Somatic Variants In Cancer Research Articles

Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC)

PurposeSeveral professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. Although these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (eg, population frequency, functional, and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation, and, importantly, affects patient care. Therefore, it is essential to address this unmet need. MethodsClinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium, and the Variant Interpretation for Cancer Consortium used a consensus approach to develop a standard operating procedure (SOP) for the classification of oncogenicity of somatic variants. ResultsThis comprehensive SOP has been developed to improve consistency in somatic variant classification and has been validated on 94 somatic variants in 10 common cancer-related genes. ConclusionThe comprehensive SOP is now available for classification of oncogenicity of somatic variants.

Massively parallel phenotyping of coding variants in cancer with Perturb-seq.

Genome sequencing studies have identified millions of somatic variants in cancer, but it remains challenging to predict the phenotypic impact of most. Experimental approaches to distinguish impactful variants often use phenotypic assays that report on predefined gene-specific functional effects in bulk cell populations. Here, we develop an approach to functionally assess variant impact in single cells by pooled Perturb-seq. We measured the impact of 200 TP53 and KRAS variants on RNA profiles in over 300,000 single lung cancer cells, and used the profiles to categorize variants into phenotypic subsets to distinguish gain-of-function, loss-of-function and dominant negative variants, which we validated by comparison with orthogonal assays. We discovered that KRAS variants did not merely fit into discrete functional categories, but spanned a continuum of gain-of-function phenotypes, and that their functional impact could not have been predicted solely by their frequency in patient cohorts. Our work provides a scalable, gene-agnostic method for coding variant impact phenotyping, with potential applications in multiple disease settings.

Standardisation of pathogenicity classification for somatic alterations in solid tumours and haematologic malignancies

BackgroundThe difficulty in interpreting somatic alterations is correlated with the increase in sequencing panel size. To correctly guide the clinical management of patients with cancer, there needs to be accurate classification of pathogenicity followed by actionability assessment. Here, we describe a specific detailed workflow for the classification of the pathogenicity of somatic variants in cancer into five categories: benign, likely benign, unknown significance, likely pathogenic and pathogenic. MethodsClassification is obtained by combining a set of eight relevant criteria in favour of either a pathogenic or a benign effect (pathogenic stand-alone, pathogenic very strong, pathogenic strong, pathogenic moderate, pathogenic supporting, benign supporting, benign strong and benign stand-alone). ResultsOur guide is concordant with the ACMG/AMP 2015 guidelines for germline variants. Interpretation of somatic variants requires considering specific criteria, such as the disease and therapeutic context, co-occurring genomic events in the tumour when available and the use of cancer-specific variant databases. In addition, the gene role in tumorigenesis (oncogene or tumour suppressor gene) also needs to be taken into consideration. ConclusionOur classification could contribute to homogenize best practices on somatic variant pathogenicity interpretation and improve interpretation consistency both within and between laboratories.

Abstract 249: Comparison of variant classification between molecular diagnostics experts and decision support software

