outlined herein, the LOH or HRD status of a test genomic DNA (such as relative to a control, standard or reference) from the information received.
  • the one or more processors may be coupled to random access memory operating under control of or in conjunction with a computer operating system.
  • the processors may be included in one or more servers, clusters, or other computers or hardware resources, or may be implemented using cloud-based resources.
  • the operating system may be, for example, a distribution of the LinuxTM operating system, the UnixTM operating system, or other open- source or proprietary operating system or platform.
  • Processors may communicate with data storage devices, such as a database stored on a hard drive or drive array or such as a computer or machine readable medium, to access or store program instructions other data.
  • Processors may further communicate via a network interface, which in turn may communicate via the one or more networks, such as the Internet or other public or private networks, such that a query or other request may be received from a client, or other device or service.
  • Such computer-implemented methods (or such methods that are assisted or supported by a computer/computing/data-processing system) may be provided as a kit or as part of a kit.
  • the bioinformatics software required to perform (part of) the computer-implemented methods may also be part of a kit, or may be provided as an individual product.
  • a computer product may also consist of a computer or machine readable medium (in any form such as disks (hard, soft disks etc.), cards (memory cards etc.), tapes, sticks (memory, USB sticks etc.), microchips, DVDs, CDs, etc.) which is storing any of the instructions, computer program, or bioinformatics software enabling a computer system to perform at least one of the analysis of the herein described methods and/or to perform at least one calculation as described herein.
  • the computer/computing/data-processing system can be set up or configurated such that it is compliant with GDPR.
  • brackets are optional further clarification of the term or word.
  • (test) genomic DNA sample obviously refers to a genomic DNA sample, and the genomic DNA sample can thus be optionally clarified by referring to a test genomic DNA sample.
  • Another example is "(further) inhibiting or impairing DNA repair”, basically meaning inhibiting or impairing DNA repair, and optionally referring to further inhibiting or impairing DNA repair.
  • This capture-based probe design included probes targeting a genome wide panel of single nucleotide polymorphisms (SNPs), sequenced at median target coverage of ⁇ 150x. In total 96,861 SNPs were selected based on population frequency with a uniform distribution across the genome. Centromere regions were excluded. The SNPs were covered by 92,723 specifically designed probes. The average size of oligonucleotide probes was 120 bp. Capture libraries were sequenced on an Illumina Novaseq (paired-end, 2x150 bp reads).
  • Paired-end reads were processed with Burrows-Wheeler Aligner (BWA, version 0.4.1) using the hgl9 human reference genome.
  • Illumina adapter sequences were trimmed from the reads using TrimGalore (version 0.4.1).
  • Duplicated reads were filtered and realigned locally around indels with the genomic analysis toolkit GATK (version 3.6 - Broad Institute). Sequencing metrics were collected with Picard (version 2.5.0).
  • Loss-of-heterozygosity features The ASCAT output was used to derive loss-of-heterozygosity (LOH) features based on the allele-specific copy number values. For each SNP, ASCAT reports the copy number value for both the major and minor allele. LOH was defined as the copy number of the minor allele being 0. Next, the genome was divided into bins of 1000 kb in size and the mean LOH across all SNPs within each bin was calculated for each sample, resulting in 2877 bin-specific LOH (or LOH/bin) features per sample. In addition to the bin features, also 23 chromosome specific features were created by calculating the mean LOH for each chromosome (LOH/chromosome features).
  • LOH/genome feature a genome-wide LOH (LOH/genome feature) was calculated from the mean LOH across all SNPs per sample. Using this procedure, we obtained 2901 LOH features in total per sample, with each feature representing a local genomic region with averaged LOH values across multiple SNPs (LOH/bin) or features covering an entire chromosome (LOH/chromosome) or the entire genome (LOH/genome). All LOH calculations were performed in R (version 3.6.3).
  • FLOH fractional LOH
  • the AUC value for the LOH+LST+TAI HRD score as approximated by the scarHRD algorithm 0.93 ("AUC original score").
  • the mean LOH/bin information In searching for an alternative method for determining the HRD status of genomic DNA of a tumor or cancer cell relying on LOH features only, but performing as well as the combined LOH + LST + TAI features, it was found that the mean LOH/bin information, the mean LOH/chromosome and the mean LOH/genome information could be used efficiently in determining the HRD status of genomic tumor or cancer DNA. Moreover, for the mean LOH/bin information, the bin size could be lowered to under 1.5 Mb and data classification of the mean LOH/bin information, the mean LOH/chromosome and the mean LOH/genome information could be used efficiently in determining the HRD status of genomic tumor or cancer DNA.
  • a cohort of 192 FFPE high-grade serous ovarian cancer samples from the PAOLA1 trial (identical to the samples used in Examples 2 and 3), composed of 132 patients administered with olaparib and 60 patients who received a placebo was examined.
  • the Myriad myChoice status was determined previously for each sample as part of the PAOLA-1 trial.
  • the Myriad myChoice HRD score was in contrast not available but approximated herein by the scarHRD algorithm (LOH (based on number of regions of LOH of intermediate size) + telomeric allelic imbalance (TAI) + large-scale transitions (LST)).
  • LH scarHRD
  • TAI telomeric allelic imbalance
  • LST large-scale transitions
  • Figure 3 shows the exemplary pipeline that was applied on all samples.
  • the PAM score constitutes the class probabilities for Myriad-positive status and is extracted using leave-one-out cross-validation from the PAM analysis.
  • a combination of LOH-features was fed to the classification algorithm: mean LOH/bin, mean LOH/chromosome, and mean genome-wide LOH (see above).
  • Figure 4 demonstrates the performance of predicting HRD status using the estimated scarHRD score ("HRD score”: LOH (based on number of regions of LOH of intermediate size)+LST+TAI) or using the herein developed alternative method ("PAM score”: mean LOH/bin features + mean LOH/chromosome features + and mean genome-wide LOH feature) with receiver-operator characteristic (ROC) curves.
  • HRD score LOH (based on number of regions of LOH of intermediate size)+LST+TAI) or using the herein developed alternative method
  • PAM score mean LOH/bin features + mean LOH/chromosome features + and mean genome-wide LOH feature
  • FIG. 5A shows the results for the patients treated with olaparib, with HRD positive patients (according to Myriad myChoice test) displaying a longer survival compared to HRD negative patients (according to Myriad myChoice test).
  • the survival curves of patients stratified by the current alternative LOH-only based data HRD classifier score (mean LOH/bin, mean LOH/chromosome, and mean genome-wide LOH features) are as good as identical.
  • Figure 5B shows the results from patients in a control group administered with a placebo. All patients display shorter survival times compared to the olaparib treated patients and again a resemblance between the current alternative LOH-only based data classifier HRD score (mean LOH/bin, mean LOH/chromosome, and mean genome-wide LOH features) and scarHRD score (LOH (based on number of regions of LOH of intermediate size)+LST+TAI features) can be observed.
  • LOH/bin mean LOH/chromosome
  • LST+TAI features scarHRD score
  • Figure 6 assessing the survival curves of samples stratified by scarHRD or the alternative method of Example 4 (results shown based on 10-fold cross-validation) but limited to using the mean LOH/bin features.
  • Figure 6A shows the results for the patients treated with olaparib, with HRD positive patients (according to Myriad myChoice test) displaying a longer survival compared to HRD negative patients (according to Myriad myChoice test).
  • the survival curves of patients stratified by the alternative LOH-only based data HRD classifier score (mean LOH/bin features only; mean LOH/chromosome and mean genome-wide LOH features omitted) are as good as identical.
  • Figure 6B shows the results for the placebo group.
  • the bin-size applied for the results depicted in Figure 6 is 1Mb (1000 kbp).
  • the same alternative method of Example 4 but limited to mean LOH/bin features was reiterated with an exemplary alternative bin size of 0.75 Mb (750 kbp) for which Kaplan-Meier curves are depicted in Figure 9 A/B (A: olaparib-treated group; B: placebo group); and with an exemplary alternative bin size of 1.25 Mb (1250 kbp) for which Kaplan-Meier curves are depicted in Figure 9 C/D (C: olaparib- treated group; D: placebo group).
  • the hazard ratio's were determined. Hazard is defined as the slope of the survival curve. The hazard ratio compares two treatments. In case of the hazard ratio being 2.0, then the rate of deaths in one treatment group is twice the rate in the other group.
  • the hazard ratio was determined for the olaparib-treated HRD-positive subject group versus the control (placebo) HRD- positive subject group, a low hazard ratio implies a beneficial result of olaparib treatment. Results are summarized in Table 1.
  • Example 5 relied on the PAM algorithm as classifier of the mean LOH/bin features only.
  • the same analysis (on the mean LOH/bin features only; same sample set) was performed using an alternative classifier, i.e. the Random Forest data classification algorithm (Breiman 2001, Machine Learning 45:5-32; Breiman and Cutler's Random Forests for Classification and Regression Version 4.7-1.1 Date 2022-01-24: https://cran.r-project.org/web/packages/randomForest/randomForest.pdf).
  • the random forest classifier was trained on the Myriad myChoice status.
  • the random forest classifier consisted of 5000 trees. At each node, 45 LOH bins were randomly selected as predictors and the minimum node size was set to 10. These parameters are exemplary and can be varied. LOH status of a bin was determined based on the copy number of the minor allele of the bin midpoint nucleotide position wherein this copy number was derived from the segment of the genomic DNA with constant copy number comprising the bin midpoint nucleotide position.
  • Figure 7 indicates that the RF classifier can distinguish between Myriad myChoice HRD positive and negative samples (as well as the scarHRD algorithm, see Figure IB). Furthermore, survival probabilities of olaparib- or placebo-treated ovarian cancer patients based on the RF classification of the LOH/bin features are near identical to the survival probabilities based on the Myriad myChoice HRD status, as illustrated in Figure 8 (A: olaparib-treated group; B: placebo group; results shown for 10-fold cross- validation). The bin-size applied for the results depicted in Figure 8 is IMbp (1000 kbp).
  • the RF classifier method was re-iterated with an exemplary alternative bin size of 0.5 Mbp (500 kbp) for which Kaplan- Meier curves are depicted in Figure 10 A/B (A: olaparib-treated group; B: placebo group); with an exemplary alternative bin size of 0.75 Mbp (750 kbp) for which Kaplan-Meier curves are depicted in Figure 10 C/D (C: olaparib-treated group; D: placebo group); with an exemplary alternative bin size of 1.25 Mbp (1250 kbp) for which Kaplan-Meier curves are depicted in Figure 10 E/F (E: olaparib-treated group; F: placebo group); and with an exemplary alternative bin size of 2 Mbp (2000 kbp) for which Kaplan-Meier curves are depicted in Figure 10 G/H (G: olaparib-treated group; H: placebo group).
  • the analyses as outlined in the foregoing Examples relied on whole-genome analysis, including dividing the whole genome in bins for purposes of determining the mean LOH/bin feature. In a next step, it was assessed whether the number of required bins could be lowered without compromising reliability of determination of LOH or HRD status of the DNA of a tumor compared to the whole-genome approach.
  • the dataset was split 50/50 into a training set and a test set.
  • Genome wide profiles for genomic instability, in particular LOH were drawn for all samples of a reference set of genomic DNA samples (the training cohort of the PAOLA-1 trial) and heterogeneity of LOH patterns between HRD positive and HRD negative samples was observed.
  • Significantly frequent LOH regions contributing to HRD were identified via a permutation test across the genome for samples of the training cohort (permutation P ⁇ 0.05).
  • Hundred LOH regions number randomly chosen with simplification in mind) with the largest weight in the model were selected.
  • the training cohort was used to train the random forest classifier model with all LOH bins (genome-wide; bin size 1 Mbp (1000 kbp)), thus resulting in the RF classifier trained on all LOH bins. Subsequently the random forest classifier model was separately retrained on the training cohort with only the top-100 most important LOH-bins, thus resulting in the second RF classifier trained on the top-100 LOH bins.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Theoretical Computer Science (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Data Mining & Analysis (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Health & Medical Sciences (AREA)
  • Evolutionary Biology (AREA)
  • Biophysics (AREA)
  • Biotechnology (AREA)
  • Bioethics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
  • Public Health (AREA)
  • Evolutionary Computation (AREA)
  • Epidemiology (AREA)
  • Databases & Information Systems (AREA)
  • Artificial Intelligence (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Genetics & Genomics (AREA)
  • Molecular Biology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)

