Tracking intratumoral heterogeneity in glioblastoma via regularized classification of single-cell RNA-Seq data.

摘要: Understanding cellular and molecular heterogeneity in glioblastoma (GBM), the most common aggressive primary brain malignancy, is a crucial step towards development of effective therapies. Besides inter-patient variability, presence multiple cell populations within tumors calls for need to develop modeling strategies able extract signatures driving tumor evolution treatment failure. With advances single-cell RNA Sequencing (scRNA-Seq), can now be dissected at level, unveiling information from their life history clinical implications. We propose classification setting based on GBM scRNA-Seq data, through sparse logistic regression, where different (neoplastic normal cells) are taken as classes. The goal identify gene features discriminating between classes, but also those shared by neoplastic clones. latter will approached via network-based twiner regularizer cells core infiltrating originated periphery, putative disease biomarkers target Our analysis supported literature identification several known players GBM. Moreover, relevance selected genes was confirmed significance survival outcomes bulk RNA-Seq well association with Gene Ontology (GO) biological process terms. presented methodology intended clones, playing similar role clones (including migrating cells), therefore potential targets therapy research. results contribute deeper understanding genetic behind GBM, disclosing novel therapeutic directions accounting heterogeneity.