Citations

Abstract INTRODUCTION We standardized positron emission tomography (PET) data across multiple cohorts and tracers to characterize the frequency of amyloid and tau PET severity along the clinical continuum. METHODS Clinical stage was defined using cohort‐specific criteria and included cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia. Amyloid severity was staged using Centiloids (CL). Tau severity was staged using a hierarchical Braak‐based schema. The cumulative probabilities of PET‐based stages were estimated using ordinal logistic regressions. RESULTS Among 10,396 individuals (mean [standard deviation] age: 71.9 [7.1] years), amyloid levels ≥ 25 CL increased with age among CU and MCI, while amyloid levels ≥ 100 CL were most common in dementia. In 3295 with tau PET, tau severity increased with amyloid and clinical stage and showed complex associations with age. Within each clinical stage, the full spectrum of amyloid and tau PET severity was observed. DISCUSSION PET‐based staging revealed heterogeneous amyloid and tau burden along the clinical continuum. Highlights PET‐based staging is feasible across multiple cohorts and PET tracers. There is heterogeneity in amyloid and tau severity across the clinical spectrum. The frequency of amyloid and tau PET severity increased with clinical severity. The likelihood of tau PET severity differed by age, amyloid, and clinical severity.

Abstract Background: MyeloMATCH is an NCI-sponsored initiative for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) employing a Master Screening and Reassessment Protocol (MSRP) to assign patients (pts) to a pt and disease biology-specific clinical trial. Several trials evaluating initial therapies are currently open, while subsequent trials dedicated to eradicating measurable residual disease during consolidation and including stem cell transplant are being developed. A pt enrolled at diagnosis may be followed longitudinally and be eligible for trials in sequential tiers throughout the treatment course. We report key data concerning the first cohort of pts enrolled on the MSRP. Methods: After pts with suspected AML or MDS consent for the MSRP, blood and marrow are sent to the myeloMATCH Molecular Diagnostics Network (MDNet) of central CLIA labs operating under a single Investigational Device Exemption to rapidly define molecular, cytogenetic, and immunophenotypic characteristics. These data are catalogued by a bioinformatics team (MATCHBox) and reviewed daily to assign participants to an available trial based on pt and disease features. A Tier Advancement Pathway (TAP) allows standard of care therapy with potential for assignment to subsequent myeloMATCH trials. Since activation in May 2024, 550 patients age≥18 were enrolled on the MSRP from 76 institutions: 48 Academic, 14 NCORP, 11 community, 3 VA. There are currently 4 open randomized AML trials (≥age 60 or unfit with mutant FLT3, ≥age 60 IDH2 mutant AML, fit age<60 ELN 2017 intermediate-risk AML, fit age<60 ELN 2017 adverse risk) and 1 MDS trial(IDH2 mutant). Patients not eligible for an open protocol were assigned to TAP. Results: Of the 550 pts consented, 491 completed screening assays; 49 had no or incomplete samples; 10 withdrew before completed analysis. Median cytogenetic turnaround time (TAT) was 69 hours (91% within 72 hours), and median Next Generation Sequencing (NGS) TAT from receipt was 49 hours (95% within 72 hours). Seventy-six pts (of 491 with assay data) were not assigned due to non-AML or non-MDS diagnoses, the most common being: B-ALL [n=11], MPN [9], benign marrow [9], APML [7], and MPAL [6]. The AML/MDS verified cohort was 415 pts, 366 (89.2%) patients with AML (45% age <60) and 49 (11.8%) with MDS. The median age at screening was 63 years (range:18-95) and 186 (45%) were female. Eighty-two percent identified as White, 7.5% Black, 3% Asian, 7% unknown, and 9% as Hispanic. The most frequent mutations in the AML pts were NPM1 (24.0%), DNMT3A (22.7%), TET2 (19.9%), NRAS (18.0%), FLT3 (18.0% ITD, TKD 7.1%), TP53 (14.5%), and CEBPA (8.7%); IDH1/2 mutation rates were 8.2% and 13.7%, respectively. Co-mutations include FLT3/NPM1 (9.8%), NPM1/IDH2 (4.4%), IDH1/2 and any RAS (3.8%). Karyotypes were normal in 40.4%, complex 20.8%, inv(16) 6.0%, MLL/KMT2A 4.6% and t(8;21) 1.9%. 29% had favorable risk disease, 29% intermediate, and 42% adverse (ELN 2017). In MDS, mutations included: TP53 (36.7%), RUNX1 (20.4%) ASXL1 (20.4%), TET2 (18.4%), spliceosome mutations (SRSF2, SF3B1, U2AF1 and ZRSR2, 28.6%) andIDH1/2 in 4.1% and 6.1%, respectively. Karyotypes were normal in 24.5%; complex in 40.8%. By IPSS-R, 2.4% were low, 73.8% intermediate, 2.4% high, and 21.4% very high risk. 82/415 (20%) pts were assigned to initial therapy protocol, with an increase in assignment rate to 44% in the most recent two months. 71/415 pts (16%) were enrolled in one of the MyeloMATCH studies. Three-hundred forty-four pts (80%) were assigned and enrolled to TAP (296 AML, 48 MDS) for potential assignment to subsequent tier trials. In the TAP AML pts, 99 (33%) have completed initial treatment, including intensive chemotherapy (IC) in 62% of pts and hypomethylating agents/venetoclax (HMA/VEN) in 38%. In pts <60 years of age, 87% received IC v. 13% with HMA/VEN, in >60 34% IC v. 66% HMA/VEN and in age 60-75 42% IC v. 58% HMA/Ven. Conclusion: MyeloMATCH has screened over 500 pts from academic and community sites in the US and Canada in 14 months, meeting the TAT goal of sample acquisition to results of <72 hours. Assignment to treatment protocols has substantially increased recently. As additional trials open (12 in development) we expect the percentage of pts enrolled on MSRP who go on to therapeutic trials will markedly increase, thereby allowing a personalized medicine approach for a broad group of US and Canadian adult pts with AML and MDS.

Abstract Introduction: Isocitrate dehydrogenase (IDH) mutations are observed in ~20% of patients with acute myeloid leukemia (AML). In the U.S., ivosidenib (ivo) is approved with or without azacitidine (aza) in the frontline (FL) and as monotherapy in relapsed/refractory (R/R) IDH1-mutated (mut) AML. Olutasidenib (oluta) is approved as monotherapy for R/R IDH1-mut AML. Enasidenib (ena) is approved as monotherapy for R/R IDH2-mut AML. This study describes real-world IDH inhibitor (IDHi) administration patterns and evaluates their outcomes in on-label settings. Methods: A retrospective review of patients with IDH1/2-mut AML who received ivosidenib, olutasidenib, or enasidenib was conducted at 12 academic institutions in the U.S. Best response at any time during treatment, including complete response (CR) and complete response with partial hematologic recovery (CRh), were graded using European Leukemia Net (ELN) 2022 criteria. Results: A total of 387 patients with IDH1- and IDH2-mut AML were treated with an IDHi in the FL or R/R setting. Median follow-up was 9.6 months (range, 0.03-90.7). Demographics include 79% White, 14% Black, 3% Asian, and 4% other/unknown race; 5% were Hispanic ethnicity; 46% were female; the median age was 70 years (range, 28-95). 70% had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1, 26% had secondary AML, and 10% had therapy-related AML. Per the ELN 2024 less-Intensive risk classification, 58% were favorable risk, 29% intermediate, 7% adverse, and 6% unknown. Overall, 209 of 387 patients (54%) received IDHi regimens on-label. In FL IDH1-mut AML (n=45), 11 received ivo monotherapy, 25 aza/ivo, and 9 used off-label combinations: 3 hypomethylating agent (HMA) + venetoclax (ven) + ivo, 5 decitabine/ivo, 1 intensive chemotherapy (IC)/ivo. Among R/R IDH1-mut AML (n=109), 47 received ivo monotherapy, 7 oluta monotherapy, and 55 off-label (31 HMA/ivo, 16 HMA/ven/ivo, 3 IC/ivo, and 5 other). 44 received off-label ena for FL treatment of IDH2-mut AML (25 ena monotherapy, 9 HMA/ena, 8 HMA/ven/ena, and 2 IC/ena). In R/R IDH2-mut AML (n=189), 119 patients received ena monotherapy and 70 received off-label combinations (25 HMA/ena, 30 HMA/ven/ena, 7 IC/ena, 6 ena/gilteritinib, and 2 other). In on-label treatment of FL IDH1-mut AML, one of four patients with response assessment of FL ivo monotherapy had a CR/CRh. Of 19 patients with response assessment on FL aza/ivo, 13 patients (68.4%) had a CR as best response (95% CI, 46.0-84.6%). Median OS was 14.9 mo (95% CI, 4.17-NR) for ivo monotherapy (n=11) and 31.9 mo (95% CI, 9.1-NR) for aza/ivo (n=25), with 0 and 2 patients receiving allogeneic stem cell transplant (allo-SCT), respectively. In R/R IDH1-mut AML, the rate of CR/CRh was 35.5% (95% CI, 21.1-53.1) for on-label ivo monotherapy. All 4 patients with response assessment for on-label oluta monotherapy had a CR. Median OS was 14.0 mo (95% CI, 10.1-22.4) for ivo monotherapy and NR for oluta monotherapy (95% CI, 10.1-NR), with 10 and 0 receiving allo-SCT, respectively. The CR/CRh rate for ivo was 38.1% (8 of 21 assessed; 95% CI, 20.8-59.1) in age < 70, and 30.0% (3 of 10 assessed; 95% CI, 10.8-60.3) in age ≥ 70. Median OS was 15.2 mo (95% CI 9.3-NR) in age < 70 (n=27) and 10.4 mo (95% CI 8.0-18) in age ≥ 70 (n=20). In R/R IDH2-mut AML, the rate of CR/CRh for on-label ena monotherapy was 38.2% (95% CI, 27.6-50.1). Median OS was 12.1 mo (95% CI, 8.2-16.6), with 5 having received allo-SCT. In demographic subgroups for ena monotherapy, 52 non-Hispanic White (NHW) patients with response assessment had a CR/CRh rate of 42.3% (95% CI, 0.30-0.56). Only 1 of 13 non-Hispanic Black (NHB) patients with response assessment had a CRh (7.7%; 95% CI, 1.4-33.0) as best response, with none attaining CR. Median OS was 13.7 mo (95% CI 10.3-26.3) in NHW (n=82), 11.2 mo (95% CI, 6.1-NR) in NHB (n=18), and 69.1 mo (95% CI, 52.6-NR) in Hispanic patients (n=8). The CR/CRh rate was 48.6% (17 of 35 assessed; 95% CI, 33.0-64.4) for age < 70, and 27.3% (9 of 33 assessed; 95% CI, 15.1-44.2) for age ≥ 70. Median OS was 12.0 mo (95% CI, 9.9-22.1) in age < 70 (n=48) and 5.8 mo (95% CI, 4.1-8.7) in age ≥ 70 (n=69). Conclusion: Practice patterns for IDH inhibitors in AML vary considerably, with high amounts of off-label use. Response rates for ivosidenib and enasidenib in the real-world setting are comparable to those of clinical trials. Descriptive analyses of demographic subgroups highlight areas in need of further research.

