Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data

Pharmacological approaches to understanding protein kinase signaling networks

Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.

Host kinase regulation of Plasmodium vivax dormant and replicating liver stages

Upon transmission to the liver, Plasmodium vivax parasites form replicating schizonts, which continue to initiate blood-stage infection, or dormant hypnozoites that reactivate weeks to months after initial infection. P. vivax phenotypes in the field vary significantly, including the ratio of schizonts to hypnozoites formed and the frequency and timing of relapse. Evidence suggests that both parasite genetics and environmental factors underly this heterogeneity. We previously demonstrated that data on the effect of a panel of kinase inhibitors with overlapping targets on Plasmodium liver stage infection, in combination with a computational approach called kinase regression (KiR), can be used to uncover novel host regulators of infection. Here, we applied KiR to evaluate the extent to which P. vivax liver-stage parasites are susceptible to changes in host kinase activity. We identified a role for a subset of host kinases in regulating schizont and hypnozoite infection and schizont size and characterized overlap as well as variability in host phosphosignaling dependencies between parasite forms and across multiple patient isolates. Striking, our data point to variability in host dependencies across P. vivax isolates, suggesting one possible origin of the heterogeneity observed across P. vivax in the field.

miRNAs in the Box: Potential Diagnostic Role for Extracellular Vesicle-Packaged miRNA-27a and miRNA-128 in Breast Cancer

Circulating extracellular vesicle (EV)-derived microRNAs (miRNAs) are now considered the next generation of cancer "theranostic" tools, with strong clinical relevance. Although their potential in breast cancer diagnosis has been widely reported, further studies are still required to address this challenging issue. The present study examined the expression profiles of EV-packaged miRNAs to identify novel miRNA signatures in breast cancer and verified their diagnostic accuracy. Circulating EVs were isolated from healthy controls and breast cancer patients and characterized following the MISEV 2018 guidelines. RNA-sequencing and real-time PCR showed that miRNA-27a and miRNA-128 were significantly down-regulated in patient-derived EVs compared to controls in screening and validation cohorts. Bioinformatics analyses of miRNA-target genes indicated several enriched biological processes/pathways related to breast cancer. Receiver operating characteristic (ROC) curves highlighted the ability of these EV-miRNAs to distinguish breast cancer patients from non-cancer controls. According to other reports, the levels of EV-miRNA-27a and EV-miRNA-128 are not associated with their circulating ones. Finally, evidence from the studies included in our systematic review underscores how the expression of these miRNAs in biofluids is still underinvestigated. Our findings unraveled the role of serum EV-derived miRNA-27a and miRNA-128 in breast cancer, encouraging further investigation of these two miRNAs within EVs towards improved breast cancer detection.

Application of High Throughput Technologies in the Development of Acute Myeloid Leukemia Therapy: Challenges and Progress

Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by extensive heterogeneity in genetics, response to therapy and long-term outcomes, making it a prototype example of development for personalized medicine. Given the accessibility to hematologic malignancy patient samples and recent advances in high-throughput technologies, large amounts of biological data that are clinically relevant for diagnosis, risk stratification and targeted drug development have been generated. Recent studies highlight the potential of implementing genomic-based and phenotypic-based screens in clinics to improve survival in patients with refractory AML. In this review, we will discuss successful applications as well as challenges of most up-to-date high-throughput technologies, including artificial intelligence (AI) approaches, in the development of personalized medicine for AML, and recent clinical studies for evaluating the utility of integrating genomics-guided and drug sensitivity testing-guided treatment approaches for AML patients.

Kinomic profile in patient-derived glioma cells during hypoxia reveals c-MET-PI3K dependency for adaptation

Hypoxic microenvironment is a hallmark of solid tumors, especially glioblastoma. The strong reliance of glioma-propagating cells (GPCs) on hypoxia-induced survival advantages is potentially exploitable for drug development. Methods: To identify key signaling pathways for hypoxia adaptation by patient-derived GPCs, we performed a kinase inhibitor profiling by screening 188 small molecule inhibitors against 130 different kinases in normoxia and hypoxia. Potential kinase candidates were prioritized for in vitro and in vivo investigations using a ranking algorithm that integrated information from the kinome connectivity network and estimated patients' survival based on expression status. Results: Hypoxic drug screen highlighted extensive modifications of kinomic landscape and a crucial functionality of c-MET-PI3K. c-MET inhibitors diminished phosphorylation of c-MET and PI3K in GPCs subjected to hypoxia, suggesting its role in the hypoxic adaptation of GPCs. Mechanistically, the inhibition of c-MET and PI3K impaired antioxidant defense, leading to oxidative catastrophe and apoptosis. Repurposed c-MET inhibitors PF04217903 and tivantinib exhibited hypoxic-dependent drug synergism with temozolomide, resulting in reduced tumor load and growth of GPC xenografts. Detailed analysis of bulk and single-cell glioblastoma transcriptomes associates the cellular subpopulation over-expressing c-MET with inflamed, hypoxic, metastatic, and stem-like phenotypes. Conclusions: Thus, our "bench to bedside (the use of patient-derived GPCs and xenografts for basic research) and back (validation with independent glioblastoma transcriptome databases)" analysis unravels the novel therapeutic indications of c-MET and PI3K/Akt inhibitors for the treatment of glioblastoma, and potentially other cancers, in the hypoxic tumor microenvironment.

