Immune DNA signature of T-cell infiltration in breast tumor exomes.

Tumor infiltrating lymphocytes (TILs) have been associated with favorable prognosis in multiple tumor types. The Cancer Genome Atlas (TCGA) represents the largest collection of cancer molecular data, but lacks detailed information about the immune environment. Here, we show that exome reads mapping to the complementarity-determining-region 3 (CDR3) of mature T-cell receptor beta (TCRB) can be used as an immune DNA (iDNA) signature. Specifically, we propose a method to identify CDR3 reads in a breast tumor exome and validate it using deep TCRB sequencing. In 1,078 TCGA breast cancer exomes, the fraction of CDR3 reads was associated with TILs fraction, tumor purity, adaptive immunity gene expression signatures and improved survival in Her2+ patients. Only 2/839 TCRB clonotypes were shared between patients and none associated with a specific HLA allele or somatic driver mutations. The iDNA biomarker enriches the comprehensive dataset collected through TCGA, revealing associations with other molecular features and clinical outcomes

SNPredict: A Machine Learning Approach for Detecting Low Frequency Variants in Cancer

Cancer is a genetic disease caused by the accumulation of DNA variants such as single nucleotide changes or insertions/deletions in DNA. DNA variants can cause silencing of tumor suppressor genes or increase the activity of oncogenes. In order to come up with successful therapies for cancer patients, these DNA variants need to be identified accurately. DNA variants can be identified by comparing DNA sequence of tumor tissue to a non-tumor tissue by using Next Generation Sequencing (NGS) technology. But the problem of detecting variants in cancer is hard because many of these variant occurs only in a small subpopulation of the tumor tissue. It becomes a challenge to distinguish these low frequency variants from sequencing errors, which are common in today\u27s NGS methods. Several algorithms have been made and implemented as a tool to identify such variants in cancer. However, it has been previously shown that there is low concordance in the results produced by these tools. Moreover, the number of false positives tend to significantly increase when these tools are faced with low frequency variants. This study presents SNPredict, a single nucleotide polymorphism (SNP) detection pipeline that aims to utilize the results of multiple variant callers to produce a consensus output with higher accuracy than any of the individual tool with the help of machine learning techniques. By extracting features from the consensus output that describe traits associated with an individual variant call, it creates binary classifiers that predict a SNP’s true state and therefore help in distinguishing a sequencing error from a true variant

Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer.

Whole exome sequencing (WES), targeted gene panel sequencing and single nucleotide polymorphism (SNP) arrays are increasingly used for the identification of actionable alterations that are critical to cancer care. Here, we compared The Cancer Genome Atlas (TCGA) and the Genomics Evidence Neoplasia Information Exchange (GENIE) breast cancer genomic datasets (array and next generation sequencing (NGS) data) in detecting genomic alterations in clinically relevant genes. We performed an in silico analysis to determine the concordance in the frequencies of actionable mutations and copy number alterations/aberrations (CNAs) in the two most common breast cancer histologies, invasive lobular and invasive ductal carcinoma. We found that targeted sequencing identified a larger number of mutational hotspots and clinically significant amplifications that would have been missed by WES and SNP arrays in many actionable genes such as PIK3CA, EGFR, AKT3, FGFR1, ERBB2, ERBB3 and ESR1. The striking differences between the number of mutational hotspots and CNAs generated from these platforms highlight a number of factors that should be considered in the interpretation of array and NGS-based genomic data for precision medicine. Targeted panel sequencing was preferable to WES to define the full spectrum of somatic mutations present in a tumor

Comprehensive outline of whole exome sequencing data analysis tools available in clinical oncology

Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use. © 2019 by the authors. Licensee MDPI, Basel, Switzerland

Evaluation of pre-analytical factors affecting plasma DNA analysis.

Pre-analytical factors can significantly affect circulating cell-free DNA (cfDNA) analysis. However, there are few robust methods to rapidly assess sample quality and the impact of pre-analytical processing. To address this gap and to evaluate effects of DNA extraction methods and blood collection tubes on cfDNA yield and fragment size, we developed a multiplexed droplet digital PCR (ddPCR) assay with 5 short and 4 long amplicons targeting single copy genomic loci. Using this assay, we compared 7 cfDNA extraction kits and found cfDNA yield and fragment size vary significantly. We also compared 3 blood collection protocols using plasma samples from 23 healthy volunteers (EDTA tubes processed within 1 hour and Cell-free DNA Blood Collection Tubes processed within 24 and 72 hours) and found no significant differences in cfDNA yield, fragment size and background noise between these protocols. In 219 clinical samples, cfDNA fragments were shorter in plasma samples processed immediately after venipuncture compared to archived samples, suggesting contribution of background DNA by lysed peripheral blood cells. In summary, we have described a multiplexed ddPCR assay to assess quality of cfDNA samples prior to downstream molecular analyses and we have evaluated potential sources of pre-analytical variation in cfDNA studies

ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.

