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.

[1]  A. Gonzalez-Perez,et al.  Rational design of cancer gene panels with OncoPaD , 2016, Genome Medicine.

[2]  E. Mardis The $1,000 genome, the $100,000 analysis? , 2010, Genome Medicine.

[3]  Marc S. Williams,et al.  ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing , 2013, Genetics in Medicine.

[4]  Michael P. Schroeder,et al.  Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations , 2017, Genome Medicine.

[5]  Anuradha Lakshminarayana,et al.  The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies , 2016, Human Genomics.

[6]  Donavan T. Cheng,et al.  Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology. , 2015, The Journal of molecular diagnostics : JMD.

[7]  Obi L. Griffith,et al.  Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples , 2018, Genetics in Medicine.

[8]  F. Collins,et al.  A new initiative on precision medicine. , 2015, The New England journal of medicine.

[9]  Moriah H Nissan,et al.  OncoKB: A Precision Oncology Knowledge Base. , 2017, JCO precision oncology.

[10]  Benjamin M. Good,et al.  Organizing knowledge to enable personalization of medicine in cancer , 2014, Genome Biology.

[11]  Emily H Turner,et al.  Actionable, pathogenic incidental findings in 1,000 participants' exomes. , 2013, American journal of human genetics.

[12]  Benjamin E. Gross,et al.  Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal , 2013, Science Signaling.

[13]  Mingming Jia,et al.  COSMIC: somatic cancer genetics at high-resolution , 2016, Nucleic Acids Res..

[14]  Alex H. Wagner,et al.  Recurrent WNT pathway alterations are frequent in relapsed small cell lung cancer , 2018, Nature Communications.

[15]  Akash Kumar,et al.  MIPgen: optimized modeling and design of molecular inversion probes for targeted resequencing , 2014, Bioinform..

[16]  Subha Madhavan,et al.  A harmonized meta-knowledgebase of clinical interpretations of cancer genomic variants , 2018, bioRxiv.

[17]  Marcin Imielinski,et al.  The cancer precision medicine knowledge base for structured clinical-grade mutations and interpretations , 2016, J. Am. Medical Informatics Assoc..

[18]  Deanna M. Church,et al.  ClinVar: public archive of relationships among sequence variation and human phenotype , 2013, Nucleic Acids Res..

[19]  Benjamin E. Gross,et al.  The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. , 2012, Cancer discovery.

[20]  B. Wood,et al.  Ultrasensitive detection of acute myeloid leukemia minimal residual disease using single molecule molecular inversion probes , 2017, Haematologica.

[21]  Obi L. Griffith,et al.  Genome Modeling System: A Knowledge Management Platform for Genomics , 2015, PLoS Comput. Biol..

[22]  L. Harris,et al.  The HER2 extracellular domain as a prognostic and predictive factor in breast cancer. , 2002, Clinical breast cancer.

[23]  Joshua F. McMichael,et al.  Optimizing cancer genome sequencing and analysis. , 2015, Cell systems.

[24]  M. Ahn,et al.  Non-small Cell Lung Cancer with Concomitant EGFR, KRAS, and ALK Mutation: Clinicopathologic Features of 12 Cases , 2016, Journal of pathology and translational medicine.

[25]  Jane C Weeks,et al.  Physicians' attitudes about multiplex tumor genomic testing. , 2014, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[26]  Steven J. M. Jones,et al.  CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer , 2017, Nature Genetics.

[27]  H. Chaturvedi,et al.  FoundationOne as a relevant tool for comprehensive genomic profiling and assessment of tumor mutation burden in the era of precision oncology in India. , 2017 .