AI Tool Predicts Which Lung Cancer Patients Will Benefit From Immunotherapy

Forskare har utvecklat VIGOR-CD8, en artificiell intelligens-modell som förutsäger CD8⁺-immunsignaturer direkt från rutinhämatoxilin-eosinfärgade biopsier. Metoden använder histopatologiska grundmodeller och virtuell genuttrycksmodellering för att identifiera lungcancerpatienter som gynnas av immunterapin nivolumab plus ipilimumab framför kemoterapi. I den blinda valideringen av CM227-studien (n = 1 111) associerades VIGOR-CD8-positiva patienter med längre överlevnad (HR 0,80, p = 0,016), oberoende av behandlingstyp och PD-L1-status. Resultatet bekräftades även i retrospektiv kohorter (HR 0,68, p = 0,0016). Georgia Institute of Technology och Bristol-Myers Squibb utvecklade metoden tillsammans med Emory och Yale. För regionvård och MedTech-investerare erbjuder VIGOR-CD8 en väg att optimera patientstratifiering utan nya laboratorieprotokoll — befintliga vävnadssamlingar räcker. Detta kan påskynda implementering av prediktiv biomarkering och minska onödig kemoterapi.

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.