Bult, Carol J.

Abstract Objective The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled “Developing a Clinical Genomic Informatics Research Agenda”. The meeting’s goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. Materials and Methods Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. Results Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. Discussion Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.

Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them.

Abstract Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62–66. ©2017 AACR.

Abstract Individual tumors, including the aggressive and difficult to treat triple-negative (ER-/PR-/HER2-) breast cancers (TNBCs) are heterogeneous collections of cells with multiple subclonal populations each contributing to the tumor. While subclonal heterogeneity is likely responsible for the development of drug resistance, identification of how tumor cell populations change over time has been difficult, largely because of the challenges in resampling tumor tissue at close time points. Here we quantify tumor evolution in human patient-derived xenografts implanted into NSG mice, which we use to test subclonal heterogeneity as a function of location within a tumor, propagation time, and drug treatment. We used high-depth (∼400x) sequencing of a targeted panel of 358 genes to quantify somatic mutation allele frequencies from 6 spatially-separated and 8 temporally-propagated xenograft samples derived from the same TNBC patient tumors. Samples ranged in age from 2-4 months post-engraftment. Although we observed a few low frequency mutations distinguishing samples, overall we found that allele frequencies of somatic mutations were well-preserved on this time scale. We then generated replicate xenografts from the same patient tumor and treated them respectively with cisplatin, doxorubicin, cyclophosphamide, docetaxel, or vehicle control for 25 days. Although again somatic mutations showed few differences in allele frequency across samples, substantial variations were seen when data were analyzed for copy number alterations. To confirm these effects we repeated the treatments for xenografts derived from two additional TNBC patients. Again we observed strong changes in tumor heterogeneity at the copy number level. This effect was particularly, but not exclusively, apparent in tumors with the greatest response to therapy. We further verified these measurements through Sanger and digital PCR sequencing on the treated mice and other mice in the same cohorts. Using a multi-sample xenograft propagation, dissection, sequencing, and computational analysis protocol, we have shown that tumor subpopulation changes in response to treatment can be quantified and distinguished from spatial or temporal effects, even for treatment time courses as short as 1 month. In triple negative breast cancer these variations are most apparent at the level of copy number variation. Our study demonstrates how patient-derived xenografts can provide detailed resolution of tumor population evolution during the manifestation of resistance. Citation Format: Hyunsoo Kim, Pooja Kumar, Francesca Menghi, Joshy George, Guru Ananda, Susan Mockus, Chengsheng Zhang, Nicholas Larson, Henry C. Chen, Yan Yang, James Keck, R. Krishnamurthy Karuturi, Charles Lee, Carol Bult, Edison Liu, Jeffrey H. Chuang. Evolution during propagation and treatment of patient-derived triple negative breast cancer xenografts. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2411.