Sheta, Lana

Abstract 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.

Abstract Clinical significance of somatic gene variants needs to be comprehensively characterized for their diagnostic, prognostic and/or therapeutic actionability in patient management. However, challenges remain due to discrepancies in interpretation and reporting of these somatic variants among different testing labs. Therefore, standardized curation, clinical interpretation and reporting of somatic variants in hematologic cancers is critical. To address this issue, the Hematologic Cancer Taskforce (HCT), composed of 52 multi-disciplinary experts including oncologists, molecular pathologists, lab directors, genomic scientists and biocurators, was formed in January 2020 within the ClinGen Somatic Cancer Clinical Domain Working Group (CDWG) with a goal to undertake systematic curation and evidence-based clinical interpretation of genes/somatic variants associated with hematologic malignancies. In collaboration with the Clinical Interpretation of Variants in Cancer (CIViC) (civicdb.org) knowledgebase, HCT members curate, edit, and verify Evidence Items for each variant extracted from peer-reviewed publications. Monthly discussions based on these Evidence Items lead to the preparation of variant Assertions, which summarize the state of the field consensus variant interpretation and include tiering based on the AMP/ASCO/CAP guidelines (PMID: 27993330). FMS-like tyrosine kinase 3 (FLT3) encodes a class III receptor tyrosine kinase expressed in hematopoietic cells. FLT3 mutations, including both internal tandem duplication (ITD) and mutations in the tyrosine kinase domain (TKD), are the most common mutations in acute myeloid leukemia (AML), occurring in approximately 30% of all AML cases. Implementing FLT3 tyrosine kinase inhibitors (TKIs) in different treatment regimens for FLT3 mutated AML patients has led to significantly improved overall survival. Type I FLT3 inhibitors, including midostaurin, gilteritinib, sunitinib, lestaurtinib, and crenolanib, bind to the ATP-binding site when the receptor is in active conformation. Type II FLT3 inhibitors, including sorafenib, ponatinib, and quizartinib, interact with a hydrophobic region directly adjacent to the ATP-binding domain that is only accessible when the receptor is inactive, which prevents receptor activation. Generally in AML cells, type I FLT3 inhibitors prevent activity for both ITD and TKD mutations, while Type II inhibitors target ITD but lack efficiency against TKD mutations. The development of TKD mutations in AML cells with ITD have proved to be a mechanism of acquired, or secondary resistance to Type II FLT3 inhibitors. The HCT is piloting curation assessments of FLT3 alterations, including ITD, TKD and non-TKD variants, in AML. So far, the HCT has curated 75 evidence items for FLT3 somatic variants. FLT3-ITD, as well as D835 and I836 were asserted as tier 1 level A variants based on the prediction of response to gilteritinib in relapsed/refractory AML (PMIDs: 27993330, 31665578, 28645776, 28516360, 27908881). Recent curation activities are focused on FLT3 D839G and N676K, as clinical trials using large AML patient cohorts are lacking in their ability to validate drug response/resistance associations of these two TKD variants due to their low frequency. Functional studies showed both variants result in increased proliferation and protection from apoptosis, supporting the oncogenic potential of these two variants (PMIDs: 26891877, 2468088). FLT3 D839G combined with ITD confers resistance to pexidartinib and ponatinib, both Type II FLT3 inhibitors (PMIDs: 25847190, 23430109). FLT3 N676K predicts response to the Type I FLT3 inhibitor, gilteritinib, when N676K is present alone or in combination with ITD. Interestingly, FLT3 N676K in the absence of ITD predicts response to sorafenib, a Type II FLT3 inhibitor (PMIDs: 32040554, 32984009). However, these results are mostly derived from in vitro studies. Two separate Tier II, Level D Assertions have been submitted for FLT3-ITD&D839G for its response to pexidartinib and ponatinib, and more evidence is being collected to form an Assertion for FLT3 N676K. The complexity of the prediction of response/resistance associated with FLT3 D839G and N676K supports the importance of evidence-based curation and collection for these variants in the context of the overall mutation profile, disease context and specific FLT3 TKIs to clearly define their clinical impact. Disclosures Pullarkat: Stemline Therapeutics: Honoraria.

Abstract 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.

Abstract 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.