Biological Basis of Breast Cancer-Related Disparities in Precision Oncology Era.
Anca-Narcisa NeaguPathea BrunoKaya R JohnsonGabriella BallestasCostel C DariePublished in: International journal of molecular sciences (2024)
Precision oncology is based on deep knowledge of the molecular profile of tumors, allowing for more accurate and personalized therapy for specific groups of patients who are different in disease susceptibility as well as treatment response. Thus, onco-breastomics is able to discover novel biomarkers that have been found to have racial and ethnic differences, among other types of disparities such as chronological or biological age-, sex/gender- or environmental-related ones. Usually, evidence suggests that breast cancer (BC) disparities are due to ethnicity, aging rate, socioeconomic position, environmental or chemical exposures, psycho-social stressors, comorbidities, Western lifestyle, poverty and rurality, or organizational and health care system factors or access. The aim of this review was to deepen the understanding of BC-related disparities, mainly from a biomedical perspective, which includes genomic-based differences, disparities in breast tumor biology and developmental biology, differences in breast tumors' immune and metabolic landscapes, ecological factors involved in these disparities as well as microbiomics- and metagenomics-based disparities in BC. We can conclude that onco-breastomics, in principle, based on genomics, proteomics, epigenomics, hormonomics, metabolomics and exposomics data, is able to characterize the multiple biological processes and molecular pathways involved in BC disparities, clarifying the differences in incidence, mortality and treatment response for different groups of BC patients.
Keyphrases
- end stage renal disease
- affordable care act
- ejection fraction
- newly diagnosed
- chronic kidney disease
- healthcare
- mass spectrometry
- metabolic syndrome
- palliative care
- physical activity
- gene expression
- type diabetes
- cardiovascular disease
- mental health
- machine learning
- peritoneal dialysis
- air pollution
- risk assessment
- single cell
- electronic health record
- data analysis