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The fast dynamics and reversibility of posttranslational modifications by the ubiquitin family pose significant challenges for research. Here we present SUMO-ID, a technology that merges proximity biotinylation by TurboID and protein-fragment complementation to find SUMO-dependent interactors of proteins of interest. We develop an optimized split-TurboID version and show SUMO interaction-dependent labelling of proteins proximal to PML and RANGAP1. SUMO-dependent interactors of PML are involved in transcription, DNA damage, stress response and SUMO modification and are highly enriched in SUMO Interacting Motifs, but may only represent a subset of the total PML proximal proteome. Likewise, SUMO-ID also allow us to identify interactors of SUMOylated SALL1, a less characterized SUMO substrate. Furthermore, using TP53 as a substrate, we identify SUMO1, SUMO2 and Ubiquitin preferential interactors. Thus, SUMO-ID is a powerful tool that allows to study the consequences of SUMO-dependent interactions, and may further unravel the complexity of the ubiquitin code. Several proteomic approaches allow the analysis of covalent protein SUMOylation, but it remains challenging to systematically study the consequences of a substrate being modified. Here, the authors combine proximity biotinylation and protein-fragment complementation to identify SUMO-dependent protein interactors.

Urine contains extracellular vesicles (EVs) that concentrate molecules and protect them from degradation. Thus, isolation and characterisation of urinary EVs could increase the efficiency of biomarker discovery. We have previously identified proteins and RNAs with differential abundance in urinary EVs from prostate cancer (PCa) patients compared to benign prostate hyperplasia (BPH). Here, we focused on the analysis of the metabolites contained in urinary EVs collected from patients with PCa and BPH. Targeted metabolomics analysis of EVs was performed by ultra-high-performance liquid chromatography-mass spectrometry. The correlation between metabolites and clinical parameters was studied, and metabolites with differential abundance in PCa urinary EVs were detected and mapped into cellular pathways. We detected 248 metabolites belonging to different chemical families including amino acids and various lipid species. Among these metabolites, 76 exhibited significant differential abundance between PCa and BPH. Interestingly, urine EVs recapitulated many of the metabolic alterations reported in PCa, including phosphathidylcholines, acyl carnitines, citrate and kynurenine. Importantly, we found elevated levels of the steroid hormone, 3beta-hydroxyandros-5-en-17-one-3-sulphate (dehydroepiandrosterone sulphate) in PCa urinary EVs, in line with the potential elevation of androgen synthesis in this type of cancer. This work supports urinary EVs as a non-invasive source to infer metabolic changes in PCa.

The dysregulation of gene expression is an enabling hallmark of cancer. Computational analysis of transcriptomics data from human cancer specimens, complemented with exhaustive clinical annotation, provides an opportunity to identify core regulators of the tumorigenic process. Here we exploit well-annotated clinical datasets of prostate cancer for the discovery of transcriptional regulators relevant to prostate cancer. Following this rationale, we identify Microphthalmia-associated transcription factor (MITF) as a prostate tumor suppressor among a subset of transcription factors. Importantly, we further interrogate transcriptomics and clinical data to refine MITF perturbation-based empirical assays and unveil Crystallin Alpha B (CRYAB) as an unprecedented direct target of the transcription factor that is, at least in part, responsible for its tumor-suppressive activity in prostate cancer. This evidence was supported by the enhanced prognostic potential of a signature based on the concomitant alteration of MITF and CRYAB in prostate cancer patients. In sum, our study provides proof-of-concept evidence of the potential of the bioinformatics screen of publicly available cancer patient databases as discovery platforms, and demonstrates that the MITF-CRYAB axis controls prostate cancer biology.

Cellular transformation and cancer progression is accompanied by changes in the metabolic landscape. Master co-regulators of metabolism orchestrate the modulation of multiple metabolic pathways through transcriptional programs, and hence constitute a probabilistically parsimonious mechanism for general metabolic rewiring. Here we show that the transcriptional co-activator peroxisome proliferator-activated receptor gamma co-activator 1α (PGC1α) suppresses prostate cancer progression and metastasis. A metabolic co-regulator data mining analysis unveiled that PGC1α is downregulated in prostate cancer and associated with disease progression. Using genetically engineered mouse models and xenografts, we demonstrated that PGC1α opposes prostate cancer progression and metastasis. Mechanistically, the use of integrative metabolomics and transcriptomics revealed that PGC1α activates an oestrogen-related receptor alpha (ERRα)-dependent transcriptional program to elicit a catabolic state and metastasis suppression. Importantly, a signature based on the PGC1α–ERRα pathway exhibited prognostic potential in prostate cancer, thus uncovering the relevance of monitoring and manipulating this pathway for prostate cancer stratification and treatment. Torrano et al.  use bioinformatics analyses to identify PGC1α as a transcriptional regulator of a metabolic program downstream of ERRα that opposes metastatic dissemination in prostate cancer.

