Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer

The tumor immune composition influences prognosis and treatment sensitivity in lung cancer. The presence of effective adaptive immune responses is associated with increased clinical benefit after immune checkpoint blockers. Conversely, immunotherapy resistance can occur as a consequence of local T-cell exhaustion/dysfunction and upregulation of immunosuppressive signals and regulatory cells. Consequently, merely measuring the amount of tumor-infiltrating lymphocytes (TILs) may not accurately reflect the complexity of tumor-immune interactions and T-cell functional states and may not be valuable as a treatment-specific biomarker. In this work, we investigate an immune-related biomarker (PhenoTIL) and its value in associating with treatment-specific outcomes in non-small cell lung cancer (NSCLC). PhenoTIL is a novel computational pathology approach that uses machine learning to capture spatial interplay and infer functional features of immune cell niches associated with tumor rejection and patient outcomes. PhenoTIL's advantage is the computational characterization of the tumor immune microenvironment extracted from H&E-stained preparations. Association with clinical outcome and major non-small cell lung cancer (NSCLC) histology variants was studied in baseline tumor specimens from 1,774 lung cancer patients treated with immunotherapy and/or chemotherapy, including the clinical trial Checkmate 057 (NCT01673867).

Automated summary

The tumor immune composition has an impact on prognosis and treatment sensitivity in lung cancer. Effective adaptive immune responses are associated with better clinical outcomes after immune checkpoint blockers, while immunotherapy resistance can occur due to T-cell exhaustion, immunosuppressive signals, and regulatory cells. This study investigates a new computational pathology approach called PhenoTIL, which uses machine learning to analyze the spatial interactions and functional features of immune cell niches associated with tumor rejection and patient outcomes in non-small cell lung cancer (NSCLC). The study demonstrates the potential of PhenoTIL as a valuable biomarker for treatment-specific outcomes in NSCLC.