BackgroundA key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison.ResultsIn this work, we describe an algorithm to distinguish somatic single nucleotide variants (SNVs) in next-generation sequencing data from germline polymorphisms in the absence of normal samples using a machine learning approach. Our algorithm was evaluated using a family of supervised learning classifications across six different cancer types and ~1600 samples, including cell lines, fresh frozen tissues, and formalin-fixed paraffin-embedded tissues; we tested our algorithm with both deep targeted and whole-exome sequencing data. Our algorithm correctly classified between 95 and 98% of somatic mutations with F1-measure ranges from 75.9 to 98.6% depending on the tumor type. We have released the algorithm as a software package called ISOWN (Identification of SOmatic mutations Without matching Normal tissues).ConclusionsIn this work, we describe the development, implementation, and validation of ISOWN, an accurate algorithm for predicting somatic mutations in cancer tissues in the absence of matching normal tissues. ISOWN is available as Open Source under Apache License 2.0 from https://github.com/ikalatskaya/ISOWN

A cancer cell-line titration series for evaluating somatic classification.

BackgroundAccurate detection of somatic single nucleotide variants and small insertions and deletions from DNA sequencing experiments of tumour-normal pairs is a challenging task. Tumour samples are often contaminated with normal cells confounding the available evidence for the somatic variants. Furthermore, tumours are heterogeneous so sub-clonal variants are observed at reduced allele frequencies. We present here a cell-line titration series dataset that can be used to evaluate somatic variant calling pipelines with the goal of reliably calling true somatic mutations at low allele frequencies.ResultsCell-line DNA was mixed with matched normal DNA at 8 different ratios to generate samples with known tumour cellularities, and exome sequenced on Illumina HiSeq to depths of >300×. The data was processed with several different variant calling pipelines and verification experiments were performed to assay >1500 somatic variant candidates using Ion Torrent PGM as an orthogonal technology. By examining the variants called at varying cellularities and depths of coverage, we show that the best performing pipelines are able to maintain a high level of precision at any cellularity. In addition, we estimate the number of true somatic variants undetected as cellularity and coverage decrease.ConclusionsOur cell-line titration series dataset, along with the associated verification results, was effective for this evaluation and will serve as a valuable dataset for future somatic calling algorithm development. The data is available for further analysis at the European Genome-phenome Archive under accession number EGAS00001001016. Data access requires registration through the International Cancer Genome Consortium's Data Access Compliance Office (ICGC DACO)

Mutational signatures in tumours induced by high and low energy radiation in Trp53 deficient mice.

Ionising radiation (IR) is a recognised carcinogen responsible for cancer development in patients previously treated using radiotherapy, and in individuals exposed as a result of accidents at nuclear energy plants. However, the mutational signatures induced by distinct types and doses of radiation are unknown. Here, we analyse the genetic architecture of mammary tumours, lymphomas and sarcomas induced by high (56Fe-ions) or low (gamma) energy radiation in mice carrying Trp53 loss of function alleles. In mammary tumours, high-energy radiation is associated with induction of focal structural variants, leading to genomic instability and Met amplification. Gamma-radiation is linked to large-scale structural variants and a point mutation signature associated with oxidative stress. The genomic architecture of carcinomas, sarcomas and lymphomas arising in the same animals are significantly different. Our study illustrates the complex interactions between radiation quality, germline Trp53 deficiency and tissue/cell of origin in shaping the genomic landscape of IR-induced tumours

Detection of Genomic Structural Variants from Next-Generation Sequencing Data

Structural variants are genomic rearrangements larger than 50?bp accounting for around 1% of the variation among human genomes. They impact on phenotypic diversity and play a role in various diseases including neurological/neurocognitive disorders and cancer development and progression. Dissecting structural variants from next-generation sequencing data presents several challenges and a number of approaches have been proposed in the literature. In this mini review, we describe and summarize the latest tools ? and their underlying algorithms ? designed for the analysis of whole-genome sequencing, whole-exome sequencing, custom captures, and amplicon sequencing data, pointing out the major advantages/drawbacks. We also report a summary of the most recent applications of third-generation sequencing platforms. This assessment provides a guided indication ? with particular emphasis on human genetics and copy number variants ? for researchers involved in the investigation of these genomic events