Patient-Derived In Vitro and In Vivo Models of Cancer

Over the last two decades, cancer researchers have taken the promise offered by the Human Genome Project and have expanded its capacity to use sequencing to identify the genomic alterations that give rise to and sustain individual tumors. This expansion has allowed researchers to identify and target highly recurrent alterations in specific cancer contexts, such as EGFR mutations in non-small cell lung cancer (Lynch et al, N Engl J Med 350:2129–2139, 2004; Sharifnia et al., Proc Natl Acad Sci U S A 111:18661–18666, 2014), BCR-ABL translocations in chronic myeloid leukemia (Deininger, Pharmacol Rev 55:401–423. https://doi.org/10.1124/pr.55.3.4, 2003; Druker et al, N Engl J Med 344. 1038–1042, 2001; Druker et al, N Engl J Med 344:1031–1037. https://doi.org/10.1056/NEJM200104053441401, 2001), or HER2 amplifications in breast cancer (Slamon et al, N Engl J Med 344:783–792. https://doi.org/10.1056/NEJM200103153441101, 2001; Solca et al, Beyond trastuzumab: second-generation targeted therapies for HER-2-positive breast cancer. In: Sibilia M, Zielinski CC, Bartsch R, Grunt TW (eds) Drugs for HER-2-positive breast cancer. Springer, Basel, pp 91–107, 2011). Despite these advances in our capacity to identify the genetic alterations that drive tumor initiation, survival, and proliferation, our ability to target these alterations to provide effective treatment options for patients in need, particularly those with rare or advanced cancers, remains limited (Gould et al, Nat Med 21:431–439. https://doi.org/10.1038/nm.3853, 2015). Patient-derived models of cancer offer one potential mechanism to overcome this barrier between the bench and bedside. Through the development and testing of patient-derived models of cancer, functional genomics efforts can identify tumor-specific drug sensitivities and thereby provide a connection between tumor genetics and effective therapeutics for patients in need of treatment options.Recognizing that cancer is a multifaceted set of disease states, the development of personalized models of cancer that can be used to compare treatment options, identify tumor-specific vulnerabilities, and guide clinical decision-making has tremendous potential for improving patient outcomes. This chapter will describe a representative set of patient-derived models of cancer, reviewing each of their strengths and weaknesses and highlighting how selecting a model to suit a specific question or context is critical. Each model comes with a unique set of pros and cons, making them more or less appropriate for each specific research or clinical question. As each model can be leveraged to gain new insights into cancer biology, the key to their deployment is to identify the most appropriate model for a specific context, while carefully considering the strengths and limitations of the selected model. When used appropriately, patient-derived models may prove to be the missing link needed to bring the promise of personalized oncology to fruition in the clinic.