The impact of adjuvant chemotherapy on overall survival in premenopausal (age≤50 years) hormone and node positive breast cancer patients with an Oncotype Dx score of 25 or less. A NCDB analysis

The impact of adjuvant chemotherapy on overall survival in premenopausal (age≤50 years) hormone and node positive breast cancer patients with an Oncotype Dx score of 25 or less. 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A NCDB analysis Prashanth Ashok Kumar, Ghanshyam Ghelani, Gowthami Koorapati, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7201172/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Dec, 2025 Read the published version in Breast Cancer Research and Treatment → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose: The RxPONDER trial showed improved outcomes in premenopausal hormone positive breast cancer (BC) with 1-3 nodes and OncotypeDx (RS) score ≤ 25 with adjuvant chemotherapy (Chemo) use. Methods: The 2010-2018 National Cancer Database was used to include M0 BC patients aged ≤50 years with N1-N3 lymph nodes stages, any T stage, and RS ≤ 25. Kaplan-Meier (KM) and multivariate (MV) propensity score (PS) weighted Cox model was used to compare survival between patients without and with chemo. Results: 8628 women were included of which 3519 (40.8%) received chemo. KM curves showed that chemo use had better survival at 10 years (93 vs 91%) compared to hormonal therapy alone. Hazard Ratio (HR) comparison between the 2 groups favored chemo [0.602(0.482,0.751)]. Subgroup analysis for mortality benefits showed favorable results in Caucasian race [0.512(0.348,0.752)], both age groups of 18-40 years [0.429(0.217,0.847) and 40-50 years [0.585(0.394,0.869)], and RS 12-25 [0.549(0.379,0.795)]. Conclusions: Based on our analysis, chemo use was noted in 40.8% of young, lymph node+ BC patients with an RS score of 0-25. This group of patients had an overall survival advantage of around 40% with chemo use, further supporting the findings of the RxPONDER trial. This benefit is of particular significance in patients with a RS of 12-25. The survival advantage was present in all patients less than 50 years, regardless of the age subgroups. Adjuvant chemotherapy Early-stage breast cancer Hormone positive breast cancer OncotypeDx Recurrence score National cancer database Figures Figure 1 Figure 2 Figure 3 Introduction Genomic assays are an important determinant for the use adjuvant chemotherapy in early-stage hormone receptor positive breast cancer (BC) patients (1) . Among these, OncotypeDX recurrence score (RS) and Mammarpint (MP), has been widely validated and recommended by several clinical guidelines to predict the risk of disease recurrence and the likelihood of benefit from adjuvant chemotherapy( 1 – 4 ).While MP risk stratifies patients as high and low risk( 4 ), the RS provides 3 categories of risk (low, intermediate, and high risk) and can guide treatment selection based on menopausal status, which offers it a unique ability in comparison to other assays( 2 , 3 ). The RxPONDER clinical trial utilized the RS in hormone positive BC who have 1–3 lymph nodes involved (N1 nodal status). The trial revealed that in premenopausal patients with a RS of 25 or lesser, combining chemotherapy with endocrine therapy (ET) resulted in longer invasive disease-free and distant relapse-free survival than ET alone( 2 ).While these results did have real world practice changing implications( 5 ), overall survival (OS) outcomes, are not available, underscoring the need for further research to optimize treatment approaches in this patient population ( 2 ). Since this is a young cohort and given that 5-year survival rates for localized BC is comparatively good > 90%, OS data from prospective data for this scenario may be challenging to obtain ( 5 , 6 ). Utilizing population-based databases may be viable and may serve as an adjunct to prospective trial data. Our group performed one such study in the N0 premenopausal cohort like the one used in TAILORx and showed an OS advantage to adjuvant chemotherapy use( 7 ). This study utilizes a similar flow where we used real-world data from a large national repository, the National Cancer Database (NCDB) to see if there is an OS difference when adjuvant chemotherapy is used for the premenopausal hormone positive BC patients who have lymph node positivity. Methods Data source The NCDB is an effort by the American College of Surgeons consisting of patient demographic, clinical, pathologic,and mortality data, collected from Commission on Cancer (CoC)-accredited facilities in the United States. The database has a high level of completeness (>70%)(8) . The study was reviewed by the SUNY Upstate Institutional Review Board (IRB) and was provided with an exempt status. Patient selection Using the participant user file (PUF) provided by NCDB, we included female BC patients between 2010 and 2018, as RS and HER2 data was available only from 2010. Patients age had to be less than or equal to 50 years. This was further stratified into 18–40 years and 40–50 years (any age >;40 years, but less than or equal to 50 was included in this group). To ensure that only BC patients were included, ICD-O-3 site Codes for BC (C500-C506, C508-C509) were used(9). Patients had to have had lymph node involvement with any N stage, and could have had any T stage, but had to be M0. Similarly, Ductal carcinoma in situ (DCIS) patients were excluded. We preferably used the American Joint Committee on Cancer (AJCC) 8th edition when available. Patients had to be hormone receptor positive [Estrogen receptor (ER) or Progesterone receptor (PR) positive and HER2-]. Patients with a RS of ≤25 were included and were stratified into 2 groups (RS 0-11 and 12-25). Both CS site specific factors and SSDI items defined in the NCDB data dictionary for HR status and Rx score were utilized. Dates of definitive surgery and chemotherapy start dates were utilized to identify patients who received adjuvant chemotherapy. Neoadjuvant chemotherapy was excluded. Patients were stratified based on whether they received adjuvant chemotherapy (Chemo+) or not (Chemo-) and survival outcomes were compared. Statistical analysis Proportions, frequencies, and chi square tests were done to assess the association of the various patient and pathologic characteristics between the 2 chemotherapy related treatment groups. To study the impact of various factors like grade, analytic stage, Charlson–Deyo Comorbidity Score (CDCC) total score, ER/PR, surgery type (regional lymph node), insurance, race, radiation (Y/N), age, facility type, hormone used, surgery site, AJCC pathological T stage, Rx score, and histology on the probability of getting chemotherapy, Multivariate logistic regression analysis was used. Backward selection was done and factors that were left in the model other than the study group include race, insurance, grade, CDCC total score, surgery type (regional lymph node), hormone used, AJCC pathological T stage, and Rx score. Kaplan-Meier curves were used and the survival rates at 5 and 10 years were provided with the 95% confidence intervals (CI) calculated after log-log transformation. The weight of chemo on OS were assessed by fitting a variety of Cox’s proportional hazards regression models: univariate model with only the adjuvant chemotherapy group; multivariate models with all factors as in the logistic regression that may relate to receiving adjuvant chemotherapy treatment; same multivariate models but with a backward selection procedure; propensity score (PS) weighted Cox model with the weighted derived from the logistic regression above. Exploratory subgroup analyses were performed, and separate multivariate Cox models were fitted within each subgroup where all other related factors were included. All analyses were performed using SAS 9.4 and a two-sided p value < 0.05 were considered significant. Results There were 8628 women who met the inclusion criteria, of which 3519 (40.8%) received chemotherapy with ET, while 5109 (59.2%) received only ET. The proportion of patients and the distribution of various factors between the 2 groups, ie, Chemo + and Chemo- is shown in Table 1 . Most patients were in the 40–50 years age range (Chemo + 76.27%, Chemo- 84.85%) with a median age of 43 and 45 years respectively, and were mostly Caucasian (Chemo + 84.2%, Chemo- 82.03%). Private insurance was the common insurance type (Chemo + 84.03%, Chemo- 83.58%). Majority of the patients were healthy with minimal comorbidities (Chemo + 90.71%, Chemo- 91.6%) and received radiation (Chemo + 71.81%, Chemo- 62.91%). On analyzing the T stage distribution, T1 (Chemo + 53.71% Chemo-64.44%) and T2 (Chemo + 39.7 Chemo-32.57%) were common. Most patients were N1 (Chemo + 92.61% Chemo-98.9%). Stage II was more frequent (Chemo + 64.28% Chemo-55.76%) followed by Stage I (Chemo + 24.67% Table 1 Univariate analysis of the distribution of various factors between Chemo + and Chemo- patients Chemo+ (%) Chemo- (%) p Age in years < .001 18–40 835 (23.73) 774 (15.15) 40–50 2684 (76.27) 4335 (84.85) Median Age in years 44 (19–49) 46 (19–49) Mean Age in years 43 45 Race 0.029^ White 2963 (84.20) 4191 (82.03) Black 281 (7.99) 453 (8.87) Others 275 (7.81) 465 (9.10) Insurance 0.057 Not Insured 91 (2.59) 104 (2.04) Private Insurance 2957 (84.03) 4270 (83.58) Government 430 (12.22) 691 (13.53) Unknown 41 (1.17) 44 (0.86) Charlson Deyo Score 0.270 0 3192 (90.71) 4680 (91.60) 1 281 (7.99) 371 (7.26) 2 39 (1.11) 43 (0.84) >=3 7 (0.20) 15 (0.29) Hormone Receptor Status < .001 ER Positive, PR Positive 2985 (97.71) 4129 (98.87) ER Positive, PR Negative 69 (2.26) 47 (1.13) ER Negative, PR Positive 1 (0.03) 0 (0.00) Radiation received < .001 No Radiation received 992 (28.19) 1895 (37.09) Radiation received 2527 (71.81) 3214 (62.91) Hormonal Therapy used < .001 Hormonal Therapy was used 3334 (94.74) 4764 (93.25) Not used 144 (4.09) 303 (5.93) Unknown 41 (1.17) 42 (0.82) OncotypeDx RS score < .001 0–11 618 (17.56) 1813 (35.49) 12–25 2901 (82.44) 3296 (64.51) Median RS 17 (0–25) 13 (0–25) Mean RS 17 13 Pathological N < .001 N1 3259 (92.61) 5053 (98.90) N2 201 (5.71) 46 (0.90) N3 59 (1.68) 10 (0.20) Pathological T < .001 AJCC Pathological T1 1890 (53.71) 3292 (64.44) AJCC Pathological T2 1397 (39.70) 1664 (32.57) AJCC Pathological T3 223 (6.34) 146 (2.86) AJCC Pathological T4 9 (0.26) 7 (0.14) NCDB Analytic Stage Group < .001 Stage I 868 (24.67) 2094 (40.99) Stage II 2262 (64.28) 2849 (55.76) Stage III 389 (11.05) 166 (3.25) ^ P value < 0.05 * Fishers exact test (used when < 5 individuals in a category) Chemo-40.99%). RS score distribution was as follows, with 0–11 (Chemo + 17.56% Chemo-35.49%) and 12–25(Chemo + 82.44% Chemo-64.51%). The mean and median RS were 17 and 13 for the 2 groups respectively. On analyzing the utilization of adjuvant chemotherapy over the years, there was an upward trend from 2010 to 2013 where chemotherapy use increased, after which a plateau with small ups and downs were observed up until 2018. This is represented in Supplemental Fig. 1. Using the odds ratios (OR) (95% CI) from multivariate logistic regression model, the likelihood of receiving adjuvant chemotherapy was studied and is shown in Supplemental Fig. 2. The likelihood of chemotherapy usage steadily declined from 2013 to 2018. Factors associated with chemotherapy use from OR estimates includes Caucasian race [African American vs Caucasian: 0.777(0.647,0.934), p = 0.0072], higher stage [II vs I: 1.825(1.598,2.084), p = < 0.0001], III vs I:3.199(1.593,6.426), p = 0.0011] and higher grade [G3 vs G1: 2.261(1.886,2.711), G2 vs G1: 1.467(1.301,1.655),p < .0001], radiation (RT) use [1.758(1.544,2.002), p < .0001), younger age [40–50 vs 18–40: 0.684(0.542,0.863),p = 0.0013], higher RS [12–25 vs 0–11: 2.325(2.065,2.618), p<;.0001], mastectomy [vs partial surgery:1.668(1.469,1.894), p < .0001] and N2N3 nodal stage [N2N3 vs N1: 2.688(1.423,5.079), p = 0.0023]. This has been represented in Fig. 1 and Supplemental Table 1. KM curves showed that Chemo + had better survival at 5 years [Chemo + vs Chemo-: 98.7(98.1,99.1)% vs 97.7(97.0,98.2)% and at 10 years [93.0(89.8,95.2)% vs 91.0(87.9,93.4)%] (Fig. 2 A and B). When stratified by age, a similar benefit was noted at 5 and 10 years. 18–40 years had a better survival at 5 years [98.4(96.5,99.2)% vs 96.3(93.4,97.9)%] and 10 years [86.0(72.6,93.1)% vs 82.8(70.0,90.5)%] with Chemo+ (Fig. 2 C). As did, 40–50 years at 5 years [98.8(98.1,99.2)% vs 97.9(97.2,98.4)%] and 10 years [94.7(91.9,96.5)% vs 92.2(88.9,94.6)%](Fig. 2 D). Survival rates at various time frames can be found in supplemental Table 3. PS weighted Hazard Ratio (HR) comparison between the 2 groups were done and showed a mortality benefit for Chemo+ [0.602(0.482,0.751)] (Table 2 ). Subgroup analysis for overall mortality benefits from chemo + showed favorable results in Caucasian race [0.512(0.348,0.752)], both age groups of 18–40 years [0.429(0.217,0.847) and 40–50 years [0.585(0.394,0.869)], both poorly differentiated [0.404(0.186,0.874)] and well-differentiated [0.386(0.165,0.903] grades and RS 12–25 [0.549(0.379,0.795)]. RS 0–11 did not reach significance [0.555(0.216,1.423]. This is shown in Fig. 3 . Survival comparison between the various demographic and clinical characteristics is shown in supplemental Fig. 3 and supplemental table 3. Table 2 Hazard ratio (HR) (chemo + vs chemo-) from univariate and multivariate Cox models. N Hazard ratio 95% Wald Confidence Limits P value Univariate Cox model 7234 0.696 0.505,0.958 0.0264 Multivariate Cox model with all factors* 7229 0.560 0.399,0.787 0.0008 Multivariate Cox model with backward selection procedure^ 7229 0.543 0.388,0.759 0.0004 Propensity score weighted Cox model # 7229 0.602 0.482,0.751 < .0001 *Other variables included in the multivariate Cox model are Age, Facility, Race, Urban/Rural 2013, Grade, ER/PR, AJCC T stage, AJCC N stage, Analytic stage group, Primary site, Radiation therapy received Y/N, Hormone used Y/N, Insurance, Oncotype score. ^After model selection, the following other variables are kept in the final model: Grade, Facility, Insurance, AJCC T stage, AJCC N stage, Race, Oncotype score. #The following factors are considered in propensity score weighting: Age, Facility, Race, Urban/Rural 2013, Grade, ER/PR, AJCC T stage, AJCC N stage, Analytic stage group, Primary site, Radiation therapy received Y/N, Hormone used Y/N, Insurance, Oncotype score. Discussion Results from the above analysis utilizing a large national real-world cohort indicated a significant improvement in overall survival (OS) among pre-menopausal women with ER/PR+/HER2-negative BC and lymph-node involvement, who had a RS of 0–25. This survival advantage was particularly observed in the Caucasian population, and for RS 12–25, while no significant benefit was observed for RS 0–11 in our study. N1 had the survival benefit, but this was expected as > 90% of our cohort were N1 patients and a very small percentage of patients had N2 or N3 nodal stage. It is thus safe to assume that our cohort did mimic the premenopausal cohort described in RxPONDER( 2 ) The TAILORx trial enrolled patients with node-negative ER+/HER2-negative breast cancer, and among pre-menopausal patients with an RS of 0–25, a greater absolute benefit with chemotherapy at 5 years was noted as RS increased. No significant benefit was seen among patients with an RS of 0–15. The invasive disease-free survival rate increased from 92.0–94.7% in women with an RS of 16–20 and from 86.3–92.1% in those with an RS of 21–25( 7 ). The RxPONDER study is a prospective study that enrolled patients aged 18 years and older with Hormone positive/HER2-negative breast cancer and nodal stage involvement. It demonstrated a chemotherapy benefit in terms of invasive disease-free survival and distant disease-free survival in pre-menopausal women across all subgroups of RS values. Importantly, the study found that the benefit did not increase with higher RS values( 2 ). Based on data from the TAILORx and RxPONDER studies, adjuvant chemotherapy has shown to improve invasive disease-free survival in Hormone positive HER2 Negative BC. The benefit of chemotherapy was observed in patients with RS > 16 in lymph node negative disease and regardless of RS in node positive disease ( 2 , 3 ). In comparison to the RxPONDER study, our research takes the form of a retrospective real-world analysis and focuses solely on pre-menopausal women aged below 50 years. Our study aimed to assess the OS benefit of adjuvant chemotherapy specifically in individuals aged 50 or below with an Oncotype DX score of 25 or less. Although the OS data from the RxPONDER study is still evolving ( 2 ), our analysis provides insight into the potential OS advantage, particularly for individuals younger than 50 years old with an RS of 12–25. While the disease free and distant relapse free survival in RxPONDER extends to all RS categories, the OS benefit in our analysis was limited to the RS 12–25 subgroup. We can thus hypothesize that with the current data, patients with RS of 12–25 have a strong case for getting adjuvant chemotherapy. We would need more prospective data and hopefully, mature survival analysis from RxPONDER to see if RS 0–11 group can omit chemotherapy( 5 ). In our subgroup analysis, it was noted that Caucasians had mortality benefit, but not African Americans. This could be due to the difference in the sample size between the racial groups in the cohort, which was predominated