Abstract INTRODUCTION: Assessing the actionability of variants identified in next-generation sequencing (NGS) of solid tumors is challenging. Recommendations from AMP/ASCO/CAP/ACMG (Li et al. JMD 2017) provide guidelines for prioritizing variants based on clinical significance and actionability. Sirohi et al. (JMD 2020) reported on interrater agreement with the AMP guidelines by distributing 51 variants from solid tumor NGS to 20 participants at 10 US academic molecular diagnostics laboratories. Initial agreement based on independent assessment amongst the 20 experts was fair (Cohen κ = 0.35). To explore the potential for decision support software to streamline interpretation and improve the reliability of tier classification, we evaluated the same variants with NAVIFY® Mutation Profiler*, a CE-IVD somatic variant interpretation tool that automatically assigns tiers based on curated public evidence. METHODS: Reverse annotation from protein or coding sequence changes was completed with TransVar (Zhou et al. Nat Methods 2015) to obtain hg38 genomic coordinates for the 51 variants. Variants were prepared in VCF files and uploaded to NAVIFY Mutation Profiler (v2.0.0.22e0193) for analysis. The software assigned tier classifications using evidence in Roche curation content version 2.33.0 (released Nov 10, 2020). The default tier for each variant was compared to the tier assigned by the most experts in Sirohi et al. If there was a tie, the higher tier was used for comparison (e.g. Tier II when there was a tie between Tier II and III). RESULTS: There was good agreement (Cohen κ = 0.61) in tier assignments for 37 variants with curation content in NAVIFY Mutation Profiler compared to consensus expert curation in Sirohi et al. Most disagreements could be explained by emerging biomedical evidence, such as phase II clinical trial results for NFE2L2 in squamous cell lung carcinoma, or recommendations in NCCN guidelines. For example, ERBB2 mutations were up-classified by NAVIFY Mutation Profiler to Tier I due to NCCN therapy recommendations, including trastuzumab in combination with lapatinib or pertuzumab for ERBB2-amplified colorectal cancer and trastuzumab emtansine for non-small cell lung cancer with ERBB2 exon 20 insertions. Of the 14 variants without curation in the database, none were actionable, and the experts had classified 11 as Tier III and 3 as Tier II. CONCLUSIONS: When curation content was available, there was high agreement between the assigned tier in NAVIFY Mutation Profiler and the most common tier assigned by 20 molecular diagnostics experts in a previously published study. The continuous growth in new biomedical evidence requires staying current with the latest guidelines and clinical trial results, or the use of regularly updated databases, to accurately interpret somatic cancer variants from NGS test results. * This product is for Research Use Only; Not for use in diagnostic procedures in the US. Citation Format: Stephanie J. Yaung, Shuba Krishna, Sidney Scudder, Maximilian Schmid, John F. Palma. Comparison of variant classification between molecular diagnostics experts and decision support software [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 249.

Clinical Interpretation Challenges of Germline-Shared Somatic Variants in Cancer.

To investigate the interpretation differences of germline-shared somatic variants. A total of 123,302 COSMIC variants associated with hematologic malignant neoplasms were used. The pathogenicity and actionability of shared variants were analyzed based on the standardized guidelines. The overall frequency of variants shared in ClinVar/HGMD and COSMIC was 10%. The pathogenicity of 54 shared variants was pathogenic/likely pathogenic (P/LP; n = 30), variants of unknown significance (n = 3), and benign/likely benign (n = 21). In total, 30 P/LP variants were reclassified to tier I/tier II (83%) and tier III (17%) variants. This is the first study about different clinical interpretations of shared variants based on the current standard guidelines. This study takes a meaningful step in bridging the interpretation gap between the somatic and germline variants.

Abstract A58: Curation of pediatric cancer variants within the Clinical Genome Resource (ClinGen)

Abstract Introduction: The Clinical Genome Resource (ClinGen) Somatic Working Group (sWG) is a multi-institution team engaged in developing processes, resources, and standards to support accurate classification of somatic variants in cancer. Existing decision support resources in cancer knowledgebases are heavily skewed towards genes and variants relevant in adult cancers; however, information to support variant interpretation in childhood cancers is limited. Here we report on the goals and progress of the Pediatric Cancer Taskforce, created within the ClinGen sWG, to lead curation efforts of actionable alterations in childhood cancers. Methods: The ClinGen sWG Pediatric Cancer Taskforce (PCT) consists of a core group of twelve members comprising geneticists, pathologists, and oncologists with expertise in different pediatric cancers and with representation from 9 leading pediatric institutions. The taskforce has a total of 35 members including volunteer-curators who work under guidance of the expert members. Curation of childhood cancer variants is conducted in collaboration with the Clinical Interpretation of Variants in Cancer (CIViC) team at Washington University in Saint Louis, using the CIViC knowledgebase (civicdb.org) and the ClinVar database as open-access curation and data-sharing platforms. Diagnostic, prognostic, and therapeutic evidence is tiered according to the AMP/ASCO/CAP guidelines for the clinical interpretation of somatic variants. PCT members are assigned specific genetic variant-tumor type associations for curation, which are then reviewed in monthly conferences to finalize assertions in CIViC. Results: The PCT has prioritized 40 genetic alterations relevant to pediatric cancer for curation based on their clinical relevance and the lack of sufficient existing curated evidence in clinical knowledgebases. To date, 4 assertions have been created and added to the database: HEY1-NCOA2 fusion in mesenchymal chondrosarcoma, KIAA1549-BRAF fusion and ACVR1 p.G328V variant in pediatric glioma, and EBF1-PDGFRB fusion in pediatric B-cell precursor acute lymphoblastic leukemia. Active curation has been initiated for NTRK fusions agnostic of tissue histology, targetable kinase fusions in Ph-like B-lymphoblastic leukemia, and common variants in selected pediatric sarcomas and brain tumors, focusing heavily on driver gene fusions in childhood cancers. 119 evidence items have been created in CIViC by the members. The PCT also works to implement more standardized and accurate classification of pediatric cancers in CIViC and other cancer resources, and to enhance search for pediatric-specific data through appropriate tagging of evidence using ontology terms. Conclusions: As molecular alterations are increasingly relevant to the care of children with cancer, the ClinGen PCT will work to develop standards, processes, and resources for efficient and accurate determination of clinical relevance of pediatric cancer variants. Citation Format: Alanna J. Church, Shruti Rao, Deborah Ritter, Arpad Danos, Kilann Krysiak, Laura B. Corson, Kevin E. Fisher, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Chimene A. Kesserwan, Theodore W. Laetsch, Donald W. Parsons, Ryan J. Schmidt, Kristen L. Sund, Wan-Hsin Lin, Malachi Griffith, Obi L. Griffith, Shashikant Kulkarni, Subha Madhavan, Angshumoy Roy, Gordana Raca. Curation of pediatric cancer variants within the Clinical Genome Resource (ClinGen) [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A58.

