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Squamous cell lung cancer (SqCLC) accounts for 20–30% of non-small cell lung cancer (NSCLC) and remains associated with a poorer prognosis compared with adenocarcinoma. Despite advances in treatment, 5-year overall survival for advanced (stage IV) disease remains below 10–15%. Unlike non-squamous NSCLC, SqCLC is characterized by a high tumor mutational burden and complex genomic landscape dominated by alterations in tumor suppressor genes and lineage survival pathways including TP53, CDKN2A, PIK3CA, FGFR1, SOX2, and the NFE2L2/KEAP1 oxidative stress pathway, as well as dysregulation of the NOTCH signaling pathway, but it harbors relatively few actionable oncogenic drivers, resulting in limited treatments for targeted therapy. HER2 alterations can occur by multiple mechanisms, including activating mutations, gene amplifications, and protein overexpression. They comprise a very small percentage of NSCLC, with HER2 mutations reported in approximately 1–3% and HER2 amplifications observed roughly in 2–4%. While HER2 alterations are well characterized in lung adenocarcinoma, the prevalence, genomic context, and clinical significance of HER2 alterations in SqCLC remain incompletely defined. Advances in next-generation sequencing have led to improved ability to detect HER2 alterations and facilitated the development of HER2 targeted therapies. Available treatments for advanced/metastatic SqCLC have been historically limited to platinum-doublet chemotherapy, with immune checkpoint inhibitors such as anti-PD-1/PD-L1 newly emerging in the past decade. Selective HER2 tyrosine kinase inhibitors and HER2 antibody/drug conjugates have shown improved efficacy in HER2-altered NSCLC as shown in DESTINY-LUNG02 and BEAMION LUNG-1 trials; however, most of the enrolled patients had non-squamous histology, with minimal or no SqCLC-specific efficacy data reported. Future progress in HER2-altered SqCLC will require inclusion of SqCLC in HER2 basket trials, incorporation of comprehensive molecular profiling and standardized HER2 testing in squamous histology. This review summarizes the current state of knowledge of HER2 biology in SqCLC and highlights areas for future directions for precision oncology in SqCLC.

Summary Functional ex vivo assays using live tumor tissues have demonstrated strong predictive accuracy for response to immune checkpoint inhibitors (ICIs) but are not scalable, requiring manual processing of large resections collected at academic centers. Here, an ex vivo live tumor fragment (LTF) platform was developed using standard-of-care biopsies from 228 patients with suspected malignancy collected across prospective, multicenter observational trials and biobanks. Hierarchical clustering of ICI-mediated changes in cytokine production identified two groups: responders and nonresponders. A binary classifier (elive index) using 8 cytokines achieved an AUC of 0.99 for cluster prediction. elive index correctly predicted clinical benefit in 93% (26/28) of patients ( P = 3.2x10 -5 ) and accurately identified 83% (10/12) of objective responders. Critically, elive responders were identified among biomarker-negative patients, highlighting the platform as a scalable approach that complements existing companion diagnostics and expands the population of patients identified to benefit from ICI therapy.

