SUMMARY: We introduce shallowHRD, a software tool to evaluate tumor homologous recombination deficiency (HRD) based on whole genome sequencing (WGS) at low coverage (shallow WGS or sWGS; ∼1X coverage). The tool, based on mining copy number alterations profile, implements a fast and straightforward procedure that shows 87.5% sensitivity and 90.5% specificity for HRD detection. shallowHRD could be instrumental in predicting response to poly(ADP-ribose) polymerase inhibitors, to which HRD tumors are selectively sensitive. shallowHRD displays efficiency comparable to most state-of-art approaches, is cost-effective, generates low-storable outputs and is also suitable for fixed-formalin paraffin embedded tissues. AVAILABILITY AND IMPLEMENTATION: shallowHRD R script and documentation are available at https://github.com/aeeckhou/shallowHRD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Copy number alterations (CNAs) are hallmarks of cancer, which are now been routinely measured by different techniques and used for diagnostic and prognostic purpose. Efficient and accurate detection of the breakpoint positions in heterogeneous cancer sample measured with intrinsic random noise and subjected to technical and biological biases is a challenging practical and methodological problem. To improve the CNA estimates, the authors present the probabilistic approach for breakpoints detection that gives confidence masks (the system of local segmentation profiles with confidence probabilities) tuned using experts estimates. The authors show that the asymmetric exponential power distribution matches well the uncertainties (jitter) in the breakpoint locations. The confidence upper and lower boundary masks for the breakpoint location are built using this function. The confidence masks are then tuned based on the medical expert annotations of the training set of the breakpoints obtained by the standard circular binary segmentation (CBS) algorithm. Comparison of modified confidence masks and experts annotations on the testing set of CNA profiles of neuroblastoma showed improvement of the CNA estimates.

MOTIVATION: Because of its low cost, amplicon sequencing, also known as ultra-deep targeted sequencing, is now becoming widely used in oncology for detection of actionable mutations, i.e. mutations influencing cell sensitivity to targeted therapies. Amplicon sequencing is based on the polymerase chain reaction amplification of the regions of interest, a process that considerably distorts the information on copy numbers initially present in the tumor DNA. Therefore, additional experiments such as single nucleotide polymorphism (SNP) or comparative genomic hybridization (CGH) arrays often complement amplicon sequencing in clinics to identify copy number status of genes whose amplification or deletion has direct consequences on the efficacy of a particular cancer treatment. So far, there has been no proven method to extract the information on gene copy number aberrations based solely on amplicon sequencing. RESULTS: Here we present ONCOCNV, a method that includes a multifactor normalization and annotation technique enabling the detection of large copy number changes from amplicon sequencing data. We validated our approach on high and low amplicon density datasets and demonstrated that ONCOCNV can achieve a precision comparable with that of array CGH techniques in detecting copy number aberrations. Thus, ONCOCNV applied on amplicon sequencing data would make the use of additional array CGH or SNP array experiments unnecessary.

SUMMARY: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks. AVAILABILITY: C++ source code is available at: http://bioinfo.curie.fr/projects/freec/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.