Oncogene addiction postulates that the survival and growth of certain tumor cells is dependent upon the activity of one oncogene, despite their multiple genetic and epigenetic abnormalities. This phenomenon provides a foundation for molecular targeted therapy and a rationale for oncogene-based stratification. We have previously reported that the Promyelocytic Leukemia protein (PML) is upregulated in triple negative breast cancer (TNBC) and it regulates cancer-initiating cell function, thus suggesting that this protein can be therapeutically targeted in combination with PML-based stratification. However, the effects of PML perturbation on the bulk of tumor cells remained poorly understood. Here we demonstrate that TNBC cells are addicted to the expression of this nuclear protein. PML inhibition led to a remarkable growth arrest combined with features of senescence in vitro and in vivo. Mechanistically, the growth arrest and senescence were associated to a decrease in MYC and PIM1 kinase levels, with the subsequent accumulation of CDKN1B (p27), a trigger of senescence. In line with this notion, we found that PML is associated to the promoter regions of MYC and PIM1, consistent with their direct correlation in breast cancer specimens. Altogether, our results provide a feasible explanation for the functional similarities of MYC, PIM1, and PML in TNBC and encourage further study of PML targeting strategies for the treatment of this breast cancer subtype.

Background: The outcome for oestrogen receptor positive (ER+) breast cancer patients has improved greatly in recent years largely due to targeted therapy. However, the presence of involved multiple synchronous lymph nodes remains associated with a poor outcome. Consequently, these patients would benefit from the identification of new prognostic biomarkers and therapeutic targets. The expression of G-protein-coupled receptor kinase-interacting protein 1 (GIT1) has recently been shown to be an indicator of advanced stage breast cancer. Therefore, we investigated its expression and prognostic value of GIT1 in a cohort of 140 ER+ breast cancer with synchronous lymph node involvement. Methods: Immunohistochemistry was employed to assess GIT1 expression in a tissue microarray (TMA) containing duplicate non-adjacent cores with matched primary tumour and lymph node tissue (n=140). GIT1 expression in tumour cells was scored and statistical correlation analyses were carried out. Results: The results revealed a sub-group of patients that displayed discordant expression of GIT1 between the primary tumour and the lymph nodes (i.e. spatial intratumoural heterogeneity). We observed that loss of GIT1 expression in the tumour cells of the metastasis was associated with a shorter time to recurrence, poorer overall survival, and a shorter median survival time. Moreover, multivariate analysis demonstrated that GIT1 expression was an independent prognostic indicator. Conclusions: GIT1 expression enabled the identification of a sub-class of ER+ patients with lymph node metastasis that have a particularly poor prognostic outcome. We propose that this biomarker could be used to further stratify ER+ breast cancer patients with synchronous lymph node involvement and therefore facilitate adjuvant therapy decision making.

Nature Cell Biology 18, 645–656 (2016); published online 23 May 2016; corrected after print 22 May 2017 In the original version of this Article, the name of author James David Sutherland was coded wrongly, resulting in it being incorrect when exported to citation databases. This has now been corrected, though no visible changes will be apparent.

This corrects the article DOI: 10.1038/ncb3357

Cellular transformation and cancer progression is accompanied by changes in the metabolic landscape. Master co-regulators of metabolism orchestrate the modulation of multiple metabolic pathways through transcriptional programs, and hence constitute a probabilistically parsimonious mechanism for general metabolic rewiring. Here we show that the transcriptional co-activator peroxisome proliferator-activated receptor gamma co-activator 1α (PGC1α) suppresses prostate cancer progression and metastasis. A metabolic co-regulator data mining analysis unveiled that PGC1α is downregulated in prostate cancer and associated with disease progression. Using genetically engineered mouse models and xenografts, we demonstrated that PGC1α opposes prostate cancer progression and metastasis. Mechanistically, the use of integrative metabolomics and transcriptomics revealed that PGC1α activates an oestrogen-related receptor alpha (ERRα)-dependent transcriptional program to elicit a catabolic state and metastasis suppression. Importantly, a signature based on the PGC1α-ERRα pathway exhibited prognostic potential in prostate cancer, thus uncovering the relevance of monitoring and manipulating this pathway for prostate cancer stratification and treatment.

ABSTRACT The fast dynamics and reversibility of posttranslational modifications by the ubiquitin family pose significant challenges for research. Here we present SUMO-ID, a technology that merges proximity biotinylation by TurboID and protein-fragment complementation to find SUMO-dependent interactors of proteins of interest. We developed an optimized split-TurboID version and show SUMO interaction-dependent labelling of proteins proximal to PML and RANGAP1. SUMO-dependent interactors of PML are involved in transcription, DNA damage, stress response and SUMO modification and are highly enriched in SUMO Interacting Motifs, but may only represent a subset of the total PML proximal proteome. Likewise, SUMO-ID also allowed us to identify novel interactors of SUMOylated SALL1, a less characterized SUMO substrate. Thus, SUMO-ID is a powerful tool that allows to study the consequences of SUMO-dependent interactions, and may further unravel the complexity of the ubiquitin code. Competing Interest Statement The authors have declared no competing interest. Footnotes * Lead Contact: Rosa Barrio rbarrioatcicbiogune.es