Studies have suggested that type 2 diabetes (T2D) is associated with a higher incidence of breast cancer and related mortality rates. T2D postmenopausal women have an ~20% increased chance of developing breast cancer, and women with T2D and breast cancer have a 50% increase in mortality compared to breast cancer patients without diabetes. This correlation has been attributed to the general activation of insulin receptor signaling, glucose metabolism, phosphatidylinositol (PI) kinases, and growth pathways. Furthermore, the presence of breast cancer specific PI kinase and/or phosphatase mutations enhance metastatic breast cancer phenotypes. We hypothesized that each of the breast cancer subtypes may have characteristic PI phosphorylation profiles that are changed in T2D conditions. Therefore, we sought to characterize the PI phosphorylation when equilibrated in normal glycemic versus hyperglycemic serum conditions. Our results suggest that hyperglycemia leads to: 1) A reduction in PI3P and PIP3, with increased PI4P that is later converted to PI(3,4)P2 at the cell surface in hormone receptor positive breast cancer; 2) a reduction in PI3P and PI4P with increased PIP3 surface expression in human epidermal growth factor receptor 2-positive (HER2+) breast cancer; and 3) an increase in di- and tri-phosphorylated PIs due to turnover of PI3P in triple negative breast cancer. This study begins to describe some of the crucial changes in PIs that play a role in T2D related breast cancer incidence and metastasis.

Epidemiological studies have proposed a link between type II diabetes and cancer via the IGF/insulin signaling pathway, which includes insulin-like peptides (IGF1, IGF2, and insulin), insulin receptors (IR-A, IR-B, IGF1R, and hybrids), and insulin substrate proteins (IRS1–6). In this study, up- and down-regulation of various components in the IGF/insulin signaling pathway are compared to clinical outcomes for cancer patients; the components include diagnosis age, overall survival, tumor invasion and vascularization, and body mass index. It was found that the up-regulation of insulin growth Factor (IGF)/insulin components was associated with overall survival and tumor invasion and vascularization, while the down-regulation of equivalent components was not associated with clinical outcomes assessed in this study. Particularly, the up-regulation of DOK5, IGF2, and IRS2 in colorectal cancer and IGF1R in liver cancer is associated with significantly decreased overall survival. Functional aberrations in either of the two proteins in co-expression pairs were identified for each cancer and correlated with overall survival and diagnosis age. Specific biomarkers proposed in this study will be further analyzed to fine-tune consistent associations that can be translated to reliable prognostic standards for the roles of IGF/insulin signaling pathway modulations that promote cancer.

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Background: Large automated electronic medical record (EMR) databases, together with natural language processing (NLP) algorithms, have the potential to be valuable tools in studying the patterns and effectiveness of treatment. Therefore, the current study sought to develop novel tools to identify bladder cancer cases, their clinical stage, and the chemotherapy they receive in electronic medical records. Methods: EMR data were obtained from Indiana University Health hospitals from 2008 to 2015.  We developed 2 novel algorithms using natural language processing (NLP) on unstructured data to identify (a) bladder cancer cases and clinical stage, and (b) chemotherapy names and line of chemotherapy. The sensitivity, specificity, PPV, and NPV for the clinical staging and treatment algorithm were calculated against the gold standard of manual chart review Results: A total of 2,559 unique bladder cancer patients were identified and stratified using the clinical staging algorithm, defined as metastatic, muscle invasive, or non-muscle invasive.  We identified 657 metastatic cases, 567 muscle invasive cases, and 604 non-muscle invasive cases. Further, we calculated the PPV for metastatic cases as 69.9%, muscle invasive as 80.4%, and non-muscle invasive as 79.1%. Next, the treatment algorithm was applied to metastatic patients to identify the type of chemotherapy received and 1st or 2nd line of therapy. The PPV for identifying the 1st and 2nd lines were 70.5% and 55.6%, respectively. The PPV for gemcitabine/carboplatin or cisplatin was 57.5%, but for methotrexate, vinblastine, doxorubicin, cisplatin, was 37.5%. Conclusion and Potential Impact: The performance of the algorithm demonstrates the potential for NLP to identify cancer cases, stage, and presence of treatment. While providing meaningful information, the accuracy of the approach suggests that a hybrid method using both NLP algorithms and manual chart review remains the most robust approach. The low performance of the algorithm to identify line of therapy further highlights the need for further NLP development in this area and emphasizes the ongoing need for either human entry or review of structured data.