Risk of major autoimmune diseases in female breast cancer patients: A nationwide, population-based cohort study.

BACKGROUND: Breast cancer is one of the most common malignancies among women. However, there remains no consensus in current literature on the incidence of autoimmune diseases among breast cancer patients. The purpose of this study was to evaluate the risks of major autoimmune diseases (MAD) including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), Sjögren's syndrome (SS) and dermatomyositis (DMtis)/polymyositis (PM) in female breast cancer patients.
METHODS: Using the Taiwanese National Health Insurance Research Database (NHIRD) records from 2003 to 2013, we identified newly-diagnosed female breast cancer patients and randomly selected females without breast cancer in the period 2007 to 2013 into a control group. We matched the two cohorts using a 1:4 ratio based on age, and the year of index date for comparison of the risk of major autoimmune diseases. We estimated and compared the relative risks of autoimmune diseases in female breast cancer patients and females without breast cancer.
RESULTS: A total of 54,311 females with breast cancer and 217,244 matched females without breast cancer were included in this study. For SLE, the incidence rates were 2.3 (breast cancer group) vs. 10.0 (control group) per 100,000 women years; for RA rates were 19.3 (breast cancer group) vs. 42.7 (control group) per 100,000 women years; and for SS rates were 20.5 (breast cancer group) vs. 38.2 (control group) per 100,000 women years. After adjusting for potential confounders, the hazard ratios (95% confidence intervals) for female breast cancer patients vs. control group were 0.04 (0.01-0.24) for SLE; 0.03 (0.02-0.04) for RA; and 0.21 (0.09-0.48) for SS.
CONCLUSION: Female breast cancer patients had lower risks of SLE, RA and SS when compared to female individuals without breast cancer. However, there was no significant difference in the risk of developing DMtis/PM between both groups.

Introduction

Breast cancer is one of the most common malignancies among women. In Taiwan, autoimmune diseases were three to four times more common in women than in men [1]. However, there remains no consensus on the incidence rates of autoimmune diseases among breast cancer patients.

Previous studies [2-4] have demonstrated that breast cancer patients showed an increased spontaneous expression of cytotoxic T-lymphocyte–associated antigen 4 (CTLA-4) on T lymphocytes in breast tissue and peripheral blood mononuclear cells, leading to impaired T cell activation. T cell activation is involved in the development of autoimmune diseases such as rheumatoid arthritis [5], systemic lupus erythematosus [6-7], Sjögren's syndrome [8-9], dermatomyositis (DMtis) [10-11] and polymyositis (PM) [10-11]. Abatacept is a fusion protein, which links the extracellular domain of CTLA-4 to the Fc region of immunoglobulin IgG1. Abatacept, which works by interfering with the immune activity of T cells, has been used in the treatment of various autoimmune diseases [12-15], and is approved for RA therapy. DMtis/PM has been found to be associated with various cancers [16-19], including breast cancer [20]. Therefore, we hypothesized that breast cancer patients may have a lower risk of major autoimmune diseases, except DMtis and PM.

The Taiwanese National Health Insurance Research Database (NHIRD) is available for research purposes and has facilitated nationwide, population-based longitudinal studies. The aim of this study was to evaluate the risk of major autoimmune diseases in female breast cancer patients using the NHIRD.

Methods

Ethics statement

The study was permitted by the Institutional Review Board (IRB) of the Taichung Veterans General Hospital (IRB number: CE17100B). The requirement for informed consent was waived, since personal information was anonymized.

Study design

This study was a retrospective cohort study.

Data source

The data set were extracted from the NHIRD database using data from the period 2003 to 2013. In 1995, Taiwan initiated a compulsory National Health Insurance (NHI) program. The NHI program currently covers over 99% of the Taiwanese population. The NHIRD includes comprehensive data regarding medication prescription, ambulatory care services, admission services and traditional medical services. Some personal data and history such as body weight, body length, alcohol use, and smoking, are not available in the NHIRD. By updating original medical records regularly, the Bureau of NHI has improved the accuracy of data in the NHIRD [21]. The NHIRD is managed by the National Health Research Institute (NHRI). The NHRI provides research data from the database to researchers after anonymizing all personal information.

