Ultra-Deep Sequencing Reveals the Mutational Landscape of Classical Hodgkin Lymphoma

The malignant Hodgkin and Reed Sternberg (HRS) cells of classical Hodgkin lymphoma (cHL) are scarce in affected lymph nodes, creating a challenge to detect driver somatic mutations. As an alternative to cell purification techniques, we hypothesized that ultra-deep exome sequencing would allow genomic study of HRS cells, thereby streamlining analysis and avoiding technical pitfalls. To test this, 31 cHL tumor/normal pairs were exome sequenced to approximately 1,000× median depth of coverage. An orthogonal error-corrected sequencing approach verified >95% of the discovered mutations. We identified mutations in genes novel to cHL including: CDH5 and PCDH7, novel stop gain mutations in IL4R, and a novel pattern of recurrent mutations in pathways regulating Hippo signaling. As a further application of our exome sequencing, we attempted to identify expressed somatic single-nucleotide variants (SNV) in single-nuclei RNA sequencing (snRNA-seq) data generated from a patient in our cohort. Our snRNA analysis identified a clear cluster of cells containing a somatic SNV identified in our deep exome data. This cluster has differentially expressed genes that are consistent with genes known to be dysregulated in HRS cells (e.g., PIM1 and PIM3). The cluster also contains cells with an expanded B-cell clonotype further supporting a malignant phenotype. This study provides proof-of-principle that ultra-deep exome sequencing can be utilized to identify recurrent mutations in HRS cells and demonstrates the feasibility of snRNA-seq in the context of cHL. These studies provide the foundation for the further analysis of genomic variants in large cohorts of patients with cHL.

SIGNIFICANCE

Our data demonstrate the utility of ultra-deep exome sequencing in uncovering somatic variants in Hodgkin lymphoma, creating new opportunities to define the genes that are recurrently mutated in this disease. We also show for the first time the successful application of snRNA-seq in Hodgkin lymphoma and describe the expression profile of a putative cluster of HRS cells in a single patient.

Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer

Somatic mutations within non-coding regions and even exons may have unidentified regulatory consequences that are often overlooked in analysis workflows. Here we present RegTools ( www.regtools.org ), a computationally efficient, free, and open-source software package designed to integrate somatic variants from genomic data with splice junctions from bulk or single cell transcriptomic data to identify variants that may cause aberrant splicing. We apply RegTools to over 9000 tumor samples with both tumor DNA and RNA sequence data. RegTools discovers 235,778 events where a splice-associated variant significantly increases the splicing of a particular junction, across 158,200 unique variants and 131,212 unique junctions. To characterize these somatic variants and their associated splice isoforms, we annotate them with the Variant Effect Predictor, SpliceAI, and Genotype-Tissue Expression junction counts and compare our results to other tools that integrate genomic and transcriptomic data. While many events are corroborated by the aforementioned tools, the flexibility of RegTools also allows us to identify splice-associated variants in known cancer drivers, such as TP53, CDKN2A, and B2M, and other genes.

Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Pancreatic ductal adenocarcinoma is a lethal disease with limited treatment options and poor survival. We studied 83 spatial samples from 31 patients (11 treatment-naïve and 20 treated) using single-cell/nucleus RNA sequencing, bulk-proteogenomics, spatial transcriptomics and cellular imaging. Subpopulations of tumor cells exhibited signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition. Mapping mutations and copy number events distinguished tumor populations from normal and transitional cells, including acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia. Pathology-assisted deconvolution of spatial transcriptomic data identified tumor and transitional subpopulations with distinct histological features. We showed coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin in tumor cells. Chemo-resistant samples contain a threefold enrichment of inflammatory cancer-associated fibroblasts that upregulate metallothioneins. Our study reveals a deeper understanding of the intricate substructure of pancreatic ductal adenocarcinoma tumors that could help improve therapy for patients with this disease.

Abstract 1194: Redesigning CIViC: Enhancing the structured curation of complex cancer variant data

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.

Abstract 1197: Refining the drug-gene interaction database for precision medicine pipelines

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.

A community approach to the cancer-variant-interpretation bottleneck

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.

Abstract 210: Advancing knowledgebase representation of pediatric cancer variants through ClinGen/CIViC collaboration

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 206: CIViC knowledgebase adapts to field experts and community input

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.

Abstract 208: Development of Evidence Statement curation algorithms to aid cancer variant interpretation

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.

Proteogenomic and metabolomic characterization of human glioblastoma

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment.

Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

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.

Abstract 3211: Evolution of the CIViC knowledgebase for community driven curation of clinical variants in cancer

With increasing adoption of next generation sequencing into clinical practice, the problem of clinical interpretation of tumor variants arises as a bottleneck for patient care, as effective annotation of these variants draws from a constantly increasing body of largely unstructured clinical and preclinical results. One model to sustain clinical variant curation is to house variant annotation behind a paywall, using access fees to fund further curation effort. An alternate approach is to leverage public curation and expert moderation to create a free public resource to house and distribute this knowledge. The Clinical Interpretation of Variants in Cancer (CIViC, www.civicdb.org) knowledgebase employs the latter approach, and is a free and open-access public resource with an intuitive user interface and flexible public API for programmatic access to all content, which is available to the public with no restrictions on usage. All data is available for retrieval without login, while registration with a free account is required to contribute curation. The provenance of all curation and revision in CIViC is viewable through the web interface, and curators may also leave public comments on all content. Selected expert editors review and revise submitted content, which is clearly labeled as accepted once it has fully undergone moderation. All content in CIViC adheres to a structured data model which follows a published standard operating procedure for curation. This data model incorporates ontologies, standards and guidelines from across the field to promote interoperability and compatibility with other efforts. The CIViC interface also allows curators and organizations to track and display summary statistics of all their activity. CIViC currently has a community of over 190 curators and 16,000 clinical and research users around the world.

CIViC continually develops and improves both new and existing features in response to user feedback as well as collaborative and internal development goals. Recently, the drugs and treatment terms used in predictive/therapeutic annotation have been normalized to the NCI Thesaurus. A conflict of interest (COI) statement is now required for all CIViC Editors, and functionality for writing and displaying the COI has been built into the interface. CIViC employs Predictive (Therapeutic), Prognostic, Diagnostic, Predisposing evidence types, and we highlight the recently introduced Functional evidence type, which has seen continued development. We will present the rationale for these changes including demonstrating how adding a Dominant Negative term better supports curation of functional genomics data sets. A focus of functional curation has been TP53, with over 50 evidence items to date. With multiple use cases for this type of data including targeted therapeutics, identification of relevant hotspots, or characterization of cancer driver mechanisms, functional evidence can be used to support conventional concepts of clinical utility and expand the CIViC data model. These developments provide a mechanism for discussion and integration of functional data into somatic variant interpretation guidelines, an area being explored but lacking expert consensus.

Citation Format: Arpad Danos, Kilannin Krysiak, Erica K. Barnell, Adam C. Coffman, Joshua F. McMichael, Susanna Kiwala, Nicholas C. Spies, Lana M. Sheta, Shahil P. Pema, Lynzey Kujan, Kaitlin A. Clark, Sydney Anderson, Amber Wollam, Brian Li, Justin Guerra, Shruti Rao, Deborah I. Ritter, Cameron J. Grisdale, Gordana Raca, Alex H. Wagner, Subha Madhavan, Malachi Griffith, Obi L. Griffith. Evolution of the CIViC knowledgebase for community driven curation of clinical variants in cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3211.

Abstract 5462: Adding CIViC knowledge to variant annotation pipelines with CIViCpy

Precision 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, and open-access knowledgebase. CIViC provides a structured framework for evaluating genomic variants of various types (e.g., fusions, SNVs) for their clinical utility based on therapeutic, prognostic, predisposing, diagnostic, and functional evidence. 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 must programmatically leverage this API, often reimplementing redundant functionality in the pursuit of common analysis tasks that are beyond the scope of the CIViC web application.

We recently published CIViCpy v1.0 (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. Here we highlight new features and informative analyses introduced since the original publication. New features include an extended variant search functionality that supports queries by variant name, HGVS, and ClinGen Allele Registry ID (CAID). In addition to the CIViC-native GRCh37 coordinates, CIViCpy now supports variant queries from multiple alternative human genome builds, including hg18 and GRCh38. CIViCpy also includes multiple easy-to-use tools for straightforward installation in a variety of compute environments.

A commonly requested feature is the annotation of user-provided variants in Variant Call Format (VCF) with CIViC data. CIViCpy now provides convenient methods, including a command line interface (CLI), for performing this task. We illustrate the use of newly added features by annotating pan-cancer VCF files from The Cancer Genome Atlas (TCGA). We show that CIViCpy is able to quickly and efficiently annotate patient variants in the context of the reported disease type, appending data about the associated CIViC Evidence and Assertions to the provided VCF files.

