Background The recent development and enormous application of parallel sequencing technology in oncology has produced immense amounts of cell-specific genetic information. However, publicly available cell-specific genetic variants are not explained by well-established guidelines. Additionally, cell-specific variants interpretation and classification has remained a challenging task and lacks standardization. The Association for Molecular Pathology (AMP), the American Society of Clinical Oncology (ASCO), and the College of American Pathologists (CAP) published the first consensus guidelines for cell-specific variants cataloging and clinical annotations. Methods AMP–ASCO–CAP recommended sources and information were downloaded and used as follows: relative knowledge in oncology clinical practice guidelines; approved, investigative or preclinical drugs; supporting literature and each gene-tumor site correlation. All information was homogenized into a single knowledgebase. Finally, we incorporated the consensus recommendations into a new computational method. Results A subset of cancer genetic variants was manually curated to benchmark our method and well-known computational algorithms. We applied the new method on freely available tumor-specific databases to produce a clinically actionable cancer somatic variants (CACSV) dataset in an easy-to-integrate format for most clinical analytical workflows. The research also showed the current challenges and limitations of using different classification systems or computational methods. Conclusion CACSV is a step toward cell-specific genetic variants standardized interpretation as it is readily adaptable by most clinical laboratory pipelines for somatic variants clinical annotations. CACSV is freely accessible at (https://github.com/tsobahytm/CACSV/tree/main/dataset).

The phosphatidylinositide 3-kinases (PI3K) and mammalian target of rapamycin-1 (mTOR1) are two key targets for anti-cancer therapy. Predicting the response of the PI3K/AKT/mTOR1 signalling pathway to targeted therapy is made difficult because of network complexities. Systems biology models can help explore those complexities but the value of such models is dependent on accurate parameterisation. Motivated by a need to increase accuracy in kinetic parameter estimation, and therefore the predictive power of the model, we present a framework to integrate kinetic data from enzyme assays into a unified enzyme kinetic model. We present exemplar kinetic models of PI3K and mTOR1, calibrated on in vitro enzyme data and founded on Michaelis-Menten (MM) approximation. We describe the effects of an allosteric mTOR1 inhibitor (Rapamycin) and ATP-competitive inhibitors (BEZ2235 and LY294002) that show dual inhibition of mTOR1 and PI3K. We also model the kinetics of phosphatase and tensin homolog (PTEN), which modulates sensitivity of the PI3K/AKT/mTOR1 pathway to these drugs. Model validation with independent data sets allows investigation of enzyme function and drug dose dependencies in a wide range of experimental conditions. Modelling of the mTOR1 kinetics showed that Rapamycin has an IC 50 independent of ATP concentration and that it is a selective inhibitor of mTOR1 substrates S6K1 and 4EBP1: it retains 40% of mTOR1 activity relative to 4EBP1 phosphorylation and inhibits completely S6K1 activity. For the dual ATP-competitive inhibitors of mTOR1 and PI3K, LY294002 and BEZ235, we derived the dependence of the IC 50 on ATP concentration that allows prediction of the IC 50 at different ATP concentrations in enzyme and cellular assays. Comparison of the drug effectiveness in enzyme and cellular assays showed that some features of these drugs arise from signalling modulation beyond the on-target action and MM approximation and require a systems-level consideration of the whole PI3K/PTEN/AKT/mTOR1 network in order to understand mechanisms of drug sensitivity and resistance in different cancer cell lines. We suggest that using these models in systems biology investigation of the PI3K/AKT/mTOR1 signalling in cancer cells can bridge the gap between direct drug target action and the therapeutic response to these drugs and their combinations.

The combined use of computational models and experimental data is an emerging tool in systems biology to help explain the mechanism of resistance to various protein inhibitors in oncological medicine. Improved understanding of the signaling network dynamics may offer novel approaches to overcoming drug resistance. The aim of this study was to analyze the signaling dynamics of the PI3K/Akt/mTOR pathway in ovarian cancer cells after treatment with different PI3K/mTOR inhibitors. Systems computational approach was applied to unwind the role of multiple feedbacks in PI3K/Akt/mTOR pathway in the responses of cancer cells bearing mutations in PTEN, PIK3CA and HER2 overexpression. In this study, a set of PI3K (LY294002), mTOR (Rapamycin) and dual PI3K/mTOR (BEZ235) inhibitors were applied to a panel of ovarian cancer cell lines under heregulin driven conditions over 10 point time-course treatment, in order to assess the association of growth inhibition with pathway activation. The evaluation of expression levels of various proteins and phospho-proteins was assessed using reverse phase protein array (RPPA) as a high throughput proteomic analysis to investigate the dynamic changes in response to the inhibitors. In part, PTEN expression status was analyzed as biomarker of the feedback-dependent pAkt activation in response to mTOR inhibition by rapamycin in different cell lines. The three inhibitors displayed a significant inhibition in cell proliferation on all the ovarian cancer cell lines following a 5-day treatment. Our results have shown a significant positive correlation between the signaling dynamics of phosphorylated Src (Tyr527) and phosphorylated PTEN (Ser380/Thr382/383) upon treatment with the drugs, consistent with the role of Src in the inactivation of PTEN. Additionally, we demonstrate that PI3K plays a role in the activation of RAS pathway, as phosphorylated cRaf (Ser259 and Ser338) and phosphorylated mTOR (Ser2481) were upregulated and showed a significant positive correlation in their signaling dynamics. Although, all the cell lines have shown a significant inhibition to cell proliferation following a 5-day treatment with the inhibitors, the signaling expression of 12 different phospho-proteins within the PI3K/mTOR and MAPK pathways differ. Rapamycin has been shown to downregulate the expression of phospho-mTOR (Ser2448) and its substrates, phospho-4E-BP1 and phospho-S6K1 but has shown an upregulation in phospho-Akt (Ser473), confirming the existence of the negative feedback loop, S6K1-Akt, upon treatment. BEZ235 presented the highest efficacy in inhibiting cell proliferation in all the ovarian cancer cell lines but failed to downregulate the phosphorylation of ERK. Due to the high overexpression of erbB2 that may cause HER2 homodimerization, the signaling expression of the ovarian cancer cell line, SKOV3, showed upregulation in all the measured phospho-proteins. We propose an extended model of PI3K/Akt/mTOR pathway in ovarian cancer, based on theoretical modeling and experimental data, highlighting possible resistance compensatory mechanisms within the pathway and with its parallel cross-talk pathway MAPK. The model highlights potential novel protein interactions and feedback loops, offering strategies for combinatorial anti-cancer therapy. Citation Format: Ghassan Tashkandi, Alexey Goltsov, Peter Mullen, Jim Bown, David Harrison, Simon Langdon. Understanding resistance mechanisms to PI3K/mTOR inhibitors in ovarian cancer model systems. [abstract]. In: Proceedings of the AACR Special Conference: Targeting the PI3K-mTOR Network in Cancer; Sep 14-17, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(7 Suppl):Abstract nr B33.