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

TitleDeep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer.
Publication TypeJournal Article
Year of Publication2023
AuthorsBarrera C, Corredor G, Viswanathan VSankar, Ding R, Toro P, Fu P, Buzzy C, Lu C, Velu P, Zens P, Berezowska S, Belete M, Balli D, Chang H, Baxi V, Syrigos K, Rimm DL, Velcheti V, Schalper K, Romero E, Madabhushi A
JournalNPJ Precis Oncol
Volume7
Issue1
Pagination52
Date Published2023 Jun 01
ISSN2397-768X
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).

DOI10.1038/s41698-023-00403-x
Alternate JournalNPJ Precis Oncol
PubMed ID37264091
PubMed Central IDPMC10235089
Grant ListUL1 TR001863 / TR / NCATS NIH HHS / United States
Related Faculty: 
Priya Velu, M.D., Ph.D.

Pathology & Laboratory Medicine 1300 York Avenue New York, NY 10065 Phone: (212) 746-6464
Surgical Pathology: (212) 746-2700