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)

PURPOSE

Several 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.

METHODS

Clinical 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.

RESULTS

This 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.

CONCLUSION

The comprehensive SOP is now available for classification of oncogenicity of somatic variants.

Open-Sourced CIViC Annotation Pipeline to Identify and Annotate Clinically Relevant Variants Using Single-Molecule Molecular Inversion Probes

PURPOSE

Clinical targeted sequencing panels are important for identifying actionable variants for patients with cancer; however, existing approaches do not provide transparent and rationally designed clinical panels to accommodate the rapidly growing knowledge within oncology.

MATERIALS AND METHODS

We used the Clinical Interpretations of Variants in Cancer (CIViC) database to develop an Open-Sourced CIViC Annotation Pipeline (OpenCAP). OpenCAP provides methods to identify variants within the CIViC database, build probes for variant capture, use probes on prospective samples, and link somatic variants to CIViC clinical relevance statements. OpenCAP was tested using a single-molecule molecular inversion probe (smMIP) capture design on 27 cancer samples from 5 tumor types. In total, 2,027 smMIPs were designed to target 111 eligible CIViC variants (61.5 kb of genomic space).

RESULTS

When compared with orthogonal sequencing, CIViC smMIP sequencing demonstrated a 95% sensitivity for variant detection (n = 61 of 64 variants). Variant allele frequencies for variants identified on both sequencing platforms were highly concordant (Pearson’s r = 0.885; n = 61 variants). Moreover, for individuals with paired tumor and normal samples (n = 12), 182 clinically relevant variants missed by orthogonal sequencing were discovered by CIViC smMIP sequencing.

CONCLUSION

The OpenCAP design paradigm demonstrates the utility of an open-source and open-access database built on attendant community contributions with peer-reviewed interpretations. Use of a public repository for variant identification, probe development, and variant interpretation provides a transparent approach to build dynamic next-generation sequencing–based oncology panels.

VIST - a Variant-Information Search Tool for precision oncology

BACKGROUND

Diagnosis and treatment decisions in cancer increasingly depend on a detailed analysis of the mutational status of a patient's genome. This analysis relies on previously published information regarding the association of variations to disease progression and possible interventions. Clinicians to a large degree use biomedical search engines to obtain such information; however, the vast majority of scientific publications focus on basic science and have no direct clinical impact. We develop the Variant-Information Search Tool (VIST), a search engine designed for the targeted search of clinically relevant publications given an oncological mutation profile.

RESULTS

VIST indexes all PubMed abstracts and content from ClinicalTrials.gov. It applies advanced text mining to identify mentions of genes, variants and drugs and uses machine learning based scoring to judge the clinical relevance of indexed abstracts. Its functionality is available through a fast and intuitive web interface. We perform several evaluations, showing that VIST's ranking is superior to that of PubMed or a pure vector space model with regard to the clinical relevance of a document's content.

CONCLUSION

Different user groups search repositories of scientific publications with different intentions. This diversity is not adequately reflected in the standard search engines, often leading to poor performance in specialized settings. We develop a search engine for the specific case of finding documents that are clinically relevant in the course of cancer treatment. We believe that the architecture of our engine, heavily relying on machine learning algorithms, can also act as a blueprint for search engines in other, equally specific domains. VIST is freely available at https://vist.informatik.hu-berlin.de/.

Comparative Analysis of Public Knowledge Bases for Precision Oncology

PURPOSE

Precision oncology depends on the availability of up-to-date, comprehensive, and accurate information about associations between genetic variants and therapeutic options. Recently, a number of knowledge bases (KBs) have been developed that gather such information on the basis of expert curation of the scientific literature. We performed a quantitative and qualitative comparison of Clinical Interpretations of Variants in Cancer, OncoKB, Cancer Gene Census, Database of Curated Mutations, CGI Biomarkers (the cancer genome interpreter biomarker database), Tumor Alterations Relevant for Genomics-Driven Therapy, and the Precision Medicine Knowledge Base.

METHODS

We downloaded each KB and restructured their content to describe variants, genes, drugs, and gene-drug associations in a common format. We normalized gene names to Entrez Gene IDs and drug names to ChEMBL and DrugBank IDs. For the analysis of clinically relevant gene-drug associations, we obtained lists of genes affected by genetic alterations and putative drug therapies for 113 patients with cancer whose cases were presented at the Molecular Tumor Board (MTB) of the Charité Comprehensive Cancer Center.

RESULTS

Our analysis revealed that the KBs are largely overlapping but also that each source harbors a notable amount of unique information. Although some KBs cover more genes, others contain more data about gene-drug associations. Retrospective comparisons with findings of the Charitè MTB at the gene level showed that use of multiple KBs may considerably improve retrieval results. The relative importance of a KB in terms of cancer genes was assessed in more detail by logistic regression, which revealed that all but one source had a notable impact on result quality. We confirmed these findings using a second data set obtained from an independent MTB.

CONCLUSION

To date, none of the existing publicly available KBs on gene-drug associations in precision oncology fully subsumes the others, but all of them exhibit specific strengths and weaknesses. Consideration of multiple KBs, therefore, is essential to obtain comprehensive results.

Variant information systems for precision oncology

BACKGROUND

The decreasing cost of obtaining high-quality calls of genomic variants and the increasing availability of clinically relevant data on such variants are important drivers for personalized oncology. To allow rational genome-based decisions in diagnosis and treatment, clinicians need intuitive access to up-to-date and comprehensive variant information, encompassing, for instance, prevalence in populations and diseases, functional impact at the molecular level, associations to druggable targets, or results from clinical trials. In practice, collecting such comprehensive information on genomic variants is difficult since the underlying data is dispersed over a multitude of distributed, heterogeneous, sometimes conflicting, and quickly evolving data sources. To work efficiently, clinicians require powerful Variant Information Systems (VIS) which automatically collect and aggregate available evidences from such data sources without suppressing existing uncertainty.

METHODS

We address the most important cornerstones of modeling a VIS: We take from emerging community standards regarding the necessary breadth of variant information and procedures for their clinical assessment, long standing experience in implementing biomedical databases and information systems, our own clinical record of diagnosis and treatment of cancer patients based on molecular profiles, and extensive literature review to derive a set of design principles along which we develop a relational data model for variant level data. In addition, we characterize a number of public variant data sources, and describe a data integration pipeline to integrate their data into a VIS.

RESULTS

We provide a number of contributions that are fundamental to the design and implementation of a comprehensive, operational VIS. In particular, we (a) present a relational data model to accurately reflect data extracted from public databases relevant for clinical variant interpretation, (b) introduce a fault tolerant and performant integration pipeline for public variant data sources, and (c) offer recommendations regarding a number of intricate challenges encountered when integrating variant data for clincal interpretation.

CONCLUSION

The analysis of requirements for representation of variant level data in an operational data model, together with the implementation-ready relational data model presented here, and the instructional description of methods to acquire comprehensive information to fill it, are an important step towards variant information systems for genomic medicine.

Integrative analysis of head and neck cancer identifies two biologically distinct HPV and three non-HPV subtypes

PURPOSE

Current classification of head and neck squamous cell carcinomas (HNSCC) based on anatomic site and stage fails to capture biologic heterogeneity or adequately inform treatment.

EXPERIMENTAL DESIGN

Here, we use gene expression-based consensus clustering, copy number profiling, and human papillomavirus (HPV) status on a clinically homogenous cohort of 134 locoregionally advanced HNSCCs with 44% HPV(+) tumors together with additional cohorts, which in total comprise 938 tumors, to identify HNSCC subtypes and discover several subtype-specific, translationally relevant characteristics.

RESULTS

We identified five subtypes of HNSCC, including two biologically distinct HPV subtypes. One HPV(+) and one HPV(-) subtype show a prominent immune and mesenchymal phenotype. Prominent tumor infiltration with CD8(+) lymphocytes characterizes this inflamed/mesenchymal subtype, independent of HPV status. Compared with other subtypes, the two HPV subtypes show low expression and no copy number events for EGFR/HER ligands. In contrast, the basal subtype is uniquely characterized by a prominent EGFR/HER signaling phenotype, negative HPV-status, as well as strong hypoxic differentiation not seen in other subtypes.

CONCLUSION

Our five-subtype classification provides a comprehensive overview of HPV(+) as well as HPV(-) HNSCC biology with significant translational implications for biomarker development and personalized care for patients with HNSCC.

Genomic profiling of a clinically annotated cohort of locoregionally advanced head and neck cancers (HNC) treated with definitive chemoradiotherapy

5517

Background: Recent advances in genomic technology and sequencing have allowed the near comprehensive characterization of genetic abnormalities in HNC, but their correlation with clinical outcome remains to be determined. We analysed a clinically annotated cohort of locoregionally advanced HNC for copy number changes and mutations and correlated with outcome in an exploratory fashion. Methods: 120 head and neck cancers (frozen tissue) and matching normal tissues/blood from patients with locoregionally advanced HNC treated with definitive chemoradiation +/- induction at the University of Chicago were obtained from our tissue bank. DNA, RNA, protein were extracted. Barcoded, multiplexed sequencing libraries for 500 head and neck cancer associated genes (Agilent capture) were built and sequenced on Illumina HiSeq next generation sequencers. 150 commonly copy number altered and HNC relevant genes were analysed for copy number alterations (CNA) using the Nanostring nCounter platform. Respective genetic aberrations in curated pathways were allocated to tumor subgroups. Exploratory analysis correlated data with induction response and progression free survival. Confirmatory HPV testing was performed using an E6, nested, multiplex PCR. Results: ≥80% of targeted sequences were successfully captured and sequenced. Commonly mutated genes included: TP53, CDKN2A, PIK3CA, NOTCH1. Of note PIK3CA mutations were enriched in HPV(+) tumors. Commonly copy number altered genes included amplifications of VEGF1, CCND1, MYC, EGFR, MDM4, CTTN, as well as PIK3CA, and deletions of CDKN2A, SUCLG2, FHIT, TGFBR2, PTPRD, IKBKG, TRAF6, and RASSF1. CCND1, and MYC were commonly co-amplified and correlated with poor progression free survival, representing a distinct HNC phenotype. Analysis for additional genes and influence on induction response will be presented. Conclusions: Targeted, medium throughput genomic profiling is feasible, and provides the majority of HNC specific genetic aberrations at a comparably low cost. Impact on outcome (CCND1/MYC amplification) and potential treatment targets (PIK3CA mutations in HPV(+) tumors) were identified.

Abstract LB-398: Detection of copy number alterations in 124 head and neck squamous cell carcinomas using the Nanostring nCounter assay

Background: Head and Neck Squamous Cell Carcinoma (HNSCC) is the 6th most common cancer worldwide. Little is known about changes in copy number (CN) in many cancer associated genes in this tumor type, which may contribute to carcinogenesis and could become useful treatment targets in the future. Many analyses of CN are complicated by the need to use formalin-fixed paraffin-embedded (FFPE) tissues posing technical challenges. We determined copy number alterations (CNA) using a novel, medium-throughput technology (NanoString nCounter) in order to determine common cancer associated CNAs and assess its performance in FFPE tissues. Results were compared with more established technologies such as SNP array, aCGH, and qPCR. Methods: We investigated CNA in 124 tumor specimens and 22 cell lines for 100 literature curated cancer genes using the NanoString nCounter. Most samples were OCT frozen tissues, with a small subset having both OCT frozen, and FFPE tissues. Slides were assessed for tumor content by a HNC pathologist and samples with at least 60% tumor content selected. DNA was extracted using standard column-based methods (Qiagen). We performed CN analysis in 124 frozen (+4 matching FFPE) HNSCC specimens and cell lines (Nanostring nCounter assay) focusing on a selection of cancer associated genes. Furthermore we used aCGH and SNP-CHIP to analyse 20 and 4 cell lines respectively two of which were covered by all three methods. FGFR1 was assessed by qPCR. For FFPE samples a special Nanostring probeset was used with 3-5 probes per gene to provide redundancy with degraded DNA samples. HPV status of samples was assessed by a nested PCR for E6. Results Copy number changes detected by Nanostring and aCGH correlated well. The Nanostring nCounter assay appeared more accurate in calling deletions, which were detected in MST1R, PBRM1, PTPRD for instance. We found amplifications in multiple samples and genes, e.g. CCND1, EGFR, MDM4, MYC, VEGFA, PAX9, ITGB4, SSND1, CTTN, FADD, FGF19, ORAOV1, PPFIA1, some of which were frequently (n>50 samples) or highly amplified (>30 copies). Some of these amplifications such as ORAOV1 and PPFIA1 seemed higher/more frequent in HPV(-) compared to HPV(+) samples. Samples with FGFR1 amplification were validated using qPCR and correlated very closely. FFPE sample processing was uncomplicated using the FFPE probeset. While some probes failed, using degraded FFPE derived DNA, the redundancy of probes allowed accurate calling of CNA that closely correlated with frozen sample results. Conclusions Copy number alterations are frequent in HNSCC and involve many cancer associated genes, including potentially targetable genes such as EGFR, MDM4, and PIK3CA. Most of the CN changes are recurrent. Amplifications and deletions to some extent differed depending on HPV status. The role and implications of these CN aberrations in a clinical setting need to be further elucidated and validated.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-398. doi:1538-7445.AM2012-LB-398

Abstract 5472: Role of Harvey Ras (HRAS) mutations in head and neck squamous cell carcinoma (HNSCC)

Background: Harvey Ras (HRAS) was recently reported being mutated in head and neck squamous cell carcinomas (HNSCC) and likely plays an important role as an oncogene. The precise role of HRAS mutations for signaling and carcinogenesis of HNSCC remains to be determined. Methods: We completed mutational screening (Sanger Sequencing) for tissues and cell lines focused on the known hotspot mutations G12X and Q61X. Furthermore we performed viability testing for various cell lines and visualized the signaling-effects by itself, in presence of PI3K-, EGFR inhibitors and likewise in combination, by immunoblotting. After suppression of HRAS using siRNA, we determined the cell-viability. Results: In our study we sequenced 100 HNSCC tumor tissues and HNSCC cell lines and identified several canonical HRAS mutations. One cell line contained a G12D HRAS mutation and was further examined. Additional two cell lines with atypical HRAS variants were identified and compared to the classic hotspot mutated cell line. The viability for the mentioned cell lines were indicative of resistance to EGFR inhibition to different degrees. The protein activation levels in important signaling pathways (PI3K/MAPK) confirmed our viability data. HRAS signaling was primarily via PI3K/AKT. Silencing HRAS showed significantly decreased viability. Conclusions: Previous studies have shown that EGFR-targeting agents remain insufficient as single targeted therapy. HRAS appears to contribute to the EGFR-resistance of HNSCC. The canonical mutation G12D appears to signal primarily via PI3K and PI3K inhibitors may be effective. The G12D cell line model indicates a central role of mutated HRAS for signaling and viability consistent with role as a driver mutation.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5472. doi:1538-7445.AM2012-5472