We utilized multiple NHIRD datasets in this study, including data on both inpatients and outpatients, as well as enrollment data from 2003 to 2013. In 2000, the NHRI randomly selected and enrolled one million representative individuals from the NHIRD, establishing the Longitudinal Health Insurance Database (LHID2000). Since we were unable to get all the data in NHIRD, we selected the non-breast cancer control group from the LHID cohort. We used LHID2000 claims data from 2003 to 2013 for analysis of the comparison cohort.

The Bureau of NHI also established a registry of patients with possible fatal illnesses, such as malignancies, SLE, RA, SS and DMtis/PM. Patients with a possible fatal illness were issued a catastrophic illness certificate and were exempt from copayment for all medications prescribed for a corresponding possible fatal illness diagnosis. However, a catastrophic illness certificate was issued only after a thorough review and validation of original medical records by two or more qualified specialists.

Definition of female breast cancer patients

Female breast cancer patients were defined as having a catastrophic illness certificate for breast cancer [International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code 174].

Definition of major autoimmune diseases

Major autoimmune diseases in this study included SLE, RA, SS and DMtis/PM. Patients with major autoimmune diseases were defined as having a catastrophic illness certificate for SLE (ICD-9-CM code 710.0), RA (ICD-9-CM code 714.x), SS (ICD-9-CM code 710.2) or DMtis (ICD-9-CM code 710.3)/PM (ICD-9-CM code 710.4).

Study outcome

The study outcome was the time from the index date to the time of the first diagnosis for major autoimmune diseases. We defined the censor date as December 31, 2013 (the last date of the database used) or the date of withdrawal from the NHI for emigration or death.

Study subjects

Newly-diagnosed female breast cancer patients from entire Taiwanese population

Using NHIRD information from 2003 to 2013, we identified 67,552 newly-diagnosed female breast cancer patients between 2007 and 2013 as the female breast cancer cohort. The index date for female breast cancer patients was defined as the first date of application for a female breast cancer catastrophic illness certificate. After excluding patients with a history of an ambulatory or inpatient visit resulting in diagnosis of any autoimmune disease including SLE (ICD-9-CM 710.1), RA (ICD-9-CM 714.0), SS (ICD-9-CM 710.2), DMtis (ICD-9-CM 710.3) and PM (ICD-9-CM 710.4), we included 62,570 newly-diagnosed female breast cancer patients as our female breast cancer cohort.

Matched non-breast cancer female individuals selected from a one million representative population

Using LHID2000 information from 2003 to 2013, we identified 430,530 female individuals without breast cancer as the control group. After excluding patients with a history of an ambulatory or inpatient visit resulting in diagnosis of any autoimmune disease including SLE (ICD-9-CM 710.1), RA (ICD-9-CM 714.0), SS (ICD-9-CM 710.2), DMtis (ICD-9-CM 710.3) and PM (ICD-9-CM 710.4), 409,340 female non-breast cancer individuals were included before matching.

Matching female breast cancer patients and female non-breast cancer individuals with a 1:4 ratio

We recorded baseline differences between the female breast cancer cohort and the female non-breast cancer individuals to conduct propensity score matching (1:4). We used a multivariable logistic regression model to calculate the propensity score. The baseline variables for calculating the propensity score included age and year of index date (index year). We used the date of the first ambulatory or inpatient visit for any reason in the index year as the index date for female non-breast cancer individuals. Those who had a major autoimmune disease diagnosis before the index date were excluded. Finally, we included 54,311 female breast cancer patients and 217,244 matched female non-breast cancer individuals as the study subjects.

Potential confounders

Potential confounders included age, Charlson comorbidity index (CCI) and concomitant medications including hormone therapy (i.e., Tamoxifen, anastrozole and letrozole) and chemotherapy (i.e., cyclophosphamide, doxorubicin, docetaxel, paclitaxel, methotrexate, fluorouracil, cisplatin, carboplatin). The CCI, as adapted by Deyo et al. [22], was used to represent the level of general comorbid medical conditions. For female breast cancer patients, breast cancer was not included for calculating CCI. The presence of a comorbidity was defined as having at least three ambulatory visits or one inpatient visit with a corresponding ICD-9-CM code within 1 year before the index date. Use of concomitant medication was defined as prescription of corresponding medications during the follow-up period.

Statistical analysis

Continuous variables are shown as a mean ± standard deviation and categorical variables as a percentage of subjects. We tested the differences between continuous variables by using Student’s t-test and between categorical variables by using Pearson’s χ2 test. We estimated incidence rate ratios (IRRs) with 95% confidence intervals (CIs) to compare the incidences of major autoimmune diseases between groups. We quantified the associations between breast cancer and the development of SLE, RA, SS, DMtis /PM by estimating hazard ratios (HRs) with 95% CIs using Cox regression analysis adjusting for age, CCI, hormone therapy and chemotherapy. Statistical analyses were performed using SAS statistical software, version 9.3 (SAS Institute, Inc., Cary, NC, USA). A p-value less than 0.05 was considered statistically significant.

Results

A total of 54,311 female breast cancer patients and 217,244 matched female non-breast cancer individuals were included. The mean ± SD age was 53.6 ± 12.7 years in both groups. Patients’ characteristics are shown in Table 1.

Table 1

Characteristics of female breast cancer patients and matched female non-breast cancer individuals.

	Female Non-breast cancer individuals	Female breast cancer patients
	(n = 217,244) (%)	(n = 54,311) (%)	P-value
Age, years (mean ± SD)	53.6 ± 12.7	53.6 ±1 2.7	1.000
CCI (mean ± SD)	0.3 ± 0.9	1.1 ± 2.1	<0.001
CCI group
0	176,864 (81.4)	37,840 (69.7)	<0.001
≥1	40,380 (18.6)	16,471 (30.3)
Concomitant medication
In the period from the index date to first SLE diagnosis date or censor date
Hormone therapy#	195 (0.1)	36,789 (67.7)	<0.001
Chemotherapy*	2,898 (1.3)	35,804 (65.9)	<0.001
In the period form the index date to first RA diagnosis date or censor date
Hormone therapy#	195 (0.1)	36,787 (67.7)	<0.001
Chemotherapy*	2,837 (1.3)	35,802 (65.9)	<0.001
In the period from the index date to first SS diagnosis or censor date
Hormone therapy#	195 (0.1)	36,788 (67.7)	<0.001
Chemotherapy*	2,897 (1.3)	35,803 (65.9)	<0.001
In the period from the index date to first PM/DMtis diagnosis date or censor date
Hormone therapy#	195 (0.1)	36,789 (67.7)	<0.001
Chemotherapy*	2,912 (1.3)	35,804 (65.9)	<0.001

Data are shown as number (%) unless specified otherwise.

CCI, Charlson comorbidity index

#Hormone therapy included Tamoxifen, Anastrozole, and Letrozole

*Chemotherapy included Cyclophosphamide, Doxorubicin, Docetaxel, Paclitaxel, Methotrexate, Fluorouracil, Cisplatin, and Carboplatin

The incidence rates of autoimmune diseases in both the female breast cancer cohort and female non-breast cancer cohort are shown in Table 2. Among female breast cancer patients, the incidence rate of SLE was 2.3 per 100,000 women years, the incidence rate of RA was 19.3 per 100,000 women years, the incidence rate of SS was 20.5 per 100,000 women years and the incidence rate of DMtis/PM was 2.3 per 100,000 women years. In the female non-breast cancer cohort, the incidence rate of SLE was 10.0 per 100,000 women years, the incidence rate of RA was 42.7 per 100,000 women years, the incidence rate of SS was 38.2 per 100,000 women years and the incidence rate of DMtis/PM was 1.7 per 100,000 women years. After adjusting for age, CCI, hormone therapy and chemotherapy, the HZs with corresponding 95% CIs for females breast cancer patients vs. control group were 0.04 (0.01–0.24) for SLE; 0.03 (0.02–0.04) for RA; and 0.21 (0.09–0.48) for SS. However, the risk of DMtis/PM was not significantly different between the female breast cancer group and the control group (HR, 0.37; 95% CI, 0.08–1.80).

Table 2

Incidence rates and risks of SLE, RA, SS and PM/DM in breast cancer females compared with matched female non-breast cancer individuals.

