Citations

Abstract Imaging-based monitoring of gliomas is hampered by treatment-related changes, as well as the insidious growth of gliomas harboring the isocitrate dehydrogenase (IDH) mutation. The IDH mutation results in elevated D-2-hydroxyglutarate (D-2-HG), which is detectable in cerebrospinal fluid (CSF) that can be accessed longitudinally from multiple compartments, including cranially via CSF access devices. As such, we evaluated the early utility of CSF D-2-HG as a serially accessible biomarker for IDH-mutant gliomas. A CLIA-approved gas chromatography mass spectrometry assay was developed for CSF D- and L-2-HG. Lumbar and cranial CSF samples were collected from patients with IDH-mutant gliomas or IDH-wild-type brain tumors and non-tumor pathologies via surgical field collection, lumbar punctures, Ommaya reservoirs, and ventriculoperitoneal shunts. First, to confirm the ability of CSF D-2-HG to detect IDH-mutant gliomas, concentrations were compared between IDH-mutant gliomas and wild-type lesions. Cranial, but not lumbar, CSF D-2-HG distinguished primary IDH-mutant gliomas from IDH-wild type lesions (cranial AUC= 0.89, 95% confidence interval (CI)= 0.80-0.97); lumbar AUC= 0.52, 95% CI=0.28-0.76). Indeed, CSF D-2-HG was significantly higher in cranial than lumbar samples from IDH-mutant glioma patients (median lumbar=0.20 μM, cranial = 1.72 μM; p<0.0001), confirming a significantly elevated abundance of CSF D-2-HG only in cranial CSF from IDH-mutant gliomas. Then, response to cytoreduction was evaluated, demonstrating that cranial CSF D-2-HG levels decreased to 0.54x of baseline with resection in seventeen patients (p=0.0129). Changes in tumor-to-CSF contact impacted the serial evaluation of CSF D-2-HG prior to versus after chemoradiation in some patients. Nevertheless, in patients with grade 4 IDH-mutant astrocytomas, serial cranial CSF D-2-HG increased with disease progression and differentiated stability from pseudoprogression when tumor-CSF contact was sufficient. In conclusion, serial cranial CSF D-2-HG shows promise as a monitoring biomarker in patients with IDH-mutant gliomas, although anatomic variables must be considered in parallel to CSF D-2-HG evaluation.

Importance Inherited pathogenic variants (PVs) in known predisposition genes can greatly increase breast cancer risk, but the combined impact of PV status, family history, and other factors on breast cancer risk in the general US population has not been well described. Objective To evaluate population-based breast cancer risk estimates for those with established PVs overall and stratified by first-degree family history of breast cancer and other factors. Design, Setting, and Participants This study used pooled data from 13 US-based breast cancer case-control studies participating in the Cancer Risk Estimates Related to Susceptibility (CARRIERS) consortium. Enrollment for individual studies occurred between 1976 and 2013, and results are based on data released March 2023, with analyses conducted from June 2022 to July 2025. Exposures PVs, breast cancer family history, self-reported race and ethnicity, and established risk factors. Main Outcomes and Measures Breast cancer rate ratios for PVs in 7 genes were estimated from the CARRIERS consortium. PV status and incidence and mortality statistics were combined using the Individualized Coherent Absolute Risk Estimation (iCARE) model to estimate conditional cumulative breast cancer risks and 95% CIs, stratified by family history and standardized to the US population. Models that incorporated population-based data and published estimates for established epidemiologic risk factors were also evaluated. Results A total of 67 692 women were studied, including 33 841 who were diagnosed with breast cancer. PVs in ATM , BRCA1 , BRCA2 , CHEK2 , and PALB2 were strongly associated with breast cancer risk, with BRCA1 and PALB2 PVs showing evidence of heterogeneity by family history. In models considering PVs, family history, and established risk factors, the estimated cumulative risks of breast cancer by age 50 years ranged from 2.4% (95% CI, 2.4-2.4) in women with no PVs and no family history to 35.5% (95% CI, 21.6-55.1) in PALB2 PV carriers with a family history. Among women who have not been diagnosed with breast cancer by age 50 years, the cumulative risk of breast cancer by age 80 years ranged from 11.1% (95% CI, 11.0-11.2) in noncarriers with no family history to 70.5% (95% CI, 52.8-83.5) for PALB2 carriers with a family history. PV-specific cumulative risk estimates varied across subgroups defined by race and ethnicity and potentially modifiable epidemiologic risk factors. Conclusions and Relevance In this study, population-based estimates of cumulative breast cancer risk for established PVs, as informed by the CARRIERS case-control sample, varied by family history and potentially modifiable risk factors. These estimates provide guidance for identifying individuals who will most benefit from enhanced screening and prevention strategies.

In women with dense breasts, adding molecular breast imaging to digital breast tomosynthesis (DBT) increased both overall and invasive cancer detection rates, while modestly raising the recall rate compared with DBT alone.

ImportanceEtiologic heterogeneity in breast carcinogenesis needs to be well characterized for targeted prevention. Associations between menopausal hormonal therapy (MHT) and oral contraceptive (OC) use and breast cancer intrinsic-like subtypes are not well understood.ObjectiveTo examine whether exogenous hormone use is differentially associated with breast cancer subtypes and to evaluate heterogeneity by intrinsic-like subtypes.Design, Setting, and ParticipantsThis study pooled data from 31 nested and population-based case-control studies involved in the Breast Cancer Association Consortium. The study population included individuals with breast cancer and control participants from 13 case-control studies nested in prospective cohorts (recruited between 1982 and 2011) and 18 population-based case-control studies (recruited between 1990 and 2013). Data analysis was performed in June 2024.ExposureMHT use (estrogen-progestin therapy [EPT] or estrogen-only therapy [ET]) in postmenopausal women and OC use in premenopausal women (never, past use, or current use).Main Outcomes and MeasuresBreast cancer intrinsic-like subtypes (luminal A–like, luminal B–like, luminal B–ERBB2 [formerly HER2 or HER2/neu]-like, ERBB2 enriched–like, or triple-negative) were determined by immunohistochemistry of tumor sections. Polytomous logistic regression was performed to estimate the association between exogenous hormones and risk of breast cancer by intrinsic-like subtypes. Analyses by subtypes were stratified by body mass index (BMI [calculated as weight in kilograms divided by height in meters squared]; healthy weight, 18.5-<25; overweight, 25-<30; or obesity, ≥30).ResultsThis study included 42 269 individuals with breast cancer (11 901 [28.2%] premenopausal and 30 368 [71.8%] postmenopausal; 23 353 [55.2%] had a known intrinsic-like subtype) and 71 072 control participants. The mean (SD) age of all participants was 57.9 (10.9) years. In postmenopausal women, associations between current MHT use (EPT or ET) and breast cancer differed by subtype. Current EPT users with healthy weight were more likely to be diagnosed with luminal A–like (odds ratio [OR], 2.51 [95% CI, 2.26-2.80]) or luminal B–ERBB2-like (OR, 1.95 [95% CI, 1.61-2.37]) subtypes. These associations were attenuated but remained for individuals with overweight (OR, 1.40 [95% CI, 1.02-1.92]) or obesity (OR, 1.68 [95% CI, 1.01-2.78]). EPT use increased the odds of being diagnosed with luminal B–like tumors solely in women with healthy weight (OR, 1.47 [95% CI, 1.17-1.86]). Current ET use was positively associated with luminal A–like disease in women with healthy weight only (OR, 1.16 [95% CI, 1.01-1.32]), showing inverse associations with higher BMI (obesity: OR, 0.65 [95% CI, 0.50-0.85]). In premenopausal women, recent OC use was associated with luminal B–ERBB2-like (OR, 1.50 [95% CI, 1.09-2.08]), ERBB2 enriched–like (OR, 2.33 [95% CI, 1.55-3.51]), and triple-negative (OR, 1.75 [95% CI, 1.33-2.29]; P < .04 for heterogeneity) tumors.Conclusions and RelevanceIn this study, clear differences were observed in associations between current EPT use and luminal-like breast cancer subtypes and other subtypes. EPT users with healthy weight were more likely to be diagnosed with luminal-like breast cancer compared with nonusers. Subtype heterogeneity was less apparent in associations of OC and ET use. Future studies on contemporary formulations, patterns of use, and routes of administration of exogenous hormone usage are warranted.

