Dataset Information

High-throughput dynamic BH3 profiling may quickly and accurately predict effective therapies in solid tumors.

ABSTRACT: Despite decades of effort, the sensitivity of patient tumors to individual drugs is often not predictable on the basis of molecular markers alone. Therefore, unbiased, high-throughput approaches to match patient tumors to effective drugs, without requiring a priori molecular hypotheses, are critically needed. Here, we improved upon a method that we previously reported and developed called high-throughput dynamic BH3 profiling (HT-DBP). HT-DBP is a microscopy-based, single-cell resolution assay that enables chemical screens of hundreds to thousands of candidate drugs on freshly isolated tumor cells. The method identifies chemical inducers of mitochondrial apoptotic signaling, a mechanism of cell death. HT-DBP requires only 24 hours of ex vivo culture, which enables a more immediate study of fresh primary tumor cells and minimizes adaptive changes that occur with prolonged ex vivo culture. Effective compounds identified by HT-DBP induced tumor regression in genetically engineered and patient-derived xenograft (PDX) models of breast cancer. We additionally found that chemical vulnerabilities changed as cancer cells expanded ex vivo. Furthermore, using PDX models of colon cancer and resected tumors from colon cancer patients, our data demonstrated that HT-DBP could be used to generate personalized pharmacotypes. Thus, HT-DBP appears to be an ex vivo functional method with sufficient scale to simultaneously function as a companion diagnostic, therapeutic personalization, and discovery tool.

SUBMITTER: Bhola PD

PROVIDER: S-EPMC8023011 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundLong non-coding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. In this study we aimed to establish a lncRNAs classifier to improve the accuracy of recurrence prediction for thymic epithelial tumors (TETs).MethodsTETs RNA sequencing (RNA-seq) data set and the matched clinicopathologic information were downloaded from the Cancer Genome Atlas. Using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a lncRNAs classifier related to recurrence. Functional analysis was conducted to investigate the potential biological processes of the lncRNAs target genes. The independent prognostic factors were identified by Cox regression model. Additionally, predictive ability and clinical application of the lncRNAs classifier were assessed, and compared with the Masaoka staging by receiver operating characteristic (ROC) analysis and decision curve analysis (DCA).ResultsFour recurrence-free survival (RFS)-related lncRNAs were identified, and the classifier consisting of the identified four lncRNAs was able to effectively divide the patients into high and low risk subgroups, with an area under curve (AUC) of 0.796 (three-year RFS) and 0.788 (five-year RFS), respectively. Multivariate analysis indicated that the lncRNAs classifier was an independent recurrence risk factor. The AUC of the lncRNAs classifier in predicting RFS was significantly higher than the Masaoka staging system. Decision curve analysis further demonstrated that the lncRNAs classifier had a larger net benefit than the Masaoka staging system.ConclusionsA lncRNAs classifier for patients with TETs was an independent risk factor for RFS despite other clinicopathologic variables. It generated more accurate estimations of the recurrence probability when compared to the Masaoka staging system, but additional data is required before it can be used in clinical practice.

Project description:Programmed death-ligand 1[PD-(L)1], cytotoxic T-lymphocyte associated protein 4 (CTLA-4), and lymphocyte-activation gene 3 (LAG-3) inhibitors are recent breakthroughs in cancer treatment, however not all patients benefit from it. Thus new therapies are under investigation, such as anti-TIGIT [anti-T-cell immunoreceptor with immunoglobulin (Ig) and immunoreceptor tyrosine-based inhibitory motif domains] antibodies. TIGIT is an immune checkpoint inhibiting lymphocyte T cells by several mechanisms. In vitro models showed its inhibition could restore antitumor response. Furthermore, its association with anti-PD-(L)1 therapies could synergistically improve survival. We carried out a review of the clinical trial about TIGIT referenced in the PubMed database, finding three published clinical trials on anti-TIGIT therapies. Vibostolimab was evaluated in a phase I alone or in combination with pembrolizumab. The combination had an objective response rate of 26% in patients with a non-small-cell lung cancer (NSCLC) naïve of anti-programmed cell death protein 1 (anti-PD-1). Etigilimab was tested in a phase I alone or in combination with nivolumab, but the study was stopped due to business reasons. In the phase II CITYSCAPE trial, tiragolumab demonstrated higher objective response rate and progression-free survival in combination with atezolizumab than atezolizumab alone in advanced PD-L1-high NSCLC. The ClinicalTrials.gov database references 70 trials of anti-TIGIT in patients with cancer, 47 of them with ongoing recruitment. Only seven were phase III, including five about patients with NSCLC, mostly with combination therapy. Data from phase I-II trials highlighted that targeting TIGIT represents a safe therapeutic approach, with an acceptable toxicity profile maintained when adding anti-PD-(L)1 antibodies. Frequent adverse events were pruritus, rash, and fatigue. Grade 3-4 adverse events were reported in nearly one in three patients. Anti-TIGIT antibodies are under development as a novel immunotherapy approach. A promising research area includes the combination with anti-PD-1 therapies in advanced NSCLCs.

Dataset Information

High-throughput dynamic BH3 profiling may quickly and accurately predict effective therapies in solid tumors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets