Haymaker, C

PURPOSE We tested the hypothesis that adding metastasis-directed therapy (MDT) to standard-of-care (SOC) systemic therapy improves progression-free survival (PFS) among patients with oligometastatic disease. METHODS EXTEND was a multicenter randomized phase II trial. Patients with 1-5 metastases were randomly assigned to MDT + SOC versus SOC in one of the six baskets (breast, pancreas, kidney, two prostate baskets, and an other basket) with basket-specific stratification and powering. PFS, the primary end point, was prespecified in the per-protocol set within each basket, across all baskets, and across all baskets excluding the prostate baskets. Exploratory end points included circulating tumor DNA (ctDNA) and immune profiling. RESULTS From 2018 through 2023, 521 patients were screened, 350 were randomly assigned, and 334 were analyzed per protocol (MDT + SOC, n = 166; SOC, n = 168). Radiotherapy was used as MDT for 98% of metastases (370/379). Overall, after a median follow-up of 53 months, PFS was improved with MDT + SOC (hazard ratio [HR], 0.54 [95% CI, 0.41 to 0.72], P < .001). Similarly, PFS was improved when excluding the prostate baskets (HR, 0.60 [95% CI, 0.40 to 0.89]). Within each basket, PFS superiority was identified for the pancreas, prostate, and other baskets, whereas the breast and kidney baskets were inconclusive. At enrollment, detectable ctDNA correlated with shorter PFS and survival; by contrast, ctDNA clearance 3 months postenrollment correlated with improved survival. MDT + SOC-induced systemic immune activation was most pronounced among baskets demonstrating PFS superiority. CONCLUSION The phase II EXTEND trial supports the addition of MDT to SOC for oligometastatic disease. Histology-specific efficacy signals were identified for phase III testing. Translational insights suggest the potential for optimizing the definition of oligometastasis using ctDNA and point to systemic immune responses as a possible mechanism of benefit from MDT.

Abstract Background: Immune checkpoint inhibitors (ICIs) have improved survival in non-small-cell lung cancer (NSCLC), yet only a subset of patients benefits, and biomarkers like PD-L1 remain limited. Here, we introduce a deep learning-based pathomics framework that utilizes routine H&E-stained slides to predict therapeutic response and survival outcomes in ICI-treated metastatic NSCLC. Methods: The study included 797 ICI-treated NSCLC patients from MD Anderson, with external validation in 280 patients from Mayo Clinic, Gustave Roussy, and the Phase III ICI-naïve LUSC Lung-MAP S1400I trial receiving nivolumab with or without ipilimumab. Path-IO (Pathology-Driven Immunotherapy Optimization) comprised of four major steps: (1) pathologist-verified tissue classifier segmented WSIs into eight compartments—Background, Bronchi, Immune, Lung, Necrosis, Stroma, Tumor, and Vessel—and was validated on TCGA and CPTAC dataset; (2) a survival prediction module generating patient-level risk scores in the MD Anderson cohort and validated across external datasets; (3) integration of Path-IO risk scores with radiomics and clinical features to improve prognostic accuracy; and (4) biological interpretability analyses correlating Path-IO risk immune contexture from multiplex immunofluorescence and transcriptomic signatures from NanoString profiling. Results: Path-IO effectively stratified patients into high and low-risk groups with significant survival differences. In the MD Anderson cohort, it achieved HR = 2.11 (p &lt; 0.001) and HR = 2.51 (p &lt; 0.001) for OS in the discovery and validation sets, and HR = 2.34 (p &lt; 0.001) and HR = 1.87 (p &lt; 0.001) for PFS, respectively. In the multicenter Phase III Lung-MAP S1400I trial, Path-IO achieved HR = 1.78 (p = 0.016) for OS and HR = 2.76 (p = 0.006) for PFS. In external validation, it predicted outcomes in the Gustave Roussy (OS = 1.97, p = 0.003; PFS = 1.51, p = 0.046) and Mayo Clinic (OS = 2.46, p = 0.007; PFS = 2.45, p = 0.027) cohorts. Path-IO outperformed PD-L1 and remained an independent predictor in multivariate analysis (p &lt; 0.001). It enabled data-driven frontline selection between anti-PD-1 monotherapy and chemo-immunotherapy, offering guidance toward more personalized treatment decisions. Integration with radiomics and clinical features further improved predictive accuracy (OS 0.63→0.75; PFS C-index 0.58→0.70), while high Path-IO risk scores correlated with immune-cold phenotypes identified by multiplex immunofluorescence and NanoString transcriptomics. Conclusions: Path-IO demonstrates that deep learning models rooted in histopathologic architecture can generate interpretable and biologically informed survival predictions in NSCLC treated with ICIs. By integrating pathology, radiology, and clinical data, Path-IO provides complementary predictive value beyond established biomarkers such as PD-L1. Citation Format: Rukhmini Bandyopadhyay, Linghzi Hong, Mihaela Aldea, Shenduo Li, Lodovica Zullo, Frank R. Rojas, Maliazurina B. Saad, Maricel C. Marin, Muhammad Waqas, Jiexin Zhang, Eman Showkatian, Claudio A. Arrechedera, Xiaoyu Han, Yuliya Kitsel, Sherif Ismail, Muhammad Aminu, Bo Zhu, Carol C. Wu, Brett W. Carter, Joe Y Chang, Zhongxing Liao, Maria R. Ghigna, Davide Soldato, Hai T. Tran, Xiuning Le, Tina Cascone, Bingnan Zhang, Haniel A. Araujo, Mehmet Altan, Simon Heeke, David Jaffray, Don L. Gibbons, Ara Vaporciyan, J Jack Lee, Neda Kalhor, Cara Haymaker, Ignacio Wistuba, John V. Heymach, Yanyan Lou, Natalie Vokes, Luisa M. Solis Soto, Jianjun Zhang, Jia Wu. Path-IO: A deep learning pathomics framework for personalized immunotherapy selection and outcome prediction in metastatic non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4003.