Citations

Abstract Background Breast cancer is etiologically heterogeneous, but which risk factors differ in their associations across tumor subtypes remains unclear. We conducted a large, pooled analysis to evaluate independent, dose-response associations between breast cancer risk factors and quantitative tumor features. Methods Analyses of 15,731 invasive breast cancers from 24 studies evaluated associations (p-trend) between reproductive and hormonal factors, body mass index (BMI), alcohol, smoking, and family history in relation to quantitative immunohistochemistry measures on tissue microarrays (ER, PR, HER2, KI67, TP53) and tumor grade. Analyses in a subset of 10 population-based studies estimated subtype-specific odds ratios (ORs) comparing cases to controls. A Bayesian False Discovery Probability (BFDP) <0.2 was used to identify associations with strong statistical evidence. Results Nulliparity and later age at menopause were associated with higher ER-positivity (p-trend=0.021 and 0.001, respectively), with corresponding OR[ER+] (95% CI) = 1.49 (1.16-1.90) for nulliparous vs. parous and 1.07 (1.03-1.11) per 5 years. Current combined menopausal hormone therapy (MHT) use was associated with lower grade (p-trend<0.001), with OR [grade1] = 3.37 (2.69-4.21) for current vs. never users. Higher BMI was associated with lower ER-positivity and higher grade in premenopausal women (p-trend<0.001 and <0.001), with OR[ER+] = 0.80 (0.74-0.87) and OR[grade1] = 0.75 (0.63-0.88) per 5 units, and with higher PR-positivity and higher grade in postmenopausal women (p-trend<0.001 and <0.001), with OR[PR+] = 1.08 (1.03-1.14) and OR[grade 3] = 1.10 (1.04-1.17) per 5 units. Conclusion This pooled analysis of 15,731 cases showed that nulliparity, age at menopause, MHT, and BMI have independent, dose-response associations with ER, PR, and grade, clarifying patterns of etiologic heterogeneity. Associations with HER2, KI67 and TP53, or other risk factors did not meet our threshold for strong evidence.

Glioblastoma is the most common form of brain cancer in adults and remains one of the most lethal forms of cancer. Treating glioblastoma is uniquely challenging due to its aggressive nature and its critical location, the brain, which must remain largely undamaged. Due to these difficulties, there is a lack of effective treatment options. Computer models simulating tumors and their response to therapy, known as digital twins, provide a promising new strategy. These model systems are mathematical equations that capture the biological knowledge of how cells interact during tumor growth and in response to treatment. By solving these equations, we can create virtual patients that provide accurate predictions of real patients’ tumor progression. This approach has the potential to improve outcomes by designing therapies for the specific needs of each patient, moving beyond the limitations of the “one-size-fits-all” approach that is currently used.

ABSTRACT Approximately 30% of patients with acute graft-versus-host disease (GVHD) develop steroid-refractory disease and have very poor outcomes. Ruxolitinib has become the standard of care for steroid-refractory acute GVHD, but it is unclear which patients derive benefit. The MAGIC Composite Score (MCS), an algorithm that combines clinical symptoms and biomarkers, has been validated to predict outcomes at the start of primary GVHD treatment. Here, we evaluated its performance at the initiation of second-line treatment in 278 patients. MCS stratified patients into three risk groups (MCS1-3), with the majority (88%) classified as intermediate or high risk. Increasing MCS score was associated with progressively higher 1-year non-relapse mortality (NRM) rates (16%, 41%, and 73%; p<0.001), lower 1-year survival (77%, 56%, and 24%; p<0.001), and lower complete response (CR) rates at day 28 (47%, 38%, and 20%, respectively; p<0.01). The area under the receiver operating characteristic curve (AUROC) for 1-year NRM was significantly higher with MCS compared to clinical symptoms alone (0.70 vs. 0.63; p=0.023). Among patients treated with ruxolitinib, higher MCS similarly predicted higher NRM and lower survival and CR rates. Patients classified as MCS2/3 had poor outcomes despite ruxolitinib, underscoring the need for novel therapies in this patient population. In conclusion the MCS is an accurate predictor of outcomes for patients who require second-line treatment and may be of use as an eligibility criterion for future clinical trials in this high-risk population. Key Points MAGIC Composite Scores that integrate clinical symptoms and biomarker scores predict outcomes of second-line therapy for acute GVHD more accurately than clinical symptoms alone. MCS identifies patients with high NRM despite treatment with ruxolitinib.

Importance Amyloid positron emission tomography (PET) is increasingly used in research and clinical settings to determine the etiology of cognitive decline and eligibility for amyloid-targeting therapies. To assist with amyloid PET evaluation and to guide clinical decision-making, images can be quantified in a standardized unit called Centiloid, the interpretation of which can vary according to the method and threshold used. Objective To collect Centiloid values from available studies and determine robust positivity cutoffs using data-driven methods and correspondence with visual reads. Data Sources PubMed search (October 2024) identified studies with Centiloid values. Corresponding authors were invited to share individual participant data. Additional data were obtained through access-controlled repositories and conference outreach (July 2024-July 2025). Study Selection Studies were included if they provided Centiloids, radiotracer, age, and sex. Data Extraction and Synthesis Each study was analyzed using a unified statistical pipeline; study estimates were pooled using random-effects meta-analysis. Main Outcomes and Measures Gaussian mixture models (GMMs) were fitted to Centiloid values for each study. In studies with a bimodal distribution (per integrated completed likelihood), single cutoffs for positivity were set as mean plus 2 SDs of the lower gaussian component. Using GMMs, a double-cutoff approach defined a lower certainty range using a 90% posterior probability cutoff for assignment to the low (amyloid-negative) vs high (amyloid-positive) component. An alternative Centiloid cutoff was derived from maximizing the correspondence (Cohen κ) with the binary visual reads when available. Results This meta-analysis included cross-sectional amyloid PET scans acquired with 5 radiotracers from 49 227 participants across 53 studies from 15 countries (mean age, 71 years; 54% female, 62% cognitively impaired). The data-driven GMM approach identified a bimodal distribution in 51 studies (n = 48 786), resulting in a single cutoff for positivity of 18 Centiloids (95% CI,16-19; I 2 = 97%). The double-cutoff approach revealed high confidence for interpreting scans as negative when Centiloid values were lower than 11 (95% CI, 9-13; I 2 = 95%) and interpreting scans as positive if Centiloid values were higher than 26 (95% CI, 24-28; I 2 = 95%). In analyses of correspondence with binary (positive or negative) visual reads of amyloid PET scans (n = 35 045; 36 studies), Centiloids were highly predictive of visual positivity (Cohen κ, 0.86; 95% CI, 0.83-0.89; I 2 = 96%) with a cutoff of 27 Centiloids (95% CI, 24-30; I 2 = 80%). Conclusions and Relevance In this individual participant data meta-analysis, positivity cutoffs converged around 18 Centiloids (data-driven) and 27 Centiloids (visual reads). Findings from a double-cutoff analysis suggest that scans in the 11 to 26 Centiloid range should be interpreted with caution depending on the context of use.

