Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges

Molecular analysis of archival diagnostic prostate cancer biopsies identifies genomic similarities in cases with progression post-radiotherapy, and those with de novo metastatic disease

BACKGROUND

It is important to identify molecular features that improve prostate cancer (PCa) risk stratification before radical treatment with curative intent. Molecular analysis of historical diagnostic formalin-fixed paraffin-embedded (FFPE) prostate biopsies from cohorts with post-radiotherapy (RT) long-term clinical follow-up has been limited. Utilizing parallel sequencing modalities, we performed a proof-of-principle sequencing analysis of historical diagnostic FFPE prostate biopsies. We compared patients with (i) stable PCa (sPCa) postprimary or salvage RT, (ii) progressing PCa (pPCa) post-RT, and (iii) de novo metastatic PCa (mPCa).

METHODS

A cohort of 19 patients with diagnostic prostate biopsies (n = 6 sPCa, n = 5 pPCa, n = 8 mPCa) and mean 4 years 10 months follow-up (diagnosed 2009-2016) underwent nucleic acid extraction from demarcated malignancy. Samples underwent 3'RNA sequencing (3'RNAseq) (n = 19), nanoString analysis (n = 12), and Illumina 850k methylation (n = 8) sequencing. Bioinformatic analysis was performed to coherently identify differentially expressed genes and methylated genomic regions (MGRs).

RESULTS

Eighteen of 19 samples provided useable 3'RNAseq data. Principal component analysis (PCA) demonstrated similar expression profiles between pPCa and mPCa cases, versus sPCa. Coherently differentially methylated probes between these groups identified ~600 differentially MGRs. The top 50 genes with increased expression in pPCa patients were associated with reduced progression-free survival post-RT (p < 0.0001) in an external cohort.

CONCLUSIONS

3'RNAseq, nanoString and 850k-methylation analyses are each achievable from historical FFPE diagnostic pretreatment prostate biopsies, unlocking the potential to utilize large cohorts of historic clinical samples. Profiling similarities between individuals with pPCa and mPCa suggests biological similarities and historical radiological staging limitations, which warrant further investigation.

Differential analysis of histopathological and genetic markers of cancer aggressiveness, and survival difference in EBV-positive and EBV-negative prostate carcinoma

Several studies have shown an association between prostate carcinoma (PCa) and Epstein-Barr virus (EBV); however, none of the studies so far have identified the histopathological and genetic markers of cancer aggressiveness associated with EBV in PCa tissues. In this study, we used previously characterized EBV-PCR-positive (n = 39) and EBV-negative (n = 60) PCa tissues to perform an IHC-based assessment of key histopathological and molecular markers of PCa aggressiveness (EMT markers, AR expression, perineural invasion, and lymphocytic infiltration characterization). Additionally, we investigated the differential expression of key oncogenes, EMT-associated genes, and PCa-specific oncomiRs, in EBV-positive and -negative tissues, using the qPCR array. Finally, survival benefit analysis was also performed in EBV-positive and EBV-negative PCa patients. The EBV-positive PCa exhibited a higher percentage (80%) of perineural invasion (PNI) compared to EBV-negative PCa (67.3%) samples. Similarly, a higher lymphocytic infiltration was observed in EBV-LMP1-positive PCa samples. The subset characterization of T and B cell lymphocytic infiltration showed a trend of higher intratumoral and tumor stromal lymphocytic infiltration in EBV-negative tissues compared with EBV-positive tissues. The logistic regression analysis showed that EBV-positive status was associated with decreased odds (OR = 0.07; p-value < 0.019) of CD3 intratumoral lymphocytic infiltration in PCa tissues. The analysis of IHC-based expression patterns of EMT markers showed comparable expression of all EMT markers, except vimentin, which showed higher expression in EBV-positive PCa tissues compared to EBV-negative PCa tissues. Furthermore, gene expression analysis showed a statistically significant difference (p < 0.05) in the expression of CDH1, AR, CHEK-2, CDKN-1B, and CDC-20 and oncomiRs miR-126, miR-152-3p, miR-452, miR-145-3p, miR-196a, miR-183-3p, and miR-146b in EBV-positive PCa tissues compared to EBV-negative PCa tissues. Overall, the survival proportion was comparable in both groups. The presence of EBV in the PCa tissues results in an increased expression of certain oncogenes, oncomiRs, and EMT marker (vimentin) and a decrease in CD3 ITL, which may be associated with the aggressive forms of PCa.

