Barrera, Cristian; Corredor, Germán; Viswanathan, Vidya Sankar; Ding, Ruiwen; Toro, Paula; Fu, Pingfu; Buzzy, Christina; Lu, Cheng; Velu, Priya; Zens, Philipp; Berezowska, Sabina; Belete, Merzu; Balli, David; Chang, Han; Baxi, Vipul; Syrigos, Konstantinos; Rimm, David L; Velcheti, Vamsidhar; Schalper, Kurt; Romero, Eduardo; ... (2023). Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer. NPJ precision oncology, 7(1), p. 52. Springer Nature 10.1038/s41698-023-00403-x
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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).
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
04 Faculty of Medicine > Service Sector > Institute of Pathology > Clinical Pathology 04 Faculty of Medicine > Service Sector > Institute of Pathology 04 Faculty of Medicine > Service Sector > Institute of Pathology > Tumour Pathology |
Graduate School: |
Graduate School for Health Sciences (GHS) |
UniBE Contributor: |
Zens, Philipp Immanuel, Berezowska, Sabina Anna |
Subjects: |
500 Science > 570 Life sciences; biology 600 Technology > 610 Medicine & health |
ISSN: |
2397-768X |
Publisher: |
Springer Nature |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
05 Jun 2023 14:29 |
Last Modified: |
11 Jun 2023 02:21 |
Publisher DOI: |
10.1038/s41698-023-00403-x |
PubMed ID: |
37264091 |
BORIS DOI: |
10.48350/183131 |
URI: |
https://boris.unibe.ch/id/eprint/183131 |
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