TPS2673 Background: The main challenge for developing CAR T therapies for solid tumors is the lack of targets that distinguish tumor from normal cells, resulting in on-target, off-tumor toxicity. Tmod logic-gated CAR T therapy addresses this challenge by incorporating 2 CARs on the same T cell: an activator targeting a marker on both tumor and normal cells, and a blocker targeting HLA-A*02 that inhibits CAR T activity against normal cells while allowing activation against tumor cells (with HLA-A*02 LOH), improving tumor selectivity and decreasing toxicity. Early safety results from 3 ongoing phase 1/2 clinical trials of logic-gated, Tmod CAR T therapy (EVEREST-1, EVEREST-2, and DENALI-1) have demonstrated manageable safety and tolerability in patients with advanced solid tumors (Grierson et al, SITC , 2024; Ward et al, SITC 2025; Specht et al, SABCS , 2025). Early efficacy results include the first ever reported complete response in a patient with non-small cell lung cancer following treatment with a CAR T-cell therapy (A2B694). A2B543 is an autologous Tmod CAR T therapy that contains the same Tmod construct as A2B694 with an added membrane-tethered IL-12 (memIL12) booster. Specifically, A2B543 is comprised of autologous Tmod cells transduced with 2 lentiviral vectors: one expressing both the HLA-A*02-targeted blocker and the mesothelin-targeted CAR activator; and a second expressing the memIL12 booster. Interleukin 12 (IL-12) is a potent, pro-inflammatory cytokine that plays a crucial role in inducing antitumor immune responses; however, systemic IL-12 can be prohibitively toxic (Jia et al, Front Immunol , 2022). In A2B543, expression of the memIL12 cassette is under the control of an NFAT promoter and is induced during antigen engagement or T cell activation. This inducible memIL12 is designed to reduce the toxicity associated with systemic IL-12 while enhancing the long-term potency and persistence of Tmod (Zhang et al, J Immunother Cancer , 2025). Methods: EVEREST-2 (NCT06051695) is a phase 1/2, open-label, nonrandomized study evaluating the safety and efficacy of A2B543 in adults with recurrent/metastatic mesothelin-expressing cancers with tumor-associated HLA-A*02 LOH, including mesothelioma, colorectal, non-small cell lung, pancreatic, or ovarian cancer. Patients are enrolled through BASECAMP-1 (NCT04981119), a master prescreening study that identifies patients with HLA LOH via next-generation sequencing and cryopreserves leukapheresis product. Upon progression, A2B543 is manufactured and then administered after lymphodepletion. The phase 1 primary objective is to evaluate the safety and tolerability of A2B543 and identify a recommended phase 2 dose (RP2D). The phase 2 primary objective is to assess overall response rate. Clinical trial information: NCT06051695 .

e20595 Background: Circadian regulation of immune function has been proposed as a modifier of immune checkpoint inhibitor (ICI) efficacy, with retrospective studies suggesting improved outcomes with earlier infusion timing. Whether these findings translate to routine oncology practice, where ICIs are administered across multiple lines, frequently combined with chemotherapy, and subject to treatment interruptions, remains uncertain. We evaluated the association between ICI infusion timing and overall survival (OS) in a real-world cohort of patients with non-small cell lung cancer (NSCLC). Methods: A retrospective cohort study included adults with NSCLC who received at least one line of ICI therapy at a community oncology center between January 2018 and December 2024. Infusion timing was defined at the patient level as morning-only ( < 1300 hours), evening-only (≥1300 hours), or mixed timing, reflecting receipt of infusions exclusively in different time-of-day categories across treatment lines. OS was measured from diagnosis to death or last follow-up. Kaplan-Meier and Cox proportional hazards models were used. Multivariable analyses adjusted for stage, ECOG performance status, body mass index, and premature ICI discontinuation. Line of therapy was excluded due to collinearity with mixed-timing classification and risk of immortal time bias. Results: A total of 127 patients were included (morning-only n = 77; mixed timing n = 36; evening-only n = 14). Staging data were available for 99.2%, and 94 deaths occurred. Median OS was 495 days for morning-only, 850 days for mixed timing, and 388 days for evening-only patients. Kaplan-Meier analysis showed no statistically significant OS differences across groups. In sensitivity analysis excluding patients with mixed infusion timing, morning-only and evening-only groups demonstrated overlapping survival curves, with no evidence of a time-of-day–specific survival advantage. In univariate analysis, mixed timing was associated with a borderline reduction in mortality compared with morning-only infusions (HR 0.63, 95% CI 0.39-1.00), while evening-only timing was not (HR 1.63, 95% CI 0.85-3.16). In multivariate analysis restricted to stage III-IV disease, infusion timing was not independently associated with OS. ECOG performance status, premature ICI discontinuation and advanced staging were the strongest predictors of mortality. Conclusions: In this real-world NSCLC cohort, ICI infusion timing was not independently associated with overall survival after adjustment for patient fitness and treatment continuity. These findings suggest that in pragmatic settings, clinical factors may outweigh subtle chronotherapy effects reported in more selected populations.

e18149 Background: Laryngeal neuroendocrine carcinoma (LNEC) is rare and biologically heterogeneous. The independent prognostic roles of tumor differentiation and stage remain incompletely defined. Methods: Patients diagnosed with LNEC (2000–2022) were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Overall survival (OS) and cancer-specific survival (CSS) were estimated using a Kaplan–Meier analysis. Tumor differentiation (Diff) was harmonized across SEER grade fields: legacy Grade Recode (through 2017) was used when available; otherwise, Grade Clinical/Pathological (2018+) codes (1=well, 2=moderate, 3=poor, 4=undifferentiated) defined Diff. Multivariable Cox regression adjusted for stage, Diff, age, and sex. Cases with missing survival time, stage, or Diff were excluded (n=97). Results: The cohort included 157 patients (median age 63 years; 63% male). Diff was well in 4%, moderate in 29%, poor in 34%, and undifferentiated in 12% of patients. At diagnosis, 22% of patients had localized, 27% had regional, and 28% had distant disease. Treatments included surgery in 31%, chemotherapy in 42%, and adjuvant radiation in 13% of patients. Median OS differed significantly by Diff, ranging from 52.5 months (mo) for well, 90 mo for moderate, 12 mo for poor, and 17 mo for undifferentiated (p<0.001). Five-year OS was 50%, 58%, 23%, and 37%, respectively, with a similar gradient for CSS (p=0.001). Survival remained differentiation-associated within surgical (p=0.004) and chemotherapeutic subgroups (p<0.001). Median OS by stage was 96 mo for localized disease, 30 mo for regional nodes only, 8.5–7.5 mo for regional direct extension ± nodes, and 11 mo for distant disease. On multivariable analysis, stage and differentiation—driven primarily by poorly differentiated tumors—were independently associated with OS. Conclusions: Both tumor differentiation and stage independently predicted survival in LNEC, indicating that tumor biology and anatomic extent each contribute to prognosis. Outcomes remain suboptimal in poorly differentiated and advanced-stage LNEC, underscoring the need for treatment optimization and clinical trial development. Variable HR (95% CI) p Differentiation (Diff) Moderate vs Well 1.76 (0.49–6.31) 0.39 Poor vs Well 6.11 (1.63–22.93) 0.007 Undifferentiated vs Well 2.28 (0.60–8.76) 0.23 Stage Nodes only vs Localized 2.21 (1.14–4.38) 0.023 Direct extension only vs Localized 14.04 (2.44–80.97) 0.003 Direct + nodes vs Localized 7.49 (3.19–17.63) <0.001 Distant vs Localized 3.34 (1.63–6.83) 0.001 Male vs Female 2.28 (1.34–3.89) 0.002

8579 Background: LOH may provide a means to target tumor versus normal cells, to augment the efficacy and safety of MSLN-targeted programs (Hecht et al. JCO . 2022). A2B694 is an autologous, logic-gated, Tmod CAR T therapy designed to improve tumor selectivity and decrease toxicity by integrating an MSLN CAR activator with an HLA-A*02 blocker (Hamburger et al. Mol Imm. 2020). Methods: The first-in-human, open-label, phase 1/2 EVEREST-2 (NCT06051695) study of A2B694 in patients with recurrent/metastatic MSLN-expressing cancers with tumor-associated HLA-A*02 LOH. The prescreening study BASECAMP-1 (NCT04981119) identifies eligible patients and cryopreserves leukapheresis product. Upon progression, A2B694 is manufactured and administered after lymphodepletion. Phase 1 primary objective: evaluate the safety and tolerability of A2B694 and identify a recommended phase 2 dose. Results: As of 05 January 2026, 13 patients were enrolled: 8 women/5 men, median age 59 years, 11 non-Hispanic White/2 Hispanic with unknown race. Tumor types included colorectal (n = 4), ovarian (n = 3), pancreatic (n = 3), non–small cell lung adenocarcinoma (NSCLC), gastro-esophageal, and mesothelioma (n = 1 each). A2B694 dose level (DL) groups were: DL1: 1×10 8 cells (n = 3), DL2: 2×10 8 cells (n = 4), DL3: 4×10 8 cells (n = 5), and DL4: 6×10 8 cells plus low-dose IL-2 (n = 1). Lymphodepletion prior to administration of A2B694 was well-tolerated, with expected, transient cytopenias. The only adverse event reported in more than 1 patient was grade 3 neutropenia. One patient had grade 3 ICANS and 1 patient had grade 1 CRS. There were no dose-limiting toxicities or new safety signals after up to 17 months follow-up. All 13 patients received A2B694, were efficacy-evaluable, and had A2B694 detected post-infusion in peripheral blood. While A2B694 was not detected in tumor biopsies collected in patients treated at DL1 (0/2), all patients treated at DL2-DL4 with available biopsies (3/3) had detectable A2B694 in the tumor microenvironment. A patient with KRAS G12V /STK11 co-mutated NSCLC who had progressed on carboplatin, pemetrexed, and pembrolizumab achieved a complete response (CR) at D90 post-infusion per RECIST 1.1 by central review and had a confirmed CR at D180. In addition, PET-CT scan and ctDNA on D190 demonstrated no evidence of disease. On D243, the patient had a CNS relapse, with an ongoing non-CNS CR per RANO-BM at D284. At M12, the patient's CT showed no new findings and persistence of A2B694 in the blood was confirmed by ddPCR. Conclusions: We report the first patient with NSCLC to have a CR after CAR T. Overall, A2B694 demonstrated manageable safety and tolerability in patients with advanced solid MSLN-expressing tumors with tumor-associated HLA-A*02 LOH. The maximum tolerated dose has not been reached; dose-escalation continues. Clinical trial information: NCT06051695 .

2646 Background: Immune checkpoint inhibitors (ICIs) have transformed the management of many advanced cancers but identifying patients who are likely or unlikely to benefit from ICI treatment remains a critical challenge. Biomarkers, such as PD-L1, mismatch repair deficiency, and tumor mutational burden, capture only static, unidimensional features of the complex tumor microenvironment, and therefore inadequately predict response to immunotherapy in patients. In contrast, live tumor fragments (LTFs) preserve the full complexity of the tumor microenvironment and enable functional ex vivo assessments of ICI activity. Previous efforts have had limited clinical applicability because they required larger resection specimens often collected at a single academic center. Here, the predictive capability of an ex vivo platform using core needle and forceps biopsies from multiple clinical sites was assessed and compared to approved biomarkers. Methods: Patients with eligible solid tumors (where ICI has an indication, including renal, non-small cell lung, bladder, colorectal, and triple-negative breast cancer, and others) were enrolled in ongoing, prospective observational trials (NCT05478538, NCT05520099, NCT06349642). Biopsies were cut into LTFs, encapsulated in hydrogel, and treated sequentially with IgG antibody followed by ICI (αPD-[L]1 with or without αCTLA-4). Cytokine production was assessed by a multiplexed bead-based immunoassay at multiple time points during ex vivo treatment. Using receiver operating characteristic and precision recall analyses, we identified 9 predictive cytokines (including IFN-γ, granzyme B, and CXCL9/10), which were used to develop the Elephas score (ELP-score) to assess cytokine response to ex vivo ICI treatment. Results: Of 167 eligible patient specimens, 85% (n = 142) passed quality control metrics and 130 were used for model development. Hierarchical clustering of cytokine production revealed that 27% (n = 35) of patient specimens exhibited a cytokine response to ICI. In a validation set of 20 tumors, collected from patients who were subsequently treated with ICI, ELP-score positivity correctly identified 9 of 11 patients (82%) who had an objective response (PR/CR) to ICI, including 2 patients who were negative for FDA-approved standard-of-care ICI biomarkers. Furthermore, 5 of 5 patients (100%) with clinical progressive disease were correctly characterized as ELP-score negative. Conclusions: Using routine biopsy specimens, the ex vivo live tumor platform accurately identifies patients with clinical response to ICI therapy, including those incorrectly identified by conventional biomarkers. Ongoing efforts will test these findings in an additional validation set. If confirmed, the ex vivo LTF platform could enable the identification of patients likely to benefit from ICI therapy. Clinical trial information: NCT05478538 , NCT05520099 , NCT06349642 .

