Disparities in compliance with the Oncotype DX breast cancer test in the United States: A National Cancer Data Base assessment

Disparities in OncotypeDx Testing and Subsequent Chemotherapy Receipt by Geography and Socioeconomic Status

BACKGROUND

OncotypeDx is a prognostic and predictive genomic assay used in early-stage hormone receptor-positive, HER2- (HR+/HER2-) breast cancer. It is used to inform adjuvant chemotherapy decisions, but not all eligible women receive testing. We aimed to assess variation in testing by demographics and geography, and to determine whether testing was associated with chemotherapy.

METHODS

For 1,615 women in the Carolina Breast Cancer Study with HR+/HER2-, Stage I-II tumors, we estimated prevalence differences (PD) and 95% confidence intervals (CI) for receipt of OncotypeDx genomic testing in association with and sociodemographic characteristics. We assessed associations between testing and chemotherapy receipt overall and by race. Finally, we calculated the proportion of eligible women receiving OncotypeDx by county-level rurality, census tract-level socioeconomic status, and Area Health Education Center regions.

RESULTS

38% (N = 609) of potentially eligible women were tested, with lower testing prevalences in Black (31%; PD, -11%; 95% CI, -16%-6%) and low-income women (24%; PD, -20%; 95% CI, -29% to -11%) relative to non-Black and higher income women. Urban participants were less likely to be tested than rural participants, though this association varied by region. Among women with low genomic risk tumors, tested participants were 29% less likely to receive chemotherapy than untested participants (95% CI, -40% to -17%). Racial differences in chemotherapy were restricted to untested women.

CONCLUSIONS

Both individual and area-level socioeconomics predict likelihood of OncotypeDx testing.

IMPACT

Variable adoption of OncotypeDx by socioeconomics and across geographic settings may contribute to excess chemotherapy among patients with HR+/HER2- cancers. See related In the Spotlight, p. 635.

Adherence Disparities and Utilization Trends of Oncotype Dx Assay: A National Cancer Database Study

INTRODUCTION

Oncotype Dx (ODX) is a genetic assay that analyzes tumor recurrence risk and provides chemotherapy recommendations for T1-T2 stage, hormone receptor-positive, human epidermal growth factor receptor-negative, and nodal-negative breast cancer patients. Despite its established validity, the utilization of this assay is suboptimal. The study aims to evaluate factors that are associated with adherence rate with the testing guidelines and examine changes in utilization trends.

METHODS

This is a retrospective study, utilizing data from the National Cancer Database from 2010 to 2017. Patients who met the ODX testing guidelines were first evaluated for testing adherence. Secondly, all patients who underwent ODX testing were assessed to evaluate the trend in ODX utilization.

RESULTS

A total of 429,648 patients met the criteria for ODX, and 43.4% of this population underwent testing. Advanced age, racial minorities, low-income status, well-differentiated tumor grade, uninsured status, and treatment at community cancer centers were associated with a decreased likelihood of receiving ODX in eligible patients. Additionally, a notable amount of testing was performed on patients who did not meet the ODX testing criteria. Among the 295,326 patients that underwent ODX testing, 16.6% of patients were node-positive and 1.8% had T3 or T4 stage tumors.

CONCLUSIONS

A considerable number of patients who were eligible for ODX did not receive it, indicating potential barriers to care and disparities in breast cancer treatment. ODX usage has been expanded to broader patient populations, indicating more research is needed to validate the effectiveness of the assay in these patient groups.

Survival Disparities in US Black Compared to White Women with Hormone Receptor Positive-HER2 Negative Breast Cancer

Black women in the US have significantly higher breast cancer mortality than White women. Within biomarker-defined tumor subtypes, disparate outcomes seem to be limited to women with hormone receptor positive and HER2 negative (HR+/HER2-) breast cancer, a subtype usually associated with favorable prognosis. In this review, we present data from an array of studies that demonstrate significantly higher mortality in Black compared to White women with HR+/HER2-breast cancer and contrast these data to studies from integrated healthcare systems that failed to find survival differences. Then, we describe factors, both biological and non-biological, that may contribute to disparate survival in Black women.

