Data report on inflammatory C–C chemokines among insulin-using women with diabetes mellitus and breast cancer

Injectable insulin use may interfere with pro-inflammatory cytokines’ production and, thus, play a role in the activation of tumor-associated macrophages - a process mainly influenced by inflammatory C–C chemokines. The data presented shows the relationship between pre-existing use of injectable insulin in women diagnosed with breast cancer and type 2 diabetes mellitus, the inflammatory C–C chemokine profiles at the time of breast cancer diagnosis, and subsequent cancer outcomes. A Pearson correlation analysis stratified by insulin use and controls is also provided. We present the observed relationship between the investigated C–C chemokines and between each of these biomarkers and previously reported adipokines levels in this study population [1].

Tumor registry query was followed by vital status ascertainment, and medical records review Luminex s -based quantitation from plasma samples was conducted for the following pro-inflammatory C-C chemokines: Chemokine ligand 2, CCL-2 (monocyte chemoattractant protein 1, MCP-1); chemokine ligand 3, CCL-3 (macrophage inflammatory protein 1α, MIP-1α); chemokine ligand 4, CCL-4 (macrophage inflammatory protein 1β, MIP-1β); and chemokine ligand 5, CCL-5 (regulated on activation normal T cell expressed and secreted, RANTES). A Luminex s 200 TM instrument with Xponent 3.1 software was used to acquire all data Data format Analyzed Experimental factors The above described pro-inflammatory C-C chemokines were determined from the corresponding plasma samples collected at the time of breast cancer diagnosis Experimental features According to a previously described study design, the dataset included 97 adult females with diabetes mellitus and newly diagnosed breast cancer (cases) and 194 matched controls (breast cancer only) [1]. Clinical and treatment history were evaluated in relationship with cancer outcomes and pro-inflammatory cytokine profiles. A biomarker correlation analysis was performed between the studied C-C chemokines and between each of them and the cytokine levels already reported elsewhere for this particular patient population [1][2][3][4][5][6][7][8][9]. The additional correlations were provided for completeness and usability of this data. Data source location United States, Buffalo, NY -42°53' 50.3592"N; 78°52' 2.658"W Data accessibility The data is with this article

Value of the data
Monocytes' infiltration and their activation to tumor-associated macrophages upon recruitment into the tumor tissue is a crucial process for tumor growth and metastasis [3]. Their mobilization is a chemotactic response mediated by tumor-derived factors, among which the C-C chemokines CCL-2, 3, 4, and 5 [4][5][6][7][8][9] The combined contribution of CCL-2, 3, 4, and 5 is responsible for the vast functionality of the macrophage phenotypes in response to changing environmental stimuli [4][5][6][7][8] This dataset represents the observed relationship between injectable insulin use, circulating proinflammatory C-C chemokines at breast cancer diagnosis and outcomes Reported data has the potential to guide future studies evaluating the impact of insulin-regulated signaling on activation of tumor-associated macrophages in breast cancer Our observations can assist further research clarifying the role of insulin in the regulation of the proinflammatory signaling leading to pro-tumorigenic activity in the breast tumor microenvironment

Data
Reported data represents the observed association between use of injectable insulin preceding breast cancer and the pro-inflammatory C-C chemokine profiles at the time of cancer diagnosis in women with diabetes mellitus (Table 1). Data in Table 2 includes the observed correlations between pro-inflammatory C-C chemokines stratified by type 2 diabetes mellitus pharmacotherapy and controls, as well as already reported biomarkers' correlation with each of the studied C-C chemokines is presented in Table 2. The details regarding adiponectin, leptin, C-reactive protein, C-peptide, tumor necrosis factor α, interleukin 1β and its receptor antagonist, interleukin 6, and interleukin 10 determination from plasma, and their association with cancer outcomes and use of injectable insulin has been previously reported [1] or is reviewed under a separate dataset [2].

Experimental design, materials and methods
This work was completed following a previously described case-control study design [1]. Briefly, the evaluation of pro-inflammatory C-C chemokine profiles 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 biomarker profiles of corresponding plasma specimen harvested at BC diagnosis and banked in the Roswell Park Cancer Institute Data Bank and Bio-Repository.

Study population
All incident breast cancer cases diagnosed at Roswell Park Cancer Institute (01/01/2003-12/31/ 2009) were considered for inclusion (n ¼2194). Medical and pharmacotherapy history were used to determine the baseline presence of diabetes following the previously described method [1].

Inclusion and exclusion criteria
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 therapysurgery, 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 or history of gestational diabetes. For a specific breakdown of excluded subjects, please see the original research article by Wintrob et al. [1].
A total of 97 female subjects with breast cancer and baseline diabetes mellitus were eligible for inclusion in this analysis.

Control-matching approach
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 [1].

Demographic and clinical data collection
Clinical and treatment history was documented as previously described [1]. 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.

Plasma specimen storage and retrieval
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 [1].

Biomarker-pharmacotherapy association analysis
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 [1].
Correlations between biomarkers stratified by type 2 diabetes mellitus pharmacotherapy and controls 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. Correlation analyses were performed using SAS Version 9.4.

Transparency document. Supporting information
Transparency data associated with this article can be found in the online version at http://dx.doi. org/10.1016/j.dib.2017.02.045.