Abstract PL03-04: Using functional genetics to optimize the treatment of cancer.
Authors: René S. Bernards
Journal: Cancer Research (2013)

Abstract Most of the current cancer therapies are ineffective, delivering benefit on average to only one in four cancer patients. If we consider the global spending on cancer drugs ($ 49 billion in 2011), this leads to the conclusion that some $ 37 billion is spent each year to only cause adverse side effects. Precision medicine aims to improve on this poor performance by selecting patients for therapy based on the genotype of the individual tumor rather than the tissue of origin. Indeed, using this approach, remarkable progress has been made in treatment of diseases like chronic myelogenous leukemia (CML) and melanoma with selective drugs that target the genetic aberrations of these tumors. With the rapid increase in the use of targeted agents, there is a parallel increase in the need to genotype the tumors of patients to make the proper therapy choice. This is most evident in lung cancer, where there are already a number of genotypes that are relevant to making the correct therapy choice. Indeed, several large academic centers are starting to genotype their patients with a sizeable panel of cancer genes for clinical decision-making. A shortcoming of this “precision medicine” approach is that responses to targeted cancer therapies are often hampered by the rapid emergence of drug resistance. For example, treatment of metastatic non-small-cell lung cancers (NSCLCs) harboring activating mutations in the gene encoding the Epidermal Growth Factor Receptor (EGFR) leads to significant increases in progression free survival. However, such responses are often short-lived resulting in much less impressive patient benefit in terms of overall survival as a result of rapid induction of drug resistance (1). Development of resistance to targeted therapies is a general phenomenon which is also seen in BCR-ABL translocated chronic myelogenous leukemias (CML) treated with imatinib (2), BRAF mutant melanomas treated with the BRAF inhibitor vemurafenib (3) and EML4-ALK translocated NSCLCs treated with the ALK inhibitor crizotinib (4). The impressive but short-lived effect of targeted therapies has led to the realization that we need to shift our focus to more efficient combination therapies. But which combinations are most likely to be effective? The rapidly increasing repertoire of targeted cancer drugs provides a seemingly endless number of possible combinations of targeted agents whose effectiveness may differ in the multitude of cancer genotypes. Until recently, combinations of drugs have been tested in the clinic following the “trial and error” approach. In my laboratory we use functional genomic approaches to find particularly powerful combinations of drugs by exploiting the concept of synthetic lethality (5). Two genes (or signaling pathways) are considered to be synthetic lethal if cells die when both genes (or pathways) are inactivated, but not when either one is inhibited individually. We use RNA interference technology to identify such synthetic lethal interactions. A particularly useful iteration of these RNAi-based synthetic lethality screens are the so called “chemical synthetic lethality screens," in which we search for genes whose suppression is synergistic with a known anti-cancer drug. Last year, we delivered proof of concept for this approach by demonstrating that inhibition of BRAF in colon cancer is only effective when EGFR is also inhibited (6). Clinical testing of this concept was initiated within 8 months after the initial publication (http://clinicaltrials.gov/ct2/show/NCT01719380?term=LGX818&amp;rank=1), underscoring that mechanism-based rational combination therapies are very attractive to pharmaceutical companies. In my presentation, I will discuss our approaches to develop powerful combination therapies in cancers through functional genetics. Citation Format: René S. Bernards. Using functional genetics to optimize the treatment of cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr PL03-04. doi:10.1158/1538-7445.AM2013-PL03-04