Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients, yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, the reiterative GAN-based training process allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

Apoptotic cell death can be initiated through the extrinsic and intrinsic signaling pathways. While cell cycle progression promotes the responsiveness to intrinsic apoptosis induced by genotoxic stress or spindle poisons, this has not yet been studied conclusively for extrinsic apoptosis. Here, we combined fluorescence-based time-lapse monitoring of cell cycle progression and cell death execution by long-term time-lapse microscopy with sampling-based mathematical modeling to study cell cycle dependency of TRAIL-induced extrinsic apoptosis in NCI-H460/geminin cells. In particular, we investigated the interaction of cell death timing and progression of cell cycle states. We not only found that TRAIL prolongs cycle progression, but in reverse also that cell cycle progression affects the kinetics of TRAIL-induced apoptosis: Cells exposed to TRAIL in G1 died significantly faster than cells stimulated in S/G2/M. The connection between cell cycle state and apoptosis progression was captured by developing a mathematical model, for which parameter estimation revealed that apoptosis progression decelerates in the second half of the cell cycle. Similar results were also obtained when studying HCT-116 cells. Our results therefore reject the null hypothesis of independence between cell cycle progression and extrinsic apoptosis and, supported by simulations and experiments of synchronized cell populations, suggest that unwanted escape from TRAIL-induced apoptosis can be reduced by enriching the fraction of cells in G1 phase. Besides novel insight into the interrelation of cell cycle progression and extrinsic apoptosis signaling kinetics, our findings are therefore also relevant for optimizing future TRAIL-based treatment strategies.

UNLABELLED: We have developed a web service that provides a comprehensive analysis of the susceptibility of cells to undergo apoptosis in response to an activation of the mitochondrial apoptotic pathway. Based on ordinary differential equations, (pre-determined) protein concentrations and release kinetics of mitochondrial pro-apoptotic factors, a network of 52 reactions and 19 reaction partners can be employed as a tool to display temporal protein profiles, to identify key regulatory proteins and to determine critical threshold concentrations required for the execution of apoptosis in HeLa cancer cells or other cell types. The web service also provides an interactive database function for the deposition of cell-type-specific quantitative data. In addition, the web service provides an output that can be compared directly to experimental results obtained from real-time single-cell experiments, making this a widely applicable systems biology tool for apoptosis and cancer researchers. AVAILABILITY: http://systemsbiology.rcsi.ie/apopto-cell.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.