Background: Current biomarker(s) for immune-oncology (IO) therapy response prediction in lung cancer are limited. Additional predictive/prognostic biomarkers are useful to help refine patient selection and guide precision therapy.
Methods: A machine learning (ML) automated method was developed to segment tumor, stroma and lymphocytes (TILs) in whole slide hematoxylin and eosin (H&E)â€“stained images of NSCLC patients. A primary set (n = 446, stage III-IV, DFCI cohort) and a secondary set (n = 523, stage I-III, Norway cohort) were used to evaluate the predictive and prognostic impact of TILs, respectively. Targeted DNA sequencing (Oncopanel) and PD-L1 protein expression data were used to evaluate independence and interaction between tumor mutational burden (TMB), PD-L1 and TIL levels with clinical outcomes after anti-PD-(L)1 monotherapy in the primary set.
Results: In the primary set, higher TIL levels was an independent predictor of IO response for both progression-free (HRadj 0.70; P = 0.003) and overall survival (HRadj 0.73; P = 0.02). In the secondary set using the identical ML algorithm, high TIL levels was an independent favorable prognostic factor regardless of endpoint (DSS, TTR), pathological stage and histology. In the PD-L1 negative subgroup of the primary set, TIL levels had superior predictive accuracy for IO response (AUC = 0.77) compared to TMB (AUC = 0.57).
Conclusion: TIL levels appear to be a robust and independent biomarker of likelihood of response to IO treatment in NSCLC. Furthermore, this study shows the potential of MLâ€“based image analysis for extracting quantitative immune information from histopathological slides.