Use of a supervised machine learning model to predict Oncotype DX risk category in node-positive patients older than 50 years of age

PURPOSE

The use of the Oncotype DX recurrence score (RS) to predict chemotherapy benefit in patients with hormone receptor-positive/HER2 negative (HR+/HER2-) breast cancer has recently expanded to include postmenopausal patients with N1 disease. RS availability is limited in resource-poor settings, however, prompting the development of statistical models that predict RS using clinicopathologic features. We sought to assess the performance of our supervised machine learning model in a cohort of patients > 50 years of age with N1 disease.

METHODS

We identified patients > 50 years of age with pT1-2N1 HR+/HER2- breast cancer and applied the statistical model previously developed in a node-negative cohort, which uses age, pathologic tumor size, histology, progesterone receptor expression, lymphovascular invasion, and tumor grade to predict RS. We measured the model's ability to predict RS risk category (low: RS ≤ 25; high: RS > 25).

RESULTS

Our cohort included 401 patients, 60.6% of whom had macrometastases, with a median of 1 positive node. The majority of patients had a low-risk observed RS (85.8%). For predicting RS category, the model had specificity of 97.3%, sensitivity of 31.8%, a negative predictive value of 87.9%, and a positive predictive value of 70.0%.

CONCLUSION

Our model, developed in a cohort of node-negative patients, was highly specific for identifying cN1 patients > 50 years of age with a low RS who could safely avoid chemotherapy. The use of this model for identifying patients in whom genomic testing is unnecessary would help decrease the cost burden in resource-poor settings as reliance on RS for adjuvant treatment recommendations increases.

Hospital Rurality and Gene Expression Profiling for Early-Stage Breast Cancer among Iowa Residents (2010-2018)

Objective

Given the challenges rural cancer patients face in accessing cancer care as well as the slower diffusion and adoption of new medical technologies among rural providers, the aim of our study was to examine trends in gene expression profiling (GEP) testing and evaluate the association between hospital rurality and receipt of GEP testing.

Methods

Data from the Iowa Cancer Registry (ICR) were used to identify women with newly diagnosed, histologically confirmed breast cancer from 2010 through 2018 who met eligibility criteria for GEP testing. Patients were allocated to the hospitals where their most definitive surgical treatment was received, and Rural-Urban Commuting Area codes were used to categorize hospitals into urban (N = 43), large rural (N = 16), and small rural (N = 48). Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression to evaluate the association between hospital rurality and GEP test use, adjusting for demographic and clinical characteristics. The association between test result and treatment received was assessed among patients who received Oncotype DX (ODX) testing.

Results

Of 6,726 patients eligible for GEP test use, 46% (N = 3,069) underwent testing with 95% receiving ODX. While overall GEP testing rates increased over time from 42% between 2010 and 2012 to 51% between 2016 and 2018 (P _trend < 0.0001), use continued to be the lowest among patients treated at hospitals in small rural areas. The odds of GEP testing remained significantly lower among patients treated at hospitals located in small rural areas (aOR 0.55; 95% CI 0.43-0.71), after adjusting for demographic and clinical characteristics. ODX recurrence scores were highly correlated with chemotherapy use across all strata of hospital rurality.

Conclusions

GEP testing continues to be underutilized, especially among those treated at small rural hospitals. Targeted interventions aimed at increasing rates of GEP testing to ensure the appropriate use of adjuvant chemotherapy may improve health outcomes and lower treatment-related costs.

Impact of genomic assays on treatment and outcomes in locally advanced breast cancer

PURPOSE

Genomic profiling in early-stage breast cancer provides prognostic and predictive information. Genomic profiling assays have not been validated in locally advanced breast cancer (LABC). We examined a large cancer registry to evaluate genomic profiling in LABC and its effect on treatment decisions and survival.

METHODS

Females with ER+/HER2- LABC who did not receive neoadjuvant therapy were selected from the National Cancer Database 2004-2017. We compared characteristics between patients with and without genomic profiling and with low genomic risk, 21-gene recurrence score ≤ 25 or low-risk 70-gene signature, treated with endocrine therapy ± chemotherapy. Propensity score methods were utilized to account for covariates that may have predicted treatment. Univariable and multivariable survival analyses were performed.