by Caucasians (> 80%). However, there are several reports that show that genomic assays like OncotypeDx may not retain its prognostic utility in African Americans. Our study highlights this issue as well and the need for more trials in with diverse racial representation ( 10 ). The fact that adjuvant chemotherapy use plateaued and declined after 2013 coincides with the advent of genomic prognostic assays like the RS and MP ( 11 ). MP was approved by FDA as early as 2007 ( 12 ). Chemo + utility trends showed expected results with chemotherapy more likely to be used with higher risk and more aggressive tumors. The fact that Caucasians in our study were more likely to receive chemo highlights the existence of healthcare disparity between racial groups. It is known that racial disparities to chemotherapy access occur especially among minority ethnicities. African Americans are predisposed to delays in access to chemotherapy due to various reasons ( 13 , 14 ) . Stabellini et al. performed a similar analysis to ours using the NCDB in ER + BC patients with 1–3 positive nodes and RS ≤ 25 ( 15 ) There were however key differences in comparison to our study. Stabellini’s cohort included all age groups, whereas our study was specific to age < 50 years. In addition, our study stratified patients by age groups (18–40 and 40–50 years), which was not pursued by the Stabellini et al. While our study involved all lymph node stages, Stabellini et al only included N1. In our methodology, we specifically excluded patients with neoadjuvant chemotherapy, which was not mentioned in the methodology of the Stabellini et al’s study( 15 ). Regardless, several results were consistent, strengthening the validity of both the studies. Patients aged less than 50 with a RS of 0–11 did not have any OS significance [HR, 0.620, 95% CI (0.170, 2.258), p = 0.469]. Those with an RS of 12–25 aged less than 50 did achieve a statistical significance [0.550 (0.338,0.895), p = 0.016] like our study. Roberts et al. conducted a SEER database analysis to assess breast cancer-specific survival in patients with hormone receptor and node-positive disease and correlated with the Oncotype Dx RS results. Their findings showed excellent 5-year outcome for patients with RS < 18 and micrometastases or one or two positive lymph nodes, which worsened with additional involved lymph nodes. It was noted that a low proportion of patients with RS < 18 received chemotherapy, suggesting a potentially limited impact of chemotherapy in this RS group ( 16 ). The benefit of chemotherapy in pre-menopausal women is likely due to the direct cytocidal effect of chemotherapy, as well as from treatment-induced ovarian function suppression/induced menopause. This notion is supported by a study conducted by Swain et al. and colleagues. In their research, in which they found that OS improved in patients who experienced amenorrhea for a duration of 6 months or more, irrespective of ER status. This suggests that ovarian function suppression or induced menopause may contribute to the improved outcomes seen in pre-menopausal women receiving chemotherapy ( 17 ). Additionally, data from The Tamoxifen and Exemestane Trial (TEXT) and the Suppression of Ovarian Function Trial (SOFT) showed that pre-menopausal women with ER+/HER2-negative BC and high recurrence risk, as defined by clinicopathologic characteristics, may experience 5–7% absolute improvement in 8-year freedom from distant recurrence with Exemestane plus Ovarian function suppression compared to Tamoxifen with or without OFS ( 18 ). The benefit extended to 12 years as well in the updated analysis ( 19 ). However, it is currently unclear on how much benefit is derived from chemotherapy compared to ovarian function suppression and there are no randomized controlled trials (RCTs) comparing this. Future RCTs should be considered to address this dilemma, as it could potentially avoid chemotherapy and related long-term complications in younger women. Studies like the ongoing Phase III NRG OFSET trial are evaluating this question may help better selection of systemic agents for this cohort( 20 ) Limitations of our study arise from its retrospective nature. Although a very extensive database, NCDB does not provide information on specific chemotherapy drugs or endocrine agents used. For instance, while we can identify patients who received chemotherapy or ET, we are unable to identify patients who received ovarian suppression function or a particular chemotherapy agent like doxorubicin. Besides, there is potential for confounding despite the use of PS matching. In conclusion, our study, along with that of Stabellini et al and the RxPONDER trial provides compulsive evidence to offer adjuvant chemotherapy for premenopausal ER + BC patients who have lymph node involvement and an RS of 12–25. Declarations Acknowledgements Our sincere gratitude to the Division of Hematology Oncology at SUNY Upstate Medical University for all the support. Our thanks to the American College of Surgeons and the American Cancer Society for providing us with the National Cancer Database PUF file without which our project would have not been possible. Author Contributions Prashanth Ashok Kumar conceptualized and designed the study, acquired, and analyzed the data and wrote the manuscript. Abirami Sivapiragasam formulated the original idea and contributed to the design, analysis, review, and supervision of the manuscript. Dongliang Wang performed data analysis, statistics, and review of the paper. Danning Huang and Ghanshyam Ghelani performed data analysis, statistics, and review of the paper. Gowthami Koorapati and Shweta Paulraj performed data analysis, statistics and review of the paper. All authors approved the final manuscript. Funding Source The work was funded by the Division of Hematology-Oncology, Upstate Cancer Center, of the Upstate Medical University. Data availability statement The datasets analyzed during the current study are not publicly available as they were provided by the American College of Surgeons. They are available through an application process to investigators in CoC- accredited cancer programs. Conflicts of interest/Disclosures The authors have no relevant financial or non-financial interests to disclose. The National Cancer Database (NCDB) is a joint project of the Commission on Cancer of the American College of Surgeons and the American Cancer Society. The data used in the study are derived from a de-identified NCDB Participant User File (PUF) file. The American College of Surgeons and the Commission on Cancer have not verified and are not responsible for the analytic or statistical methodology employed, or the conclusions drawn from these data by the investigators. Ethics approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. The SUNY Upstate IRB has reviewed the project and has determined this project does not meet the definition of human subject research under the purview of the IRB according to federal regulations. IRB no: 1778292. Consent to participate Not applicable. Data was obtained from the NCDB PUF file. 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Supplementary Files Supplement.docx Cite Share Download PDF Status: Published Journal Publication published 08 Dec, 2025 Read the published version in Breast Cancer Research and Treatment → Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviews received at journal 04 Sep, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 06 Aug, 2025 Reviewers invited by journal 03 Aug, 2025 Editor assigned by journal 24 Jul, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 23 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7201172","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495540934,"identity":"80a6afc1-3b99-46f0-b9df-a0cbd4ecce1b","order_by":0,"name":"Prashanth Ashok Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACZhDBxmDAwN4AZBhYkKKF5wBIiwSxVoG0SCSAWERoMTjO/vBzRZmdsbnkG9MNPwokGPjbuxPwaznMkCx55lyymeXsHLObPUCHSZw5uwGvFrPDDAckG9uYbQxu55jd4AFqMZDIJaSFsflnY1u9jcHNM2Y3/xCnhZkNaMthM4MbPGa3ibLF/jAbm2XDuePGlj1pZbdlDCR4CPpFsv/445sNZdWG29kPb7v55o+NHH97L34tcGDAwGEAonmIUw7Rwv6AeNWjYBSMglEwogAADgREIrkLzJcAAAAASUVORK5CYII=","orcid":"","institution":"George Washington University Medical Faculty Associates","correspondingAuthor":true,"prefix":"","firstName":"Prashanth","middleName":"Ashok","lastName":"Kumar","suffix":""},{"id":495540936,"identity":"b8e6100b-889f-4001-9abe-c771e724c505","order_by":1,"name":"Ghanshyam Ghelani","email":"","orcid":"","institution":"Upstate University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ghanshyam","middleName":"","lastName":"Ghelani","suffix":""},{"id":495540937,"identity":"ee91b852-2b69-4537-bfc0-08f416b410b5","order_by":2,"name":"Gowthami Koorapati","email":"","orcid":"","institution":"Mercy One Waterloo Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Gowthami","middleName":"","lastName":"Koorapati","suffix":""},{"id":495540939,"identity":"191cc8a7-6af8-4d3c-983b-66fc14bc57eb","order_by":3,"name":"Shweta Paulraj","email":"","orcid":"","institution":"MedStar Washington Hospital Center","correspondingAuthor":false,"prefix":"","firstName":"Shweta","middleName":"","lastName":"Paulraj","suffix":""},{"id":495540941,"identity":"f7853111-b80c-42db-a90a-1d920802ec63","order_by":4,"name":"Dongliang Wang","email":"","orcid":"","institution":"Upstate University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongliang","middleName":"","lastName":"Wang","suffix":""},{"id":495540943,"identity":"2ef635ca-d549-4c42-b468-e4fd086d2d71","order_by":5,"name":"Danning Huang","email":"","orcid":"","institution":"Upstate University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Danning","middleName":"","lastName":"Huang","suffix":""},{"id":495540944,"identity":"399b4cbb-c71b-4b02-a93f-490e17c822e2","order_by":6,"name":"Abirami Sivapiragasam","email":"","orcid":"","institution":"Upstate University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Abirami","middleName":"","lastName":"Sivapiragasam","suffix":""}],"badges":[],"createdAt":"2025-07-24 04:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7201172/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7201172/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10549-025-07868-3","type":"published","date":"2025-12-08T15:58:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88411812,"identity":"7dac9947-e5cc-4306-acf7-1cee7dd9b4c8","added_by":"auto","created_at":"2025-08-06 08:27:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91348,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOdds Ratio estimates for the likelihood of receiving chemotherapy with 95% CI\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7201172/v1/0184df08797f504a3a594920.jpg"},{"id":88411811,"identity":"3402f95f-d12d-49a5-95bb-c85f90b057ef","added_by":"auto","created_at":"2025-08-06 08:27:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92429,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier survival curves for chemo+ vs Chemo- for overall cohort (A and B), 18-40 years (C), 40-50 years (D).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7201172/v1/18bbf5604012d683677029f3.jpg"},{"id":88411814,"identity":"d4df16ca-4f80-4e0f-a77a-8dee9062daca","added_by":"auto","created_at":"2025-08-06 08:27:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of the impact of various subgroups on mortality with chemotherapy use using multivariate analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7201172/v1/2fd2c8d997b70de218753404.jpg"},{"id":98244302,"identity":"0aaeb840-b93e-4a7c-8488-7a0e8ca8ae7c","added_by":"auto","created_at":"2025-12-15 16:14:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1191088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7201172/v1/19944fff-6d66-4b64-9598-c0f20729b0c0.pdf"},{"id":88411815,"identity":"14946cd2-0c26-42d2-be28-a3ea685e19c1","added_by":"auto","created_at":"2025-08-06 08:27:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":208560,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7201172/v1/e2dd8fbfc96f661b9c7f6122.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe impact of adjuvant chemotherapy on overall survival in premenopausal (age≤50 years) hormone and node positive breast cancer patients with an Oncotype Dx score of 25 or less. A NCDB analysis\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGenomic assays are an important determinant for the use adjuvant chemotherapy in early-stage hormone receptor positive breast cancer (BC) patients \u003csup\u003e(1)\u003c/sup\u003e. Among these, OncotypeDX recurrence score (RS) and Mammarpint (MP), has been widely validated and recommended by several clinical guidelines to predict the risk of disease recurrence and the likelihood of benefit from adjuvant chemotherapy(\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).While MP risk stratifies patients as high and low risk(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), the RS provides 3 categories of risk (low, intermediate, and high risk) and can guide treatment selection based on menopausal status, which offers it a unique ability in comparison to other assays(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe RxPONDER clinical trial utilized the RS in hormone positive BC who have 1–3 lymph nodes involved (N1 nodal status). The trial revealed that in premenopausal patients with a RS of 25 or lesser, combining chemotherapy with endocrine therapy (ET) resulted in longer invasive disease-free and distant relapse-free survival than ET alone(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).While these results did have real world practice changing implications(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), overall survival (OS) outcomes, are not available, underscoring the need for further research to optimize treatment approaches in this patient population (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Since this is a young cohort and given that 5-year survival rates for localized BC is comparatively good \u0026gt; 90%, OS data from prospective data for this scenario may be challenging to obtain (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Utilizing population-based databases may be viable and may serve as an adjunct to prospective trial data. Our group performed one such study in the N0 premenopausal cohort like the one used in TAILORx and showed an OS advantage to adjuvant chemotherapy use(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This study utilizes a similar flow where we used real-world data from a large national repository, the National Cancer Database (NCDB) to see if there is an OS difference when adjuvant chemotherapy is used for the premenopausal hormone positive BC patients who have lymph node positivity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData source\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NCDB is an effort by the American College of Surgeons consisting of patient demographic, clinical, pathologic,and mortality data, collected from Commission on Cancer (CoC)-accredited facilities in the United States. The database has a high level of completeness (\u0026gt;70%)(8) . The study was reviewed by the SUNY Upstate Institutional Review Board (IRB) and was provided with an exempt status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePatient selection\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the participant user file (PUF) provided by NCDB, we included female BC patients between 2010 and 2018, as RS and HER2 data was available only from 2010. Patients age had to be less than or equal to 50 years. This was further stratified into 18\u0026ndash;40 years and 40\u0026ndash;50 years (any age \u0026gt;;40 years, but less than or equal to 50 was included in this group). To ensure that only BC patients were included, ICD-O-3 site Codes for BC (C500-C506, C508-C509) were used(9). Patients had to have had lymph node involvement with any N stage, and could have had any T stage, but had to be M0. Similarly, Ductal carcinoma in situ (DCIS) patients were excluded. We preferably used the American Joint Committee on Cancer (AJCC) 8th edition when available. Patients had to be hormone receptor positive [Estrogen receptor (ER) or Progesterone receptor (PR) positive and HER2-]. Patients with a RS of \u0026le;25 were included and were stratified into 2 groups (RS 0-11 and 12-25). Both CS site specific factors and SSDI items defined in the NCDB data dictionary for HR status and Rx score were utilized. Dates of definitive surgery and chemotherapy start dates were utilized to identify patients who received adjuvant chemotherapy. Neoadjuvant chemotherapy was excluded. Patients were stratified based on whether they received adjuvant chemotherapy (Chemo+) or not (Chemo-) and survival outcomes were compared.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProportions, frequencies, and chi square tests were done to assess the association of the various patient and pathologic characteristics between the 2 chemotherapy related treatment groups. To study the impact of various factors like grade, analytic stage, Charlson\u0026ndash;Deyo Comorbidity Score (CDCC) total score, ER/PR, surgery type (regional lymph node), insurance, race, radiation (Y/N), age, facility type, hormone used, surgery site, AJCC pathological T stage, Rx score, and histology on the probability of getting chemotherapy, Multivariate logistic regression analysis was used. Backward selection was done and factors that were left in the model other than the study group include race, insurance, grade, CDCC total score, surgery type (regional lymph node), hormone used, AJCC pathological T stage, and Rx score. Kaplan-Meier curves were used and the survival rates at 5 and 10 years were provided with the 95% confidence intervals (CI) calculated after log-log transformation. The weight of chemo on OS were assessed by fitting a variety of Cox\u0026rsquo;s proportional hazards regression models: univariate model with only the adjuvant chemotherapy group; multivariate models with all factors as in the logistic regression that may relate to receiving adjuvant chemotherapy treatment; same multivariate models but with a backward selection procedure; propensity score (PS) weighted Cox model with the weighted derived from the logistic regression above. Exploratory subgroup analyses were performed, and separate multivariate Cox models were fitted within each subgroup where all other related factors were included. All analyses were performed using SAS 9.4 and a two-sided p value \u0026lt; 0.05 were considered significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThere were 8628 women who met the inclusion criteria, of which 3519 (40.8%) received chemotherapy with ET, while 5109 (59.2%) received only ET. The proportion of patients and the distribution of various factors between the 2 groups, ie, Chemo\u0026thinsp;+\u0026thinsp;and Chemo- is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Most patients were in the 40\u0026ndash;50 years age range (Chemo\u0026thinsp;+\u0026thinsp;76.27%, Chemo- 84.85%) with a median age of 43 and 45 years respectively, and were mostly Caucasian (Chemo\u0026thinsp;+\u0026thinsp;84.2%, Chemo- 82.03%). Private insurance was the common insurance type (Chemo\u0026thinsp;+\u0026thinsp;84.03%, Chemo- 83.58%). Majority of the patients were healthy with minimal comorbidities (Chemo\u0026thinsp;+\u0026thinsp;90.71%, Chemo- 91.6%) and received radiation (Chemo\u0026thinsp;+\u0026thinsp;71.81%, Chemo- 62.91%). On analyzing the T stage distribution, T1 (Chemo\u0026thinsp;+\u0026thinsp;53.71% Chemo-64.44%) and T2 (Chemo\u0026thinsp;+\u0026thinsp;39.7 Chemo-32.57%) were common. Most patients were N1 (Chemo\u0026thinsp;+\u0026thinsp;92.61% Chemo-98.9%). Stage II was more frequent (Chemo\u0026thinsp;+\u0026thinsp;64.28% Chemo-55.76%) followed by Stage I (Chemo\u0026thinsp;+\u0026thinsp;24.67%\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate analysis of the distribution of various factors between Chemo\u0026thinsp;+\u0026thinsp;and Chemo- patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChemo+ (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChemo- (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge in years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u0026ndash;40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e835 (23.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e774 (15.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2684 (76.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4335 (84.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedian Age in years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (19\u0026ndash;49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 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colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2963 (84.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4191 (82.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e281 (7.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e453 (8.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e275 (7.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e465 (9.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInsurance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot Insured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 (2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104 (2.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate Insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2957 (84.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4270 (83.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e430 (12.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e691 (13.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCharlson Deyo Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3192 (90.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4680 (91.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e281 (7.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e371 (7.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (1.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;=3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (0.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHormone Receptor Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER Positive, PR Positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2985 (97.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4129 (98.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER Positive, PR Negative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (2.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (1.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER Negative, PR Positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRadiation received\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Radiation received\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e992 (28.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1895 (37.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiation received\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2527 (71.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3214 (62.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHormonal Therapy used\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHormonal Therapy was used\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3334 (94.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4764 (93.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot used\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (4.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e303 (5.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (1.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42 (0.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOncotypeDx RS score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u0026ndash;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e618 (17.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1813 (35.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u0026ndash;25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2901 (82.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3296 (64.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMedian RS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (0\u0026ndash;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (0\u0026ndash;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean RS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePathological N\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3259 (92.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5053 (98.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e201 (5.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePathological T\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAJCC Pathological T1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1890 (53.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3292 (64.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAJCC Pathological T2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1397 (39.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1664 (32.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAJCC Pathological T3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e223 (6.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146 (2.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAJCC Pathological T4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (0.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNCDB Analytic Stage Group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e868 (24.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2094 (40.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2262 (64.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2849 (55.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e389 (11.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e166 (3.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e^ P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e* Fishers exact test (used when \u0026lt;\u0026thinsp;5 individuals in a category)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eChemo-40.99%). RS score distribution was as follows, with 0\u0026ndash;11 (Chemo\u0026thinsp;+\u0026thinsp;17.56% Chemo-35.49%) and 12\u0026ndash;25(Chemo\u0026thinsp;+\u0026thinsp;82.44% Chemo-64.51%). The mean and median RS were 17 and 13 for the 2 groups respectively.