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Mukherjee, Sach

Publications and source records attributed to Mukherjee, Sach.

Decoupling of the PI3K pathway via mutation necessitates combinatorial treatment in HER2+ breast cancer

We report here on experimental and theoretical efforts to determine how best to combine drugs that inhibit HER2 and AKT in HER2 + breast cancers. We accomplished this by measuring cellular and molecular responses to lapatinib and the AKT inhibitors (AKT i ) GSK690693 and GSK2141795 in a panel of 22 HER2 + breast cancer cell lines carrying wild type or mutant PIK3CA. We observed that combinations of lapatinib plus AKT i were synergistic in HER2 /PIK3CA mut cell lines but not in HER2 + /PIK3CA wt cell lines. We measured changes in phospho-protein levels in 15 cell lines after treatment with lapatinib, AKT i or lapatinib + AKT i to shed light on the underlying signaling dynamics. This revealed that p-S6RP levels were less well attenuated by lapatinib in HER2 + /PIK3CA mut cells compared to HER2 + /PIK3CA wt cells and that lapatinib + AKT i reduced p-S6RP levels to those achieved in HER2 + /PIK3CA wt cells with lapatinib alone. We also found that that compensatory up-regulation of p-HER3 and p-HER2 is blunted in PIK3CA mut cells following lapatinib + AKT i treatment. Responses of HER2 + SKBR3 cells transfected with lentiviruses carrying control or PIK3CA mut sequences were similar to those observed in HER2 + /PIK3CA mut cell lines but not in HER2 + /PIK3CA wt cell lines. We used a nonlinear ordinary differential equation model to support the idea that PIK3CA mutations act as downstream activators of AKT that blunt lapatinib inhibition of downstream AKT signaling and that the effects of PIK3CA mutations can be countered by combining lapatinib with an AKT i . This combination does not confer substantial benefit beyond lapatinib in HER2 + /PIK3CA wt cells.

60 APPLIED LIFE SCIENCES↗

Causal network inference using biochemical kinetics

Motivation: Networks are widely used as structural summaries of biochemical systems. Statistical estimation of networks is usually based on linear or discrete models. However, the dynamics of biochemical systems are generally non-linear, suggesting that suitable non-linear formulations may offer gains with respect to causal network inference and aid in associated prediction problems. Results: We present a general framework for network inference and dynamical prediction using time course data that is rooted in nonlinear biochemical kinetics. This is achieved by considering a dynamical system based on a chemical reaction graph with associated kinetic parameters. Both the graph and kinetic parameters are treated as unknown; inference is carried out within a Bayesian framework. This allows prediction of dynamical behavior even when the underlying reaction graph itself is unknown or uncertain. Results, based on (i) data simulated from a mechanistic model of mitogen-activated protein kinase signaling and (ii) phosphoproteomic data from cancer cell lines, demonstrate that non-linear formulations can yield gains in causal network inference and permit dynamical prediction and uncertainty quantification in the challenging setting where the reaction graph is unknown. Availability and implementation: MATLAB R2014a software is available to download from warwick.ac.uk/chrisoates.

60 APPLIED LIFE SCIENCES↗