Receptor tyrosine kinases (RTKs) are high-affinity cell surface receptors for growth factors that are frequently deregulated in cancer. Signaling through these receptors has been associated with increased cancer cell proliferation and resistance to cytotoxic therapies. To block this detrimental signaling, many companies are developing inhibitory antibodies against various RTKs. A key challenge in clinical studies is the optimal stratification of patients who may benefit from these therapies. For an RTK targeted antibody, the detection of the respective growth factor in the tumor microenvironment may be an important bio-marker. Beyond the physical presence of the growth factor, the decision whether a cancer cell will respond to growth factor-induced signals is governed by complex intra-cellular signaling networks. We compared different approaches to predict cellular responses and will highlight a hybrid approach that combines mechanistic modeling based on ordinary differential equations with a machine learning algorithm. The models are trained on in vitro drug response screens and then applied to predict response in patient samples. The mechanistic models are trained on quantitative data from signal transduction studies as well as RNAseq data for cellular characterization. Using the hybrid approach, a correlation between growth factor expression in the tumor microenvironment and its predicted response was identified. This supports the hypothesis of addiction of tumors to growth factors abundant in the tumor microenvironment, and might enable more robust patient stratification in the future.

Citation Format: Helge Hass, Kristina Masson, Sibylle Wohlgemuth, Violette Paragas, John E. Allen, Mark Sevecka, Emily Pace, Jens Timmer, Joerg Stelling, Gavin MacBeath, Birgit Schoeberl, Andreas Raue. Predicting ligand-dependent tumors from multi-dimensional signaling features [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1312.