Summary: Stratification of cancer patients into distinct molecular subgroups based on multi-omics data is an important issue in the context of precision medicine. Here, we present MOVICS, an R package for multi-omics integration and visualization in cancer subtyping. MOVICS provides a unified interface for 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporates the most commonly used downstream analyses in cancer subtyping researches, including characterization and comparison of identified subtypes from multiple perspectives, and verification of subtypes in external cohort using two model-free approaches for multiclass prediction. MOVICS also creates feature rich customizable visualizations with minimal effort. By analysing two published breast cancer cohort, we signifies that MOVICS can serve a wide range of users and assist cancer therapy by moving away from the ‘one-size-fits-all’ approach to patient care. Availability and implementation: MOVICS package and online tutorial are freely available at https://github.com/xlucpu/MOVICS. Supplementary information: Supplementary data are available at Bioinformatics online.

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The heterogeneity of the immune microenvironment leads to different responses in immune checkpoint blockade therapy. We aimed to propose a robust molecular classification system to investigate the relevance of the immune microenvironment subtype and prognosis of prostate cancer patients, as well as the therapeutic response to immune checkpoint blockade therapy. A total of 1,557 prostate cancer patients were enrolled, including 69 real‐world samples from our institute (titled the AHMU‐PC cohort). The non‐negative matrix factorization algorithm was employed to virtually microdissect patients. The immune enrichment was characterized by a high enrichment of T cell‐, B cell‐, NK cell‐, and macrophage‐associated signatures, by which patients were subclassified into nonimmune and immune classes. Subsequently, the immune class was dichotomized into immune‐activated and immune‐suppressed subtypes based on the stromal signature, represented by the activation of WNT/TGF‐β, TGF‐β1, and C‐ECM signatures. Approximately 14.9% to 24.3% of patients belonged to the immune‐activated subtype, which was associated with favorable recurrence‐free survival outcomes. In addition, patients in the immune‐activated subtype were predicted to benefit more from anti‐PD‐1/PD‐L1 therapy. In conclusion, our study identifies a novel immune molecular classifier that is closely related to clinical prognosis and provides novel insights into immunotherapeutic strategies for prostate cancer patients.

The antiandrogen enzalutamide (Enz) has improved survival in castration resistant prostate cancer (CRPC) patients. However, most patients eventually develop Enz resistance that may involve inducing the androgen receptor (AR) splicing variant 7 (ARv7). Here we report that high expression of monoamine oxidase-A (MAO-A) is associated with positive ARv7 detection in CRPC patients following Enz treatment. Targeting MAO-A with phenelzine or clorgyline, the FDA-approved drugs for antidepression, resensitize the Enz resistant (EnzR) cells to Enz treatment and further suppress EnzR cell growth in vitro and in vivo. Our findings suggest that Enz-increased ARv7 expression can transcriptionally enhance MAO-A expression resulting in Enz resistance via altering the hypoxia HIF-1α signals. Together, our results show that targeting the Enz/ARv7/MAO-A signaling with the antidepressants phenelzine or clorgyline can restore Enz sensitivity to suppress EnzR cell growth, which may indicate that these antidepression drugs can overcome the Enz resistance to further suppress the EnzR CRPC.