Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images With Outcomes of Immunotherapy in Patients With NSCLC.

Autor:	Rakaee M; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.; Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway.; Department of Clinical Pathology, University Hospital of North Norway, Tromso, Norway., Adib E; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.; Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts., Ricciuti B; Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts., Sholl LM; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts., Shi W; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts., Alessi JV; Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts., Cortellini A; Department of Surgery and Cancer, Imperial College London, London, United Kingdom., Fulgenzi CAM; Department of Surgery and Cancer, Imperial College London, London, United Kingdom.; Department of Medical Oncology, University Campus Bio-Medico, Rome, Italy., Viola P; Department of Cellular Pathology, Imperial College London NHS Trust, London, United Kingdom., Pinato DJ; Department of Surgery and Cancer, Imperial College London, London, United Kingdom.; Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy., Hashemi S; Department of Pulmonology, Amsterdam UMC, Amsterdam, the Netherlands., Bahce I; Department of Pulmonology, Amsterdam UMC, Amsterdam, the Netherlands., Houda I; Department of Pulmonology, Amsterdam UMC, Amsterdam, the Netherlands., Ulas EB; Department of Pulmonology, Amsterdam UMC, Amsterdam, the Netherlands., Radonic T; Department of Pathology, Amsterdam UMC, Amsterdam, the Netherlands., Väyrynen JP; Cancer and Translational Medicine Research Unit, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland., Richardsen E; Department of Clinical Pathology, University Hospital of North Norway, Tromso, Norway., Jamaly S; Department of Medical Biology, UiT The Arctic University of Norway, Tromso, Norway., Andersen S; Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway.; Department of Oncology, University Hospital of North Norway, Tromso, Norway., Donnem T; Department of Clinical Medicine, UiT The Arctic University of Norway, Tromso, Norway.; Department of Oncology, University Hospital of North Norway, Tromso, Norway., Awad MM; Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts., Kwiatkowski DJ; Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts.
Jazyk:	angličtina
Zdroj:	JAMA oncology [JAMA Oncol] 2023 Jan 01; Vol. 9 (1), pp. 51-60.
DOI:	10.1001/jamaoncol.2022.4933
Abstrakt:	Importance: Currently, predictive biomarkers for response to immune checkpoint inhibitor (ICI) therapy in lung cancer are limited. Identifying such biomarkers would be useful to refine patient selection and guide precision therapy. Objective: To develop a machine-learning (ML)-based tumor-infiltrating lymphocytes (TILs) scoring approach, and to evaluate TIL association with clinical outcomes in patients with advanced non-small cell lung cancer (NSCLC). Design, Setting, and Participants: This multicenter retrospective discovery-validation cohort study included 685 ICI-treated patients with NSCLC with median follow-up of 38.1 and 43.3 months for the discovery (n = 446) and validation (n = 239) cohorts, respectively. Patients were treated between February 2014 and September 2021. We developed an ML automated method to count tumor, stroma, and TIL cells in whole-slide hematoxylin-eosin-stained images of NSCLC tumors. Tumor mutational burden (TMB) and programmed death ligand-1 (PD-L1) expression were assessed separately, and clinical response to ICI therapy was determined by medical record review. Data analysis was performed from June 2021 to April 2022. Exposures: All patients received anti-PD-(L)1 monotherapy. Main Outcomes and Measures: Objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) were determined by blinded medical record review. The area under curve (AUC) of TIL levels, TMB, and PD-L1 in predicting ICI response were calculated using ORR. Results: Overall, there were 248 (56%) women in the discovery cohort and 97 (41%) in the validation cohort. In a multivariable analysis, high TIL level (≥250 cells/mm2) was independently associated with ICI response in both the discovery (PFS: HR, 0.71; P = .006; OS: HR, 0.74; P = .03) and validation (PFS: HR = 0.80; P = .01; OS: HR = 0.75; P = .001) cohorts. Survival benefit was seen in both first- and subsequent-line ICI treatments in patients with NSCLC. In the discovery cohort, the combined models of TILs/PD-L1 or TMB/PD-L1 had additional specificity in differentiating ICI responders compared with PD-L1 alone. In the PD-L1 negative (<1%) subgroup, TIL levels had superior classification accuracy for ICI response (AUC = 0.77) compared with TMB (AUC = 0.65). Conclusions and Relevance: In these cohorts, TIL levels were robustly and independently associated with response to ICI treatment. Patient TIL assessment is relatively easily incorporated into the workflow of pathology laboratories at minimal additional cost, and may enhance precision therapy.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu