skip to main content

Explore Research Products in the NSF-PAR

  • NSF Public Access
  • Search Results
  • A Pooled Case-only Analysis of Reproductive Risk Factors and Breast Cancer Subtype Among Black Women in the Southeastern United States
Title: A Pooled Case-only Analysis of Reproductive Risk Factors and Breast Cancer Subtype Among Black Women in the Southeastern United States
Background: We investigated the association between reproductive risk factors and breast cancer subtype in Black women. On the basis of the previous literature, we hypothesized that the relative prevalence of specific breast cancer subtypes might differ according to reproductive factors. Methods: We conducted a pooled analysis of 2,188 (591 premenopausal, 1,597 postmenopausal) Black women with a primary diagnosis of breast cancer from four studies in the southeastern United States. Breast cancers were classified by clinical subtype. Case-only polytomous logistic regression models were used to estimate ORs and 95% confidence intervals (CI) for HER2+ and triple-negative breast cancer (TNBC) status in relation to estrogen receptor–positive (ER+)/HER2− status (referent) for reproductive risk factors. Results: Relative to women who had ER+/HER2− tumors, women who were age 19–24 years at first birth (OR, 1.78; 95% CI, 1.22–2.59) were more likely to have TNBC. Parous women were less likely to be diagnosed with HER2+ breast cancer and more likely to be diagnosed with TNBC relative to ER+/HER2− breast cancer. Postmenopausal parous women who breastfed were less likely to have TNBC [OR, 0.65 (95% CI, 0.43–0.99)]. Conclusions: This large pooled study of Black women with breast cancer revealed etiologic heterogeneity among breast cancer subtypes. Impact: Black parous women who do not breastfeed are more likely to be diagnosed with TNBC, which has a worse prognosis, than with ER+/HER2− breast cancer.  more » « less
Award ID(s):
1359140 1659752
NSF-PAR ID:
10290357
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Cancer epidemiology biomarkers prevention
ISSN:
1055-9965
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; ; ( , npj Breast Cancer)
    Abstract

    Approaches for rapidly identifying patients at high risk of early breast cancer recurrence are needed. Image-based methods for prescreening hematoxylin and eosin (H&E) stained tumor slides could offer temporal and financial efficiency. We evaluated a data set of 704 1-mm tumor core H&E images (2–4 cores per case), corresponding to 202 participants (101 who recurred; 101 non-recurrent matched on age and follow-up time) from breast cancers diagnosed between 2008–2012 in the Carolina Breast Cancer Study. We leveraged deep learning to extract image information and trained a model to identify recurrence. Cross-validation accuracy for predicting recurrence was 62.4% [95% CI: 55.7, 69.1], similar to grade (65.8% [95% CI: 59.3, 72.3]) and ER status (66.3% [95% CI: 59.8, 72.8]). Interestingly, 70% (19/27) of early-recurrent low-intermediate grade tumors were identified by our image model. Relative to existing markers, image-based analyses provide complementary information for predicting early recurrence.

     
    more » « less
  2. ; ; ; ; ; ; ; ; ; ; et al ( , JAMA Network Open)
    Importance

    Persistence of COVID-19 symptoms beyond 2 months, or long COVID, is increasingly recognized as a common sequela of acute infection.

     Objectives

    To estimate the prevalence of and sociodemographic factors associated with long COVID and to identify whether the predominant variant at the time of infection and prior vaccination status are associated with differential risk.

     Design, Setting, and Participants

    This cross-sectional study comprised 8 waves of a nonprobability internet survey conducted between February 5, 2021, and July 6, 2022, among individuals aged 18 years or older, inclusive of all 50 states and the District of Columbia.

     Main Outcomes and Measures

    Long COVID, defined as reporting continued COVID-19 symptoms beyond 2 months after the initial month of symptoms, among individuals with self-reported positive results of a polymerase chain reaction test or antigen test.

     Results

    The 16 091 survey respondents reporting test-confirmed COVID-19 illness at least 2 months prior had a mean age of 40.5 (15.2) years; 10 075 (62.6%) were women, and 6016 (37.4%) were men; 817 (5.1%) were Asian, 1826 (11.3%) were Black, 1546 (9.6%) were Hispanic, and 11 425 (71.0%) were White. From this cohort, 2359 individuals (14.7%) reported continued COVID-19 symptoms more than 2 months after acute illness. Reweighted to reflect national sociodemographic distributions, these individuals represented 13.9% of those who had tested positive for COVID-19, or 1.7% of US adults. In logistic regression models, older age per decade above 40 years (adjusted odds ratio [OR], 1.15; 95% CI, 1.12-1.19) and female gender (adjusted OR, 1.91; 95% CI, 1.73-2.13) were associated with greater risk of persistence of long COVID; individuals with a graduate education vs high school or less (adjusted OR, 0.67; 95% CI, 0.56-0.79) and urban vs rural residence (adjusted OR, 0.74; 95% CI, 0.64-0.86) were less likely to report persistence of long COVID. Compared with ancestral COVID-19, infection during periods when the Epsilon variant (OR, 0.81; 95% CI, 0.69-0.95) or the Omicron variant (OR, 0.77; 95% CI, 0.64-0.92) predominated in the US was associated with diminished likelihood of long COVID. Completion of the primary vaccine series prior to acute illness was associated with diminished risk for long COVID (OR, 0.72; 95% CI, 0.60-0.86).