Abstract Background: Hypomethylating agent (HMA) with venetoclax (VEN) is used in clinical practice for frontline management of patients with myelodysplastic syndrome (MDS) based on encouraging data from phase 1 clinical trial (Garcia JS et al. Blood 2025). While the randomized clinical trial (VERONA-NCT04401748) did not show a survival benefit of azacitidine-VEN, the study had important differences such as exclusion of therapy related MDS and being conducted in several non-academic US centers. To better understand the impact of this regimen and its effects in subgroups, we conducted a real-world analysis of frontline MDS patients treated mainly at academic medical centers with HMA-VEN and compared to HMA monotherapy. Methods: This is a multicenter retrospective study of 19 US academic medical centers. We included contemporary cohorts of adults (age≥18 years) with MDS (intermediate, high or very high risk by IPSS-R) who were treated outside of clinical trials with either upfront combination of HMA-VEN or HMA monotherapy. Data on patient-related, disease-related and treatment-related variables were collected through chart review. Descriptive statistics were used for baseline characteristics. Overall survival (OS) and event free survival (EFS: progression, leukemic transformation, death as events) analysis was conducted using Kaplan-Meier method. Determinants of outcomes were analyzed using multivariable regression models. All analyses were conducted at a significance level of p<0.05. Results: Among 1198 patients with intermediate, high or very-high risk MDS, 313 patients received upfront VEN in combination with HMA [HMA-VEN] and 885 patients received HMA monotherapy. Median age was 70 (63-76) years and majority of patients had MDS with excess blasts-1 (34% overall, HMA-VEN=26%, HMA=37%) or MDS with excess-blasts-2 (41% overall, HMA-VEN=60%, HMA=34%). Disease risk by IPSS-R was intermediate (29% overall, HMA-VEN=18%, HMA=33%), high (30% overall, HMA-VEN=31%, HMA=30%) or very-high risk (41% overall, HMA-VEN=51%, HMA=37%, p<0.001). Therapy related MDS (t-MDS) was seen in 27% of patients (HMA-VEN=33%, HMA=25%, p=0.004), 34% had TP53 mutation (HMA-VEN=40%, HMA=32%, p=0.005) and 33% underwent allogeneic hematopoietic stem cell transplantation (AHSCT) (HMA-VEN=42%, HMA=30%, p<0.001). The most common mutations detected at baseline included TP53 (34%), ASXL1 (22%), DNMT3A (16%), RUNX1 (16%), TET2 (14%), SRSF2 (13%), and U2AF1 (11%). The response rate by IWG 2006 criteria was significantly higher with HMA-VEN than HMA monotherapy (overall 81% vs 39%, CR: 17% vs 9.6%; marrow CR: 56% vs 14%, p<0.001). In multivariable analysis accounting for upfront treatment, AHSCT, genomic mutations, baseline bone marrow blasts and t-MDS, patients treated with HMA-VEN in the upfront setting had significantly improved EFS (median EFS 10.7 vs. 7.8 mon, HR 0.83, 95%CI 0.72-0.96, p=0.01) and OS (median OS 22.4 vs. 18.1 mon, HR 0.79, 95%CI 0.64-0.97, p=0.02). In addition, TP53 mutation status (OS: HR 1.88, 95% CI 1.57-2.26, p<0.001, EFS: HR 1.43, 95% CI 1.23-1.65, p<0.001), subsequent AHSCT (OS: HR 0.34, 95% CI 0.27-0.41, p<0.001; EFS: HR 0.47, 95% CI 0.40-0.54, p<0.001), and therapy related MDS (OS: HR 1.20, 95% CI 1.00-1.44, p=0.04, EFS: HR 1.10, 95% CI 0.96-1.27, p=0.2) were significant predictors on multivariable analysis. When separately investigated in subgroups, an OS benefit of HMA-VEN over HMA was seen in patients undergoing subsequent AHSCT (median OS 40.6 vs. 30 mon, p=0.05), bone marrow blast 10-19% (median OS 25.1 vs. 17.4 mon, P=0.01), de novo MDS (median OS 30 vs. 20.1 mon, P=0.003) and absence of TP53 mutation (median OS 34.9 mon vs. 23.1 mon, p=0.004). The benefit of HMA-VEN over HMA was also seen across molecular mutations including presence of ASXL1 mutation (median OS 35.2 vs 20.9 mon, p=0.03). Conclusions: In this large multicenter retrospective analysis from US academic medical centers, we found that HMA-VEN therapy significantly improved OS in certain subsets of high risk MDS including patients with 10-19% blasts, denovo MDS, subsequent AHSCT and those without TP53 mutation. While our analysis is limited by its retrospective design, prospectively exploring subgroups of MDS patients who could benefit from HMA-VEN therapy is needed.