\u003c/p\u003e\u003cp\u003eOn analyzing the utilization of adjuvant chemotherapy over the years, there was an upward trend from 2010 to 2013 where chemotherapy use increased, after which a plateau with small ups and downs were observed up until 2018. This is represented in Supplemental Fig.\u0026nbsp;1. Using the odds ratios (OR) (95% CI) from multivariate logistic regression model, the likelihood of receiving adjuvant chemotherapy was studied and is shown in Supplemental Fig.\u0026nbsp;2. The likelihood of chemotherapy usage steadily declined from 2013 to 2018.\u003c/p\u003e\u003cp\u003eFactors associated with chemotherapy use from OR estimates includes Caucasian race [African American vs Caucasian: 0.777(0.647,0.934), p\u0026thinsp;=\u0026thinsp;0.0072], higher stage [II vs I: 1.825(1.598,2.084), p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.0001], III vs I:3.199(1.593,6.426), p\u0026thinsp;=\u0026thinsp;0.0011] and higher grade [G3 vs G1: 2.261(1.886,2.711), G2 vs G1: 1.467(1.301,1.655),p\u0026thinsp;\u0026lt;\u0026thinsp;.0001], radiation (RT) use [1.758(1.544,2.002), p\u0026thinsp;\u0026lt;\u0026thinsp;.0001), younger age [40\u0026ndash;50 vs 18\u0026ndash;40: 0.684(0.542,0.863),p\u0026thinsp;=\u0026thinsp;0.0013], higher RS [12\u0026ndash;25 vs 0\u0026ndash;11: 2.325(2.065,2.618), p\u0026lt;;.0001], mastectomy [vs partial surgery:1.668(1.469,1.894), p\u0026thinsp;\u0026lt;\u0026thinsp;.0001] and N2N3 nodal stage [N2N3 vs N1: 2.688(1.423,5.079), p\u0026thinsp;=\u0026thinsp;0.0023]. This has been represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplemental Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eKM curves showed that Chemo\u0026thinsp;+\u0026thinsp;had better survival at 5 years [Chemo\u0026thinsp;+\u0026thinsp;vs Chemo-: 98.7(98.1,99.1)% vs 97.7(97.0,98.2)% and at 10 years [93.0(89.8,95.2)% vs 91.0(87.9,93.4)%] (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). When stratified by age, a similar benefit was noted at 5 and 10 years. 18\u0026ndash;40 years had a better survival at 5 years [98.4(96.5,99.2)% vs 96.3(93.4,97.9)%] and 10 years [86.0(72.6,93.1)% vs 82.8(70.0,90.5)%] with Chemo+ (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). As did, 40\u0026ndash;50 years at 5 years [98.8(98.1,99.2)% vs 97.9(97.2,98.4)%] and 10 years [94.7(91.9,96.5)% vs 92.2(88.9,94.6)%](Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Survival rates at various time frames can be found in supplemental Table\u0026nbsp;3.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePS weighted Hazard Ratio (HR) comparison between the 2 groups were done and showed a mortality benefit for Chemo+ [0.602(0.482,0.751)] (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subgroup analysis for overall mortality benefits from chemo\u0026thinsp;+\u0026thinsp;showed favorable results in Caucasian race [0.512(0.348,0.752)], both age groups of 18\u0026ndash;40 years [0.429(0.217,0.847) and 40\u0026ndash;50 years [0.585(0.394,0.869)], both poorly differentiated [0.404(0.186,0.874)] and well-differentiated [0.386(0.165,0.903] grades and RS 12\u0026ndash;25 [0.549(0.379,0.795)]. RS 0\u0026ndash;11 did not reach significance [0.555(0.216,1.423]. This is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Survival comparison between the various demographic and clinical characteristics is shown in supplemental Fig.\u0026nbsp;3 and supplemental table 3.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHazard ratio (HR) (chemo\u0026thinsp;+\u0026thinsp;vs chemo-) from univariate and multivariate Cox models.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% Wald Confidence Limits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnivariate Cox model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.505,0.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0264\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultivariate Cox model with all factors*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.399,0.787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMultivariate Cox model with backward selection procedure^\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.388,0.759\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePropensity score weighted Cox model\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.482,0.751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e*Other variables included in the multivariate Cox model are Age, Facility, Race, Urban/Rural 2013, Grade, ER/PR, AJCC T stage, AJCC N stage, Analytic stage group, Primary site, Radiation therapy received Y/N, Hormone used Y/N, Insurance, Oncotype score.\u003c/p\u003e\u003cp\u003e^After model selection, the following other variables are kept in the final model: Grade, Facility, Insurance, AJCC T stage, AJCC N stage, Race, Oncotype score.\u003c/p\u003e\u003cp\u003e#The following factors are considered in propensity score weighting: Age, Facility, Race, Urban/Rural 2013, Grade, ER/PR, AJCC T stage, AJCC N stage, Analytic stage group, Primary site, Radiation therapy received Y/N, Hormone used Y/N, Insurance, Oncotype score.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eResults from the above analysis utilizing a large national real-world cohort indicated a significant improvement in overall survival (OS) among pre-menopausal women with ER/PR+/HER2-negative BC and lymph-node involvement, who had a RS of 0\u0026ndash;25. This survival advantage was particularly observed in the Caucasian population, and for RS 12\u0026ndash;25, while no significant benefit was observed for RS 0\u0026ndash;11 in our study. N1 had the survival benefit, but this was expected as \u0026gt;\u0026thinsp;90% of our cohort were N1 patients and a very small percentage of patients had N2 or N3 nodal stage. It is thus safe to assume that our cohort did mimic the premenopausal cohort described in RxPONDER(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe TAILORx trial enrolled patients with node-negative ER+/HER2-negative breast cancer, and among pre-menopausal patients with an RS of 0\u0026ndash;25, a greater absolute benefit with chemotherapy at 5 years was noted as RS increased. No significant benefit was seen among patients with an RS of 0\u0026ndash;15. The invasive disease-free survival rate increased from 92.0\u0026ndash;94.7% in women with an RS of 16\u0026ndash;20 and from 86.3\u0026ndash;92.1% in those with an RS of 21\u0026ndash;25(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The RxPONDER study is a prospective study that enrolled patients aged 18 years and older with Hormone positive/HER2-negative breast cancer and nodal stage involvement. It demonstrated a chemotherapy benefit in terms of invasive disease-free survival and distant disease-free survival in pre-menopausal women across all subgroups of RS values. Importantly, the study found that the benefit did not increase with higher RS values(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Based on data from the TAILORx and RxPONDER studies, adjuvant chemotherapy has shown to improve invasive disease-free survival in Hormone positive HER2 Negative BC. The benefit of chemotherapy was observed in patients with RS\u0026thinsp;\u0026gt;\u0026thinsp;16 in lymph node negative disease and regardless of RS in node positive disease (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn comparison to the RxPONDER study, our research takes the form of a retrospective real-world analysis and focuses solely on pre-menopausal women aged below 50 years. Our study aimed to assess the OS benefit of adjuvant chemotherapy specifically in individuals aged 50 or below with an Oncotype DX score of 25 or less. Although the OS data from the RxPONDER study is still evolving (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), our analysis provides insight into the potential OS advantage, particularly for individuals younger than 50 years old with an RS of 12\u0026ndash;25. While the disease free and distant relapse free survival in RxPONDER extends to all RS categories, the OS benefit in our analysis was limited to the RS 12\u0026ndash;25 subgroup. We can thus hypothesize that with the current data, patients with RS of 12\u0026ndash;25 have a strong case for getting adjuvant chemotherapy. We would need more prospective data and hopefully, mature survival analysis from RxPONDER to see if RS 0\u0026ndash;11 group can omit chemotherapy(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In our subgroup analysis, it was noted that Caucasians