RESULTS

Of 18,437 patients with LABC, 1258 (7%) had genomic profiling and 1022 (81%) had low genomic risk results. 562 patients (55%) with low genomic risk received chemotherapy and endocrine therapy (chemoendocrine). Patients who received chemoendocrine therapy were younger, had fewer comorbidities, presented with higher stage disease, had higher grade tumors, more frequently had partial mastectomy, and more often received radiation than those who received endocrine therapy alone. On multivariable analysis, endocrine therapy alone was associated with worse OS compared to chemoendocrine therapy (HR 1.77, 95% CI 1.13-2.78, p = 0.013).

CONCLUSION

In women with LABC and low genomic risk, endocrine therapy alone was associated with worse OS compared to chemoendocrine therapy. This suggests that genomic profiling is not predictive in LABC. Accordingly, genomic profiling should not be routinely utilized to make adjuvant treatment decisions in LABC in the absence of further data which shows a benefit.

Redefining precision cancer prevention to promote health equity

Precision cancer prevention as it is currently envisioned is a targeted, molecular-based approach to intercept carcinogenesis before cancer develops or before it becomes untreatable. Unfortunately, due to systemic biases, current precision cancer prevention interventions might not be effective in all populations, especially in minoritized communities. In addition, not all cancer risk is attributable to genetic or even biological factors, but includes social determinants of health (SDH). Here, we propose a broader framework for precision cancer prevention, anchored in optimizing the benefits to harms for all people. We propose that precision cancer prevention considers not only what is being delivered, but also for whom, where, and how, with a goal of achieving cancer prevention health equity.

Oncotype DX Test Receipt among Latina/Hispanic Women with Early Invasive Breast Cancer in New Jersey: A Registry-Based Study

Oncotype DX® (ODX) is a valid test of breast cancer (BC) recurrence risk and chemotherapy benefit. The purpose of this study was to examine prevalence of and factors associated with receipt of ODX testing among eligible Latinas/Hispanics diagnosed with BC. Sociodemographic and tumor data of BC cases diagnosed between 2008 and 2017 among Latina/Hispanic women (n = 5777) were from the New Jersey State Cancer Registry (NJSCR). Eligibility for ODX testing were based on National Comprehensive Cancer Network guidelines. Multivariable logistic regression models of ODX receipt among eligible women were used to estimate adjusted odds ratios (AOR) and 95% confidence intervals (CI) by demographic and clinicopathologic factors. One-third of Latinas/Hispanics diagnosed with BC were eligible for ODX testing. Among the eligible, 60.9% received ODX testing. Older age (AOR 0.08, 95% CI: 0.04, 0.14), low area-level SES (AOR 0.58, 95% CI: 0.42, 0.52), and being uninsured (AOR 0.58, 95% CI: 0.39, 0.86) were associated with lower odds of ODX testing. While there was relatively high ODX testing among eligible Latina/Hispanic women with BC in New Jersey, our findings suggest that age, insurance status, and area-level SES contribute to unequal access to genetic testing in this group, which might impact BC outcomes.

Breast cancer, screening and diagnostic tools: All you need to know

Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.

Oncotype DX Predictive Nomogram for Recurrence Score Output: The Novel System ADAPTED01 Based on Quantitative Immunochemistry Analysis

PURPOSE

Oncotype DX (ODX) predicts breast cancer recurrence risk, guiding the choice of adjuvant treatment. In many countries, access to the test is not always available. We used correlation between phenotypical tumor characteristics, quantitative classical immunohistochemistry (IHC), and recurrence score (RS) assessed by ODX to develop a decision supporting system for clinical use.

PATIENTS AND METHODS

Breast cancer patients who underwent ODX testing between 2014 and 2018 were retrospectively included in the study. The data selected for analysis were age, menopausal status, and pathologic and IHC features. IHC was performed with standardized quantitative methods. The data set was split into two subsets: 70% for the training set and 30% for the internal validation set. Statistically significant features were included in logistic models to predict RS ≤ 25 or ≤ 20. Another set was used for external validation to test reproducibility of prediction models.

