Proteomics-based interrogation of the kinome and its implications for precision oncology

Non-kinase off-target inhibitory activities of clinically-relevant kinase inhibitors

Protein kinases are responsible for a myriad of cellular functions, such as cell cycle, apoptosis, and proliferation. Because of this, kinases make excellent targets for therapeutics. During the process to identify clinical kinase inhibitor candidates, kinase selectivity profiles of lead inhibitors are typically obtained. Such kinome selectivity screening could identify crucial kinase anti-targets that might contribute to drug toxicity and/or reveal additional kinase targets that potentially contribute to the efficacy of the compound via kinase polypharmacology. In addition to kinome panel screening, practitioners also obtain the inhibition profiles of a few non-kinase targets, such as ion-channels and select GPCR targets to identify compounds that might possess potential liabilities. Often ignored is the possibility that identified kinase inhibitors might also inhibit or bind to the other proteins (greater than 20,000) in the cell that are not kinases, which may be relevant to toxicity or even additional mode of drug action. This review highlights various inhibitors, which have been approved by the FDA or are currently undergoing clinical trials, that also inhibit other non-kinase targets. The binding poses of the drugs in the binding sites of the target kinases and off-targets are analyzed to understand if the same features of the compounds are critical for the polypharmacology.

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

Proteome-based molecular subtyping and therapeutic target prediction in gastric cancer

Different molecular classifications for gastric cancer (GC) have been proposed based on multi-omics platforms with the long-term goal of improved precision treatment. However, the GC (phospho)proteome remains incompletely characterized, particularly at the level of tyrosine phosphorylation. In addition, previous multiomics-based stratification of patient cohorts has lacked identification of corresponding cell line models and comprehensive validation of broad or subgroup-selective therapeutic targets. To address these knowledge gaps, we applied a reverse approach, undertaking the most comprehensive (phospho)proteomic analysis of GC cell lines to date and cross-validating this using publicly available data. Mass spectrometry (MS)-based (phospho)proteomic and tyrosine phosphorylation datasets were subjected to individual or integrated clustering to identify subgroups that were subsequently characterized in terms of enriched molecular processes and pathways. Significant congruence was detected between cell line proteomic and specific patient-derived transcriptomic subclassifications. Many protein kinases exhibiting 'outlier' expression or phosphorylation in the cell line dataset exhibited genomic aberrations in patient samples and association with poor prognosis, with casein kinase I isoform delta/epsilon (CSNK1D/E) being experimentally validated as potential therapeutic targets. Src family kinases were predicted to be commonly hyperactivated in GC cell lines, consistent with broad sensitivity to the next-generation Src inhibitor eCF506. In addition, phosphoproteomic and integrative clustering segregated the cell lines into two subtypes, with epithelial-mesenchyme transition (EMT) and proliferation-associated processes enriched in one, designated the EMT subtype, and metabolic pathways, cell-cell junctions, and the immune response dominating the features of the other, designated the metabolism subtype. Application of kinase activity prediction algorithms and interrogation of gene dependency and drug sensitivity databases predicted that the mechanistic target of rapamycin kinase (mTOR) and dual specificity mitogen-activated protein kinase kinase 2 (MAP2K2) represented potential therapeutic targets for the EMT and metabolism subtypes, respectively, and this was confirmed using selective inhibitors. Overall, our study provides novel, in-depth insights into GC proteomics, kinomics, and molecular taxonomy and reveals potential therapeutic targets that could provide the basis for precision treatments.

A Semiautomated Proteomics and Phosphoproteomics Protocol for the Identification of Novel Therapeutic Targets and Predictive Biomarkers in In Vivo Xenograft Models of Pediatric Cancers

Genomic profiling has identified therapeutic targets for precision treatment of certain cancers, but many patients lack actionable mutations. Additional omics approaches, like proteomics and phosphoproteomics, are essential for comprehensive mapping of cancer-associated molecular phenotypes. In vivo models, such as cell line and patient-derived xenografts (PDX), offer valuable insights into cancer biology and treatment strategies.This chapter presents a semiautomated high-throughput workflow for integrated proteomics and phosphoproteomics analysis on the Kingfish platform coupled with MagReSyn® Zr-IMAC HP. It enhances protein extraction from in vivo xenograft samples and provides better insights into cancers with poor prognosis. The approach successfully identified over 11,000 unique phosphosites and ~6000 proteins in SJSA-1 pediatric osteosarcoma xenografts, demonstrating its efficacy. This workflow is a valuable tool for studying tumor biology and developing precision oncology strategies.

Clustering of colon, lung, and other cancer susceptibility genes with protein tyrosine phosphatases and protein kinases in multiple short genomic regions

Interactions of large gene families are poorly understood. We found that human, mouse, and rat colon and lung cancer susceptibility genes, presently considered as separate gene families, were frequently pairwise linked. The orthologous mouse map positions of 142 of 159 early discovered colon and lung cancer susceptibility genes formed 41 genomic clusters conserved >70 million years. These linked gene pairs concordantly affected both tumors and their majority was linked with two other gene families - protein tyrosine phosphatases and cancer driver protein kinases. 25% of both protein tyrosine phosphatases and protein kinases mapped <1 cM from a colon or lung cancer susceptibility gene, and 50% in <3 cM. Similar linkage was detected with most other human susceptibility genes that controlled 29 different cancer types. This concentration of tumor susceptibility genes with protein tyrosine phosphatases and driver protein kinases in multiple relatively short genomic regions suggests their possible functional diversity.

Shifting Gears in Precision Oncology-Challenges and Opportunities of Integrative Data Analysis

For decades, research relating to modification of host immunity towards antitumor response activation has been ongoing, with the breakthrough discovery of immune-checkpoint blockers. Several biomarkers with potential predictive value have been reported in recent studies for these novel therapies. However, with the plethora of therapeutic options existing for a given cancer entity, modern oncology is now being confronted with multifactorial interpretation to devise “the best therapy” for the individual patient. Into the bargain come the multiverse guidelines for established and emerging diagnostic biomarkers, as well as the complex interplay between cancer cells and tumor microenvironment, provoking immense challenges in the therapy decision-making process. Through this review, we present various molecular diagnostic modalities and techniques, such as genomics, immunohistochemistry and quantitative image analysis, which have the potential of becoming powerful tools in the development of an optimal treatment regime when analogized with patient characteristics. We will summarize the underlying complexities of these methods and shed light upon the necessary considerations and requirements for data integration. It is our hope to provide compelling evidence to emphasize on the need for inclusion of integrative data analysis in modern cancer therapy, and thereupon paving a path towards precision medicine and better patient outcomes.