The Proteogenomic Landscape of Curable 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.

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Tumor evolution metrics predict recurrence beyond 10 years in locally advanced prostate cancer

Cancer evolution lays the groundwork for predictive oncology. Testing evolutionary metrics requires quantitative measurements in controlled clinical trials. We mapped genomic intratumor heterogeneity in locally advanced prostate cancer using 642 samples from 114 individuals enrolled in clinical trials with a 12-year median follow-up. We concomitantly assessed morphological heterogeneity using deep learning in 1,923 histological sections from 250 individuals. Genetic and morphological (Gleason) diversity were independent predictors of recurrence (hazard ratio (HR) = 3.12 and 95% confidence interval (95% CI) = 1.34-7.3; HR = 2.24 and 95% CI = 1.28-3.92). Combined, they identified a group with half the median time to recurrence. Spatial segregation of clones was also an independent marker of recurrence (HR = 2.3 and 95% CI = 1.11-4.8). We identified copy number changes associated with Gleason grade and found that chromosome 6p loss correlated with reduced immune infiltration. Matched profiling of relapse, decades after diagnosis, confirmed that genomic instability is a driving force in prostate cancer progression. This study shows that combining genomics with artificial intelligence-aided histopathology leads to the identification of clinical biomarkers of evolution.

Molecular characterization and clinical relevance of metabolic signature subtypes in gastric cancer

Metabolic reprogramming dictates tumor molecular attributes and therapeutic potentials. However, the comprehensive metabolic characteristics in gastric cancer (GC) remain obscure. Here, metabolic signature-based clustering analysis identifies three subtypes with distinct molecular and clinical features: MSC1 showed better prognosis and upregulation of the tricarboxylic acid (TCA) cycle and lipid metabolism, combined with frequent TP53 and RHOA mutation; MSC2 had moderate prognosis and elevated nucleotide and amino acid metabolism, enriched by intestinal histology and mismatch repair deficient (dMMR); and MSC3 exhibited poor prognosis and enhanced glycan and energy metabolism, accompanied by diffuse histology and frequent CDH1 mutation. The Shandong Provincial Hospital (SDPH) in-house dataset with matched transcriptomic, metabolomic, and spatial-metabolomic analysis also validated these findings. Further, we constructed the metabolic subtype-related prognosis gene (MSPG) scoring model to quantify the activity of individual tumors and found a positive correlation with cuproptosis signaling. In conclusion, comprehensive recognition of the metabolite signature can enhance the understanding of diversity and heterogeneity in GC.

Characteristics of the immune environment in prostate cancer as an adjunct to immunotherapy

Background and Aims

The tumor microenvironment (TME) exerts an important role in carcinogenesis and progression. Several investigations have suggested that immune cell infiltration (ICI) is of high prognostic importance for tumor progression and patient survival in many tumors, particularly prostate cancer. The pattern of immune infiltration of PCa, on the other hand, has not been thoroughly understood.

Methods

The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets on PCa were obtained, and several datasets were merged into one data set using the "ComBat" algorithm. The ICI profiles of PCa patients were then to be uncovered by two computer techniques. The unsupervised clustering method was utilized to identify three ICI patterns in tumor samples, and Principal Component Analysis (PCA) was conducted to estimate the ICI score.

Results

Three different clusters of three ICIs were identified in 1341 PCa samples, which also correlated with different clinical features/characteristics and biological pathways. Patients with PCa are classified into high and low subtypes based on the ICI scores extracted from immune-associated signature genes. High ICI score subtypes are associated with a worse prognosis, which may intrigue the activation of cancer-related and immune-related pathways such as pathways involving Toll-like receptors, T-cell receptors, JAK-STAT, and natural killer cells. The ICI score was linked to tumor mutation load and immune/cancer-relevant signaling pathways, which explain prostate cancer's poor prognosis.

Conclusion

The findings of this study not only advanced our knowledge of the mechanism of immune response in the prostate tumor microenvironment but also provided a novel biomarker, that is, the ICI score, for disease prognosis and guiding precision immunotherapy.

Prostate cancer reshapes the secreted and extracellular vesicle urinary proteomes

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Prediction of Clinically Significant Prostate Cancer by a Specific Collagen-related Transcriptome, Proteome, and Urinome Signature

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.

Single-cell proteomics by mass spectrometry: Advances and implications in cancer research

Cancer harbours extensive proteomic heterogeneity. Inspired by the prior success of single-cell RNA sequencing (scRNA-seq) in characterizing minute transcriptomics heterogeneity in cancer, researchers are now actively searching for information regarding the proteomics counterpart. Therefore recently, single-cell proteomics by mass spectrometry (SCP) has rapidly developed into state-of-the-art technology to cater the need. This review aims to summarize application of SCP in cancer research, while revealing current development progress of SCP technology. The review also aims to contribute ideas into research gaps and future directions, ultimately promoting the application of SCP in cancer research.

Integration of proteomics in the molecular tumor board

Cancer remains one of the most complex and challenging diseases in mankind. To address the need for a personalized treatment approach for particularly complex tumor cases, molecular tumor boards (MTBs) have been initiated. MTBs are interdisciplinary teams that perform in-depth molecular diagnostics to cooperatively and interdisciplinarily advise on the best therapeutic strategy. Current molecular diagnostics are routinely performed on the transcriptomic and genomic levels, aiming to identify tumor-driving mutations. However, these approaches can only partially capture the actual phenotype and the molecular key players of tumor growth and progression. Thus, direct investigation of the expressed proteins and activated signaling pathways provide valuable complementary information on the tumor-driving molecular characteristics of the tissue. Technological advancements in mass spectrometry-based proteomics enable the robust, rapid, and sensitive detection of thousands of proteins in minimal sample amounts, paving the way for clinical proteomics and the probing of oncogenic signaling activity. Therefore, proteomics is currently being integrated into molecular diagnostics within MTBs and holds promising potential in aiding tumor classification and identifying personalized treatment strategies. This review introduces MTBs and describes current clinical proteomics, its potential in precision oncology, and highlights the benefits of multi-omic data integration.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

Resistance to androgen-deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform into emergent aggressive variant prostate cancer (AVPC), which has neuroendocrine (NE)-like features. In this work, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflect and retain key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. We compared 15 NE versus 33 AdCa samples, which included six different PDX tumors for each group in biological replicates, and identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of concordance from PDX tumor-matched protein and mRNA revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

IMPLICATIONS

Overall, our study highlights the importance of protein-based identification when compared with RNA and provides a rich resource of new and feasible targets for clinical assay development and in understanding the underlying biology of these tumors.