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, an SDK for downstream applications and rapid analysis. The new features discussed here allow users to easily incorporate CIViC knowledge into their variant annotation pipelines. CIViCpy (civicpy.org) is fully documented, open-source, and freely available online.

Citation Format: Susanna Kiwala, Alex H. Wagner, Adam C. Coffman, Joshua F. McMichael, Kelsy C. Cotto, Thomas B. Mooney, Erica K. Barnell, Kilannin Krysiak, Arpad M. Danos, Jason M. Walker, Obi L. Griffith, Malachi Griffith. Adding CIViC knowledge to variant annotation pipelines with CIViCpy [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5462.

CIViCpy: A Python Software Development and Analysis Toolkit for the CIViC Knowledgebase

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.

Standard operating procedure for curation and clinical interpretation of variants in cancer

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 Interpretation 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 and meeting abstracts, and currently holds over 6300 Evidence Items for over 2300 variants derived from over 400 genes. CIViC has seen increased adoption by, and also undertaken collaboration with, a wide range of users and organizations involved in research. To enhance CIViC’s clinical value, regular submission to the ClinVar database and pursuit of other regulatory approvals is necessary. For this reason, a formal peer reviewed curation guideline and discussion of the underlying principles of curation is needed. We present here the CIViC knowledge model, standard operating procedures (SOP) for variant curation, and detailed examples to support community-driven curation of cancer variants.

Regulated Phosphosignaling Associated with Breast Cancer Subtypes and Druggability

Aberrant phospho-signaling is a hallmark of cancer. We investigated kinase-substrate regulation of 33,239 phosphorylation sites (phosphosites) in 77 breast tumors and 24 breast cancer xenografts. Our search discovered 2134 quantitatively correlated kinase-phosphosite pairs, enriching for and extending experimental or binding-motif predictions. Among the 91 kinases with auto-phosphorylation, elevated EGFR, ERBB2, PRKG1, and WNK1 phosphosignaling were enriched in basal, HER2-E, Luminal A, and Luminal B breast cancers, respectively, revealing subtype-specific regulation. CDKs, MAPKs, and ataxia-telangiectasia proteins were dominant, master regulators of substrate-phosphorylation, whose activities are not captured by genomic evidence. We unveiled phospho-signaling and druggable targets from 113 kinase-substrate pairs and cascades downstream of kinases, including AKT1, BRAF and EGFR. We further identified kinase-substrate-pairs associated with clinical or immune signatures and experimentally validated activated phosphosites of ERBB2, EIF4EBP1, and EGFR. Overall, kinase-substrate regulation revealed by the largest unbiased global phosphorylation data to date connects driver events to their signaling effects.

Pathogenic Germline Variants in 10,389 Adult Cancers

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.

Adapting crowdsourced clinical cancer curation in CIViC to the ClinGen minimum variant level data community-driven standards

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.

Integrative omics analyses broaden treatment targets in human cancer

Background

Although large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers.

Methods

To overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO.

Results

Within the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability.

Conclusions

Our results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0564-z) contains supplementary material, which is available to authorized users.

Abstract 3289: Implementing evolving clinical standards in a variant interpretation knowledgebase

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.

Abstract 2266: Development and validation of a cancer variant capture panel using the open-sourced CIViC database

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.

Integrated analysis of germline and somatic variants in ovarian cancer

We report the first large-scale exome-wide analysis of the combined germline-somatic landscape in ovarian cancer. Here we analyse germline and somatic alterations in 429 ovarian carcinoma cases and 557 controls. We identify 3,635 high confidence, rare truncation and 22,953 missense variants with predicted functional impact. We find germline truncation variants and large deletions across Fanconi pathway genes in 20% of cases. Enrichment of rare truncations is shown in BRCA1, BRCA2 and PALB2. In addition, we observe germline truncation variants in genes not previously associated with ovarian cancer susceptibility (NF1, MAP3K4, CDKN2B and MLL3). Evidence for loss of heterozygosity was found in 100 and 76% of cases with germline BRCA1 and BRCA2 truncations, respectively. Germline-somatic interaction analysis combined with extensive bioinformatics annotation identifies 222 candidate functional germline truncation and missense variants, including two pathogenic BRCA1 and 1 TP53 deleterious variants. Finally, integrated analyses of germline and somatic variants identify significantly altered pathways, including the Fanconi, MAPK and MLL pathways.

