Targeting Ovarian Carcinoma with TSP-1:CD47 Antagonist TAX2 Activates Anti-Tumor Immunity

TAX2 peptide is a cyclic peptide that acts as an orthosteric antagonist for thrombospondin-1 (TSP-1) interaction with CD47. TAX2 was first described for its anti-angiogenic activities and showed anti-cancer efficacy in numerous preclinical models. Here, we aimed at providing an extensive molecular characterization of TAX2 mode of action, while evaluating its potential in ovarian cancer therapy. Multidisciplinary approaches were used to qualify a TAX2 drug candidate in terms of stability, solubility and potency. Then, efficacy studies, together with benchmark experiments, were performed in relevant mouse models of ovarian carcinoma. TAX2 peptide appears to be stable and soluble in clinically relevant solvents, while displaying a favorable safety profile. Moreover, clinical data mining allowed for the identification of TSP-1 as a relevant pharmacological target in ovarian cancer. In mice, TAX2 therapy inhibits ovarian tumor growth and metastatic dissemination, while activating anti-cancer adaptive immunity. Interestingly, TAX2 also synergizes when administered in combination with anti-PD-1 immune checkpoint inhibitiors. Altogether, our data expose TAX2 as an optimized candidate with advanced preclinical characterization. Using relevant syngeneic ovarian carcinoma models, we highlighted TAX2’s ability to convert poorly immunogenic tumors into ones displaying effective anti-tumor T-cell immunity.

Novel Insights into Data Mining to Improve the Specificity of Pharmacovigilance and Prevent Adverse Drug Reactions in Psychiatric Patients

The aim of this perspective is to provide a review upon the fundamental computational methods deployed in data mining as applied to healthcare data, with particular regards to patient records of psychiatric patients. Albeit clinical data mining has advanced over the years, further research is needed to improve the specificity of pharmacovigilance and prevent adverse drug reactions in psychiatric patients. From describing the main principles and present challenges of data mining to its most-novel applications in clinical psychiatry, this literature review highlights current research gaps that have to be filled to increase the efficacy of psychiatric drugs nowadays, thus improving patient outcomes and decreasing hospitalization costs.

SEC23A Is an Independent Prognostic Biomarker in Bladder Cancer Correlated With MAPK Signaling

Clinical data mining and bioinformatics analysis can be employed effectively to elucidate the function and underlying mechanisms of the gene of interest. Here, we have proposed a framework for the identification and validation of independent biomarkers in human cancer and for mechanistic profiling using gene sets enrichment analysis and pathway analysis. This is followed by validation with in vitro experiments. Using this framework to analyze the clinical relevance of SEC23A, we have discovered the prognostic potential of SEC23A in different cancers and identified SEC23A as an independent prognostic factor for poor prognosis in bladder cancer, which implicates SEC23A, for the first time, as an oncogene. Bioinformatic analyses have elucidated an association between SEC23A expression and the upregulation of the MAPK signaling pathway. Using the T24 human bladder cell line, we confirmed that knockdown of SEC23A expression could effectively impact the MAPK signaling pathway. Further, through PCR verification, we showed that MEF2A, one of the key genes of the MAPK signaling pathway, might be a downstream factor of the SEC23A gene.

ClinFlow – An Interactive Application for Clinical Data Mining

Analyzing clinical data comes with many challenges. Medical expertise combined with statistical and programming knowledge must go hand-in-hand when applying data mining methods on clinical datasets. This work aims at bridging the gap between clinical expertise and computer science knowledge by providing an application for clinical data analysis with no requirement for statistical programming knowledge. Our tool allows clinical researchers to conduct data processing and visualization in an interactive environment, thus providing an assisting tool for clinical studies. The application was experimentally evaluated with an analysis of Type 1 Diabetes clinical data. The results obtained with the tool are in line with the domain literature, demonstrating the value of our application in data exploration and hypothesis testing.

Clinical data mining reveals analgesic effects of lapatinib in cancer patients

Microsomal prostaglandin E2 synthase 1 (mPGES-1) is recognized as a promising target for a next generation of anti-inflammatory drugs that are not expected to have the side effects of currently available anti-inflammatory drugs. Lapatinib, an FDA-approved drug for cancer treatment, has recently been identified as an mPGES-1 inhibitor. But the efficacy of lapatinib as an analgesic remains to be evaluated. In the present clinical data mining (CDM) study, we have collected and analyzed all lapatinib-related clinical data retrieved from clinicaltrials.gov. Our CDM utilized a meta-analysis protocol, but the clinical data analyzed were not limited to the primary and secondary outcomes of clinical trials, unlike conventional meta-analyses. All the pain-related data were used to determine the numbers and odd ratios (ORs) of various forms of pain in cancer patients with lapatinib treatment. The ORs, 95% confidence intervals, and P values for the differences in pain were calculated and the heterogeneous data across the trials were evaluated. For all forms of pain analyzed, the patients received lapatinib treatment have a reduced occurrence (OR 0.79; CI 0.70–0.89; P = 0.0002 for the overall effect). According to our CDM results, available clinical data for 12,765 patients enrolled in 20 randomized clinical trials indicate that lapatinib therapy is associated with a significant reduction in various forms of pain, including musculoskeletal pain, bone pain, headache, arthralgia, and pain in extremity, in cancer patients. Our CDM results have demonstrated the significant analgesic effects of lapatinib, suggesting that lapatinib may be repurposed as a novel type of analgesic.