AI-derived CD8⁺ cytotoxic T-cell immune signatures from baseline H&E images to predict immunotherapy benefit over chemotherapy in non–small cell lung cancer: Blinded validation in CheckMate-227 (CM227).

医学 肿瘤科 易普利姆玛 免疫疗法 内科学 无容量 化疗 比例危险模型 肺癌 队列 生存分析 免疫系统 回顾性队列研究 总体生存率 阶段(地层学) 活检 癌症 多元分析 临床试验 免疫学 组织病理学 试验预测值 前瞻性队列研究 无进展生存期 生物标志物 基因签名 队列研究
作者
Cristian Barrera,Wiem Safta,Pushkar Mutha,Mohammadhadi Khorrami,Diederik J. Grootendorst,Oana Mustatea,Nathanial Eddy,Tilak Pathak,Miguel Lopez de Rodas Gregorio,Kurt A. Schalper,Suresh S. Ramalingam,Vamsidhar Velcheti,Anant Madabhushi
出处
期刊:Journal of Clinical Oncology [Lippincott Williams & Wilkins]
卷期号:44 (16_suppl): 8534-8534
标识
DOI:10.1200/jco.2026.44.16_suppl.8534
摘要

8534 Background: Immunotherapy (IO) has transformed treatment for non–small cell lung cancer (NSCLC), but not all patients (pts) benefit, underscoring the need for predictive biomarkers to guide IO versus chemotherapy (Ch). CheckMate 227 (CM227; NCT02477826) showed a survival benefit of first-line nivolumab plus ipilimumab (nivo+ipi) over Ch in stage IV NSCLC. CD8⁺ T cells are key mediators of antitumor immunity and are associated with IO benefit. We developed an artificial intelligence (AI)–based pipeline (VIGOR-CD8) that predicts spatial CD8⁺ immune signatures from routine baseline H&E whole-slide images using histopathology foundation model embeddings (H-Optimus-0) and virtual gene expression modeling (HE2Gene), and evaluated its ability to identify pts who do and do not derive IO benefit over Ch in CM227. Methods: 1,598 pts with advanced NSCLC were analyzed, including multi-institutional retrospective cohorts (n = 487; 65 for patch-level CD8⁺ prediction and overall survival (OS), 422 for patient-level OS) and a blinded CM227 validation subset (n = 1,111). For orthogonal validation, 86,470 H&E patches were co-registered with quantitative CD8⁺ immunofluorescence. H-Optimus-0 and HE2Gene immune-related embeddings were used to train a random forest classifier to predict patch-level CD8⁺ probability. Patch-level probabilities were aggregated into a patient-level CD8⁺ signature and dichotomized into biomarker-positive (B⁺) and biomarker-negative (B⁻) groups by the training-set median. Cox models assessed the impact of VIGOR-CD8 on OS. In CM227, prognostic and predictive utility were evaluated using treatment-specific analyses; investigators were blinded to outcomes, and models were trained on independent, non-overlapping cohorts. Results: VIGOR-CD8 was associated with longer OS in the testing cohort (n = 422; HR 0.68, 95% CI 0.53–0.87, p = 0.00163) and CM227 (n = 1,111; HR 0.8, 95% CI 0.67–0.96, p = 0.016), irrespective of treatment type and PD-L1 expression. Among pts with evaluable PD-L1, B⁺ pts treated with nivo+ipi had superior OS versus Ch (n = 617; HR 0.72, 95% CI 0.58–0.90, p = 0.003), while in B⁻ pts there was no significant OS difference between treatment arms (n = 130; HR 1.18, 95% CI 0.79–1.75, p = 0.436), supporting a predictive rather than purely prognostic role for VIGOR-CD8. Conclusions: An AI-derived CD8⁺ immune signature from routine baseline H&E slides was associated with favorable OS in CM227 and predicted differential benefit from nivo+ipi versus Ch. VIGOR-CD8 may help identify advanced NSCLC pts most likely to benefit from first-line dual IO, but further validation in independent and prospective trials is warranted.

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