Personalized Cancer Models for Target Discovery and Precision Medicine

Abstract

Although cancer research is progressing at an exponential rate, translating this knowledge to develop better cancer drugs and more effectively match drugs to patients is lagging. Genome profiling of tumors provides a snapshot of the genetic complexity of individual tumors, yet this knowledge is insufficient to guide therapy for most patients. Model systems, usually cancer cell lines or mice, have been instrumental in cancer research and drug development, but translation of results to the clinic is inefficient, in part, because these models do not sufficiently reflect the complexity and heterogeneity of human cancer. Here, we discuss the potential of combining genomics with high-throughput functional testing of patient-derived tumor cells to overcome key roadblocks in both drug target discovery and precision medicine.

Figure 1.

Every patient’s tumor has a unique mosaic of genomic alterations. Shown are genomic alterations in serous ovarian cancer from TCGA taken from CBioPortal. Amplification of c-MYC MYCN and LMYC is seen in ~80 % of cases. TP53 is mutated in over 95% of cases. However, these common alterations are embedded within many additional patient-specific genomic alterations which may influence drug response. Red: amplified, Blue: deleted, Black: mutated.

Figure 2.. Patient derived models for precision oncology.

Shown are key steps to enable functional drug testing on patient derived organoids and generation of a functional atlas of cancer.

Figure 3.. A target discovery and validation engine.

1. A range of models can be used for target discovery. a) Isogenic cell pairs with one of the pair engineered to carry a single lesion such as a tumor suppressor deletion or oncogene mutation. This system is the cleanest to identify synthetic lethal genes. b) Tumor cells derived from GEMMs and other mouse models of cancer. Tumors can be derived from mice of the same genetic background and experimental conditions thus reducing genetic and experimental variables. c) Low passage patient derived tumor cells. The advantage of these models is their genetic and biologic relevance to actual patients. Comparing results across a set of complementary models increases confidence in target validity.2. The use of arrayed siRNAs as target discovery tool enables testing one gene at a time and also can be used in primary cells from diverse models. Integration of functional screen results with genomic data also helps to prioritize targets.3. A range of tools and preclinical model systems are used to validate targets and link to candidate biomarkers. 4. The spectrum of evidence gathered can be marshalled to warrant clinical testing of candidate targeted agents.