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. Author manuscript; available in PMC: 2025 Jul 7.
Published in final edited form as: Ann Surg. 2025 Mar 27;284(2):288–295. doi: 10.1097/SLA.0000000000006710

The Effect of Rurality on Time to Surgery and Overall Survival among Women with Breast Cancer

Kriyana P Reddy 1, Cara L Berkowitz 1,2, Kathleen Jarrell 1,3, Rachel Berger 1, Sarah Hulse 1, Leisha C Elmore 1,4, Rebecca Fishman 1,5, Alina M Mateo 1,5, Dahlia M Sataloff 1,5, Julia C Tchou 1,6,7, Jennifer Q Zhang 1,6, Anne Marie McCarthy 8, Oluwadamilola M Fayanju 1,6,7,9
PMCID: PMC12230759  NIHMSID: NIHMS2094542  PMID: 40143820

Introduction

Prolonged time to surgery (TTS) has been associated with lower disease-specific and overall survival in patients with breast cancer.1 Significant surgical treatment delays have been documented in racial and ethnic minority populations, making TTS a potential focal point for reducing disparities in breast cancer outcomes.2,3 However, our understanding of the extent of rural-urban disparities in TTS in the United States (US) remains limited. Emerging evidence has found that surgical treatment delays are more common among urban patients than rural patients, despite evidence that rural patients face greater travel burdens and are more likely to be diagnosed at advanced often stage relative to urban patients.4–7 The prevailing paradigm around timely receipt of care and health care access among rural patients has centered on distance to care. Patients residing in rural areas must traverse significantly longer distances to treatment facilities relative to urban patients, which can be a source of disparities in stage at diagnosis, receipt of guideline-concordant care, timeliness of care, and survival.8–10 This is further complicated by lower density of both specialist and generalist providers, limited technological infrastructure (e.g., broadband access), and socioeconomic deprivation in rural areas.10–12

However, no previous studies have assessed whether the relationship between rurality and TTS is mediated by the geographic distance between the residence of a patient with breast cancer and her primary treatment facility. The extent to which rural-urban differences in TTS have differential implications for OS in rural versus urban patients is also unknown. To address this gap, we sought to examine (1) whether and to what extent geographic proximity to care mediates the relationship between rurality and prolonged TTS; (2) the functional relationship between geographic proximity to care and TTS; and (3) how much rural-urban differences in TTS might affect overall survival (OS) in women with non-metastatic breast cancer?

Methods

As this study utilized publicly available de-identified data, it was deemed exempt from review by the institutional review board at the University of Pennsylvania. The guidelines recommended by the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement guidelines for reporting observational studies were followed.13

Data Source & Study Cohort

The National Cancer Database (NCDB) is a national oncology outcomes database comprised of over 1,500 Commission on Cancer (CoC) accredited cancer programs in the US and Puerto Rico. Approximately 72% of all newly diagnosed cancer cases in the US are reported in the NCDB.14

We identified all adult (≥18 years) females diagnosed in 2004–2019 with Stage 0-III breast cancer receiving surgery as first course of treatment and having non-missing data on rural/urban residential status in the NCDB. For each patient, we identified age; race and ethnicity (Non-Hispanic [NH] White, NH Black, NH American Indian/Alaskan Native, NH Asian or Pacific Islander, NH Other, Hispanic); insurance status (private/managed care, uninsured, Medicaid, Medicare, other public insurance); geographic region of residence (Midwest, Northeast, South, West), ZIP-level median household income (HHI) (≥$63,333, $50–354-$63,332, $40,227-$50,353, <$40,227); type of the reporting facility based on accreditation category by the CoC (Comprehensive Community Cancer Program, Academic/Research Program, Community Cancer Program, Integrated Network Cancer Program), Charlson-Deyo Comorbidity Coefficient (CDCC), tumor grade, tumor size, tumor receptor status (hormone receptor-positive [HR+]/HER2-, HER2+, hormone receptor-negative [HR-]/HER2-, i.e., triple-negative [TN]), rural/urban designation of the patient’s residence, geographic proximity to treatment facility, and TTS.15

Rural/urban designation in the NCDB is estimated using 2013 US Department of Agriculture Economic Research Service rural-urban continuum codes (RUCC), which are county-level codes that consider population size and proximity to metropolitan areas.16 Rurality was defined in this study as it is defined in the NCDB—counties categorized as “Completely rural or <2,500 urban population adjacent to a metro area” or “Completely rural or <2,500 urban population not adjacent to a metro area” (RUCC codes 8 and 9). Components of the urban and rural categories in NCDB are provided in Supplemental Table 1.17 Geographic proximity to treatment facility was measured using the “CROWFLY” variable, which measures great circle distance (i.e., distance between centroid of patient’s residential ZIP code or city and latitude/longitude of treating hospital) in miles between the patient’s residence and the hospital that reported their case. This variable was Winsorized at the 99th percentile to mitigate right skew.