Development and validation of Houston Methodist Variant Viewer version 3: updates to our application for interpretation of next-generation sequencing data.

ObjectivesInformatics tools that support next-generation sequencing workflows are essential to deliver timely interpretation of somatic variants in cancer. Here, we describe significant updates to our laboratory developed bioinformatics pipelines and data management application termed Houston Methodist Variant Viewer (HMVV).Materials and MethodsWe collected feature requests and workflow improvement suggestions from the end-users of HMVV version 1. Over 1.5 years, we iteratively implemented these features in five sequential updates to HMVV version 3.ResultsWe improved the performance and data throughput of the application while reducing the opportunity for manual data entry errors. We enabled end-user workflows for pipeline monitoring, variant interpretation and annotation, and integration with our laboratory information system. System maintenance was improved through enhanced defect reporting, heightened data security, and improved modularity in the code and system environments.Discussion and ConclusionValidation of each HMVV update was performed according to expert guidelines. We enabled an 8× reduction in the bioinformatics pipeline computation time for our longest running assay. Our molecular pathologists can interpret the assay results at least 2 days sooner than was previously possible. The application and pipeline code are publicly available at https://github.com/hmvv.

Comparative Study of NGS Platform Ion Torrent Personal Genome Machine and Therascreen Rotor-Gene Q for the Detection of Somatic Variants in Cancer.

Molecular profiling of a tumor allows the opportunity to design specific therapies which are able to interact only with cancer cells characterized by the accumulation of several genomic aberrations. This study investigates the usefulness of next-generation sequencing (NGS) and mutation-specific analysis methods for the detection of target genes for current therapies in non-small-cell lung cancer (NSCLC), metastatic colorectal cancer (mCRC), and melanoma patients. We focused our attention on EGFR, BRAF, KRAS, and BRAF genes for NSCLC, melanoma, and mCRC samples, respectively. Our study demonstrated that in about 2% of analyzed cases, the two techniques did not show the same or overlapping results. Two patients affected by mCRC resulted in wild-type (WT) for BRAF and two cases with NSCLC were WT for EGFR according to PGM analysis. In contrast, these samples were mutated for the evaluated genes using the therascreen test on Rotor-Gene Q. In conclusion, our experience suggests that it would be appropriate to confirm the WT status of the genes of interest with a more sensitive analysis method to avoid the presence of a small neoplastic clone and drive the clinician to correct patient monitoring.

A case for expert curation: an overview of cancer curation in the Clinical Genome Resource (ClinGen).

We describe the Clinical Genome Resource (ClinGen) cancer-related curation activities and the importance of curation to the evolving state of variant interpretation in a clinical context for both pediatric and adult cancer patients. We highlight specific examples from the CDH1 and PTEN Variant Curation Expert Panels (VCEPs) of the FDA-recognized process by which ClinGen VCEPs specify the American College of Medical Genetics and Genomics/Association of Molecular Pathology evidence code to develop variant classifications. We also review gene curations performed within the Hereditary Cancer Clinical Domain. We describe the parallel efforts for curation of somatic cancer variants from the Somatic Cancer Working Group. The ClinGen Germline/Somatic Committee is working to improve incorporation of both hereditary and somatic variant data to aid clinical interpretation. These ClinGen efforts rely on broad data sharing and detailed phenotypic and molecular information from published case studies to provide expert-curated variant interpretation to the cancer community.

