Measuring Cancer Hallmark Mediation of the TET1 Glioma Survival Effect with Linked Neural-Network Based Mediation Experiments

Applications for open access normalized synthesis in metastatic prostate cancer trials

Recent metastatic castration-resistant prostate cancer (mCRPC) clinical trials have integrated homologous recombination and DNA repair deficiency (HRD/DRD) biomarkers into eligibility criteria and secondary objectives. These trials led to the approval of some PARP inhibitors for mCRPC with HRD/DRD indications. Unfortunately, biomarker-trial outcome data is only discovered by reviewing publications, a process that is error-prone, time-consuming, and laborious. While prostate cancer researchers have written systematic evidence reviews (SERs) on this topic, given the time involved from the last search to publication, an SER is often outdated even before publication. The difficulty in reusing previous review data has resulted in multiple reviews of the same trials. Thus, it will be useful to create a normalized evidence base from recently published/presented biomarker-trial outcome data that one can quickly update. We present a new approach to semi-automating normalized, open-access data tables from published clinical trials of metastatic prostate cancer using a data curation and SER platform. Clinicaltrials.gov and Pubmed.gov were used to collect mCRPC clinical trial publications with HRD/DRD biomarkers. We extracted data from 13 publications covering ten trials that started before 22nd Apr 2021. We extracted 585 hazard ratios, response rates, duration metrics, and 543 adverse events. Across 334 patients, we also extracted 8,180 patient-level survival and biomarker values. Data tables were populated with survival metrics, raw patient data, eligibility criteria, adverse events, and timelines. A repeated strong association between HRD and improved PARP inhibitor response was observed. Several use cases for the extracted data are demonstrated via analyses of trial methods, comparison of treatment hazard ratios, and association of treatments with adverse events. Machine learning models are also built on combined and normalized patient data to demonstrate automated discovery of therapy/biomarker relationships. Overall, we demonstrate the value of systematically extracted and normalized data. We have also made our code open-source with simple instructions on updating the analyses as new data becomes available, which anyone can use even with limited programming knowledge. Finally, while we present a novel method of SER for mCRPC trials, one can also implement such semi-automated methods in other clinical trial domains to advance precision medicine.

Differential expression of microRNAs targeting genes associated with the development of high-grade gliomas

Background

Highly malignant high-grade gliomas are tumors of the central nervous system (CNS). They are solid tumors arising from transformed cells of the brain and/or the spinal cord. In recent years, the expression of genes and regulating miRNAs in glial brain tumors has been actively studied. The present study is devoted to assessing the expression levels of miR-215-5p, miR-22-3p, miR-122-5p, miR-107, miR-324-5p, miR-34a-5p, miR-155. -5p, miR-21-5p, miR-497-5p, miR-330-3p, miR-146a-5p, miR-92a-1-5p, miR-326 and target genes EGFR, SMAD4, SMAD7, SMO, NOTCH1, NOTCH2, HIF1A, EGLIN1/3, KDM1B, KDM1A, MSI1, MSI2, TET1 in high-grade glioma tissues.

Results

As a result of the analysis of the levels of relative expression of the studied genes, there are significant changes (p < 0.05) in tumor tissue for genes: EGFR, SMAD4, SMAD7, SMO, HIF1A, EGLN1/3. We obtained data on a significant change (p < 0.05) in the levels of relative expression for microRNA: hsa-miR-215-5p, hsa-miR-22-3p, hsa-miR-122-5p, hsa-miR-107, hsa-miR-324-5p, hsa-miR-155-5p, hsa-miR-21-5p, hsa-miR-330-3p, hsa-miR-326. Data on the association of overall survival in patients with high-grade glioma and the level of relative expression of the EGFR and HIF1A genes were obtained. The obtained data demonstrate the association of overall survival of patients with high-grade glioma and the level of relative expression of EGFR, HIF1A and hsa-miR-22-3p, hsa-miR-107 and hsa-miR-330-3p.

Conclusions

The obtained data on the expression of genes and microRNAs expand the understanding of the biology of the development of high-grade glial tumors. These data demonstrate new potential therapeutic and prognostic goals in high-grade gliomas.