Spatial transcriptomics analysis of co-targeted PIM and PI3 K/mTOR in multikinase inhibitor and single kinase inhibitor combination-treated patient-derived prostate cancer explants

Prostate cancer is a multifocal heterogeneous disease that remains the second leading cause of male death worldwide. These deaths are caused mainly by metastasis. Moreover, PIM and PI3 K/mTOR pathways are frequently dysregulated in prostate cancer, and may lead to an invasion, increased metastasis and decreased survival. However, the interconnection of these pathways has been shown to mediate anti-tumour drug resistance. Furthermore, current treatments exhibit issues with toxicity. The novel preclinical multikinase PIM/PI3 K/mTOR inhibitor, AUM302, versus clinical trial investigated - a combination of the PI3 K/mTOR inhibitor BEZ235 (Dactolisib) and PIM inhibitor, AZD-1208 has been analyzed in our laboratory using a cohort of cancer explants emanating from our PEOPLE: PatiEnt prOstate samPLes for rEsea ch study and our current SCREEN study. This cohort has a high Gleason grade score of ≥8. Therefore, this study aims to assess the adequacy of the prostate cancer ex vivo models and the efficacy and mechanism of action of the drugs. Using the Nanostring GeoMX DSP technology, we aim to analyze the spatial molecular profile of the co-targeted therapy treated ex vivo models to decipher the effects of heterogeneity on the co-targeted therapies efficacy. Tissue microarrays of co-targeted treated thirty ex vivo 3 mm cores derived from 5 patients will be analyzed. Morphology markers, including PAN CK positive and PAN CK negative, will be used to guide the selection of 300 regions of interest (ROI) comprising benign and tumours. ROI will be segmented and profiled using immunofluorescence. The morphological markers will define these segments into areas of illumination (AOIs) using a combination of the absence or presence of αSMA and pSTAT3. The AOIs will generate multiple expression profiles for the related ROI. We intend to use this flexible, high-dimensional spatial profiling to identify the spatial genomic signatures and phosphorylation sites in cancer-targeted therapies. Our findings will contribute to understanding how the spatial landscape of the tumour microenvironment enhances the efficacy of anti-tumour drugs and what subset of patients are more likely to benefit from such therapy. No conflict of interest.