<p>Clonal evolution of mutations from diagnosis to relapse. These graphs depict the Variant Allele Frequency (VAF) of each patient's mutations at diagnosis and relapse. Note: Mutations absent at diagnosis or relapse are depicted with VAF of zero. Gene names are noted to the left of each variant and are available in Supplemental Table 2.</p>
<p>Clonal cluster analysis. Mutations were clustered using DBSCAN based on the VAF and graphed according to timepoint. Panels A-T illustrate possible scenarios of how individual mutations may originate, evolve, and resolve based on VAF.</p>
The mission of the National Cancer Institute's (NCI) Office of Cancer Genomics (OCG) is to advance the molecular understanding of cancers in order to improve clinical outcomes through precision medicine. Although vast amounts of genomic data are available for many types of cancers, identifying genetic alterations in rare and pediatric cancers is still a challenge. Efficient bioinformatics tools to analyze, manage, store, and access data are also necessary for the research community. To develop effective and targeted treatments, clinically accurate genotypic and phenotypic research models are much needed. OCG's programs focus on addressing these challenges through multidisciplinary, collaborative research efforts.The four initiatives of OCG support research on structural, functional, and translational genomics, as well as development of next-generation cancer models. The Cancer Genome Characterization Initiative (CGCI) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) programs use transcriptomic, genomic, and epigenomic approaches to examine genetic alterations between various tumors and matched normal tissues. CGCI projects focus on HIV-associated and rare cancers such as Burkitt lymphoma, while TARGET focuses on high-risk childhood cancers. The goals of these programs are to attain insights into key mutations that drive tumors and genetic abnormalities specific to cancer subtypes and to develop effective and less toxic therapies for patients. CGCI and TARGET data are available to the research community through data matrices on the OCG website. OCG also recently launched the Pediatric Genomic Data Inventory (PGDI) as a new resource for investigators to access molecular characterization data.The Cancer Target Discovery and Development (CTD2) Network advances cancer research by bridging the knowledge gap between cancer genomics and precision oncology. The Network aims to understand the cancer metastasis, tumor heterogeneity, and drug resistance to develop optimal combinations of small-molecules or immunotherapy with small molecules. As a community resource program, the CTD2 Network develops and provides access to data, tools, methods, and reagents through the Data Portal and the Dashboard. The Human Cancer Models Initiative (HCMI) is an international consortium that is generating novel human tumor-derived culture models from a wide variety of cancer types including rare and understudied cancers. The models, together with related clinical and genomic data, will be available as a resource to the world-wide research community.OCG's policies on data usage, as well as guides to accessing data, are explained on the OCG website (https://ocg.cancer.gov/). Researchers, potential collaborators, and interested members of the public are encouraged to visit the OCG webpages or contact OCG at ocg@mail.nih.gov.Citation Format: Cindy W. Kyi, Pamela C. Birriel, Tanja M. Davidsen, Martin L. Ferguson, Patee Gesuwan, Nicholas B. Griner, Yiwen He, Subhashini Jagu, Eva Tonsing-Carter, Daniela S. Gerhard. NCI Office of Cancer Genomics: Promoting multidisciplinary research to translate findings into the clinic and advance precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4341.
Abstract Patient-derived xenografts and cell lines have been the underpinning of functional characterization and drug discovery efforts in cancer. The use of these models is often under the assumption that these systems are renewable, faithful representations of original progenitor tumor cell populations. To test this assumption, we performed whole exome (92X median coverage) and genome sequencing (36X median coverage) analysis of cell line and patient-derived mouse xenografts (PDXs) originating from 7 neuroblastoma patients. Data are from1 primary tumor, 3 PDXs, and 15 neuroblastoma cell lines cultured from tumor, bone marrow, or blood. The cell lines consisted of 4 pre-/post-therapy pairs and 3 pairs established and maintained in either hyperoxia (room air i.e. “standard” cell culture) or physiologic (bone marrow hypoxia = 5%) oxygen. 7 lymphoblastoid or fibroblast cell lines were used as matched normals to identify somatic mutations. Subclonal population structures were inferred from somatic mutation calls calibrated for copy number state and tumor purity. In all cell lines and xenografts, we observed 1-2 additional subclonal populations, primarily supported by deep coverage from exome sequencing. In nearly every case, we observed shifts in the proportional representation of genetic subclones and many subclones showed additional mutations not evident in the progenitor tissue or cancer line derived in parallel. Comparison of three cell line pairs established in bone marrow level hypoxia versus room air found only ∼40% of coding mutations in each line were shared (average 82 mutations per line), suggesting significant genetic impact of growing tumor cells in the two different culture conditions. Matched PDXs from these cases had only ∼17% of coding mutations shared across all three models. The greatest genetic similarity was seen between paired cell lines established from tissue obtained pre-/post-therapy from the same patient (36 coding mutations shared, 14 private to diagnosis and 13 private to progression). However, a second pre/post-therapy cell line pair did not share any coding mutations, although they did have 585 non-coding mutations in common (of 4,033 and 2,480 in each line), assuring that the relapse was derived from a diagnostic tumor clone. These results highlight a need for comprehensive subclonal analysis of human cancer laboratory models to better inform design and interpretation of biological and preclinical therapeutic studies. Citation Format: Maya Schonbach, Arnavaz Danesh, Jeff Bruce, Tito Woodburn, Tanja Davidsen, Leandro Hermida, Patee Gesuwan, Jaime Guidry Auvil, Oliver Hampton, David Wheeler, Julie Gastier-Foster, Malcolm Smith, Daniela Gerhard, John M. Maris, Patrick Reynolds, Trevor J. Pugh. Fidelity of subclonal representation in human neuroblastoma-derived cell line and patient-derived xenograft models: A report from the NCI-TARGET project. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 484. doi:10.1158/1538-7445.AM2015-484
