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TRAIL is a TNF family ligand that trimerizes TRAIL-R1 (DR4) or TRAIL-R2 (DR5) to induce apoptosis, necroptosis, and/or NF-κB activation in receptor-bearing cells. We previously identified TRAILshort as a splice variant of TRAIL that lacks cysteine 230, cannot trimerize, and acts as a dominant-negative ligand that blocks TRAIL-mediated apoptosis. TRAILshort is expressed on cell surfaces and within extracellular vesicles, enabling it to confer TRAIL resistance to both producing and bystander cells. In this study, we showed that elevated TRAILshort levels were associated with chronic viral infections, cancer, and autoimmune diseases, suggesting a link to impaired immune regulation. Using unbiased phosphoproteomics and mechanistic studies, we demonstrated that TRAILshort binding to DR5 recruited and activated the phosphatase Src homology region 2 domain–containing phosphatase 1 (SHP-1), leading to zeta-chain-associated protein kinase 70 (ZAP-70) dephosphorylation, disruption of ZAP-70–CD3ζ interactions, and impaired T cell receptor signaling, thereby reducing T cell activation, proliferation, and cytokine production in response to antigen or CD3/CD28 ligation. Genetic or pharmacologic SHP-1 inhibition reverses these effects. In humanized mouse models, TRAILshort promoted the persistence of transformed mouse embryonic fibroblasts (MEFs) and L428 and antagonized CD19-directed CAR T cell activity, revealing TRAILshort as an immunomodulator of T cell function with therapeutic implications, including blocking TRAILshort to restore T cell immunity or delivering TRAILshort to enforce tolerance. TRAILshort, a variant of TRAIL, suppresses T cell function and immune responses in cancer and infectious diseases, highlighting its potential as a therapeutic target.

LBA5 Background: Available 2L therapies offer limited clinical benefit in mPDAC with reported mPFS of 3–4 mo and mOS of 6–7 mo. Aberrant RAS pathway activation is the key driver of PDAC, with oncogenic RAS mutations (mut) identified in >90% of cases, most commonly at codon G12. Daraxonrasib is an oral, RAS(ON) multi-selective, tri-complex inhibitor of the active, GTP-bound state of mutant and wild type RAS. Methods: RASolute 302 (NCT06625320) is a global, randomized, open-label, Ph 3 study in pts with 2L mPDAC and ECOG PS 0-1. Pts were randomized 1:1 to receive daraxonrasib 300 mg PO QD or investigator’s choice of SOC cytotoxic chemo. Dual primary endpoints were OS and PFS by BICR in the RAS G12 mutant (RAS G12) population. Key secondary endpoints included OS and PFS by BICR in the overall population, ORR by BICR in RAS G12 and overall populations. Results: 248 pts were randomized to daraxonrasib and 252 to chemo. Baseline characteristics were balanced between arms. At data cutoff (Feb 10, 2026; mFU: 8.5 mo), all primary and key secondary endpoints were met. Statistically significant, clinically meaningful improvements in OS and PFS were observed with daraxonrasib vs chemo in the RAS G12 and overall populations (Table). Gr ≥3 TRAEs occurred in 43.6% of pts receiving daraxonrasib vs 57.5% receiving chemo. The most common (≥10%) Gr ≥3 TRAEs were rash (13.7%) and stomatitis (12.0%) for daraxonrasib; neutrophil decrease (18.2%) and anemia (16.4%) for chemo. TRSAEs occurred in 10.8% of pts receiving daraxonrasib vs 18.7% receiving chemo. Discontinuation due to TRAEs occurred in 1.2% of pts for daraxonrasib vs 11.2% for chemo. Median/mean daraxonrasib dose intensity was 93.1%/84.7%. Conclusions: Daraxonrasib demonstrated unprecedented improvements in OS and PFS vs chemo in pts with 2L mPDAC with or without an identified tumor RAS mut. Daraxonrasib was generally well tolerated, with a manageable safety profile and with no new safety signals. Results support daraxonrasib as the new SOC for 2L mPDAC. Clinical trial information: NCT06625320 . RAS G12 Overall b Daraxonrasibn=228 Chemo n=231 Daraxonrasib n=248 Chemo n=252 OS Median; mos(95% CI) 13.2(10.0–NE) 6.6(5.4–8.2) 13.2(10.0–NE) 6.7(5.8–8.0) HR(95% CI)P-value 0.40(0.30-0.54)<0.0001 0.40(0.30-0.53)<0.0001 PFS a Median; mos(95% CI) 7.3(6.3–8.1) 3.5(2.9–3.8) 7.2(5.7–7.5) 3.6(2.9–4.2) HR(95% CI)P-value 0.45(0.34-0.59)<0.0001 0.49(0.38-0.64)<0.0001 ORR a %(95% CI) 33.2(27.0–39.9) 11.8(7.8–16.8) 31.6(25.8–38.0) 11.2(7.5–15.9) a By BICR. b RAS G12, G13 or Q61 mut or no RAS mut identified.

e16334 Background: Liver cancer remains a leading cause of cancer-related mortality in the United States. Although overall mortality has increased over the past two decades, age-specific patterns and demographic disparities are not fully characterized. Here, we analyzed U.S. liver cancer mortality trends from 1999 to 2023, with a focus on age-related, racial and ethnic, and geographic differences. Methods: Mortality data for U.S. adults aged ≥25 years from 1999 to 2023 were obtained from the CDC WONDER database (ICD-10 codes C22.0–C22.9). Age-adjusted mortality rates (AAMRs) were standardized to the 2000 U.S. population. Joinpoint regression was used to estimate annual percent change (APC) and average annual percent change (AAPC) across age groups, sex, race/ethnicity, U.S. census regions, and levels of urbanization. Results: From 1999 to 2023, overall liver cancer AAMRs increased from 6.95 to 10.1 per 100,000. Marked age-specific differences were observed. Mortality among adults aged 45–54 and 55–64 years peaked in the mid-2010s and subsequently experienced significant declines through 2023 (recent APCs < −4.5% for both groups; P < 0.001). Specifically, while the overall AAPC for the 55–64 age group was +1.89%, mortality significantly reversed after 2016 (APC, −4.59%). In contrast, mortality continued to rise among older adults, including those aged 65–74 years (AAPC, +2.26%), 75–84 years (AAPC, +1.69%), and aged ≥85 years (AAPC, +1.77%) (all P < 0.001). Differences in mortality trends were also identified across racial and ethnic groups. Sustained increases were observed among Non-Hispanic White individuals (AAPC, +1.89%; P < 0.001), while Hispanic/Latino populations experienced declines after 2013 (APC, −0.67%; P = 0.003). Non-Hispanic Black individuals demonstrated a significant decrease in mortality between 2017 and 2020 (APC, −3.82%; P = 0.03). Regionally, liver cancer mortality declined in the Northeast after 2013 (APC, −0.66%; P < 0.001), whereas no significant post-peak declines were observed in the South or West. Conclusions: U.S. liver cancer mortality trends exhibit substantial heterogeneity by age, race and ethnicity, and geographic region. The recent, sharp declines among middle-aged adults (APCs < −4.5%) likely reflect the success of large-scale viral hepatitis screenings and highly effective antiviral therapies, whereas continued increases among older adults may relate to the cumulative impact of metabolic risk factors and delayed diagnosis. Persistent racial and regional disparities highlight the need for targeted, age-specific prevention and surveillance strategies.

e16102 Background: Esophageal cancer remains a significant cause of cancer mortality in the United States. While overall trends have been reported, less is known regarding contemporary long-term patterns and specific disparities across demographic and geographic strata. We aimed to characterize esophageal cancer mortality trajectories and identify populations with divergent outcomes by age, race/ethnicity, geographic region, and urbanization status. Methods: We analyzed esophageal cancer mortality data from 1999 to 2023 using the Centers for Disease Control and Prevention (CDC) WONDER database. Age-adjusted mortality rates (AAMRs) were standardized to the 2000 U.S. standard population. Joinpoint regression analysis was utilized to estimate the annual percent change (APC) and average annual percent change (AAPC) with 95% confidence intervals (CIs). Analyses were stratified by age, sex, race/ethnicity, census region, state, and urbanization level. Results: Overall esophageal cancer mortality in the U.S. declined significantly from 1999 to 2023 (AAPC, -0.81%; 95% CI, -0.97% to -0.65%; P < 0.001). Declines were observed across most age groups, with the steepest reductions among adults aged 45-54 years (AAPC, -1.68%; 95% CI, -2.01% to -1.35%) and 55-64 years (AAPC, -1.25%; 95% CI, -1.51% to -0.99%). Marked racial and ethnic disparities were identified. Non-Hispanic (NH) Black individuals experienced the most rapid decline (AAPC, -4.07%; 95% CI, -4.42% to -3.72%). Conversely, trends among NH White individuals remained stable overall (AAPC, -0.05%; P = 0.59), characterized by an initial increase from 1999 to 2005 (APC, 1.14%; P = 0.002) followed by a modest decline from 2005 to 2023 (APC, -0.44%; P < 0.001). Regionally, the West demonstrated the largest decline (AAPC, -1.13%; 95% CI, -1.26% to -0.99%), followed by the Northeast (AAPC, -1.05%). West Virginia was the only state to exhibit a significant increase in mortality (AAPC, 1.01%; 95% CI, 0.38% to 1.64%; P = 0.003). Mortality declined significantly in metropolitan areas (AAPC, -1.09%; P < 0.001), while trends in nonmetropolitan areas remained stagnant (AAPC, 0.48%; P = 0.12). Conclusions: Despite a national decline in esophageal cancer mortality, substantial disparities persist across racial, geographic, and urbanization groups. The dramatic improvement among NH Black populations contrasts with the relative stability in NH White populations and the lack of progress in nonmetropolitan areas. The significant rise in mortality in West Virginia warrants targeted investigation. These findings underscore the critical need for population-tailored strategies to ensure equitable progress in cancer prevention and care.

e13663 Background: Artificial intelligence (AI) in oncology, including computer vision and radiomics, has expanded rapidly with substantial federal support. However, the extent to which growth in funding and publications is accompanied by growth in prospective interventional evaluation is not well quantified. We conducted an ecosystem-level analysis to compare temporal trends in federal awards, peer-reviewed publications, and interventional clinical trial registrations in AI-oncology. Methods: We performed a cross-sectional landscape analysis spanning 2015–2025 using three public data sources: (1) federally funded extramural awards indexed in NIH RePORTER (including NIH/NCI and other agencies where available); (2) interventional studies registered on ClinicalTrials.gov; and (3) PubMed-indexed publications. Records were identified using a consistent Boolean query requiring AI-related terms (“artificial intelligence,” “machine learning,” “deep learning,” “neural network,” “large language model,” “computer vision,” “natural language processing,” “radiomics”) AND oncology-related terms (“cancer,” “oncology,” “tumor,” “neoplasm,” “carcinoma,” “melanoma,” “leukemia,” “lymphoma”). Annual absolute counts were extracted (without normalization) to preserve volume differences across the ecosystem. We summarized growth over time and calculated publication-to-trial and grant-to-output ratios. Results: Between 2015 and 2025, AI-oncology federal awards totaled 11,503. PubMed-indexed AI-oncology publications increased 38-fold from 558 (2015) to 21,414 (2025), totaling 81,782. Interventional AI-oncology trial registrations increased from 2 (2015) to 84 (2025), totaling 372 across the period. Overall, the publication-to-interventional-trial ratio was 220:1; interventional trial registrations represented 0.45% of publication volume. For every federal award, approximately 7 publications were produced, but only 0.03 interventional trials were registered. Conclusions: Across 2015–2025, AI-oncology funding and publications increased dramatically, while interventional trial registrations remained comparatively sparse. This persistent publication-to-trial gap highlights a translational bottleneck and supports targeted initiatives to accelerate prospective validation, such as dedicated funding mechanisms, multi-center evaluation networks, and trial-ready implementation infrastructure, to realize clinical benefit from AI in cancer care.

e12691 Background: Breast cancer prognosis is influenced not only by tumor-specific factors but also by systemic inflammatory responses. Elevated circulating neutrophils after surgery may reflect one such systemic inflammatory response that promotes a pro-tumorigenic environment. Neutrophil extracellular traps (NETs) have been recognized as promoters of cancer activity. However, there have been no studies looking at neutrophil count alone as an independent inflammatory marker in cancer. Clinical prognostic significance of post-operative neutrophil counts remains incompletely understood. If proven significant, neutrophil count is a simple, widely available measure to predict post-operative outcomes in patients with breast cancer. Methods: Data from the Mayo Clinic Platform were retrospectively analyzed, including only breast cancer patients with normal BMI and neutrophil counts recorded within one week after breast surgery. Patients who underwent radical mastectomy were excluded. Patients were stratified into two groups based on neutrophil count: high (>7×10⁹/L) and normal (1.7–7×10⁹/L). Clinical outcomes assessed included age distribution and 5-year overall survival. Age differences were evaluated using Cohen’s D, and Kaplan-Meier survival curves and hazard ratio estimation were performed using the Mayo Clinic Platform's tool. Results: A total of 899 patients were included in the study cohort: 303 in the high neutrophil group (Cohort A) and 596 in the normal neutrophil group (Cohort B). Age distributions were comparable between groups (Cohort A mean 59.2 ± 13.1 years; Cohort B mean 59.1 ± 14.3 years; Cohen's d = 0.0079). High neutrophil counts were significantly associated with reduced 5-year survival (77.8% vs. 84.5%) and an increased risk of mortality (HR 1.63; 95% CI: 1.16–2.29; P = 0.0041). Conclusions: Recent literature supports that surgery-induced NET formation may drive metastatic progression. Findings from this retrospective analysis at Mayo Clinic suggest that elevated neutrophil counts after breast cancer surgery are associated with poorer survival and may reflect systemic inflammation. While markers like NLR and NPAR have prognostic value, neutrophil count alone is a practical, accessible biomarker for clinical use. Limitations of this study include potential confounding factors such as lifestyle and comorbidities, and lack of subgroup analysis by breast cancer type, stage, and other treatments in addition to surgery. Further prospective studies are still needed for validation. Five-year survival rate comparison. Statistic High Neutrophil counts Normal Neutrophil counts Patient Count 303 596 1 Month Survival 99% 99.8% 5 Month Survival 97% 99.3% 10 Month Survival 94.4% 97.3% 30 Month Survival 85.4% 91.9% 60 Month Survival 77.8% 84.5% Hazard Ratio* 1.63 (95% CI: 1.16–2.29) P = 0.0041.

1110 Background: CDK4/6 inhibitors with endocrine therapy are widely used for HR+/HER2– metastatic breast cancer (BC). While studies have shown similar progression-free outcomes across agents, overall survival (OS) data are limited. Since OS is a definitive endpoint and BMI may influence outcomes, this study examines BMI-dependent OS differences among CDK4/6 inhibitors. Methods: A retrospective cohort study was conducted using the Mayo Clinic Platform full AMC1 dataset. Postmenopausal women with BC receiving palbociclib/ribociclib/abemaciclib plus aromatase inhibitor (AI) were included. Patients with prior use of fulvestrant, anthracyclines, radiation therapy, GnRH agonists, or history of MACE, heart failure, myocardial infarction, or stroke before BC diagnosis were excluded. Patients were stratified by BMI and CDK4/6 inhibitor. The primary outcome was 5-year OS. Results: Among 1,989 patients (palbociclib: 868; ribociclib: 465; abemaciclib: 656), palbociclib was associated with significantly worse OS compared to ribociclib in BMI <25 (HR 2.91; 95% CI: 1.76–4.79; P < 0.01) and BMI ≥30 (HR 1.91; 95% CI: 1.21–3.01; P < 0.01). Compared to abemaciclib, palbociclib showed worse OS: BMI <25 (HR 1.73; P < 0.01), BMI 25–30 (HR 1.56; P < 0.01), and BMI ≥30 (HR 1.82; P < 0.01). No significant OS differences were observed between ribociclib and abemaciclib. Palbociclib patients were older, particularly in BMI <25 (Cohen's D = 0.39 vs. ribociclib; 0.27 vs. abemaciclib) and