Abstract PR01: Identifying clinically important somatic mutations through a knowledge-based approach

Large-scale tumor sequencing projects, like The Cancer Genome Atlas (TCGA), have implicated thousands of somatic mutations in cancer. These initiatives have incentivized many improvements in somatic variant detection. However, we have observed that important pathogenic variants are often missed due to stringent filtering, tumor heterogeneity, tumor contamination of normal, low tumor purity, alignment challenges, and other issues. These idiosyncrasies can impede variant detection algorithms from reliably calling even the most clinically relevant variants. To rescue this missed variation we devised a knowledge based variant identification strategy. We mined the literature and other variant databases for pathogenic variation and assembled them into an integrated Database of Curated Mutations (DoCM - www.docm.info). The DoCM contains 488 variants across 63 genes implicated in 34 cancer types. We developed an algorithm to identify any pathogenic variant signal, for all variants in the DoCM, in aligned sequence data. As a proof of principle, we applied this approach to four cancer types sequenced by TCGA: acute myeloid leukemia (AML), breast cancer, ovarian carcinoma, and uterine corpus endometrial carcinoma. Obvious sequencing and alignment errors, like variants in homopolymer runs, were excluded from subsequent analysis by manual review. Across these four TCGA projects, which includes 1,840 individuals, 1,757 clinically relevant variants were identified, 1,223 of which had not been previously reported in TCGA studies. To validate this approach, custom capture probes were designed for all of the DoCM variants, new libraries constructed and deep sequencing performed on 96 tumor and matched normal samples from the AML and breast cancer TCGA projects. Following this strategy, we were able to confirm the rescue of clinically relevant somatic mutations that were missed in the original TCGA analysis. We propose a knowledge-driven variant detection approach be considered as standard practice to avoid false-negative calls of events likely to be clinically relevant

Citation Format: Benjamin J. Ainscough, Malachi Griffith, Jason Kunisaki, Adam Coffman, Joshua F. McMichael, James M. Eldred, Jason R. Walker, Robert S. Fulton, Richard K. Wilson, Obi L. Griffith, Elaine R. Mardis. Identifying clinically important somatic mutations through a knowledge-based approach. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR01.

Abstract A2-42: Identifying clinically important somatic mutations through a knowledge-based approach

Large-scale tumor sequencing projects, like The Cancer Genome Atlas (TCGA), have implicated thousands of somatic mutations in cancer. These initiatives have incentivized many improvements in somatic variant detection. However, we have observed that important pathogenic variants are often missed due to stringent filtering, tumor heterogeneity, tumor contamination of normal, low tumor purity, alignment challenges, and other issues. These idiosyncrasies can impede variant detection algorithms from reliably calling even the most clinically relevant variants. To rescue this missed variation we devised a knowledge based variant identification strategy. We mined the literature and other variant databases for pathogenic variation and assembled them into an integrated Database of Curated Mutations(DoCM - www.docm.info). The DoCM contains 488 variants across 63 genes implicated in 34 cancer types. We developed an algorithm to identify any pathogenic variant signal, for all variants in the DoCM, in aligned sequence data. As a proof of principle, we applied this approach to four cancer types sequenced by TCGA: acute myeloid leukemia (AML), breast cancer, ovarian carcinoma, and uterine corpus endometrial carcinoma. Obvious sequencing and alignment errors, like variants in homopolymer runs, were excluded from subsequent analysis by manual review. Across these four TCGA projects, which includes 1,840 individuals, 1,757 clinically relevant variants were identified, 1,223 of which had not been previously reported in TCGA studies. To validate this approach, custom capture probes were designed for all of the DoCM variants, new libraries constructed and deep sequencing performed on 96 tumor and matched normal samples from the AML and breast cancer TCGA projects. Following this strategy, we were able to confirm the rescue of clinically relevant somatic mutations that were missed in the original TCGA analysis. We propose a knowledge-driven variant detection approach be considered as standard practice to avoid false-negative calls of events likely to be clinically relevant.

Citation Format: Benjamin J. Ainscough, Malachi Griffith, Jason Kunisaki, Adam Coffman, Joshua F. McMichael, James M. Eldred, Jason R. Walker, Robert S. Fulton, Richard K. Wilson, Obi L. Griffith, Elaine R. Mardis. Identifying clinically important somatic mutations through a knowledge-based approach. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-42.

DGIdb 2.0: mining clinically relevant drug-gene interactions

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that consolidates disparate data sources describing drug-gene interactions and gene druggability. It provides an intuitive graphical user interface and a documented application programming interface (API) for querying these data. DGIdb was assembled through an extensive manual curation effort, reflecting the combined information of twenty-seven sources. For DGIdb 2.0, substantial updates have been made to increase content and improve its usefulness as a resource for mining clinically actionable drug targets. Specifically, nine new sources of drug-gene interactions have been added, including seven resources specifically focused on interactions linked to clinical trials. These additions have more than doubled the overall count of drug-gene interactions. The total number of druggable gene claims has also increased by 30%. Importantly, a majority of the unrestricted, publicly-accessible sources used in DGIdb are now automatically updated on a weekly basis, providing the most current information for these sources. Finally, a new web view and API have been developed to allow searching for interactions by drug identifiers to complement existing gene-based search functionality. With these updates, DGIdb represents a comprehensive and user friendly tool for mining the druggable genome for precision medicine hypothesis generation.

Genome Modeling System: A Knowledge Management Platform for Genomics

In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.

Clonal architectures and driver mutations in metastatic melanomas

To reveal the clonal architecture of melanoma and associated driver mutations, whole genome sequencing (WGS) and targeted extension sequencing were used to characterize 124 melanoma cases. Significantly mutated gene analysis using 13 WGS cases and 15 additional paired extension cases identified known melanoma genes such as BRAF, NRAS, and CDKN2A, as well as a novel gene EPHA3, previously implicated in other cancer types. Extension studies using tumors from another 96 patients discovered a large number of truncation mutations in tumor suppressors (TP53 and RB1), protein phosphatases (e.g., PTEN, PTPRB, PTPRD, and PTPRT), as well as chromatin remodeling genes (e.g., ASXL3, MLL2, and ARID2). Deep sequencing of mutations revealed subclones in the majority of metastatic tumors from 13 WGS cases. Validated mutations from 12 out of 13 WGS patients exhibited a predominant UV signature characterized by a high frequency of C->T transitions occurring at the 3′ base of dipyrimidine sequences while one patient (MEL9) with a hypermutator phenotype lacked this signature. Strikingly, a subclonal mutation signature analysis revealed that the founding clone in MEL9 exhibited UV signature but the secondary clone did not, suggesting different mutational mechanisms for two clonal populations from the same tumor. Further analysis of four metastases from different geographic locations in 2 melanoma cases revealed phylogenetic relationships and highlighted the genetic alterations responsible for differential drug resistance among metastatic tumors. Our study suggests that clonal evaluation is crucial for understanding tumor etiology and drug resistance in melanoma.

Age-related mutations associated with clonal hematopoietic expansion and malignancies

Several genetic alterations characteristic of leukemia and lymphoma have been detected in the blood of individuals without apparent hematological malignancies. The Cancer Genome Atlas (TCGA) provides a unique resource for comprehensive discovery of mutations and genes in blood that may contribute to the clonal expansion of hematopoietic stem/progenitor cells. Here, we analyzed blood-derived sequence data from 2,728 individuals from TCGA and discovered 77 blood-specific mutations in cancer-associated genes, the majority being associated with advanced age. Remarkably, 83% of these mutations were from 19 leukemia and/or lymphoma-associated genes, and nine were recurrently mutated (DNMT3A, TET2, JAK2, ASXL1, TP53, GNAS, PPM1D, BCORL1 and SF3B1). We identified 14 additional mutations in a very small fraction of blood cells, possibly representing the earliest stages of clonal expansion in hematopoietic stem cells. Comparison of these findings to mutations in hematological malignancies identified several recurrently mutated genes that may be disease initiators. Our analyses show that the blood cells of more than 2% of individuals (5-6% of people older than 70 years) contain mutations that may represent premalignant events that cause clonal hematopoietic expansion.

Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts

To characterize patient-derived xenografts (PDXs) for functional studies, we made whole-genome comparisons with originating breast cancers representative of the major intrinsic subtypes. Structural and copy number aberrations were found to be retained with high fidelity. However, at the single-nucleotide level, variable numbers of PDX-specific somatic events were documented, although they were only rarely functionally significant. Variant allele frequencies were often preserved in the PDXs, demonstrating that clonal representation can be transplantable. Estrogen-receptor-positive PDXs were associated with ESR1 ligand-binding-domain mutations, gene amplification, or an ESR1/YAP1 translocation. These events produced different endocrine-therapy-response phenotypes in human, cell line, and PDX endocrine-response studies. Hence, deeply sequenced PDX models are an important resource for the search for genome-forward treatment options and capture endocrine-drug-resistance etiologies that are not observed in standard cell lines. The originating tumor genome provides a benchmark for assessing genetic drift and clonal representation after transplantation.

Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia

BACKGROUND

Many mutations that contribute to the pathogenesis of acute myeloid leukemia (AML) are undefined. The relationships between patterns of mutations and epigenetic phenotypes are not yet clear.

METHODS

We analyzed the genomes of 200 clinically annotated adult cases of de novo AML, using either whole-genome sequencing (50 cases) or whole-exome sequencing (150 cases), along with RNA and microRNA sequencing and DNA-methylation analysis.

RESULTS

AML genomes have fewer mutations than most other adult cancers, with an average of only 13 mutations found in genes. Of these, an average of 5 are in genes that are recurrently mutated in AML. A total of 23 genes were significantly mutated, and another 237 were mutated in two or more samples. Nearly all samples had at least 1 nonsynonymous mutation in one of nine categories of genes that are almost certainly relevant for pathogenesis, including transcription-factor fusions (18% of cases), the gene encoding nucleophosmin (NPM1) (27%), tumor-suppressor genes (16%), DNA-methylation-related genes (44%), signaling genes (59%), chromatin-modifying genes (30%), myeloid transcription-factor genes (22%), cohesin-complex genes (13%), and spliceosome-complex genes (14%). Patterns of cooperation and mutual exclusivity suggested strong biologic relationships among several of the genes and categories.

CONCLUSIONS

We identified at least one potential driver mutation in nearly all AML samples and found that a complex interplay of genetic events contributes to AML pathogenesis in individual patients. The databases from this study are widely available to serve as a foundation for further investigations of AML pathogenesis, classification, and risk stratification. (Funded by the National Institutes of Health.).

Abstract LB-265: Patient-derived xenografts from advanced luminal-type breast cancer: insights into endocrine therapy resistance

Deeper understanding the mechanisms by which luminal-type breast cancer develops resistance to endocrine therapy and development of novel strategies to treat these patients requires model systems recapitulate human breast cancer as accurately as possible. An increasing body of work suggests patient derived xenografts (PDX) may represent an informative model for development of novel therapeutics. We therefore established seven xenograft tumor lines from late-stage breast cancer patients with estrogen positive (ER+) disease. To date five ER+ PDX lines have been tested for responses to estradiol treatment in overiectomized NOD/SCID mice. Three showed estradiol independent-growth, one estrogen-stimulated growth and in one estradiol-induced a regression. These patterns mimicked the clinical phenotypes of each patient, tracking survival and responses to serial endocrine treatments. To define new mechanisms for resistance, whole genome DNA sequencing, RNA sequencing and Reverse Phase Protein Assay analysis was conducted. These studies identified an ESR1/YAP1 balanced translocation in a PDX model and tumor of origin showing low levels of ER, paradoxical high level expression form luminal genes and extreme ET resistance. The ESR1 YAP1 fusion maintained the N terminal DNA binding motif of ESR1, but the hormone binding and AF2 motifs were replaced with the C terminal transactivation domain of YAP1. Expression ESR1 YAP1 in ER+ breast cancer models down-regulated ER and induced estrogen independent growth. PDX endocrine phenotypes parallel tumor of origin responses to endocrine therapy and revel novel mechanism for endocrine therapy resistance.

Citation Format: Matthew J. Ellis, Shunqiang Li, Dong Shen, Li Ding, Robert Crowder, Jeiya Shao, Rodrigo Goncalves, Yu Tao, Jingqin Luo, Aleix Prat, Wenbin Liu, Ana Maria Gonzalez-Angulo, Shuying Liu, Joshua F. McMichael, Chris Miller, Dave Larson, Robert S. Fulton, Tom Mooney, Jeremy Hoog, Li Lin, Therese Giuntoli, Caroline Bumb, Crystal Cooper, Rebecca Aft, Robert T. Kitchens, Stephen N. Johnson, Chanpheng Phommaly, Megha Shiyam Kavuri, Katherine DeSchryver, Austin Lin, YiYu Dong, Cynthia X. Ma, Timothy Pluard, Michael Naughton, Ron Bose, Rama Suresh, Reida G. McDowell, Loren Michel, Richard Wilson, Shaomeng Wang, Christopher Maher, Gordon B. Mills, Charles Perou, Elaine R. Mardis. Patient-derived xenografts from advanced luminal-type breast cancer: insights into endocrine therapy resistance. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-265. doi:10.1158/1538-7445.AM2013-LB-265

