The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

Background: There is a considerable need to incorporate biomarkers of resistance to new antiandrogen agents in the management of castration‐resistant prostate cancer (CRPC).Methods: We conducted a phase II trial of enzalutamide in first‐line chemo‐naïve asymptomatic or minimally symptomatic mCRPC and analyzed the prognostic value ofTMPRSS2‐ERGand other biomarkers, including circulating tumor cells (CTCs), androgen receptor splice variant (AR‐V7) in CTCs and plasma Androgen Receptor copy number gain (AR‐gain). These biomarkers were correlated with treatment response and survival outcomes and developed a clinical–molecular prognostic model using penalized cox‐proportional hazard model. This model was validated in an independent cohort.Results: Ninety‐eight patients were included.TMPRSS2‐ERGfusion gene was detected in 32 patients with no differences observed in efficacy outcomes. CTC detection was associated with worse outcome and AR‐V7 in CTCs was associated with increased rate of progression as best response. Plasma AR gain was strongly associated with an adverse outcome, with worse median prostate specific antigen (PSA)‐PFS (4.2 vs. 14.7 m;p < 0.0001), rad‐PFS (4.5 vs. 27.6 m;p < 0.0001), and OS (12.7 vs. 38.1 m;p < 0.0001). The clinical prognostic model developed in PREVAIL was validated (C‐Index 0.70) and the addition of plasma AR (C‐Index 0.79;p < 0.001) increased its prognostic ability. We generated a parsimonious model including alkaline phosphatase (ALP); PSA and AR gain (C‐index 0.78) that was validated in an independent cohort.Conclusions: TMPRSS2‐ERGdetection did not correlate with differential activity of enzalutamide in first‐line mCRPC. However, we observed that CTCs and plasma AR gain were the most relevant biomarkers.

The prognosis for patients with metastatic castration-resistant prostate cancer (mCRPC) varies, being influenced by blood-related factors such as transcriptional profiling and immune cell ratios. We aimed to address the contribution of distinct whole blood immune cell components to the prognosis of these patients. This study analyzed pre-treatment blood samples from 152 chemotherapy-naive mCRPC patients participating in a phase 2 clinical trial (NCT02288936) and a validation cohort. We used CIBERSORT-X to quantify 22 immune cell types and assessed their prognostic significance using Kaplan–Meier and Cox regression analyses. Reduced CD8 T-cell proportions and elevated monocyte levels were substantially connected with a worse survival. High monocyte counts correlated with a median survival of 32.2 months versus 40.3 months for lower counts (HR: 1.96, 95% CI 1.11–3.45). Low CD8 T-cell levels were associated with a median survival of 31.8 months compared to 40.3 months for higher levels (HR: 1.97, 95% CI 1.11–3.5). These findings were consistent in both the trial and validation cohorts. Multivariate analysis further confirmed the independent prognostic value of CD8 T-cell counts. This study highlights the prognostic implications of specific blood immune cells, suggesting they could serve as biomarkers in mCRPC patient management and should be further explored in clinical trials.

Metaproteomics is as a promising technique for studying the human gut microbiota, because it can reveal the taxonomic profile and also shed light on the functional role of the microbial community. Nevertheless, methods for extracting proteins from stool samples continue to evolve, in the pursuit of optimal protocols for moistening and dispersing the stool sample and for disrupting microbial cells which are two critical steps for ensuring good protein recovery. Here, we evaluated different stool sample processing and microbial cell disruption methods for metaproteomic analyses of human gut microbiota. An unsupervised principal component analysis showed that different methods produced similar human gut microbial taxonomic profiles. An unsupervised two-way hierarchical clustering analysis identified the microbial taxonomic signatures associated with each method. Proteobacteria and Bacteroidetes identification was favored by moistening the stool samples during processing and by disrupting cells with medium-sized glass beads. Ascomycota identification was enhanced by using large-sized glass beads during sample processing for stool dispersion. Euryarchaeota identification was improved with a combination of small and medium-sized glass beads for cell disruption. Assessments of the relative abundance of Firmicutes, Actinobacteria and Spirochaetes improved when ultrasonication was performed before cell disruption with glass beads. The latter method also increased the overall number of identified proteins. Taxonomic and protein functional analyses of metaproteomic data derived from stool samples from six healthy individuals showed common taxonomic profiles. We also detected certain proteins involved in microbial functions relevant to the host and related mostly to particular taxa, such as B12 biosynthesis and short chain fatty acid production carried out mainly by members in the Prevotella genus and the Firmicutes phylum, respectively. Finally, in this metaproteomic study we identified several human proteins, mostly related to the anti-microbial response, which could contribute to determining the beneficial and detrimental relationships between gut microbiota and human cells in particular human diseases.

Background/Objectives: Despite advances in metastatic renal cell carcinoma (mRCC), long-term survival is poor and biomarkers are lacking. Optimal integration of cytoreductive nephrectomy (CN) in the current era remains controversial. CABOPRE, the first phase II trial assessing perioperative cabozantinib in mRCC patients eligible for CN, aimed to determine its effects in this setting, integrating tumor outcomes with molecular profiling. Methods: CABOPRE (NCT06377722) is a multicentre, single-arm, phase II study conducted in Spanish academic hospitals. Patients with resectable metastatic clear cell RCC (ccRCC) eligible for post-treatment CN received perioperative cabozantinib (60 mg/day for 12 weeks; n = 18). The primary endpoint was the objective response rate (ORR) at 12 weeks assessed by RECIST v1.1. Exploratory analyses included longitudinal plasma miRNA and spatial transcriptomic profiling to identify candidate biomarkers. The trial closed early because of slow accrual, after enrolling 18 of 50 planned patients and without completing the 26-patient first stage of the Simon two-stage design; the planned interim analysis was therefore never performed. Results: Because the two-stage design was not completed, the primary endpoint could not be formally evaluated against the prespecified decision rule. Among 15 evaluable patients, the objective response rate was 26.7% (95% CI, 7.8–55.1), with stable disease in 10 (66.7%) and progressive disease in 1 (6.7%). Cabozantinib demonstrated a manageable safety profile (Grade 1–2 adverse events). In exploratory biomarker analyses, miR-126-3p upregulation and VEGFA/CCND1 downregulation were associated with tumor shrinkage, and the adhesion molecules ITGAV and ICAM1 were upregulated in these patients. Because no direct vascular measurements were performed, these changes are described as treatment-associated and hypothesis-generating rather than as predictors of response or evidence of vascular normalization. Conclusions: The observed response rate was close to the 25% null rate, and the prespecified efficacy rule could not be applied. CABOPRE provides preliminary evidence that perioperative cabozantinib is active and represents a viable backbone for neoadjuvant combination strategies in selected patients with potentially resectable mRCC. The identified molecular heterogeneity and stromal-immune signatures provide a biological rationale for the prospective evaluation of cabozantinib-IO combinations in this setting.

The severe acute respiratory syndrome coronavirus (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) is highly transmissible and has been responsible for a pandemic associated with a high number of deaths. The clinical management of patients and the optimal use of resources are two important factors in reducing this mortality, especially in scenarios of high incidence. To this end, it is necessary to develop tools that allow early triage of patients with the minimal use of diagnostic tests and based on readily accessible data, such as electronic medical records. This work proposes the use of a machine learning model that allows the prediction of mortality and risk of hospitalization using simple demographic characteristics and comorbidities, using a COVID-19 dataset of 86867 patients. In addition, we developed a new method designed to deal with data imbalance problems. The model was able to predict with high accuracy (89-93%, ROC-AUC = 0.94) the patient’s final status (expired/discharged) and with medium accuracy the risk of hospitalization (71-73%, ROC-AUC = 0.75). These models were obtained by assembling and using easily obtainable clinical characteristics (2 demographic characteristics and 19 predictors of comorbidities). The most relevant features of these models were the following patient characteristics: age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure.