Unraveling the tumor-microenvironment through a radiogenomic-based multiomic approach to predict outcomes of immunotherapy in non-small cell lung cancer.

Background: The tumor microenvironment (TME) plays a critical role in influencing immune checkpoint inhibitor (ICI) therapy outcomes in advanced non-small cell lung cancer (NSCLC). This study aimed to develop a radiomics model reflecting an ICI-favorable TME based on whole transcriptome sequencing (WTS).

Methods: This multi-center retrospective cohort study included training (n = 120), internal validation (n = 319), and external validation (n = 150) cohorts of advanced NSCLC patients who received ICI as first- or second-line therapy. The radiomics model (rTME) was developed based on the TME score, which reflected ICI-favorable immune cell compositions. The model's performance was assessed using the C-index, and survival outcomes were also evaluated.

Results: In the training cohort, high rTME scores were associated with significantly prolonged progression-free survival (PFS) (median 4.1 vs. 2.9 months, p = 0.024) and overall survival (OS) (median 15.0 vs. 8.4 months, p = 0.030). Similar trends were observed in the internal validation cohort for PFS (median 3.3 vs. 2.1 months, p = 0.004) and OS (median 13.9 vs. 7.3 months, p = 0.004), as well as in the external validation cohort for OS (median 15.5 vs. 7.3 months, p = 0.008). Integrating clinical variables improved predictive accuracy in both the training and internal validation cohorts.

Conclusion: Our radiomics model, reflecting the ICI-favorable immune cell expression in the TME, showed a positive association with ICI outcomes in NSCLC patients. Integrating radiomics and clinical variables enhances prognostic accuracy, demonstrating the model's potential utility in guiding ICI therapy decisions.