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CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC’s functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.

As guidelines, therapies, and literature on cancer variants expand, the lack of consensus variant interpretations impedes clinical applications. CIViC is a public domain, crowd-sourced, and adaptable knowledgebase of evidence for the Clinical Interpretation of Variants in Cancer, designed to reduce barriers to knowledge sharing and alleviate the variant interpretation bottleneck.

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, and other web-based sources. Drug, gene, and interaction data are normalized and merged into conceptual groups. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major version release of this database. A primary focus of this update was integration with crowdsourced efforts, leveraging the Drug Target Commons for community-contributed interaction data, Wikidata to facilitate term normalization, and export to NDEx for drug-gene interaction network representations. Seven new sources have been added since the last major version release, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include the introduction of a more sophisticated Query Score for interaction search results, an updated Interaction Score, the inclusion of interaction directionality, and several additional improvements to search features, data releases, licensing documentation and the application framework.

Personalized cancer vaccines designed to target neoantigens represent a promising new treatment paradigm in oncology. In contrast to classical idiotype vaccines, we hypothesized that ‘polyvalent’ vaccines could be engineered for the personalized treatment of follicular lymphoma (FL) using neoantigen discovery by combined whole exome sequencing (WES) and RNA sequencing (RNA-Seq). Fifty-eight tumor samples from 57 patients with FL underwent WES and RNA-Seq. Somatic and B-cell clonotype neoantigens were predicted and filtered to identify high-quality neoantigens. B-cell clonality was determined by alignment of B-cell receptor (BCR) CDR3 regions from RNA-Seq data, grouping at the protein level, and comparison to the BCR repertoire of RNA-Seq data from healthy individuals. An average of 52 somatic mutations per patient (range: 2-172) were identified, and two or more (median: 15) high-quality neoantigens were predicted for 56 of 58 samples. The predicted neoantigen peptides were composed of missense mutations (76%), indels (9%), gene fusions (3%), and BCR sequences (11%). Building off of these preclinical analyses, we initiated a pilot clinical trial using personalized neoantigen vaccination combined with PD-1 blockade in patients with relapsed or refractory FL (#NCT03121677). Synthetic long peptide (SLP) vaccines were successfully synthesized for and administered to all four patients enrolled to date. Initial results demonstrate feasibility, safety, and potential immunologic and clinical responses. Our study suggests that a genomics-driven personalized cancer vaccine strategy is feasible for patients with FL, and this may overcome prior challenges in the field.

Bam-readcount is a utility for generating low-level information about sequencing data at specific nucleotide positions. Originally designed to help filter genomic mutation calls, the metrics it outputs are useful as input for variant detection tools and for resolving ambiguity between variant callers . In addition, it has found broad applicability in diverse fields including tumor evolution, single-cell genomics, climate change ecology, and tracking community spread of SARS-CoV-2. Here we report on the release of version 1.0 of this tool, which adds CRAM support, among other improvements. It is released under a permissive MIT license and available at https://github.com/genome/bam-readcount.

The Clinical Interpretation of Variants in Cancer (CIViC; www.civicdb.org) knowledgebase is a curation platform designed to capture evidence from the published literature which support or refute the significance of genomic variants in various cancer types. Since the launch of the beta user interface in 2014, this knowledgebase has undergone substantial evolution and redesign to support the needs of the clinical and research communities. Significant work has been done by the community to release guidelines and resources to support cancer variant interpretation. CIViC is a crowd sourced, expert moderated resource with a community of over 300 active curators. In response to community feedback, evolving guidelines and improved understanding of the genetics of cancer, we have developed CIViC 2.0, a major redesign that expands the capabilities of this widely used resource. CIViC 2.0 has been designed to support complex variant relationships (termed molecular profiles in CIViC) such as the poor prognostic impact of the combination of variants in FLT3, DNMT3A and NPM1 in acute myeloid leukemia. These highly structured molecular profiles link details about each constituent variant, including genomic coordinates, HGVS, aliases, and additional metadata. Molecular profiles will also support the absence of a variant, which has become critical in targeted therapy decision making such as colorectal cancer where EGFR expression without a KRAS variant is an indication for targeted inhibitor therapy. These molecular profiles also support structural variants. While fusions have always been supported, CIViC 2.0 will support complex genomic coordinate or cytoband specific queries. Significant technical changes have improved the speed and performance of the UI and the underlying API. A complete redesign of the API, utilizing GraphQL, empowers users to ask more complex questions and more deeply explore this highly curated data. More robust data schemas and comprehensive validation of evidence structure will allow for the programmatic submission of new evidence. By utilizing site-wide full text faceted search, identifying relevant content in summaries, evidence items, or even curator comments is straightforward. The CIViC 2.0 redesign supports the ever-increasing complexity of cancer variant information and provides a powerful tool to explore and utilize the carefully curated data within the knowledgebase for a multitude of questions related to basic, translational, and clinical research. Citation Format: Kilannin C. Krysiak, Adam C. Coffman, Susanna Kiwala, Joshua F. McMichael, Arpad M. Danos, Jason Saliba, Cameron J. Grisdale, Jake Lever, Lana Sheta, Shruti Rao, Alex H. Wagner, Malachi Griffith, Obi L. Griffith. Redesigning CIViC: Enhancing the structured curation of complex cancer variant data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1194.