Exploiting vulnerabilities in cancer signalling networks to combat targeted therapy resistance

Drug resistance remains one of the greatest challenges facing precision oncology today. Despite the vast array of resistance mechanisms that cancer cells employ to subvert the effects of targeted therapy, a deep understanding of cancer signalling networks has led to the development of novel strategies to tackle resistance both in the first-line and salvage therapy settings. In this review, we provide a brief overview of the major classes of resistance mechanisms to targeted therapy, including signalling reprogramming and tumour evolution; our discussion also focuses on the use of different forms of polytherapies (such as inhibitor combinations, multi-target kinase inhibitors and HSP90 inhibitors) as a means of combating resistance. The promise and challenges facing each of these polytherapies are elaborated with a perspective on how to effectively deploy such therapies in patients. We highlight efforts to harness computational approaches to predict effective polytherapies and the emerging view that exceptional responders may hold the key to better understanding drug resistance. This review underscores the importance of polytherapies as an effective means of targeting resistance signalling networks and achieving durable clinical responses in the era of personalised cancer medicine.

Identifying host regulators and inhibitors of liver stage malaria infection using kinase activity profiles

Plasmodium parasites have extensive needs from their host hepatocytes during the obligate liver stage of infection, yet there remains sparse knowledge of specific host regulators. Here we assess 34 host-targeted kinase inhibitors for their capacity to eliminate Plasmodium yoelii-infected hepatocytes. Using pre-existing activity profiles of each inhibitor, we generate a predictive computational model that identifies host kinases, which facilitate Plasmodium yoelii liver stage infection. We predict 47 kinases, including novel and previously described kinases that impact infection. The impact of a subset of kinases is experimentally validated, including Receptor Tyrosine Kinases, members of the MAP Kinase cascade, and WEE1. Our approach also predicts host-targeted kinase inhibitors of infection, including compounds already used in humans. Three of these compounds, VX-680, Roscovitine and Sunitinib, each eliminate >85% of infection. Our approach is well-suited to uncover key host determinants of infection in difficult model systems, including field-isolated parasites and/or emerging pathogens.

Host kinases facilitate Plasmodium liver stage (LS) infection, but systematic accounting of important players is lacking. Here, the authors use a computational approach and kinase activity profiles to identify host kinase regulators of LS infection and drugs that could eliminate parasite burden.

Progress towards a public chemogenomic set for protein kinases and a call for contributions

Protein kinases are highly tractable targets for drug discovery. However, the biological function and therapeutic potential of the majority of the 500+ human protein kinases remains unknown. We have developed physical and virtual collections of small molecule inhibitors, which we call chemogenomic sets, that are designed to inhibit the catalytic function of almost half the human protein kinases. In this manuscript we share our progress towards generation of a comprehensive kinase chemogenomic set (KCGS), release kinome profiling data of a large inhibitor set (Published Kinase Inhibitor Set 2 (PKIS2)), and outline a process through which the community can openly collaborate to create a KCGS that probes the full complement of human protein kinases.

Kinase activity ranking using phosphoproteomics data (KARP) quantifies the contribution of protein kinases to the regulation of cell viability

Cell survival is regulated by a signaling network driven by the activity of protein kinases; however, determining the contribution that each kinase in the network makes to such regulation remains challenging. Here, we report a computational approach that uses mass spectrometry-based phosphoproteomics data to rank protein kinases based on their contribution to cell regulation. We found that the scores returned by this algorithm, which we have termed kinase activity ranking using phosphoproteomics data (KARP), were a quantitative measure of the contribution that individual kinases make to the signaling output. Application of KARP to the analysis of eight hematological cell lines revealed that cyclin-dependent kinase (CDK) 1/2, casein kinase (CK) 2, extracellular signal-related kinase (ERK), and p21-activated kinase (PAK) were the most frequently highly ranked kinases in these cell models. The patterns of kinase activation were cell-line specific yet showed a significant association with cell viability as a function of kinase inhibitor treatment. Thus, our study exemplifies KARP as an untargeted approach to empirically and systematically identify regulatory kinases within signaling networks.

Exploiting receptor tyrosine kinase co-activation for cancer therapy

Studies over the past decade have shown that many cancers have evolved receptor tyrosine kinase (RTK) co-activation as a mechanism to drive tumour progression and limit the lethal effects of therapy. This review summarises the general principles of RTK co-activation and discusses approaches to exploit this phenomenon in cancer therapy and drug discovery. Computational strategies to predict kinase co-dependencies by integrating drug screening data and kinase inhibitor selectivity profiles will also be described. We offer a perspective on the implications of RTK co-activation on tumour heterogeneity and cancer evolution and conclude by surveying emerging computational and experimental approaches that will provide insights into RTK co-activation biology and deliver new developments in effective cancer therapies.

An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer

BACKGROUND

Triple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis.

RESULTS

We integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. We validated our predictions using published and new experimental data.

CONCLUSIONS

In summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies.