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In addition to the cellular pathways and expression programs that define phenotypes, several individual genes and their encoded proteins play important roles in PC pathobiology and serve as targets for therapeutics. These targets include FOLH1 which encodes prostate-specific membrane antigen (PSMA), where PSMA-conjugated radioligands are being evaluated for imaging as well to focally direct therapeutic doses of radioisotopes30,31,32. The histone methyltransferase enhancer of zeste homolog 2 (EZH2) is involved in cellular reprogramming/transdifferentiation and may contribute to AR-directed therapy resistance33,34. The antiapoptotic protein BCL2 and checkpoint kinase inhibitor Wee1 are upregulated in SCNPCs and therapeutics directed toward these proteins inhibit SCNPC growth in preclinical models35. DSP quantitation of each of these targets demonstrated that interindividual heterogeneity with measurements closely aligned with RNAseq-based levels (Fig. 2g–j). Notably, the expression variation between patients suggests that a precision approach may be required to establish efficacy specifically in patients most likely to benefit by virtue of target expression.
DSP identifies limited intraindividual diversity in metastatic PC phenotypes Adventure Is Calling And I Must Go Mug
Most patients with mPC have multiple sites of tumor dissemination that may include lymph nodes, bone, and various soft tissues such as liver, adrenal, and lung. The similarities and differences in the phenotypes and genotypes between the disseminated tumor sites will impact the utility of sampling any individual site for overall tumor classification and the performance of a predictive biomarker for a given therapy. We next compared the DSP-based phenotypic classification of metastasis within each patient and also evaluated expression programs indicating cell proliferation (CCP) status, FGF/MAPK activity, and RB1 loss. Overall, there was high concordance in the phenotype call within a given patient with 82% of randomly sampled pairs of tumor ROIs from the same patient having the same phenotype classification (Fig. 3a). In contrast, only 54% of ROIs were phenotypically concordant when randomly comparing ROIs across all tumors and all patients. The scores for pathway activities were also generally concordant between metastasis from the same patient, with substantially greater diversity across patients (Fig. 3 and Fig. S1). However, there were notable exceptions: in seven patients, tumors were classified into different phenotype categories. For example, patient 15-096 had one tumor classified as AR+/NE+, whereas a separate metastasis was AR+/NE− (Fig. 3b–d). Patient 12-011 had one tumor classified as AR+/NE− and one tumor AR+/NE+ and patient 15-010 had one tumor classified as ARlow/NE− and one tumor AR−/NE+ (Fig. 3e, f). At the individual gene level, there was clear evidence of divergent gene expression for transcripts that comprise NE differentiation, for example, SYP, ASCL1, and ONECUT2. The intertumor heterogeneity included transcripts encoding proteins of potential utility in therapeutics, such as the cell surface protein NCAM1, to which antibodies and CAR-T cells have been developed (Fig. 3e, f)36. Though unusual, these findings indicate that therapy resistance can occur via distinct mechanisms and may require combinations of treatments to address diverse drivers of progression within an individual patient.
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