RESULTS

The internal set included 407 patients. Mean (range) age was 53.7 (31-80) years, and 222 patients (54.55%) were > 50 years old. ODX results showed 67 patients (16.6%) had RS between 0 and 10, 272 patients between 11 and 25 (66.8%), and 68 patients > 26 (16.6%). Logistic regression analysis showed that RS score (for threshold ≤ 25) was significantly associated with estrogen receptor (P = .004), progesterone receptor (P < .0001), and Ki-67 (P < .0001). Generalized linear regression resulted in a model that had an area under the receiver operating characteristic curve (AUC) of 92.2 (sensitivity 84.2%, specificity 80.1%) and that was well calibrated. The external validation set (183 patients) analysis confirmed the model performance, with an AUC of 82.3 and a positive predictive value of 91%. A nomogram was generated for further prospective evaluation to predict RS ≤ 25.

CONCLUSION

RS was related to quantitative IHC in patients with RS ≤ 25, with a good performance of the statistical model in both internal and external validation. A nomogram for enhancing clinical approach in a cost-effective manner was developed. Prospective studies must test this application in clinical practice.

Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers

Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient's specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens. The genomic data were divided into a training set of N = 835 cases and a validation set consisting of 1315 hormone sensitive, 634 triple negative breast cancer (TNBC) and 1365 breast cancer patients with information on neoadjuvant chemotherapy responses. Patients were selected by the following criteria: estrogen receptor (ER) status, lymph node invasion, recurrence free survival. The k-means classification algorithm delineated clusters with low- and high- expression of genes related to recurrence of disease; a multivariate Cox's proportional hazard model defined recurrence risk for disease. Classifier genes were validated by Immunohistochemistry (IHC) using tissue microarray sections containing both normal and cancerous tissues and by evaluating findings deposited in the human protein atlas repository. Based on the leave-on-out cross validation procedure of 4 independent data sets we identified 51-genes associated with disease relapse and selected 10, i.e. TOP2A, AURKA, CKS2, CCNB2, CDK1 SLC19A1, E2F8, E2F1, PRC1, KIF11 for in depth validation. Expression of the mechanistically linked disease regulated genes significantly correlated with recurrence free survival among ER-positive and triple negative breast cancer patients and was independent of age, tumor size, histological grade and node status. Importantly, the classifier genes predicted pathological complete responses to neoadjuvant chemotherapy (P < 0.001) with high expression of these genes being associated with an improved therapeutic response toward two different anthracycline-taxane regimens; thus, highlighting the prospective for precision medicine. Our study demonstrates the potential of classifier genes to predict risk for disease relapse and treatment response to chemotherapies. The classifier genes enable rational selection of patients who benefit best from a given chemotherapy thus providing the best possible care. The findings encourage independent clinical validation.

Age-related disparities in older women with breast cancer

Improvements in breast cancer (BC) mortality rates have not been seen in the older adult community, and the fact that older adults are more likely to die from their cancer than younger women establishes a major health disparity. Studies have identified that despite typically presenting with more favorable histology, older women present with more advanced disease, which may be related in part to delayed diagnosis. This is supported by examination of screening practices in older adults. Older women have a worse prognosis than younger women in both early stage disease, and more advanced and metastatic disease. Focus on the treatment of older adults has often concentrated on avoiding overtreatment, but in fact undertreatment may be one reason for the age-related differences in outcomes, and treatments need to be individualized for every older adult, and take into account patient preferences and functional status and not chronologic age alone. Given the aging population in the US, identifying methods to improve early diagnosis in this population and identify additional factors will be important to reducing this age-related disparity.

Tumour-Infiltrating Immune Cell-Based Subtyping and Signature Gene Analysis in Breast Cancer Based on Gene Expression Profiles

