gdc-0068 and Prostatic-Neoplasms

Inter- and intra-patient molecular heterogeneity of primary and metastatic prostate cancer (PCa) confers variable clinical outcome and poses a formidable challenge in disease management. High-throughput integrative genomics and functional approaches have untangled the complexity involved in this disease and revealed a spectrum of diverse aberrations prevalent in various molecular subtypes, including ETS fusion negative. Emerging evidence indicates that SPINK1 upregulation, mutations in epigenetic regulators or chromatin modifiers, and SPOP are associated with the ETS-fusion negative subtype. Additionally, patients with defects in a DNA-repair pathway respond to poly-(ADP-ribose)-polymerase (PARP) inhibition therapies. Furthermore, a new class of immunogenic subtype defined by CDK12 biallelic loss has also been identified in ETS-fusion-negative cases. This review focuses on the emerging molecular underpinnings driving key oncogenic aberrations and advancements in therapeutic strategies of this disease.

Topics: Cyclin-Dependent Kinases; DNA Repair; Epigenetic Repression; ETS Motif; Gene Expression Regulation, Neoplastic; Genomics; Humans; Loss of Heterozygosity; Male; Molecular Targeted Therapy; Nuclear Proteins; Phosphatidylethanolamine Binding Protein; Piperazines; Poly (ADP-Ribose) Polymerase-1; Precision Medicine; Prostatic Neoplasms; Proteomics; Proto-Oncogene Proteins c-akt; Proto-Oncogene Proteins c-raf; Pyrimidines; Repressor Proteins; Signal Transduction; Trypsin Inhibitor, Kazal Pancreatic

Hyperglycemia is a major adverse event of phosphatidylinositol 3-kinase/AKT inhibitor class of cancer therapeutics. Machine learning (ML) methodologies can identify and highlight how explanatory variables affect hyperglycemia risk.. Using data from clinical trials of the AKT inhibitor ipatasertib (IPAT) in the metastatic castrate-resistant prostate cancer setting, we trained an XGBoost ML model to predict the incidence of grade ≥2 hyperglycemia (HGLY ≥ 2). Of the 1,364 patients included in our analysis, 19.4% (n = 265) of patients had HGLY ≥2 events with a median time of first onset of 28 days (range, 0-753 days), and 30.0% (n = 221) of patients on an IPAT regimen had at least one HGLY ≥2 event compared with 7.0% (n = 44) of patients on placebo.. An 11-variable XGBoost model predicted HGLY ≥2 events well with an AUROC of 0.83 ± 0.02 (mean ± standard deviation). Using SHapley Additive exPlanations analysis, we found IPAT exposure and baseline HbA1c levels to be the strongest predictors of HGLY ≥2, with additional predictivity of baseline measurements of fasting glucose, magnesium, and high-density lipoproteins.. The findings support using patients' prediabetic status as a key factor for hyperglycemia monitoring and/or trial exclusion criteria. Additionally, the model and relationships between explanatory variables and HGLY ≥2 described herein can help identify patients at high risk for hyperglycemia and develop rational risk mitigation strategies.

Topics: Humans; Hyperglycemia; Machine Learning; Male; Prostatic Neoplasms; Protein Kinase Inhibitors; Proto-Oncogene Proteins c-akt; Risk Factors

Topics: Animals; Apoptosis; Benzamides; Cell Proliferation; Drug Resistance, Neoplasm; Gene Expression Regulation, Neoplastic; Humans; Male; Mice; Nitriles; Phenylthiohydantoin; Phosphatidylinositol 3-Kinases; Piperazines; Prostatic Neoplasms; Proto-Oncogene Proteins c-akt; Pyrimidines; Receptors, Androgen; Receptors, Glucocorticoid; Tumor Cells, Cultured; Xenograft Model Antitumor Assays