Abstract Purpose: The role of tumor-infiltrating lymphocytes (TIL) remains unclear in hormone receptor (HR)–positive/HER2-negative breast cancer, particularly in young patients, whose immune microenvironment could be altered by age-related host and tumor differences. Experimental Design: Patients with stage I to III HR-positive/HER2-negative tumors were identified from a prospective cohort study of patients with breast cancer diagnosed at age ≤40 years. Multiplexed immunofluorescence and semiautomated quantitative software measured cytotoxic T, non–CD8 T, T regulatory, exhausted T, and PDL1+ cells in stroma and tumor. Univariate analyses assessed differences in clinicopathologic characteristics by high versus low immune infiltration, divided based on median. TIL subtypes were evaluated as a continuous variable per 10% increase in Cox regression analyses for invasive breast cancer–free survival, distant disease–free survival (DDFS), and overall survival, adjusted for clinicopathologic parameters. Results: Among 390 patients, high immune infiltration was associated with increasing age, Black race, grade 3 tumors, and metaplastic or micropapillary histologic subtypes. Over a median follow-up of 8 years, higher stromal and intratumoral non–CD8 T-cell infiltration, T regulatory–cell infiltration, and PDL1 expression were associated with improved invasive breast cancer–free survival. Higher intratumoral non–CD8 T-cell infiltration, T regulatory–cell infiltration, and PDL1 expression were associated with improved DDFS; higher stromal PDL1 expression was also associated with improved DDFS. Higher intratumoral cytotoxic T-cell infiltration and PDL1 expression were associated with improved overall survival. Conclusions: Characterization of immune subpopulations could help refine the prognostic value of TILs in young patients with HR-positive breast cancer, who may benefit from risk stratification for treatment individualization. See related commentary by Salgado and Kok, p. 2133

Introduction: We aimed to prove integration of advanced machine learning methods within a robust ensemble framework can enhance clinical decision-support for neurologists managing patients in the emergency department (ED). Methods: We engineered an ensemble framework leveraging the capabilities of the Gemini 1.5-pro-002 large language model (LLM). The model was enhanced using prompt engineering and retrieval-augmented generation (RAG). Predictive modeling achieved by combining eXtreme Gradient Boosting (XGBoost) and logistic regression for optimal accuracy in clinical decision-making. Key clinical outcomes, such as admission and mortality, were assessed. A random subset of 100 cases was reviewed by three senior neurologists to evaluate the alignment of the AI’s predictions with expert clinical judgment. Results: We retrospectively analyzed 1368 consecutive ED patients who underwent neurological consultations, assessing their clinical features, diagnostic tests, and admission outcomes. Patients admitted were typically older and had higher mortality rates, shorter intervals to neurological evaluation, and a higher incidence of acute stroke compared to those discharged. For the primary analysis (n = 250), the Neuro artificial intelligence (AI) model demonstrated significant performance metrics, achieving an area under the curve (AUC) of 0.88 for general admission predictions in comparison to actual outcomes, an AUC of 0.86 for neurological department admissions, 0.93 for long-term mortality risk, and 1 for 48 h mortality risk. Our Neuro AI model predictions showed a strong correlation with expert consensus (Pearson correlation 0.79, p < 0.001), indicating its ability to provide consistent support amid divergent clinical opinions. Conclusions: Our Neuro AI model accurately predicted hospital admissions (AUC = 0.88) and neurological department admissions (AUC = 0.86), demonstrating strong alignment with expert clinical judgment.

