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Data Brief. 2017 April; 11: 391–402.
Published online 2017 February 16. doi:  10.1016/j.dib.2017.02.031
PMCID: PMC5331147

Insulin secretagogue use and circulating inflammatory C–C chemokine levels in breast cancer patients

Abstract

Monocytes’ infiltration into the tumor tissue and their activation to tumor-associated macrophages is an essential step in tumor development, also playing a critical role in an eventual metastasis. Stimulation of endogenous insulin production by oral insulin secretagogue treatment has the potential to interfere with the production and release of C–C chemokines, a group of potent inflammatory cytokines acting as monocyte chemo-attractants and influencing their behavior in the tumor microenvironment.

Studied plasma samples were collected under a previously reported study design involving a population of women diagnosed with breast cancer presenting with or without type 2 diabetes mellitus at the time of breast cancer diagnosis (Wintrob et al., 2017, 2016) [1,2]. The data presented here shows the relationship between pre-existing use of insulin secretagogue, the inflammatory C–C chemokine profiles at the time of breast cancer diagnosis, and subsequent cancer outcomes. A Pearson correlation analysis stratified by secretagogue use and controls was implemented to evaluate the relationship between the investigated biomarkers and respectively each of these biomarkers and the other relevant reported cytokine datasets derived from the same patient population (Wintrob et al., 2017, 2016) [1,2].

Keywords: Inflammation, Inflammatory cytokines, Secretagogue, C–C chemokines, CCL-2, CCL-3, CCL-4, CCL-5, Breast cancer, Diabetes, Monocyte infiltration, Activated macrophage, Cancer outcomes, Cancer prognosis

Specifications table

Table thumbnail

Value of the data

  • • Monocytes’ mobilization to the tumor location is a chemotactic response mediated by pro-inflammatory C–C chemokine ligands: CCL-2, CCL-3, CCL-4, and CCL-5 [3]. Their combined contribution determines specific tumor environment changes many of which are responsible for metastasis.
  • • CCL-2 was the first described tumor-derived factor while later has been found to also be elevated among type 2 diabetes patients [4], [5]. CCL-2 promotes tumor metastasis through secretion of CCL-3. Given its crucial role, CCl-2 is currently explored as a diagnostic and prognostic biomarker [6], [7], [8], [9]. CCL-4 and CCL-5 are reported to facilitate metastasis and contribute to disease progression [10], [11], [12]. CCL-5 is currently considered as a therapeutic target for breast cancer [13].
  • • Present data shows the observed relationship between history of insulin secretagogue use, circulating C–C chemokines at breast cancer diagnosis and cancer outcomes.
  • • This data provides additional detail for the design of future studies investigating the relationship between insulin production and inflammation leading to breast cancer metastasis.
  • • Our observations have the potential to guide research investigating the use of C–C chemokines as diagnostic and/or prognostic indicators.

1. Data

Reported data represents the observed association between use of insulin secretagogues preceding breast cancer and the 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 the measured biomarkers stratified by type 2 diabetes mellitus pharmacotherapy and controls, as well as correlations with other inflammatory adipokines reported by us in the past: tumor necrosis factor α, interleukin 1β and its receptor antagonist, and interleukin 6. The details regarding tumor necrosis factor α, interleukin 1β and its receptor antagonist, and interleukin 6 determination from plasma, their association with cancer outcomes and use of insulin secretagogues has been previously reported [1], [2].

Table 1
Pro-inflammatory Cytokine Associations with Secretagogue Use.
Table 2
Pro-inflammatory Cytokine Correlations by Secretagogue Use.

2. Experimental design, materials and methods

Evaluation of pro-inflammatory cytokine profiles association with insulin secretagogue 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 pro-inflammatory cytokine profiles of corresponding plasma specimen harvested at BC diagnosis and banked in the Roswell Park Cancer Institute Data Bank and Bio-Repository.

2.1. Study population

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 [1], [2].

2.2. 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 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. [1].

A total of 97 female subjects with breast cancer and baseline diabetes mellitus were eligible for inclusion in this analysis.

2.3. 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].

2.4. 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. The specific treatment groups have been defined according to the mechanism of action of their respective diabetes pharmacotherapy. Receiving any of the following pharmacotherapies alone or in combination: sulfonylureas (glimepiride, glipizide, and glyburide), meglitinides (nateglinide, repaglinide), alpha-glucosidase inhibitors (acarbose, miglitol), glucagon-like peptide-1 receptor agonists (exenatide, liraglutide), led to assigning the subject to the “any secretagogue” user group, whereas the “no secretagogue” user group included patients receiving one or more of the following treatment options: biguanides (metformin) and thiazolidinediones (pioglitazone, rosiglitazone) or no oral pharmacotherapy [1]. Of note is that each of the two groups, any secretagogue and no secretagogue, included 11 and respectively 9 insulin users.

2.5. 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.

2.6. Luminex® assays

A total of 5 biomarkers – 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) – were quantified according to the manufacturer protocol. The HCYTOMAG-60K Luminex® biomarker panel (Millipore Corporation, Billerica, MA) was utilized in this study. Tumor necrosis factor α, interleukin 1β, interleukin 1β receptor antagonist, interleukin 6, and interleukin 10 determinations were done according to the manufacturer protocol as previously reported [1], [2].

2.7. 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 Kruskal–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.

Funding sources

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).

Acknowledgments

Authors acknowledge the valuable help of Dr. Chi-Chen Hong with case-control matching.

Footnotes

Transparency documentTransparency data associated with this article can be found in the online version at 10.1016/j.dib.2017.02.031.

Transparency document. Supplementary material

Supplementary material

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