Tumour-infiltrating immune cells have been indicated to play an important role in prognosis prediction and therapy sensitivity for breast cancer. In recent years, estimating the abundance of immune cells based on tumour transcriptome data has provided a novel way to analyse the clinical significance of various immune cell subsets. This study integrated breast cancer tissue transcriptome datasets from the Gene Expression Omnibus (GEO), the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. A novel breast cancer immunotyping and a new prognostic model based on tumour-infiltrating immune cell subsets have been established, aiming to provide new clues regarding prognostic prediction and precision therapy for breast cancer. The key differentially expressed gene between different breast cancer immunotypes has also been identified. We performed unsupervised clustering analysis and construct a novel immunotyping which could classify breast cancer cases into immunotype A (B_cell^high NK^high CD8⁺_T^high CD4⁺_memory_T_activated^high γδT^low Mast_cell_activated^low Neutrophil^low) and immunotype B (B_cell^low NK^low CD8⁺_T^low CD4⁺_memory_T_activated^low γδT^high Mast_cell_activated^high Neutrophil^high) in luminal B, HER2-enriched and basal-like subtypes. The 5-year (85.7% vs. 73.4%) and 10-year OS (75.60% vs. 61.73%) of immunotype A population were significantly higher than those of immunotype B. A novel tumour-infiltrating immune cell-based prognostic model had also been established and the result immunorisk score (IRS) could serve as a new prognostic factor for luminal B, HER2-enriched and basal-like breast cancer. The higher IRS was, the worse prognosis was. We further screened the differentially expressed genes between immunotype A and B and identified a novel breast cancer immune-related gene, prostaglandin D2 synthase (PTGDS) and higher PTGDS mRNA expression level was positively correlated with earlier TNM stage. Immune-related signaling pathways analysis and immune cell subsets correlation analysis revealed that PTGDS expression was related with abundance of B cells, CD4⁺ T cells and CD8⁺ T cells, which was finally validated by immunohistochemical and immunofluorescence staining. We established a novel immunotyping and a tumour-infiltrating immune cell-based prognostic prediction model in luminal B, HER2-enriched and basal-like breast cancer by analyzing the prognostic significance of multiple immune cell subsets. A novel breast cancer immune signature gene PTDGS was discovered, which might serve as a protective prognostic factor and play an important role in breast cancer development and lymphocyte-related immune response.

Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence

PURPOSE

Identifying imaging phenotypes and understanding their relationship with prognostic markers and patient outcomes can allow for a noninvasive assessment of cancer. The purpose of this study was to identify and validate intrinsic imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans and evaluate their prognostic performance in predicting 10 years recurrence.

EXPERIMENTAL DESIGN

Pretreatment DCE-MRI scans of 95 women with primary invasive breast cancer with at least 10 years of follow-up from a clinical trial at our institution (2002-2006) were retrospectively analyzed. For each woman, a signal enhancement ratio (SER) map was generated for the entire segmented primary lesion volume from which 60 radiomic features of texture and morphology were extracted. Intrinsic phenotypes of tumor heterogeneity were identified via unsupervised hierarchical clustering of the extracted features. An independent sample of 163 women diagnosed with primary invasive breast cancer (2002-2006), publicly available via The Cancer Imaging Archive, was used to validate phenotype reproducibility.

RESULTS

Three significant phenotypes of low, medium, and high heterogeneity were identified in the discovery cohort and reproduced in the validation cohort (P < 0.01). Kaplan-Meier curves showed statistically significant differences (P < 0.05) in recurrence-free survival (RFS) across phenotypes. Radiomic phenotypes demonstrated added prognostic value (c = 0.73) predicting RFS.

CONCLUSIONS

Intrinsic imaging phenotypes of breast cancer tumor heterogeneity at primary diagnosis can predict 10-year recurrence. The independent and additional prognostic value of imaging heterogeneity phenotypes suggests that radiomic phenotypes can provide a noninvasive characterization of tumor heterogeneity to augment personalized prognosis and treatment.

Adjuvant chemotherapy and survival among patients 70 years of age and younger with node-negative breast cancer and the 21-gene recurrence score of 26-30

Background

The benefits of chemotherapy in node-negative, hormone receptor-positive, and human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients with the 21-gene recurrence score (RS) of 18–30, particularly those with RS 26–30, are not known.

Methods

Using the Surveillance, Epidemiology, and End Results (SEER) data, we retrospectively identified 29,137 breast cancer patients with the 21-gene RS of 18–30 diagnosed between 2004 and 2015. Mortality risks according to the RS and chemotherapy use were compared by the Kaplan-Meier method and Cox’s proportional hazards model.

