Citations

2036 Background: Patients with oncogene-driven metastatic non–small cell lung cancer (NSCLC) treated with CNS-penetrant tyrosine kinase inhibitors (TKIs) frequently develop brain metastases (BM) while continuing targeted therapy, and outcomes of adding local CNS-directed therapy in this setting are not well defined. We evaluated outcomes associated with local CNS therapy after post-TKI BM diagnosis. Methods: We conducted a retrospective cohort study using the TriNetX Global Collaborative Network. Adults with NSCLC treated with osimertinib or lorlatinib who subsequently had a coded diagnosis of BM (ICD-10 C79.31) at least 1 month after TKI initiation and evidence of continued TKI use were included. Patients were stratified by receipt of CNS-directed local therapy (SRS/SBRT or neurosurgery) within 3 months of the index BM diagnosis versus no local therapy. The primary outcome was all-cause mortality within 180 days of the index event. Secondary outcomes included hospitalization and systemic corticosteroid use. Cohorts were balanced using 1:1 propensity score matching on demographics, comorbidities, extracranial disease, laboratory values, and TKI exposure. Survival analyses were performed, and proportional hazards assumptions were validated using Schoenfeld residuals (p>0.05). Results: Among 1,157 eligible patients, 1,053 matched pairs were generated. Within 180 days of the index brain metastasis diagnosis, mortality was lower among patients who received local CNS therapy compared with those who did not (10.9% vs 18.1%), corresponding to a hazard ratio (HR) for death of 0.54 (95% CI 0.43–0.68; p<0.001). Survival probabilities at 180 days were 88.7% in the local therapy cohort and 80.2% in the no local therapy cohort. Hospitalization within 180 days occurred in 20.3% of patients receiving local therapy and 22.4% of those without local therapy (p=0.24), with a nonsignificant trend toward delayed hospitalization (HR 0.84, 95% CI 0.69–1.01). Systemic corticosteroid use was common and similar between groups (61.3% vs 59.4%; p=0.40), with no significant difference in time to steroid initiation. In an osimertinib-only sensitivity analysis, 180-day mortality was 10.5% among patients receiving local CNS therapy compared with 19.4% among those without local therapy (HR 0.48, 95% CI 0.37–0.61; p<0.001). Conclusions: In this multi-institutional real-world cohort of patients with NSCLC receiving osimertinib or lorlatinib who developed BM, receipt of local CNS-directed therapy within 3 months was associated with lower 6-month mortality compared with continuation of targeted therapy alone, with consistent findings in an osimertinib-only sensitivity analysis. These results suggest a potential survival benefit of local CNS intervention in selected patients, while acknowledging residual confounding related to intracranial disease burden and clinical indication.

Background Black adults experience disproportionately higher rates of Alzheimer's disease and related dementias (ADRD), yet remain underrepresented in blood-based biomarker research. Understanding how plasma biomarkers relate to cognitive performance is essential for equitable detection and monitoring of ADRD. Objective We examined cross-sectional and longitudinal associations of both core and non-core plasma biomarkers and cognition in a community-based cohort of Black adults. Methods Participants from the ARCHES study completed baseline plasma biomarker assessments and neuropsychological testing, including a Preclinical Alzheimer Cognitive Composite (PACC) score and the Montreal Cognitive Assessment (MoCA). Plasma biomarkers reflecting amyloid, tau, neurodegeneration, and astrocytic activation were quantified using immunoprecipitation–mass spectrometry and ultrasensitive immunoassay platforms. Linear regression was used to evaluate cross-sectional associations between biomarkers and cognition. Linear mixed-effects models examined whether baseline biomarker levels were associated with cognitive change over one year, adjusting for age, sex, and education. Results The sample included 334 participants with a mean baseline age of 64.6 years (SD = 10.1; range, 45–92.9). Cross-sectionally, higher brain-derived phosphorylated tau 181 was associated with poorer MoCA score (p = 0.04), and neurofilament light chain (NfL) level was also associated with lower PACC score (p = 0.04). Longitudinally, higher baseline NfL and glial fibrillary acidic protein (GFAP) were associated with faster cognitive decline (p < 0.001 and p = 0.018). Conclusions Non-core NfL and GFAP biomarkers are associated with both cross-sectional and longitudinal cognitive performance. These findings highlight the importance of inclusive biomarker research and suggest non-core biomarkers may be particularly informative for characterizing cognitive aging and decline in this population.

