Utilizing the Luminex Magnetic Bead-Based Suspension Array for Rapid Multiplexed Phosphoprotein Quantification

A bead-based GPCR phosphorylation immunoassay for high-throughput ligand profiling and GRK inhibitor screening

Analysis of agonist-driven phosphorylation of G protein-coupled receptors (GPCRs) can provide valuable insights into the receptor activation state and ligand pharmacology. However, to date, assessment of GPCR phosphorylation using high-throughput applications has been challenging. We have developed and validated a bead-based immunoassay for the quantitative assessment of agonist-induced GPCR phosphorylation that can be performed entirely in multiwell cell culture plates. The assay involves immunoprecipitation of affinity-tagged receptors using magnetic beads followed by protein detection using phosphorylation state-specific and phosphorylation state-independent anti-GPCR antibodies. As proof of concept, five prototypical GPCRs (MOP, C5a1, D1, SST2, CB2) were treated with different agonizts and antagonists, and concentration-response curves were generated. We then extended our approach to establish selective cellular GPCR kinase (GRK) inhibitor assays, which led to the rapid identification of a selective GRK5/6 inhibitor (LDC8988) and a highly potent pan-GRK inhibitor (LDC9728). In conclusion, this versatile GPCR phosphorylation assay can be used extensively for ligand profiling and inhibitor screening.

A G protein-coupled receptors (GPCRs) phosphorylation assay for cell culture plates can be used for ligand profiling and inhibitor screening, as evidenced by the identification of two GRK inhibitor compounds.

Individualized Prediction of Drug Response and Rational Combination Therapy in NSCLC Using Artificial Intelligence-Enabled Studies of Acute Phosphoproteomic Changes

We hypothesize that the study of acute protein perturbation in signal transduction by targeted anticancer drugs can predict drug sensitivity of these agents used as single agents and rational combination therapy. We assayed dynamic changes in 52 phosphoproteins caused by an acute exposure (1 hour) to clinically relevant concentrations of seven targeted anticancer drugs in 35 non-small cell lung cancer (NSCLC) cell lines and 16 samples of NSCLC cells isolated from pleural effusions. We studied drug sensitivities across 35 cell lines and synergy of combinations of all drugs in six cell lines (252 combinations). We developed orthogonal machine-learning approaches to predict drug response and rational combination therapy. Our methods predicted the most and least sensitive quartiles of drug sensitivity with an AUC of 0.79 and 0.78, respectively, whereas predictions based on mutations in three genes commonly known to predict response to the drug studied, for example, EGFR, PIK3CA, and KRAS, did not predict sensitivity (AUC of 0.5 across all quartiles). The machine-learning predictions of combinations that were compared with experimentally generated data showed a bias to the highest quartile of Bliss synergy scores (P = 0.0243). We confirmed feasibility of running such assays on 16 patient samples of freshly isolated NSCLC cells from pleural effusions. We have provided proof of concept for novel methods of using acute ex vivo exposure of cancer cells to targeted anticancer drugs to predict response as single agents or combinations. These approaches could complement current approaches using gene mutations/amplifications/rearrangements as biomarkers and demonstrate the utility of proteomics data to inform treatment selection in the clinic.

Suspension array for multiplex immunoassay of five common endocrine disrupter chemicals

A low cost and effective indirect competitive method is reported to detect five EDCs, 17-beta-estradiol (E2), estriol (E3), bisphenol A (BPA), diethylstilbestrol (DES), and nonylphenol (NP) simultaneously, based on suspension array technology (SAT). Five kinds of complete antigens (E2-BSA, E3-BSA, BPA-BSA, DES-BPA, NP-BSA) were coupled to different encoding microspheres using purpose-made solutions in our laboratory instead of commercially available amino coupling kits; the method was further optimized for determination and reducing  the cost. Encoding and signaling fluorescence of the particles are determined at 635/532 nm emission wavelengths. High-throughput curves of five EDCs were draw and the limit of detection (LOD) were between 0.0010 ng mL^-1 ~ 0.0070 ng mL^-1. Compared with traditional ELISA methods, the SAT exhibited better specificity and sensitivity. Experiments using spiked milk and tap water samples were also carried out, and the recovery was between 85 and 110%; the results also confirmed good repeatability and reproducibility. It illustrated great potential of the present strategy in the detection of EDCs in actual samples.