Abstract Dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia, pathologically defined by the presence of Lewy bodies. Peripheral and central inflammation are increasingly recognized in DLB in clinical, post-mortem and animal studies. Finding clinically relevant biomarkers of inflammation in DLB will support the identification of novel pathways for disease-modifying therapies or use in clinical trials of immunomodulatory agents. Whilst there are cross-sectional studies of inflammation markers in DLB, there is limited evidence on the association between these markers and cognitive decline over time. Twenty participants with DLB underwent blood sampling for serum inflammatory markers, paired with PET imaging of the translocator protein (TSPO) and up to 4 years of longitudinal cognitive testing. Thirty participants with Alzheimer’s disease—comprising both Alzheimer’s dementia and/or mild cognitive impairment with biomarker evidence of amyloid pathology (AD/MCI+)—and 28 controls were also recruited for group comparisons. Data from 42 baseline cytokine immunoassays and TSPO PET were used as predictors of longitudinal cognitive scores in linear mixed-effects models. Partial least squares regression was used to test the association between peripheral and central inflammation. Using peripheral inflammatory markers as single predictors, we identified 14 associated with either a slower or faster rate of cognitive decline in DLB, whilst no single marker was predictive of decline in AD/MCI+. As many inflammatory markers were highly correlated, we used principal component analysis to identify a cytokine component associated with reduced cognitive decline in both DLB and AD/MCI+, that overlapped with the single markers identified in the previous analysis. A separate component was associated with cognitive decline in AD/MCI+ or DLB with Alzheimer’s dementia co-pathology (ascertained by amyloid PET). Widespread TSPO binding was associated with reduced cognitive decline in DLB, whilst a fronto-temporal pattern was associated with more rapid cognitive decline in both DLB and AD/MCI+. There were associations between peripheral cytokines and TSPO PET in AD/MCI+, but these were not significant in DLB. Overall, peripheral and central inflammation predicted cognitive decline in DLB. Specific patterns associated with both faster and slower rates of decline were identified. These profiles had both overlapping and contrasting associations when compared to AD/MCI+. Collectively, these data add to a body of evidence suggesting clinically relevant levels of inflammation in DLB. Future studies in larger, multi-site cohorts with multiple biomarker sampling points are required to understand the impact and dynamics of inflammation across all stages of the disease.

The NCCN Clinical Practice Guidelines for Breast Cancer include recommendations for clinical management of patients with carcinoma in situ, invasive breast cancer, Paget’s disease, Phyllodes tumor, inflammatory breast cancer, and management of breast cancer during pregnancy. The panel convenes annually to update recommendations based on a review of recently published clinical trials. This selection from the NCCN Guidelines for Breast Cancer focuses on the recommendations for the management of recurrent or stage IV (M1) metastatic breast cancer. It summarizes the treatment strategies stratified by tumor biology, including hormone receptor status and HER2 expression, as well as clinical factors such as prior therapy and disease-free interval. Throughout all treatment lines, supportive care, quality of life preservation, toxicity minimization, and clinical trial participation are emphasized as integral components of patient care.

TAF7, a core subunit of the general transcription factor TFIID, regulates transcription and additionally functions as an RNA chaperone that directs nascent nuclear RNAs to cytoplasmic polysomes, thereby influencing protein synthesis. Here, we identify a set of novel cytoplasmic interactors and a previously unappreciated cytoplasmic role for TAF7 in oncogenesis. BioID-mediated proximity labeling identified STAT3 and the CCT and WASH complexes as prominent cytoplasmic interactors of TAF7. These interactions were validated by co-immunoprecipitation and proximity ligation assays. We further showed that cytoplasmic TAF7 levels increased during oncogenesis in proportion to cancer cell pathogenicity. Notably, TAF7 engagement with STAT3, CCT and WASH complexes is markedly enhanced during cellular transformation, consistent with their functions in cancer progression. Together, these findings reveal a cytoplasmic TAF7 interactome and implicate elevated cytoplasmic TAF7 as associated with oncogenesis.

Racial and ethnic survival disparities exist in pediatric and young adult acute lymphoblastic leukemia (ALL), with differential treatment toxicity and setting of care as possible drivers. Whether treatment cost and utilization disparities exist, or how they may contribute to differences in ALL survival, is unknown. Patients with ALL diagnosed between 2000 and 2019 at ages 1 to 24 years were identified using ICD-9/10 codes in the administrative claims data from the OptumLabs Data Warehouse and the Surveillance, Epidemiology, and End Results program. Sex, race and ethnicity, age, year, and subtype of ALL diagnosis were collected. Reimbursed treatment cost and utilization (number of inpatient and outpatient days) were computed over the initial 8 months of treatment. Regression models assessed associations with demographic and clinical characteristics. Survival analyses assessed associations between race and ethnicity and overall survival. Among 374 individuals with ALL, treatment costs were 57% lower for Black patients than for White patients (estimate 0.43, 95% CI: 0.19-0.96; P =0.04). Utilization was similar across groups, though Black patients had 24% fewer outpatient visits (estimate 0.76, 95% CI: 0.58-0.99; P =0.04). No significant differences in survival were detected, but observations are consistent with previous reports. Despite a small sample size, the statistically significantly lower cost and utilization among Black relative to White ALL patients are concerning and warrant further research.

