Most cancer cells harbor multiple drivers whose epistasis and interactions with expression
context clouds drug and drug combination sensitivity prediction. We constructed a …

During the last decade, our understanding of cancer cell signaling networks has significantly
improved, leading to the development of various targeted therapies that have elicited …

Cancer cells have genetic alterations that often directly affect intracellular protein signaling
processes allowing them to bypass control mechanisms for cell death, growth and division …

Mechanistic models of how single cells respond to different perturbations can help integrate
disparate big data sets or predict response to varied drug combinations. However, the …

Crosstalk and complexity within signaling pathways and their perturbation by oncogenes
limit component-by-component approaches to understanding human disease. Network …

Recent progress in our understanding of cancer mostly relies on the systematic profiling of
patient samples with high-throughput techniques like transcriptomics. With this approach …

Rapid advancements in high-throughput omics technologies and experimental protocols
have led to the generation of vast amounts of scale-specific biomolecular data on cancer …

Patient heterogeneity precludes cancer treatment and drug development; hence,
development of methods for finding prognostic markers for individual treatment is urgently …

Mechanistic models are essential to deepen the understanding of complex diseases at the
molecular level. Nowadays, high-throughput molecular and phenotypic characterizations …

Monotherapy clinical trials with mutation-targeted kinase inhibitors, despite some success in
other cancers, have yet to impact glioblastoma (GBM). Besides insufficient blood–brain …