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

• Type of Material: Article
• Authors: Anant Madabhushi, Emory University; Cristian Barrera, Emory University; Germán Corredor, Emory University; Vidya Viswanathan, Emory University; Ruiwen Ding, Case Western Reserve University; Paula Toro, Cleveland Clinic; Pingfu Fu, Case Western Reserve University; Christina Buzzy, Case Western Reserve University; Cheng Lu, Emory University; Priya Velu, Weill Cornell Medical College; Phillipp Zens, University of Bern; Sabina Berezowska, University of Bern; Merzu Belete, Bristol Myers Squibb; David Balli, Bristol Myers Squibb; Han Chang, Bristol Myers Squibb; Vipul Baxi, Bristol Myers Squibb; Konstantinos Syrigos, National and Kapodistrian University of Athens; David L Rimm, Yale University; Vamsidhar Velcheti, New York University; Kurt Schalper, Yale University; Eduardo Romero, Universidad Nacional de Colombia
• Language: English
• Date: 2023-06-01
• Publisher: NATURE PORTFOLIO
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2023
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41698-023-00403-x
• Title of Journal or Parent Work: NPJ PRECISION ONCOLOGY
• Volume: 7
• Issue: 1
• Start Page: 52
• End Page: 52
• Keywords: Oncology; TUMOR-INFILTRATING LYMPHOCYTES; T-CELLS; Life Sciences & Biomedicine; DOCETAXEL; SURVIVAL; Science & Technology
• Research Categories: Engineering, Biomedical; Health Sciences, Pathology

Abstract

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).