Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model’s predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a “two-wave killing” temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.
Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first line treatment for patients with advanced non-small cell lung cancer (NSCLC) who have specific hyperactive mutations in EGFR. Though patients have seen improvements in survival after EGFR TKI treatment, resistance commonly develops as not all NSCLC cells die following treatment. Indeed, EGFR inhibitor treatment induces both cell-cycle arrest (allowing for cell survival) and death in cancer cells. The FOXO family of transcription factors have been implicated in both death and arrest responses following EGFR inhibition. This is consistent with FOXOs function in regulating both arrest and apoptotic genes programs. However, it is unknown how the FOXO network ‘decides' between these two oppositional programs to enact following treatment with EGFR inhibitors. Protein dynamics, meaning how the levels or location of a protein changes over time, is a mechanism cells use to encode information about cellular fates. The p53 transcription system is an established example of a dynamically regulated system–it responds to multiple inputs and is capable of enacting multiple outputs (gamma radiation causes oscillations of p53 levels that result in cellular arrest while UV causes a sustained increase of p53 levels that result in cellular death). FOXO's network is similar to the p53 system (multiple input, multiple output), making it a good candidate for dynamic regulation. We have tagged Foxo3a with a fluorescent marker (Venus) at its endogenous locus in PC9 NSCLC cells using CRISPR/Cas9 to determine whether FOXO dynamics dictate cell fate following EGFR TKI treatment. Using live cell microscopy on this line, we have acquired FOXO nuclear cytoplasmic shuttling dynamics as well as cell fate (death/arrest) in single cells. Using time-series analyses we are trying to determine if there are dynamic patterns associated with each fate following EGFR inhibition. Through our studies, we hope to gain a better understanding of this non-genetic mechanism cells use to determine their fate. Our long-term goal is to identify dynamic patterns that push cancer cells to more terminal fates following treatment. Citation Format: Julie M. Huynh, Kayenat S. Aryeh, Lisa Shanks, Andrew L. Paek. Role of FOXO transcription factor dynamics in determining cell fates following EGFR inhibition [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2471.
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