Alternative splicing in prostate cancer progression and therapeutic resistance

Prostate cancer (CaP) remains the second leading cause of cancer deaths in western men. CaP mortality results from diverse molecular mechanisms that mediate resistance to the standard of care treatments for metastatic disease. Recently, alternative splicing has been recognized as a hallmark of CaP aggressiveness. Alternative splicing events cause treatment resistance and aggressive CaP behavior and are determinants of the emergence of the two major types of late-stage treatment-resistant CaP, namely castration-resistant CaP (CRPC) and neuroendocrine CaP (NEPC). Here, we review recent multi-omics data that are uncovering the complicated landscape of alternative splicing events during CaP progression and the impact that different gene transcript isoforms can have on CaP cell biology and behavior. We discuss renewed insights in the molecular machinery by which alternative splicing occurs and contributes to the failure of systemic CaP therapies. The potential for alternative splicing events to serve as diagnostic markers and/or therapeutic targets is explored. We conclude by considering current challenges and promises associated with splicing-modulating therapies, and their potential for clinical translation into CaP patient care.

Bioinspired Tumor Calcification-Guided Early Diagnosis and Eradication of Hepatocellular Carcinoma

Tumor calcification is found to be associated with the benign prognostic, and which shows considerable promise as a somewhat predictive index of the tumor response clinically. However, calcification is still a missing area in clinical cancer treatment. A specific strategy is proposed for inducing tumor calcification through the synergy of calcium peroxide (CaO2)-based microspheres and transcatheter arterial embolization for the treatment of hepatocellular carcinoma (HCC). The persistent calcium stress in situ specifically leads to powerful tumor calcioptosis, resulting in diffuse calcification and a high-density shadow on computed tomography that enables clear localization of the in vivo tumor site and partial delineation of tumor margins in an orthotopic HCC rabbit model. This osmotic calcification can facilitate tumor clinical diagnosis, which is of great significance in differentiating tumor response during early follow-up periods. Proteome and phosphoproteome analysis identify that calreticulin (CALR) is a crucial target protein involved in tumor calcioptosis. Further fluorescence molecular imaging analysis also indicates that CALR can be used as a prodromal marker of calcification to predict tumor response at an earlier stage in different preclinical rodent models. These findings suggest that upregulated CALR in association with tumor calcification, which may be broadly useful for quick visualization of tumor response.

moPepGen: Rapid and Comprehensive Proteoform Identification

Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteoform diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively enumerates proteoforms in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it detects and quantifies previously unobserved noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient identification and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.

Uncovering PROTAC Sensitivity and Efficacy by Multidimensional Proteome Profiling: A Case for STAT3

Proteolysis-targeting chimera (PROTAC) is a powerful technology that can effectively trigger the degradation of target proteins. The intricate interplay among various factors leads to a heterogeneous drug response, bringing about significant challenges in comprehending drug mechanisms. Our study applied data-independent acquisition-based mass spectrometry to multidimensional proteome profiling of PROTAC (DIA-MPP) to uncover the efficacy and sensitivity of the PROTAC compound. We profiled the signal transducer and activator of transcription 3 (STAT3) PROTAC degrader in six leukemia and lymphoma cell lines under multiple conditions, demonstrating the pharmacodynamic properties and downstream biological responses. Through comparison between sensitive and insensitive cell lines, we revealed that STAT1 can be regarded as a biomarker for STAT3 PROTAC degrader, which was validated in cells, patient-derived organoids, and mouse models. These results set an example for a comprehensive description of the multidimensional PROTAC pharmacodynamic response and PROTAC drug sensitivity biomarker exploration.

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

CD99 Modulates the Proteomic Landscape of Ewing Sarcoma Cells and Related Extracellular Vesicles

Ewing sarcoma (EWS) is an aggressive pediatric bone tumor characterized by unmet clinical needs and an incompletely understood epigenetic heterogeneity. Here, we considered CD99, a major surface molecule hallmark of EWS malignancy. Fluctuations in CD99 expression strongly impair cell dissemination, differentiation, and death. CD99 is also loaded within extracellular vesicles (EVs), and the delivery of CD99-positive or CD99-negative EVs dynamically exerts oncogenic or oncosuppressive functions to recipient cells, respectively. We undertook mass spectrometry and functional annotation analysis to investigate the consequences of CD99 silencing on the proteomic landscape of EWS cells and related EVs. Our data demonstrate that (i) the decrease in CD99 leads to major changes in the proteomic profile of EWS cells and EVs; (ii) intracellular and extracellular compartments display two distinct signatures of differentially expressed proteins; (iii) proteomic changes converge to the modulation of cell migration and immune-modulation biological processes; and (iv) CD99-silenced cells and related EVs are characterized by a migration-suppressive, pro-immunostimulatory proteomic profile. Overall, our data provide a novel source of CD99-associated protein biomarkers to be considered for further validation as mediators of EWS malignancy and as EWS disease liquid biopsy markers.