had mortality benefit, but not African Americans. This could be due to the difference in the sample size between the racial groups in the cohort, which was predominated by Caucasians (\u0026gt;\u0026thinsp;80%). However, there are several reports that show that genomic assays like OncotypeDx may not retain its prognostic utility in African Americans. Our study highlights this issue as well and the need for more trials in with diverse racial representation (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The fact that adjuvant chemotherapy use plateaued and declined after 2013 coincides with the advent of genomic prognostic assays like the RS and MP (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). MP was approved by FDA as early as 2007 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Chemo\u0026thinsp;+\u0026thinsp;utility trends showed expected results with chemotherapy more likely to be used with higher risk and more aggressive tumors. The fact that Caucasians in our study were more likely to receive chemo highlights the existence of healthcare disparity between racial groups. It is known that racial disparities to chemotherapy access occur especially among minority ethnicities. African Americans are predisposed to delays in access to chemotherapy due to various reasons (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eStabellini et al. performed a similar analysis to ours using the NCDB in ER\u0026thinsp;+\u0026thinsp;BC patients with 1\u0026ndash;3 positive nodes and RS\u0026thinsp;\u0026le;\u0026thinsp;25 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) There were however key differences in comparison to our study. Stabellini\u0026rsquo;s cohort included all age groups, whereas our study was specific to age\u0026thinsp;\u0026lt;\u0026thinsp;50 years. In addition, our study stratified patients by age groups (18\u0026ndash;40 and 40\u0026ndash;50 years), which was not pursued by the Stabellini et al. While our study involved all lymph node stages, Stabellini et al only included N1. In our methodology, we specifically excluded patients with neoadjuvant chemotherapy, which was not mentioned in the methodology of the Stabellini et al\u0026rsquo;s study(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Regardless, several results were consistent, strengthening the validity of both the studies. Patients aged less than 50 with a RS of 0\u0026ndash;11 did not have any OS significance [HR, 0.620, 95% CI (0.170, 2.258), p\u0026thinsp;=\u0026thinsp;0.469]. Those with an RS of 12\u0026ndash;25 aged less than 50 did achieve a statistical significance [0.550 (0.338,0.895), p\u0026thinsp;=\u0026thinsp;0.016] like our study. Roberts et al. conducted a SEER database analysis to assess breast cancer-specific survival in patients with hormone receptor and node-positive disease and correlated with the Oncotype Dx RS results. Their findings showed excellent 5-year outcome for patients with RS\u0026thinsp;\u0026lt;\u0026thinsp;18 and micrometastases or one or two positive lymph nodes, which worsened with additional involved lymph nodes. It was noted that a low proportion of patients with RS\u0026thinsp;\u0026lt;\u0026thinsp;18 received chemotherapy, suggesting a potentially limited impact of chemotherapy in this RS group (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe benefit of chemotherapy in pre-menopausal women is likely due to the direct cytocidal effect of chemotherapy, as well as from treatment-induced ovarian function suppression/induced menopause. This notion is supported by a study conducted by Swain et al. and colleagues. In their research, in which they found that OS improved in patients who experienced amenorrhea for a duration of 6 months or more, irrespective of ER status. This suggests that ovarian function suppression or induced menopause may contribute to the improved outcomes seen in pre-menopausal women receiving chemotherapy (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Additionally, data from The Tamoxifen and Exemestane Trial (TEXT) and the Suppression of Ovarian Function Trial (SOFT) showed that pre-menopausal women with ER+/HER2-negative BC and high recurrence risk, as defined by clinicopathologic characteristics, may experience 5\u0026ndash;7% absolute improvement in 8-year freedom from distant recurrence with Exemestane plus Ovarian function suppression compared to Tamoxifen with or without OFS (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The benefit extended to 12 years as well in the updated analysis (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). However, it is currently unclear on how much benefit is derived from chemotherapy compared to ovarian function suppression and there are no randomized controlled trials (RCTs) comparing this. Future RCTs should be considered to address this dilemma, as it could potentially avoid chemotherapy and related long-term complications in younger women. Studies like the ongoing Phase III NRG OFSET trial are evaluating this question may help better selection of systemic agents for this cohort(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eLimitations of our study arise from its retrospective nature. Although a very extensive database, NCDB does not provide information on specific chemotherapy drugs or endocrine agents used. For instance, while we can identify patients who received chemotherapy or ET, we are unable to identify patients who received ovarian suppression function or a particular chemotherapy agent like doxorubicin. Besides, there is potential for confounding despite the use of PS matching.\u003c/p\u003e\u003cp\u003eIn conclusion, our study, along with that of Stabellini et al and the RxPONDER trial provides compulsive evidence to offer adjuvant chemotherapy for premenopausal ER\u0026thinsp;+\u0026thinsp;BC patients who have lymph node involvement and an RS of 12\u0026ndash;25.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur sincere gratitude to the Division of Hematology Oncology at SUNY Upstate Medical University for all the support. Our thanks to the American College of Surgeons and the American Cancer Society for providing us with the National Cancer Database PUF file without which our project would have not been possible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrashanth Ashok Kumar conceptualized and designed the study, acquired, and analyzed the data and wrote the manuscript. Abirami Sivapiragasam formulated the original idea and contributed to the design, analysis, review, and supervision of the manuscript. Dongliang Wang performed data analysis, statistics, and review of the paper. Danning Huang and Ghanshyam Ghelani performed data analysis, statistics, and review of the paper. Gowthami Koorapati and Shweta Paulraj performed data analysis, statistics and review of the paper. \u0026nbsp;All authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding Source\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work was funded by the Division of Hematology-Oncology, Upstate Cancer Center, of the Upstate Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData availability statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are not publicly available as they were provided by the American College of Surgeons. They are available through an application process to investigators in CoC- accredited cancer programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflicts of interest/Disclosures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. The National Cancer Database (NCDB) is a joint project of the Commission on Cancer of the American College of Surgeons and the American Cancer Society. The data used in the study are derived from a de-identified NCDB Participant User File (PUF) file. The American College of Surgeons and the Commission on Cancer have not verified and are not responsible for the analytic or statistical methodology employed, or the conclusions drawn from these data by the investigators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. The SUNY Upstate IRB has reviewed the project and has determined this project does not meet the definition of human subject research under the purview of the IRB according to federal regulations. IRB no: 1778292.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. Data was obtained from the NCDB PUF file. It is a Health Insurance Portability and Accountability Act (HIPAA) complaint data file where patient information is de-identified.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAssi HI, Alameh IA, Khoury J, Abdul Halim N, El Karak F, Farhat F, et al. Impact of Commercialized Genomic Tests on Adjuvant Treatment Decisions in Early Stage Breast Cancer Patients. J Oncol. 2020;2020:9238084.