A machine learning and directed network optimization approach to uncover TP53 regulatory patterns.

TP53, the Guardian of the Genome, is the most frequently mutated gene in human cancers and the functional characterization of its regulation is fundamental. To address this we employ two strategies: machine learning to predict the mutation status of TP53from transcriptomic data, and directed regulatory networks to reconstruct the effect of mutations on the transcipt levels of TP53 targets. Using data from established databases (Cancer Cell Line Encyclopedia, The Cancer Genome Atlas), machine learning could predict the mutation status, but not resolve different mutations. On the contrary, directed network optimization allowed to infer the TP53 regulatory profile across: (1) mutations, (2) irradiation in lung cancer, and (3) hypoxia in breast cancer, and we could observe differential regulatory profiles dictated by (1) mutation type, (2) deleterious consequences of the mutation, (3) known hotspots, (4) protein changes, (5) stress condition (irradiation/hypoxia). This is an important first step toward using regulatory networks for the characterization of the functional consequences of mutations, and could be extended to other perturbations, with implications for drug design and precision medicine.

The Challenge of Combining Chemo- and Radiotherapy with Checkpoint Kinase Inhibitors.

Preclinical models of cancer have demonstrated enhanced efficacy of cell-cycle checkpoint kinase inhibitors when used in combination with genotoxic agents. This combination therapy is predicted to be exquisitely toxic to cells with a deficient G1-S checkpoint or cells with a genetic predisposition leading to intrinsic DNA replication stress, as these cancer cells become fully dependent on the intra-S and G2-M checkpoints for DNA repair and cellular survival. Therefore, abolishing remaining cell-cycle checkpoints after damage leads to increased cell death in a tumor cell-specific fashion. However, the preclinical success of these drug combinations is not consistently replicated in clinical trials. Here, we provide a perspective on the translation of preclinical studies into rationally designed clinical studies. We will discuss successes and failures of current treatment combinations and drug regimens and provide a detailed overview of all clinical trials using ATR, CHK1, or WEE1 inhibitors in combination with genotoxic agents. This highlights the need for revised patient stratification and the use of appropriate pharmacodynamic biomarkers to improve the success rate of clinical trials.