Abstract 445: A validated genomic reference material with known content applicable to panel assays requiring low frequency variant allele detection

Abstract Prospective and GCP testing of tumors for important somatic variants in cancer is becoming more commonplace. Currently, there is a lack of a reliable and perpetual publicly available genomic material that can be used across a wide range of genomic testing methods for assessing the potential accuracy of a given test. A great variety of the next-gen sequencing (NGS) methods and processes currently in place now or in the future will have heterogeneous genomic targets, limits of detection, input material, and cost requirements. The variety of these tests extends from small panels (targeting 10 or more genes) to larger panels (targeting > 100 genes) to comprehensive tests (several hundred genes or WES/WGS). There is considerable variability in the sensitivity requirements of various assays with some designed to detect variant allele frequencies (VAF) of 10% or more while others wish to detect VAF < 1%. In addition, the testing itself has several layers of complexity: 1) the size and complex nature of the human genome; 2) the additional complexity of the damaged genome in cancer cells; 3) the amount and origin of the input material; and 4) the complex instruments and software algorithms necessary to proficiently perform the tests. The SEQC2 consortium has extensively evaluated and documented a pooled cell line reference material that has > 40,000 known variants with the great majority having allele frequencies < 10% in the standard pool. This effort evaluated DNA pooled from 11 cell lines, 10 of which are from distinct cancers, used for generating RNA material for the Universal Human Reference RNA (UHRR). This consortium defined ground truth (primarily SNVs, Indels) for each cell line using related and orthogonal genomic testing methods (multiple platforms, enrichment methods, and software algorithms) so that the content of various standardized admixtures and pools of the material will be known. The final results were validated by additional sequencing of the individual pools and through ddPCR testing of a large number of identified variants. The result is a series of reference materials with a large number and variety of validated variants at various VAF, suitable for a wide range of testing, including variants with VAF < 1%. These materials can be used as an ongoing reference material when developing new assays, conducting proficiency and reproducibility tests, or for quality control purposes when running regulated assays (positive and negative control variants). Citation Format: Wendell Jones, Joshua Xu, Binsheng Gong, Onco-panel Working Group of the Sequencing QualityControl Phase 2 (SEQC2) Consortium. A validated genomic reference material with known content applicable to panel assays requiring low frequency variant allele detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 445.

Maftools: efficient and comprehensive analysis of somatic variants in cancer.

Numerous large-scale genomic studies of matched tumor-normal samples have established the somatic landscapes of most cancer types. However, the downstream analysis of data from somatic mutations entails a number of computational and statistical approaches, requiring usage of independent software and numerous tools. Here, we describe an R Bioconductor package, Maftools, which offers a multitude of analysis and visualization modules that are commonly used in cancer genomic studies, including driver gene identification, pathway, signature, enrichment, and association analyses. Maftools only requires somatic variants in Mutation Annotation Format (MAF) and is independent of larger alignment files. With the implementation of well-established statistical and computational methods, Maftools facilitates data-driven research and comparative analysis to discover novel results from publicly available data sets. In the present study, using three of the well-annotated cohorts from The Cancer Genome Atlas (TCGA), we describe the application of Maftools to reproduce known results. More importantly, we show that Maftools can also be used to uncover novel findings through integrative analysis.

Abstract 432: Establishment of reference samples for the detection of somatic variants in cancer

Abstract The FDA is well aware of the crucial role that next generation sequencing (NGS) plays in precision medicine, and this technology is already leveraged in regulatory decision-making. However, lack of well-characterized and community-validated reference samples and data benchmarks creates a potential challenge for development and review of NGS applications. To address this challenge, 60+ institutions and 160+ scientists have come together to form a somatic mutation working group within the FDA-led Sequencing Quality Control Phase II (SEQC2) consortium. We present genomic samples with high-confidence germline and somatic mutation data in order to establish a community standard against which new tools and platforms can be evaluated and benchmarked. Our initial reference samples are a triple-negative breast cancer cell line: HCC1395, and its B lymphocyte derived normal cell line: HCC1395BL. To minimize bias, we sequenced these two cell lines at up to 10 sequencing centers using multiple sequencing platforms. In totality, we have performed whole genome sequencing of the two cell lines to coverage depths of 2400X on Illumina HiSeq, 450X on NovaSeq, 600X on 10X Genomics and 50X on PacBio. Additionally, we performed whole exome sequencing at 1200X on HiSeq and 100X on Ion Torrent. Lastly, we sequenced (RNAseq) >5000 single cells for cellular heterogeneity studies. We applied machine learning algorithms to define high confidence mutation calls with datasets from 8 NGS platforms combined with 9 bioinformatics analysis pipelines. Each mutation in the “gold standard” dataset was annotated with confidence scores and supporting evidence. To independently evaluate accuracy of our call set, we selected a subset of mutation calls for additional validation using orthogonal methods, including ddPCR. To summarize, we present the first release of high-confidence germline variants and somatic mutations on a pair of well characterized cell lines. We plan to continuously advance accuracy and completeness of our reference sample data, in order to serve as a standard for the community for years to come. Citation Format: Li Tai Fang, Wenming Xiao, Somatic Mutation Working Group, SEQC2 Consortium. Establishment of reference samples for the detection of somatic variants in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 432.

Semi-automated platform for efficient and accurate annotation and interpretation of somatic variants in cancer.

e18597 Background: Rapid advances in NGS technology have enabled high throughput generation of data allowing for more comprehensive approaches to precision medicine and improved cancer patient care. However, efficient and timely variant interpretation and reporting to guide physicians’ therapeutic decisions remains an unmet need. Manual interpretation of variants is onerous, unreproducible, and non-scalable. At the same time, full automation of highly-nuanced clinical interpretation without human intervention is still at a nascent stage and has high error rates. Herein, we describe development of a semi-automated platform that augments accuracy and efficiency of clinical interpretation and reporting by over 20-fold. Methods: To facilitate accurate and efficient variant interpretation and clinical reporting we developed and validated GENEKEEPER (KEW, Inc.; Cambridge, MA), a cloud-based knowledgebase and reporting tool. The underlying database architecture seamlessly integrates patient sequence data with numerous resources including, the internal knowledgebase, trial registries, professional guidelines, and other public databases. The internal knowledgebase includes over 40,000 variants from > 3,000 patient tumor specimens, annotated by PHD/MD expert analysts to be consistent with the AMP guidelines. Results: In an analysis of 20 sequenced tumors, curated using our platform, we noted a 10-fold increase in efficiency and 2-fold increase in accuracy as compared to manual curation. We also observed a near 100% congruency of reported clinical trials and therapies between different curators. The flexible architecture enabled facile implementation of AMP guidelines to enhance standardization and accuracy of variant interpretation. Overall the platform demonstrated a 20-fold increase in efficacy in report generation. Conclusions: Semi-automated curation platforms promote precision and efficacy in analyzing and reporting clinically relevant variants to guide informed treatment decisions. GENEKEEPER significantly reduces the laboriousness of manual curation and will empower clinical laboratories to exponentially scale-up NGS operations.

A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal.

A key constraint in genomic testing in oncology is that matched normal specimens are not commonly obtained in clinical practice. Thus, while well-characterized genomic alterations do not require normal tissue for interpretation, a significant number of alterations will be unknown in whether they are germline or somatic, in the absence of a matched normal control. We introduce SGZ (somatic-germline-zygosity), a computational method for predicting somatic vs. germline origin and homozygous vs. heterozygous or sub-clonal state of variants identified from deep massively parallel sequencing (MPS) of cancer specimens. The method does not require a patient matched normal control, enabling broad application in clinical research. SGZ predicts the somatic vs. germline status of each alteration identified by modeling the alteration’s allele frequency (AF), taking into account the tumor content, tumor ploidy, and the local copy number. Accuracy of the prediction depends on the depth of sequencing and copy number model fit, which are achieved in our clinical assay by sequencing to high depth (>500x) using MPS, covering 394 cancer-related genes and over 3,500 genome-wide single nucleotide polymorphisms (SNPs). Calls are made using a statistic based on read depth and local variability of SNP AF. To validate the method, we first evaluated performance on samples from 30 lung and colon cancer patients, where we sequenced tumors and matched normal tissue. We examined predictions for 17 somatic hotspot mutations and 20 common germline SNPs in 20,182 clinical cancer specimens. To assess the impact of stromal admixture, we examined three cell lines, which were titrated with their matched normal to six levels (10–75%). Overall, predictions were made in 85% of cases, with 95–99% of variants predicted correctly, a significantly superior performance compared to a basic approach based on AF alone. We then applied the SGZ method to the COSMIC database of known somatic variants in cancer and found >50 that are in fact more likely to be germline.

Oncodomains: A protein domain-centric framework for analyzing rare variants in tumor samples.

The fight against cancer is hindered by its highly heterogeneous nature. Genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare somatic variants present only in a small fraction of lesions. Such rare somatic variants dominate the landscape of genomic mutations in cancer, yet efforts to correlate somatic mutations found in one or few individuals with functional roles have been largely unsuccessful. Traditional methods for identifying somatic variants that drive cancer are ‘gene-centric’ in that they consider only somatic variants within a particular gene and make no comparison to other similar genes in the same family that may play a similar role in cancer. In this work, we present oncodomain hotspots, a new ‘domain-centric’ method for identifying clusters of somatic mutations across entire gene families using protein domain models. Our analysis confirms that our approach creates a framework for leveraging structural and functional information encapsulated by protein domains into the analysis of somatic variants in cancer, enabling the assessment of even rare somatic variants by comparison to similar genes. Our results reveal a vast landscape of somatic variants that act at the level of domain families altering pathways known to be involved with cancer such as protein phosphorylation, signaling, gene regulation, and cell metabolism. Due to oncodomain hotspots’ unique ability to assess rare variants, we expect our method to become an important tool for the analysis of sequenced tumor genomes, complementing existing methods.

Validation of OncoPanel: A Targeted Next-Generation Sequencing Assay for the Detection of Somatic Variants in Cancer.

- The analysis of somatic mutations across multiple genes in cancer specimens may be used to aid clinical decision making. The analytical validation of targeted next-generation sequencing panels is important to assess accuracy and limitations. - To report the development and validation of OncoPanel, a custom targeted next-generation sequencing assay for cancer. - OncoPanel was designed for the detection of single-nucleotide variants, insertions and deletions, copy number alterations, and structural variants across 282 genes with evidence as drivers of cancer biology. We implemented a validation strategy using formalin-fixed, paraffin-embedded, fresh or frozen samples compared with results obtained by clinically validated orthogonal technologies. - OncoPanel achieved 98% sensitivity and 100% specificity for the detection of single-nucleotide variants, and 84% sensitivity and 100% specificity for the detection of insertions and deletions compared with single-gene assays and mass spectrometry-based genotyping. Copy number detection achieved 86% sensitivity and 98% specificity compared with array comparative genomic hybridization. The sensitivity of structural variant detection was 74% compared with karyotype, fluorescence in situ hybridization, and polymerase chain reaction. Sensitivity was affected by inconsistency in the detection of FLT3 and NPM1 alterations and IGH rearrangements due to design limitations. Limit of detection studies demonstrated 98.4% concordance across triplicate runs for variants with allele fraction greater than 0.1 and at least 50× coverage. - The analytical validation of OncoPanel demonstrates the ability of targeted next-generation sequencing to detect multiple types of genetic alterations across a panel of genes implicated in cancer biology.

Identification of Genomic Somatic Variants in Cancer: From Discovery to Actionability.

The perfect method to discover and validate actionable somatic variants in cancer has not yet been developed, yet significant progress has been made toward this goal. There have been huge increases in the throughput and cost of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) sequencing technologies that have led to the burgeoning possibility of using sequencing data in clinical settings. Discovery of somatic mutations is relatively simple and has been improved recently due to laboratory methods optimization, bioinformatics algorithms development, and the expansion of various databases of population genomic information. Tiered systems of evidence evaluation are currently being used to classify genomic variants for clinicians to more rapidly and accurately determine actionability of these aberrations. These efforts are complicated by the intricacies of communicating sequencing results to physicians and supporting its biological relevance, emphasizing the need for increasing education of clinicians and administrators, and the ongoing development of ethical standards for dealing with incidental results. This chapter will focus on general aspects of DNA and RNA tumor sequencing technologies, data analysis and interpretation, assessment of biological and clinical relevance of genomic aberrations, ethical aspects of germline sequencing, and how these factors impact cancer personalized care.