Neoantigens are novel peptide sequences resulting from somatic mutations in tumors that upon loading onto major histocompatibility complex (MHC) molecules allow recognition by T cells. Accurate neoantigen identification is thus critical for designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly inferring whether the presenting peptide sequence can successfully induce an immune response. As the majority of somatic mutations are SNVs, changes between wildtype and mutant peptide are subtle and require cautious interpretation. An important, yet underappreciated, variable in neoantigen-prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific HLA alleles. While a subset of peptide positions are presented to the T-cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T-cell responses. However, a systematic method for determining anchor locations for the wide range of HLA alleles present in the population and application of these to evaluate MT/WT peptide pairs arising in tumors has not been reported. As a result, many neoantigen studies have either failed to adequately consider this crucial factor or have used conventional assumptions to guide their neoantigen identification process. Here, we provide a computational workflow for predicting anchor locations for a wide range of HLA alleles, using a reference dataset generated from clinical and The Cancer Genome Atlas (TCGA) patient samples. We calculated high probability anchor positions for different peptide lengths for over 300 common HLA alleles. Analysis of these results showed clusters of different anchor trends among the HLA alleles analyzed. A subset of these HLA anchor results were orthogonally validated using protein crystallography structures. Analysis of 923 tumor samples showed that 7-41% of neoantigen candidates were potentially misclassified in the neoantigen selection process and can be rescued using allele-specific knowledge of anchor positions. These anchor predictions are currently undergoing experimental validation using both peptide-MHC stability assays as well as fluorescence-based competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, such as pVACtools, we hope to formalize and streamline the identification process for relevant clinical studies. Citation Format: Huiming Xia, Joshua McMichael, Michelle Becker-Hapak, Onyinyechi C. Onyeador, Rico Buchli, Ethan McClain, Patrick Pence, Suangson Supabphol, Megan M. Richters, Anamika Basu, Cody A. Ramirez, Cristina Puig-Saus, Kelsy C. Cotto, Jasreet Hundal, Susanna Kiwala, S. Peter Goedegebuure, Tanner M. Johanns, Gavin P. Dunn, Antoni Ribas, Christopher A. Miller, William Gillanders, Todd A. Fehniger, Obi L. Griffith, Malachi Griffith. Computational prediction of MHC anchor locations guide neoantigen prediction and prioritization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5639.

Neoantigen vaccines have demonstrated strong efficacy in the treatment of cancers in the realm of personalized medicine when used in combination with checkpoint blockade therapy. Numerous clinical trials involving these therapies are underway across the world. Accurate identification and prioritization of neoantigens is highly relevant to the design of these trials, and for predicting treatment response and understanding mechanisms of resistance. With the advent of whole exome and RNA sequencing technologies, researchers and clinicians are now able to computationally predict neoantigens and design personalized vaccines based on patient-specific mutation information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized medicine, including somatic variant identification and expression, peptide processing and transportation, peptide-MHC binding and stability, recognition by cytotoxic T cells, etc. There has been a rapid development of computational tools that attempt to account for these complexities. Pipelines have been developed to allow researchers to run an ensemble of many potentially informative tools for individual patients. However, output results from these complex pipelines are often unorganized, difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. Additionally, while current pipelines analyze neoantigens from the perspective of variants, transcripts and peptides, they fail to present the underlying details for efficient prioritization of the candidates. In addition to variant detection, gene expression and predicted peptide binding affinities, recent studies have also shown the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature of neoantigen features, presenting all relevant information to experimentalists and immunogenomics tumor boards for candidate selection is a difficult challenge. In response, we have created pVACview, an interactive tool designed to aid in the prioritization and selections of neoantigen candidates for personalized cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select and export their neoantigen candidates. The tool allows users to visualize neoantigen candidates across three different levels, including variant, transcript and peptide level information. We hope pVACview will allow researchers and clinicians to analyze and prioritize neoantigen candidates with greater efficiency and accuracy. The application is available as part of the pVACtools pipeline at https://github.com/griffithlab/pVACtools and the online server can be accessed at pvacview.org. Citation Format: Huiming Xia, Susanna Kiwala, Zachary L. Skidmore, Bryan Fisk, S. Peter Goedegebuure, Christopher A. Miller, William E. Gillanders, Obi L. Griffith, Malachi Griffith. pVACview: An integrative visualization tool for efficient neoantigen prioritization and selection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5036.

Neoantigens are tumor-specific peptides on the cell surface that can be recognized by the adaptive immune system. Personalized immunotherapies, such as cancer vaccines, rely on neoantigen prediction to identify sequences that can activate T cells to recognize and destroy the tumor. The majority of cancer vaccine trials have utilized neoantigens derived from missense mutations and small insertions and deletions. However, other mutation types could contribute to the overall neoantigen landscape, such as aberrantly spliced transcripts arising from cis-acting regulatory mutations. In this study, we explore the potential immunogenicity of alternative splicing events by creating pVACsplice, a tool to expand the capability of pVACtools, a suite of tools for neoantigen prediction (http://www.pvactools.org). pVACsplice assembles alternative transcripts from tumor-specific splicing patterns, identifies sequence changes by comparison to a reference, and predicts neoantigens from the novel regions. To verify the accuracy of alternative transcript assembly, we ran pVACsplice with HCC1395 cell line samples and performed long-read sequencing to detect the transcripts in vitro. Matched whole exome sequencing and RNA sequencing datasets from glioblastoma, melanoma, and colorectal cancer cohorts will also be analyzed with pVACsplice to obtain binding affinity estimates. We will compare these results to neoantigen predictions from other mutation sources and across cancer types to discover the prevalence of immunogenic splicing events. Finally, we will perform immunogenicity testing with a set of high quality candidates to validate our predictions. We hope to increase the number of candidates for patients’ vaccines by adding this functionality to our standard neoantigen prediction workflow. This tool could help generate a more accurate portrait of the neoantigen landscape in tumors, and in turn, enhance responses to personalized immunotherapies. Citation Format: Megan M. Richters, Kelsy C. Cotto, Susanna Kiwala, Huiming Xia, Beatriz M. Carreno, Gavin P. Dunn, Antoni Ribas, Obi L. Griffith, Malachi Griffith. pVACsplice: Predicting neoantigens from tumor-specific alternative splicing events derived from cis-acting regulatory mutations using whole exome and RNA sequencing data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1215.

