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Granulocyte colony-stimulating factor (G-CSF) and granulocyte macrophage colony-stimulating factor (GM-CSF) are cytokines of particular interest in oncology from the perspective of neutropenia management (Mehta et al., 2015 ) and also as indirect activators of tumor-associated macrophages and modifiers of tumor microenvironment. Associated with poor breast cancer survival and unfavorable hormone receptor status (Wintrob et al., 2017 ), insulin may also influence hematopoiesis, thus interfering with colony stimulating factor production. Although G-CSF has been linked to exacerbating insulin resistance (Ordelheide et al., 2016 ), thus far no study linked insulin treatment and hematopoietic cytokines production. Additionally, IL-7 is the primary driver of T and B cell differentiation, maturation, and response (Corfe and Paige, 2012 ) and its elevated levels have been associated with poor prognosis in breast cancer.
The data presented here is among the first to show a relationship between pre-existing use of injectable insulin in women diagnosed with breast cancer and type 2 diabetes mellitus, hematopoietic cytokine profiles at time of breast cancer diagnosis, and subsequent cancer outcomes. A Pearson correlation analysis evaluating the relationship between G-CSF, GM-CSF, and IL-7 stratified by insulin use, controls, as well as by estrogen and progesterone receptor status is also provided.
Value of the data
Reported data represents the observed association between use of injectable insulin preceding breast cancer and the hematopoietic cytokine profiles at the time of cancer diagnosis in women with diabetes mellitus (Table 1). Data in Table 2 includes the observed correlations between hematopoietic cytokine stratified by type 2 diabetes mellitus pharmacotherapy and controls. Interferon α2 and γ correlation with each of the studied hematopoietic cytokine is presented in Table 2, however, the details regarding these biomarkers’ determination from plasma, association with cancer outcomes and use of injectable insulin is reported in a distinct dataset . Table 3 provides the observed correlations stratified by hormone receptor status.
Evaluation of hematopoietic cytokine profile association with injectable insulin use and BC outcomes was carried out under two protocols approved by both Roswell Park Cancer Institute (EDR154409 and NHR009010) and the State University of New York at Buffalo (PHP0840409E). Demographic and clinical patient information was linked with cancer outcomes and hematopoietic cytokine profiles of corresponding plasma specimen harvested at BC diagnosis and banked in the Roswell Park Cancer Institute Data Bank and Bio-Repository.
All incident breast cancer cases diagnosed at Roswell Park Cancer Institute (01/01/2003-12/31/2009) were considered for inclusion (n=2194). Medical and pharmacotherapy history were used to determine the baseline presence of diabetes.
All adult women with pre-existing diabetes at breast cancer diagnosis having available banked treatment-naïve plasma specimens (blood collected prior to initiation of any cancer-related therapy - surgery, radiation or pharmacotherapy) in the Institute׳s Data Bank and Bio-Repository were included.
Subjects were excluded if they had prior cancer history or unclear date of diagnosis, incomplete clinical records, type 1 or unclear diabetes status. For a specific breakdown of excluded subjects, please see the original research article by Wintrob et al. .
A total of 97 female subjects with breast cancer and baseline diabetes mellitus were eligible for inclusion in this analysis.
Each of the 97 adult female subjects with breast cancer and diabetes mellitus (defined as “cases”) was matched with two other female subjects diagnosed with breast cancer, but without baseline diabetes mellitus (defined as “controls”). The following matching criteria were used: age at diagnosis, body mass index category, ethnicity, menopausal status and tumor stage (as per the American Joint Committee on Cancer). Some matching limitations applied .
Clinical and treatment history was documented as previously described . Vital status was obtained from the Institute׳s Tumor Registry, a database updated biannually with data obtained from the National Comprehensive Cancer Networks’ Oncology Outcomes Database. Outcomes of interest were breast cancer recurrence and/or death.
All the plasma specimens retrieved from long-term storage were individually aliquoted in color coded vials labeled with unique, subject specific barcodes. Overall duration of freezing time was accounted for all matched controls ensuring that the case and matched control specimens had similar overall storage conditions. Only two instances of freeze-thaw were allowed between biobank retrieval and biomarker analyses: aliquoting procedure step and actual assay.
A total of 3 biomarkers (granulocyte colony stimulating factor, granulocyte macrophage colony stimulating factor, and interleukin- 7) were quantified according to the manufacturer protocol. The HCYTOMAG-60K Luminex® biomarker panel (Millipore Corporation, Billerica, MA) was utilized in this study. Interferon α2 and γ determinations were done according to the manufacturer protocol as reported in our dataset focusing on Th1/Th2 cytokines׳ determinations .
Biomarker cut-point optimization was performed for each analyzed biomarker. Biomarker levels constituted the continuous independent variable that was subdivided into two groups that optimized the log rank test among all possible cut-point selections yielding a minimum of 10 patients in any resulting group. Quartiles were also constructed. The resultant biomarker categories were then tested for association with type 2 diabetes mellitus therapy and controls by Fisher׳s exact test. The continuous biomarker levels were also tested for association with diabetes therapy and controls across groups by the Kruskall-Wallis test and pairwise by the Wilcoxon rank sum. Multivariate adjustments were performed accounting for age, tumor stage, body mass index, estrogen receptor status, and cumulative comorbidity. The biomarker analysis was performed using R Version 2.15.3. Please see the original article for an illustration of the analysis workflow .
Correlations between biomarkers stratified by type 2 diabetes mellitus pharmacotherapy, controls, and hormone receptor status were assessed by the Pearson method. Correlation models were constructed both with and without adjustment for age, body mass index, and the combined comorbidity index. The correlation stratification by hormone receptor status excluded 3 subjects that were estrogen receptor negative but progesterone receptor positive due to insufficient numbers to compute confidence intervals and correlation significance. Correlation analyses were performed using SAS Version 9.4.
This research was funded by the following grant awards: Wadsworth Foundation Peter Rowley Breast Cancer Grant awarded to A.C.C. (UB Grant number 55705, Contract CO26588).
Authors acknowledge the valuable help of Dr. Chi-Chen Hong with case-control matching.
Transparency documentTransparency data associated with this article can be found in the online version at doi:10.1016/j.dib.2017.02.037.