Mass Spectrometry-Based Proteogenomics: New Therapeutic Opportunities for Precision Medicine

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

A proteomic landscape of pharmacologic perturbations for functional relevance

Pharmacological perturbation studies based on protein-level signatures are fundamental for drug discovery. In the present study, we used a mass spectrometry (MS)-based proteomic platform to profile the whole proteome of the breast cancer MCF7 cell line under stress induced by 78 bioactive compounds. The integrated analysis of perturbed signal abundance revealed the connectivity between phenotypic behaviors and molecular features in cancer cells. Our data showed functional relevance in exploring the novel pharmacological activity of phenolic xanthohumol, as well as the noncanonical targets of clinically approved tamoxifen, lovastatin, and their derivatives. Furthermore, the rational design of synergistic inhibition using a combination of histone methyltransferase and topoisomerase was identified based on their complementary drug fingerprints. This study provides rich resources for the proteomic landscape of drug responses for precision therapeutic medicine.

Low-dose exercise protects the heart against established myocardial infarction via IGF-1-upregulated CTRP9 in male mice

Regular exercise is recommended as an important component of therapy for cardiovascular diseases in clinical practice. However, there are still major challenges in prescribing an optimized exercise regimen to individual patients with established cardiac disease. Here, we tested the effects of different exercise doses on cardiac function in mice with established myocardial infarction (MI). Exercise was introduced to mice with MI after 4 weeks of surgery. Low-dose exercise (15 min/day for 8 weeks) improved mortality and cardiac function by increasing 44.39% of ejection fractions while inhibiting fibrosis by decreasing 37.74% of distant region. Unlike higher doses of exercise, low-dose exercise consecutively upregulated cardiac expression of C1q complement/tumor necrosis factor-associated protein 9 (CTRP9) during exercise (>1.5-fold). Cardiac-specific knockdown of CTRP9 abolished the protective effects of low-dose exercise against established MI, while cardiac-specific overexpression of CTRP9 protected the heart against established MI. Mechanistically, low-dose exercise upregulated the transcription factor nuclear receptor subfamily 2 group F member 2 by increasing circulating insulin-like growth factor 1 (IGF-1), therefore, upregulating cardiac CTRP9 expression. These results suggest that low-dose exercise protects the heart against established MI via IGF-1-upregulated CTRP9 and may contribute to the development of optimized exercise prescriptions for patients with MI.

A pathway activity-based proteomic classifier stratifies prostate tumors into two subtypes

Prostate cancer (PCa) is the second most common cancer in males worldwide. The risk stratification of PCa is mainly based on morphological examination. Here we analyzed the proteome of 667 tumor samples from 487 Chinese PCa patients and characterized 9576 protein groups by PulseDIA mass spectrometry. Then we developed a pathway activity-based classifier concerning 13 proteins from seven pathways, and dichotomized the PCa patients into two subtypes, namely PPS1 and PPS2. PPS1 is featured with enhanced innate immunity, while PPS2 with suppressed innate immunity. This classifier exhibited a correlation with PCa progression in our cohort and was further validated by two published transcriptome datasets. Notably, PPS2 was significantly correlated with poor biochemical recurrence (BCR)/metastasis-free survival (log-rank P-value < 0.05). The PPS2 was also featured with cell proliferation activation. Together, our study presents a novel pathway activity-based stratification scheme for PCa.

Comprehensive analysis of alternative splicing across multiple transcriptomic cohorts reveals prognostic signatures in prostate cancer

BACKGROUND

Alternative splicing (AS) plays a crucial role in transcriptomic diversity and is a hallmark of cancer that profoundly influences the development and progression of prostate cancer (PCa), a prevalent and potentially life-limiting cancer among men. Accumulating evidence has highlighted the association between AS dysregulation and the onset and progression of PCa. However, a comprehensive and integrative analysis of AS profiles at the event level, utilising data from multiple high-throughput cohorts and evaluating the prognosis of PCa progression, remains lacking and calls for thorough exploration.

RESULTS

We identified a differentially expressed retained intron event in ZWINT across three distinct cohorts, encompassing an original array-based dataset profiled by us previously and two RNA sequencing (RNA-seq) datasets. Subsequent in-depth analyses of these RNA-seq datasets revealed  141 altered events, of which 21 demonstrated a significant association with patients' biochemical recurrence-free survival (BCRFS). We formulated an AS event-based prognostic signature, capturing six pivotal events in genes CYP4F12, NFATC4, PIGO, CYP3A5, ALS2CL, and FXYD3. This signature effectively differentiated  high-risk patients diagnosed with PCa, who experienced shorter BCRFS, from their low-risk counterparts. Notably, the signature's predictive power surpassed traditional clinicopathological markers in forecasting 5-year BCRFS, demonstrating robust performance in both internal and external validation sets. Lastly, we constructed a novel nomogram that integrates patients' Gleason scores with pathological tumour stages, demonstrating improved prognostication of BCRFS.

CONCLUSIONS

Prediction of clinical progression remains elusive in PCa. This research uncovers novel splicing events associated with BCRFS, augmenting existing prognostic tools, thus potentially refining clinical decision-making.

Exploring the pan-cancer landscape of posttranscriptional regulation

Understanding the mechanisms underlying cancer gene expression is critical for precision oncology. Posttranscriptional regulation is a key determinant of protein abundance and cancer cell behavior. However, to what extent posttranscriptional regulatory mechanisms impact protein levels and cancer progression is an ongoing question. Here, we exploit cancer proteogenomics data to systematically compare mRNA-protein correlations across 14 different human cancer types. We identify two clusters of genes with particularly low mRNA-protein correlations across all cancer types, shed light on the role of posttranscriptional regulation of cancer driver genes and drug targets, and unveil a cohort of 55 mutations that alter systems-wide posttranscriptional regulation. Surprisingly, we find that decreased levels of posttranscriptional control in patients correlate with shorter overall survival across multiple cancer types, prompting further mechanistic studies into how posttranscriptional regulation affects patient outcomes. Our findings underscore the importance of a comprehensive understanding of the posttranscriptional regulatory landscape for predicting cancer progression.