Abstract Background We examined how the COVID-19 pandemic influenced influenza vaccination, healthcare engagement, and patient satisfaction. Methods We retrospectively analyzed influenza vaccination records and patient experience surveys of adult primary care patients in southeastern Minnesota during two 3-year phases: Prepandemic (1 January 2017–28 February 2020) and Pandemic-Plus (1 March 2020–31 December 2023). Vaccination status was defined as “always vaccinated” (AV), “never vaccinated” (NV), or “intermittently vaccinated” (IV) for seasonal influenza. Results During the Pandemic-Plus phase, 7.0% (n = 3556) of the Prepandemic AV and 48.7% (n = 16 710) of the Prepandemic IV patients never received another influenza vaccine. Compared to AV, IV and NV patients were more likely to reside in areas with greater socioeconomic deprivation (odds ratio [OR], 1.58 [95% confidence interval {CI}, 1.53–1.62] and 1.99 [1.94–2.05], respectively), have a high school education or less (2.86 [2.74–2.98] and 3.38 [3.23–3.53]), and report healthcare disengagement (1.59 [1.55–1.64] and 4.21 [4.09–4.33]). After adjusting for Area Deprivation Index and medical comorbidities, healthcare disengagement increased among the NV versus AV between phases (3.33 [3.24–3.41] and 4.23 [4.10–4.35]). In a subgroup analysis those with severe comorbidities were less likely AV (NV vs AV: 1.21 [1.14–1.27]) and more dissatisfied with care (NV vs AV: 1.25 [1.18–1.33]). Conclusions The COVID-19 pandemic altered vaccination behaviors and healthcare satisfaction, especially among those at high risk of developing influenza-related complications. Medical providers and public health officials should be aware of factors associated with vaccine refusal to better target interventions.

