Lou, Yanyan

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.

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%)