Dysregulated miRNA Expression and Androgen Receptor Loss in Racially Distinct Triple-Negative Breast Cancer.

Androgen receptor (AR)-negative triple-negative breast cancer (TNBC), often termed quadruple-negative breast cancer (QNBC), disproportionately impacts women of African descent, leading to poorer overall survival (OS). MiRNAs regulate the expression of gene drivers involved in critical signaling pathways in TNBC, such as the gene, and their expression varies across races and breast cancer subtypes. This study investigates whether differentially expressed miRNAs influence AR transcription, potentially contributing to the observed disparities between African American (AA) and European American (EA) QNBC patients.

Association of neighborhood social vulnerability with ovarian cancer survival.

Objective: Social determinants of health (SDOH) impact cancer outcomes. The CDC Social Vulnerability Index (SVI) integrates scores for four neighborhood-based SDOH domains (socioeconomic status, household characteristics, minority status, and housing type/transportation) to assess neighborhood social vulnerability (NSV). While NSV has been associated with overall cancer mortality and lung, breast, colon, and endometrial cancer-specific mortality, the relationship between NSV as defined by the SVI and ovarian cancer outcomes remains unknown.

Cell cycle traverse rate predicts long-term outcomes in a multi-institutional cohort of patients with triple-negative breast cancer.

Background: Ki67 index (KI) and mitotic index (MI) are proliferation markers with established prognostic value in breast carcinomas. While KI is evaluated immunohistochemically and reported as a percentage, MI is determined visually and reflects total mitotic cells in 10 high-power fields. Our objective was to integrate KI and MI into a novel metric; the cell cycle traverse rate (CCTR).

MammOnc-DB, an integrative breast cancer data analysis platform for target discovery.

Breast cancer (BCa) is one of the most common malignancies among women worldwide. It is a complex disease that is characterized by morphological and molecular heterogeneity. In the early stages of the disease, most BCa cases are treatable, particularly hormone receptor-positive and HER2-positive tumors.

Quantitative proteomics reveals serum proteome alterations during metastatic disease progression in breast cancer patients.

Background: Tumor recurrence and metastatic progression remains the leading cause for breast cancer related mortalities. However, the proteomes of patient- matched primary breast cancer (BC) and metastatic lesions have not yet been identified, due to the lack of clinically annotated longitudinal samples. In this study, we evaluated the global-proteomic landscape of BC patients with and without distant metastasis as well as compared the proteome of distant metastatic disease with its corresponding primary BC, within the same patient.

A novel role for KIFC1-MYH9 interaction in triple-negative breast cancer aggressiveness and racial disparity.

African American (AA) women are twice as likely to develop triple-negative breast cancer (TNBC) as women of European descent. Additionally, AA women with TNBC present a much more aggressive disease course than their European American (EA) counterparts. Thus, there is an unmet clinical need to identify race-specific biomarkers and improve survival outcomes in AA patients with TNBC.

Demographic disparities in the limited awareness of alcohol use as a breast cancer risk factor: empirical findings from a cross-sectional study of U.S. women.

Background: Alcohol use is an established yet modifiable risk factor for breast cancer. However, recent research indicates that the vast majority of U.S.

Integrative spatial omics reveals distinct tumor-promoting multicellular niches and immunosuppressive mechanisms in African American and European American patients with TNBC.

Racial disparities in triple-negative breast cancer (TNBC) outcomes have been reported. However, the biological mechanisms underlying these disparities remain unclear. We integrated imaging mass cytometry and spatial transcriptomics, to characterize the tumor microenvironment (TME) of African American (AA) and European American (EA) patients with TNBC.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.

Background: Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment in NAC response in patients with TNBC remains unclear.

Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning.

Background: Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients.

Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer.

Background: Pathological complete response (pCR) is associated with favorable prognosis in patients with triple-negative breast cancer (TNBC). However, only 30-40% of TNBC patients treated with neoadjuvant chemotherapy (NAC) show pCR, while the remaining 60-70% show residual disease (RD). The role of the tumor microenvironment (TME) in NAC response in patients with TNBC remains unclear.

Associations between health insurance status, neighborhood deprivation, and treatment delays in women with breast cancer living in Georgia.

Background: Little is known regarding the association between insurance status and treatment delays in women with breast cancer and whether this association varies by neighborhood socioeconomic deprivation status.

Methods: In this cohort study, we used medical record data of women diagnosed with breast cancer between 2004 and 2022 at two Georgia-based healthcare systems. Treatment delay was defined as >90 days to surgery or >120 days to systemic treatment.

Deep learning based registration of serial whole-slide histopathology images in different stains.

For routine pathology diagnosis and imaging-based biomedical research, Whole-slide image (WSI) analyses have been largely limited to a 2D tissue image space. For a more definitive tissue representation to support fine-resolution spatial and integrative analyses, it is critical to extend such tissue-based investigations to a 3D tissue space with spatially aligned serial tissue WSIs in different stains, such as Hematoxylin and Eosin (H&E) and Immunohistochemistry (IHC) biomarkers. However, such WSI registration is technically challenged by the overwhelming image scale, the complex histology structure change, and the significant difference in tissue appearances in different stains.

Predicting neoadjuvant treatment response in triple-negative breast cancer using machine learning.

Background: Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30%â€"40% of TNBC patients.

Acceptability of Primary Care Counseling and Brief Educational Messages to Increase Awareness about Alcohol and Breast Cancer Risks among Bisexual and Lesbian Women.

This research had two aims: (1) to assess how often bisexual and lesbian women self-report screening and counseling for alcohol use in primary care settings; and (2) understand how bisexual and lesbian women respond to brief messages that alcohol increases breast cancer risk. The study sample consisted of 4891 adult U.S.

PLK1 and AURKB phosphorylate survivin differentially to affect proliferation in racially distinct triple-negative breast cancer.

Protein diversity due to alternative mRNA splicing or post-translational modifications (PTMs) plays a vital role in various cellular functions. The mitotic kinases polo-like kinase 1 (PLK1) and Aurora B (AURKB) phosphorylate survivin, an inhibitor of apoptosis (IAP) family member, thereby regulating cell proliferation. PLK1, AURKB, and survivin are overexpressed in triple-negative breast cancer (TNBC), an aggressive breast cancer subtype.

A Case Series Exploration of Multi-Regional Expression Heterogeneity in Triple-Negative Breast Cancer Patients.

Extensive intratumoral heterogeneity (ITH) is believed to contribute to therapeutic failure and tumor recurrence, as treatment-resistant cell clones can survive and expand. However, little is known about ITH in triple-negative breast cancer (TNBC) because of the limited number of single-cell sequencing studies on TNBC. In this study, we explored ITH in TNBC by evaluating gene expression-derived and imaging-derived multi-region differences within the same tumor.

Association of Race and Area Deprivation With Breast Cancer Survival Among Black and White Women in the State of Georgia.

Importance: Increasing evidence suggests that low socioeconomic status and geographic residence in disadvantaged neighborhoods contribute to disparities in breast cancer outcomes. However, little epidemiological research has sought to better understand these disparities within the context of location.

Objective: To examine the association between neighborhood deprivation and racial disparities in mortality among Black and White patients with breast cancer in the state of Georgia.

Spatial Attention-Based Deep Learning System for Breast Cancer Pathological Complete Response Prediction with Serial Histopathology Images in Multiple Stains.

In triple negative breast cancer (TNBC) treatment, early prediction of pathological complete response (PCR) from chemotherapy before surgical operations is crucial for optimal treatment planning. We propose a novel deep learning-based system to predict PCR to neoadjuvant chemotherapy for TNBC patients with multi-stained histopathology images of serial tissue sections. By first performing tumor cell detection and recognition in a cell detection module, we produce a set of feature maps that capture cell type, shape, and location information.

Barriers to breast cancer screening in Atlanta, GA: results from the Pink Panel survey at faith-based institutions.

Purpose: Our research sought to describe barriers to mammography screening among a sample of predominantly Black women in metropolitan Atlanta, Georgia.

Methods: The Pink Panel project convened community leaders from faith-based institutions to administer an offline survey to women via convenience sampling at fourteen churches in Atlanta in late 2019 and early 2020. With the COVID-19 pandemic, the research team switched to an online survey.

Computational benchmarking of putative KIFC1 inhibitors.

The centrosome in animal cells is instrumental in spindle pole formation, nucleation, proper alignment of microtubules during cell division, and distribution of chromosomes in each daughter cell. Centrosome amplification involving structural and numerical abnormalities in the centrosome can cause chromosomal instability and dysregulation of the cell cycle, leading to cancer development and metastasis. However, disturbances caused by centrosome amplification can also limit cancer cell survival by activating mitotic checkpoints and promoting mitotic catastrophe.

A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.

Motivation: Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eosin and two immunohistochemical biomarkers (Ki67 and PHH3).