Authors
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
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 |
|
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