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a publicly accessible resource that aggregates 102,426 gene records and 57,498 drug records from 40 drug-gene interaction data sources to aid both researchers and clinicians in identifying associations between genes of interest and available drugs and therapeutics. By using peer-reviewed data sources and publications, DGIdb represents a stand-alone resource with over 100,000 drug-gene interaction claims across 30 interaction types to drive hypothesis generation in precision medicine and interpretation pipelines. The background process that normalizes drugs to a harmonized ontological concept has been upgraded. These improvements have increased concept normalization for drugs by 20% and are now available as a stand-alone service for use (https://normalize.cancervariants.org/therapy/). Leveraging our platform’s ability to find relationships between disease-critical genes and available therapeutics, DGIdb has been used in clinical interpretation pipelines to find drugs for specific diseases with an emphasis on regulatory approval status. DGIdb now uses annotations from Drugs@FDA as an additional source to provide more accurate descriptors for market and maturity status of drugs, when available. Lastly, to enhance the annotation potential for DGIdb in precision medicine pipelines, we have updated our druggable gene category sources with an additional curated list of 2,217 genes. Used alone or in combination with existing categories-such as the heavily-utilized ‘clinically actionable’ category-this additional source will give precision medicine and interpretation pipelines the power to find concise, actionable annotations for specific diseases including pediatric cancers and epilepsy. These lists are managed and maintained as a publicly-available resource to provide up-to-date annotations on disease-associated genes as they become available. Citation Format: Matthew Cannon, James Stevenson, Kori Kuzma, Colin O'Sullivan, Katherine Miller, Olivia Grischow, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Dorian Morrissey, Kelsy Cotto, Obi Griffith, Malachi Griffith, Alex Wagner. Refining the drug-gene interaction database for precision medicine pipelines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1197.

Recent efforts to design personalized cancer immunotherapies use predicted neoantigens, but most neoantigen prediction strategies do not consider proximal (nearby) variants that alter the peptide sequence and may influence neoantigen binding. We evaluated somatic variants from 430 tumors to understand how proximal somatic and germline alterations change the neoantigenic peptide sequence and also affect neoantigen binding predictions. On average, 241 missense somatic variants were analyzed per sample. Of these somatic variants, 5% had one or more in-phase missense proximal variants. Without incorporating proximal variant correction for major histocompatibility complex class I neoantigen peptides, the overall false discovery rate (incorrect neoantigens predicted) and the false negative rate (strong-binding neoantigens missed) across peptides of lengths 8-11 were estimated as 0.069 (6.9%) and 0.026 (2.6%), respectively.

Neoantigens are novel peptide sequences resulting from sources such as somatic mutations in tumors. Upon loading onto major histocompatibility complex (MHC) molecules, they can trigger recognition by T cells. Accurate neoantigen identification is thus critical for both designing cancer vaccines and predicting response to immunotherapies. Neoantigen identification and prioritization relies on correctly predicting whether the presenting peptide sequence can successfully induce an immune response. As the majority of somatic mutations are single nucleotide variants, changes between wildtype and mutated peptides are typically subtle and require cautious interpretation. A potentially underappreciated variable in neoantigen-prediction pipelines is the mutation position within the peptide relative to its anchor positions for the patient’s specific MHC molecules. While a subset of peptide positions are presented to the T-cell receptor for recognition, others are responsible for anchoring to the MHC, making these positional considerations critical for predicting T-cell responses. We computationally predicted high probability anchor positions for different peptide lengths for 328 common HLA alleles and identified unique anchoring patterns among them. Analysis of 923 tumor samples shows that 6-38% of neoantigen candidates are potentially misclassified and can be rescued using allelespecific knowledge of anchor positions. A subset of anchor results were orthogonally validated using protein crystallography structures. Representative anchor trends were experimentally validated using peptide-MHC stability assays and competition binding assays. By incorporating our anchor prediction results into neoantigen prediction pipelines, we hope to formalize, streamline and improve the identification process for relevant clinical studies.One Sentence SummaryNeoantigen prediction accuracy is significantly influenced by the mutation position within the neoantigen and its relative position to the patient’s allele-specific MHC anchor locations.

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from various sources including publications, databases, and other web-based sources in one resource. These drug, gene, and interaction claims are normalized and grouped to identify aliases, merge concepts, and reduce redundancy. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major update of this database. Seven new sources have been added, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include further development of automatic jobs for routine data updates, more sophisticated query scores for interaction search results, extensive manual curation of interaction source link outs, and the inclusion of interaction directionality. A major focus of this update was integration with crowd-sourced efforts, including leveraging the curation activities of Drug Target Commons, using Wikidata to facilitate term normalization, and integrating into NDEx for producing network representations.

PURPOSE Precision oncology depends on the matching of tumor variants to relevant knowledge describing the clinical significance of those variants. We recently developed the Clinical Interpretations for Variants in Cancer (CIViC; civicdb.org ) crowd-sourced, expert-moderated, and open-access knowledgebase. CIViC provides a structured framework for evaluating genomic variants of various types (eg, fusions, single-nucleotide variants) for their therapeutic, prognostic, predisposing, diagnostic, or functional utility. CIViC has a documented application programming interface for accessing CIViC records: assertions, evidence, variants, and genes. Third-party tools that analyze or access the contents of this knowledgebase programmatically must leverage this application programming interface, often reimplementing redundant functionality in the pursuit of common analysis tasks that are beyond the scope of the CIViC Web application. METHODS To address this limitation, we developed CIViCpy ( civicpy.org ), a software development kit for extracting and analyzing the contents of the CIViC knowledgebase. CIViCpy enables users to query CIViC content as dynamic objects in Python. We assess the viability of CIViCpy as a tool for advancing individualized patient care by using it to systematically match CIViC evidence to observed variants in patient cancer samples. RESULTS We used CIViCpy to evaluate variants from 59,437 sequenced tumors of the American Association for Cancer Research Project GENIE data set. We demonstrate that CIViCpy enables annotation of > 1,200 variants per second, resulting in precise variant matches to CIViC level A (professional guideline) or B (clinical trial) evidence for 38.6% of tumors. CONCLUSION The clinical interpretation of genomic variants in cancers requires high-throughput tools for interoperability and analysis of variant interpretation knowledge. These needs are met by CIViCpy, a software development kit for downstream applications and rapid analysis. CIViCpy is fully documented, open-source, and available free online.

Harmonization of cancer variant representation, efficient communication, and free distribution of clinical variant‐associated knowledge are central problems that arise with increased usage of clinical next‐generation sequencing. The Clinical Genome Resource (ClinGen) Somatic Working Group (WG) developed a minimal variant level data (MVLD) representation of cancer variants, and has an ongoing collaboration with Clinical Interpretations of Variants in Cancer (CIViC), an open‐source platform supporting crowdsourced and expert‐moderated cancer variant curation. Harmonization between MVLD and CIViC variant formats was assessed by formal field‐by‐field analysis. Adjustments to the CIViC format were made to harmonize with MVLD and support ClinGen Somatic WG curation activities, including four new features in CIViC: (1) introduction of an assertions feature for clinical variant assessment following the Association of Molecular Pathologists (AMP) guidelines, (2) group‐level curation tracking for organizations, enabling member transparency, and curation effort summaries, (3) introduction of ClinGen Allele Registry IDs to CIViC, and (4) mapping of CIViC assertions into ClinVar submission with automated submissions. A generalizable workflow utilizing MVLD and new CIViC features is outlined for use by ClinGen Somatic WG task teams for curation and submission to ClinVar, and provides a model for promoting harmonization of cancer variant representation and efficient distribution of this information.

Childhood cancers are driven by unique profiles of somatic genetic alterations, with a significant contribution from predisposing germline variants. Understanding the genomic landscape of pediatric cancers is complicated by their rarity, the heterogeneity of variation within a given disease, and the complex forms of structural variation they contain. Variants in childhood disease may differ from those in adult versions of the same cancer type, or may have different clinical significance. Currently, pediatric variants are underrepresented in cancer variant databases, and an urgent need exists for their publicly available expert curation. To address this, the Pediatric Cancer Taskforce (PCT) was formed within the Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group (CDWG) (https://www.clinicalgenome.org/working-groups/somatic/). The PCT is a multi-institutional group of 39 members with broad experience in childhood cancer and variant curation, whose work consists of standardization and classification of genetic variants in pediatric cancers. The CIViC knowledgebase (www.civicdb.org) is a freely available resource for Clinical Interpretation of Variants in Cancer, which leverages public curation and expert moderation to address the problem of annotating the large volume of clinically actionable cancer variants. PCT curators work together with PCT expert members and the CIViC team on variant curation, and have submitted over 230 Evidence Items and over 10 Assertions to CIViC. To further address issues specific to pediatric curation, the PCT is working with CIViC to develop new pediatric-specific CIViC features and modifications of the data model that will aid in pediatric curation. A pediatric user interface, as well as representation of large scale structural and copy number variation are being developed for version two of CIViC, expected to be released in 1-2 years, which will enable curation of a new class of structural variants often encountered in pediatric cancer. A novel standard operating procedure for childhood cancer curation in CIViC is being developed by PCT experts, curators and the CIViC team. This SOP will cover topics including curation of structural variants, as well as pediatric-specific variant tiering guidelines which take into account the sparse nature of evidence in pediatric cases. A companion resource, CIViCmine (http://bionlp.bcgsc.ca/civicmine/), will be further developed to incorporate pediatric data. These and other joint efforts of the PCT and CIViC will significantly enhance pediatric variant representation for public use, to support the care of children with cancer. Citation Format: Arpad Danos, Wan-Hsin Lin, Jason Saliba, Angshumoy Roy, Alanna J. Church, Shruti Rao, Deborah Ritter, Kilannin Krysiak, Alex Wagner, Erica Barnell, Lana Sheta, Adam Coffman, Susanna Kiwala, Joshua F. McMichael, Laura Corson, Kevin Fisher, Heather E. Williams, Matthew Hiemenz, Katherine A. Janeway, Jianling Ji, Kesserwan A. Chimene, Laura Fuqua, Lisa Dyer, Huiling Xu, Jeffrey Jean, Laveniya Satgunaseelan, Liying Zhang, Ted W. Laetsch, Donald W. Parsons, Ryan Schmidt, Lynn M. Schriml, Kristen L. Sund, Shashikant Kulkarni, Subha Madhavan, Xinjie Xu, Rashmi Kanagal-Shamana, Marian Harris, Yasmine Akkari, Nurit Paz Yacov, Panieh Terraf, Malachi Griffith, Obi L. Griffith, Gordana Raca. Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration [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 210.

