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.

The dichotomous role of the glycolytic metabolism pathway in cancer metastasis: Interplay with the complex tumor microenvironment and novel therapeutic strategies.

Cancer metastasis to distant organs is initiated by tumor cells that disseminate from primary heterogeneous tumors. The subsequent growth and survival of tumor metastases depend on different metabolic changes, which constitute one of the enigmatic properties of tumor cells. Aerobic glycolysis, 'the Warburg effect', contributes to tumor energy supply, by oxidizing glucose in a faster manner compared to oxidative phosphorylation, leading to an increased lactate production by lactate dehydrogenase A (LDH-A), which in turn affects the immune response. Surrounding stromal cells contribute to feedback mechanisms further prompting the acquisition of pro-invasive metabolic features. Hence, therapeutic strategies targeting the glycolytic pathway are intensively investigated, with a special interest on their anti-metastatic properties. Various small molecules, such as LDH-A inhibitors, have shown pre-clinical activity against different cancer types, and blocking LDH-A could also help in designing future complimentary therapies. Modulation of specific targets in cells with an altered glycolytic metabolism should indeed result in a milder and distinct toxicity profile, compared to conventional cytotoxic therapy, while a combination treatment with vitamin C leading to increasing reactive oxygen species levels, should further inhibit cancer cell survival and invasion. In this review we describe the impact of metabolic reprogramming in cancer metastasis, the contribution of lactate in this aberrant process and its effect on oncogenic processes. Furthermore, we discuss experimental compounds that target glycolytic metabolism, such as LDH-A inhibitors, and their potential to improve current and experimental therapeutics against metastatic tumors.

Kinase activities in pancreatic ductal adenocarcinoma with prognostic and therapeutic avenues.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited number of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a direct read-out of aberrant signaling and the resultant clinically relevant phenotype. Mass spectrometry (MS)-based proteomics and phosphoproteomics were applied to 42 PDAC tumors. Data encompassed over 19 936 phosphoserine or phosphothreonine (pS/T; in 5412 phosphoproteins) and 1208 phosphotyrosine (pY; in 501 phosphoproteins) sites and a total of 3756 proteins. Proteome data identified three distinct subtypes with tumor intrinsic and stromal features. Subsequently, three phospho-subtypes were apparent: two tumor intrinsic (Phos1/2) and one stromal (Phos3), resembling known PDAC molecular subtypes. Kinase activity was analyzed by the Integrative iNferred Kinase Activity (INKA) scoring. Phospho-subtypes displayed differential phosphorylation signals and kinase activity, such as FGR and GSK3 activation in Phos1, SRC kinase family and EPHA2 in Phos2, and EGFR, INSR, MET, ABL1, HIPK1, JAK, and PRKCD in Phos3. Kinase activity analysis of an external PDAC cohort supported our findings and underscored the importance of PI3K/AKT and ERK pathways, among others. Interestingly, unfavorable patient prognosis correlated with higher RTK, PAK2, STK10, and CDK7 activity and high proliferation, whereas long survival was associated with MYLK and PTK6 activity, which was previously unknown. Subtype-associated activity profiles can guide therapeutic combination approaches in tumor and stroma-enriched tissues, and emphasize the critical role of parallel signaling pathways. In addition, kinase activity profiling identifies potential disease markers with prognostic significance.

Phosphoproteomics guides effective low-dose drug combinations against pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited set of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a readout of aberrant signaling and has the potential to identify new targets and guide treatment decisions. Using two-step sequential phosphopeptide enrichment, we generate a comprehensive phosphoproteome and proteome of nine PDAC cell lines, encompassing more than 20,000 phosphosites on 5,763 phospho-proteins, including 316 protein kinases. By using integrative inferred kinase activity (INKA) scoring, we identify multiple (parallel) activated kinases that are subsequently matched to kinase inhibitors. Compared with high-dose single-drug treatments, INKA-tailored low-dose 3-drug combinations against multiple targets demonstrate superior efficacy against PDAC cell lines, organoid cultures, and patient-derived xenografts. Overall, this approach is particularly more effective against the aggressive mesenchymal PDAC model compared with the epithelial model in both preclinical settings and may contribute to improved treatment outcomes in PDAC patients.

Blood-circulating EV-miRNAs, serum TARC, and quantitative FDG-PET features in classical Hodgkin lymphoma.

Blood-based biomarkers are gaining interest for response evaluation in classical Hodgkin lymphoma (cHL). However, it is unknown how blood-based biomarkers relate to quantitative 18F-FDG-PET features. We correlated extracellular vesicle-associated miRNAs (EV-miRNA), serum TARC, and complete blood count (CBC) with PET features (e.g., metabolic tumor volume [MTV], dissemination and intensity features) in 30 cHL patients at baseline. EV-miR127-3p, EV-miR24-3p, sTARC, and several CBC parameters showed weak to strong correlations with MTV and dissemination features, but not with intensity features. Two other EV-miRNAs only showed weak correlations with PET features. Therefore, blood-based biomarkers may be complementary to PET features, which warrants further exploration of combining these biomarkers in prognostic models.

High-dose intravenous vitamin C, a promising multi-targeting agent in the treatment of cancer.

Mounting evidence indicates that vitamin C has the potential to be a potent anti-cancer agent when administered intravenously and in high doses (high-dose IVC). Early phase clinical trials have confirmed safety and indicated efficacy of IVC in eradicating tumour cells of various cancer types. In recent years, the multi-targeting effects of vitamin C were unravelled, demonstrating a role as cancer-specific, pro-oxidative cytotoxic agent, anti-cancer epigenetic regulator and immune modulator, reversing epithelial-to-mesenchymal transition, inhibiting hypoxia and oncogenic kinase signalling and boosting immune response. Moreover, high-dose IVC is powerful as an adjuvant treatment for cancer, acting synergistically with many standard (chemo-) therapies, as well as a method for mitigating the toxic side-effects of chemotherapy. Despite the rationale and ample evidence, strong clinical data and phase III studies are lacking. Therefore, there is a need for more extensive awareness of the use of this highly promising, non-toxic cancer treatment in the clinical setting. In this review, we provide an elaborate overview of pre-clinical and clinical studies using high-dose IVC as anti-cancer agent, as well as a detailed evaluation of the main known molecular mechanisms involved. A special focus is put on global molecular profiling studies in this respect. In addition, an outlook on future implications of high-dose vitamin C in cancer treatment is presented and recommendations for further research are discussed.

Extracellular vesicle miRNA predict FDG-PET status in patients with classical Hodgkin Lymphoma.

Minimally-invasive tools to assess tumour presence and burden may improve clinical management. FDG-PET (metabolic) imaging is the current gold standard for interim response assessment in patients with classical Hodgkin Lymphoma (cHL), but this technique cannot be repeated frequently. Here we show that microRNAs (miRNA) associated with tumour-secreted extracellular vesicles (EVs) in the circulation of cHL patients may improve response assessment. Small RNA sequencing and qRT-PCR reveal that the relative abundance of cHL-expressed miRNAs, miR-127-3p, miR-155-5p, miR-21-5p, miR-24-3p and let-7a-5p is up to hundred-fold increased in plasma EVs of cHL patients pre-treatment when compared to complete metabolic responders (CMR). Notably, in partial responders (PR) or treatment-refractory cases (n = 10) the EV-miRNA levels remain elevated. In comparison, tumour specific copy number variations (CNV) were detected in cell-free DNA of 8 out of 10 newly diagnosed cHL patients but not in patients with PR. Combining EV-miR-127-3p and/or EV-let-7a-5p levels, with serum TARC (a validated protein cHL biomarker), increases the accuracy for predicting PET-status (n = 129) to an area under the curve of 0.93 (CI: 0.87-0.99), 93.5% sensitivity, 83.8/85.0% specificity and a negative predictive value of 96%. Thus the level of tumour-associated miRNAs in plasma EVs is predictive of metabolic tumour activity in cHL patients. Our findings suggest that plasma EV-miRNA are useful for detection of small residual lesions and may be applied as serial response prediction tool.

To Combine or Not Combine: Drug Interactions and Tools for Their Analysis. Reflections from the EORTC-PAMM Course on Preclinical and Early-phase Clinical Pharmacology.

Combination therapies are used in the clinic to achieve cure, better efficacy and to circumvent resistant disease in patients. Initial assessment of the effect of such combinations, usually of two agents, is frequently performed using in vitro assays. In this review, we give a short summary of the types of analyses that were presented during the Preclinical and Early-phase Clinical Pharmacology Course of the Pharmacology and Molecular Mechanisms Group, European Organization for Research and Treatment on Cancer, that can be used to determine the efficacy of drug combinations. The effect of a combination treatment can be calculated using mathematical equations based on either the Loewe additivity or Bliss independence model, or a combination of both, such as Chou and Talalay's median-drug effect model. Interactions can be additive, synergistic (more than additive), or antagonistic (less than additive). Software packages CalcuSyn (also available as CompuSyn) and Combenefit are designed to calculate the extent of the combined effects. Interestingly, the application of machine-learning methods in the prediction of combination treatments, which can include pharmacogenomic, genetic, metabolomic and proteomic profiles, might contribute to further refinement of combination regimens. However, more research is needed to apply appropriate rules of machine learning methods to ensure correct predictive models.