	SLE	RA	SS	DMtis/PM
Female non-breast cancer individuals (n = 217,244)
Event (%)	82 (0.04)	348 (0.16)	312 (0.14)	14 (0.006)
Person-years	816,360	815,557	815,713	816,581
IR, /105 years	10.0	42.7	38.2	1.7
Breast cancer females (n = 54,311)
Event (%)	4 (0.01)	33 (0.06)	35 (0.06)	4 (0.01)
Person-years	170,627	170,560	170,561	170,627
IR, /105 years	2.3	19.3	20.5	2.3
Breast cancer females compared with female non-breast cancer individuals
IRR (95% CI)	0.23 (0.09–0.64)	0.45 (0.32–0.65)	0.54 (0.38–0.76)	1.37 (0.45–4.15)
HR# (95% CI)
Crude	0.23 (0.08–0.62)	0.45 (0.32–0.64)	0.55 (0.39–0.78)	1.35 (0.44–4.10)
Adjusted*	0.04 (0.01–0.24)	0.03 (0.02–0.04)	0.21 (0.09–0.48)	0.37 (0.08–1.80)

Abbreviations: SLE, systemic lupus erythematosus; RA, rheumatoid arthritis; SS, Sjögren's syndrome; DMtis, dermatomyositis; PM, polymyositis; IRR, incidence rate ratio; CI, confidence interval; HR, hazard ratio

#Using Cox proportional hazard regression model

*Adjusted variables included age, Charlson comorbidity index, hormone therapy and chemotherapy

Discussion

This nationwide, population-based, matched cohort study was the first to investigate the risks of developing major autoimmune diseases including SLE, RA, SS, DMtis /PM, among female breast cancer patients. After adjusting for age, CCI, hormone therapy and chemotherapy, we found that patients with female breast cancer had a significant lower risk of developing SLE, RA, and SS, but not DMtis/PM compared to the control group.

The mechanism of lower autoimmune disease incidence rates in female breast cancer patients remains unknown. Jaberipour et al. [2] showed higher CTLA-4 expression and higher activity of regulatory T (Treg) cells in the peripheral blood mononuclear cells of female breast cancer patients. Mao et al. [3] demonstrated abnormal expression and dysregulation of CTLA-4 in female breast cancer patients. Khalife et al. [4] found overexpression of Treg-related markers and higher activity of Treg cells in the peripheral blood mononuclear cells of female breast cancer patients. Treg cells play an important role in the regulation of immune activities and maintain tolerance to self-antigens by suppressing antigen-presenting cells via CTLA-4. Overexpression of Treg-related markers and higher activity of Treg cells affect the immune system in female breast cancer patients. Therefore, we hypothesized that female breast cancer patients may have a decreased risk of autoimmune diseases and our results showed female breast cancer patients had a significant lower risk of developing SLE, RA and SS. On the other hand, several clinical immunotherapy studies [23-24] used Ipilimumab, a monoclonal antibody which targeted to CTLA-4 and activated the immune system, and showed rheumatic and musculoskeletal adverse effects in their trials.

DMtis and PM are chronic inflammatory diseases, and DMtis/PM patients have skeletal, muscle and skin symptoms. After reviewing DMtis/PM patients, Fang et al. [20] demonstrated a high frequency of malignancies (17.2%), and found the most common malignancies were nasopharyngeal cancer and female breast cancer. The cause of DMtis/PM is not clear. However, pathology studies have shown autoantibody in muscle biopsies, as discussed in previous studies [25-26]. According to Engel et al. [27], muscle biopsies showed CD4+ and CD8+ T cells and B cells in DMtis/PM patients. Of the other autoantibodies discussed in recent studies [28-29], the most common in DMtis and PM patients were anti-SSA, anti-Ro-52 and anti-PMScl [30-31]. Shah et al. [32] demonstrated that DMtis-associated autoantibodies were detectable in female breast cancer patients who had clinically specific rheumatic diseases. These cancer-associated autoantibodies may explain why the incidence rates of DMtis/PM were not significantly different between the female breast cancer cohort and the female non-breast cancer cohort.

This study has some limitations that should be addressed. Firstly, the accuracy of diagnoses based on data from NHIRD was of concern. However, the use of the catastrophic illness certificate to confirm the diagnosis of breast cancer and MADs may have increased the accuracy of diagnoses. Secondly, some potential confounders including smoking, drinking and over-the counter medications were not included in the claims data. Thirdly, treatment options varied depending on the particular disease, patients’ condition, physicians’ discretion and period of diagnosis. All of these could have affected individual immune responses, the incidence rates and risks of developing autoimmune diseases. Finally, these results may not be generalized to non-Taiwanese populations. In order to eliminate confounding factors, we adjusted for variables, including age, CCI, hormone therapy and chemotherapy. Further studies are still required to clarify the mechanisms of different autoimmune diseases in breast cancer patients.