     Conclusions and Relevance

    This study suggests that long COVID is prevalent and associated with female gender and older age, while risk may be diminished by completion of primary vaccination series prior to infection.

     
    more » « less
  3. ; ; ; ; ; ; ; ; ; ; et al ( , Paediatric and Perinatal Epidemiology)
    Abstract Background

    The metabolic changes that ultimately lead to gestational diabetes mellitus (GDM) likely begin before pregnancy. Cannabis use might increase the risk of GDM by increasing appetite or promoting fat deposition and adipogenesis.

     Objectives

    We aimed to assess the association between preconception cannabis use and GDM incidence.

     Methods

    We analysed individual‐level data from eight prospective cohort studies. We identified the first, or index, pregnancy (lasting ≥20 weeks of gestation with GDM status) after cannabis use. In analyses of pooled individual‐level data, we used logistic regression to estimate study‐type‐specific odds ratios (OR) and 95% confidence intervals (CI), adjusting for potential confounders using random effect meta‐analysis to combine study‐type‐specific ORs and 95% CIs. Stratified analyses assessed potential effect modification by preconception tobacco use and pre‐pregnancy body mass index (BMI).

     Results

    Of 17,880 participants with an index pregnancy, 1198 (6.7%) were diagnosed with GDM. Before the index pregnancy, 12.5% of participants used cannabis in the past year. Overall, there was no association between preconception cannabis use in the past year and GDM (OR 0.97, 95% CI 0.79, 1.18). Among participants who never used tobacco, however, those who used cannabis more than weekly had a higher risk of developing GDM than those who did not use cannabis in the past year (OR 2.65, 95% CI 1.15, 6.09). This association was not present among former or current tobacco users. Results were similar across all preconception BMI groups.

     Conclusions

    In this pooled analysis of preconception cohort studies, preconception cannabis use was associated with a higher risk of developing GDM among individuals who never used tobacco but not among individuals who formerly or currently used tobacco. Future studies with more detailed measurements are needed to investigate the influence of preconception cannabis use on pregnancy complications.

     
    more » « less
  4. ( , Cancer Informatics)
    Known genes in the breast cancer study literature could not be confirmed whether they are vital to breast cancer formations due to lack of convincing accuracy, although they may be biologically directly related to breast cancer based on present biological knowledge. It is hoped vital genes can be identified with the highest possible accuracy, for example, 100% accuracy and convincing causal patterns beyond what has been known in breast cancer. One hope is that finding gene-gene interaction signatures and functional effects may solve the puzzle. This research uses a recently developed competing linear factor analysis method in differentially expressed gene detection to advance the study of breast cancer formation. Surprisingly, 3 genes are detected to be differentially expressed in TNBC and non-TNBC (Her2, Luminal A, Luminal B) samples with 100% sensitivity and 100% specificity in 1 study of triple-negative breast cancers (TNBC, with 54 675 genes and 265 samples). These 3 genes show a clear signature pattern of how TNBC patients can be grouped. For another TNBC study (with 54 673 genes and 66 samples), 4 genes bring the same accuracy of 100% sensitivity and 100% specificity. Four genes are found to have the same accuracy of 100% sensitivity and 100% specificity in 1 breast cancer study (with 54 675 genes and 121 samples), and the same 4 genes bring an accuracy of 100% sensitivity and 96.5% specificity in the fourth breast cancer study (with 60 483 genes and 1217 samples). These results show the 4-gene-based classifiers are robust and accurate. The detected genes naturally classify patients into subtypes, for example, 7 subtypes. These findings demonstrate the clearest gene-gene interaction patterns and functional effects with the smallest numbers of genes and the highest accuracy compared with findings reported in the literature. The 4 genes are considered to be essential for breast cancer studies and practice. They can provide focused, targeted researches and precision medicine for each subtype of breast cancer. New breast cancer disease types may be detected using the classified subtypes, and hence new effective therapies can be developed. 
    more » « less
  5. ; ; ; ( , Frontiers in Genetics)
    Tumor subtype and menopausal status are strong predictors of breast cancer (BC) prognosis. We aimed to find and validate subtype- or menopausal-status-specific changes in tumor DNA methylation (DNAm) associated with all-cause mortality or BC progression. Associations between site-specific tumor DNAm and BC prognosis were estimated among The Cancer Genome Atlas participants ( n = 692) with Illumina Infinium HumanMethylation450 BeadChip array data. All-cause mortality and BC progression were modeled using Cox proportional hazards models stratified by tumor subtypes, adjusting for age, race, stage, menopausal status, tumor purity, and cell type proportion. Effect measure modification by subtype and menopausal status were evaluated by incorporating a product term with DNAm. Site-specific inference was used to identify subtype- or menopausal-status-specific differentially methylated regions (DMRs) and functional pathways. The validation of the results was carried out on an independent dataset (GSE72308; n = 180). We identified a total of fifteen unique CpG probes that were significantly associated ( P ≤ 1 × 10 − 7 with survival outcomes in subtype- or menopausal-status-specific manner. Seven probes were associated with overall survival (OS) or progression-free interval (PFI) for women with luminal A subtype, and four probes were associated with PFI for women with luminal B subtype. Five probes were associated with PFI for post-menopausal women. A majority of significant probes showed a lower risk of OS or BC progression with higher DNAm. We identified subtype- or menopausal-status-specific DMRs and functional pathways of which top associated pathways differed across subtypes or menopausal status. None of significant probes from site-specific analyses met genome-wide significant level in validation analyses while directions and magnitudes of coefficients showed consistent pattern. We have identified subtype- or menopausal-status-specific DNAm biomarkers, DMRs and functional pathways associated with all-cause mortality or BC progression, albeit with limited validation. Future studies with larger independent cohort of non-post-menopausal women with non-luminal A subtypes are warranted for identifying subtype- and menopausal-status-specific DNAm biomarkers for BC prognosis. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email