Statistical Analysis

Baseline characteristics of patients in the study cohort were summarized using frequencies (proportions) for categorical variables and medians (IQRs) for continuous variables. We compared baseline characteristics of patients in the study cohort between subgroups defined by TTS (<30 days, 30–60 days, 61–90 days, and >90 days). Proportions for categorical variables were compared using chi-squared tests, and distributions of continuous variables were compared using Kruskal-Wallis tests.

A mediation analysis with logistic regression was performed to assess the relationship between rurality, proximity to care, and prolonged TTS. The conceptual framework underlying our statistical model was established using a directed acyclic graph (DAG). The dependent variable was prolonged TTS, the independent variable of interest was rurality, and the mediator was geographic proximity to treatment facility. In the primary analysis, prolonged TTS was defined as >60 days, which has been identified in the literature as the specific timepoint at which OS is significantly reduced in breast cancer patients.18 In the sensitivity analysis, prolonged TTS was defined as >90 days. Models included patient age, race/ethnicity, insurance status, geographic region of residence, ZIP-level median HHI, facility type, CDCC, and tumor grade, size, and receptor status as covariates. The effect of rurality on prolonged TTS was decomposed into a direct effect and indirect effect, and the proportion of the total effect of rurality on prolonged TTS that is mediated by geographic proximity was calculated (Figure 1).

Figure 1.

Figure 1.

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Directed acyclic graph and results of multivariate logistic regression mediation analysis measuring the association between rurality and prolonged TTS as mediated by geographic proximity to care

To determine the functional relationship between proximity to care and TTS, we fit univariate and multivariate linear regressions using restricted cubic splines (RCS) with four knots. The dependent variable was TTS (in days), and the independent variable of interest was distance to treatment facility (in miles). In these models, TTS was Winsorized at the 99th percentile to mitigate a right skew. In the multivariate model, we adjusted for patient age, race/ethnicity, insurance type, geographic region of residence, ZIP-level median HHI, facility type, CDCC, and tumor grade, size, and receptor status. RCS curves with 95% CI were plotted to show the relationship between distance to treatment facility and TTS.

To determine the implications of rural-urban differences in TTS for OS, we built univariate and multivariate Cox Proportional Hazard models separately assessing OS in patients residing in rural areas versus patients residing in urban areas. All survival analyses were restricted to patients with invasive, non-metastatic disease, as survival among patients with non-invasive disease is nearly 100%.19,20 OS was measured during the period between receipt of surgery and most recent follow-up or death. The independent variable of interest was TTS, operationalized as a categorical variable (<30 days, 31–60 days, 61–90 days, or >90 days). Multivariate models included covariates for patient-level age, race/ethnicity, insurance status, geographic region of residence, ZIP-level median HHI, facility type, CDCC, tumor grade, tumor size, tumor receptor status, rural/urban residential status, and geographic proximity to treatment facility. The relationship between TTS and OS was summarized with Kaplan-Meier survival curves for rural and urban patient subgroups.

Variance inflation factors (VIFs) were calculated to assess for multicollinearity in multivariate models. VIFs in all models for all covariates were <4, suggesting no multicollinearity between included covariates. All analyses were performed in R version 4.3.1. Statistical testing was 2-tailed, with P<0.05 considered statistically significant.

