The interaction of tumor cells with their environment and particularly the immune system is increasingly recognized as a key factor of tumorigenesis and tumor progression. However, the expression levels of genes alone cannot capture directly the complexity of these interactions, and a more elaborated framework is necessary. We propose a new cell-cell interactions (CCIs) feature space based on scRNA-Seq data of multiple patient samples to classify cancers into subtypes at the patient level. Specifically, we transform gene expression profiles into a redefined feature space that takes into account interactions between ligands and receptors across all possible pairs of cell types for each patient. To handle the ultrahigh dimensionality arising from the hundreds of thousands of CCIs, we propose an ensemble feature selection algorithm to prioritize the most discriminatory CCIs. Our machine learning pipeline identifies a set of CCIs that accurately classifies cancer samples into subtypes. We apply our computational framework, named pCCI, to the Pancreatic Ductal Adenocarcinoma and Glioblastoma Multiforme subtypes classification, achieving high classification performance. Our work provides compelling evidence that the CCI space can successfully reveal differences in the microenvironment of different cancer subtypes and identify novel pharmacological targets.

Deep learning methods for RNA sequencing data have exploded in the recent years due to the advent of singlecell RNA sequencing (scRNA-seq), which enables the study of multiple cells per-patient simultaneously. However, in the case of rare cell types, data scarcity continues to exist, posing several challenges, while preventing the exploitation of deep learning models’ full predictive power. Generating realistic synthetic cells to augment the data could allow for more informative subsequent downstream analyses. Herein, we introduce Mask-cscGAN, a conditional generative adversarial network (GAN) that generates realistic synthetic cells with desired characteristics managing also to model genes’ sparsity through learning a mask of zeros. Employed for the augmentation of a glioblastoma multiforme (GBM) malignant cells dataset, Mask-cscGAN generates realistic synthetic cells of desired cancer subtypes. Generating cells of a rare cancer subtype, Mask-cscGAN improves the classification performance of the rare cancer subtype by 12.29%. MaskcscGAN is the first to generate realistic synthetic cells belonging to specified cancer subtypes, and augmentation with MaskcscGAN outperforms state-of-the-art methods in rare cancer subtype classification.

MOTIVATION: Biomarker discovery is one of the most frequent pursuits in bioinformatics and is crucial for precision medicine, disease prognosis, and drug discovery. A common challenge of biomarker discovery applications is the low ratio of samples over features for the selection of a reliable not-redundant subset of features, but despite the development of efficient tree-based classification methods, such as the extreme gradient boosting (XGBoost), this limitation is still relevant. Moreover, existing approaches for optimizing XGBoost do not deal effectively with the class imbalance nature of the biomarker discovery problems, and the presence of multiple conflicting objectives, since they focus on the training of a single-objective model. In the current work, we introduce MEvA-X, a novel hybrid ensemble for feature selection (FS) and classification, combining a niche-based multiobjective evolutionary algorithm (EA) with the XGBoost classifier. MEvA-X deploys a multiobjective EA to optimize the hyperparameters of the classifier and perform FS, identifying a set of Pareto-optimal solutions and optimizing multiple objectives, including classification and model simplicity metrics. RESULTS: The performance of the MEvA-X tool was benchmarked using one omics dataset coming from a microarray gene expression experiment, and one clinical questionnaire-based dataset combined with demographic information. MEvA-X tool outperformed the state-of-the-art methods in the balanced categorization of classes, creating multiple low-complexity models and identifying important nonredundant biomarkers. The best-performing run of MEvA-X for the prediction of weight loss using gene expression data yields a small set of blood circulatory markers which are sufficient for this precision nutrition application but need further validation. AVAILABILITY AND IMPLEMENTATION: https://github.com/PanKonstantinos/MEvA-X.