Abstract 3646: Highly sensitive and cost-effective detection of somatic cancer variants using single-molecule, real-time sequencing

Abstract Next-Generation Sequencing (NGS) technologies allow for molecular profiling of cancer samples with high sensitivity and speed at reduced cost. For efficient profiling of cancer samples, it is important that the NGS methods used are not only robust but also capable of accurately detecting low-frequency somatic mutations. Single Molecule, Real-Time (SMRT®) Sequencing offers several advantages, including the ability to sequence single molecules with very high accuracy (>QV40) using the circular consensus sequencing (CCS) approach. The availability of genetically defined, human genomic reference standards provides an industry standard for the development and quality control of molecular assays for studying cancer variants. Here we characterize SMRT Sequencing for the detection of low-frequency somatic variants using the Quantitative Multiplex DNA Reference Standards from Horizon Diagnostics, combined with amplification of the variants using the Multiplicom Tumor Hotspot MASTR Plus assay. First, we sequenced a reference standard containing precise allelic frequencies from 1% to 24.5% for major oncology targets verified using digital PCR. This reference material recapitulates the complexity of tumor composition and serves as a well-characterized control. The control sample was amplified using the Multiplicom Tumor Hotspot MASTR Plus assay that targets 252 amplicons (121-254 bp) from 26 relevant cancer genes, which includes all 11 variants in the control sample. We also sequenced a second sample containing a series of mixes, each with known mutations, at levels below 10% and down to 0.01%. PCR-amplified targets were sequenced and analyzed using SMRT Sequencing to identify the variants and determine the observed frequency. The random error profile and high-accuracy CCS reads make it possible to accurately detect low-frequency somatic variants. Citation Format: Steve Kujawa, Anand Sethuraman, Kevin Eng, Primo Baybayan, Lien Heyrman, Jurgen Del Favero. Highly sensitive and cost-effective detection of somatic cancer variants using single-molecule, real-time sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3646.

Abstract 4260: Standardized decision support in next-generation sequencing (NGS) reports of somatic cancer variants

Abstract Introduction: Of hundreds to thousands of somatic mutations that exist in each cancer genome, a large number are unique and non-recurrent variants. Many such variants occur in tumor genes that have well-established biological and clinical relevance and are putative targets of molecular therapy, however, most variants are still of unknown significance. Prioritizing and reporting genetic variants identified via NGS technologies remains a major challenge. Methods: Based on a systematic framework for cancer variant annotation/prioritization, we propose a structured molecular pathology report using standardized terminology in order to best inform oncology clinical practice. In parallel with the variant interpretation pipeline, we developed a comprehensive knowledge database (Kdb) that integrates tumor types, genes, variants, response/resistance patterns to approved and experimental agents and PubMed identifiers. Results: As physicians rely heavily on limited subsets of data and frequently apply rules based on strength of evidence in the decision-making process, NGS results are presented in a very discrete manner, avoiding detailed descriptions of each genomic aberration. Reportable variants are grouped in three categories: (i) actionable, which support treatment recommendation, enrollment in clinical trials and/or have prognostic or diagnostic implications; (ii) biologically relevant but not clearly actionable, including novel variants lacking functional preclinical validation in known cancer genes; and (iii) variants of unknown significance. Predictive associations are reported according to tumor type and classified in a hierarchical way based on the strength of evidence: (i) late trials; (ii) early trials; (iii) case reports; and (iv) preclinical data being explored in clinical trials. In our internal Kdb, tumor types with the largest number of emerging predictive associations are acute leukemias, non-small cell lung cancer, brain tumors, melanoma, colorectal, ovarian and breast cancers, comprising more than 100 unique gene - drug interactions. Further interpretation of gene variants requires a team with strong background in cancer biology, careful evaluation of the published literature and ability to match a patient's tumor genotype to clinical trials in the context of multiple aberrations. Conclusions: With large amounts of data being generated as high throughput sequencing assays enter the clinical realm, there is a growing need to better communicate relevant findings in a timely manner while remaining cognizant of the potential consequences of misuse or overinterpretation of genomic information. The scientific knowledge on actionable events should be presented the report, so that physicians can make evidence-based decisions and patients may benefit from treatment with matched targeted agents. We hope that our experience in this process will help other institutions implement similar programs. Citation Format: Rodrigo Dienstmann, Fei Dong, Darrell Borger, Dora Dias Santagata, Leif W. Ellisen, Long P. Le, A. John Iafrate. Standardized decision support in next-generation sequencing (NGS) reports of somatic cancer variants. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4260. doi:10.1158/1538-7445.AM2014-4260

Integrative Annotation of Variants from 1092 Humans: Application to Cancer Genomics

Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations ("ultrasensitive") and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, "motif-breakers"). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.