Precision Oncology: The Road Ahead. Issue 10 (October 2017)

• Record Type: Journal Article
• Authors: Senft, Daniela; Leiserson, Mark D.M.; Ruppin, Eytan; Ronai, Ze'ev A.
• Is Part Of: Trends in molecular medicine. Volume 23:Issue 10(2017)
• Page Start: 874
• Page End: 898
• Publication Date: 2017-10
• Subjects: Molecular biology -- Periodicals; Pathology, Molecular -- Periodicals; Physiology, Pathological -- Periodicals; 572.8
• DOI: 10.1016/j.molmed.2017.08.003
• Languages: English
• ISSNs: 1471-4914

Abstract

Abstract : Current efforts in precision oncology largely focus on the benefit of genomics-guided therapy. Yet, advances in sequencing techniques provide an unprecedented view of the complex genetic and nongenetic heterogeneity within individual tumors. Herein, we outline the benefits of integrating genomic and transcriptomic analyses for advanced precision oncology. We summarize relevant computational approaches to detect novel drivers and genetic vulnerabilities, suitable for therapeutic exploration. Clinically relevant platforms to functionally test predicted drugs/drug combinations for individual patients are reviewed. Finally, we highlight the technological advances in single cell analysis of tumor specimens. These may ultimately lead to the development of next-generation cancer drugs, capable of tackling the hurdles imposed by genetic and phenotypic heterogeneity on current anticancer therapies. Trends: Genomics-driven cancer therapy benefits a subset of patients, although there are clear shortcomings to this approach. Using genomics as a single 'biomarker' to inform therapy is insufficient to comprehensively predict efficient therapeutic approaches. By providing information about active pathways, the inclusion of transcriptomic data reveals a more comprehensive and, thus, accurate molecular profile, which likely improves the choice of therapy. Available patient-derived functional models (e.g., organoids or patient-derived xenografts) are promising for testing multiple drugs and/or drug combinations in a clinically relevant time-frame. Mining available data sets can allow researchers to comprehensively map the processes that drive cancer and reveal novel vulnerabilities. Intratumor heterogeneity remains one of the biggest challenges in reaching sustained therapeutic responses to cancer treatment. Integrating additional factors (immune, metabolome, and microbiome) could pinpoint novel putative therapeutic approaches and combinational drug therapies, in an effort to overcome tumor heterogeneity.