Conclusion

This nationwide, population-based cohort study showed that female breast cancer patients had lower risks of SLE, RA and SS when compared to female individuals without breast cancer. However, there was no significant difference in the risk of developing DMtis/PM between both groups. Further studies are warranted to clarify the mechanisms of these diseases in female breast cancer patients.

19 Jul 2019

PONE-D-19-15961

Risk of Major Autoimmune Diseases in Patients with Female Breast Cancer: a Nationwide, Population-based Cohort Study.

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Reviewer #1: This study makes use of the large Taiwanese National Health Insurance Research Database (NHIRD) to compare the incidence rates of autoimmune diseases in breast patients and propensity score matched non-breast cancer individuals. The study design is well powered, data analysis is clean, and results are unambiguous.

I only have some minor comments—

1) In title the expression “in patients with female breast cancer” is awkward. Please consider rephrase it, for example, as “in female patients with breast cancer”?

2) There is a typo in Abstract “1:4” instead of “1:40”.

Reviewer #2: General comments

The idea is novel, and inspired by very up-to-date science: the importance of immune responses in breast cancer. In the US, there about 300 clinical studies registered on this issue. This study illustrates the potential for immunotherapy.

The paper needs English editing to give an impression of serious science. Too many typing errors that I can correct them all. I also think many sentences can be written better, for example, I think this heading is better “Risk of Major Autoimmune Diseases in female breast Cancer: a Population-based Cohort Study”. First sentence in the abstract “Breast cancer is one of common malignancy among women” illustrates the low level of proficiency in English.

Method: The method needs to be better described and justified. Why not using all the data in NHRID? Then they need not to match and later adjust for covariates. This is a case-control cohort study. Breast cancers are recorded during 2007-13, while non-breast cancer patients are recorded during the period 2003-2013. This can make problems, I think. If not, please explain. Why not use all the population in NHRID, split them in breast cancer patients and not breast cancer patients at start, and then follow them up until disease (and censor them). Then you have a prospective cohort study, and less variables to adjust for. The potential risk of biased outcome/false positive reporting in this study is related to the adjustment.

In Table 2, I would like to see estimates before adjustment of adjuvant therapy. Adjustment for CIC and adjuvant therapy may generate over-adjustment. You adjust for the same disease twice, unless you think that adjuvant therapy directly influence on the risk of autoimmune disease.

Minor comments

Abstract, Method: the ratio is 1:4, I think. You are not matching on sex, I think. You are excluding males.

Page numbers are missing. Would be fine with line numbers too.

Introduction, second sentence. Delete “prevalence”. Only focus on incidence, because women live longer than men do. Thus, comparing prevalence rates is not meaningful.

Last sentence, last paragraph, introduction (especially 6th last line): bad English, too long sentence. Furthermore, the aim/hypothesis to study has already been presented in the previous paragraph.

Data source, first paragraph, forth sentence: The word “claims” is not necessary. Sufficient to write “… comprehensive data regarding…” Last sentence: “provides data” and not “databases” to researchers.

I think “catastrophic illness” is not the proper word to use. “Possible fatal illness”, is probably better.

Definition of female breast cancer patients: CIC is not defined. And not MAD either, in the next paragraph.

Potential confounders: gender??? I suggest excluding all male breast cancer patients from start and only study women. Then adjusting for gender is not necessary.

Statistical analyses: I think you have done a case-control cohort study.

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Reviewer #2: Yes: Per Henrik Zahl

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Submitted filename: Review on AID and BC in Taiwan.docx

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6 Aug 2019

Response to reviewers:

Dear Editors-in-Chief and Reviewers,

Reviewer #1: This study makes use of the large Taiwanese National Health Insurance Research Database (NHIRD) to compare the incidence rates of autoimmune diseases in breast patients and propensity score matched non-breast cancer individuals. The study design is well powered, data analysis is clean, and results are unambiguous.