Abstract

La présente invention concerne des procédés de détermination de la perte d'état d'hétérozygotie (LOH) d'une tumeur et des procédés de détermination de l'état de défaut de recombinaison homologue (HRD) d'une tumeur. De tels procédés se traduisent en outre par des moyens de prédiction de la réponse d'un sujet atteint d'un cancer à des traitements ou des thérapies comprenant des agents endommageant l'ADN et/ou des agents inhibant ou affectant la réparation de l'ADN ; et par l'utilisation de tels traitements ou thérapies lorsque l'état LOH ou HRD du sujet atteint d'un cancer est positif.
PCT/EP2024/078316 2023-10-10 2024-10-09 Procédé de détermination de perte d'état d'hétérozygotie d'une tumeur Pending WO2025078387A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
EP23202680 2023-10-10
EP23202680.7 2023-10-10

Publications (1)

Publication Number Publication Date
WO2025078387A1 true WO2025078387A1 (fr) 2025-04-17

Family

ID=88315452

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2024/078316 Pending WO2025078387A1 (fr) 2023-10-10 2024-10-09 Procédé de détermination de perte d'état d'hétérozygotie d'une tumeur

Country Status (1)

Country Link
WO (1) WO2025078387A1 (fr)

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011106541A2 (fr) 2010-02-24 2011-09-01 Myriad Genetics, Inc. Méthodes de diagnostic impliquant une perte d'hétérozygosité
WO2011160063A2 (fr) 2010-06-18 2011-12-22 Myriad Genetics, Inc. Procédés et matériaux pour évaluer la perte d'hétérozygotie
WO2012027224A1 (fr) 2010-08-24 2012-03-01 Dana-Farber Cancer Institute, Inc. Méthodes de prédiction d'une réponse anticancéreuse
WO2013096843A1 (fr) 2011-12-21 2013-06-27 Myriad Genetics, Inc. Procédés et matériaux pour évaluer la perte d'hétérozygotie
WO2013130347A1 (fr) 2012-02-23 2013-09-06 The Children's Hospital Corporation Méthodes de prédiction d'une réponse anticancéreuse
WO2013182645A1 (fr) 2012-06-07 2013-12-12 Institut Curie Procédé de détection de l'inactivation de la voie de recombination homologue (brca1/2) dans des tumeurs humaines
WO2014165785A2 (fr) 2013-04-05 2014-10-09 Myriad Genetics, Inc. Méthodes et substances destinées à évaluer le déficit de recombinaison homologue
WO2015086473A1 (fr) 2013-12-09 2015-06-18 Institut Curie Procede pour la detection de l'inactivation de voie de recombinaison homologue (brca1/2) dans des tumeurs humaines
WO2016025958A1 (fr) 2014-08-15 2016-02-18 Myriad Genetics, Inc. Procédés et matériaux permettant d'évaluer une déficience de recombinaison homologue
US20210327536A1 (en) * 2019-02-12 2021-10-21 Tempus Labs, Inc. Detection of Human Leukocyte Antigen Loss of Heterozygosity
WO2022150063A1 (fr) 2021-01-10 2022-07-14 Act Genomics (Ip) Co., Ltd. Procédé de détermination d'une déficience de recombinaison homologue et kit associé
US20230175053A1 (en) * 2019-07-30 2023-06-08 Menarini Silicon Biosystems S.P.A. Method for analysing loss-of-heterozygosity (loh) following deterministic restriction-site whole genome amplification (drs-wga).
WO2024083971A1 (fr) 2022-10-19 2024-04-25 Vib Vzw Procédé de détermination de perte d'état d'hétérozygosité d'une tumeur

