Abstract: The present invention is directed to the probes for detecting known and unknown fusion genes, related methods of detection of fusion genes, uses and kits related thereto. In particular, the invention relates to methods of diagnosing and monitoring of a cancer.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a variant calling module, genomic variant scenarios on sequencing reads from an enriched patient genomic sample comprising a combination of a first repeat pattern and a second repeat pattern, such as repeats of homopolymer (single nucleotide) and/or heteropolymer (multiple nucleotide) basic motifs. The variant calling module may estimate the probability distribution of the length of the first repeat pattern and the probability distribution of the repeat pattern length measurements in patient data to the distribution of the repeat pattern length measurements in control data, in order to remove biases possibly induced by the next generation sequencing laboratory setup both in control and patient data.

Abstract: Provided may be a computer-implemented method for estimating a tumor fraction in a patient sample, comprising the steps of obtaining a catalog of tumor specific variants and whole genome sequencing data from the patient sample. Further, the method may comprise aligning reads to a reference genome; determining a measure of the signal supporting the presence, in the patient sample read alignment file, of variants in the catalog of tumor specific variants; and determining a measure of the noise associated with variants similar to variants in the catalog of tumor specific variants in the patient sample read alignment file. The method may comprise estimating, over iterations, k, the fraction of tumor (eTF) DNA in the patient sample given the measure of the signal and the measure of the noise at all tumor specific positions; and generating a final eTF and a list of somatic variants in the patient sample.

Abstract: A computer-implemented method may obtain variant calling data for the tumor sample. The method may identify, in the variant calling data and in view of at least one population database, a list of germline variants for the tumor sample along each chromosome. The method may identify, in the variant calling data, a list of candidate somatic variants. The method may filter out likely germline variants from the list of candidate somatic variants to retain only likely somatic variants, filtering out the likely germline variants further comprising the steps of estimating a probability of each candidate somatic variant i being a germline variant (“Pgermline(i)”); and determine whether a candidate somatic variant i is germline or somatic, to retain only the likely somatic variants in the list of candidate somatic variants, determining the tumor mutational burden (TMB) value for the tumor sample.

Abstract: The present invention is directed to the probes for detecting known and unknown fusion genes, related methods of detection of fusion genes, uses and kits related thereto. In particular, the invention relates to methods of diagnosing and monitoring of a cancer.

Abstract: Methods are disclosed for adding adapters to fragmented nucleic acids for next generation sequencing, including providing numerical codes based on variable adapter molecular barcode lengths on both sides of the fragmented nucleic acids and identifying reads from the same fragment based on both barcodes. The methods and products allow for the amplification of the fragmented nucleic acids when there is a low yield of isolated fragmented nucleic acids and also for efficient and reliable detection of low-frequency mutations including in subpopulations of cells within a subject.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low sequencing coverage in a high throughput sequencing workflow as may be routinely employed in a diversity of clinical oncology setups. The genomic data analyzer may arrange the aligned read data coverage from chromosome arms or full chromosomes to form a coverage data signal array possibly as an image. The trained machine learning model may process the coverage data signal array to determine whether a chromosomal spatial instability (CSI) such as for instance a genomic instability caused by a homologous repair or recombination deficiency (HRD) is present in the tumor sample. The latter indication may guide the choice of a preferred anticancer treatment for the tumor.

Abstract: The characterization, classification and reporting of MSI status of patient genomic samples may be provided by high throughput genomic analysis of a set of microsatellite marker loci. The patient sample may only comprise a low fraction of somatic DNA relative to germline DNA. A multi-parametric background model may be used to infer at least two parameters respectively characterizing the sample variant fraction and the MSI genomic alterations of the patient DNA sample relative to a reference background model of the MSS repeat length distribution, without the need to use a germline control sample. A local MSI score may be calculated as a function of the at least two parameters to characterize the MSI status at each locus, and a global composite MSI score may be calculated over all tested loci to characterize and report the overall MSI status for the patient sample.

Abstract: A genomic data analyzer may process the next generation sequencing data of a patient sample to identify whether a variant is present (positive variant calling), absent at a high confidence (negative variant calling), or equivocal (possible false negative calling) as falling under a calculated limit of detection (LOD). This LOD estimate corresponds the lowest variant allele fraction (VAF) detectable at the required sensitivity (true positive rate). The presently disclosed genomic data analyzer may improve any legacy variant caller by automatically calculating the limitations of variant calling detection for a user-defined sensitivity and minimal VAF of interest for any variant genomic position and/or mutation, depending on analytical factors of the NGS assay and workflow such as the sample type, the DNA sample amount and the NGS assay library conversion rate (LCR), and/or its molecular barcoding capability, as well as its NGS assay error profile.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low sequencing coverage in a high throughput sequencing workflow as may be routinely employed in a diversity of clinical oncology setups. The genomic data analyzer may arrange the aligned read data coverage from chromosome arms or full chromosomes to form a coverage data signal array possibly as an image. The trained machine learning model may process the coverage data signal array to determine whether a chromosomal spatial instability (CSI) such as for instance a genomic instability caused by a homologous repair or recombination deficiency (HRD) is present in the tumor sample. The latter indication may guide the choice of a preferred anticancer treatment for the tumor.

Abstract: A genomic data analyzer may be configured to detect and characterize, with a machine learning model such as a trained convolutional neural network, the presence of a genomic instability in a tumor sample. The genomic data analyzer may use whole genome sequencing reads as input data even at low sequencing coverage in a high throughput sequencing workflow as may be routinely employed in a diversity of clinical oncology setups. The genomic data analyzer may arrange the aligned read data coverage from chromosome arms or full chromosomes to form a coverage data signal array possibly as an image. The trained machine learning model may process the coverage data signal array to determine whether a chromosomal spatial instability (CSI) such as for instance a genomic instability caused by a homologous repair or recombination deficiency (HRD) is present in the tumor sample. The latter indication may guide the choice of a preferred anticancer treatment for the tumor.

Abstract: A method to manage raw genomic data (SAM/BAM files) in a privacy preserving manner in a biobank. By using order preserving encryption of the reads' positions, the method provides a requested range of nucleotides to a medical unit, without revealing the locations of the short reads (which include the requested nucleotides) to the biobank. The method prevents the leakage of extra information in the short reads to the medical unit by masking the encrypted short reads at the biobank. That is, specific parts of the genomic data for which the medical unit is not authorized or the patient prefers to keep secret are masked at the biobank, without revealing any information to the biobank.