Aldea, Mihaela

Abstract Background There is limited data on immune checkpoint inhibitor (ICI) use in patients with non-small cell lung cancer (NSCLC) and known pericardial effusions. Methods This international multi-center observational retrospective study included patients with advanced NSCLC with or without pericardial effusions receiving ICIs. Immune-related adverse events (irAEs) were graded per CTCAE v5.0. Overall survival (OS) and progression-free survival (PFS) were estimated by the Kaplan-Meier method. Cox proportional hazards multivariable models assessed the association between survival outcomes and clinicopathologic variables, and an inverse probability of treatment weighting (IPTW) model compared outcomes between the two groups. Results A total of 1248 patients with advanced NSCLC treated with ICIs across 13 institutions were included. Of these, 85 patients (6.8%) had pre-existing pericardial effusions. Compared to controls, patients with pericardial effusions were younger (median age 63 vs 67 years, P = .007), diagnosed more frequently with PD-L1 positive tumors (96% vs 76%, P < .001), and had a higher median number of baseline metastatic sites (3 vs 1, P < .001). IrAEs were similar between both groups (42% vs 38%), and no cardiac irAEs occurred in patients with pericardial effusions. PFS (median 5.6 vs 4.6 months, P = .3) and OS (median 10.2 vs 14.7 months, P = .50) were not statistically different between patients with pericardial effusions and the controls, respectively. In both multivariable and IPTW models, pericardial effusions were not associated with PFS or OS. Conclusions Among patients with NSCLC and pre-existing pericardial effusions, ICIs demonstrate meaningful clinical benefit similar to those without effusions, with no new cardiac or severe toxicities.

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 < 0.001) and HR = 2.51 (p < 0.001) for OS in the discovery and validation sets, and HR = 2.34 (p < 0.001) and HR = 1.87 (p < 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 < 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.

Abstract Purpose: In RET-rearranged non–small cell lung cancer (NSCLC), treatment options after first-generation selective RET inhibitors (SRI) are limited, and the value of SRI retreatment remains unclear. This study evaluates outcomes of SRI retreatment. Experimental Design: This multicenter retrospective study included patients with advanced RET-rearranged NSCLC who received ≥2 SRI-based therapy lines. Of 411 SRI-treated patients, 41 (10%) underwent SRI retreatment. Outcomes included objective response rate (ORR), progression-free survival (PFS), 6-month PFS, time to treatment failure, adverse events (AE). Results: Among 41 patients, 14 (34%) discontinued the initial SRI because of toxicity and 27 (66%) because of progression. After discontinuation for toxicity, all switched to an alternate SRI, achieving an ORR of 67%, median PFS of 9.9 months, and 6-month PFS of 80.3%. AEs reoccurred in nine patients (64%), with grade ≥3 AEs in 3 (21%) who switched at full dose. Among patients who discontinued because of progression, SRI monotherapy (n = 13) achieved an ORR of 23%, median PFS of 7 months, and 6-month PFS of 61.5%. Benefit was observed in patients with brain-only or oligoprogressive disease or dose reduction on first SRI. Combination therapy (n = 14; targeted agents, n = 11; chemotherapy, n = 3) achieved an ORR of 39%, median PFS of 4 months, and 6-month PFS of 27.3%. Conclusions: Same-class SRI switch after toxicity is feasible and clinically active but warrants cautious, dose-adjusted switching to mitigate toxicities. The efficacy of SRI rechallenge after progression seems limited overall. However, selected patients, such as those with brain-only or oligoprogressive disease, may derive benefit.

Abstract Oncogenic translocations involving the MET gene have been reported in several cancer types, but detailed clinicogenomic characterization of these cancers is not well defined. In addition, prospective clinical trials evaluating the antitumor activity of MET inhibitors in MET rearrangement–positive cancers are limited. In this study, in a pan-cancer analysis of >46,000 solid tumors with comprehensive genomic profiling, we identified oncogenic MET rearrangements in ∼0.04% of cancers. Preliminary analysis from a phase II clinical trial of the type I MET tyrosine kinase inhibitor (TKI) vebreltinib in MET fusion–positive solid tumors demonstrated an objective response rate of 50% and disease control rate of 79%, with antitumor activity seen in diverse cancer types, including lung adenocarcinoma and intrahepatic cholangiocarcinoma, among others. Similar to MET exon 14–altered lung cancer, secondary mutations in the kinase domain can confer resistance to MET TKIs in MET fusion–positive cancers. Overall, these data categorize MET rearrangements as actionable targets in solid tumors. Significance: MET rearrangement–positive cancers are not well-characterized, and optimal treatment strategies are yet to be defined. Through comprehensive genomic analysis, preclinical modeling, and preliminary results of a phase II clinical trial, we demonstrate that MET fusions are a unique molecular subtype of cancers targetable with vebreltinib, a TKI in development.