CIViC (civicdb.org) is an open access, expertly moderated knowledgebase for crowdsourcing Clinical Interpretations of Variants in Cancer. Stakeholders globally-including those in government, academia, industry and medicine-use CIViC to find and curate actionable interpretations of genomic variants in their therapeutic, prognostic, predisposing, diagnostic and functional contexts. Through engagement with curators and leaders in the field, CIViC has implemented several features including Assertions, Organizations and expanded help documentation. The foundational unit of CIViC is the Evidence Item, which describes the clinical relevance of a specific variant curated from a single published source within peer-reviewed literature or ASCO abstract. Assertions aggregate Evidence Items for a given variant-disease or variant-disease-therapy combination. In response to the 2017 AMP-ASCO-CAP guidelines and collaborations with ClinGen, Assertions were modified to integrate ACMG variant pathogenicity classifications, AMP-ASCO-CAP tier designations and associations with NCCN guidelines and FDA approvals to provide a ‘state of the field' interpretation. At present, 12 Assertions spanning eight variants have been submitted by CIViC to ClinVar with one-star submitter status (Submitter ID: 506594), and CIViC has been cited as supporting information in two variants. Assertions exemplify CIViC's responsiveness to new field guidelines, expert collaborators' recommendations and its contributions to other resources. To enhance community involvement, CIViC created Organization-attributed actions. Each action performed by a curator is tagged with their Organization. Curators may switch between Organizations if they belong to more than one. Currently, nine Organizations are recognized in CIViC, the largest being ClinGen with 79 members, 4 sub-organizations and over 24,000 actions. Organizations enable groups to prominently display and track their submissions, activity, and users. CIViC's wide adoption has necessitated the development of robust educational material. CIViC has created nine YouTube videos, one of which is linked by the NIH ITCR homepage. CIViC has migrated help documents to a stand alone site (civic.readthedocs.io) and has made over 60 page modifications since 2019. Help documentation expansion was fueled by user feedback via the CIViC interface, collaborator meetings and in-person events (Curation Jamborees). Improved documentation allows CIViC to grow at scale, unhindered by the need for direct training. CIViC's rapid adaptation to the needs of the community is derived from its open access nature, commitment to data provenance, active connection with users, and abundance of educational material. CIViC rapidly integrates the guidelines, regulatory standards and community recommendations in a freely accessible resource that is flexible enough to evolve with the dynamic field of cancer genomics. Citation Format: Lana M. Sheta, Arpad M. Danos, Jason Saliba, Kilannin Krysiak, Alex H. Wagner, Erica K. Barnell, Susanna Kiwala, Joshua F. McMichael, Adam Coffman, Shahil Pema, Lynzey Kujan, Kelsy C. Cotto, Cody Ramirez, Zachary L. Skidmore, Cameron J. Grisdale, Shruti Rao, Subha Madhaven, Malachi Griffith, Obi L. Griffith. CIViC knowledgebase adapts to field experts and community input [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 206.

The Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase (civicdb.org) is an open access, centralized hub for structured, community curated and expertly moderated relationships between genomic variants and cancer. Evidence is curated from peer-reviewed, published literature and is classified into one of five Types: Predisposing, Diagnostic, Prognostic, Predictive (therapeutic), or Functional. The robustness of the Evidence is conveyed through the assignment of Levels with the first three derived from patient studies (Validated, Clinical, Case Study), Preclinical, generated from in vivo or in vitro data, and Inferential, which describes indirect associations. Each Evidence Item requires an Evidence Statement written in the curator's own words summarizing the source's results regarding the variant's clinical impact. Collaborations with groups like ClinGen have generated a significant influx of new curators, increasing the demand for detailed principles regarding data prioritization in the Evidence Statement in order to streamline the curation process. The curation community would benefit from simpler, visual guides through the complex decisions needed to appropriately and consistently curate Evidence Items. We are devoting significant effort to continue the development of straightforward Evidence curation algorithms (decision trees) similar to those used in clinical molecular testing labs to aid CIViC curators. Previously published guidelines on development of these statements are the basis of our Evidence algorithms. Obvious inflection points for curators are clearly identified with specific details noted for each to optimize decision efficiency. As the predominant Evidence Type comprising 57% of all CIViC submissions, 58% of referenced patient trials, and 92% of Preclinical submissions, Predictive Evidence is the initial focus of our pilot guidelines with Diagnostic and Prognostic to follow. Within the Predictive Evidence Type, clinical trials, case studies, and preclinical Levels each require vastly different Evidence Statement details and ultimately the creation of three separate, uniquely modeled algorithms. The implementation of these algorithms will assist in streamlining both curation and the expert review process. Notably, a template is not being created, as the preservation of curator style and voice is important to maintain the community feel of the database. To ensure the highest level of clarity, our team is utilizing specific novice and experienced curators to assist with the development process. As these algorithms pass the pilot phase, they are being tested as curator training tools. Ultimately, these guidelines will be used to encourage independence in curators and to enhance the Evidence already contained in CIViC. Citation Format: Jason Saliba, Lana Sheta, Kilannin Krysiak, Arpad Danos, Alex Marr, Erica Barnell, Shahil Pema, Wan-Hsin Lin, Panieh Terraf, Joshua F. McMichael, Cameron J. Grisdale, Shruti Rao, Susanna Kiwala, Adam Coffman, Alex Wagner, Obi L. Griffith, Malachi Griffith. Development of Evidence Statement curation algorithms to aid cancer variant interpretation [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 208.

The drug–gene interaction database (DGIdb, www.dgidb.org) consolidates, organizes and presents drug–gene interactions and gene druggability information from papers, databases and web resources. DGIdb normalizes content from 30 disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API) and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included 24 sources were updated. Six new resources were added, bringing the total number of sources to 30. These updates and additions of sources have cumulatively resulted in 56 309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and anti-neoplastic drug–gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes and drug–gene interactions, including listings of PubMed IDs, interaction type and other interaction metadata.

PurposePrecision oncology is dependent upon the matching of tumor variants to relevant knowledge describing the clinical significance of those variants. We recently developed the Clinical Interpretations for Variants in Cancer (CIViC; civicdb.org) crowd-sourced, expert-moderated, open-access knowledgebase, providing a structured framework for evaluating genomic variants of various types (e.g., fusions, SNVs) for their therapeutic, prognostic, predisposing, diagnostic, or functional utility. CIViC has a documented API for accessing CIViC records: Assertions, Evidence, Variants, and Genes. Third-party tools that analyze or access the contents of this knowledgebase programmatically must leverage this API, often reimplementing redundant functionality in the pursuit of common analysis tasks that are beyond the scope of the CIViC web application.MethodsTo address this limitation, we developed CIViCpy (civicpy.org), a software development kit (SDK) for extracting and analyzing the contents of the CIViC knowledgebase. CIViCpy enables users to query CIViC content as dynamic objects in Python. We assess the viability of CIViCpy as a tool for advancing individualized patient care by using it to systematically match CIViC evidence to observed variants in patient cancer samples.ResultsWe used CIViCpy to evaluate variants from 59,437 sequenced tumors of the AACR Project GENIE dataset. We demonstrate that CIViCpy enables annotation of >1,200 variants per second, resulting in precise variant matches to CIViC level A (professional guideline) or B (clinical trial) evidence for 38.6% of tumors.ConclusionsThe clinical interpretation of genomic variants in cancers requires high-throughput tools for interoperability and analysis of variant interpretation knowledge. These needs are met by CIViCpy, an SDK for downstream applications and rapid analysis. CIViCpy (civicpy.org) is fully documented, open-source, and freely available online.

Manually curated variant knowledgebases and their associated knowledge models are serving an increasingly important role in distributing and interpreting variants in cancer. These knowledgebases vary in their level of public accessibility, and the complexity of the models used to capture clinical knowledge. CIViC (Clinical Interpretations of Variants in Cancer - www.civicdb.org) is a fully open, free-to-use cancer variant interpretation knowledgebase that incorporates highly detailed curation of evidence obtained from peer-reviewed publications. Currently, the CIViC knowledge model consists of four main components: Genes, Variants, Evidence Items, and Assertions. Each component has an associated knowledge model and methods for curation. Gene and Variant data contextualize the genomic region(s) involved in the clinical statement. Evidence Items provide structured associations between variants and their clinically predictive/therapeutic, prognostic, diagnostic, predisposing, and functional implications. Finally, CIViC Assertions summarize collections of CIViC Evidence Items for a specific Disease, Variant, and Clinical Significance with incorporation of clinical and technical guidelines. Here we present the CIViC knowledge model, curation standard operating procedures, and detailed examples to support community-driven curation of cancer variants.

Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector–based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at http://www.pvactools.org.

Follicular lymphoma (FL) is the most common indolent non-Hodgkin's lymphoma; however, it remains incurable with conventional therapies and is poorly responsive to checkpoint blockade. Additionally, because FL develops so slowly (and often asymptomatically), a major research focus has been to avoid chemotherapy treatments to limit the potential development of treatment-related side effects and the risk of therapy-related second cancers. The mutational processes that lead to lymphomagenesis and progression also produce tumor-specific mutant antigens (TSMAs) that can be targeted by the immune system to control malignancies. Personalized cancer vaccines designed for these TSMAs represent a promising new strategy for treatment of FL. However, the feasibility of this approach and precisely how to optimize effective vaccine design, informed by next-generation sequencing data, are not fully understood. We hypothesize that (tumor/normal) whole-exome sequencing (WES) and (tumor) RNA sequencing (RNA-Seq) can be used to predict patient HLA typing and neoepitopes to engineer personalized cancer vaccines for FL. DNA and RNA from 58 patients' FL biopsies underwent WES and RNA-Seq. pVACtools and MiXCR predicted potential somatic and B-cell clonotype neoantigens, which were filtered to identify high-quality TSMAs. B-cell oligoclonality was determined by comparison to B-cell receptor (BCR) repertoire profiling of healthy individual lymph nodes. RNA-seq data allowed us to identify expressed TSMAs. Complementary in silico analysis based on mRNA-based peptide reconstruction and custom HLA affinity binding predictions were performed. An average of 52 somatic mutations per patient (range: 2-172) were identified. At least one high-quality TSMA was predicted for 57 of 58 patients. Five or more TSMA candidates were identified for 52 (90%) patients with a mean of 17 predicted peptides per patient (range: 0-45). 81% (813/1,004) of the total predicted TSMA peptides arose from missense mutations, 9% (94/1,004) from indels, and 10% (97/1,004) from BCR. 78% (45/58) of patients have both somatic and BCR vaccine candidates, while 21% (12/58) of patients had only somatic vaccine candidates. No fusion genes were identified within the cohort that could have been a source of neoepitope candidates. Predicted TSMAs were identified in multiple genes recurrently mutated in lymphoma (e.g., BCL2). There was a high prediction concordance with the orthogonal BostonGene Vaccine Module V1 pipeline. These preclinical results led to a first-in-human pilot trial of personalized TSMA vaccine combined with anti-PD-1 mAb for rel/ref FL patients (NCT03121677), with one response observed within 4 patients evaluable for response to date. TSMA peptides suitable for cancer vaccines were identified for most FL patients via next-generation sequencing, MiXCR and pVACtools. This preclinical study suggests that FL patients will be candidates for TSMA vaccine clinical trials, and pilot clinical results provide proof of concept for this approach. Citation Format: Cody A. Ramirez, Felix Frenkel, Olga Plotnikova, Vladislav Belousov, Alexander Bagaev, Elena Ocheredko, Susanna Kiwala, Jasreet Hundal, Zachary L. Skidmore, Marcus Watkins, Michelle Becker-Hapak, Thomas B. Mooney, Jason Walker, Catrina Fronick, Robert Fulton, Robert Schreiber, Nancy L. Bartlett, Brad Kahl, Ravshan Ataullakhanov, Malachi Griffith, Obi Griffith, Todd A. Fehniger. Identification of predicted neoantigen vaccine candidates in follicular lymphoma patients [abstract]. In: Proceedings of the AACR Virtual Meeting: Advances in Malignant Lymphoma; 2020 Aug 17-19. Philadelphia (PA): AACR; Blood Cancer Discov 2020;1(3_Suppl):Abstract nr PO-56.