I only have some minor comments—

1) In title the expression “in patients with female breast cancer” is awkward. Please consider rephrase it, for example, as “in female patients with breast cancer”?

Response: We had rephrased it as ‘in female breast cancer’.

2) There is a typo in Abstract “1:4” instead of “1:40”.

Response: We correct it.

Reviewer #2: General comments

General comments

The idea is novel, and inspired by very up-to-date science: the importance of immune responses in breast cancer. In the US, there about 300 clinical studies registered on this issue. This study illustrates the potential for immunotherapy.

The paper needs English editing to give an impression of serious science. Too many typing errors that I can correct them all. I also think many sentences can be written better, for example, I think this heading is better “Risk of Major Autoimmune Diseases in female breast Cancer: a Population-based Cohort Study”. First sentence in the abstract “Breast cancer is one of common malignancy among women” illustrates the low level of proficiency in English.

Response: We have rephrased the title. Changes have been made pertaining to language and grammar.

Method: The method needs to be better described and justified. Why not using all the data in NHRID? Then they need not to match and later adjust for covariates. This is a case-control cohort study. Breast cancers are recorded during 2007-13, while non-breast cancer patients are recorded during the period 2003-2013. This can make problems, I think. If not, please explain. Why not use all the population in NHRID, split them in breast cancer patients and not breast cancer patients at start, and then follow them up until disease (and censor them). Then you have a prospective cohort study, and less variables to adjust for. The potential risk of biased outcome/false positive reporting in this study is related to the adjustment.

Response:

1. Because we were unable to get all the data in NHIRD, the only way to get matched non-breast cancer individuals was to use the one million representative population in the LHID2000. We have added a statement in Line 8 of page 6: “Because we were unable to get all the data in NHIRD, we selected the non-breast cancer comparison cohort from the population in the LHID2000.”

2. The periods of datasets for the female breast cancer cohort and the comparison cohort are the same (i.e., from 2003 to 2013). To identify new cases of breast cancer during 2007–2013, we excluded patients who had a diagnosis of breast cancer before 2007. To eliminate the differences of diagnosis and treatment in different era, the comparison cohort was also matched for the diagnosis date of female breast cancer patients.

In Table 2, I would like to see estimates before adjustment of adjuvant therapy. Adjustment for CIC and adjuvant therapy may generate over-adjustment. You adjust for the same disease twice, unless you think that adjuvant therapy directly influence on the risk of autoimmune disease.

Response:

1. Chemotherapy and hormone therapy may affect lymphocytes and other immune cells, affecting the risk of autoimmune disease.

2. CCI was used to represent the level of general comorbid medical conditions. For female breast cancer patients, breast cancer was not included for calculating CCI. Therefore, we did not adjust for the same disease twice. We ahve added a statment in Line 8 of page 9: “For female breast cancer patients, breast cancer was not included for calculating CCI. ”

Minor comments

Abstract, Method: the ratio is 1:4, I think. You are not matching on sex, I think. You are excluding males.

Response: Since we have excluded males, we did not match the sex. We have removed “sex” in the list of matched variables.

Page numbers are missing. Would be fine with line numbers too.

Response: We have added continuous page and line numbers.

Introduction, second sentence. Delete “prevalence”. Only focus on incidence, because women live longer than men do. Thus, comparing prevalence rates is not meaningful.

Response: We have corrected it according to your suggestion.

Last sentence, last paragraph, introduction (especially 6th last line): bad English, too long sentence. Furthermore, the aim/hypothesis to study has already been presented in the previous paragraph.

Response: We have revised the statement for concision a follows. “The aim of this study was to investigate the risks of major autoimmune diseases in female breast cancer patients using the NHIRD.” We have deleted a redundant statement regarding the hypothesis.

Data source, first paragraph, forth sentence: The word “claims” is not necessary. Sufficient to write “… comprehensive data regarding…” Last sentence: “provides data” and not “databases” to researchers.

Response: We have corrected it based on your suggestions.

I think “catastrophic illness” is not the proper word to use. “Possible fatal illness”, is probably better.

Response: We have used “possible fatal illness” to describe the characteristics of these diseases. However, the official name of the certificate for patients with these “possible fatal illness” is “catastrophic illness certificate” in Taiwan.

Definition of female breast cancer patients: CIC is not defined. And not MAD either, in the next paragraph.

