Dataset Information

Clinical and molecular correlates of PD-L1 expression in patients with lung adenocarcinomas.

ABSTRACT:

Background

Programmed death-ligand 1 (PD-L1) expression is the only FDA-approved biomarker for immune checkpoint inhibitors (ICIs) in patients with lung adenocarcinoma, but sensitivity is modest. Understanding the impact of molecular phenotype, clinical characteristics, and tumor features on PD-L1 expression is largely unknown and may improve prediction of response to ICI.

Patients and methods

We evaluated patients with lung adenocarcinoma for whom PD-L1 testing and targeted next-generation sequencing (using MSK-IMPACT) was performed on the same tissue sample. Clinical and molecular features were compared across PD-L1 subgroups to examine how molecular phenotype associated with tumor PD-L1 expression. In patients treated with anti-PD-(L)1 blockade, we assessed how these interactions impacted efficacy.

Results

A total of 1586 patients with lung adenocarcinoma had paired PD-L1 testing and targeted next-generation sequencing. PD-L1 negativity was more common in primary compared to metastatic samples (P < 0.001). The distribution of PD-L1 expression (lymph nodes enriched for PD-L1 high; bones predominantly PD-L1 negative) and predictiveness of PD-L1 expression on ICI response varied by organ. Mutations in KRAS, TP53, and MET significantly associated with PD-L1 high expression (each P < 0.001, Q < 0.001) and EGFR and STK11 mutations associated with PD-L1 negativity (P < 0.001, Q = 0.01; P = 0.001, Q < 0.001, respectively). WNT pathway alterations also associated with PD-L1 negativity (P = 0.005). EGFR and STK11 mutants abrogated the predictive value of PD-L1 expression on ICI response.

Conclusion

PD-L1 expression and association with ICI response vary across tissue sample sites. Specific molecular features are associated with differential expression of PD-L1 and may impact the predictive capacity of PD-L1 for response to ICIs.

SUBMITTER: Schoenfeld AJ

PROVIDER: S-EPMC7523592 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Clinical and molecular correlates of PD-L1 expression in patients with lung adenocarcinomas.

Schoenfeld A J AJ Rizvi H H Bandlamudi C C Sauter J L JL Travis W D WD Rekhtman N N Plodkowski A J AJ Perez-Johnston R R Sawan P P Beras A A Egger J V JV Ladanyi M M Arbour K C KC Rudin C M CM Riely G J GJ Taylor B S BS Donoghue M T A MTA Hellmann M D MD

Annals of oncology : official journal of the European Society for Medical Oncology 20200206 5

<h4>Background</h4>Programmed death-ligand 1 (PD-L1) expression is the only FDA-approved biomarker for immune checkpoint inhibitors (ICIs) in patients with lung adenocarcinoma, but sensitivity is modest. Understanding the impact of molecular phenotype, clinical characteristics, and tumor features on PD-L1 expression is largely unknown and may improve prediction of response to ICI.<h4>Patients and methods</h4>We evaluated patients with lung adenocarcinoma for whom PD-L1 testing and targeted next- ...[more]

PMID: 32178965

Similar Datasets

Project description:BackgroundWe aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD-L1) expression in advanced stage lung adenocarcinoma.MethodsThis retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD-L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c-statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability.ResultsAmong 153 patients, 53 patients were classified as PD-L1 positive and 100 patients as PD-L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad-score by radiomic analysis was higher in the PD-L1 positive group than in the PD-L1 negative group with a statistical significance (-0.378 ± 1.537 vs. -1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c-statistic = 0.646 vs. 0.550, P = 0.0299).ConclusionsQuantitative CT radiomic features can predict PD-L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression.Key pointsSignificant findings of the study Quantitative CT radiomic features can help predict PD-L1 expression, whereas none of the qualitative imaging findings is associated with PD-L1 positivity. What this study adds A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD-L1 expression.

Project description:BackgroundVascular endothelial growth factor C (VEGFC), an activator of lymphangiogenesis, is newly identified as an immunomodulator which can regulate the immune system so that tumor cells more easily escape immune surveillance. Evidence has shown programmed cell death-ligand 1 (PD-L1) can also suppress the immune response. Nevertheless, the clinical significance of co-expression of VEGFC and PD-L1 for predicting outcomes in patients with lung adenocarcinoma has not yet been determined.MethodsA total of 114 patients with lung adenocarcinoma who underwent surgeries at Tianjin Medical University Cancer Institute and Hospital between December 2011 and September 2016 were retrospectively reviewed. Tissue specimens were collected for immunohistochemistry of VEGFC and PD-L1 which were analyzed with an H-score system.ResultsIn this study, 57 (50.0%) and 47 (41.2%) patients were classified as VEGFC high expression and PD-L1 high expression. Co-expression was observed in 33 (28.9%) patients. In addition, a positive correlation was found between VEGFC and PD-L1 (P = 0.0398, r = 0.1937). In a univariate analysis, both progression-free survival (PFS) and overall survival (OS) were significantly worse in the VEGFC high expression group and the PD-L1 high expression group, respectively. Furthermore, VEGFC/PD-L1 co-expression showed a worse OS (P = 0.03) and PFS survival (P = 0.01) than the other groups.ConclusionsTaken together, these results indicate that VEGFC/PD-L1 co-expression can forecast both poor OS and PFS in patients with resected lung adenocarcinoma. Co-expression of VEGFC and PD-L1 may serve as a significant prognostic factor for patients with lung adenocarcinoma.Key pointsVEGFC/PD-L1 co-expression forecasts poor survival in patients with resected lung adenocarcinoma. VEGFC/PD-L1 co-expression may be used as a prognostic indicator and provide the theoretical possibility to screen the optimal population with a combination of anti-VEGFC and anti-PD-L1 therapy in the clinical treatment.

Dataset Information

Clinical and molecular correlates of PD-L1 expression in patients with lung adenocarcinomas.

Background

Patients and methods

Results

Conclusion

Publications

Clinical and molecular correlates of PD-L1 expression in patients with lung adenocarcinomas.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets