Du, Kaili

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.

1673 Background: Survivorship care plans (SCPs) summarize cancer treatments and provide evidence-based recommendations for surveillance, screening, and management of treatment-related complications. Despite the availability of guidelines and templates, SCPs remain underutilized in routine practice due to the time-intensive and error-prone nature of manual creation. Empowering cancer survivors to use publicly available large language models (LLM)–based chatbots, such as ChatGPT, may offer a feasible solution to this problem. Methods: Standardized patient profiles were developed for six common cancers: breast, colorectal, non–small cell lung, small cell lung, prostate cancer, and diffuse large B-cell lymphoma. Profiles were reviewed by a primary care physician and an oncologist to ensure clinical accuracy and representativeness of common survivorship scenarios. Using lay language to simulate real-world patient input, ChatGPT (version 5.2, publicly available at the time of the study) was prompted with each profile using the question, “What follow-up care do I need?” Common survivorship symptoms for each cancer were also tested. Responses were evaluated across five domains: medical accuracy (concordance with current ASCO or NCCN guidelines), thoroughness, patient safety and risk framing, clarity and patient comprehension, and actionability. Five-point Likert scales were used (1 = not at all; 5 = very much). Scores were averaged across two independent physician raters. Readability was assessed using the Flesch Reading Ease Score. Results: ChatGPT-generated SCPs demonstrated high medical accuracy (mean score 4.25/5), with no factual errors or hallucinations identified on manual review. Patient safety and risk framing scored 3.75/5; symptom red flags and care escalation guidance were appropriately stated in most scenarios. Thoroughness was moderate (3.50/5), with key survivorship elements, such as smoking cessation counseling or genetic risk considerations; sometimes omitted unless explicitly prompted. Readability was limited, with a mean Flesch Reading Ease Score of 40.5, corresponding to a college reading level and exceeding recommended readability standards for patient education materials. Clarity (3.38/5) and actionability (3.38/5) were similarly constrained due to dense medical language. Conclusions: ChatGPT demonstrated high medical accuracy in generating survivorship care plans across six common cancers, supporting the feasibility of using publicly available LLMs to guide survivorship care. However, improvements in comprehensiveness, readability, and patient-centered actionability are necessary before such tools can be safely integrated into clinical survivorship workflows.

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.