Tumor-educated blood platelets (TEPs) are implicated as central players in the systemic and local responses to tumor growth, thereby altering their RNA profile. We determined the diagnostic potential ...of TEPs by mRNA sequencing of 283 platelet samples. We distinguished 228 patients with localized and metastasized tumors from 55 healthy individuals with 96% accuracy. Across six different tumor types, the location of the primary tumor was correctly identified with 71% accuracy. Also, MET or HER2-positive, and mutant KRAS, EGFR, or PIK3CA tumors were accurately distinguished using surrogate TEP mRNA profiles. Our results indicate that blood platelets provide a valuable platform for pan-cancer, multiclass cancer, and companion diagnostics, possibly enabling clinical advances in blood-based “liquid biopsies”.
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•Tumors “educate” platelets (TEPs) by altering the platelet RNA profile•TEPs provide a RNA biosource for pan-cancer, multiclass, and companion diagnostics•TEP-based liquid biopsies may guide clinical diagnostics and therapy selection•A total of 100–500 pg of total platelet RNA is sufficient for TEP-based diagnostics
Best et al. show that mRNA sequencing of tumor-educated blood platelets distinguishes cancer patients from healthy individuals with 96% accuracy, differentiates between six primary tumor types of patients with 71% accuracy, and identifies several genetic alterations found in tumors.
Abstract
Kinases are a prime target of drug development efforts with >60 drug approvals in the past two decades. Due to the research into this protein family, a wealth of data has been accumulated ...that keeps on growing. KLIFS—Kinase–Ligand Interaction Fingerprints and Structures—is a structural database focusing on how kinase inhibitors interact with their targets. The aim of KLIFS is to support (structure-based) kinase research through the systematic collection, annotation, and processing of kinase structures. Now, 5 years after releasing the initial KLIFS website, the database has undergone a complete overhaul with a new website, new logo, and new functionalities. In this article, we start by looking back at how KLIFS has been used by the research community, followed by a description of the renewed KLIFS, and conclude with showcasing the functionalities of KLIFS. Major changes include the integration of approved drugs and inhibitors in clinical trials, extension of the coverage to atypical kinases, and a RESTful API for programmatic access. KLIFS is available at the new domain https://klifs.net.
The Landscape of Atypical and Eukaryotic Protein Kinases Kanev, Georgi K.; de Graaf, Chris; de Esch, Iwan J.P. ...
Trends in pharmacological sciences (Regular ed.),
November 2019, 2019-11-00, 20191101, Letnik:
40, Številka:
11
Journal Article
Recenzirano
Odprti dostop
Kinases are attractive anticancer targets due to their central role in the growth, survival, and therapy resistance of tumor cells. This review explores the two primary kinase classes, the eukaryotic ...protein kinases (ePKs) and the atypical protein kinases (aPKs), and provides a structure-centered comparison of their sequences, structures, hydrophobic spines, mutation and SNP hotspots, and inhibitor interaction patterns. Despite the limited sequence similarity between these two classes, atypical kinases commonly share the archetypical kinase fold but lack conserved eukaryotic kinase motifs and possess altered hydrophobic spines. Furthermore, atypical kinase inhibitors explore only a limited number of binding modes both inside and outside the orthosteric binding site. The distribution of genetic variations in both classes shows multiple ways they can interfere with kinase inhibitor binding. This multilayered review provides a research framework bridging the eukaryotic and atypical kinase classes.
Despite their low sequence similarity, most atypical kinases share the same characteristic eukaryotic protein kinase fold.The atypical kinases lack the highly conserved kinase motifs (e.g., the GxGxxG motif) and contain a mirrored catalytic HRD motif.Atypical kinases have altered regulatory and catalytic hydrophobic spines compared with the eukaryotic kinases.SNPs and cancer mutations occurring in the catalytic cleft of the kinases are mutually exclusive in location in both atypical and eukaryotic kinases and can interfere with inhibitor binding.Atypical kinase inhibitors currently explore a limited number of binding modes compared with eukaryotic kinase inhibitors.Selective kinase inhibitors can be designed by rationally exploiting selectivity hotspots or by targeting alternative allosteric binding sites.