The origin and evolution of mutations in acute myeloid leukemia

Most mutations in cancer genomes are thought to be acquired after the initiating event, which may cause genomic instability and drive clonal evolution. However, for acute myeloid leukemia (AML), normal karyotypes are common, and genomic instability is unusual. To better understand clonal evolution in AML, we sequenced the genomes of M3-AML samples with a known initiating event (PML-RARA) versus the genomes of normal karyotype M1-AML samples and the exomes of hematopoietic stem/progenitor cells (HSPCs) from healthy people. Collectively, the data suggest that most of the mutations found in AML genomes are actually random events that occurred in HSPCs before they acquired the initiating mutation; the mutational history of that cell is "captured" as the clone expands. In many cases, only one or two additional, cooperating mutations are needed to generate the malignant founding clone. Cells from the founding clone can acquire additional cooperating mutations, yielding subclones that can contribute to disease progression and/or relapse.

Whole-genome analysis informs breast cancer response to aromatase inhibition

To correlate the variable clinical features of estrogen receptor positive (ER+) breast cancer with somatic alterations, we studied pre-treatment tumour biopsies accrued from patients in a study of neoadjuvant aromatase inhibitor (AI) therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to hematopoietic disorders. Mutant MAP3K1 was associated with Luminal A status, low grade histology and low proliferation rates whereas mutant TP53 associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon AI treatment. Pathway analysis demonstrated mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in ER+ breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumor biology but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing.

Abstract LB-423: Whole genome comparisons of pre- and post- aromatase inhibitor treatment in estrogen receptor positive breast cancer

Background: Estrogen receptors are over-expressed in around 70% of breast cancer cases. The genetic changes that occur during aromatase inhibitor (AI) treatment are not well understood and may differ depending upon the patient's response phenotype. Methods: We performed whole genome sequencing (WGS) of matched blood, pre-treatment, and post-treatment biopsy samples from 22 estrogen receptor positive breast cancer patients treated with neoadjuvant aromatase inhibitors. For 5 cases, we performed the whole genome sequencing (WGS) on patients’ matched normal, two pre AI-treatment, and two post AI-treatment DNA isolates from biopsy samples. We validated all putative coding and non-coding somatic mutations using deep sequencing. By comparing the validated somatic mutations from pre- and post- AI treatment biopsy samples, we were able to determine the alterations in the tumor genomes. In every case we defined the clonal architecture of each pair of pre-treatment and post-treatment biopsy samples by comparing the variant allele frequencies from thousands of validated somatic mutations. Results: Comparisons of the two pre AI-treatment biopsy samples from the same patient indicates that the variant allele frequencies of mutations showed high concordances in all 5 cases, 0.74 to 0.95 range of correlation coefficient. Only a small percentage of somatic mutations were detected in one pre-treatment sample and not the other (4.65% overall). In comparing the somatic variations between pre-treatment and matched post-treatment biopsy samples in 22 cases, we found that patients with good clinical response to AI treatment retained known driver mutations only in their pre-treatment tumors. Conversely, those patients with poor clinical response presented new driver mutations in their post-treatment samples. Furthermore, the variant allele frequency for most mutated genes decreased in post AI treatment samples for patients with good AI treatment response; on the contrary, the variant allele frequency increased for patients with poor clinical response. Conclusions: From WGS of matched normal, pre-treatment, and post-treatment biopsy samples, we identified new driver genes mutated in patients with poor clinical response, while patients with good clinical response had lost mutated driver genes in their post-treatment biopsy samples. The genetic landscape revealed by WGS of pre-treatment and post-treatment biopsy samples reveals mutational repertoires are remodeled by AI therapy. This finding suggests deep sequencing of AI treated samples will be necessary to reveal the complete complement of mutations present in a patient's tumor.

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-423. doi:1538-7445.AM2012-LB-423

Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing

Most patients with acute myeloid leukemia (AML) die from progressive disease after relapse, which is associated with clonal evolution at the cytogenetic level^1,2. To determine the mutational spectrum associated with relapse, we sequenced the primary tumor and relapse genomes from 8 AML patients, and validated hundreds of somatic mutations using deep sequencing; this allowed us to precisely define clonality and clonal evolution patterns at relapse. Besides discovering novel, recurrently mutated genes (e.g. WAC, SMC3, DIS3, DDX41, and DAXX) in AML, we found two major clonal evolution patterns during AML relapse: 1) the founding clone in the primary tumor gained mutations and evolved into the relapse clone, or 2) a subclone of the founding clone survived initial therapy, gained additional mutations, and expanded at relapse. In all cases, chemotherapy failed to eradicate the founding clone. The comparison of relapse-specific vs. primary tumor mutations in all 8 cases revealed an increase in transversions, probably due to DNA damage caused by cytotoxic chemotherapy. These data demonstrate that AML relapse is associated with the addition of new mutations and clonal evolution, which is shaped in part by the chemotherapy that the patients receive to establish and maintain remissions.