Results

Among the breast cancer patients with the RS 18–30, 21% of them had RS 26–30. Compared to breast cancer patients with RS 18–25, patients with RS 26–30 had more aggressive tumor characteristics and chemotherapy use and increased risk of breast cancer-specific mortality and overall mortality. In breast cancer patients who were aged ≤ 70 years and had RS of 26–30, chemotherapy administration was associated with a 32% lower risk of breast cancer-specific mortality (hazard ratio [HR], 0.68; 95% confidence interval [CI], 0.47–0.99) and a 42% lower risk of overall mortality (HR, 0.58; 95% CI, 0.44–0.76). Survival benefits were most pronounced in breast cancer patients who were younger or had grade III tumor.

Conclusions

The 21-gene RS of 18–30 showed heterogeneous outcomes, and the RS 26–30 was a significant prognostic factor for an increased risk of mortality. Adjuvant chemotherapy could improve the survival of node-negative, hormone receptor-positive, and HER2-negative breast cancer patients with the 21-gene RS 26–30 and should be considered for patients, especially younger patients or patients with high-grade tumors.

Electronic supplementary material

The online version of this article (10.1186/s13058-019-1190-4) contains supplementary material, which is available to authorized users.

Evaluating patterns of utilization of gene signature panels and impact on treatment patterns in patients with ductal carcinoma in situ of the breast

BACKGROUND

Broad patterns of use of the gene signature panel Oncotype DX DCIS and its large-scale impact on postoperative administration of radiation therapy in women with ductal carcinoma in situ of the breast remain unclear. This study sought to evaluate the patterns of use of this gene signature panel in women with ductal carcinoma in situ and the impact of these tools on postoperative radiation therapy administration.

METHODS

The National Cancer Database was queried for women with ductal carcinoma in situ treated with breast-conserving therapy who had information regarding whether a gene signature panel was performed between 2010 and 2015. Demographic characteristics, the characteristics of their ductal carcinoma in situ, and whether they received postoperative radiation therapy were compared among patients who did have a gene signature panel performed and those who did not. Patterns of radiation therapy administration were also evaluated based on the recurrence risk score by the gene signature panel.

RESULTS

Gene signature panel use increased over time, with a sharp increase in utilization occurring in 2015 (8.0% in 2015 vs 4.4% in 2014, P < .001). Patients with estrogen receptor-positive ductal carcinoma in situ were somewhat more likely to have a gene signature panel ordered (3.9% estrogen receptor positive vs 1.7% estrogen receptor negative, P < .001), as were patients with lower-grade ductal carcinoma in situ (4.5% grade I/II vs 3.1% grade III, P < .001). Gene signature panel utilization was associated with a decrease in the administration of postoperative radiation therapy (48.6% gene signature panel vs 83.4% no gene signature panel, P < .001). Among patients in whom a gene signature panel was performed, postoperative radiation therapy was administered in 81.9%, 72.0%, and 35.9% of patients with high-, intermediate-, and low-recurrence scores, respectively.

CONCLUSION

Gene signature panel use in patients with ductal carcinoma in situ has increased over time and is more commonly used in women with lower-risk, clinicopathologic features to determine the magnitude of benefit afforded by radiation therapy. Gene signature panel use is associated with decreased rates of postoperative radiation therapy administration, particularly among patients with scores suggesting a low rate of recurrence.

Evidence-based approaches to reduce cancer health disparities: Discover, develop, deliver, and disseminate

The Texas Center for Health Disparities (TCHD) at the University of North Texas Health Science Center is a National Institute on Minority Health and Health Disparities-funded, specialized center of excellence for health disparities. TCHD organized its 12^th annual conference focusing on "Evidence-Based Approaches to Reduce Cancer Health Disparities: Discover, Develop, Deliver, and Disseminate." At this conference, experts in health care, biomedical sciences, and public health gathered to discuss the current status and strategies for reducing cancer health disparities. The meeting was conducted in three sessions on breast cancer, prostate cancer, and colorectal cancer disparities, in addition to roundtable discussions and a poster session. Each session highlighted differences in the effects of cancer, based on factors such as race/ethnicity, gender, socioeconomic status, and geographical location. In each session, expert speakers presented their findings, and this was followed by a discussion panel made up of experts in that field and cancer survivors, who responded to questions from the audience. This article summarizes the approaches to fundamental, translational, clinical, and public health issues in cancer health disparities discussed at the conference.