Results

Baseline Characteristics of Study Cohort

Of 3,446,070 patients with breast cancer in the NCDB diagnosed in 2004–2019, 1,979,194 patients met inclusion criteria. The median (IQR) TTS overall was 33 (19–53) days. Of those patients, 887,134 (44.8%) had TTS <30 days, 696,330 (35.1%) had TTS 31–60 days, 170,813 had TTS 61–90 (8.6%) days, and 224,197 (11.3%) had TTS >90 days (Figure 2). Overall, 28,210 (1.4%) of patients resided in rural areas. Patients with TTS >90 days had a median (IQR) age of 57 (47–66) years, whereas patients with TTS <30 days had a median (IQR) age of 62 (53–71) years (P<0.001). A higher proportion of patients with TTS >90 days were NH Black compared to patients with TTS <30 days (17.1% vs 9.5%, P<0.001). Further, 15.6% of patients with TTS >90 days had ZIP-level median HHI of <$40,227, compared to 12.7% of patients with TTS <30 days (P<0.001). Patients with TTS >90 days were less likely to be treated at a Comprehensive Community Cancer program compared to those with TTS <30 days (33.7% vs 43.1%, P<0.001). Among patients with TTS >90 days, 20% had TN breast cancer, whereas 8.4% of patients with TTS <30 days had TN breast cancer (P<0.001). Patients with TTS>90 days also had a longer median distance to treatment facility than patients with TTS <30 days (9.3 vs. 8.9 miles, P<0.001) (Table 1). Baseline characteristics of rural versus urban patients in the study cohort are provided in Supplemental Table 2. Notably, the median (IQR) distance to treatment facility among patients residing in rural areas was 44.7 (27–70.8) miles, compared to 8.8 (4.3–17.7) miles among patients residing in urban areas (P<0.001).

Figure 2.

Figure 2.

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CONSORT Diagram of Study Cohort, Women with Stage 0-III breast cancer in the National Cancer Database, 2004–2019, who received upfront surgery and had available rural/urban residence data

Table 1.

Select baseline characteristics, Women with Stage 0-III breast cancer in the National Cancer Database, 2004–2019, by time to surgery who received upfront surgery and had available rural/urban residence data

TTS <30 Days
(n = 887,134)		 TTS 30–60 Days
(n = 696,330)		 TTS 61–90 Days
(n = 170,813)		 TTS >90 days
(n =224,917)		 P-Value
Age, Median (IQR) 62 (53–71) 62 (53–71) 61 (51–69) 57 (47–66) <0.001
Race and Ethnicity, n (%) <0.001
 Non-Hispanic (NH) White 692,853 (78.1%) 533,526 (76.6%) 117,983 (69.1%) 144,966 (64.5%)
 NH Black 84,530 (9.5%) 71,377 (10.3%) 24,332 (14.2%) 38,401 (17.1%)
 NH American Indian/Alaskan Native 2,337 (0.3%) 1,874 (0.3%) 485 (0.3%) 823 (0.4%)
 NH Asian or Pacific Islander 33,181 (3.7%) 27,388 (3.9%) 8,023 (4.7%) 11,203 (5.0%)
 NH Other 4,060 (0.5%) 3,574 (0.5%) 1,124 (0.7%) 1,617 (0.7%)
 Hispanic 41,232 (4.6%) 38,916 (5.6%) 14,457 (8.5%) 22,203 (9.9%)
Insurance Status, n (%) <0.001
 Private/Managed Care 453,215 (51.1%) 354,605 (50.9%) 86,371 (50.6%) 122,606 (54.5%)
 Uninsured 11,693 (1.3%) 8,875 (1.3%) 3,491 (2.0%) 7,536 (3.4%)
 Medicaid 43,338 (4.9%) 37,102 (5.3%) 14,147 (8.3%) 26,557 (11.8%)
 Medicare 359,417 (40.5%) 279,054 (40.1%) 61,955 (36.3%) 61,924 (27.5%)
 Other Public 8,942 (1.0%) 7,414 (1.1%) 2,119 (1.2%) 2,957 (1.3%)
Tumor Size, mm, Median (IQR) 15 (8–23) 14 (9–22) 15 (9–24) 25 (15–40) <0.001
Receptor Status, n (%) <0.001
 HR+/HER2− 506,298 (57.1%) 439,998 (63.2%) 102,370 (59.9%) 98,310 (43.7%)
 HER2+ 86,850 (9.8%) 59,664 (8.6%) 14,255 (8.3%) 53,482 (23.8%)
 HR−/HER2− 74,589 (8.4%) 46,126 (6.6%) 9,780 (5.7%) 45,066 (20.0%)
Rural/Urban Designation, n (%) <0.001
 Rural 14,809 (1.7%) 8,859 (1.3%) 1,872 (1.1%) 2,670 (1.2%)
 Urban 872,325 (98.3%) 687,471 (98.7%) 168,941 (98.9%) 222,247 (98.8%)
Geographic Proximity to Treatment Facility, miles, Median (IQR) 8.9 (4.2–18.1) 8.9 (4.3–17.9) 9 (4.4–18.5) 9.3 (4.6–19.1) <0.001
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Association of Geographic Proximity to Care and Rurality with Prolonged TTS

