Approaches to identify kinase dependencies in cancer signalling networks

Implementation of Clinical Phosphoproteomics and Proteomics for Personalized Medicine

The identification of biomarkers for companion diagnostics is revolutionizing the development of treatments tailored to individual patients in different disease areas including cancer. Precision medicine is most frequently based on the detection of genomic markers that correlate with the efficacy of selected targeted therapies. However, since nongenetic mechanisms also contribute to disease biology, there is a considerable interest of using proteomic techniques as additional source of biomarkers to personalize therapies. In this chapter, we describe label-free mass spectrometry methods for proteomic and phosphoproteomic analysis compatible with routine analysis of clinical samples. We also outline bioinformatic pipelines based on statistical learning that use these proteomics datasets as input to quantify kinase activities and predict drug responses in cancer cells.

Computational Analysis of Cholangiocarcinoma Phosphoproteomes Identifies Patient-Specific Drug Targets

Cholangiocarcinoma is a form of hepatobiliary cancer with an abysmal prognosis. Despite advances in our understanding of cholangiocarcinoma pathophysiology and its genomic landscape, targeted therapies have not yet made a significant impact on its clinical management. The low response rates of targeted therapies in cholangiocarcinoma suggest that patient heterogeneity contributes to poor clinical outcome. Here we used mass spectrometry-based phosphoproteomics and computational methods to identify patient-specific drug targets in patient tumors and cholangiocarcinoma-derived cell lines. We analyzed 13 primary tumors of patients with cholangiocarcinoma with matched nonmalignant tissue and 7 different cholangiocarcinoma cell lines, leading to the identification and quantification of more than 13,000 phosphorylation sites. The phosphoproteomes of cholangiocarcinoma cell lines and patient tumors were significantly correlated. MEK1, KIT, ERK1/2, and several cyclin-dependent kinases were among the protein kinases most frequently showing increased activity in cholangiocarcinoma relative to nonmalignant tissue. Application of the Drug Ranking Using Machine Learning (DRUML) algorithm selected inhibitors of histone deacetylase (HDAC; belinostat and CAY10603) and PI3K pathway members as high-ranking therapies to use in primary cholangiocarcinoma. The accuracy of the computational drug rankings based on predicted responses was confirmed in cell-line models of cholangiocarcinoma. Together, this study uncovers frequently activated biochemical pathways in cholangiocarcinoma and provides a proof of concept for the application of computational methodology to rank drugs based on efficacy in individual patients. SIGNIFICANCE: Phosphoproteomic and computational analyses identify patient-specific drug targets in cholangiocarcinoma, supporting the potential of a machine learning method to predict personalized therapies.

Integrated phosphoproteomics and transcriptional classifiers reveal hidden RAS signaling dynamics in multiple myeloma

A major driver of multiple myeloma (MM) is thought to be aberrant signaling, yet no kinase inhibitors have proven successful in the clinic. Here, we employed an integrated, systems approach combining phosphoproteomic and transcriptome analysis to dissect cellular signaling in MM to inform precision medicine strategies. Unbiased phosphoproteomics initially revealed differential activation of kinases across MM cell lines and that sensitivity to mammalian target of rapamycin (mTOR) inhibition may be particularly dependent on mTOR kinase baseline activity. We further noted differential activity of immediate downstream effectors of Ras as a function of cell line genotype. We extended these observations to patient transcriptome data in the Multiple Myeloma Research Foundation CoMMpass study. A machine-learning-based classifier identified surprisingly divergent transcriptional outputs between NRAS- and KRAS-mutated tumors. Genetic dependency and gene expression analysis revealed mutated Ras as a selective vulnerability, but not other MAPK pathway genes. Transcriptional analysis further suggested that aberrant MAPK pathway activation is only present in a fraction of RAS-mutated vs wild-type RAS patients. These high-MAPK patients, enriched for NRAS Q61 mutations, have inferior outcomes, whereas RAS mutations overall carry no survival impact. We further developed an interactive software tool to relate pharmacologic and genetic kinase dependencies in myeloma. Collectively, these predictive models identify vulnerable signaling signatures and highlight surprising differences in functional signaling patterns between NRAS and KRAS mutants invisible to the genomic landscape. These results will lead to improved stratification of MM patients in precision medicine trials while also revealing unexplored modes of Ras biology in MM.

Targeting kinases with thymoquinone: a molecular approach to cancer therapeutics

Kinases are enzymes that are important for cellular functions, but their overexpression has strong connections with carcinogenesis, rendering them important targets for anticancer drugs. Thymoquinone (TQ) is a natural compound with proven anticancer activities, at least in preclinical studies. TQ can target several kinases, including phosphoinositide 3-kinase (PI3K), mitogen-activated protein kinase (MAPK), Janus kinase/signal transducers and activators of transcription (JAK/STAT), polo-like kinase 1 (PLK1), and tyrosine kinase in different cancer cells and animal models. Inhibiting the activity of kinases or suppressing their expression might be among the mechanisms of TQ anticancer activity. In this review, we discuss the role of TQ in kinase regulation in different cancer models.

Computational Methods for Understanding Mass Spectrometry–Based Shotgun Proteomics Data

Computational proteomics is the data science concerned with the identification and quantification of proteins from high-throughput data and the biological interpretation of their concentration changes, posttranslational modifications, interactions, and subcellular localizations. Today, these data most often originate from mass spectrometry–based shotgun proteomics experiments. In this review, we survey computational methods for the analysis of such proteomics data, focusing on the explanation of the key concepts. Starting with mass spectrometric feature detection, we then cover methods for the identification of peptides. Subsequently, protein inference and the control of false discovery rates are highly important topics covered. We then discuss methods for the quantification of peptides and proteins. A section on downstream data analysis covers exploratory statistics, network analysis, machine learning, and multiomics data integration. Finally, we discuss current developments and provide an outlook on what the near future of computational proteomics might bear.