Deciphering the Transcription Factor Landscape in Neuroendocrine Prostate Cancer Progression: A Novel Approach to Understand NE Transdifferentiation

Background and Objective

Prostate cancer (PCa) is a leading cause of cancer mortality in men, with neuroendocrine prostate cancer (NEPC) representing a particularly resistant subtype. The role of transcription factors (TFs) in the progression from prostatic adenocarcinoma (PRAD) to NEPC is poorly understood. This study aims to identify and analyze lineage-specific TF profiles in PRAD and NEPC and illustrate their dynamic shifts during NE transdifferentiation.

Methods

A novel algorithmic approach was developed to evaluate the weighted expression of TFs within patient samples, enabling a nuanced understanding of TF landscapes in PCa progression and TF dynamic shifts during NE transdifferentiation.

Results

unveiled TF profiles for PRAD and NEPC, identifying 126 shared TFs, 46 adenocarcinoma-TFs, and 56 NEPC-TFs. Enrichment analysis across multiple clinical cohorts confirmed the lineage specificity and clinical relevance of these lineage-TFs signatures. Functional analysis revealed that lineage-TFs are implicated in pathways critical to cell development, differentiation, and lineage determination. Novel lineage-TF candidates were identified, offering potential targets for therapeutic intervention. Furthermore, our longitudinal study on NE transdifferentiation highlighted dynamic TF expression shifts and delineated a three-phase hypothesis for the process comprised of de-differentiation, dormancy, and re-differentiation. and proposing novel insights into the mechanisms of PCa progression.

Conclusion

The lineage-specific TF profiles in PRAD and NEPC reveal a dynamic shift in the TF landscape during PCa progression, highlighting three distinct phases of NE transdifferentiation.

Experimental prognostic model integrating N6-methyladenosine-related programmed cell death genes in colorectal cancer

Colorectal cancer (CRC) intricacies, involving dysregulated cellular processes and programmed cell death (PCD), are explored in the context of N6-methyladenosine (m6A) RNA modification. Utilizing the TCGA-COADREAD/CRC cohort, 854 m6A-related PCD genes are identified, forming the basis for a robust 10-gene risk model (CDRS) established through LASSO Cox regression. qPCR experiments using CRC cell lines and fresh tissues was performed for validation. The CDRS served as an independent risk factor for CRC and showed significant associations with clinical features, molecular subtypes, and overall survival in multiple datasets. Moreover, CDRS surpasses other predictors, unveiling distinct genomic profiles, pathway activations, and associations with the tumor microenvironment. Notably, CDRS exhibits predictive potential for drug sensitivity, presenting a novel paradigm for CRC risk stratification and personalized treatment avenues.

Identification of tumor-agnostic biomarkers for predicting prostate cancer progression and biochemical recurrence