DNMT3A mutations in acute myeloid leukemia

BACKGROUND

The genetic alterations responsible for an adverse outcome in most patients with acute myeloid leukemia (AML) are unknown.

METHODS

Using massively parallel DNA sequencing, we identified a somatic mutation in DNMT3A, encoding a DNA methyltransferase, in the genome of cells from a patient with AML with a normal karyotype. We sequenced the exons of DNMT3A in 280 additional patients with de novo AML to define recurring mutations.

RESULTS

A total of 62 of 281 patients (22.1%) had mutations in DNMT3A that were predicted to affect translation. We identified 18 different missense mutations, the most common of which was predicted to affect amino acid R882 (in 37 patients). We also identified six frameshift, six nonsense, and three splice-site mutations and a 1.5-Mbp deletion encompassing DNMT3A. These mutations were highly enriched in the group of patients with an intermediate-risk cytogenetic profile (56 of 166 patients, or 33.7%) but were absent in all 79 patients with a favorable-risk cytogenetic profile (P<0.001 for both comparisons). The median overall survival among patients with DNMT3A mutations was significantly shorter than that among patients without such mutations (12.3 months vs. 41.1 months, P<0.001). DNMT3A mutations were associated with adverse outcomes among patients with an intermediate-risk cytogenetic profile or FLT3 mutations, regardless of age, and were independently associated with a poor outcome in Cox proportional-hazards analysis.

CONCLUSIONS

DNMT3A mutations are highly recurrent in patients with de novo AML with an intermediate-risk cytogenetic profile and are independently associated with a poor outcome. (Funded by the National Institutes of Health and others.).

Genome remodelling in a basal-like breast cancer metastasis and xenograft

Massively parallel DNA sequencing technologies provide an unprecedented ability to screen entire genomes for genetic changes associated with tumor progression. Here we describe the genomic analyses of four DNA samples from an African-American patient with basal-like breast cancer: peripheral blood, the primary tumor, a brain metastasis, and a xenograft derived from the primary tumor. The metastasis contained two de novo mutations and a large deletion not present in the primary tumor, and was significantly enriched for 20 shared mutations. The xenograft retained all primary tumor mutations, and displayed a mutation enrichment pattern that paralleled the metastasis (16 of 20 genes). Two overlapping large deletions, encompassing CTNNA1, were present in all three tumor samples. The differential mutation frequencies and structural variation patterns in metastasis and xenograft compared to the primary tumor suggest that secondary tumors may arise from a minority of cells within the primary.

Recurring mutations found by sequencing an acute myeloid leukemia genome

BACKGROUND

The full complement of DNA mutations that are responsible for the pathogenesis of acute myeloid leukemia (AML) is not yet known.

METHODS

We used massively parallel DNA sequencing to obtain a very high level of coverage (approximately 98%) of a primary, cytogenetically normal, de novo genome for AML with minimal maturation (AML-M1) and a matched normal skin genome.

RESULTS

We identified 12 acquired (somatic) mutations within the coding sequences of genes and 52 somatic point mutations in conserved or regulatory portions of the genome. All mutations appeared to be heterozygous and present in nearly all cells in the tumor sample. Four of the 64 mutations occurred in at least 1 additional AML sample in 188 samples that were tested. Mutations in NRAS and NPM1 had been identified previously in patients with AML, but two other mutations had not been identified. One of these mutations, in the IDH1 gene, was present in 15 of 187 additional AML genomes tested and was strongly associated with normal cytogenetic status; it was present in 13 of 80 cytogenetically normal samples (16%). The other was a nongenic mutation in a genomic region with regulatory potential and conservation in higher mammals; we detected it in one additional AML tumor. The AML genome that we sequenced contains approximately 750 point mutations, of which only a small fraction are likely to be relevant to pathogenesis.

CONCLUSIONS

By comparing the sequences of tumor and skin genomes of a patient with AML-M1, we have identified recurring mutations that may be relevant for pathogenesis.