Abstract Breast cancer risk assessment tools are widely used in clinical practice to guide decisions regarding screening timing and modality, lifestyle interventions, genetic testing, preventive therapy, and risk-reducing surgery. Although a number of tools designed for the general population are used in practice, they face a series of challenges including: (i) modest discriminatory ability due to lack of a unified model that flexibly incorporates a comprehensive set of risk factors; (ii) inability to produce subtype-specific risk estimates, relevant for tailoring screening and/or preventive strategies (e.g. prophylactic endocrine therapy that is effective only for hormone receptor positive tumors); (iii) lack of data from diverse populations to build models that work well for all women; and, (iv) scarcity of opportunities for comparative validation of different models. The Breast Cancer Risk Prediction Project (BCRPP), a collaboration currently including 20 prospective cohorts from the National Cancer Institute’s Cohort Consortium, is addressing these challenges by harmonizing, sharing, and analyzing data on over 1.8 million women, over 115, 000 of whom have developed breast cancer. The BCRPP is developing a comprehensive tool that will predict breast cancer risk, overall and by subtypes, across major racial and ethnic groups in the United States. This tool incorporates known and emerging risk factors (e.g. reproductive history, anthropometry, smoking, alcohol intake, medication use, mammography, family history, and measured genotypes). In parallel to model building and validation efforts, BCRPP investigators are developing privacy-preserving web and mobile interfaces to deliver the risk tools to patients and clinicians. Here we present descriptive data on participating cohorts (e.g. dates of enrollment, ages at enrollment, length of follow up, numbers of cases by subtype), summarize missing data patterns and our approach to model development and validation that accounts for heterogeneity in missingness patterns across cohorts, and describe data governance procedures and data sharing infrastructure (including pooled and federated analyses, to account for policy limitations on sharing individual data). Descriptive information and procedures for requesting and accessing data are available via the BCRPP data platform (https://epidataplatforms.cancer.gov/bcrpp/). Although originally assembled for risk model development, these data on a large and diverse sample of women with extensive harmonized risk factors and covariates can be a resource to address other outstanding questions in breast cancer epidemiology across time, the lifecourse, and diverse populations. Citation Format: Peter Kraft, Laura Beane-Freeman, Julie Palmer, Ellen O'Meara, Jeanine Genkinger, James Lacey, Thomas Rohan, Lauren Teras, Marian L. Neuhouser, Kala Visvanathan, Celine Vachon, Roger Milne, Christopher Haiman, Yu Chen, Heather Eliassen, Cari Kitahara, Katie O'Brien, Emily White, Garnet Anderson, I-Min Lee, Archie Campbell, Renee Fortner, Ylva Lagerros, Sven Sandin, Mia Gaudet, Montserrat Garcia-Closas, Nilanjan Chatterjee. The Breast Cancer Risk Prediction Project: a resource of 1.8 million women with diverse backgrounds from 20 prospective cohorts to improve breast cancer risk prediction and advance breast cancer epidemiology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3583.

ABSTRACT Advancements in transmission electron microscopy (TEM) have enabled in-depth studies of biological specimens, offering new avenues to large-scale imaging experiments with subcellular resolution. Mitochondrial structure is of growing interest in cancer biology due to its crucial role in regulating the multi-faceted functions of mitochondria. We and others have established the crucial role of mitochondria in triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer with limited therapeutic options. Building upon our previous work demonstrating the functional role of mitochondrial structure dynamics in the metabolic adaptations and survival of chemotherapy-refractory TNBC cells, we sought to extend those findings to a large-scale analysis of transmission electron micrographs. Here we present a novel U-Net artificial intelligence (AI) model for automatic annotation and assessment of mitochondrial morphology and feature quantification. Our model is trained on 11,039 manually annotated mitochondria across 125 micrographs derived from a variety of orthotopic patient-derived xenograft (PDX) mouse model tumors and adherent cell cultures. The model achieves an F1 score of 0.85 on test micrographs at the pixel level. To validate the ability of our model to detect expected mitochondrial structural changes, we utilized micrographs from mouse primary skeletal muscle cells genetically modified to lack Dynamin-related protein 1 (Drp1). We subjected in vitro and in vivo TNBC models to conventional chemotherapy treatments commonly used for clinical management of TNBC, including doxorubicin, carboplatin, paclitaxel, and docetaxel (DTX). We found substantial within-sample heterogeneity of mitochondrial structure in both in vitro and in vivo TNBC models. In four of five PDX models, in vivo treatment with DTX elicited significant alteration in mitochondrial elongation and/or area. We went on to compare mammary tumors and matched lung metastases in a highly metastatic PDX model of TNBC, uncovering significant increase in mitochondrial elongation in metastatic lesions compared to their matched primary mammary tumor. The successful application of our AI model to capture mitochondrial structure marks a step forward in high-throughput analysis of mitochondrial structures, enhancing our understanding of how morphological changes may relate to chemotherapy efficacy and mechanism of action. Our large, manually curated electron micrograph dataset - now publicly available - serves as a unique resource for developing, benchmarking, and applying computational models, while further advancing investigations into mitochondrial morphology and its impact on breast cancer biology. This study provides proof of concept that mitochondrial structural remodeling is an additional layer of cellular reprogramming accompanying therapeutic resistance TNBC that merits further investigation.

Abstract Background The COVID-19 pandemic has underscored challenges with patient adherence to medical directives, particularly vaccine acceptance. While sociodemographic factors have been linked to non-COVID-19 vaccine hesitancy, the relationships between patient experience, the COVID-19 pandemic, and adherence to routine vaccinations are uncertain.Figure 1:Yearly influenza vaccine refusal, influenza vaccine administrations, and overall patient satisfaction since establishing care with a primary care provider from 2017-2023.Panel A (Influenza Refusals): the number of vaccine refusals among individuals who are Intermittently Vaccinated and Never Vaccinated for seasonal influenza since establishing care with a primary care provider over time.Panel B (Influenza Vaccinations): the number of vaccines administered over time since establishing care with a primary care provider among those who are Always Vaccinated and Intermittently Vaccinated for seasonal influenza.Panel C (Overall Satisfaction): the number of patients who indicated that they were maximally satisfied with their overall care following any interaction with the healthcare system over time. Methods We retrospectively analyzed influenza vaccination records of adult primary care patients in Olmsted County, Minnesota. Vaccination history was examined during two phases: Pre-Pandemic (1/1/2017—2/28/2020) and Pandemic-Plus (3/1/2020—12/31/2023). Vaccination status was defined as “always vaccinated” (AV), “never vaccinated” (NV), or “intermittently missed years of vaccinations” (IV) for seasonal influenza. Patient dissatisfaction was determined if patients negatively ranked their overall quality of care on standard patient experience surveys. We explored the relationship between influenza vaccination rates, healthcare engagement, and patient satisfaction.Figure 2:Multinomial logistic regression of patient satisfaction on vaccination status across the phases of the COVID-19 Pandemic, adjusted for engagement in the health care system, participation, and area of deprivation index (ADI) for patients that were (A) intermittently vaccinated compared to always vaccinated and (B) never vaccinated comparted to always vaccinated. The “Pre-pandemic phase” refers to influenza immunization data and patient satisfaction data prior to March 2020. The “Pandemic-plus phase” refers to immunization data and patient satisfaction data from March 2020 to January 2024. Low ADI ranking refers to areas with a low level of deprivation, whereas high ADI ranking refers to areas with a high level of deprivation. Individuals were considered low if they came from an area with a state ADI ranking from 1 to 5 and were considered high if they came from an area with a state ADI ranking from 6 to 10. Results From 1/1/2017 to 12/31/2023, 21% of individuals were AV (n=36,632), 52% were IV (n=90,776), and 27% were NV (n=46,418) for seasonal influenza. Post-pandemic onset, 7% (n=3,556) of the Pre-Pandemic AV and 32% (n=16,710) of the Pre-Pandemic IV never received another influenza vaccine. In the Pandemic-Plus phase, the IV and NV had a higher likelihood of disengagement with the healthcare system [odds ratio (OR) (95% CI): 1.59 (1.55, 1.64); 4.21 (4.09, 4.33)], residing in areas of greater deprivation [1.58 (1.53, 1.62); 1.99 (1.94, 2.05)], and self-identifying their race as Black [2.29 (2.09, 2.51); 3.29 (3.01, 3.60)] compared to the AV. After adjusting for healthcare engagement and area of deprivation, dissatisfaction OR increased from 1.03 (0.99,1.07) Pre-Pandemic to 1.07 (1.03,1.12) in the Pandemic-Plus phase among IV compared to AV. Similarly, the OR of dissatisfaction rose from 1.11 (1.05, 1.18) Pre-Pandemic to 1.16 (1.09, 1.24) in the Pandemic-Plus phase among those who were NV compared to those AV. Conclusion Higher healthcare dissatisfaction was observed among those inconsistently adherent to vaccination recommendations. Targeted efforts to enhance patient satisfaction may boost vaccination rates. Disclosures All Authors: No reported disclosures

Abstract Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significance Integration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.