Neuroblastoma and other pediatric tumors show a paucity of gene mutations, which has sparked an interest in their epigenetic regulation. Several tumor types include phenotypically divergent cells, ...resembling cells from different lineage development stages. It has been proposed that super-enhancer-associated transcription factor (TF) networks underlie lineage identity, but the role of these enhancers in intratumoral heterogeneity is unknown. Here we show that most neuroblastomas include two types of tumor cells with divergent gene expression profiles. Undifferentiated mesenchymal cells and committed adrenergic cells can interconvert and resemble cells from different lineage differentiation stages. ChIP-seq analysis of isogenic pairs of mesenchymal and adrenergic cells identified a distinct super-enhancer landscape and super-enhancer-associated TF network for each cell type. Expression of the mesenchymal TF PRRX1 could reprogram the super-enhancer and mRNA landscapes of adrenergic cells toward a mesenchymal state. Mesenchymal cells were more chemoresistant in vitro and were enriched in post-therapy and relapse tumors. Two super-enhancer-associated TF networks, which probably mediate lineage control in normal development, thus dominate epigenetic control of neuroblastoma and shape intratumoral heterogeneity.
Diffuse brain infiltration by glioma cells causes detrimental disease progression, but its multicellular coordination is poorly understood. We show here that glioma cells infiltrate the brain ...collectively as multicellular networks. Contacts between moving glioma cells are adaptive epithelial-like or filamentous junctions stabilized by N-cadherin, β-catenin and p120-catenin, which undergo kinetic turnover, transmit intercellular calcium transients and mediate directional persistence. Downregulation of p120-catenin compromises cell-cell interaction and communication, disrupts collective networks, and both the cadherin and RhoA binding domains of p120-catenin are required for network formation and migration. Deregulating p120-catenin further prevents diffuse glioma cell infiltration of the mouse brain with marginalized microlesions as the outcome. Transcriptomics analysis has identified p120-catenin as an upstream regulator of neurogenesis and cell cycle pathways and a predictor of poor clinical outcome in glioma patients. Collective glioma networks infiltrating the brain thus depend on adherens junctions dynamics, the targeting of which may offer an unanticipated strategy to halt glioma progression.
Neuroblastoma is a childhood tumour of the peripheral sympathetic nervous system. The pathogenesis has for a long time been quite enigmatic, as only very few gene defects were identified in this ...often lethal tumour. Frequently detected gene alterations are limited to MYCN amplification (20%) and ALK activations (7%). Here we present a whole-genome sequence analysis of 87 neuroblastoma of all stages. Few recurrent amino-acid-changing mutations were found. In contrast, analysis of structural defects identified a local shredding of chromosomes, known as chromothripsis, in 18% of high-stage neuroblastoma. These tumours are associated with a poor outcome. Structural alterations recurrently affected ODZ3, PTPRD and CSMD1, which are involved in neuronal growth cone stabilization. In addition, ATRX, TIAM1 and a series of regulators of the Rac/Rho pathway were mutated, further implicating defects in neuritogenesis in neuroblastoma. Most tumours with defects in these genes were aggressive high-stage neuroblastomas, but did not carry MYCN amplifications. The genomic landscape of neuroblastoma therefore reveals two novel molecular defects, chromothripsis and neuritogenesis gene alterations, which frequently occur in high-risk tumours.
The blood–brain barrier (BBB) represents a major obstacle to delivering drugs to the central nervous system (CNS), resulting in the lack of effective treatment for many CNS diseases including brain ...cancer. To accelerate CNS drug development, computational prediction models could save the time and effort needed for experimental evaluation. Here, we studied BBB permeability focusing on active transport (influx and efflux) as well as passive diffusion using previously published and self-curated data sets. We created prediction models based on physicochemical properties, molecular substructures, or their combination to understand which mechanisms contribute to BBB permeability. Our results show that features that predicted passive diffusion over membranes overlap with features that explain endothelial permeation of approved CNS-active drugs. We also identified physical properties and molecular substructures that positively or negatively predicted BBB transport. These findings provide guidance toward identifying BBB-permeable compounds by optimally matching physicochemical and molecular properties to BBB transport mechanisms.
Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting.
We provide here a ...proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid. Adverse event frequencies were shown as colors to assess the ratio between individual and combined drug effects. To capture these patterns, we trained a convolutional neural network (CNN) autoencoder using 7,300 single-drug heat maps. In addition, statistical synergy analyses were performed on the basis of BLISS independence or χ2 testing.
The trained CNN model was able to decode patterns, showing that adverse events occur in global rather than isolated and unique patterns. Patterns were not likely to be attributed to disease symptoms given their relatively limited contribution to drug-associated adverse events. Pattern recognition was validated using trial data from ClinicalTrials.gov and drug combination data. We examined the adverse event interactions of 140 drug combinations known to be avoided in the clinic and found that near all of them showed additive rather than synergistic interactions, also when assessed statistically.
Our study provides a framework for analyzing adverse events and suggests that adverse drug interactions commonly result in additive effects with a high level of overlap of adverse event patterns. These real-world insights may advance the implementation of new combination therapies in clinical practice.
Purpose
CXCR4 (over)expression is found in multiple human cancer types, while expression is low or absent in healthy tissue. In glioblastoma it is associated with a poor prognosis and more extensive ...infiltrative phenotype. CXCR4 can be targeted by the diagnostic PET agent
68
GaGa-Pentixafor and its therapeutic counterpart
177
LuLu-Pentixather. We aimed to investigate the expression of CXCR4 in glioblastoma tissue to further examine the potential of these PET agents.
Methods
CXCR4 mRNA expression was examined using the R2 genomics platform. Glioblastoma tissue cores were stained for CXCR4. CXCR4 staining in tumor cells was scored. Stained tissue components (cytoplasm and/or nuclei of the tumor cells and blood vessels) were documented. Clinical characteristics and information on IDH and
MGMT
promoter methylation status were collected. Seven pilot patients with recurrent glioblastoma underwent
68
GaGa-Pentixafor PET; residual resected tissue was stained for CXCR4.
Results
Two large mRNA datasets (
N
= 284;
N
= 540) were assesed. Of the 191 glioblastomas, 426 cores were analyzed using immunohistochemistry. Seventy-eight cores (23 tumors) were CXCR4 negative, while 18 cores (5 tumors) had both strong and extensive staining. The remaining 330 cores (163 tumors) showed a large inter- and intra-tumor variation for CXCR4 expression; also seen in the resected tissue of the seven pilot patients—not directly translatable to
68
GaGa-Pentixafor PET results. Both mRNA and immunohistochemical analysis showed CXCR4 negative normal brain tissue and no significant correlation between CXCR4 expression and IDH or
MGMT
status or survival.
Conclusion
Using immunohistochemistry, high CXCR4 expression was found in a subset of glioblastomas as well as a large inter- and intra-tumor variation. Caution should be exercised in directly translating ex vivo CXCR4 expression to PET agent uptake. However, when high CXCR4 expression can be identified with
68
GaGa-Pentixafor, these patients might be good candidates for targeted radionuclide therapy with
177
LuLu-Pentixather in the future.
Many therapies in clinical trials are based on single drug-single target relationships. To further extend this concept to multi-target approaches using multi-targeted drugs, we developed a machine ...learning pipeline to unravel the target landscape of kinase inhibitors. This pipeline, which we call 3D-KINEssence, uses a new type of protein fingerprints (3D FP) based on the structure of kinases generated through a 3D convolutional neural network (3D-CNN). These 3D-CNN kinase fingerprints were matched to molecular Morgan fingerprints to predict the targets of each respective kinase inhibitor based on available bioactivity data. The performance of the pipeline was evaluated on two test sets: a sparse drug-target set where each drug is matched in most cases to a single target and also on a densely-covered drug-target set where each drug is matched to most if not all targets. This latter set is more challenging to train, given its non-exclusive character. Our model’s root-mean-square error (RMSE) based on the two datasets was 0.68 and 0.8, respectively. These results indicate that 3D FP can predict the target landscape of kinase inhibitors at around 0.8 log units of bioactivity. Our strategy can be utilized in proteochemometric or chemogenomic workflows by consolidating the target landscape of kinase inhibitors.
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