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Human tissue cultures of lung cancer predict patient susceptibility to immune-checkpoint inhibition.

ABSTRACT: Despite novel immunotherapies being approved and established for the treatment of non-small cell lung cancer (NSCLC), ex vivo models predicting individual patients' responses to immunotherapies are missing. Especially immune modulating therapies with moderate response rates urge for biomarkers and/or assays to determine individual prediction of treatment response and investigate resistance mechanisms. Here, we describe a standardized ex vivo tissue culture model to investigate individual tumor responses. NSCLC tissue cultures preserve morphological characteristics of the baseline tumor specimen for up to 12 days ex vivo and also maintain T-cell function for up to 10 days ex vivo. A semi-automated analysis of proliferating and apoptotic tumor cells was used to evaluate tissue responses to the PD-1 inhibitor nivolumab (n = 12), from which two cases could be successfully correlated to the clinical outcome. T-cell responses upon nivolumab treatment were investigated by flow cytometry and multispectral imaging. Alterations in the frequency of the Treg population and reorganization of tumor tissues could be correlated to nivolumab responsiveness ex vivo. Thus, our findings not only demonstrate the functionality of T cells in NSCLC slice cultures up to 10 days ex vivo, but also suggests this model for stratifying patients for treatment selection and to investigate in depth the tumor-associated T-cell regulation.

SUBMITTER: Junk D

PROVIDER: S-EPMC8464600 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundUtilization of alternative transcription start sites through alterations in epigenetic promoter regions causes reduced expression of immunogenic N-terminal peptides, which may facilitate immune evasion in early gastric cancer. We hypothesized that tumors with high alternate promoter utilization would be resistant to immune checkpoint inhibition in metastatic gastric cancer.Patients and methodsTwo cohorts of patients with metastatic gastric cancer treated with immunotherapy were analyzed. The first cohort (N = 24) included patients treated with either nivolumab or pembrolizumab. Alternate promoter utilization was measured using the NanoString® (NanoString Technologies, Seattle, WA, USA) platform on archival tissue samples. The second cohort was a phase II clinical trial of patients uniformly treated with pembrolizumab (N = 37). Fresh tumor biopsies were obtained, and transcriptomic analysis was carried out on RNAseq data. Alternate promoter utilization was correlated to T-cell cytolytic activity, objective response rate and survival.ResultsIn the first cohort 8 of 24 (33%) tumors were identified to have high alternate promoter utilization (APhigh), and this was used to define the APhigh tertile of the second cohort (13 APhigh of 37). APhigh tumors exhibited decreased markers of T-cell cytolytic activity and lower response rates (8% versus 42%, P = 0.03). Median progression-free survival was lower in the APhigh group (55 versus 180 days, P = 0.0076). In multivariate analysis, alternative promoter utilization was an independent predictor of immunotherapy survival [hazard ratio 0.29, 95% confidence interval 0.099-0.85, P = 0.024). Analyzing tumoral evolution through paired pre-treatment and post-treatment biopsies, we observed consistent shifts in alternative promoter utilization rate associated with clinical response.ConclusionA substantial proportion of metastatic gastric cancers utilize alternate promoters as a mechanism of immune evasion, and these tumors may be resistant to anti-PD1 immune checkpoint inhibition. Alternate promoter utilization is thus a potential mechanism of resistance to immune checkpoint inhibition, and a novel predictive biomarker for immunotherapy.Trial registrationClinicalTrials.gov Identifier: NCT#02589496.

Project description:BackgroundThe development of immune checkpoint inhibitors (ICIs) is a revolutionary milestone in the field of immune-oncology. However, the low response rate is the major problem of ICI treatment. The recent studies showed that response rate to single-agent programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibition in unselected non-small cell lung cancer (NSCLC) patients is 25% so that researchers defined several biomarkers to predict the response of immunotherapy in ICIs treatment. Common biomarkers like tumor mutational burden (TMB) and PD-L1 expression have several limitations, such as low accuracy and inadequately validated cutoff value.MethodsTwo published and an unpublished ICIs treatment NSCLC cohorts with 129 patients were collected and divided into a training cohort (n = 53), a validation cohort (n = 22), and two independent test cohorts (n = 34 and n = 20). We identified six immune-related pathways whose mutational status was significantly associated with overall survival after ICIs treatment. Then these pathways mutational status combined with TMB, PD-L1 expression and intratumor heterogeneity were incorporated to build a Bayesian-regularization neural networks (BRNN) model to predict the ICIs treatment response.ResultsWe firstly proved that TMB, PD-L1, and mutant-allele tumor heterogeneity (MATH) were independent biomarkers. The survival analysis of six immune-related pathways revealed the mutational status could distinguish overall survival after ICIs treatment. When predicting immunotherapy efficacy, the overall accuracy of area under curve (AUC) in validation cohort reaches 0.85, outperforming previous predictors in either sensitivity or specificity. And the AUC in two independent test cohorts reach 0.74 and 0.80.ConclusionWe developed a pathway-model that could predict the efficacy of ICIs in NSCLC patients. Our study made a significant contribution to solving the low prediction accuracy of immunotherapy of single biomarker. With the accumulation of larger data sets, further studies are warranted to refine the predictive performance of the approach.

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Human tissue cultures of lung cancer predict patient susceptibility to immune-checkpoint inhibition.

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