Patent Citations (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011106541A2 (fr) 2010-02-24 2011-09-01 Myriad Genetics, Inc. Méthodes de diagnostic impliquant une perte d'hétérozygosité
WO2011160063A2 (fr) 2010-06-18 2011-12-22 Myriad Genetics, Inc. Procédés et matériaux pour évaluer la perte d'hétérozygotie
WO2012027224A1 (fr) 2010-08-24 2012-03-01 Dana-Farber Cancer Institute, Inc. Méthodes de prédiction d'une réponse anticancéreuse
WO2013096843A1 (fr) 2011-12-21 2013-06-27 Myriad Genetics, Inc. Procédés et matériaux pour évaluer la perte d'hétérozygotie
WO2013130347A1 (fr) 2012-02-23 2013-09-06 The Children's Hospital Corporation Méthodes de prédiction d'une réponse anticancéreuse
WO2013182645A1 (fr) 2012-06-07 2013-12-12 Institut Curie Procédé de détection de l'inactivation de la voie de recombination homologue (brca1/2) dans des tumeurs humaines
WO2014165785A2 (fr) 2013-04-05 2014-10-09 Myriad Genetics, Inc. Méthodes et substances destinées à évaluer le déficit de recombinaison homologue
WO2015086473A1 (fr) 2013-12-09 2015-06-18 Institut Curie Procede pour la detection de l'inactivation de voie de recombinaison homologue (brca1/2) dans des tumeurs humaines
WO2016025958A1 (fr) 2014-08-15 2016-02-18 Myriad Genetics, Inc. Procédés et matériaux permettant d'évaluer une déficience de recombinaison homologue
US20170283879A1 (en) * 2014-08-15 2017-10-05 Myriad Genetics, Inc. Methods and materials for assessing homologous recombination deficiency
US20210327536A1 (en) * 2019-02-12 2021-10-21 Tempus Labs, Inc. Detection of Human Leukocyte Antigen Loss of Heterozygosity
US20230175053A1 (en) * 2019-07-30 2023-06-08 Menarini Silicon Biosystems S.P.A. Method for analysing loss-of-heterozygosity (loh) following deterministic restriction-site whole genome amplification (drs-wga).
WO2022150063A1 (fr) 2021-01-10 2022-07-14 Act Genomics (Ip) Co., Ltd. Procédé de détermination d'une déficience de recombinaison homologue et kit associé
WO2024083971A1 (fr) 2022-10-19 2024-04-25 Vib Vzw Procédé de détermination de perte d'état d'hétérozygosité d'une tumeur

Non-Patent Citations (20)

* Cited by examiner, † Cited by third party
Title
ABKEVICH ET AL., BR J CANCER, vol. 107, 2012, pages 1776 - 1782
BARRETINA-GINESTA MARIA-PILAR: "DNA damaging agents in ovarian cancer", EUROPEAN JOURNAL OF CANCER. SUPPLEMENT, vol. 15, 1 August 2020 (2020-08-01), GB, pages 67 - 72, XP093119161, ISSN: 1359-6349, DOI: 10.1016/j.ejcsup.2020.06.001 *
BIRKBAK ET AL., CANCER DISCOV, vol. 2, 2012, pages 366 - 375
BREIMAN, MACHINE LEARNING, vol. 45, 2001, pages 5 - 32
BREIMANCUTLER'S, RANDOM FORESTS FOR CLASSIFICATION AND REGRESSION, 24 January 2022 (2022-01-24), Retrieved from the Internet <URL:https://cran.r-project.org/web/packages/randomForest/randomForest.pdf>
CHEN ET AL., J BIG DATA, vol. 7, 2020, pages 52
MILLER ET AL., ANNALS ONCOL, vol. 31, 2020, pages 1606 - 1622
OLSON RANDAL S ET AL: "Automating Biomedical Data Science Through Tree-Based Pipeline Optimization", 15 March 2016, SAT 2015 18TH INTERNATIONAL CONFERENCE, AUSTIN, TX, USA, SEPTEMBER 24-27, 2015; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER, BERLIN, HEIDELBERG, PAGE(S) 123 - 137, ISBN: 978-3-540-74549-5, XP047338765 *
POPOVA ET AL., CANCER RES, vol. 72, 2012, pages 5454 - 5462
POPOVA ET AL., CANCER RES, vol. 72, pages 5454 - 5462
PUJADE-LAURAINE ET AL., INT J GYNECOL CANCER, vol. 31, no. 3, 2021, pages 208
RAY-COQUARD ET AL., N ENGL J MED, vol. 381, 2019, pages 2416 - 2428
ROSS ET AL., BIOINFORMATICS, vol. 37, 2021, pages 1909 - 1911
SAMBROOK ET AL.: "Protocols in Molecular Biology (Supplement 100", 2012, COLD SPRING HARBOR PRESS
STOVER ET AL., GYNECOLOGIC ONCOLOGY, vol. 159, 2020, pages 887 - 898
TIMMS ET AL., BREAST CANCER RES, vol. 16, 2014, pages 475
TIMMS ET AL., J CLIN ONCOL, vol. 38, no. 15, 2020, pages 1586
VAN LOO ET AL., PROC NATL ACAD SCI USA, vol. 107, 2010, pages 16910 - 16915
WANG ET AL., CLIN CANCER RES, vol. 18, 2012, pages 5806 - 5815
WANG ET AL., PLOS GENET, vol. 17, no. 5, 2021, pages 1009557

Similar Documents

Publication Publication Date Title
US12221656B2 (en) Methods and materials for assessing homologous recombination deficiency
US20250223655A1 (en) Methods and materials for assessing homologous recombination deficiency
Widman et al. Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment
Hu et al. 5mC regulator-mediated molecular subtypes depict the hallmarks of the tumor microenvironment and guide precision medicine in bladder cancer
DK2582847T3 (en) METHODS AND MATERIALS TO ASSESS loss of heterozygosity
EP4605937A1 (fr) Procédé de détermination de perte d&#39;état d&#39;hétérozygosité d&#39;une tumeur
US20210238668A1 (en) Biterminal dna fragment types in cell-free samples and uses thereof
CA3212968A1 (fr) Prediction de la reponse a des traitements chez des patients atteints d&#39;un carcinome renal a cellules claires
CN115176035A (zh) 患者对放射治疗之响应的分子预测因子
US20200294622A1 (en) Subtyping of TNBC And Methods
WO2025078387A1 (fr) Procédé de détermination de perte d&#39;état d&#39;hétérozygotie d&#39;une tumeur
CN115862876B (zh) 基于免疫微环境基因群预测肺腺癌患者预后的装置
WO2025078404A1 (fr) Procédés pour déterminer la réponse d&#39;une tumeur à des agents endommageant l&#39;adn ou à des agents inhibant ou altérant la réparation de l&#39;adn
Parekh et al. Cabazitaxel Versus Abiraterone or Enzalutamide for Poor-prognosis Metastatic Castration-resistant Prostate Cancer After Docetaxel: A Phase 2 Trial with a Circulating Tumor DNA Analysis
Kim et al. A Comparative Genomic Analysis of Left-and Right-Sided Colon Cancer Using Real-World Data from the AACR Project GENIE BPC Dataset
HK40098329A (en) Methods and materials for assessing homologous recombination deficiency
HK40098329B (en) Methods and materials for assessing homologous recombination deficiency
TW202505037A (zh) 用於評估乳癌亞型中同源重組缺陷之方法及材料
CN116837103A (zh) Zfhx3基因突变可作为sclc免疫治疗生物标志物
JP2021501422A (ja) テモゾロミド反応予測因子及び方法
NZ712663B2 (en) Methods and materials for assessing homologous recombination deficiency

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 24787150

Country of ref document: EP

Kind code of ref document: A1