Modeling cancer drug response through drug-specific informative genes.

Recent advances in pharmacogenomics have generated a wealth of data of different types whose analysis have helped in the identification of signatures of different cellular sensitivity/resistance responses to hundreds of chemical compounds. Among the different data types, gene expression has proven to be the more successful for the inference of drug response in cancer cell lines. Although effective, the whole transcriptome can introduce noise in the predictive models, since specific mechanisms are required for different drugs and these realistically involve only part of the proteins encoded in the genome. We analyzed the pharmacogenomics data of 961 cell lines tested with 265 anti-cancer drugs and developed different machine learning approaches for dissecting the genome systematically and predict drug responses using both drug-unspecific and drug-specific genes. These methodologies reach better response predictions for the vast majority of the screened drugs using tens to few hundreds genes specific to each drug instead of the whole genome, thus allowing a better understanding and interpretation of drug-specific response mechanisms which are not necessarily restricted to the drug known targets.

Hierarchical Organization Endows the Kinase Domain with Regulatory Plasticity.

The functional diversity of kinases enables specificity in cellular signal transduction. Yet how more than 500 members of the human kinome specifically receive regulatory inputs and convey information to appropriate substrates-all while using the common signaling output of phosphorylation-remains enigmatic. Here, we perform statistical co-evolution analysis, mutational scanning, and quantitative live-cell assays to reveal a hierarchical organization of the kinase domain that facilitates the orthogonal evolution of regulatory inputs and substrate outputs while maintaining catalytic function. We find that three quasi-independent "sectors"-groups of evolutionarily coupled residues-represent functional units in the kinase domain that encode for catalytic activity, substrate specificity, and regulation. Sector positions impact both disease and pharmacology: the catalytic sector is significantly enriched for somatic cancer mutations, and residues in the regulatory sector interact with allosteric kinase inhibitors. We propose that this functional architecture endows the kinase domain with inherent regulatory plasticity.

Kinome-wide decoding of network-attacking mutations rewiring cancer signaling.

Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks.

Unmasking determinants of specificity in the human kinome.

Protein kinases control cellular responses to environmental cues by swift and accurate signal processing. Breakdowns in this high-fidelity capability are a driving force in cancer and other diseases. Thus, our limited understanding of which amino acids in the kinase domain encode substrate specificity, the so-called determinants of specificity (DoS), constitutes a major obstacle in cancer signaling. Here, we systematically discover several DoS and experimentally validate three of them, named the αC1, αC3, and APE-7 residues. We demonstrate that DoS form sparse networks of non-conserved residues spanning distant regions. Our results reveal a likely role for inter-residue allostery in specificity and an evolutionary decoupling of kinase activity and specificity, which appear loaded on independent groups of residues. Finally, we uncover similar properties driving SH2 domain specificity and demonstrate how the identification of DoS can be utilized to elucidate a greater understanding of the role of signaling networks in cancer (Creixell et al., 2015 [this issue of Cell]).

Enrichment of Leishmania donovani ATP-binding proteins using a staurosporine capture compound.

Trypanosomatid parasites of the genus Leishmania cause severe human diseases collectively termed leishmaniasis. Parasite ATP-binding proteins have emerged as potent targets for chemotherapeutic intervention. However, many parasite-specific ATP-binding proteins may escape current efforts in drug target identification, validation and deconvolution due to the lack of sequence conservation and functional annotation of these proteins in early branching eukaryotic trypanosomatids. Here, we selectively enriched for ATP-binding proteins from Leishmania donovani axenic promastigote and amastigote total protein extracts utilizing a Capture Compound™ (CC) linked to the ATP-competitive inhibitor staurosporine. As judged by in-gel kinase activity assay and competitive inhibition with free staurosporine, the CC specifically enriched for parasite phosphotransferases. Comparative nanoLC-MS(n) analysis identified 70 captured proteins, including 24 conserved protein kinases, and 32 hypothetical proteins with potential ATP-binding function. We identified conserved signature sequence motifs characteristic for staurosporine-binding protein kinases, and identified the hypothetical proteins LinJ.20.0280 and LinJ.09.1630 as novel ATP-binding proteins. Thus, functional enrichment procedures such as described here, combined with bio-informatics analyses and activity assays, provide powerful tools for the discovery of parasite-specific ATP-binding proteins that escape homology-based identification, which can be subsequently targeted for pharmacological intervention. BIOLOGICAL SIGNIFICANCE: Functional enrichment using a Capture Compound™ linked to the ATP-competitive inhibitor staurosporine provides a powerful new tool for the discovery of parasite-specific ATP-binding proteins that escape homology-based identification, which can be subsequently targeted for pharmacological intervention.