Pos0420 Temporal Association Between Breast Cancer and Systemic Sclerosis: A Cross-Sectional Analysis in an Urban Institution

Background: Prior studies have demonstrated an increased risk of malignancy in patients with Systemic Sclerosis (SSc). Breast cancer was found to be among the most prevalent malignancies in patients with SSc by several studies, while a meta-analysis did not show an increased risk of breast cancer in such population.[1-3] Objectives: Our study investigates the temporal relationship between breast cancer and SSc to elucidate the time period in which patients may be at highest risk. Methods: We used ICD codes to gather a retrospective cohort of patients with SSc within a large, urban, tertiary center. Manual chart review was conducted to obtain the dates of diagnosis of breast cancer. Descriptive statistics were used to show the distribution of demographic characteristics in patients with and without a history of breast cancer. T-tests, Chi-square tests, and Fisher's exact tests were used to study the differences in the distribution of patients' characteristics. Patients' timelines of breast cancer and SSc diagnosis are presented individually. The intervals between the two diagnoses were calculated, the distribution of which is shown in a bar chart. Multivariable logistic regression analysis was conducted to study the association between breast cancer and patient characteristics. Propensity score matching was used to study the average treatment effect of MMF in breast cancer. Stata, R, and Excel were used for data analysis and visualization. Results: We identified 32 (5.13%) breast cancer cases in a 625-patient cohort (81.6% female). All breast cancer patients were female. The breast cancer group was older (median age 74 vs 61, P<0.01) (Table 1). Non-MMF immunosuppressant use was associated with a higher risk of breast cancer (OR 2.36; 95%CI=1.01, 5.52; P = 0.047), in a multivariable logistic regression model. MMF use was associated with reduced odds of breast cancer diagnosis. Through propensity score matching, we observed that MMF use resulted in an average of 10.3% decrease in the odds of breast cancer. Of the 26 patients with complete documentation of both breast cancer and SSc diagnosis dates, their individual timelines are presented in Figure 1. Five (19.2%) patients were diagnosed with both breast cancer and SSc within 12 months. 13 (50%) patients were diagnosed with breast cancer within 3 years of their SSc diagnosis. Seven (26.9%) patients were diagnosed with breast cancer ≤ 3 years before SSc diagnosis while one (3.8%) patient was diagnosed with breast cancer ≤ 3 years after SSc diagnosis. In addition, seven (26.9%) patients were diagnosed with breast cancer > 3 years before SSc diagnosis while six (23.1%) patient was diagnosed with breast cancer > 3 years after SSc diagnosis. Conclusion: Our findings suggest a temporal association between onset of breast cancer and SSc, with most patients being diagnosed within three years of SSc diagnosis. Interestingly, more patients were diagnosed with breast cancer before than after SSc diagnosis. Prospective studies with larger cohorts are warranted to further investigate this temporal association. Table 1. Demographic characteristics of SSc patients with demographic characteristics (N = 32) REFERENCES: [1] Moinzadeh P, Fonseca C, Hellmich M, et al. Association of anti-RNA polymerase III autoantibodies and cancer in scleroderma. Arthritis Res Ther. 2014;16(1):R53. Published 2014 Feb 14. doi:10.1186/ar4486. [2] Shah AA, Hummers LK, Casciola-Rosen L, Visvanathan K, Rosen A, Wigley FM. Examination of autoantibody status and clinical features associated with cancer risk and cancer-associated scleroderma. Arthritis Rheumatol. 2015;67(4):1053-1061. doi:10.1002/art.39022. [3] Bonifazi M, Tramacere I, Pomponio G, et al. Systemic sclerosis (scleroderma) and cancer risk: systematic review and meta-analysis of observational studies. Rheumatology (Oxford). 2013;52(1):143-154. doi:10.1093/rheumatology/kes303. Acknowledgements: I sincerely thank my mentor, Dr. Dobrowolski, for her guidance and support. Special thanks to Celestine, who provided invaluable contributions to this project. I am also grateful to my coauthors for their contributions. Disclosure of Interests: None declared.