Limitations of Explainability for Established Prognostic Biomarkers of Prostate Cancer

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Scaffold protein MAPK8IP2 expression is a robust prognostic factor in prostate cancer associated with AR signaling activity

Mitogen-activated protein kinase-8-interacting protein 2 (MAPK8IP2) is a scaffold protein that modulates MAPK signal cascades. Although MAPK pathways were heavily implicated in prostate cancer progression, the regulation of MAPK8IP2 expression in prostate cancer is not yet reported. We assessed MAPK8IP2 gene expression in prostate cancer related to disease progression and patient survival outcomes. MAPK8IP2 expression was analyzed using multiple genome-wide gene expression datasets derived from The Cancer Genome Atlas (TCGA) RNA-sequence project and complementary DNA (cDNA) microarrays. Multivariable Cox regressions and log-rank tests were used to analyze the overall survival outcome and progression-free interval. MAPK8IP2 protein expression was evaluated using the immunohistochemistry approach. The quantitative PCR and Western blot methods analyzed androgen-stimulated MAPK8IP2 expression in LNCaP cells. In primary prostate cancer tissues, MAPK8IP2 mRNA expression levels were significantly higher than those in the case-matched benign prostatic tissues. Increased MAPK8IP2 expression was strongly correlated with late tumor stages, lymph node invasion, residual tumors after surgery, higher Gleason scores, and preoperational serum prostate-specific antigen (PSA) levels. MAPK8IP2 upregulation was significantly associated with worse overall survival outcomes and progression-free intervals. In castration-resistant prostate cancers, MAPK8IP2 expression strongly correlated with androgen receptor (AR) signaling activity. In cell culture-based experiments, MAPK8IP2 expression was stimulated by androgens in AR-positive prostate cancer cells. However, MAPK8IP2 expression was blocked by AR antagonists only in androgen-sensitive LNCaP but not castration-resistant C4-2B and 22RV1 cells. These results indicate that MAPK8IP2 is a robust prognostic factor and therapeutic biomarker for prostate cancer. The potential role of MAPK8IP2 in the castration-resistant progression is under further investigation.

Are There Limits in Explainability of Prognostic Biomarkers? Scrutinizing Biological Utility of Established Signatures

Prognostic biomarkers can have an important role in the clinical practice because they allow stratification of patients in terms of predicting the outcome of a disorder. Obstacles for developing such markers include lack of robustness when using different data sets and limited concordance among similar signatures. In this paper, we highlight a new problem that relates to the biological meaning of already established prognostic gene expression signatures. Specifically, it is commonly assumed that prognostic markers provide sensible biological information and molecular explanations about the underlying disorder. However, recent studies on prognostic biomarkers investigating 80 established signatures of breast and prostate cancer demonstrated that this is not the case. We will show that this surprising result is related to the distinction between causal models and predictive models and the obfuscating usage of these models in the biomedical literature. Furthermore, we suggest a falsification procedure for studies aiming to establish a prognostic signature to safeguard against false expectations with respect to biological utility.