SITC cancer immunotherapy resource document: a compass in the land of biomarker discovery

Since the publication of the Society for Immunotherapy of Cancer's (SITC) original cancer immunotherapy biomarkers resource document, there have been remarkable breakthroughs in cancer immunotherapy, in particular the development and approval of immune checkpoint inhibitors, engineered cellular therapies, and tumor vaccines to unleash antitumor immune activity. The most notable feature of these breakthroughs is the achievement of durable clinical responses in some patients, enabling long-term survival. These durable responses have been noted in tumor types that were not previously considered immunotherapy-sensitive, suggesting that all patients with cancer may have the potential to benefit from immunotherapy. However, a persistent challenge in the field is the fact that only a minority of patients respond to immunotherapy, especially those therapies that rely on endogenous immune activation such as checkpoint inhibitors and vaccination due to the complex and heterogeneous immune escape mechanisms which can develop in each patient. Therefore, the development of robust biomarkers for each immunotherapy strategy, enabling rational patient selection and the design of precise combination therapies, is key for the continued success and improvement of immunotherapy. In this document, we summarize and update established biomarkers, guidelines, and regulatory considerations for clinical immune biomarker development, discuss well-known and novel technologies for biomarker discovery and validation, and provide tools and resources that can be used by the biomarker research community to facilitate the continued development of immuno-oncology and aid in the goal of durable responses in all patients.

Perturbation Theory Machine Learning Modeling of Immunotoxicity for Drugs Targeting Inflammatory Cytokines and Study of the Antimicrobial G1 Using Cytometric Bead Arrays

ChEMBL biological activities prediction for 1-5-bromofur-2-il-2-bromo-2-nitroethene (G1) is a difficult task for cytokine immunotoxicity. The current study presents experimental results for G1 interaction with mouse Th1/Th2 and pro-inflammatory cytokines using a cytometry bead array (CBA). In the in vitro test of CBA, the results show no significant differences between the mean values of the Th1/Th2 cytokines for the samples treated with G1 with respect to the negative control, but there are moderate differences for cytokine values between different periods (24/48 h). The experiments show no significant differences between the mean values of the pro-inflammatory cytokines for the samples treated with G1, regarding the negative control, except for the values of tumor necrosis factor (TNF) and Interleukin (IL6) between the group treated with G1 and the negative control at 48 h. Differences occur for these cytokines in the periods (24/48 h). The study confirmed that the antimicrobial G1 did not alter the Th1/Th2 cytokines concentration in vitro in different periods, but it can alter TNF and IL6. G1 promotes free radicals production and activates damage processes in macrophages culture. In order to predict all ChEMBL activities for drugs in other experimental conditions, a ChEMBL data set was constructed using 25 biological activities, 1366 assays, 2 assay types, 4 assay organisms, 2 organisms, and 12 cytokine targets. Molecular descriptors calculated with Rcpi and 15 machine learning methods were used to find the best model able to predict if a drug could be active or not against a specific cytokine, in specific experimental conditions. The best model is based on 120 selected molecular descriptors and a deep neural network with area under the curve of the receiver operating characteristic of 0.904 and accuracy of 0.832. This model predicted 1384 G1 biological activities against cytokines in all ChEMBL data set experimental conditions.

Differences in Signaling Patterns on PI3K Inhibition Reveal Context Specificity in KRAS-Mutant Cancers

It is increasingly appreciated that drug response to different cancers driven by the same oncogene is different and may relate to differences in rewiring of signal transduction. We aimed to study differences in dynamic signaling changes within mutant KRAS (KRAS MT), non-small cell lung cancer (NSCLC), colorectal cancer, and pancreatic ductal adenocarcinoma (PDAC) cells. We used an antibody-based phosphoproteomic platform to study changes in 50 phosphoproteins caused by seven targeted anticancer drugs in a panel of 30 KRAS MT cell lines and cancer cells isolated from 10 patients with KRAS MT cancers. We report for the first time significant differences in dynamic signaling between colorectal cancer and NSCLC cell lines exposed to clinically relevant equimolar concentrations of the pan-PI3K inhibitor pictilisib including a lack of reduction of p-AKTser473 in colorectal cancer cell lines (P = 0.037) and lack of compensatory increase in p-MEK in NSCLC cell lines (P = 0.036). Differences in rewiring of signal transduction between tumor types driven by KRAS MT cancers exist and influence response to combination therapy using targeted agents.