Integration of proteomic and clinical data for the prediction of response to immune checkpoint inhibitor therapy in non-small cell lung cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e21110-e21110
Author(s):  
Yuval Shaked ◽  
Michal Harel ◽  
Coren Lahav ◽  
Eyal Jacob ◽  
Itamar Sela ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Host Response ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Resistance Mechanisms ◽  
Small Cell ◽  
Plasma Samples ◽  
Small Cell Lung ◽  
Nsclc Patients

e21110 Background: Immune checkpoint inhibitor (ICI) therapy represents one of the most promising cancer treatments to date. However, despite unprecedented rates of durable response, only a small proportion of patients benefits from this approach. Major efforts are therefore required to characterize treatment resistance mechanisms, as well as to identify reliable biomarkers for response. We have previously shown that in response to various types of cancer therapy, including ICIs, the host may induce pro-tumorigenic processes that can promote therapy resistance. Here we examined systemic host-response proteomic profiles in non-small cell lung cancer (NSCLC) patients, aiming to discover biomarkers for response to ICI therapy and to unravel underlying resistance mechanisms. Methods: As part of our ongoing PROPHETIC clinical trial (NCT04056247), plasma samples were obtained at baseline (T0) and early-on treatment (T1; following the first treatment) from 120 NSCLC patients receiving ICI therapy. Proteomic profiling of the plasma samples was performed using proximity-extension assay (PEA) technology; validation was carried out for a fraction of the samples using ELISA-based arrays. To identify a proteomic signature that predicts clinical outcome, machine learning algorithms were applied following a random separation of the cohort into a discovery set and a validation set. Results: A proteomic signature predictive of response to treatment was identified and validated. Bioinformatic analysis identified potential mechanisms of resistance based on differentially expressed proteins associated with pro-tumorigenic biological processes. Statistical analysis of the clinical data identified multiple novel differential clinical parameters between responders and non-responders, either at baseline or by comparing T0 to T1, which may suggest host-mediated effects. Conclusions: Our study demonstrates the potential clinical utility of analyzing the host response to ICI therapy, in particular for the discovery of novel predictive biomarkers for NSCLC patient stratification. Clinical trial information: NCT04056247.

scholarly journals Clinical implications of bronchoscopy for immune checkpoint inhibitor-related pneumonitis in patients with non-small cell lung cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Osamu Nishiyama ◽  
Shigeki Shimizu ◽  
Koji Haratani ◽  
Kosuke Isomoto ◽  
Junko Tanizaki ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Acute Lung Injury ◽  
Lung Injury ◽  
Small Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Small Cell ◽  
Small Cell Lung ◽  
Nsclc Patients

Abstract Background The utility of bronchoscopy for patients with suspected immune checkpoint inhibitor (ICI)-related pneumonitis is currently debatable. The purpose of this study was to examine the findings of bronchoalveolar lavage (BAL) analysis and transbronchial lung biopsy (TBLB) in non-small cell lung cancer (NSCLC) patients with ICI-related pneumonitis, and to elucidate the clinical significance of bronchoscopy for this health condition. Patients and methods Consecutive NSCLC patients treated with ICIs, diagnosed with ICI-related pneumonitis after undergoing bronchoscopy between October 2015 and March 2019 were retrospectively screened. Findings of BAL fluid analysis and/or TBLB specimen histology were reviewed. Results Twelve patients underwent bronchoscopy for the diagnosis of ICI-related pneumonitis, ten of whom underwent BAL. An increase in the proportion of lymphocytes higher than 20% was observed in all ten patients. An increase in the proportion of neutrophils (> 10%) and eosinophils (> 10%) was observed in two and one patient, respectively. TBLB specimens were analyzed for eight patients. Major histologic findings included alveolitis in seven (87.5%) and organizing pneumonia (OP) in five (62.5%) patients. Other findings included acute lung injury and fibrosis. All twelve patients demonstrated favorable outcomes. Conclusion A major characteristic of BAL analysis in ICI-related pneumonitis with NSCLC was an increased proportion of lymphocytes. The histologic features of lung tissue included alveolitis and/or OP. Acute lung injury and fibrosis were observed. Although the necessity of bronchoscopy should be determined on a case-by-case basis, it is necessary to assess these parameters when proper differential diagnosis is needed.

Deep-learning analysis of H&E images to define three immune phenotypes to reveal loss-of-target in excluded immune cells as a novel resistance mechanism of immune checkpoint inhibitor in non-small cell lung cancer.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 3120-3120
Author(s):  
Sehhoon Park ◽  
Chan-Young Ock ◽  
Minje Jang ◽  
Jiwon Shin ◽  
Sarah Lee ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Cells ◽  
Immune Checkpoint Inhibitor ◽  
Validation Cohort ◽  
Checkpoint Inhibitor ◽  
Resistance Mechanism ◽  
Small Cell ◽  
Small Cell Lung ◽  
Training Cohort ◽  
Nsclc Patients

3120 Background: Discovery of predictive biomarker to enrich the responder of immune checkpoint inhibitor (ICI) in PD-L1-low ( < 50%) non-small cell lung cancer (NSCLC) is still challenging. Recent study showed that loss of heterozygosity (LOH) of HLA led to immune evasion. In the current study, we hypothesized that 3 immune phenotype (3IP): inflamed, excluded and desert would be reliably classified by deep-learning algorithm of H&E image, called Lunit-SCOPE, which would dictate the responder in PD-L1-low NSCLC patients and discover a unique resistance pathway in excluded phenotype. Methods: Lunit-SCOPE was trained with 1,824 H&E Whole-Slide Image (WSI) of NSCLC from Samsung Medical Center (SMC). WSI was divided into patches (~10 high-power fields) which was classified for 3IP, based on both quantity and localization of immune cells. The 3IP was trained and optimized by considering clinical outcome of 119 NSCLC patients with PD-(L)1 inhibitor (training cohort, patches = 25,897), and validated in 62 patients enrolled in LC-biomarker study (NCT03578185, validation cohort, patches = 8,929). Tumor Proportion Score (TPS) of PD-L1 22C3 immunohistochemistry was assessed by pathologists. Tumor Mutational Burden (TMB) was calculated as number of nonsynonymous alterations throughout whole-exome and HLA LOH was called by LOHHLA algorithm. Results: Interactive analysis to classify 3IP in training cohort showed that 8,726 (33.7%), 10,965 (42.3%), and 6,206 (24.0%) patches were classified as inflamed, excluded, and desert, respectively. In validation cohort, median progression-free survival (mPFS) of inflamed phenotype was 10.1 m, significantly prolonged compared to either excluded phenotype (3.0 m, P= 0.0053) or desert phenotype (1.4 m, P= 0.0011). Inflamed phenotype independently dictated favorable ICI outcome in PD-L1-low (TPS < 50%, mPFS of inflamed: 14.3 m vs excluded/desert: 1.4 m, P= 0.0233) as well as in PD-L1-high (TPS≥50%, 10.1 m vs 4.2 m, P= 0.0361), respectively. Excluded phenotype had higher TMB compared to inflamed phenotype had (median 177 vs 107), and HLA LOH was also enriched in excluded phenotype (31.0%) compared to inflamed (17.6%) and desert (16.7%) phenotypes. Conclusions: Lunit-SCOPE based 3IP classification can predict ICI outcome especially in PD-L1-low ( < 50%) patients. Excluded phenotype showed poor ICI outcome even with high TMB, partially explained by HLA LOH resulting in loss-of-target, as a novel resistance mechanism of ICI.

scholarly journals Efficacy of Immune Checkpoint Inhibitor Monotherapy for Advanced Non-Small-Cell Lung Cancer with ALK Rearrangement

2020 ◽  
Vol 21 (7) ◽  
pp. 2623 ◽  
Author(s):  
Yuko Oya ◽  
Hiroaki Kuroda ◽  
Takeo Nakada ◽  
Yusuke Takahashi ◽  
Noriaki Sakakura ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Small Cell ◽  
Egfr Mutations ◽  
Small Cell Lung ◽  
Alk Rearrangement ◽  
Nsclc Patients

Programmed death-ligand 1 (PD-L1) expression is a predictor of immune checkpoint inhibitor (ICI) treatment efficacy. The clinical efficacy of ICIs for non-small-cell lung cancer (NSCLC) patients harboring major mutations, such as EGFR or ALK mutations, is limited. We genotyped 190 patients with advanced lung adenocarcinomas who received nivolumab or pembrolizumab monotherapy, and examined the efficacy in NSCLC patients with or without major mutations. Among the patients enrolled in the genotyping study, 47 patients harbored EGFR mutations, 25 patients had KRAS mutations, 5 patients had a HER2 mutation, 6 patients had a BRAF mutation, and 7 patients had ALK rearrangement. The status of PD-L1 expression was evaluated in 151 patients, and the rate of high PD-L1 expression (≥50%) was significantly higher in patients with ALK mutations. The progression-free survival was 0.6 (95% CI: 0.2–2.1) months for ALK-positive patients and 1.8 (95% CI: 1.2–2.1) months for EGFR-positive patients. All patients with ALK rearrangement showed disease progression within three months from the initiation of anti-PD-1 treatment. Our data suggested that ICI treatment was significantly less efficacious in patients with ALK rearrangement than in patients with EGFR mutations, and PD-L1 expression was not a critical biomarker for ICI treatment for patients with one of these mutations.

scholarly journals Identification of NOTCH4 mutation as a response biomarker for immune checkpoint inhibitor therapy

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Junyu Long ◽  
Dongxu Wang ◽  
Xu Yang ◽  
Anqiang Wang ◽  
Yu Lin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Bladder Cancer ◽  
Cancer Patients ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Small Cell ◽  
Small Cell Lung ◽  
Esophagogastric Cancer ◽  
Clinical Benefits

Abstract Background Immune checkpoint inhibitor (ICI) therapy elicits durable antitumor responses in patients with many types of cancer. Genomic mutations may be used to predict the clinical benefits of ICI therapy. NOTCH homolog-4 (NOTCH4) is frequently mutated in several cancer types, but its role in immunotherapy is still unclear. Our study is the first to study the association between NOTCH4 mutation and the response to ICI therapy. Methods We tested the predictive value of NOTCH4 mutation in the discovery cohort, which included non-small cell lung cancer, melanoma, head and neck squamous cell carcinoma, esophagogastric cancer, and bladder cancer patients, and validated it in the validation cohort, which included non-small cell lung cancer, melanoma, renal cell carcinoma, colorectal cancer, esophagogastric cancer, glioma, bladder cancer, head and neck cancer, cancer of unknown primary, and breast cancer patients. Then, the relationships between NOTCH4 mutation and intrinsic and extrinsic immune response mechanisms were studied with multiomics data. Results We collected an ICI-treated cohort (n = 662) and found that patients with NOTCH4 mutation had better clinical benefits in terms of objective response rate (ORR: 42.9% vs 25.9%, P = 0.007), durable clinical benefit (DCB: 54.0% vs 38.1%, P = 0.021), progression-free survival (PFS, hazard ratio [HR] = 0.558, P < 0.001), and overall survival (OS, HR = 0.568, P = 0.006). In addition, we validated the prognostic value of NOTCH4 mutation in an independent ICI-treated cohort (n = 1423). Based on multiomics data, we found that NOTCH4 mutation is significantly associated with enhanced immunogenicity, including a high tumor mutational burden, the expression of costimulatory molecules, and activation of the antigen-processing machinery, and NOTCH4 mutation positively correlates activated antitumor immunity, including infiltration of diverse immune cells and various immune marker sets. Conclusions Our findings indicated that NOTCH4 mutation serves as a novel biomarker correlated with a better response to ICI therapy.

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