Biology subjects

Califano, A.

Publications and source records attributed to Califano, A..

4 recordsLinked to original sources

bioRxiv2018

Elucidating synergistic dependencies in lung adenocarcinoma by proteome-wide signaling-network analysis

Signaling pathway models are largely based on the compilation of literature data from heterogeneous cellular contexts. Indeed, de novo reconstruction of signaling interactions from large-scale molecular profiling is still lagging, compared to similar efforts in transcriptional and protein-protein interaction networks. To address this challenge, we introduce a novel algorithm for the systematic inference of protein kinase pathways, and applied it to published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 TKs and 415 inferred, LUAD-specific substrates, which were validated at >60% accuracy by SILAC assays, including \"novel substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer. This systematic, data-driven model supported drug response prediction on an individual sample basis, including accurate prediction and validation of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, thus contributing to current precision oncology efforts.

systems biology↗

bioRxiv2018

Systematic Elucidation and Validation of OncoProtein-Centric Molecular Interaction Maps

The largely incomplete and tissue-independent nature of cancer pathways represents a key limitation to the ability to elucidate mechanistic determinants of cancer phenotypes and to predict adaptive response to targeted therapy. To address these challenges, we propose replacing canonical cancer pathways with a more accurate, comprehensive, and context-specific architecture - dubbed a Protein-Centric molecular interaction Map (PC-Map) - representing modulators, effectors, and cognate binding-partners of any oncoprotein of interest. To reconstruct these complex molecular architectures de novo, we introduce a novel OncoSig algorithm. Validation of a lung adenocarcinoma specific (LUAD) KRAS-centric PC-Map recapitulated known KRAS biology and, more critically, identified a novel repertoire of proteins eliciting synthetic lethality in KRASG12D LUAD organoid cultures. Showing the generalizable nature of the algorithm, we elucidated PC-Maps for ten recurrently mutated oncoproteins, including KRAS, in distinct tumor contexts. This revealed a highly context-specific nature of cancers regulatory and signaling architectures to an unprecedented degree of resolution.

systems biology↗

bioRxiv2018

Transcriptional deconvolution reveals consistent functional subtypes of pancreatic cancer epithelium and stroma

Bulk tumor tissues comprise intermixed populations of neoplastic cells and multiple lineages of stromal cells. We used laser capture microdissection and RNA sequencing to disentangle the transcriptional programs active in the malignant epithelium and stroma of pancreatic ductal adenocarcinoma (PDA). This led to the development of a new algorithm (ADVOCATE) that accurately predicts the compartment fractions of bulk tumor samples and can computationally purify bulk gene expression data from PDA. We also present novel stromal subtypes, derived from 110 microdissected PDA stroma samples, that were enriched in extracellular matrix- and immune-associated processes. Finally, we applied ADVOCATE to systematically evaluate cross-compartment subtypes spanning four patient cohorts, revealing consistent functional classes and survival associations despite substantial compositional differences.

systems biology↗

bioRxiv2018

Quantitative Assessment of Protein Activity in Orphan Tissues and Single Cells Using the metaVIPER Algorithm

We and others have shown that transition and maintenance of biological states is controlled by master regulator protein, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, a novel algorithm designed to assess protein activity in tissue-independent by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactome. We confirmed the algorithms value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

systems biology↗