Proteogenomics in Cancer: Then and Now

For years, the paths of sequencing technologies and mass spectrometry have occurred in isolation, with each developing its own unique culture and expertise. These two technologies are crucial for inspecting complementary aspects of the molecular phenotype across the central dogma. Integrative multiomics strives to bridge the analysis gap among different fields to complete more comprehensive mechanisms of life events and diseases. Proteogenomics is one integrated multiomics field. Here in this review, we mainly summarize and discuss three aspects: workflow of proteogenomics, proteogenomics applications in cancer research, and the SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis of proteogenomics in cancer research. In conclusion, proteogenomics has a promising future as it clarifies the functional consequences of many unannotated genomic abnormalities or noncanonical variants and identifies driver genes and novel therapeutic targets across cancers, which would substantially accelerate the development of precision oncology.

Complementary analysis of proteome-wide proteomics reveals changes in RNA binding protein-profiles during prostate cancer progression

BACKGROUND

Accumulating evidence indicates importance of RNA regulation in cancer. This includes events such as splicing, translation, and regulation of noncoding RNAs, functions which are governed by RNA binding proteins (RBPs).

AIMS

To find which RBPs could be relevant for prostate cancer, we performed systematic screening of RBP expression in clinical prostate cancer.

METHODS AND RESULTS

We interrogated four proteome-wide proteomics datasets including tumor samples of primary, castration resistant, and metastatic prostate cancer. We found that, while the majority of RBPs are expressed but not significantly altered during prostate cancer development and progression, expression of several RBPs increases in advanced disease. Interestingly, most of the differentially expressed RBPs are not targets of differential posttranscriptional phosphorylation during disease progression. The RBPs undergoing expression changes have functions in, especially, poly(A)-RNA binding, nucleocytoplasmic transport, and cellular stress responses, suggesting that these may play a role in formation of castration resistance. Pathway analyzes indicate that increased ribosome production and chromatin-related functions of RBPs are also linked to castration resistant and metastatic prostate cancers. We selected a group of differentially expressed RBPs and studied their role in cultured prostate cancer cells. With siRNA screens, several of these were indicated in survival (DDX6, EIF4A3, PABPN1), growth (e.g., EIF5A, HNRNPH2, LRRC47, and NVL), and migration (e.g., NOL3 and SLTM) of prostate cancer cells. Our analyzes further show that RRP9, a U3 small nucleolar protein essential for ribosome formation, undergoes changes at protein level during metastasis in prostate cancer.

CONCLUSION

In this work, we recognized significant molecular alterations in RBP profiles during development and evolution of prostate cancer. Our study further indicates several functionally significant RBPs warranting further investigation for their functions and possible targetability in prostate cancer.

Global impact of somatic structural variation on the cancer proteome

Both proteome and transcriptome data can help assess the relevance of non-coding somatic mutations in cancer. Here, we combine mass spectrometry-based proteomics data with whole genome sequencing data across 1307 human tumors spanning various tissues to determine the extent somatic structural variant (SV) breakpoint patterns impact protein expression of nearby genes. We find that about 25% of the hundreds of genes with SV-associated cis-regulatory alterations at the mRNA level are similarly associated at the protein level. SVs associated with enhancer hijacking, retrotransposon translocation, altered DNA methylation, or fusion transcripts are implicated in protein over-expression. SVs combined with altered protein levels considerably extend the numbers of patients with tumors somatically altered for critical pathways. We catalog both SV breakpoint patterns involving patient survival and genes with nearby SV breakpoints associated with increased cell dependency in cancer cell lines. Pan-cancer proteogenomics identifies targetable non-coding alterations, by virtue of the associated deregulated genes.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Editorial: The Application of Proteogenomics to Urine Analysis for the Identification of Novel Biomarkers of Prostate Cancer: An Exploratory Study

In this editorial context, we aim to leverage the potential of proteogenomics, which integrates genomic and proteomic data, to discover novel biomarkers that can aid in the diagnosis and management of prostate cancer. We highlight the importance of proteogenomics for understanding the functional consequences of somatic mutations in cancer and demonstrating how proteogenomic analysis can provide insights into the effects of genetic alterations on the proteomic landscape and identify potential therapeutic targets. This article also emphasizes the potential of urine analysis for the detection of prostate cancer. Overall, our editorial paper provides general insights on the application of proteogenomics to urine analysis for the identification of novel biomarkers of prostate cancer.

The Akt/mTOR and MNK/eIF4E pathways rewire the prostate cancer translatome to secrete HGF, SPP1 and BGN and recruit suppressive myeloid cells

Cancer is highly infiltrated by myeloid-derived suppressor cells (MDSCs). Currently available immunotherapies do not completely eradicate MDSCs. Through a genome-wide analysis of the translatome of prostate cancers driven by different genetic alterations, we demonstrate that prostate cancer rewires its secretome at the translational level to recruit MDSCs. Among different secreted proteins released by prostate tumor cells, we identified Hgf, Spp1 and Bgn as the key factors that regulate MDSC migration. Mechanistically, we found that the coordinated loss of Pdcd4 and activation of the MNK/eIF4E pathways regulate the mRNAs translation of Hgf, Spp1 and Bgn. MDSC infiltration and tumor growth were dampened in prostate cancer treated with the MNK1/2 inhibitor eFT508 and/or the AKT inhibitor ipatasertib, either alone or in combination with a clinically available MDSC-targeting immunotherapy. This work provides a therapeutic strategy that combines translation inhibition with available immunotherapies to restore immune surveillance in prostate cancer.

The Identification of CELSR3 and Other Potential Cell Surface Targets in Neuroendocrine Prostate Cancer

Although recent efforts have led to the development of highly effective androgen receptor (AR)-directed therapies for the treatment of advanced prostate cancer, a significant subset of patients will progress with resistant disease including AR-negative tumors that display neuroendocrine features [neuroendocrine prostate cancer (NEPC)]. On the basis of RNA sequencing (RNA-seq) data from a clinical cohort of tissue from benign prostate, locally advanced prostate cancer, metastatic castration-resistant prostate cancer and NEPC, we developed a multi-step bioinformatics pipeline to identify NEPC-specific, overexpressed gene transcripts that encode cell surface proteins. This included the identification of known NEPC surface protein CEACAM5 as well as other potentially targetable proteins (e.g., HMMR and CESLR3). We further showed that cadherin EGF LAG seven-pass G-type receptor 3 (CELSR3) knockdown results in reduced NEPC tumor cell proliferation and migration in vitro. We provide in vivo data including laser capture microdissection followed by RNA-seq data supporting a causal role of CELSR3 in the development and/or maintenance of the phenotype associated with NEPC. Finally, we provide initial data that suggests CELSR3 is a target for T-cell redirection therapeutics. Further work is now needed to fully evaluate the utility of targeting CELSR3 with T-cell redirection or other similar therapeutics as a potential new strategy for patients with NEPC.

Significance

The development of effective treatment for patients with NEPC remains an unmet clinical need. We have identified specific surface proteins, including CELSR3, that may serve as novel biomarkers or therapeutic targets for NEPC.

Prostate Cancer Reshapes the Secreted and Extracellular Vesicle Urinary Proteomes

Urine is a complex biofluid that reflects both overall physiologic state and the state of the genitourinary tissues through which it passes. It contains both secreted proteins and proteins encapsulated in tissue-derived extracellular vesicles (EVs). To understand the population variability and clinical utility of urine, we quantified the secreted and EV proteomes from 190 men, including a subset with prostate cancer. We demonstrate that a simple protocol enriches prostatic proteins in urine. Secreted and EV proteins arise from different subcellular compartments. Urinary EVs are faithful surrogates of tissue proteomes, but secreted proteins in urine or cell line EVs are not. The urinary proteome is longitudinally stable over several years. It can accurately and non-invasively distinguish malignant from benign prostatic lesions, and can risk-stratify prostate tumors. This resource quantifies the complexity of the urinary proteome, and reveals the synergistic value of secreted and EV proteomes for translational and biomarker studies.

Whole-genome DNA methylation and DNA methylation-based biomarkers in lung squamous cell carcinoma

Exploring early detection methods through comprehensive evaluation of DNA methylation for lung squamous cell carcinoma (LUSC) patients is of great significance. By using different machine learning algorithms for feature selection and model construction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, five methylation biomarkers in LUSC (along with mapped genes) were identified including cg14823851 (TBX4), cg02772121 (TRIM15), cg10424681 (C6orf201), cg12910906 (ARHGEF4), and cg20181079 (OR4D11), achieving extremely high sensitivity and specificity in distinguishing LUSC from normal samples in independent cohorts. Pyrosequencing assay verified DNA methylation levels, meanwhile qRT-PCR and immunohistochemistry results presented their accordant methylation-related gene expression statuses in paired LUSC and normal lung tissues. The five methylation-based biomarkers proposed in this study have great potential for the diagnosis of LUSC and could guide studies in methylation-regulated tumor development and progression.

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

CrypticProteinDB: an integrated database of proteome and immunopeptidome derived non-canonical cancer proteins

Translated non-canonical proteins derived from noncoding regions or alternative open reading frames (ORFs) can contribute to critical and diverse cellular processes. In the context of cancer, they also represent an under-appreciated source of targets for cancer immunotherapy through their tumor-enriched expression or by harboring somatic mutations that produce neoantigens. Here, we introduce the largest integration and proteogenomic analysis of novel peptides to assess the prevalence of non-canonical ORFs (ncORFs) in more than 900 patient proteomes and 26 immunopeptidome datasets across 14 cancer types. The integrative proteogenomic analysis of whole-cell proteomes and immunopeptidomes revealed peptide support for a nonredundant set of 9760 upstream, downstream, and out-of-frame ncORFs in protein coding genes and 12811 in noncoding RNAs. Notably, 6486 ncORFs were derived from differentially expressed genes and 340 were ubiquitously translated across eight or more cancers. The analysis also led to the discovery of thirty-four epitopes and eight neoantigens from non-canonical proteins in two cohorts as novel cancer immunotargets. Collectively, our analysis integrated both bottom-up proteogenomic and targeted peptide validation to illustrate the prevalence of translated non-canonical proteins in cancer and to provide a resource for the prioritization of novel proteins supported by proteomic, immunopeptidomic, genomic and transcriptomic data, available at https://www.maherlab.com/crypticproteindb.

Using cancer proteomics data to identify gene candidates for therapeutic targeting

Gene-level associations obtained from mass-spectrometry-based cancer proteomics datasets represent a resource for identifying gene candidates for functional studies. When recently surveying proteomic correlates of tumor grade across multiple cancer types, we identified specific protein kinases having a functional impact on uterine endometrial cancer cells. This previously published study provides just one template for utilizing public molecular datasets to discover potential novel therapeutic targets and approaches for cancer patients. Proteomic profiling data combined with corresponding multi-omics data on human tumors and cell lines can be analyzed in various ways to prioritize genes of interest for interrogating biology. Across hundreds of cancer cell lines, CRISPR loss of function and drug sensitivity scoring can be readily integrated with protein data to predict any gene's functional impact before bench experiments are carried out. Public data portals make cancer proteomics data more accessible to the research community. Drug discovery platforms can screen hundreds of millions of small molecule inhibitors for those that target a gene or pathway of interest. Here, we discuss some of the available public genomic and proteomic resources while considering approaches to how these could be leveraged for molecular biology insights or drug discovery. We also demonstrate the inhibitory effect of BAY1217389, a TTK inhibitor recently tested in a Phase I clinical trial for the treatment of solid tumors, on uterine cancer cell line viability.

Integrative multi-omics and drug-response characterization of patient-derived prostate cancer primary cells

Prostate cancer (PCa) is the second most prevalent malignancy in males across the world. A greater knowledge of the relationship between protein abundance and drug responses would benefit precision treatment for PCa. Herein, we establish 35 Chinese PCa primary cell models to capture specific characteristics among PCa patients, including gene mutations, mRNA/protein/surface protein distributions, and pharmaceutical responses. The multi-omics analyses identify Anterior Gradient 2 (AGR2) as a pre-operative prognostic biomarker in PCa. Through the drug library screening, we describe crizotinib as a selective compound for malignant PCa primary cells. We further perform the pharmacoproteome analysis and identify 14,372 significant protein-drug correlations. Surprisingly, the diminished AGR2 enhances the inhibition activity of crizotinib via ALK/c-MET-AKT axis activation which is validated by PC3 and xenograft model. Our integrated multi-omics approach yields a comprehensive understanding of PCa biomarkers and pharmacological responses, allowing for more precise diagnosis and therapies.

Variant biomarker discovery using mass spectrometry-based proteogenomics

Genomic diversity plays critical roles in risk of disease pathogenesis and diagnosis. While genomic variants-including single nucleotide variants, frameshift variants, and mis-splicing isoforms-are commonly detected at the DNA or RNA level, their translated variant protein or polypeptide products are ultimately the functional units of the associated disease. These products are often released in biofluids and could be leveraged for clinical diagnosis and patient stratification. Recent emergence of integrated analysis of genomics with mass spectrometry-based proteomics for biomarker discovery, also known as proteogenomics, have significantly advanced the understanding disease risk variants, precise medicine, and biomarker discovery. In this review, we discuss variant proteins in the context of cancers and neurodegenerative diseases, outline current and emerging proteogenomic approaches for biomarker discovery, and provide a comprehensive proteogenomic strategy for detection of putative biomarker candidates in human biospecimens. This strategy can be implemented for proteogenomic studies in any field of enquiry. Our review timely addresses the need of biomarkers for aging related diseases.

ALAN is a computational approach that interprets genomic findings in the context of tumor ecosystems

Gene behavior is governed by activity of other genes in an ecosystem as well as context-specific cues including cell type, microenvironment, and prior exposure to therapy. Here, we developed the Algorithm for Linking Activity Networks (ALAN) to compare gene behavior purely based on patient -omic data. The types of gene behaviors identifiable by ALAN include co-regulators of a signaling pathway, protein-protein interactions, or any set of genes that function similarly. ALAN identified direct protein-protein interactions in prostate cancer (AR, HOXB13, and FOXA1). We found differential and complex ALAN networks associated with the proto-oncogene MYC as prostate tumors develop and become metastatic, between different cancer types, and within cancer subtypes. We discovered that resistant genes in prostate cancer shared an ALAN ecosystem and activated similar oncogenic signaling pathways. Altogether, ALAN represents an informatics approach for developing gene signatures, identifying gene targets, and interpreting mechanisms of progression or therapy resistance.

Workflow enabling deepscale immunopeptidome, proteome, ubiquitylome, phosphoproteome, and acetylome analyses of sample-limited tissues

Serial multi-omic analysis of proteome, phosphoproteome, and acetylome provides insights into changes in protein expression, cell signaling, cross-talk and epigenetic pathways involved in disease pathology and treatment. However, ubiquitylome and HLA peptidome data collection used to understand protein degradation and antigen presentation have not together been serialized, and instead require separate samples for parallel processing using distinct protocols. Here we present MONTE, a highly sensitive multi-omic native tissue enrichment workflow, that enables serial, deep-scale analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome, and acetylome from the same tissue sample. We demonstrate that the depth of coverage and quantitative precision of each 'ome is not compromised by serialization, and the addition of HLA immunopeptidomics enables the identification of peptides derived from cancer/testis antigens and patient specific neoantigens. We evaluate the technical feasibility of the MONTE workflow using a small cohort of patient lung adenocarcinoma tumors.

Mass Spectrometry Contribution to Pediatric Cancers Research

For over four decades, mass spectrometry-based methods have provided a wealth of information relevant to various challenges in the field of cancers research. These challenges included identification and validation of novel biomarkers for various diseases, in particular for various forms of cancer. These biomarkers serve various objectives including monitoring patient response to the various forms of therapy, differentiating subgroups of the same type of cancer, and providing proteomic data to complement datasets generated by genomic, epigenetic, and transcriptomic methods. The same proteomic data can be used to provide prognostic information and could guide scientists and medics to new and innovative targeted therapies The past decade has seen a rapid emergence of epigenetics as a major contributor to carcinogenesis. This development has given a fresh momentum to MS-based proteomics, which demonstrated to be an unrivalled tool for the analyses of protein post-translational modifications associated with chromatin modifications. In particular, high-resolution mass spectrometry has been recently used for systematic quantification of chromatin modifications. Data generated by this approach are central in the search for new therapies for various forms of cancer and will help in attempts to decipher antitumor drug resistance. To appreciate the contribution of mass spectrometry-based proteomics to biomarkers discovery and to our understanding of mechanisms behind the initiation and progression of various forms of cancer, a number of recent investigations are discussed. These investigations also include results provided by two-dimensional gel electrophoresis combined with mass spectrometry.

Phenotypic heterogeneity analysis of APC-mutant colon cancer by proteomics and phosphoproteomics identifies RAI14 as a key prognostic determinant in East Asians and Westerners

Adenomatous polyposis coli (APC) is an important tumor suppressor and is mostly linked to the regulation of the WNT/β-catenin signaling pathway. APC mutation has been identified as an early event in more than 80% of sporadic colorectal cancers (CRCs). Moreover, prognostic differences are observed in CRC patients with APC mutations. Although previous genomics studies have investigated the roles of concomitant gene mutations in determining the phenotypic heterogeneity of APC-mutant tumors, valuable prognostic determinants for APC-mutant CRC patients are still lacking. Based on the proteome and phosphoproteome data, we classified APC-mutant colon cancer patients and revealed genomic, proteomic and phosphoproteomic heterogeneity in APC-mutant tumors. More importantly, we identified RAI14 as a key prognostic determinant for APC-mutant but not APC-wildtype colon cancer patients. The heterogeneity and the significance of prognostic biomarkers in APC-mutant tumors were further validated in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) colon cancer cohort. In addition, we found that colon cancer patients with high expression of RAI14 were less responsive to chemotherapy. Knockdown of RAI14 in cell lines led to reduced cell migration and changes in epithelial-mesenchymal transition (EMT)-related markers. Mechanistically, knockdown of RAI14 remodeled the phosphoproteome associated with cell adhesion, which might affect EMT marker expression and promote F-actin degradation. Collectively, this work describes the phenotypic heterogeneity of APC-mutant tumors and identifies RAI14 as an important prognostic determinant for APC-mutant colon cancer patients. The prognostic utility of RAI14 in APC-mutant colon cancer will provide early warning and increase the chance of successful treatment.

Mass spectrometry-based targeted proteomics for analysis of protein mutations

Cancers are caused by accumulated DNA mutations. This recognition of the central role of mutations in cancer and recent advances in next-generation sequencing, has initiated the massive screening of clinical samples and the identification of 1000s of cancer-associated gene mutations. However, proteomic analysis of the expressed mutation products lags far behind genomic (transcriptomic) analysis. With comprehensive global proteomics analysis, only a small percentage of single nucleotide variants detected by DNA and RNA sequencing have been observed as single amino acid variants due to current technical limitations. Proteomic analysis of mutations is important with the potential to advance cancer biomarker development and the discovery of new therapeutic targets for more effective disease treatment. Targeted proteomics using selected reaction monitoring (also known as multiple reaction monitoring) and parallel reaction monitoring, has emerged as a powerful tool with significant advantages over global proteomics for analysis of protein mutations in terms of detection sensitivity, quantitation accuracy and overall practicality (e.g., reliable identification and the scale of quantification). Herein we review recent advances in the targeted proteomics technology for enhancing detection sensitivity and multiplexing capability and highlight its broad biomedical applications for analysis of protein mutations in human bodily fluids, tissues, and cell lines. Furthermore, we review recent applications of top-down proteomics for analysis of protein mutations. Unlike the commonly used bottom-up proteomics which requires digestion of proteins into peptides, top-down proteomics directly analyzes intact proteins for more precise characterization of mutation isoforms. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale targeted detection and quantification of important protein mutations are discussed.

Rewiring of the N-Glycome with prostate cancer progression and therapy resistance

An understanding of the molecular features associated with prostate cancer progression (PCa) and resistance to hormonal therapy is crucial for the identification of new targets that can be utilized to treat advanced disease and prolong patient survival. The glycome, which encompasses all sugar polymers (glycans) synthesized by cells, has remained relatively unexplored in the context of advanced PCa despite the fact that glycans have great potential value as biomarkers and therapeutic targets due to their high density on the cell surface. Using imaging mass spectrometry (IMS), we profiled the N-linked glycans in tumor tissue derived from 131 patients representing the major disease states of PCa to identify glycosylation changes associated with loss of tumor cell differentiation, disease remission, therapy resistance and disease recurrence, as well as neuroendocrine (NE) differentiation which is a major mechanism for therapy failure. Our results indicate significant changes to the glycosylation patterns in various stages of PCa, notably a decrease in tri- and tetraantennary glycans correlating with disease remission, a subsequent increase in these structures with the transition to therapy-resistant PCa, and downregulation of complex N-glycans correlating with NE differentiation. Furthermore, both nonglucosylated and monoglucosylated mannose 9 demonstrate aberrant upregulation in therapy-resistant PCa which may be useful therapeutic targets as these structures are not normally presented in healthy tissue. Our findings characterize changes to the tumor glycome that occur with hormonal therapy and the development of castration-resistant PCa (CRPC), identifying several glycan markers and signatures which may be useful for diagnostic or therapeutic purposes.

A first-in-class HBO1 inhibitor WM-3835 inhibits castration-resistant prostate cancer cell growth in vitro and in vivo

The prognosis and overall survival of castration-resistant prostate cancer (CRPC) patients are poor. The search for novel and efficient anti-CRPC agents is therefore extremely important. WM-3835 is a cell-permeable, potent and first-in-class HBO1 (KAT7 or MYST2) inhibitor. Here in primary human prostate cancer cells-derived from CRPC patients, WM-3835 potently inhibited cell viability, proliferation, cell cycle progression and in vitro cell migration. The HBO1 inhibitor provoked apoptosis in the prostate cancer cells. It failed to induce significant cytotoxicity and apoptosis in primary human prostate epithelial cells. shRNA-induced silencing of HBO1 resulted in robust anti-prostate cancer cell activity as well, and adding WM-3835 failed to induce further cytotoxicity in the primary prostate cancer cells. Conversely, ectopic overexpression of HBO1 further augmented primary prostate cancer cell proliferation and migration. WM-3835 inhibited H3-H4 acetylation and downregulated several pro-cancerous genes (CCR2, MYLK, VEGFR2, and OCIAD2) in primary CRPC cells. Importantly, HBO1 mRNA and protein levels are significantly elevated in CRPC tissues and cells. In vivo, daily intraperitoneal injection of WM-3835 potently inhibited pPC-1 xenograft growth in nude mice, and no apparent toxicities detected. Moreover, intratumoral injection of HBO1 shRNA adeno-associated virus (AAV) suppressed the growth of primary prostate cancer xenografts in nude mice. H3-H4 histone acetylation and HBO1-dependent genes (CCR2, MYLK, VEGFR2, and OCIAD2) were remarkably decreased in WM-3835-treated or HBO1-silenced xenograft tissues. Together, targeting HBO1 by WM-3835 robustly inhibits CRPC cell growth.

Identification of molecular subtypes and a prognostic signature based on chromatin regulators related genes in prostate cancer

The clinical and molecular phenotypes of prostate cancer (PCa) exhibit substantial heterogeneity, ranging from indolent to metastatic disease. In this study, we aimed to identify PCa subtypes and construct a gene signature that can predict the recurrence-free survival (RFS) of PCa patients based on chromatin regulators genes (CRGs). Strikingly, we identified two heterogeneous subtypes with distinct clinical and molecular characteristics. Furthermore, by performing differential analysis between the two CRGs subtypes, we successfully constructed a gene signature to predict PCa prognosis. The signature, comprising four genes (MXD3, SSTR1, AMH and PPFIA2), was utilized to classify PCa patients into two risk groups; the high-risk group was characterized by poor prognosis and more aggressive clinical features. Moreover, we investigated the immune profile, mutation landscape and molecular pathways in each of the groups. Additionally, drug-susceptibility testing was performed to explore sensitive drugs for high-risk patients. Furthermore, we found that MXD3 downregulation suppressed the proliferation of PCa cell lines in vitro. Overall, our results highlight the signature based on CRGs as a powerful tool for predicting RFS of PCa patients, as well as an indicator for personalized treatment of those patients.

Quantitative proteomic studies addressing unmet clinical needs in sarcoma

Sarcoma is a rare and complex disease comprising over 80 malignant subtypes that is frequently characterized by poor prognosis. Challenges in clinical management include uncertainties in diagnosis and disease classification, limited prognostic and predictive biomarkers, incompletely understood disease heterogeneity among and within subtypes, lack of effective treatment options, and limited progress in identifying new drug targets and novel therapeutics. Proteomics refers to the study of the entire complement of proteins expressed in specific cells or tissues. Advances in proteomics have included the development of quantitative mass spectrometry (MS)-based technologies which enable analysis of large numbers of proteins with relatively high throughput, enabling proteomics to be studied on a scale that has not previously been possible. Cellular function is determined by the levels of various proteins and their interactions, so proteomics offers the possibility of new insights into cancer biology. Sarcoma proteomics therefore has the potential to address some of the key current challenges described above, but it is still in its infancy. This review covers key quantitative proteomic sarcoma studies with findings that pertain to clinical utility. Proteomic methodologies that have been applied to human sarcoma research are briefly described, including recent advances in MS-based proteomic technology. We highlight studies that illustrate how proteomics may aid diagnosis and improve disease classification by distinguishing sarcoma histologies and identify distinct profiles within histological subtypes which may aid understanding of disease heterogeneity. We also review studies where proteomics has been applied to identify prognostic, predictive and therapeutic biomarkers. These studies traverse a range of histological subtypes including chordoma, Ewing sarcoma, gastrointestinal stromal tumors, leiomyosarcoma, liposarcoma, malignant peripheral nerve sheath tumors, myxofibrosarcoma, rhabdomyosarcoma, synovial sarcoma, osteosarcoma, and undifferentiated pleomorphic sarcoma. Critical questions and unmet needs in sarcoma which can potentially be addressed with proteomics are outlined.

Comparative Proteomic and Transcriptomic Analysis of the Impact of Androgen Stimulation and Darolutamide Inhibition

Several inhibitors of androgen receptor (AR) function are approved for prostate cancer treatment, and their impact on gene transcription has been described. However, the ensuing effects at the protein level are far less well understood. We focused on the AR signaling inhibitor darolutamide and confirmed its strong AR binding and antagonistic activity using the high throughput cellular thermal shift assay (CETSA HT). Then, we generated comprehensive, quantitative proteomic data from the androgen-sensitive prostate cancer cell line VCaP and compared them to transcriptomic data. Following treatment with the synthetic androgen R1881 and darolutamide, global mass spectrometry-based proteomics and label-free quantification were performed. We found a generally good agreement between proteomic and transcriptomic data upon androgen stimulation and darolutamide inhibition. Similar effects were found both for the detected expressed genes and their protein products as well as for the corresponding biological programs. However, in a few instances there was a discrepancy in the magnitude of changes induced on gene expression levels compared to the corresponding protein levels, indicating post-transcriptional regulation of protein abundance. Chromatin immunoprecipitation DNA sequencing (ChIP-seq) and Hi-C chromatin immunoprecipitation (HiChIP) revealed the presence of androgen-activated AR-binding regions and long-distance AR-mediated loops at these genes.

Clinical proteomics towards multiomics in cancer

Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.

Prostate Cancer Transcriptomic Regulation by the Interplay of Germline Risk Alleles, Somatic Mutations, and 3D Genomic Architecture

Prostate cancer is one of the most heritable human cancers. Genome-wide association studies have identified at least 185 prostate cancer germline risk alleles, most noncoding. We used integrative three-dimensional (3D) spatial genomics to identify the chromatin interaction targets of 45 prostate cancer risk alleles, 31 of which were associated with the transcriptional regulation of target genes in 565 localized prostate tumors. To supplement these 31, we verified transcriptional targets for 56 additional risk alleles using linear proximity and linkage disequilibrium analysis in localized prostate tumors. Some individual risk alleles influenced multiple target genes; others specifically influenced only distal genes while leaving proximal ones unaffected. Several risk alleles exhibited widespread germline-somatic interactions in transcriptional regulation, having different effects in tumors with loss of PTEN or RB1 relative to those without. These data clarify functional prostate cancer risk alleles in large linkage blocks and outline a strategy to model multidimensional transcriptional regulation.

SIGNIFICANCE

Many prostate cancer germline risk alleles are enriched in the noncoding regions of the genome and are hypothesized to regulate transcription. We present a 3D genomics framework to unravel risk SNP function and describe the widespread germline-somatic interplay in transcription control. This article is highlighted in the In This Issue feature, p. 2711.