PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 5, 2026Cancer Research0 citations

Abstract 7748: Immune-related RNA-seq biomarker-based clustering reveals heterogeneous immunotherapy responses and guides subtype-specific strategies in metastatic NSCLC

View Full Paper
JLJiyon LyuSFS. Franch-ExpósitoSKSanghwa Kim

Key Points

  • This research aims to explore the immune landscape in metastatic non-small cell lung cancer (mNSCLC) and its association with varied responses to immunotherapy.
  • Utilized RNA-seq molecular clustering based on immune-related biomarkers in a cohort of 2,235 mNSCLC patients.
  • Applied unsupervised clustering to define four distinct immune subtypes.
  • Conducted Kaplan-Meier analysis for overall survival (rwOS) and progression-free survival (rwPFS).
  • Analyzed tumor mutational burden (TMB) and immune cell composition using QuantiSeq.
  • Identified four immune subtypes with significant differences in survival outcomes, the poorest in Cluster 1 and best in Cluster 3.
  • Found significant differences in RNA-seq biomarker expression and TAM score across clusters (p < 0.001).
  • Cluster 1 showed a higher prevalence of squamous/current smokers, while Cluster 2 was more frequent in non-squamous/never smokers.
  • Differential prevalence of TMB-high and PD-L1-positive cases was noted across clusters.

Abstract

Abstract Metastatic non-small cell lung cancer (mNSCLC) represents a highly heterogeneous disease with variable clinical outcomes under first-line immunotherapy plus chemotherapy. To better understand immune landscape features associated with heterogeneous response to immunotherapy, we performed biomarker-driven RNA-seq molecular clustering using known immune-related markers TIGIT, FOXP3, CD274 (PD-L1), and tumor-associated macrophage (TAM) score. We analyzed a real-world cohort of 2,235 mNSCLC patients with pre-treatment tumor biopsies in the de-identified Tempus database treated with first-line PD-(L)1 plus chemotherapy. Unsupervised clustering of RNA-seq data defined four distinct immune subtypes. Real-world overall survival (rwOS) and progression-free survival (rwPFS) were assessed via Kaplan-Meier analysis with a log-rank test. Pathway enrichment using hallmark gene sets, tumor mutational burden (TMB), and immune cell composition using QuantiSeq were analyzed. Expression levels of RNA-seq biomarkers and TAM score were significantly different across identified clusters (ANOVA; p 0.001). These clusters also showed significantly differential prevalence of TMB-high and PD-L1-positive (IHC) (Chi-squared; p 0.001, respectively), as well as characteristic pathway enrichment and immune profiles. Non-squamous/Never smoker were more frequent in Cluster 2, whereas Squamous/Current smoker were predominant in Cluster 1 (Chi-squared; Histology/Smoking, p 0.05, respectively). Survival differed significantly, being poorest in Cluster 1 and best in Cluster 3 (rwOS/rwPFS, p 0.001) (Table 1). This biomarker-driven RNA-seq analysis identified four immune clusters of mNSCLC with differential survival outcomes. This study provides a foundation for understanding tumor heterogeneity and supports the use of immune biomarkers to enable patient stratification for therapeutic combinations. Citation Format: Jiyon Lyu, Sebastià Franch-Expósito, Sanghwa Kim, Liam Il-Young Chung, Ronald Min, Sung Hwan Lee, Shinkyo Yoon, Michelle M. Stein, JACOB MERCER, Paul Fields, Bella Kim, Young Kwang Chae. Immune-related RNA-seq biomarker-based clustering reveals heterogeneous immunotherapy responses and guides subtype-specific strategies in metastatic NSCLC abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7748.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Lyu et al. (2026) studied this question.

synapsesocial.com/papers/69d1fd13a79560c99a0a2d58https://doi.org/10.1158/1538-7445.am2026-7748
Ask AI
Helpful
Bookmark
Share
View Full Paper