Liss, Michael A.

Abstract Background: Missing risk factors, whether random or not measured at all, across different hospitals and patients provide challenges, both for the developers of online clinical risk tools and the patients trying to use such tools. This paper provides a development and end-user solution to the commonly encountered limitations of clinical risk tool based decision making due to missing information.Methods: Six state-of-the-art logistic regression approaches accommodating missing data were compared using prostate cancer data from ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG). An additional large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach.Results: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history.Conclusion: Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

Abstract Although the Sonic hedgehog (SHH) signaling pathway has been implicated in promoting malignant phenotypes of prostate cancer, details on how it is activated and exerts its oncogenic role during prostate cancer development and progression is less clear. Here, we show that GLI3, a key SHH pathway effector, is transcriptionally upregulated during androgen deprivation and posttranslationally stabilized in prostate cancer cells by mutation of speckle-type POZ protein (SPOP). GLI3 is a substrate of SPOP-mediated proteasomal degradation in prostate cancer cells and prostate cancer driver mutations in SPOP abrogate GLI3 degradation. Functionally, GLI3 is necessary and sufficient for the growth and migration of androgen receptor (AR)–positive prostate cancer cells, particularly under androgen-depleted conditions. Importantly, we demonstrate that GLI3 physically interacts and functionally cooperates with AR to enrich an AR-dependent gene expression program leading to castration-resistant growth of xenografted prostate tumors. Finally, we identify an AR/GLI3 coregulated gene signature that is highly correlated with castration-resistant metastatic prostate cancer and predictive of disease recurrence. Together, these findings reveal that hyperactivated GLI3 promotes castration-resistant growth of prostate cancer and provide a rationale for therapeutic targeting of GLI3 in patients with castration-resistant prostate cancer (CRPC). Implications: We describe two clinically relevant mechanisms leading to hyperactivated GLI3 signaling and enhanced AR/GLI3 cross-talk, suggesting that GLI3-specific inhibitors might prove effective to block prostate cancer development or delay CRPC.