Abstract 5037: New concepts for quantifying the benefits of mono and combination therapy in an era of big data

I will describe new (unpublished) approaches to quantifying drug response at two points in the drug development pipeline: pre-clinical studies in cell lines and clinical trials of combination therapies in patient populations. Drug sensitivity and resistance in cell lines is conventionally quantified by IC50 or Emax values, but these measures suffer from a fundamental flaw when applied to growing cells: they are highly sensitive to cell division number, which varies with cell line, experimental condition, seeding density etc. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity while obscuring important biological insights and interfering with biomarker discovery. I will describe alternative growth rate inhibition (GR) metrics that are insensitive to division number and can directly measure both endpoint sensitivity and adaptive drug resistance. Theory and experiments show that GR50 and GRmax are superior to IC50 and Emax for assessing the effects of drugs in dividing cells. GR metrics promise to improve our ability to score drug sensitivity in specific-derived tumor cells, improves data reproducibility, and increase the translational potential of pharmacogenomics data. In patients, combination therapy improves tumor control compared to monotherapy and the development of combinations is motivated in most cases by pre-clinical data on synergism in cell lines. However I will describe a different way in which combinations can provide clinical benefit. Based on analysis of between-patient variability in existing trial data and a large set patient derived tumor xenograft (PDX) mice published by Gao H, Korn JM, Ferretti S, et al. (Nature medicine 2015;21:1318-25), I will argue for a simple principle: in nearly two-thirds of cases analyzed, efficacious combinations work simply by improving the likelihood that a tumor will experience an outlier response to a single drug. Thus, even in the absence of additive or synergistic tumor inhibition, combinations are generally superior to monotherapies. Superiority by “independent action” provides a principle on which to design new combinations and a scientific rationale for the use of combination therapies in poorly understood cancers whenever toxicity is acceptable. These studies, at two different points of the drug discovery pipeline, illustrate the value of combining new first-principles theories about drug mechanism of action with “big data” that is increasingly available on pre-clinical and clinical drug response. Conversely, in the absence of conceptual innovation, we miss important mechanistic insights hidden in existing data. The purpose of such insights, when obtained, is drive new laboratory and real-world experiments. I will discuss how the process of coupling computation and experimentation works in practice. Citation Format: Peter K. Sorger, Marc Hafner, Mario Niepel, Caitlin Mills, Adam Palmer, Mohammed Fallahi Sichani. New concepts for quantifying the benefits of mono and combination therapy in an era of big data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5037. doi:10.1158/1538-7445.AM2017-5037