Predicting treatment outcome using kinome activity profiling in HER2+ breast cancer biopsies.

In this study, we measured the kinase activity profiles of 32 pre-treatment tumor biopsies of HER2-positive breast cancer patients. The aim of this study was to assess the prognostic potential of kinase activity levels, to identify potential mechanisms of resistance and to predict treatment success of HER2-targeted therapy combined with chemotherapy. Indeed, our system-wide kinase activity analysis allowed us to link kinase activity to treatment response. Overall, high kinase activity in the HER2-pathway was associated with good treatment outcome. We found eleven kinases differentially regulated between treatment outcome groups, including well-known players in therapy resistance, such as p38a, ERK, and FAK, and an unreported one, namely MARK1. Lastly, we defined an optimal signature of four kinases in a multiple logistic regression diagnostic test for prediction of treatment outcome (AUC = 0.926). This kinase signature showed high sensitivity and specificity, indicating its potential as predictive biomarker for treatment success of HER2-targeted therapy.

Innovative strategies for measuring kinase activity to accelerate the next wave of novel kinase inhibitors.

The development of protein kinase inhibitors (PKIs) has gained significance owing to their therapeutic potential for diseases like cancer. In addition, there has been a rise in refining kinase activity assays, each possessing unique biological and analytical characteristics crucial for PKI development. However, the PKI development pipeline experiences high attrition rates and approved PKIs exhibit unexploited potential because of variable patient responses. Enhancing PKI development efficiency involves addressing challenges related to understanding the PKI mechanism of action and employing biomarkers for precision medicine. Selecting appropriate kinase activity assays for these challenges can overcome these attrition rate issues. This review delves into the current obstacles in kinase inhibitor development and elucidates kinase activity assays that can provide solutions.

Novel kinome profiling technology reveals drug treatment is patient and 2D/3D model dependent in glioblastoma.

Glioblastoma is the deadliest brain cancer. One of the main reasons for poor outcome resides in therapy resistance, which adds additional challenges in finding an effective treatment. Small protein kinase inhibitors are molecules that have become widely studied for cancer treatments, including glioblastoma. However, none of these drugs have demonstrated a therapeutic activity or brought more benefit compared to the current standard procedure in clinical trials. Hence, understanding the reasons of the limited efficacy and drug resistance is valuable to develop more effective strategies toward the future. To gain novel insights into the method of action and drug resistance in glioblastoma, we established in parallel two patient-derived glioblastoma 2D and 3D organotypic multicellular spheroids models, and exposed them to a prolonged treatment of three weeks with temozolomide or either the two small protein kinase inhibitors enzastaurin and imatinib. We coupled the phenotypic evidence of cytotoxicity, proliferation, and migration to a novel kinase activity profiling platform (QuantaKinome™) that measured the activities of the intracellular network of kinases affected by the drug treatments. The results revealed a heterogeneous inter-patient phenotypic and molecular response to the different drugs. In general, small differences in kinase activation were observed, suggesting an intrinsic low influence of the drugs to the fundamental cellular processes like proliferation and migration. The pathway analysis indicated that many of the endogenously detected kinases were associated with the ErbB signaling pathway. We showed the intertumoral variability in drug responses, both in terms of efficacy and resistance, indicating the importance of pursuing a more personalized approach. In addition, we observed the influence derived from the application of 2D or 3D models in in vitro studies of kinases involved in the ErbB signaling pathway. We identified in one 3D sample a new resistance mechanism derived from imatinib treatment that results in a more invasive behavior. The present study applied a new approach to detect unique and specific drug effects associated with pathways in in vitro screening of compounds, to foster future drug development strategies for clinical research in glioblastoma.

Multiomics profiling of paired primary and recurrent glioblastoma patient tissues.

