Immune-Related RNA-Seq Biomarker-Based Clustering Reveals Heterogeneous Immunotherapy Responses and Guides Subtype-Specific Strategies in Metastatic NSCLC

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.

 

Table 1.

	Cluster 1 (Immune-desert) N=713	Cluster 2 (TAM-enriched) N=402	Cluster 3 (Immune-hot) N=813	Cluster 4 (Myeloid-inflamed, PD-L1-high) N=302	p-value
Median Survival Time (Months) rwOS/rwPFS	11.5/5.95 mo	14.8/7.33 mo	18.1/8.15 mo	16.7/6.84 mo	Log-rank test; p <0.001
RNA-Seq Biomarkers (TIGIT, FOXP3, CD274 (PD-L1)) and TAM Score	Uniformly low expression of all markers	High TAM but low TIGIT/FOXP3/PD-L1	High TIGIT/FOXP3/PD-L1 with elevated TAM	High PD-L1 with low TIGIT/FOXP3	ANOVA test; p <0.001
Pathway Enrichment TME: tumor microenvironment	↑ Oncogenic signalings and proliferation ↓ Immune -related pathways	↑ TME remodeling pathways	↑ Immune/inflammatory signaling (e.g., IFNγ) and TME remodeling pathways	↑ Proliferation and DNA-repair pathways	Welch ANOVA + Games-Howell or Kruskal-Wallis and Dunn (BH) test; adjusted p <0.05
Immune Cell Composition	↓ Lymphoid and myeloid cell infiltration	↑ M1/M2 macrophage	Broad infiltration (↑ CD8, CD4, Treg, B, NK)	↑ Myeloid cell infiltration	Kruskal-Wallis and Dunn (BH) test; adjusted p <0.05
TMB-High (TMB ≥10 mut/Mb)	283 (34%)	92 (20%)	254 (26%)	124 (35%)	Chi-squared test; p <0.001
PD-L1-Positive (IHC; TPS ≥ 1%)	203 (36%)	174 (54%)	421 (68%)	213 (94%)	Chi-squared test; p <0.001
Tumor Histology					Chi-squared test; p <0.001
Squamous	232 (33%)	73 (18%)	221 (27%)	84 (28%)
Non-Squamous	444 (62%)	315 (78%)	555 (68%)	201 (67%)
NOS	37 (5.2%)	14 (3.5%)	37 (4.6%)	17 (5.6%)
Smoking Status					Chi-squared test; p <0.05
Current Smoker	118 (62%)	44 (46%)	113 (55%)	48 (59%)
Never Smoker	16 (8.4%)	23 (24%)	32 (15%)	12 (15%)
Ex-Smoker	56 (29%)	29 (30%)	62 (30%)	21 (26%)
Unknown	523	306	606	221