In the primary analysis, the total effect of rurality on prolonged TTS (i.e., TTS >60 days) corresponded to an aOR of 0.89 (95% CI 0.86–0.93, P<0.01). The direct effect of rurality on prolonged TTS corresponded to an aOR of 0.84 (95% CI 0.79–0.89, P<0.01), and the indirect effect corresponded to an aOR of 1.10 (95% CI 1.09–1.10, P<0.01). The proportion of the total effect of rurality on prolonged TTS that was mediated by geographic proximity to treatment facility was 52% (95% CI 12%−92%, P<0.01). Similar results were obtained in the sensitivity analysis modeling TTS >90 days (Figure 1). Crude and adjusted ORs for all covariates included in the mediation analyses are provided in Supplemental Table 3.

Functional Relationship Between Distance to Treatment Facility and TTS

After adjustment for patient-level clinical and sociodemographic factors, RCS estimation showed that the relationship between distance to treatment facility and TTS is non-linear (P<0.001) and non-monotonic. As distance to treatment facility increases from 0 to 10 miles, TTS increases. As distance increases from 10 to 22 miles, TTS decreases, and as distance increases past 22 miles, TTS increases (Figure 3). The crude relationship between distance to treatment facility and TTS was similar, exhibiting non-linearity and non-monotonicity (Supplemental Figure 1).

Figure 3.

Figure 3.

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Multivariate restricted cubic spline curve from regression of distance to treatment facility to predict time to surgerya

a Model adjusted for age, race/ethnicity, insurance type, geographic region, median household income, Charlson-Deyo comorbidity score, tumor grade, tumor size, receptor status, facility type, and rural/urban status. All values of distance to treatment facility and time to surgery were Winsorized at the 99th percentile to account for outliers.

Association Between TTS and OS in Rural versus Urban Patients

Multivariate analysis of the association between TTS and OS in adult women with invasive, non-metastatic breast cancer receiving surgery as a first course of therapy showed that prolonged TTS has similar effects on OS for rural and urban patients. Among both rural and urban patients, TTS >60 days was associated with reduced OS relative to TTS <30 days (Figure 4). Among patients residing in rural areas, TTS 61–90 days (HR 1.37, 95% CI 1.14–1.63, P<0.001) and TTS >90 days (HR 1.73, 95% CI 1.51–1.98, P<0.001) were associated with increased mortality, whereas increased distance to treatment facility (HR 0.89, 95% CI 0.83–0.96, P=0.003) was associated with reduced mortality (Supplemental Table 4). Among patients residing in urban areas, TTS 61–90 days (HR 1.18, 95% CI 1.15–1.20, P<0.001) and TTS >90 days (HR 1.75, 95% CI 1.72–1.78, P<0.001) were associated with increased mortality, whereas increased distance to treatment facility (HR 0.87, 95% CI 0.86–0.89, P<0.001) were associated with reduced mortality (Supplemental Table 5). Unadjusted survival curves showing the relationship between TTS and OS for rural patients, urban patients, and overall are provided in Supplemental Figures 2–3. The results of the multivariate Cox model assessing the association between TTS and OS are provided in Supplemental Figure 4 and Supplemental Table 6.

Figure 4.

Figure 4.

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Multivariate model of association between time to surgery and overall survival among (A) rural and (B) urban women with Stage 0-III breast cancer in the National Cancer Database, 2004–2019, who received upfront surgery and had available rural/urban residence data

Discussion

In this study assessing the relationship between geographic proximity to care, rurality, TTS, and survival after diagnosis with non-metastatic breast cancer, we found that the distance between a patient’s residence and where she receives treatment is a mediator of the relationship between rurality and TTS. The effect of rurality on TTS mediated by geographic proximity to care is associated with increased risk of delayed care, whereas the direct effect of rurality on TTS is associated with reduced risk of delayed care. Further, the relationship between distance to treatment facility and TTS is non-linear. Despite rural-urban differences in TTS, we found that TTS >60 days correlates with reduced OS in patients residing in both rural and urban areas. Taken together, these findings suggest that there is an intrinsic protective effect of rurality on TTS that is distinct from the adverse effect of distance to care and that rural-urban differences in TTS on their own do not explain rural-urban differences in OS in patients with invasive, non-metastatic disease.

