For more than 4 decades, a survival gap has persisted between black and white women with breast cancer in the United States; age-adjusted mortality rates are 28 per 100,000 among black women and 20 per 100,000 among white women. Much of what we know about the root causes of this disparity, as well as possible solutions, comes from research using different types of data, ranging from high-dimensional genomic data to large population-based data sources and linkages with insurance claims records. We will provide a brief orientation on the research that has defined our understanding of breast cancer disparities to date, as well as promising emerging data sources and methods that may take us further in the quest to close the racial survival gap and provide better cancer care to vulnerable populations.
What Is the Meaning of Big Data for Oncology Research?
The term "big data" is used in many different industries. It is colloquial rather than scientific, with varying meaning based on the context in which the term is used. A general definition of big data centers on three defining features: size, complexity, and technology. That is, the data are of such enormous quantity and complexity that “manipulation and management present significant logistical challenges” using traditional data computing and technology methods. Big data are also frequently described using 4 to 10 key attributes known as the “Vs” of big data. The four most common Vs are volume, velocity, variety, and veracity. Depending on the field of research, each of these “V” attributes carries a different level of importance, and presents different challenges, to data users. Extensive research and technology developments are now emerging that help data users deal with each of the big Vs in their efforts to transform large and complex datasets into valuable and actionable information.
In cancer disparities research, big data resources can be powerful because the size (volume) and breadth (variety) of data captured means that, compared with clinical trial data or data from academic centers or consortia, the information may better represent vulnerable patient groups, such as minorities or elderly patients. Similarly, the tools and analytic methods developed for working with these data can model and measure how multiple complex factors interact to reveal specific disparities in care. For the purposes of this discussion, we will highlight the use of certain big data resources and analytic methods for cancer disparities research, including large linked administrative and cancer registry datasets, aggregated data from electronic health records (EHRs), and genomic data. A model for integrating these data sources to fully understand cancer disparities is illustrated in Figure 1.
Major Contributors to Racial Disparities in Breast Cancer
It is widely acknowledged that both social and biological factors contribute to the survival gap between black women and white women with breast cancer. The epidemiologic basis of racial disparities in breast cancer has been defined using data from the Surveillance, Epidemiology, and End Results (SEER) Program national network of cancer registries, as well as analyses of national cancer incidence and mortality data from the American Cancer Society. We know from these large, longitudinal, national data sources that black women, particularly those under 50 years of age, have a disproportionately large burden (measured as relative frequency) of the biologically aggressive triple-negative breast cancer subtype, as well as a more advanced disease stage at presentation.[6,7] Genomic sequencing data, which enable more precise molecular characterization of breast cancers, have been used in studies such as the Carolina Breast Cancer Study and The Cancer Genome Atlas project, to identify additional biological differences within clinically defined groups, including higher proportions of poor-prognosis molecular subtypes among young black women whose clinical markers indicate hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative disease. This variation within clinical subtypes may contribute to racial disparities in breast cancer outcome in these HR-positive HER2-negative patients, who would usually be expected to have a favorable prognosis.[8-10]
While unrecognized biological variation likely explains a portion of within-subtype variability in survival outcomes, we also have ample documentation, primarily from large cancer registry datasets linked to administrative claims data, that treatment disparities are prominent in breast cancer care and contribute to differences in disease outcomes. Beginning with surgical therapy, black women are less likely to receive any definitive surgery for early-stage disease, and less likely to receive morbidity-sparing sentinel lymph node biopsy when eligible. There is substantial evidence that these disparities are partially explained by the concentration of surgical treatment for black patients within lower-volume and lower-quality hospitals that are less likely to be integrated into research networks.[13-15] Adjuvant breast radiation therapy (RT) is more often delayed among black women; delays are linked to both site of care (with access to smaller surgical facilities and/or those with onsite RT available linked to higher odds of accessing RT, compared with access to large governmental facilities only) and breast cancer mortality. With respect to adjuvant chemotherapy, initiation of chemotherapy appears to be relatively equal by race. However, black women more often have delays between surgery and chemotherapy, and may discontinue chemotherapy and biologic therapy prematurely; both treatment patterns are demonstrably related to survival decrements.[18-20] Black women with HER2-overexpressing breast cancer receive trastuzumab, a highly effective but costly targeted therapy, at dramatically lower rates than white women. Among eligible patients, black women initiate adjuvant hormonal therapy at lower rates than white women and have more problems with adherence; this disparity appears to be concentrated among younger patients and patients treated with chemotherapy. With regard to genomic testing, black women are less likely to receive guideline-concordant gene expression profile testing to help predict the benefit from chemotherapy in those with HR-positive disease.[23,24]
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