1615 Background: Immune checkpoint inhibitors (ICIs) have revolutionized lung cancer treatment but cause adverse events, including immune-related colitis (ir-colitis). Accurate identification of ir-colitis is critical for improving patient outcomes and clinical research. However, current methods relying on ICD codes have substantial limitations: non-specific categories that fail to capture the cases or distinguish cases from infectious or ischemic etiologies, as well as incomplete coding that leads to missed cases. Large language models (LLMs), with advanced natural language understanding capabilities, offer a scalable approach for automated phenotyping using unstructured clinical notes. We aimed to develop and evaluate an LLM-based approach for identifying ir-colitis in patients with lung cancer receiving ICIs and compare it with ICD code based method. Methods: We identified 5,278 lung cancer patients with ICI administration at Mayo Clinic (2012-2025). To investigate ir-colitis occurrence within two years after ICI initiation, patients were stratified into three ICD-based cohorts: (1) IR-Colitis, with clinician-assigned ir-colitis-specific diagnosis codes; (2) Non-IR Colitis, with colitis diagnosis codes excluding ir-colitis; and (3) No-Colitis, absent of any colitis-related codes. We used three open-source LLMs to process clinical notes : Llama4, MedGemma, and Qwen. Prompts were designed to extract both gold standard evidence (explicit ir-colitis terminology, such as "immune-mediated colitis" and "checkpoint inhibitor colitis") and supportive evidence (such as pathology and lab results). Results: Llama4 demonstrated superior accuracy among three LLMs in manual evaluation and was thus selected for all cohort analyses. Among ICD-based IR-Colitis patients (n = 339), 82.0% had confirmatory evidence while 18% lacked evidence, suggesting potential false-positive coding. In the Non-IR Colitis cohort (n = 297), LLM-based extraction identified 43.8% patients in fact had ir-colitis, demonstrating substantial misclassification. In the No-colitis cohort (n = 4,642, random sample of 996 for analysis), the LLM identified 10.7% with documented ir-colitis, revealing substantial missing by administrative coding. Conclusions: LLM-based phenotyping demonstrates superior sensitivity for identifying ir-colitis compared to ICD codes alone, successfully uncovering cases missed by administrative coding. This scalable approach enables more comprehensive case identification for immunotherapy safety research and clinical surveillance. ICD-Based Cohorts LLM-Identified ir-Colitis Gold Standard Evidence Supportive Evidence Any Evidence ir-Colitis (n = 339) 277/339 (81.7%) 275/339 (81.1%) 278/339 (82.0%) Non-ir-Colitis (n = 297) 129/297 (43.4%) 126/297 (42.4%) 130/297 (43.8%) No Colitis (n = 996) 106/996 (10.6%) 104/996 (10.4%) 107/996 (10.7%)

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.

Background: In the last decade, a surge of advancements in and approvals of immunotherapies has revolutionized the treatment of non-small-cell lung cancer (NSCLC). Even as an increasing number of immune checkpoint inhibitors and antibody–drug conjugates gained FDA approval for the remarkable response and durable remission they imparted to some patients, persistent challenges of primary and acquired resistance, imperfect predictive biomarkers, and toxicity remain. Methods: This review identifies and summarizes the most significant emerging clinical data being reported for immunotherapies in NSCLC. Results: We found that the most recent clinical trials are centered on antibody–drug conjugates, bispecific and multispecific antibodies, cellular therapies, and immunocytokines, while other ongoing trials continue to explore new monoclonal antibodies as well as a variety of immunotherapy modalities. Conclusions: Immunotherapy in NSCLC remains an area of rapid advancement, with the potential for further improvements in safety and efficacy as monotherapy and in multimodal approaches.