Importance Obesity is associated with a higher risk of recurrence, mortality, comorbidities, treatment-related adverse effects, and poor quality of life in patients with breast cancer. Scalable interventions are needed to promote weight loss in this population. Objective To evaluate the impact of a remotely delivered weight loss intervention (WLI) on weight change at 1 year in patients with breast cancer and obesity and to explore factors associated with weight change. Design, Setting, and Participants The Breast Cancer Weight Loss trial is a phase 3, randomized clinical trial evaluating the impact of a telephone-based WLI on invasive disease–free survival and other outcomes in women with obesity and early breast cancer at 637 sites across the US and Canada. Participants were enrolled to the study between August 2016 and February 2021. Participants included women with stage II to III, ERBB2 -negative breast cancer and a body mass index (BMI) of 27 or higher. Interventions Participants were randomized to a 2-year, telephone-based WLI plus health education or health education alone control group. Main Outcome and Measures The primary end point for this prespecified secondary analysis was weight change at 1 year. Weight was measured at baseline and 1 year, and changes in weight were compared between groups. Weight change was evaluated with a linear mixed-effects model including treatment group, weight over time, a time-by-group interaction, menopausal status, race and ethnicity, and hormone receptor status. Results A total of 3180 women with breast cancer and BMI of 27 and higher were included in the study; 1591 were randomized to the WLI and 1589 to the control group. At baseline, the mean (SD) age of participants was 53.4 (10.6), and the mean (SD) BMI was 34.4 (5.6). The racial and ethnic breakdown included 406 (12.8%) Black, 231 (7.3%) Hispanic or Latino, 2906 (91.4%) non-Hispanic, and 2555 (80.3%) White participants. WLI participants lost a mean of 4.3 kg (95% CI 3.9-4.6 kg), or 4.7% (95% CI, 4.3%-5.0%) of baseline body weight at 1 year vs control participants, who gained 0.9 kg (95% CI, 0.5-1.3 kg), or 1.0% (95% CI 0.1%-1.4%) of baseline body weight ( P &amp;lt; .001). Participants randomized to WLI experienced significant weight loss (vs control group participants) across demographic and tumor factors. WLI effect differed significantly by menopausal status, with postmenopausal participants having greater weight loss than premenopausal participants, and by race and ethnicity, with Black and Hispanic participants having less weight loss compared to other races and ethnicities. Conclusions and Relevance In this secondary analysis of a randomized clinical trial, a telephone-based WLI induced significant weight loss in patients with breast cancer with overweight and obesity across demographic and treatment factors. Further follow-up of the Breast Cancer Weight Loss trial will evaluate whether the WLI improves disease outcomes. Trial Registration ClinicalTrials.gov Identifier: NCT02750826

1586 Background: Black and Hispanic breast cancer (BC) survivors have a higher prevalence of obesity and experience less success with weight loss interventions (WLI) than White BC survivors. The BWEL trial (Alliance A011401; NCT02750826) is a phase III randomized trial evaluating the impact of a 2-year telephone-based WLI on invasive disease-free survival in participants (pts) with stage II-III HER2-negative BC and a BMI ≥ 27 kg/m 2 . At 12-months, the WLI induced significant weight loss across demographic factors, including race and ethnicity. However, Black and Hispanic pts lost less weight and completed fewer calls than White pts. Here, we evaluate intervention adherence, engagement and tool utilization in BWEL pts by race and ethnicity. Methods: BWEL randomized pts to a WLI plus health education (HE) or HE alone. WLI pts received semi-structured telephone-based health coaching, delivered in English or Spanish, and received an activity monitor and wireless scale. Pts self-reported race and ethnicity. Mean values for call duration, call density (time to complete the initial 12-week intensive intervention phase), intervention attrition, and frequency of Fitbit use and weight tracking over 12-months were compared by race and ethnicity, comparing least squares means with Tukey-Kramer adjustment for multiple comparisons with adjusted p-values. Results: Of 3181 