Response:

1. To avoid confusion with CCI, we have removed the abbreviation of CIC and used “catastrophic illness certificate” in the manuscript.

2. Because MAD is not a common abbreviation, we have deleted this abbreviation and used the entire definition “major autoimmune disease” in manuscript.

Potential confounders: gender??? I suggest excluding all male breast cancer patients from start and only study women. Then adjusting for gender is not necessary.

Response: Because we have excluded males, we have removed “gender” in the list of potential confounders.

Statistical analyses: I think you have done a case-control cohort study.

Response: This study is a retrospective cohort study and not case-control study. Therefore, we have used the Cox regression analysis.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

4 Sep 2019

[EXSCINDED]

PONE-D-19-15961R1

Risk of Major Autoimmune Diseases in Female Breast Cancer Patients: A Nationwide, Population-based Cohort Study

PLOS ONE

Dear Dr. Chien-Chih Chen,

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #2: All comments have been addressed

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Reviewer #2: Partly

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Reviewer #2: Yes

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Reviewer #2: (No Response)

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Reviewer #2: Abstract, lines 12-14: ….selected females diagnosed with breast cancer in the period 2007 to 2013 into a control group.

Abstract, line 15. Delete sex. Not necessary to match on sex when you only study women.

Abstract, lines 19-21. ….For SLE, the incidence rates were 2.3 (BC group) vs 10.0 (control group) per 100,000 women year; for RA rates were 19.3 (BC group) vs 42.7 (control group) per 100,000 women year; and for SS rates were 20.5 (BC group) vs 38.2 (control group) per 100,000 women year.

Do not use 10 raised to the power of fifth in a medical journal.

Abstract, page 3 , lines 1-4: After adjusting for potential confounders, the hazard ratio (HZ) with corresponding confidence interval (CI) for females breast cancer patients vs control group were 0.04 (95% CI: 0.01-0.24) for SLE; 0.03 (95% CI: 0.02-0.04) for RA; and 0.21 (95% CI: 0.09-0.48) for SS.

Page 4, line 2: ….one of the most common….

Page 5, lines 13-14: The data set were extracted from the NHIRD database using data from the period 2003 to 2013.

Page 6, lines 5 -6: …..,including data on both inpatients and outpatients,….

Page 6, lines 9-10:….,we selected the non-breast cancer control group from the LHID cohort.

Page 7, line 13: delete leaving and write “emigration”. Is there any other way of leaving the database than emigration or death?

Page 8, line 6: …,as the control group.

Page 8, line 10: including 409,340 female non-breast cancer individuals before matching.

Page 9, line 17…,by using Student’s t-test,….by using Pearson’s…

Page 9, line18; confidence interval has been defined above.

Page 10, line 2: HZ has been defined before.

Page 10, lines 13-14: …. ,the incidence rate of SLE was 2.3 per 100,000 women years, the incidence rate of RA was 19.3 per 100,000 women years, the incidence rate of SS was 20.1 per 100,000 women years and the incidence rate of DMtis/PM was 2.3 per 100,000 women years.

Page 10, lines 15-16: Do as above.

Page 10, line 19: , the hazard ratio (HZ) with corresponding confidence interval (CI) for females breast cancer patients vs control group were 0.04 (95% CI: 0.01-0.24) for SLE; 0.03 (95% CI: 0.02-0.04) for RA; and 0.21 (95% CI: 0.09-0.48) for SS.

Page 12, line 5: …had a significant lower risk….

Page 13, line 6. Do not use the word “claim data”. This is register data.

Page 15, Table 1. Spelling error in the line under “Concomitant medication”. FiRst

Page 15, Table 1: “Time to first SLE”… The number look like real numbers with percentage in parenthesis and not life times. Please correct or explain.

**********

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Reviewer #2: Yes: Per-Henrik Zahl

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Submitted filename: Review on PONE-R1-2019 .docx

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6 Sep 2019

Dear Editors-in-Chief and Reviewers,

Reviewer #2:

Abstract, lines 12-14: ….selected females diagnosed with breast cancer in the period 2007 to 2013 into a control group.

Response: We rephrased abstract. We identified newly-diagnosed female breast cancer patients and randomly selected females without breast cancer in the period 2007 to 2013 into a control group.

