Author Correction: Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma.
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ABSTRACT:
SUBMITTER: Patterson A
PROVIDER: S-EPMC10514182 | biostudies-literature | 2023 Sep
REPOSITORIES: biostudies-literature
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Author Correction: Mutated processes predict immune checkpoint inhibitor therapy benefit in metastatic melanoma.
Patterson Andrew A Auslander Noam N
Nature communications 20230921 1
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Project description:Immune Checkpoint Inhibitor (ICI) therapy has revolutionized treatment for advanced melanoma; however, only a subset of patients benefit from this treatment. Despite considerable efforts, the Tumor Mutation Burden (TMB) is the only FDA-approved biomarker in melanoma. However, the mechanisms underlying TMB association with prolonged ICI survival are not entirely understood and may depend on numerous confounding factors. To identify more interpretable ICI response biomarkers based on tumor mutations, we train classifiers using mutations within distinct biological processes. We evaluate a variety of feature selection and classification methods and identify key mutated biological processes that provide improved predictive capability compared to the TMB. The top mutated processes we identify are leukocyte and T-cell proliferation regulation, which demonstrate stable predictive performance across different data cohorts of melanoma patients treated with ICI. This study provides biologically interpretable genomic predictors of ICI response with substantially improved predictive performance over the TMB.
| S-EPMC9485158 | biostudies-literature
Project description:The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity.
| S-EPMC10302726 | biostudies-literature
Project description:The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy, so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity.
2023-05-31 | MSV000092069 | MassIVE
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.
| S-EPMC6442650 | biostudies-literature
Project description:In the version of this article originally published, there was an error in the URL linked to by an accession code in the data availability section of the methods. The erroneous URL was: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100351 . The correct URL is: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115821 . The error has been corrected in the HTML and PDF versions of this article.
| S-EPMC9639231 | biostudies-literature
Project description:Personalized cell therapy targeting tumor antigens with expanded tumor-infiltrating lymphocytes (TILs) has shown great promise in metastatic melanoma (MM) since the 90s. However, MM was first-in line to benefit from the wave of checkpoint inhibitors (CPI), which shifted the focus of immunotherapy almost fully to immune CPI. Still, the majority of patients fail to benefit from CPI treatment, raising the intriguing question on how TIL therapy may fit into the changing landscape of melanoma treatment. We took advantage of data from a unique cohort of patients with MM treated with T-cell therapy in consecutive clinical trials at our institution across the last 10 years. Based on detailed data on patient characteristics, pre-TIL and post-TIL treatments and long-term follow-up, we were able to address the important issue of how TIL therapy can be positioned in the current CPI era. We found that previous progression on anticytotoxic T-lymphocyte-associated protein 4 do not seem to harm neither rate nor duration of response to TIL therapy. Importantly, even in the hard-to-treat population of patients who progressed on antiprogrammed cell death protein 1 (anti-PD-1), an objective response rate of 32% was achieved, including durable responses. Yet, median progression-free survival was reduced in this anti-PD-1 refractory population. Trial registration number: ClinicalTrials.gov ID: NCT00937625, NCT02379195 and NCT02354690.
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Project description:Targeted therapies (TT) and immune checkpoint inhibitors (ICI) have become increasingly important in the treatment of metastatic malignant melanoma in recent years. We examined implementation and effectiveness of these new therapies over time in Germany with a focus on regional differences. We analyzed data from 12 clinical cancer registries in 8 federal states in Germany over the period 2000-2016. A total of 3871 patients with malignant melanoma in Union internationale contre le cancer (UICC) stage IV at primary diagnosis (synchronous metastases) or with metachronous metastases were included. We investigated differences in survival of patients treated with new and conventional therapies by log-rank tests for Kaplan-Meier curves. Cox regression models were estimated to adjust therapy effects for demographic, regional, and prognostic factors. New systemic therapies were increasingly applied throughout Germany. TT were most frequently documented in Eastern Germany (East: 11.2%; West: 6.3%), whereas ICI therapies were more frequently used in Western Germany (East: 1.7%; West: 3.9%). TT had a relevant influence on patient survival (hazard ratio (HR) = 0.831; 95%-CI = (0.729; 0.948)). Survival was worse in Eastern Germany (HR = 1.470; 95%-CI = (1.347; 1.604)) relative to Western Germany. Treatment and survival prospects of patients with melanoma differed considerably between Western and Eastern Germany. The differences in regional medication behavior and survival require further exploration.
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Project description:In BRAFV600mut metastatic melanoma, the combination of BRAF and MEK inhibitors (BRAFi, MEKi) has undergone multiple resistance mechanisms, limiting its clinical benefit and resulting in the need for response predicting biomarkers. Based on phase III clinical trial data, several studies have previously explored baseline genomic features associated with response to BRAFi + MEKi. Using a targeted approach that combines the examination of mRNA expression and DNA alterations in a subset of genes, we performed an analysis of baseline genomic alterations involved in MAPK inhibitors' resistance in a real-life cohort of BRAFV600mut metastatic melanoma patients. Twenty-seven patients were included in this retrospective study, and tumor samples were analyzed when the BRAFi + MEKi therapy was initiated. The clinical characteristics of our cohort were consistent with previously published studies. The BRAFi + MEKi treatment was initiated in seven patients as a following-line treatment, and had a specific transcriptomic profile exhibiting 14 genes with lower mRNA expression. However, DNA alterations in CCND1, RB1, and MET were only observed in patients who received BRAFi + MEKi as the first-line treatment. Furthermore, KIT mRNA expression was significantly higher in patients showing clinical benefit from the combined therapy, emphasizing the tumor-suppressor role of KIT already described within the context of BRAF-mutant melanoma.
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Project description: Not available
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Project description:Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma. Adult patients that were treated with ICI for advanced melanoma were retrospectively identified from ten participating centers. A deep learning model (DLM) was trained on volumes of lesions on baseline CT to predict clinical benefit. The DLM was compared to and combined with a model of known clinical predictors (presence of liver and brain metastasis, level of lactate dehydrogenase, performance status and number of affected organs). A total of 730 eligible patients with 2722 lesions were included. The DLM reached an area under the receiver operating characteristic (AUROC) of 0.607 [95%CI 0.565-0.648]. In comparison, a model of clinical predictors reached an AUROC of 0.635 [95%CI 0.59 -0.678]. The combination model reached an AUROC of 0.635 [95% CI 0.595-0.676]. Differences in AUROC were not statistically significant. The output of the DLM was significantly correlated with four of the five input variables of the clinical model. The DLM reached a statistically significant discriminative value, but was unable to improve over known clinical predictors. The present work shows that the assessment over known clinical predictors is an essential step for imaging-based prediction and brings important nuance to the almost exclusively positive findings in this field.
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