Abstract Background Data on respiratory syncytial virus (RSV) acute respiratory infection (ARI) among community-dwelling U.S. adults aged 18 to 64 years, particularly those with high-risk conditions (HRC), remain limited. This study estimated incidence and attack rates of RSV-ARI and RSV lower respiratory tract disease (LRTD) in adults with and without HRCs. Methods A multistate, community-based prospective cohort of adults aged 18 to 64 years with and without HRCs was followed from October 2022 to September 2024. Incidence rates and annual attack rates of RSV-ARI and RSV-LRTD were calculated by HRC presence and age group. Results Of 3375 adults with at least 1 HRC, the RSV-ARI incidence rate was 17.9 per 1000 person-years (attack rates: year 1, 1.87%; year 2, 1.56%), compared with 14.8 per 1000 person-years (year 1, 1.33%; year 2, 1.50%) among adults without HRCs. RSV-LRTD incidence was 10.0 per 1000 person-years in adults with HRCs versus 5.7 per 1000 person-years in those without. Asthma, congenital immunodeficiency, and immunosuppressive medication use were associated with increased incidence of RSV-ARI and RSV-LRTD; whereas adults with solid organ transplant and chronic kidney disease had a significantly higher incidence rate of RSV-LRTD compared to those without these conditions. Adults aged 18 to 49 years with HRCs had significantly higher RSV-LRTD incidence (9.6 vs 4.3 per 1000 person-years). After adjustment for age and sex, having ≥1 HRC was associated with a 62% increased RSV-LRTD risk, rising to 117% among adults aged 18 to 49 years. Conclusions Adults with HRCs—particularly asthma, congenital immunodeficiency, chronic kidney disease, immunosuppressive medication use, and solid organ transplant—experienced higher RSV-ARI and RSV-LRTD incidence. The elevated RSV-LRTD burden was most pronounced in adults aged 18 to 49 years with HRCs.

Abstract Background Approximately 12,000 adolescents and young adults (ages 15-39, AYAs) are diagnosed with a primary central nervous system (CNS) tumor each year. DNA methylation profiling has transformed CNS tumor classification by refining diagnostic accuracy and identifying biologically distinct subtypes, but its application has not been systematically evaluated in the AYA population. In this study, we examined the spectrum of CNS tumor types across age groups, with a focus on the AYA population. Methods We assembled a large dataset of CNS tumor samples with age annotations and methylation profiles matching with high confidence to a CNS tumor type using the NCI/Bethesda classifier. Prevalence of tumor type, methylation class and DNA copy number aberrations were compared across age strata (pediatric, AYA and adult). Results The cohort of 21,099 CNS tumors included 5139 tumors from AYAs (24%), which showed a mixed pattern, with tumors typical of childhood (e.g. medulloblastoma) as well as those common in older adults (e.g. glioblastoma). Several tumor types were specifically enriched in the AYA, including IDH-mutant astrocytoma, pleomorphic xanthoastrocytoma, posterior fossa group B ependymoma, and diffuse hemispheric glioma, H3 G34-mutant, among others. Distinct methylation subclasses of multiple tumor types were observed in the AYA, and patterns of genomic aberrations showed age-specific distributions. Conclusion Large-scale methylation profiling revealed unique classification patterns of CNS tumors in AYAs, with specific tumor types, subclasses, and genomic alterations enriched in this population. These data may serve as a valuable reference resource for better understanding the spectrum of CNS tumors affecting AYA patients.