Our findings support emerging evidence that rural patients have shorter TTS compared to urban patients, despite rural patients residing farther from their primary point of care than urban patients.4,5,21 There are several potential explanations for this trend. First, rural patients have lower rates of preventive and diagnostic imaging as well as barriers to accessing specialist care and receiving second opinions, suggesting that rural patients may ultimately see reduced TTS due to receiving less complex and potentially less rigorous pre-operative care.22–26 Qualitative studies have found that due to limited resources in rural areas, provider and/or facility choice is less relevant in the decision-making process for rural patients. Rural patients may opt to be seen at nearby facilities with more availability to take on new patients than high-volume centers in urban areas that tend to have long wait times.23 Moreover, rural patients may seek help for symptoms of breast cancer more quickly than urban patients.27

The complex differences in social values and community-centered beliefs across the rural-urban continuum may offer insight into the protective benefit of rurality on TTS observed in this study. Rural Americans are more likely to regard their communities as close-knit and safe compared to individuals residing in urban areas.28,29 Individuals’ sense of community has been positively associated with exposure to and engagement in health-promoting behaviors.30 Solidarity within rural communities can motivate collective community action to support community members when they are diagnosed with breast cancer. This support can be emotional, spiritual, psychological, or even financial in nature, as the concept of affordability of health care in rural areas may go beyond immediate access to finances and can include a sense of belonging within the community.28 Sense of community and social intimacy extends to the patient-physician relationship as well—rural patients tend to have long, continuous relationships with their physicians, which may improve shared decision-making and motivate patients to pursue cancer treatment quickly following diagnosis.31,32 Policy interventions aimed at reducing prolonged TTS among urban patients may, therefore, benefit from focusing on providing navigation resources to patients and enhancing shared decision-making between physicians and patients.33–36

Still, it is worth noting the role of intersectionality when considering the rural patient experience. There are significant racial and ethnic disparities in access to timely cancer care within rural populations.4 In this study, we also found that NH Black and Hispanic race/ethnicity were independently associated with increased odds of prolonged TTS and that race/ethnicity was a significant predictor of survival in urban patients but not rural patients. Existing research on rurality and race/ethnicity has focused on the White American experience, as rural America remains nearly 75% NH White. Further work is necessary to shed light on the health access challenges and adverse health outcomes experienced by the near 25% of rural Americans who are not White.28

Notably, our study found that increased distance to treatment facility is associated with reduced mortality for patients residing in both rural and urban areas. This trend may reflect patients’ decision-making process in choosing to pursue care at one facility over another. It is possible that patients may choose to travel to a farther facility if they have the means to do so and if they are seeking advanced treatments, highly specialized care, and engagement with clinical trials among other factors. Patients may also choose to travel to a more distant facility in lieu of a local facility if the facility that is farther away offers sooner appointments, has higher performance ratings, or comes recommended by family and friends. Taken together, these factors could confer prognostic survival benefits if patients traveling farther to get care are ultimately receiving faster, higher quality, more advanced, and/or more specialized care.

Reassuringly, our study found that the critical time point for reduced survival due to TTS among urban and rural patients is the same; TTS >60 days corresponds to an increased risk of mortality among all breast cancer patients, which aligns with emerging evidence.18,37 Rural-urban disparities in 5-year survival among breast cancer patients have not narrowed over time.38 However, prior research has found that geographic location, itself, does not predict survival. Our study similarly found that rurality was not a significant predictor of survival in multivariate analysis of survival among women with invasive breast cancer in the study cohort (Supplemental Table 6). Rather, social and behavioral determinants of health (e.g., racial residential segregation, poverty, access to transportation) and related differences between rural and urban populations predict disparities in survivorship across the rural-urban continuum.38,39 Moreover, rural-urban disparities in survival are most pronounced among NH Black and Hispanic patients.38 The overall Black-White disparity in breast cancer mortality among women with breast cancer in the US has consistently widened for the past 40 years due, in part, to disparities in obtaining high-quality, timely care and, to a lesser extent, racial differences in rates of TN tumors.40 It is imperative that the complex interplay between race/ethnicity and geography be further illuminated and analyzed in future studies as a steppingstone to designing policy interventions for closing rural-urban and racial/ethnic gaps in breast cancer mortality.

Limitations

There are several limitations to this study. First, while this study draws conclusions from a large sample, the NCDB only includes patients who are treated at CoC accredited programs, which may preclude generalizability of the findings to patients treated at non-CoC sites. For instance, it is possible that patients residing in rural areas tend to get care in non-accredited sites in closer proximity to their area of residence. It is also possible that those rural patients receiving care at accredited sites farther from their residence tend to be wealthier and better resourced than patients who may lack the same means to access care farther away and are, consequently, restricted to finding care locally. These nuances have implications for TTS as it relates to rurality and warrant further investigation, potentially through a cohort study where rurality and patients’ choices to pursue treatment at a specific facility can be ascertained more clearly. Second, it is important to note that the NCDB is a hospital- not population-based dataset, thus the patients included in it may have privileges and characteristics that distinguish them from patients who were not included. Since CoC sites are less likely to be in rural areas, it is likely that this study does not capture a fully representative sample of patients residing in rural areas. By some estimates, as many as 20% of Americans live in rural areas, while RUCC codes 8 and 9 in the NCDB reflect only 1.4% of the US population. However, definitions of rurality in the context of public health research have evolved over time, and estimates of the rural population in the US vary considerably across government agencies, which employ different definitions and methods for identifying rural areas. The definition of rurality in the NCDB is narrower than the definition of rurality in other studies, which often include RUCC codes 4 to 9 (i.e., all non-metropolitan counties) and reflect 15% of the US population. Thus, one significant limitation of the NCDB definition of rurality is that it may not adequately capture the rural population in the US as it is more broadly defined in other studies.16,41,42 On the other hand, one benefit of the NCDB’s definition of rurality is that the rural-urban continuum codes used in the NCDB account for not only population size but also proximity to metropolitan counties and commuter movement data, which may provide a more complete picture of rurality as well as some indication of access to care than population estimates alone.43 Third, the NCDB variable for distance to treatment facility is calculated as a great circle distance, i.e., as the crow flies, which may not be a good representation of travel time from a patient’s residence to treatment facility. Fourth, travel time may differ among rural versus urban patients due to traffic, availability of public transit, and other factors that are not captured by NCDB data. Fifth, there is no data in the NCDB regarding the training background of surgeons operating on breast cancer patients, precluding any granular understanding of how differential access to specialized care influences rural-urban disparities in TTS. Sixth, geography is known to have a complex relationship with social determinants of health that are not captured in the NCDB. Thus, there are other factors (e.g., food security, smoking status, housing stability) that may influence both TTS and OS but could not be assessed in this analysis. Seventh, we were unable to identify patients who did not receive surgery due to distance to treatment facility. It is possible some patients residing in rural areas did not receive treatment at all due to challenges with geographic distance to care. Further work is needed to understand the role of rurality and distance to treatment facility on receipt versus non-receipt of treatment in breast cancer patients residing in rural areas. Lastly, as CROWLFY measures the great circle distance between a patient’s residence and the reporting facility, rather than the actual travel time to the facility, CROWLFY may under-estimate patients’ true travel times to facilities, especially for patients residing in rural areas.

Conclusion

Among women with Stage 0-III breast cancer receiving surgery as first course of treatment, geographic proximity to care mediates the relationship between rurality and TTS. There is an intrinsic protective effect of rurality on TTS that is distinct from the adverse effect of distance to care and rural-urban differences in TTS in isolation do not explain rural-urban differences in OS among patients with invasive, non-metastatic breast cancer. Further work is needed to identify characteristics of rurality that offer a protective effect on time to care as well as modifiable factors that can act as focal points for interventions to mitigate the detrimental effects of prolonged TTS on OS particularly for marginalized, paradoxically underserved patients in urban areas.

Supplementary Material

Supplement

Funding:

Dr. Fayanju is supported by the National Institutes of Health (NIH) under Award Numbers 7K08CA241390-03 (PI: Fayanju) and P50CA244690 (PI: Bekelman) as well as the Breast Cancer Research Foundation (BCRF) and philanthropic funds from the Haas family. This work is also supported by the NIH under Award Number 2P30CA016520-45 (PI: Vonderheide). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Funders had no role in the design or conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Disclosures:

Dr. Fayanju reports research support outside the current work from Gilead Sciences, Inc.

Footnotes

Meeting Presentation: A portion of this work was presented at the 2024 Annual Meeting of the Society of Surgical Oncology.

Data Sharing:

The data upon which this study is based is publicly available.

References

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Supplementary Materials

Supplement
NIHMS2094542-supplement-Supplement.docx (832.1KB, docx)

Data Availability Statement

The data upon which this study is based is publicly available.

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