BACKGROUND: Despite maximal therapy with surgery, chemotherapy, and radiotherapy, glioblastoma (GBM) patients have a median survival of only 15 months. Almost all patients inevitably experience symptomatic tumor recurrence. A hallmark of this tumor type is the large heterogeneity between patients and within tumors itself which relates to the failure of standardized tumor treatment. In this study, tissue samples of paired primary and recurrent GBM tumors were investigated to identify individual factors related to tumor progression. METHODS: Paired primary and recurrent GBM tumor tissues from 8 patients were investigated with a multiomics approach using transcriptomics, proteomics, and phosphoproteomics. RESULTS: In the studied patient cohort, large variations between and within patients are observed for all omics analyses. A few pathways affected at the different omics levels partly overlapped if patients are analyzed at the individual level, such as synaptogenesis (containing the SNARE complex) and cholesterol metabolism. Phosphoproteomics revealed increased STMN1(S38) phosphorylation as part of ERBB4 signaling. A pathway tool has been developed to visualize and compare different omics datasets per patient and showed potential therapeutic drugs, such as abobotulinumtoxinA (synaptogenesis) and afatinib (ERBB4 signaling). Afatinib is currently in clinical trials for GBM. CONCLUSIONS: A large variation on all omics levels exists between and within GBM patients. Therefore, it will be rather unlikely to find a drug treatment that would fit all patients. Instead, a multiomics approach offers the potential to identify affected pathways on the individual patient level and select treatment options.

Tumour kinome re-wiring governs resistance to palbociclib in oestrogen receptor positive breast cancers, highlighting new therapeutic modalities.

Combination of CDK4/6 inhibitors and endocrine therapy improves clinical outcome in advanced oestrogen receptor (ER)-positive breast cancer, however relapse is inevitable. Here, we show in model systems that other than loss of RB1 few gene-copy number (CN) alterations are associated with irreversible-resistance to endocrine therapy and subsequent secondary resistance to palbociclib. Resistance to palbociclib occurred as a result of tumour cell re-wiring leading to increased expression of EGFR, MAPK, CDK4, CDK2, CDK7, CCNE1 and CCNE2. Resistance altered the ER genome wide-binding pattern, leading to decreased expression of 'classical' oestrogen-regulated genes and was accompanied by reduced sensitivity to fulvestrant and tamoxifen. Persistent CDK4 blockade decreased phosphorylation of tuberous sclerosis complex 2 (TSC2) enhancing EGFR signalling, leading to the re-wiring of ER. Kinome-knockdown confirmed dependency on ERBB-signalling and G2/M-checkpoint proteins such as WEE1, together with the cell cycle master regulator, CDK7. Noteworthy, sensitivity to CDK7 inhibition was associated with loss of ER and RB1 CN. Overall, we show that resistance to CDK4/6 inhibitors is dependent on kinase re-wiring and the redeployment of signalling cascades previously associated with endocrine resistance and highlights new therapeutic networks that can be exploited upon relapse after CDK4/6 inhibition.

PaDuA: A Python Library for High-Throughput (Phospho)proteomics Data Analysis.

The increased speed and sensitivity in mass spectrometry-based proteomics has encouraged its use in biomedical research in recent years. Large-scale detection of proteins in cells, tissues, and whole organisms yields highly complex quantitative data, the analysis of which poses significant challenges. Standardized proteomic workflows are necessary to ensure automated, sharable, and reproducible proteomics analysis. Likewise, standardized data processing workflows are also essential for the overall reproducibility of results. To this purpose, we developed PaDuA, a Python package optimized for the processing and analysis of (phospho)proteomics data. PaDuA provides a collection of tools that can be used to build scripted workflows within Jupyter Notebooks to facilitate bioinformatics analysis by both end-users and developers.

A System-wide Approach to Monitor Responses to Synergistic BRAF and EGFR Inhibition in Colorectal Cancer Cells.

Intrinsic and/or acquired resistance represents one of the great challenges in targeted cancer therapy. A deeper understanding of the molecular biology of cancer has resulted in more efficient strategies, where one or multiple drugs are adopted in novel therapies to tackle resistance. This beneficial effect of using combination treatments has also been observed in colorectal cancer patients harboring the BRAF(V600E) mutation, whereby dual inhibition of BRAF(V600E) and EGFR increases antitumor activity. Notwithstanding this success, it is not clear whether this combination treatment is the only or most effective treatment to block intrinsic resistance to BRAF inhibitors. Here, we investigate molecular responses upon single and multi-target treatments, over time, using BRAF(V600E) mutant colorectal cancer cells as a model system. Through integration of transcriptomic, proteomic and phosphoproteomics data we obtain a comprehensive overview, revealing both known and novel responses. We primarily observe widespread up-regulation of receptor tyrosine kinases and metabolic pathways upon BRAF inhibition. These findings point to mechanisms by which the drug-treated cells switch energy sources and enter a quiescent-like state as a defensive response, while additionally compensating for the MAPK pathway inhibition.