pts randomized to the study between 08/2016 and 02/2021, 1591 pts were allocated to the WLI arm. 80.5% of pts were White, 12.8% Black, and 7.1% Hispanic. Average BMI was 34.5 (±5.7) kg/m2. Compared to White pts, Black pts had fewer days of Fitbit usage (113.6 vs. 159.8, p<0.0001) and weight tracking (77.9 vs. 135.6 days, p< 0.0001). Hispanic pts had fewer days of Fitbit usage (108.8 vs. 154.9, p= 0.001) and weight tracking (87 vs. 129.5 days, p=0.0002) compared to non-Hispanic pts. There were no differences in attrition rate, average call duration, or call density by race or ethnicity. Conclusions: In a phase III WLI trial, engagement with tools designed to support weight loss was significantly lower in Black and Hispanic pts. Future work is needed to explore ways to enhance engagement and improve weight loss outcomes for racial and ethnic minority pts. Support: U10CA180821, U10CA180882, UG1CA189823; https://acknowledgments.alliancefound.org . Clinical trial information: NCT02750826 . Measure of engagement Race Ethnicity WhiteN=1281 BlackN=204 p-value Non-HispanicN=1459 HispanicN=113 p-value Withdrew from intervention n (%) 58 (4.5%) 13 (6.4%) 0.56 70 (4.8%) 6 (5.3%) 0.51 Call Duration (min)Mean (SD) 34.5 (7.6) 34.6 (9.1) 0.99 34.6 (7.8) 33.3 (7.9) 0.25 Call density(weeks)Mean (SD) 14.2 (7.2) 14.2 (8.8) 0.99 14.2 (7.3) 14.2 (9.7) 0.99 Days of Fitbit UseMean (SD) 159.8 (132.2) 113.6 (124.7) < 0.0001 154.9 (131.8) 108.8 (125.7) 0.001 Days of weight trackingMean (SD) 135.6 (109.5) 77.9 (87.5) < 0.0001 129.5 (108.8) 87 (96.5) 0.0002

561 Background: Obesity and other lifestyle factors are associated with breast cancer (BC) risk and outcomes. These relationships appear to vary in pre- vs postmenopausal women. Here we explore associations between pathological features, diet quality and physical activity (PA) in patients (pts) with BC enrolled in the BWEL (Alliance for Clinical Trials in Oncology A011401) trial, stratifying for menopausal status. Methods: BWEL is a phase III trial evaluating the impact of a weight loss intervention on disease outcomes in 3180 pts with stage II-III HER2– BC and body mass index ≥27 kg/m 2 . The first 542 BWEL pts underwent assessment of PA (7-Day PA Recall) and diet (24-hour Diet Recall) at enrollment. Healthy Eating Index 2020 (HEI) score (0-100, higher value = healthier diet) and minutes/week (min/wk) of moderate/vigorous PA (MVPA) were calculated. Estrogen receptor (ER) and progesterone receptor (PR) expression level (%) were abstracted from pathology reports. Analyses of links between HEI score, MVPA min/wk, % ER/PR, stratified by menopausal status, were conducted using t-tests; differential associations by menopausal status used linear regression models with interaction term. Results: In 523 pts with available pathology data, 76.5% had ER/PR+ BC, 56.2% were postmenopausal. Median HEI score was 50.1 (range 18.1-96.4), median MVPA min/wk was 0 (range 0-630), median time from diagnosis to enrollment was 10.0 months (range 2.4-13.1). In postmenopausal pts, higher diet quality was linked to increased % ER and PR (HEI above vs below median, mean % ER 78.0 vs 66.1, p=0.012; mean % PR 57.7 vs 43.1, p=0.004). The relationship between HEI score and % ER/PR differed significantly by menopausal status (interaction term of menopausal status and % ER: p=0.006; and % PR: p=0.012); there were no significant associations between HEI scores and % ER/PR in premenopausal pts. There were no significant associations between PA and % ER/PR. Conclusions: Healthier diet was associated with higher % ER and PR in postmenopausal pts with BC, but no relationship was seen in premenopausal pts. These data suggest that lifestyle factors may influence BC pathologic features related to outcomes in older women. Support: U10CA180821, U10CA180882, UG1CA189823, U24CA196171, https://acknowledgments.alliancefound.org . Clinical trial information: NCT02750826 . % ER mean (SD) p value % PR mean (SD) p value Post-Menopausal HEI* score≤50.1 [n=134]>50.1 [n=156] 66.1 (43.3)78.0 (36.8) 0.012 43.1 (41.8)57.7 (41.0) 0.004 MVPA † min/wk0 [n=147]≥1 [n=147] 74.5 (39.3)70.3 (41.2) 0.37 50.0 (41.8)53.0 (42.0) 0.41 Pre-Menopausal HEI* score≤50.1 [n=124]>50.1 [n=103] 70.5 (38.5)65.5 (43.0) 0.15 53.5 (41.1)49.1 (43.0) 0.43 MVPA † min/wk0 [n=112]≥1 [n=113] 66.3 (41.0)66.3 (41.2) 0.99 49.5 (41.4)52.6 (42.7) 0.59 *Healthy Eating Index. † Moderate/Vigorous Physical Activity.

e12562 Background: Previous research has suggested that breast cancer (BC) diagnosed during pregnancy or the postpartum period exhibits more aggressive clinical features, possibly due to pregnancy-related hormonal changes, immune modulation, and alterations in the mammary microenvironment compared to nulligravid or women many years out from pregnancy. Emerging data have demonstrated potential worse outcomes for patients (pts) diagnosed in post-partum. This study evaluated the influence of timing of diagnosis in relation to pregnancy history and recency of childbirth on long-term outcomes in a modern cohort of young pts with early-stage BC. Methods: Pts with stage I–III BC age < 40y in the prospective Young Women’s Breast Cancer Study (YWBCS) were categorized at diagnosis as Nulligravid (no prior pregnancy), Nulliparous (prior pregnancy without live birth), Currently Pregnant, or Parous (subdivided by time since last childbirth: within 5 years [≤ 5y PP], and 5–10 years postpartum [5–10y PP]). Baseline characteristics and outcomes were compared across BC subtypes (estrogen receptor+/HER2- [ER+/HER2-], HER2+, and triple-negative BC [TNBC]). Prognostic analyses for distant recurrence-free survival (DRFS) used Cox models. Results: 859 pts were included, 257 (30%) nulligravid, 50 (6%) nulliparous, 37 (4%) pregnant, and 515 (60%) parous (348 [68%] were ≤5y PP, and 167 [32%] were 5–10y PP). Median follow-up was 133 (6–209) months. Median age at diagnosis was lower in nulligravid pts (33 vs. 35–38 years); T1 tumors were most common in pts >5y PP (62%), and N0 status was highest in nulligravid pts (64%) (all p < 0.0001). Pregnant pts had higher rates of TNBC (27%), and T3 tumors (14%), N1 status, N1 (41%), and Grade 3 (81%) tumors compared to other groups (all p < 0.0001). Overall, only tumor size (T2-T4 vs T1, HR 2.03; 95% CI 1.37–3.03) and nodal status (N1 vs N0, HR 2.22; 1.48–3.34; N2/3 vs N0, HR 3.23; 1.96-5.33) were independently associated with worse DRFS; pregnancy history was not: nulligravid + nulliparous pts had no significant differences in DRFS compared to Pregnant (HR 1.46; 0.75–2.86), Parous <5y PP (HR 0.86; 0.58–1.26), and Parous 5–10y PP (HR 0.89; 0.54–1.48). Subgroup analyses by BC subtype (Table) and sensitivity analyses (excluding currently pregnant pts or separating nulligravid and nulliparous pts) demonstrated no significant associations between pregnancy history and DRFS. Conclusions: In this cohort study of very young patients with early breast cancer with contemporary treatment including management of breast cancer during pregnancy, pregnancy history and recency of pregnancy were not independent predictors of long-term disease outcomes. DRFS results by subgroup. Pregnancy Group N (%) HR (95% CI) ER+/HER2- Nulligravid + Nulliparous 173 (38.1) Ref Pregnant 16 (3.5) 0.93 (0.32–2.70) Parous ≤5y PP 177 (39.0) 0.77 (0.45–1.29) Parous 5–10y PP 88 (19.4) 0.78 (0.39–1.53) HER2+ Nulligravid + Nulliparous 77 (30.8) Ref Pregnant 11 (4.4) 3.27 (0.80–13.38) Parous ≤5y PP 113 (45.2) 1.50 (0.60–3.75) Parous 5–10y PP 49 (19.6) 1.45 (0.45–4.66) TNBC Nulligravid + Nulliparous 56 (36.8) Ref Pregnant 10 (6.6) 1.92 (0.61–6.09) Parous ≤5y PP 57 (37.5) 0.68 (0.28–1.63) Parous 5–10y PP 29 (19.1) 1.16 (0.43–3.14)