8054 Background: Follicular lymphoma (FL) is incurable with conventional therapies and poorly responsive to immune checkpoint blockade. There is a need for new therapies without long-term complications of chemotherapy and with curative potential. We hypothesize that FL contains tumor-specific mutant antigens (TSMAs) that can be targeted by the immune system by vaccination. Recent reports have highlighted the potential for unique immunoglobulin peptides to elicit immune response in lymphomas. We utilized whole exome sequencing (WES) and RNA sequencing (RNA-Seq) of FL patient samples to infer HLA genotype, and predict TSMAs with the goal of designing a personalized cancer vaccine, supported by recent reports of this approach in solid cancers. Methods: DNA and RNA from 58 patients’ FL biopsies underwent WES and RNA-Seq. pVACtools and MiXCR predicted potential somatic and B-cell clonotype neoantigens, which were filtered to identify high quality TSMAs. B-cell oligoclonality was determined by comparison to B-cell receptor (BCR) repertoire profiling of healthy individual lymph nodes. RNA-seq data allowed us to identify expressed TSMAs. Complementary in silico analysis based on mRNA-based peptide reconstruction and custom HLA affinity binding predictions were performed. Results: An average of 52 somatic mutations per patient (range: 2-172) were identified. At least one high quality TSMA was predicted for 57 of 58 patients. Five or more TSMA candidates were identified for 52 (90%) patients with a mean of 17 predicted peptides per patient (range: 0-45). 81% (813/1,004) of the total predicted TSMA peptides arose from missense mutations, 9% (94/1,004) from indels, and 10% (97/1,004) from BCR. 78% (45/58) of patients have both somatic and BCR vaccine candidates, while 21% (12/58) of patients had only somatic vaccine candidates. Predicted TSMAs were identified in multiple genes recurrently mutated in lymphoma (e.g., BCL2). There was a high prediction concordance with the orthogonal BostonGene Vaccine Module V1 pipeline. These pre-clinical results led to a first-in-human pilot trial of personalized TSMA vaccine combined with anti-PD-1 mAb for rel/ref FL patients (NCT03121677), with one response observed within 4 patients evaluable for response to date. Conclusions: TSMA peptides suitable for cancer vaccines were identified for most FL patients via next-generation sequencing, MiXCR and pVACtools. This pre-clinical study suggests that FL patients will be candidates for TSMA vaccine clinical trials and pilot clinical results provide proof of concept for this approach.

The application of modern high-throughput genomics to the study of cancer genomes has exploded in the past few years, yielding unanticipated insights into the myriad and complex combinations of genomic alterations that lead to the development of cancers. Coincident with these genomic approaches have been computational analyses that are capable of multiplex evaluations of genomic data toward specific therapeutic end points. One such approach is called "immunogenomics" and is now being developed to interpret protein-altering changes in cancer cells in the context of predicted preferential binding of these altered peptides by the patient's immune molecules, specifically human leukocyte antigen (HLA) class I and II proteins. One goal of immunogenomics is to identify those cancer-specific alterations that are likely to elicit an immune response that is highly specific to the patient's cancer cells following stimulation by a personalized vaccine. The elements of such an approach are outlined herein and constitute an emerging therapeutic option for cancer patients.

The Clinical Interpretations of Variants in Cancer (CIViC) database was designed as a fully open-access resource specifically focusing on the subset of cancer variants with well-defined clinical information associated to them and targeting a broad user base in cancer, including clinicians, researchers and patient advocates. CIViC follows a crowd-sourced curation model with expert moderation, and emphasizes transparency in that all changes, approvals, and discussion of content are tracked and displayed in the interface. Evidence items (EIDs) make up the fundamental unit of CIViC. EIDs connect a predictive, prognostic, diagnostic or predisposing clinical significance to a variant in the context of a specific disease, and each EID links back to specific published and peer-reviewed evidence. Multiple EIDs of high value can be used to make clinical assertions. While the CIViC variant is intentionally broad and can capture specific SNVs and Indels, while also admitting umbrella variants such as “EGFR mutation,” it is also anchored to a single gene entity (pulled directly from the NCBI Entrez database). Two drawbacks result from this strict association. First, clinical information associated with multiple genes is difficult to implement in knowledgebases like CIViC. In cases where two variants co-occur on the same gene, such as the erlotinib-sensitizing EGFR L858R and resistance mutation T790M, CIViC allows a “L858R and T790M” variant to be created under the EGFR gene. In other cases, such as FLT3 internal tandem duplication and DNMT3A mutation in AML, there is no CIViC variant that can capture this combination. In addition, non-gene entities important in cancer such as viral oncogene, microsatellite instability, or loss of chromosome region are not possible to represent. To address this, we are preparing CIViC V2, which will be implemented via modification of the existing database and web interface. The architecture around the EID as fundamental unit of CIViC will remain unchanged, but the CIViC variant will be re-envisioned, introducing the concepts of region and genotype. A variant will link to one or more regions, and region will be drawn from a list of types—genome, chromosome, rearrangement, gene and pathogen—which can be expanded when needed. The notion of complex genotype will be realized as a combination of variants drawn from multiple regions in either an additive manner (HER2 overexpression AND PIK3CA mutation) or employing more complex variant combinations using OR and NOT. Multicomponent genotypes will point back to EID collections associated with each individual component, while also enabling users to write EIDs unique to that genotype, which can, in turn, be used to create genotype clinical assertions. This new scheme will be presented via refactoring of the UI, and allow for a highly flexible concept of region and genotype reflecting the growing understanding of the combinatorial nature of variants in cancer. Citation Format: Arpad Danos, Kilannin Krysiak, Alex Wagner, Susanna Kiwala, Joshua McMichael, Adam Coffman, Erica Barnell, Yang-Yang Feng, Benjamin Ainscough, Cody Ramirez, Malachi Griffith, Obi Griffith. Expanding the CIViC variant to complex combinations of regions in the cancer genome [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 1290.

Background: The Clinical Interpretations of Variants in Cancer database (CIViC) mitigates the variant interpretation bottleneck currently hindering clinical adoption of customized treatment plans for oncology patients. The knowledgebase stores evidence items derived from peer-reviewed publications detailing the therapeutic, prognostic, predisposing, and diagnostic implications of genetic variants in cancer. The knowledgebase is freely accessible without login requirements, fees, or exclusive access, making it uniquely able to attract contributions from volunteer domain experts. Here we present a method to create a dynamic capture panel derived from a community consensus of clinically-relevant variants using the CIViC knowledgebase. Methods: Using curated Sequence Ontology IDs that specify the variant type (eg., missense mutation, fusion), we elucidated variants that could be evaluated by DNA-sequencing. A CIViC score was developed for each DNA-based variant to identify high-quality variants. This score takes into account the evidence level (eg. case-study, clinical data) and the curated trust rating of variant-associated evidence statements. Variants that attained a CIViC score > 20 were eligible for the CIViC capture panel. CIViC-curated variant coordinates were used to design single molecule molecular inversion probes (smMIPs) for all eligible variants. Sequencing data from 27 samples across five tumor subtypes were compared using both the smMIPs assay and whole exome sequencing to assess panel performance. Results: Of the 497 DNA-based variants in CIViC, 150 attained a CIViC score > 20 points and were targetable via smMIPs assay. Of these 150 variants, 134 had at least one evidence item that was derived from a clinical study, thereby supporting the CIViC score's ability to identify high quality variants. We designed 2,124 smMIPs to assess the 150 CIViC variants. This required tiling protein-coding exons for 41 genes and developing targeted smMIPs for 95 single nucleotide variants, insertions, and deletions. The CIViC panel design showed extensive overlap with variants identified in the 27 samples using whole exome sequencing. Predicted overlap ranged from 2-12 variants per sample with an average of 4.1 variants per sample. Discussion: Using expert crowd-sourced curation to drive production of the CIViC capture panel creates an impartial method for panel development. Future validation of the initial capture probe design will allow for automated development of probes for additional variants in the database as curated evidence increases. Citation Format: Erica K. Barnell, Malachi Griffith, Katie M. Campbell, Kilannin Krysiak, Joshua F. McMichael, Adam C. Coffman, Susanna Kiwala, Obi L. Griffith. Development and validation of a cancer variant capture panel using the open-sourced CIViC database [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 2266.

The rapid expansion of clinical sequencing and targeted therapies has driven the accelerated evolution of clinical guidelines and regulatory standards surrounding sequence variants. Organizations such as the NCCN, FDA, ACMG, etc., continue to incorporate sequence variants into a variety of clinical contexts. The pace of these changes has led to several disparities between guidelines and resources used for the interpretation of clinically identified variants. 1) Despite the inherent lag in creating such standards through expert consensus, this evolution outpaces their incorporation into many resources used for clinical sequencing analysis. 2) Guidelines are often not readily accessible in programmatic, structured ways to promote rapid updates, including many guidelines only available in human-readable text formats. 3) These guidelines apply to various clinical contexts (drug response, prognosis, etc.), each requiring their own schema changes for their proper incorporation. 4) Levels of specificity provided by these guidelines are not conducive to matching specific genomic coordinates (e.g., FGFR2 fusion) or maintaining evidence provenance. We recently developed the Clinical Interpretations for Variants in Cancer (CIViC; civicdb.org) crowd-sourced, open-access knowledgebase, providing a structured framework for evaluating genomic variants of various types (e.g., fusions, SNVs) for their therapeutic, prognostic, predisposing or diagnostic utility. Since then, the CIViC resource has continued to evolve and has tackled the challenges above. This crowd-sourced model of both data entry and application development leverages a wide network of users with various expertise to adapt the resource at a pace beyond what a single organization could achieve. We have introduced a new entity to CIViC that aggregates individual evidence items into a single clinical assertion, allowing guideline incorporation at varying levels of specificity. For example, classification of germline variants by ACMG guidelines assigns individual evidence to specific codes (e.g., PVS1, PP1) that are aggregated to classify a variant as benign or pathogenic. The CIViC assertion provides the variant classification and supporting evidence that can be readily re-evaluated by any user, not just the initial submitter, and reviews and tracks any changes. An assertion can be created for highly specific variants (e.g., EGFR T790M), which can be submitted to ClinVar or more generic variants (ALK fusions) unsupported by many resources. Assertions support searchable, structured, and versioned annotations including FDA companion tests and drug approvals, germline ACMG categories, somatic AMP/ASCO variant levels, NCCN and WHO guidelines. The CIViC knowledgebase provides rapid integration of the latest guidelines and regulatory standards in a freely accessible resource that is flexible enough to evolve with this rapidly changing field. Citation Format: Kilannin Krysiak, Arpad M. Danos, Alex H. Wagner, Susanna Kiwala, Joshua F. McMichael, Adam C. Coffman, Erica K. Barnell, Yan-Yang Feng, Benjamin J. Ainscough, Cody A. Ramirez, Malachi Griffith, Obi L. Griffith. Implementing evolving clinical standards in a variant interpretation knowledgebase [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 3289.