Background: Germline mutations in the BRCA1 or BRCA2 (BRCA) genes predispose to hereditary breast and ovarian cancer and, mostly in the case of BRCA2, are also prevalent in cases of pancreatic and prostate malignancies. Tumours from these patients tend to lose both copies of the wild-type BRCA gene, which makes them exquisitely sensitive to platinum drugs and poly(ADP-ribose) polymerase inhibitors (PARPi), treatments of choice in these disease settings. Reversion secondary mutations with the capacity of restoring BRCA protein expression have been documented in the literature as bona fide mechanisms of resistance to these treatments. Patients and methods: We analysed published sequencing data of BRCA genes (from tumour or circulating tumour DNA) in 327 patients with tumours harbouring mutations in BRCA1 or BRCA2 (234 patients with ovarian cancer, 27 with breast cancer, 13 with pancreatic cancer, 11 with prostate cancer and 42 with a cancer of unknown origin) that progressed on platinum or PARPi treatment. Results: We describe 269 cases of reversion mutations in 86 patients in this cohort (26.0%). Detailed analyses of the reversion events highlight that most amino acid sequences encoded by exon 11 in BRCA1 and BRCA2 are dispensable to generate resistance to platinum or PARPi, whereas other regions are more refractory to sizeable amino acid losses. They also underline the key role of mutagenic end-joining DNA repair pathways in generating reversions, especially in those affecting BRCA2, as indicated by the significant accumulation of DNA sequence microhomologies surrounding deletions leading to reversion events. Conclusions: Our analyses suggest that pharmacological inhibition of DNA end-joining repair pathways could improve durability of drug treatments by preventing the acquisition of reversion mutations in BRCA genes. They also highlight potential new therapeutic opportunities when reversions result in expression of hypomorphic versions of BRCA proteins, especially with agents targeting the response to DNA replication stress.

Synthetic lethality is a promising concept in cancer research, potentially opening new possibilities for the development of more effective and selective treatments. Here, we present a computational method to predict and exploit synthetic lethality in cancer metabolism. Our approach relies on the concept of genetic minimal cut sets and gene expression data, demonstrating a superior performance to previous approaches predicting metabolic vulnerabilities in cancer. Our genetic minimal cut set computational framework is applied to evaluate the lethality of ribonucleotide reductase catalytic subunit M1 (RRM1) inhibition in multiple myeloma. We present a computational and experimental study of the effect of RRM1 inhibition in four multiple myeloma cell lines. In addition, using publicly available genome-scale loss-of-function screens, a possible mechanism by which the inhibition of RRM1 is effective in cancer is established. Overall, our approach shows promising results and lays the foundation to build a novel family of algorithms to target metabolism in cancer.

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell-line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell-line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology.

Background Schlafen 11 (SLFN11) has been linked with response to DNA-damaging agents (DDA) and PARP inhibitors. An in-depth understanding of several aspects of its role as a biomarker in cancer is missing, as is a comprehensive analysis of the clinical significance of SLFN11 as a predictive biomarker to DDA and/or DNA damage-response inhibitor (DDRi) therapies. Methods We used a multidisciplinary effort combining specific immunohistochemistry, pharmacology tests, anticancer combination therapies and mechanistic studies to assess SLFN11 as a potential biomarker for stratification of patients treated with several DDA and/or DDRi in the preclinical and clinical setting. Results SLFN11 protein associated with both preclinical and patient treatment response to DDA, but not to non-DDA or DDRi therapies, such as WEE1 inhibitor or olaparib in breast cancer. SLFN11-low/absent cancers were identified across different tumour types tested. Combinations of DDA with DDRi targeting the replication-stress response (ATR, CHK1 and WEE1) could re-sensitise SLFN11-absent/low cancer models to the DDA treatment and were effective in upper gastrointestinal and genitourinary malignancies. Conclusion SLFN11 informs on the standard of care chemotherapy based on DDA and the effect of selected combinations with ATR, WEE1 or CHK1 inhibitor in a wide range of cancer types and models.

Motivation The development of computational tools exploiting -omics data and high-quality genome-scale metabolic networks for the identification of novel drug targets is a relevant topic in Systems Medicine. Metabolic Transformation Algorithm (MTA) is one of these tools, which aims to identify targets that transform a disease metabolic state back into a healthy state, with potential application in any disease where a clear metabolic alteration is observed. Results Here, we present a robust extension to MTA (rMTA), which additionally incorporates a worst-case scenario analysis and minimization of metabolic adjustment to evaluate the beneficial effect of gene knockouts. We show that rMTA complements MTA in the different datasets analyzed (gene knockout perturbations in different organisms, Alzheimer’s disease and prostate cancer), bringing a more accurate tool for predicting therapeutic targets. Availability and implementation rMTA is freely available on The Cobra Toolbox: https://opencobra.github.io/cobratoolbox/latest/. Supplementary information Supplementary data are available at Bioinformatics online.

Herein, we provide a novel, context-specific reconstruction procedure based on metaproteomic and taxonomic data. Without previous knowledge of a high-quality, genome-scale metabolic networks for each different member in a bacterial community, we propose a meta-network approach, where the expression levels and taxonomic assignments of proteins are used as the most relevant clues for inferring an active set of reactions. Our approach was applied to draft the context-specific metabolic networks of two different naphthalene-enriched communities derived from an anthropogenically influenced, polyaromatic hydrocarbon contaminated soil, with (CN2) or without (CN1) bio-stimulation. We were able to capture the overall functional differences between the two conditions at the metabolic level and predict an important activity for the fluorobenzoate degradation pathway in CN1 and for geraniol metabolism in CN2. Experimental validation was conducted, and good agreement with our computational predictions was observed. We also hypothesize different pathway organizations at the organismal level, which is relevant to disentangle the role of each member in the communities. The approach presented here can be easily transferred to the analysis of genomic, transcriptomic and metabolomic data.

Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).

With the emergence of metabolic networks, novel mathematical pathway concepts were introduced in the past decade, aiming to go beyond canonical maps. However, the use of network-based pathways to interpret 'omics' data has been limited owing to the fact that their computation has, until very recently, been infeasible in large (genome-scale) metabolic networks. In this review article, we describe the progress made in the past few years in the field of network-based metabolic pathway analysis. In particular, we review in detail novel optimization techniques to compute elementary flux modes, an important pathway concept in this field. In addition, we summarize approaches for the integration of metabolic pathways with gene expression data, discussing recent advances using network-based pathway concepts.