BMI ≥30 (0.28 vs. ribociclib; 0.23 vs. abemaciclib). In the BMI 25–30 group, palbociclib group was also older than ribociclib group (Cohen's D = 0.26). Conclusions: In postmenopausal BC patients on AIs and CDK4/6 inhibitors, palbociclib was associated with worse OS in BMI < 25 and BMI ≥ 30 groups, while ribociclib and abemaciclib showed similar outcomes across BMI groups. Palbociclib patients were older than those on other drugs, which may partially confound results; however, in the BMI 25–30 group, despite similar age differences, no survival difference was observed between palbociclib and ribociclib, suggesting age alone may have limited impact. These findings indicate a potential interaction between BMI and drug choice, and highlight the need for prospective studies to confirm and adjust for these variables. Limitations include the observational design and inability to perform multivariable regression analyses to adjust for confounders. Overall survival comparisons by CDK4/6 inhibitor and BMI subgroup. Comparison BMI Group Hazard Ratio (95% CI) P-value Palbociclib vs Ribociclib <25 2.91 (1.76 – 4.79) < 0.01 25 – 30 1.34 (0.87 – 2.071) 0.17 ≥30 1.91 (1.21 – 3.01) < 0.01 Palbociclib vs Abemaciclib <25 1.73 (1.29 – 2.32) < 0.01 25 – 30 1.56 (1.09 – 2.23) < 0.01 ≥30 1.82 (1.15 – 2.88) < 0.01 Ribociclib vs Abemaciclib <25 1.27 (0.81 – 1.99) 0.29 25 – 30 0.87 (0.54 – 1.43) 0.59 ≥30 1.05 (0.63 – 1.75) 0.85

10585 Background: Prior studies suggest that thyroid cancer (TC) survivors may have an elevated risk of secondary malignancies. However, whether this risk differs by TC histology, specifically with breast cancer (BC), remains unclear. We aimed to investigate the association between TC subtype—papillary thyroid carcinoma (PTC) versus medullary thyroid carcinoma (MTC)—and subsequent BC risk, adjusting for demographic factors. Methods: We conducted a retrospective cohort study using the SEER (2000–2022), including adults with a primary, microscopically confirmed PTC or MTC. Subsequent BC occurrence was defined as a binary outcome (yes/no). Multivariable logistic regression was used to estimate adjusted odds ratios (aORs) for MTC versus PTC, controlling for age (<50 vs ≥50 years), sex, and race. Stratified analyses by age and race were also performed. Stata 15 was used for statistical analyses. Results: 14,840 patients (14,563 PTC, 277 MTC) were included. MTC patients were older (mean 59.08±12.66 vs 56.16±12.98 years, p<0.001) and had a higher proportion of males (44.4% vs 29.4%, p<0.001). The overall incidence of secondary BC was lower in MTC patients (7.6% vs 22.5%, p<0.001). In unadjusted logistic regression, MTC was associated with significantly lower BC risk (OR=0.28, 95% CI: 0.18–0.44, p<0.001). After adjustment, MTC remained significantly protective (aOR=0.34, 95% CI: 0.22–0.55, p<0.001). Stratified analyses demonstrated consistent findings across age groups (<50: aOR=0.10, 95% CI: 0.03–0.42; ≥50: aOR=0.34, 95% CI: 0.21–0.54), , as well as white and Hispanic patients. Conclusions: Survivors of MTC have a substantially lower risk of developing secondary BC compared to PTC survivors. This association persists after adjustment for age, sex, and race, and is consistent across age and most racial subgroups. However, different BC types were not assessed due to limited number of BC patients among MTC survivors, and the small MTC sample size and potential survival differences between groups may affect the results. These findings highlight a possible effect of TC subtype on BC risk, suggesting the potential influence of underlying biological or genetic mechanisms. Baseline demographic characteristics of patients with papillary and medullary thyroid carcinoma. Characteristic Papillary Thyroid Carcinoma (PTC) (N=14,563) Medullary Thyroid Carcinoma (MTC) (N=277) P-value Age at Diagnosis 0.0002* Mean ± SD, years 56.16 ± 12.98 59.08 ± 12.66 Median, years 57 61 Sex, n (%) <0.001† Female 10,288 (70.6) 154 (55.6) Male 4,275 (29.4) 123 (44.4) Race, n (%) 0.015† White 10,263 (70.5) 197 (71.1) Black 921 (6.3) 15 (5.4) American Indian 77 (0.5) 1 (0.4) Asian 1,379 (9.5) 13 (4.7) Hispanic 1,903 (13.1) 50 (18.1) SD: Standard Deviation. *P-value from two-sample t-test (equal variances assumed). †P-value from chi-square test for the overall distribution across categories.

Abstract RATIONALE: Chronic lower respiratory diseases (CLRD) confer higher risks from respiratory viral infections, hence CLRD patients are prioritized for influenza, pneumococcal, and COVID-19 vaccination. Nonetheless, vaccine deferral and declination remain common. Given that COVID-19 vaccination is particularly polarizing, and attitudes toward it may differ from other vaccines, we assessed COVID-19 vaccination behaviors and attitudes in adults with versus without CLRD and/or smoking history, which is strongly associated with CLRD risk. METHODS: We pooled data from 10 cohorts participating in the Collaborative Cohort of Cohorts for COVID-19 Research (C4R) that collected pre-pandemic data on self-reported physician diagnosis of asthma or COPD. C4R assessed COVID-19 vaccination status through three waves of standardized questionnaires (2020-2022, 2021-2023, 2023-2024). Prompt COVID-19 vaccination was defined as self-report of receiving at least one COVID-19 vaccine by May 1, 2021. Cumulative COVID-19 vaccine count was self-reported on the Wave 3 Questionnaire. Vaccine attitudes and receipt of influenza and/or pneumococcal vaccines were self-reported on the Wave 1 or 2 Questionnaire. Age-adjusted Poisson regression was used to test associations of vaccination behaviors with CLRD and smoking status at the most recent pre-pandemic exam. RESULTS: Of 20,034 participants (pre-pandemic COPD, 9.3%; asthma, 11.7%; current smoking, 13.4%; former smoking, 36.3%), 64.2% reported prompt COVID-19 vaccination. Among the subset of 10,626 participants completing the Wave 3 Questionnaire in 2023-24, the median COVID-19 vaccine count was 3 (Q1:2, Q3:4). Compared to participants without CLRD, prompt vaccination was more likely in those with asthma (RR=1.04, 95%CI:1.01-1.07, p=0.008) and less likely with COPD (RR=0.94, 95%CI:0.91-0.98, p=0.001), but there were no differences in doses for asthma and COPD. Compared to never smokers, former smoking participants were more likely to receive prompt vaccination (RR=1.11, 95%CI:1.08-1.13, p&lt;0.0001) and more vaccine doses (RR=1.03, 95%CI:1.01-1.05, p=0.015); conversely, current smoking was inversely associated with prompt vaccination (RR=0.94, 95%CI:0.91-0.97, p=0.001) and was associated with fewer doses (RR=0.93, 95%CI:0.89-0.96, p&lt;0.0001). Participants with CLRD versus without CLRD were more likely to report receiving influenza and pneumococcal vaccines, but they were more likely to believe that vaccines are unsafe or ineffective (figure). CONCLUSIONS: COPD was associated with lower likelihood of prompt COVID-19 vaccination. Current smoking was associated with both lower likelihood of prompt COVID-19 vaccination and a lower number of COVID-19 vaccine doses by 2023-24. These associations may be partly attributed to negative vaccine attitudes and beliefs. How the COVID-19 pandemic may have influenced vaccine behaviors and attitudes merits additional study.