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.

The gene encoding tumor protein p53 is the most frequently mutated gene in human cancer. Mutations in both coding and non-coding regions of TP53 can disrupt the regulatory function of the transcription factor, but the functional impact of different somatic mutations on the global TP53 regulon is complex and poorly understood. To address this, we first proceed with a machine learning (ML) approach, and then propose an integrated computational network modelling approach that reconstructs signalling networks using a comprehensive collection of experimental and predicted regulons, and compares their topology. We evaluate both these approaches in a scrutinized pan-cancer analysis of matched genomics and transcriptomics data from 1,457 cell lines (22 cancer types) and 12,531 clinical samples (54 cancer sub-types). Using a ML approach based on penalized generalized linear regression we were able to predict TP53 mutation, but failed to resolve different mutation types. Thus, to infer the impact of different TP53 mutations we compared the topological characteristics of the optimized and reconstructed (upwards of twenty thousand) gene networks. We demonstrate that by accounting for TP53 mutation characteristics such as i) mutation type (e.g. missense, nonsense), ii) deleterious consequences of the mutation, or iii) mapping to previously identified hotspots, we can infer a much richer understanding of gene expression regulation, than when simply grouping samples based on their mutation/wild type or gene expression status. Our study highlights a powerful strategy exploiting signalling networks to systematically characterize the functional impact of the full spectrum of somatic mutations. This approach can be applied in general to genetic variation, with clear implications for, but not limited to, the biomedical domain and precision medicine.

miRNAs are post-transcriptional regulators of gene expression, controlling biological processes from development to pathogenesis. We asked whether the reshaped functional miRNA landscape in cancers is driven by altered transcription of its precursors, or altered biogenesis and maturation of miRNAs. Integrated analysis of genomic and transcriptomic data in 9,111 samples across 10 cancer types and healthy tissues revealed a recurrent genomic switch from DICER-dependent to non-canonical Argonaute-mediated, DICER-independent, miRNA biogenesis. Experimental validation in AGO2-amplified clinical samples and cancer cell lines confirmed that canonical miRNAs can undergo maturation in a DICER-independent manner, and that elevated Argonaute levels promote selective maturation of the oncogenic miR-106b/25 cluster as shown by the altered ratio of mature miRNA to immature pri-miRNA levels. The preferential maturation of these oncogenic miRNAs, whose processing bypasses DICER1, promotes cancer progression and predicts poor prognosis. This highlights the evolution of non-canonical AGO2-dependent oncomiR processing as a novel driver pathway in cancer.