Abstract Background Markerless tumor tracking (MTT) using single‐energy (SE) kilovoltage (kV) imaging has been proposed as a technique for lung tumor motion management. However, bony structures can obscure the tumor and make tracking challenging. Dual‐energy (DE) subtraction imaging can suppress the bone and therefore enhance tumor visibility, potentially improving tracking accuracy. Purpose To develop a machine learning model that predicts the reliability of MTT for SE and DE imaging using phantom and patient data. Methods Images were collected at high and low energy (120/60 kV) using fast‐kV switching on dynamic thorax phantom with 5–15 diameter targets and 20 lung cancer patients, including one patient with two tumor locations. DE images were then generated off‐line using weighted logarithmic subtraction. Separately, a template‐based tracking algorithm was used to track the tumor on both phantom and patient images. For phantom data, the ground truth (GT) was defined by the cos 4 waveform programmed into the motion controller. Since the GT was unknown for patient data, it was estimated using a Kalman filter and further refined using visual inspection. The tracking success rate (TSR) was subsequently calculated. Based on TSR, patients were stratified into three tracking groups: good, moderate, and poor. A logistic regression (LR) model was implemented with tracking features, including match score, peak to side‐peak ratio (PSR), x ‐ and y ‐velocity. To address the distribution differences between phantom and patient data, a domain indicator and interaction features were also incorporated. The model was trained on phantom and 17 patient data using leave‐one‐out cross‐validation, and its performance was evaluated on three held‐out patients, one from each tracking group. Separate LR models were developed for each imaging modality, resulting in SE‐LR and DE‐LR models. For each held‐out patient, the confusion matrix, sensitivity, and specificity were reported. Results For both SE‐LR and DE‐LR models, PSR has the largest coefficient value, indicating that it is the most influential factor when determining the reliability of tracking. At a specificity of 95%, the DE‐LR model achieved significantly higher sensitivity than the SE‐LR model (0.908 vs. 0.731, p < 0.01 for phantom data and 0.657 vs. 0.589, p < 0.01 for patient data). In the three held‐out patients, the DE‐LR model outperformed the SE‐LR model across all metrics. The largest gains were found in the patient with good tracking quality. Conclusions The proposed LR models successfully predicted the reliability of SE‐ and DE‐MTT. Across phantom and patient datasets, the DE‐LR model consistently demonstrated improved performance compared with the SE‐LR model, suggesting that DE imaging may be beneficial for MTT applications.

Background Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC) and lacks effective treatment options. Polyphyllin B (PB), a saponin extracted from Paris formosana Hayata, has shown potential in cancer therapy. However, its anti-TNBC effects and mechanisms are not well studied. This study investigated the effects and underlying mechanisms of PB in TNBC. Methods CCK8 assay, Wound healing assay, Transwell migration experiments, and clonogenic assay were performed to assess the effects of PB in three TNBC cells lines. Iron level, lipid ROS, and MDA levels were assessed as ferroptosis markers. Mechanistic studies were conducted using molecular docking, Western blot, immunofluorescence, RT-qPCR, Nrf2 overexpression assays and establishment of a TNBC mouse model. Results PB treatment resulted in significant cell death and inhibited the migration of TNBC cells. Accumulation of iron and MDA suggested that PB induces ferroptosis. In vitro and in vivo , PB downregulated the expression of nuclear factor E2-related factor 2 (NRF2) and sequestosome-1 (P62) and upregulated the expression of Kelch-like ECH-associated protein 1 (KEAP1). Furthermore, it modulated the expression of key ferroptosis regulators, including acyl-CoA synthetase long-chain family member 4 (ACSL4) and glutathione peroxidase 4 (GPX4). In a TNBC mouse model, PB suppressed tumor growth and increased malondialdehyde accumulation. Conclusion PB inhibits cell proliferation and migration and induces ferroptosis in TNBC through the inactivation of NRF2. PB is a promising candidate for TNBC treatment that warrants further investigation.

ABSTRACT Age‐related changes in human hematopoietic stem cells (HSCs) often form the basis for clonal hematopoiesis (CH), which presages the development of overt myeloid neoplasm with variable risk. CH can also arise after exposure to chemotherapy, radiotherapy, or immune interventions. In this study, we performed clinico‐genomic profiling of therapy‐related CH (t‐CH) ( n = 67) versus de novo CH ( n = 123) from a multidisciplinary CHIP Clinic. The most enriched mutations in t‐CH were TET2 (26.2%), DNMT3A (22.4%), and TP53 (8.4%). Median latency period from the time of initial exposure to the diagnosis of t‐CH was 6.96 ± 1.10 years. Patients with t‐CH had inferior event‐free survival compared to de novo CH (median 29.9 months versus 122.1 months, p = 0.0045). Overall survival was also shorter in t‐CH versus de novo CH (median 55.3 months vs. 129.2 months, p = 0.043). Genes that imparted significantly increased risk for progression from t‐CH to overt therapy‐related myeloid neoplasm (t‐MN) included JAK2 (relative risk (RR) 9.42, p = 0.0001), RUNX1 (RR 7.11, p = 0.001), EZH2 (RR 7.11, p = 0.001), and TET2 (RR 13.4, p = 0.01). Clonal dynamic modeling of patients with t‐CH who progressed to overt t‐MN showed an increase in the relative clonal fraction and new clonal outgrowth. In the t‐CH cohort, higher mean variant allele frequency was observed in progressors versus non‐progressors (77.1% vs. 30.6%, p = 0.017). This study sheds light onto outcomes for CH based on differing clinical ontogeny and has implications for previvorship for secondary malignancies.

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.

Introduction The unproven stem cell intervention (SCI) industry markets stem cell products to patients navigating chronic and serious conditions who seek alternative options when conventional treatments no longer offer relief or improvement. This market is often characterized by providers using deceptive advertising to misinform potential consumers to seek stem cells that have not been shown to be safe and/or effective through clinical studies. Objective This study examines how patients describe interpersonal and institutional trust in relation to their interest in and decisions about unproven SCIs among two distinct and divergent chronic disease patient groups: patients with little or no interest in unproven SCIs (low seekers) and those with high interest or have undertaken an unproven SCI (high seekers). Methods A semi-structured interview guide was developed deductively and modified inductively based on the Unified Theory of Health Behavior. Qualitative interviews among 36 patients and carers were conduct and transcripts were analyzed using constant comparison based on the principles of grounded theory. Results Trust varies across social contexts as low seekers expressed strong reservations about the commercialization elements of the SCI industry placing greater confidence in conventional medicine. In contrast, high seekers questioned the advice of conventional providers and motives of medical regulators. Across both, personal relationships were commonly described as influential in how participants evaluated unproven SCIs. Conclusion These findings suggest that communication efforts in clinical settings should account for the relational and emotional dimensions of trust. Interventions aimed at countering misleading information and promoting informed choices should engage trusted messengers and address the broader social and psychological contexts in which patients may seek unproven SCIs.

Abstract Inflammatory bowel disease (IBD) is a chronic immune-mediated disorder of the gastrointestinal tract whose genetic basis is only partly resolved because most risk variants identified by genome-wide association studies (GWAS) lie in non-coding regions, limiting direct gene assignment and biological interpretation 1,2 . Here we analysed whole-exome and whole-genome sequencing data from 86,213 IBD cases and 478,363 controls of European ancestry. We identified 68 IBD genes directly implicated by conditionally independent protein-coding associations across the allele frequency spectrum. Many newly implicated IBD genes are supported by orthogonal genomic or pleiotropic evidence, pointing to disease-related pathways and nominating targets with therapeutic relevance. We further identified allelic series and non-additive effects at key loci such as NOD2 and TYK2. These results show that large-scale sequencing can resolve disease genes and pathways that remain ambiguous from non-coding association alone, providing a more direct route from human genetics to biological insight and therapeutic hypotheses.