Abstract, line 15. Delete sex. Not necessary to match on sex when you only study women.

Response: We have deleted ‘sex’.

Abstract, lines 19-21. ….For SLE, the incidence rates were 2.3 (BC group) vs 10.0 (control group) per 100,000 women year; for RA rates were 19.3 (BC group) vs 42.7 (control group) per 100,000 women year; and for SS rates were 20.5 (BC group) vs 38.2 (control group) per 100,000 women year. Do not use 10 raised to the power of fifth in a medical journal.

Response: We rephrased abstract.

Abstract, page 3 , lines 1-4: After adjusting for potential confounders, the hazard ratio (HZ) with corresponding confidence interval (CI) for females breast cancer patients vs control group were 0.04 (95% CI: 0.01-0.24) for SLE; 0.03 (95% CI: 0.02-0.04) for RA; and 0.21 (95% CI: 0.09-0.48) for SS.

Response: We rephrased abstract: After adjusting for potential confounders, the hazard ratios (95% confidence intervals) for female breast cancer patients vs. control group were 0.04 (0.01–0.24) for SLE; 0.03 (0.02–0.04) for RA; and 0.21 (0.09–0.48) for SS.

Page 4, line 2: ….one of the most common….

Response: We rephrased introduction: Breast cancer is one of the most common malignancies among women.

Page 5, lines 13-14: The data set were extracted from the NHIRD database using data from the period 2003 to 2013.

Response: We rephrased method.

Page 6, lines 5 -6: …..,including data on both inpatients and outpatients,….

Response: We rephrased method.

Page 6, lines 9-10:….,we selected the non-breast cancer control group from the LHID cohort.

Response: We rephrased method.

Page 7, line 13: delete leaving and write “emigration”. Is there any other way of leaving the database than emigration or death?

Response: We rephrased method. We defined the censor date as December 31, 2013 (the last date of the database used) or the date of withdrawal from the NHI for emigration or death.

Page 8, line 6: …,as the control group.

Response: We rephrased method.

Page 8, line 10: including 409,340 female non-breast cancer individuals before matching.

Response: We rephrased method.

Page 9, line 17…,by using Student’s t-test,….by using Pearson’s…

Response: We rephrased method.

Page 9, line18; confidence interval has been defined above.

Response: Because CI was not defined before in the revised manuscript, so we kept the definition of HZ here.

Page 10, line 2: HZ has been defined before.

Response: Because HZ was not defined before in the revised manuscript, so we kept the definition of HZ here.

Page 10, lines 13-14: …. ,the incidence rate of SLE was 2.3 per 100,000 women years, the incidence rate of RA was 19.3 per 100,000 women years, the incidence rate of SS was 20.1 per 100,000 women years and the incidence rate of DMtis/PM was 2.3 per 100,000 women years.

Response: We rephrased results.

Page 10, lines 15-16: Do as above.

Response: We rephrased results.

Page 10, line 19: , the hazard ratio (HZ) with corresponding confidence interval (CI) for females breast cancer patients vs control group were 0.04 (95% CI: 0.01-0.24) for SLE; 0.03 (95% CI: 0.02-0.04) for RA; and 0.21 (95% CI: 0.09-0.48) for SS.

Response: We rephrased results.

Page 12, line 5: …had a significant lower risk….

Response: We rephrased discussion.

Page 13, line 6. Do not use the word “claim data”. This is register data.

Response: We rephrased discussion.

Page 15, Table 1. Spelling error in the line under “Concomitant medication”. FiRst

Response: We correct spelling error.

Page 15, Table 1: “Time to first SLE”… The number look like real numbers with percentage in parenthesis and not life times. Please correct or explain.

Response: We correct Table 1.

Thank you for your helpful suggestion.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

10 Sep 2019

Risk of Major Autoimmune Diseases in Female Breast Cancer Patients: A Nationwide, Population-based Cohort Study

PONE-D-19-15961R2

Dear Dr. Chien-Chih Chen,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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Pirkko L. Härkönen, M.D., Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

12 Sep 2019

PONE-D-19-15961R2

Risk of Major Autoimmune Diseases in Female Breast Cancer Patients: A Nationwide, Population-based Cohort Study

Dear Dr. Chen:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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With kind regards,

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on behalf of

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Academic Editor

PLOS ONE