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BACKGROUND AND OBJECTIVE: While collagen density has been associated with poor outcomes in various cancers, its role in prostate cancer (PCa) remains elusive. Our aim was to analyze collagen-related transcriptomic, proteomic, and urinome alterations in the context of detection of clinically significant PCa (csPCa, International Society of Urological Pathology [ISUP] grade group ≥2). METHODS: Comprehensive analyses for PCa transcriptome (n = 1393), proteome (n = 104), and urinome (n = 923) data sets focused on 55 collagen-related genes. Investigation of the cellular source of collagen-related transcripts via single-cell RNA sequencing was conducted. Statistical evaluations, clustering, and machine learning models were used for data analysis to identify csPCa signatures. KEY FINDINGS AND LIMITATIONS: Differential expression of 30 of 55 collagen-related genes and 34 proteins was confirmed in csPCa in comparison to benign prostate tissue or ISUP 1 cancer. A collagen-high cancer cluster exhibited distinct cellular and molecular characteristics, including fibroblast and endothelial cell infiltration, intense extracellular matrix turnover, and enhanced growth factor and inflammatory signaling. Robust collagen-based machine learning models were established to identify csPCa. The models outcompeted prostate-specific antigen (PSA) and age, showing comparable performance to multiparametric magnetic resonance imaging (mpMRI) in predicting csPCa. Of note, the urinome-based collagen model identified four of five csPCa cases among patients with Prostate Imaging-Reporting and Data System (PI-IRADS) 3 lesions, for which the presence of csPCa is considered equivocal. The retrospective character of the study is a limitation. CONCLUSIONS AND CLINICAL IMPLICATIONS: Collagen-related transcriptome, proteome, and urinome signatures exhibited superior accuracy in detecting csPCa in comparison to PSA and age. The collagen signatures, especially in cases of ambiguous lesions on mpMRI, successfully identified csPCa and could potentially reduce unnecessary biopsies. The urinome-based collagen signature represents a promising liquid biopsy tool that requires prospective evaluation to improve the potential of this collagen-based approach to enhance diagnostic precision in PCa for risk stratification and guiding personalized interventions. PATIENT SUMMARY: In our study, collagen-related alterations in tissue, and urine were able to predict the presence of clinically significant prostate cancer at primary diagnosis.
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Multiple myeloma (MM) is a plasma cell malignancy of the bone marrow. Despite therapeutic advances, MM remains incurable, and better risk stratification as well as new therapies are therefore highly needed. The proteome of MM has not been systematically assessed before and holds the potential to uncover insight into disease biology and improved prognostication in addition to genetic and transcriptomic studies. Here we provide a comprehensive multiomics analysis including deep tandem mass tag-based quantitative global (phospho)proteomics, RNA sequencing, and nanopore DNA sequencing of 138 primary patient-derived plasma cell malignancies encompassing treatment-naive MM, plasma cell leukemia and the premalignancy monoclonal gammopathy of undetermined significance, as well as healthy controls. We found that the (phospho)proteome of malignant plasma cells are highly deregulated as compared with healthy plasma cells and is both defined by chromosomal alterations as well as posttranscriptional regulation. A prognostic protein signature was identified that is associated with aggressive disease independent of established risk factors in MM. Integration with functional genetics and single-cell RNA sequencing revealed general and genetic subtype-specific deregulated proteins and pathways in plasma cell malignancies that include potential targets for (immuno)therapies. Our study demonstrates the potential of proteogenomics in cancer and provides an easily accessible resource for investigating protein regulation and new therapeutic approaches in MM.
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Cancer immunotherapies using chimeric antigen receptor (CAR) T cells have tremendous potential and proven clinical efficacy against a number of malignancies. Research and development are emerging to deepen the knowledge of CAR T cell efficacy and extend the therapeutic potential of this novel therapy. To this end, functional characterization of CAR T cells plays a central role in consecutive phases across fundamental research and therapeutic development, with increasing needs for standardization. The functional characterization of CAR T cells is typically achieved by assessing critical effector functions, following co-culture with cell lines expressing the target antigen. However, the use of target cell lines poses several limitations, including alterations in cell fitness, metabolic state or genetic drift due to handling and culturing of the cells, which would increase variabilities and could lead to inconsistent results. Moreover, the use of target cell lines can be work and time intensive, and introduce significant background due to the allogenic responses of T cells. To overcome these limitations, we developed a synthetic bead-based platform ("Artificial Targets") to characterize CAR T cell function in vitro. These synthetic microparticles could specifically induce CAR T cell activation, as measured by CD69 and CD137 (4-1BB) upregulation. In addition, engagement with Artificial Targets resulted in induction of multiple effector functions of CAR T cells mimicking the response triggered by target cell lines including cytotoxic activity, as assessed by exposure of CD107a (LAMP-1), expression and secretion of cytokines, as well as cell proliferation. Importantly, in contrast to target cells, stimulation with Artificial Targets showed limited unspecific CAR T cell proliferation. Finally, Artificial Targets demonstrated flexibility to engage multiple costimulatory molecules that can synergistically enhance the CAR T cell function and represented a powerful tool for modulating CAR T cell responses. Collectively, our results show that Artificial Targets can specifically activate CAR T cells for essential effector functions that could significantly advance standardization of functional assessment of CAR T cells, from early development to clinical applications.