Outcomes in breast cancer-does ethnicity matter?

[Impact of precariousness on breast cancer care in the Île-de-France region: Results of the DESSEIN study]

INTRODUCTION

Precariousness has been associated with an increase in breast cancer mortality, but the links between precariousness, stage at diagnosis and care pathways are little explored. The objective of the DESSEIN study was to assess the impact of precariousness on disease and care pathways.

METHODS

Prospective observational study in Île-de-France comparing precarious and non-precarious patients consulting for breast cancer and followed for 1 year.

RESULTS

In total, 875 patients were included between 2016 and 2019 in 19 institutions: 543 non-precarious patients and 332 precarious patients. Precarious patients had a more advanced stage at diagnosis (55% T1 vs. 63%, 30% N+ vs 19%, P=0.0006), had a higher risk of not receiving initially planned treatment (4 vs. 1%, P=0.004), and participated less in clinical trials (5 vs. 9%, P=0.03). Non-use of supportive oncology care was 2 times more frequent among patients in precarious situations (P<0.001). During treatment, 33% of deprived patients reported a loss of income, compared with 24% of non-deprived patients (P<0.001). At 12 months from diagnosis, lay-offs were 2 times more frequent in precarious patients (P=0.0001).

DISCUSSION

Precariousness affects all stages of the cancer history and care pathway. Particular attention needs to be paid to vulnerable populations, considering issues of accessibility and affordability of care, health literacy and possible implicit bias from the care providers.

Association Between Polymorphisms in DNA Damage Repair Pathway Genes and Female Breast Cancer Risk

Breast cancer risk have been discussed to be associated with polymorphisms in genes as well as abnormal DNA damage repair function. This study aims to assess the relationship between genes single nucleotide polymorphisms (SNPs) related to DNA damage repair and female breast cancer risk in Chinese population. A case-control study containing 400 patients and 400 healthy controls was conducted. Genotype was identified using the sequence MassARRAY method and expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER-2) in tumor tissues was analyzed by immunohistochemistry assay. The results revealed that ATR rs13091637 decreased breast cancer risk influenced by ER, PR (CT/TT vs. CC: adjusted odds ratio [OR] = 1.54, 95% confidence interval [CI]: 1.04-2.27, p = 0.032; CT/TT vs. CC: adjusted OR = 1.63, 95%CI: 1.14-2.35, p = 0.008) expression. Stratified analysis revealed that PALB2 rs16940342 increased breast cancer risk in response to menstrual status (AG/GG vs. AA: adjusted OR = 1.72, 95%CI: 1.13-2.62, p = 0.011) and age of menarche (AG/GG vs. AA: adjusted OR = 1.54, 95%CI: 1.03-2.31, p = 0.037), whereas ATM rs611646 and Ku70 rs132793 were associated with reduced breast cancer risk influenced by menarche (GA/AA vs. GG: adjusted OR = 0.50, 95%CI: 0.30-0.95, p = 0.033). In a summary, PALB2 rs16940342, ATR rs13091637, ATM rs611646, and Ku70 rs132793 were associated with breast cancer risk.

Breast cancer insights from Northern Israel: a comprehensive analysis of survival rates among Jewish and Arab women

This study investigates breast cancer survival rates between 2000 and 2022 in northern Israel, focusing on ethnicity, socioeconomic status, age at diagnosis, and the Charlson Comorbidity Index. Analyzing data from Clalit Health Services, we studied 8,431 breast cancer patients (6,395 Jewish, 2,036 Arab). We compared five- and ten-year survival rates across different demographics. Ethnicity showed a minor impact on survival (OR 1.12, 95% CI: 0.93 - 1.35). Socioeconomic status had a significant effect, with a higher level of improving survival (OR 2.50, 95% CI: 2.04 - 3.08). Age was crucial; women 18-39 had better survival than 60-100, but no significant difference was found between the 18-39 and 40-59 age groups [OR (CI 0.90 - 1.53, p = 0.231)]. For the Charlson Comorbidity Index, women with scores of 3-10 showed lower survival compared to scores of 0 and 1-2. There was a notable improvement in five-year survival rates among patients aged 18-59 diagnosed from 2009-2018 (90.7%) compared to 2000-2008 (86.9%) (p = 0.0046), but not in patients aged 60-100. The study highlights that socioeconomic status, age, and comorbidity scores are significant in determining survival rates for breast cancer. The improvement in survival rates for younger patients diagnosed more recently reflects advancements in treatment and care. This research provides valuable insights into the factors affecting breast cancer survival rates, underscoring the role of socioeconomic status, age, and comorbidities while also highlighting the progress in breast cancer treatment over recent years.

Evaluation of breast tumor morphologies from African American and Caucasian patients

The primary aim of this research was to investigate potential differences of breast tumor morphologies across African American and Caucasian racial groups by utilizing machine learning (ML) and artificial intelligence (AI) methods. While breast cancer disparities can partially be attributed to social determinants of health, tumor biology also contributes to survival outcomes. The rate of breast tumor growth is largely dependent on the extracellular matrix (ECM). Current research suggests the cellular components of the ECM may vary among racial and ethnic populations, and this may contribute to the incidence of cancer in African Americans. We utilized a supervised AI method to evaluate morphological differences between African American and Caucasian breast cancer tumors. Images used for analysis were downloaded from the Cancer Genome Atlas (TCGA) biorepository stored in the NIH Genomic Data Commons (GDC) data portal. We designed an ML classifier using the AlexNet model provided in PyTorch's torchvision package. The model was pre-trained and adapted via transfer learning resulting in a classification accuracy of 92.1% when using our breast cancer tumor image database split into 80% of training set and 20% of testing set. We interpreted the results of the AlexNet and ResNet50 models using LIME and saliency mapping as the explainers. Based on the images from our bi-racial testing set, this study confirmed significant variations of tumor and ECM regions in the different racial groups evaluated. Based on these findings, further analysis and characterization may provide new insight into disparities associated with the incidence of breast cancer.