Treatment strategies for unresectable locally advanced non-small cell lung cancer in the real-life ESME cohort

Immunogenic cell death-based prognostic model for predicting the response to immunotherapy and common therapy in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a malignant tumor in the respiratory system. The efficacy of current treatment modalities varies greatly, and individualization is evident. Therefore, finding biomarkers for predicting treatment prognosis and providing reference and guidance for formulating treatment options is urgent. Cancer immunotherapy has made distinct progress in the past decades and has a significant effect on LUAD. Immunogenic Cell Death (ICD) can reshape the tumor's immune microenvironment, contributing to immunotherapy. Thus, exploring ICD biomarkers to construct a prognostic model might help individualized treatments. We used a lung adenocarcinoma (LUAD) dataset to identify ICD-related differentially expressed genes (DEGs). Then, these DEGs were clustered and divided into subgroups. We also performed variance analysis in different dimensions. Further, we established and validated a prognostic model by LASSO Cox regression analysis. The risk score in this model was used to evaluate prognostic differences by survival analysis. The treatment prognosis of various therapies were also predicted. LUAD samples were divided into two subgroups. The ICD-high subgroup was related to an immune-hot phenotype more sensitive to immunotherapy. The prognostic model was constructed based on six ICD-related DEGs. We found that high-risk score patients responded better to immunotherapy. The ICD prognostic model was validated as a standalone factor to evaluate the ICD subtype of individual LUAD patients, which might contribute to more effective therapies.

Treatment of unresectable stage III NSCLC: Real world cohort study and literature review

INTRODUCTION

Until recently, the treatment for patients with locally advanced unresectable stage III non-small cell lung cancer (NSCLC) was combined chemoradiotherapy (CRT), delivered either concurrently (cCRT) or sequentially (sCRT). There is limited data on the outcomes and safety of CRT in a real-world setting. We conducted a real-world cohort analysis of our Leuven Lung Cancer Group (LLCG) experience with CRT for unresectable stage III NSCLC, prior to the era of consolidation treatment with immunotherapy.

PATIENTS AND METHODS

In this observational, real-world monocentric cohort study, a total of 163 consecutive patients were included. They were diagnosed with unresectable stage III primary NSCLC and treated with CRT between January 1st, 2011, and December 31st, 2018. Patient and tumor characteristics, treatment patterns, toxicity, and primary outcome parameters such as PFS, OS and pattern of relapse were captured.

RESULTS

CRT was concurrent in 108 patients, sequential in 55. Overall tolerability was good, with two thirds of patients without severe adverse events such as severe febrile neutropenia, ≥ grade 2 pneumonitis, or ≥ grade 3 esophagitis. All registered adverse events were more frequent in the cCRT group compared to the sCRT group. Median PFS was 13.2 months (95% CI 10.3-16.2), median OS was 23.3 months (95% CI 18.3-28.0), with a 47.5% survival rate at 2 years, and 29.4% at five years.

CONCLUSIONS

This study provides a clinically relevant benchmark on the outcomes and toxicity of concurrent and sequential chemoradiotherapy in unresectable stage III NSCLC in a real-world setting in the pre-PACIFIC era.

A bioinformatics-based immune-related prognostic index for lung adenocarcinoma that predicts patient response to immunotherapy and common treatments

Background

There is increasing evidence of the effectiveness of immune checkpoint blockade (ICB) therapy for the treatment of lung adenocarcinoma (LUAD). However, the benefits of ICB therapy vary among LUAD patients. Due to the research dimension, existing biomarkers, such as programmed death-ligand 1 (PD-L1) expression and tumor mutation burden (TMB), could not reflect the complex tumor environment, and had low prediction accuracy of ICB. Therefore, we aimed to uncover a prognostic biomarker that could also predict whether a patient would benefit from ICB therapy and other common treatments from multiple dimensions, so as to improve the prediction accuracy of pre-treatment patients.

Methods

Based on the LUAD dataset retrieved from The Cancer Genome Atlas (TCGA) database, 50 immune-related hub genes were identified using weighted gene co-expression network analysis and univariate Cox regression analyses. An immune-related gene prognostic index (IRGPI) was constructed using a Cox proportional-hazards model based on 15 genes and validated using GSE72094 dataset. We tested its prognostic accuracy by Kaplan-Meier (K-M) survival curves of the two datasets and assessed its predictive power by comparing area under curve (AUC) of IRGPI with existing biomarkers. Subsequently, we analyzed the molecular and immune characteristics, and evaluated the benefits of ICB by PD-L1 expression and Tumor Immune Dysfunction and Exclusion (TIDE) analysis, predicted the inhibitory concentration 50 of common treatments drugs for two IRGPI score-related subgroups.

Results

Patients in the IRGPI-high subgroup had lower overall survival (OS) than patients in the IRGPI-low subgroup in K-M survival curve in two cohorts. And IRGPI has AUC values of 0.715, 0.724, and 0.743 in 1, 2, and 3 years, respectively. A higher tumor mutation burden and PD-L1 expression and the tumor microenvironment (TME) landscape demonstrated that IRGPI-high subgroup patients may respond better to ICB therapy. Genomics of Drug Sensitivity in Cancer (GDSC) analysis indicated that the IRGPI-high subgroup showed greater sensitivity to chemotherapy.

Conclusions

IRGPI is a prospective biomarker for evaluating whether a patient will benefit from ICB therapy and other treatments, and distinguishing patients with different molecular and immune characteristics.