\u003c/li\u003e\n\u003cli\u003eKalinsky K, Barlow WE, Gralow JR, Meric-Bernstam F, Albain KS, Hayes DF, et al. 21-Gene Assay to Inform Chemotherapy Benefit in Node-Positive Breast Cancer. N Engl J Med. 2021;385(25):2336-47.\u003c/li\u003e\n\u003cli\u003eSparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS, Hayes DF, et al. Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer. N Engl J Med. 2018;379(2):111-21.\u003c/li\u003e\n\u003cli\u003ePiccart M, van \u0026apos;t Veer LJ, Poncet C, Lopes Cardozo JMN, Delaloge S, Pierga JY, et al. 70-gene signature as an aid for treatment decisions in early breast cancer: updated results of the phase 3 randomised MINDACT trial with an exploratory analysis by age. Lancet Oncol. 2021;22(4):476-88.\u003c/li\u003e\n\u003cli\u003eWeiser R, Haque W, Polychronopoulou E, Hatch SS, Kuo YF, Gradishar WJ, et al. The 21-gene recurrence score in node-positive, hormone receptor-positive, HER2-negative breast cancer: a cautionary tale from an NCDB analysis. Breast Cancer Res Treat. 2021;185(3):667-76.\u003c/li\u003e\n\u003cli\u003esurvival rates for breast cancer | American Cancer Society [Internet][cited 2024 Jul 15] [Available from: https://www.cancer.org/cancer/types/breast-cancer/understanding-a-breast-cancer-\u003c/li\u003e\n\u003cli\u003ediagnosis/breast-cancer-survival-rates.html.\u003c/li\u003e\n\u003cli\u003eAshok Kumar P, Wang D, Huang D, Sivapiragasam A. Adjuvant Chemotherapy in Premenopausal Patients With Hormone-Positive Breast Cancer With a Recurrence Score of 16-25: A Retrospective Analysis Using the National Cancer Database. JCO Precis Oncol. 2024;8:e2300390.\u003c/li\u003e\n\u003cli\u003ePalis BE, Janczewski LM, Browner AE, Cotler J, Nogueira L, Richardson LC, et al. The National Cancer Database Conforms to the Standardized Framework for Registry and Data Quality. Ann Surg Oncol. 2024;31(9):5546-59.\u003c/li\u003e\n\u003cli\u003eSite Recode ICD-O-3/WHO 2008 SEER Data reporting tools 2024 [cited 2024 7/21/2025]. Available from: https://seer.cancer.gov/siterecode/icdo3_dwhoheme/index.html.\u003c/li\u003e\n\u003cli\u003eHoskins KF, Danciu OC, Ko NY, Calip GS. Association of Race/Ethnicity and the 21-Gene Recurrence Score With Breast Cancer-Specific Mortality Among US Women. JAMA Oncol. 2021;7(3):370-8.\u003c/li\u003e\n\u003cli\u003eKwa M, Makris A, Esteva FJ. Clinical utility of gene-expression signatures in early stage breast cancer. Nat Rev Clin Oncol. 2017;14(10):595-610.\u003c/li\u003e\n\u003cli\u003eSchmidt C. Mammaprint Reveals Who Can Skip Chemotherapy for Breast Cancer. J Natl Cancer Inst. 2016;108(8).\u003c/li\u003e\n\u003cli\u003eGallups SF, Connolly MC, Bender CM, Rosenzweig MQ. Predictors of Adherence and Treatment Delays among African American Women Recommended to Receive Breast Cancer Chemotherapy. Womens Health Issues. 2018;28(6):553-8.\u003c/li\u003e\n\u003cli\u003eGreen AK, Aviki EM, Matsoukas K, Patil S, Korenstein D, Blinder V. Racial disparities in chemotherapy administration for early-stage breast cancer: a systematic review and meta-analysis. Breast Cancer Res Treat. 2018;172(2):247-63.\u003c/li\u003e\n\u003cli\u003eStabellini N, Cao L, Towe CW, Luo X, Amin AL, Montero AJ. Adjuvant chemotherapy is associated with an overall survival benefit regardless of age in ER+/HER2- breast cancer pts with 1-3 positive nodes and oncotype DX recurrence score 20 to 25: an NCDB analysis. Front Oncol. 2023;13:1115208.\u003c/li\u003e\n\u003cli\u003eRoberts MC, Miller DP, Shak S, Petkov VI. Breast cancer-specific survival in patients with lymph node-positive hormone receptor-positive invasive breast cancer and Oncotype DX Recurrence Score results in the SEER database. Breast Cancer Res Treat. 2017;163(2):303-10.\u003c/li\u003e\n\u003cli\u003eSwain SM, Jeong JH, Geyer CE, Jr., Costantino JP, Pajon ER, Fehrenbacher L, et al. Longer therapy, iatrogenic amenorrhea, and survival in early breast cancer. N Engl J Med. 2010;362(22):2053-65.\u003c/li\u003e\n\u003cli\u003eFrancis PA, Pagani O, Fleming GF, Walley BA, Colleoni M, L\u0026aacute;ng I, et al. Tailoring Adjuvant Endocrine Therapy for Premenopausal Breast Cancer. New England Journal of Medicine. 2018;379(2):122-37.\u003c/li\u003e\n\u003cli\u003ePagani O, Walley BA, Fleming GF, Colleoni M, L\u0026aacute;ng I, Gomez HL, et al. Adjuvant Exemestane With Ovarian Suppression in Premenopausal Breast Cancer: Long-Term Follow-Up of the Combined TEXT and SOFT Trials. J Clin Oncol. 2023;41(7):1376-82.\u003c/li\u003e\n\u003cli\u003eA Phase III Adjuvant Trial Evaluating the Addition of Adjuvant Chemotherapy to Ovarian Function Suppression Plus Endocrine Therapy in Premenopausal Patients With pN0-1, ER-Positive/HER2-Negative Breast Cancer and an Oncotype Recurrence Score Less Than or Equal to 25 (OFSET) [Internet]. 2023. Available from: https://clinicaltrials.gov/study/NCT05879926.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Adjuvant chemotherapy, Early-stage breast cancer, Hormone positive breast cancer, OncotypeDx, Recurrence score, National cancer database","lastPublishedDoi":"10.21203/rs.3.rs-7201172/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7201172/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e\u003c/em\u003e The RxPONDER trial showed improved outcomes in premenopausal hormone positive breast cancer (BC) with 1-3 nodes and OncotypeDx (RS) score ≤ 25 with adjuvant chemotherapy (Chemo) use.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/em\u003e The 2010-2018 National Cancer Database was used to include M0 BC patients aged ≤50 years with N1-N3 lymph nodes stages, any T stage, and RS ≤ 25. Kaplan-Meier (KM) and multivariate (MV) propensity score (PS) weighted Cox model was used to compare survival between patients without and with chemo.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/em\u003e 8628 women were included of which 3519 (40.8%) received chemo. KM curves showed that chemo use had better survival at 10 years (93 vs 91%) compared to hormonal therapy alone. Hazard Ratio (HR) comparison between the 2 groups favored chemo [0.602(0.482,0.751)]. Subgroup analysis for mortality benefits showed favorable results in Caucasian race [0.512(0.348,0.752)], both age groups of 18-40 years [0.429(0.217,0.847) and 40-50 years [0.585(0.394,0.869)], and RS 12-25 [0.549(0.379,0.795)].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003c/em\u003e Based on our analysis, chemo use was noted in 40.8% of young, lymph node+ BC patients with an RS score of 0-25. This group of patients had an overall survival advantage of around 40% with chemo use, further supporting the findings of the RxPONDER trial. This benefit is of particular significance in patients with a RS of 12-25. The survival advantage was present in all patients less than 50 years, regardless of the age subgroups.\u003c/p\u003e","manuscriptTitle":"The impact of adjuvant chemotherapy on overall survival in premenopausal (age≤50 years) hormone and node positive breast cancer patients with an Oncotype Dx score of 25 or less. A NCDB analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 08:27:36","doi":"10.21203/rs.3.rs-7201172/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-20T01:18:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T18:21:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-04T22:24:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328547458196075646357663251455167969834","date":"2025-08-19T14:00:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259338872181451956962415716126473019905","date":"2025-08-07T16:04:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115513249272916860644907042912070654041","date":"2025-08-06T17:10:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-03T15:34:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-24T09:04:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-24T09:02:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research and Treatment","date":"2025-07-24T03:53:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"be20b9ac-ad2a-4b48-9856-c504b255fddb","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:07:25+00:00","versionOfRecord":{"articleIdentity":"rs-7201172","link":"https://doi.org/10.1007/s10549-025-07868-3","journal":{"identity":"breast-cancer-research-and-treatment","isVorOnly":false,"title":"Breast Cancer Research and Treatment"},"publishedOn":"2025-12-08 15:58:14","publishedOnDateReadable":"December 8th, 2025"},"versionCreatedAt":"2025-08-06 08:27:36","video":"","vorDoi":"10.1007/s10549-025-07868-3","vorDoiUrl":"https://doi.org/10.1007/s10549-025-07868-3","workflowStages":[]},"version":"v1","identity":"rs-7201172","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7201172","identity":"rs-7201172","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}