The diverse clinical outcomes of prostate cancer have led to the development of gene signature assays predicting disease progression. Improved prostate cancer progression biomarkers are needed as current RNA biomarker tests have varying success for intermediate prostate cancer. Interest grows in universal gene signatures for invasive carcinoma progression. Early breast and prostate cancers share characteristics, including hormone dependence and BRCA1/2 mutations. Given the similarities in the pathobiology of breast and prostate cancer, we utilized the NanoString BC360 panel, comprising the validated PAM50 classifier and pathway-specific signatures associated with general tumor progression as well as breast cancer-specific classifiers. This retrospective cohort of primary prostate cancers (n=53) was stratified according to biochemical recurrence (BCR) status and the CAPRA-S to identify genes related to high-risk disease. Two public cohort (TCGA-PRAD and GSE54460) were used to validate the results. Expression profiling of our cohort uncovered associations between PIP and INHBA with BCR and high CAPRA-S score, as well as associations between VCAN, SFRP2, and THBS4 and BCR. Despite low levels of the ESR1 gene compared to AR, we found strong expression of the ER signaling signature, suggesting that BCR may be driven by ER-mediated pathways. Kaplan-Meier and univariate Cox proportional hazards regression analysis indicated the expression of ESR1, PGR, VCAN, and SFRP2 could predict the occurrence of relapse events. This is in keeping with the pathways represented by these genes which contribute to angiogenesis and the epithelial-mesenchymal transition. It is likely that VCAN works by activating the stroma and remodeling the tumor microenvironment. Additionally, SFRP2 overexpression has been associated with increased tumor size and reduced survival rates in breast cancer and among prostate cancer patients who experienced BCR. ESR1 influences disease progression by activating stroma, stimulating stem/progenitor prostate cancer, and inducing TGF-β. Estrogen signaling may therefore serve as a surrogate to AR signaling during progression and in hormone-refractory disease, particularly in prostate cancer patients with stromal-rich tumors. Collectively, the use of agnostic biomarkers developed for breast cancer stratification has facilitated a precise clinical classification of patients undergoing radical prostatectomy and highlighted the therapeutic potential of targeting estrogen signaling in prostate cancer.

Endocrine Disrupting Chemicals Influence Hub Genes Associated with Aggressive Prostate Cancer

Prostate cancer (PCa) is one of the most frequently diagnosed cancers among men in the world. Its prevention has been limited because of an incomplete understanding of how environmental exposures to chemicals contribute to the molecular pathogenesis of aggressive PCa. Environmental exposures to endocrine-disrupting chemicals (EDCs) may mimic hormones involved in PCa development. This research aims to identify EDCs associated with PCa hub genes and/or transcription factors (TF) of these hub genes in addition to their protein-protein interaction (PPI) network. We are expanding upon the scope of our previous work, using six PCa microarray datasets, namely, GSE46602, GSE38241, GSE69223, GSE32571, GSE55945, and GSE26126, from the NCBI/GEO, to select differentially expressed genes based on |log2FC| (fold change) ≥ 1 and an adjusted p-value < 0.05. An integrated bioinformatics analysis was used for enrichment analysis (using DAVID.6.8, GO, KEGG, STRING, MCODE, CytoHubba, and GeneMANIA). Next, we validated the association of these PCa hub genes in RNA-seq PCa cases and controls from TCGA. The influence of environmental chemical exposures, including EDCs, was extrapolated using the chemical toxicogenomic database (CTD). A total of 369 overlapping DEGs were identified associated with biological processes, such as cancer pathways, cell division, response to estradiol, peptide hormone processing, and the p53 signaling pathway. Enrichment analysis revealed five up-regulated (NCAPG, MKI67, TPX2, CCNA2, CCNB1) and seven down-regulated (CDK1, CCNB2, AURKA, UBE2C, BUB1B, CENPF, RRM2) hub gene expressions. Expression levels of these hub genes were significant in PCa tissues with high Gleason scores ≥ 7. These identified hub genes influenced disease-free survival and overall survival of patients 60-80 years of age. The CTD studies showed 17 recognized EDCs that affect TFs (NFY, CETS1P54, OLF1, SRF, COMP1) that are known to bind to our PCa hub genes, namely, NCAPG, MKI67, CCNA2, CDK1, UBE2C, and CENPF. These validated differentially expressed hub genes can be potentially developed as molecular biomarkers with a systems perspective for risk assessment of a wide-ranging list of EDCs that may play overlapping and important role(s) in the prognosis of aggressive PCa.

Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer

Background

Most prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients.

Methods

Prognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan-Meier (K-M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model.

Results

A total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues.

Conclusion

A credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients.

Proteomic Distributions in CD34+ Microvascular Niche Patterns of Glioblastoma

The poor clinical prognosis and microvascular patterns of glioblastoma (GBM) are of serious concern to many clinicians and researchers. However, very few studies have examined the correlation between microvascular niche patterns (MVNPs) and proteomic distribution. In this study, CD34 immunofluorescence staining and matrix-assisted laser desorption ionization-mass spectrometry imaging (MALDI-IMS) technology were used to investigate the protein distributions in MVNPs. CD34+ microvascular phenotype could be divided into four types: microvascular sprouting (MS), vascular cluster (VC), vascular garland (VG), and glomeruloid vascular proliferation (GVP). Based on such characteristics, MVNPs were divided into two types by cluster analysis, namely, type I, comprising primarily MS and VC, and type II, comprising many VGs and GVPs. Survival analysis indicated the type of MVNPs to be an independent prognostic factor for progression-free and overall survival in GBM. MALDI-IMS results showed the peaks at m/z 1037 and 8960 to exhibit stronger ion signals in type II, while those at m/z 3240 and 3265 exhibited stronger ion signals in type I. The findings may assist future research on therapy and help predict prognosis in GBM. However, due to the limited number of studies, more well-designed studies are warranted to further verify our results.

NLRP3 inflammasome promoted the malignant progression of prostate cancer via the activation of caspase-1

It is widely accepted that inflammation is an important risk for the development of prostate cancer (PCa). The objective of this study was designed to investigate the potential molecular mechanism of NLR family, pyrin domain-containing protein 3 (NLRP3) inflammasome in the malignant progression of PCa. The expression level of NLRP3 was evaluated by quantitative real-time polymerase chain reaction (qRT-PCR) and fluorescence in situ hybridization. The effects of NLRP3 in the development of PCa by applying gain- and loss-of-function assays in LNCaP and PC3 cell lines were detected by CCK-8, TUNEL, and Transwell migration assays. The underlying mechanism of NLRP3 and caspase-1 in PCa was examined by the rescue experiments, western blotting, and qRT-PCR assays. In addition, the promoting effect of NLRP3 inflammasome was performed with an animal subcutaneous tumorigenesis experiment in vivo. The upregulation of NLRP3 was confirmed in PCa tissues and cell lines. Functionally, using CCK-8, TUNEL, and Transwell migration assays, these results showed that activation of NLRP3/caspase-1 inflammasome by LPS + ATP could enhance the ability of proliferation and migration; and decrease the apoptosis of LNCaP and PC3 cell lines. Western blotting assay showed that the activation of caspase-1 would increase after the stimulation of NLRP3 inflammasome by LPS + ATP. Moreover, the overexpression of NLRP3 promoted, while the knockdown of NLRP3 inhibited the malignant progression in PCa cell lines by positively regulating caspase-1. In addition, the rescue experiments revealed the association among NLRP3 and caspase-1, which showed that the overexpression vectors/inhibitors of caspase-1 could reverse the effect of knockdown/overexpression of NLRP3 in PCa cell lines in vitro. Finally, In in vivo experiment, the suppression of NLRP3 knockdown impaired tumor growth of PCa. Collectively, these results indicated that NLRP3 inflammasome played a vital role in promoting the malignant progression of PCa via the activation of caspase-1. Together, our findings provided insight into the mechanisms of NLRP3/caspase-1 inflammasome and revealed an alternative and potential target for the clinical diagnosis and treatment of PCa.

Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse

Prostate cancer is heterogeneous and patients would benefit from methods that stratify those who are likely to respond to systemic therapy. Here, we employ single-cell assays for transposase-accessible chromatin (ATAC) and RNA sequencing in models of early treatment response and resistance to enzalutamide. In doing so, we identify pre-existing and treatment-persistent cell subpopulations that possess regenerative potential when subjected to treatment. We find distinct chromatin landscapes associated with enzalutamide treatment and resistance that are linked to alternative transcriptional programs. Transcriptional profiles characteristic of persistent cells are able to stratify the treatment response of patients. Ultimately, we show that defining changes in chromatin and gene expression in single-cell populations from pre-clinical models can reveal as yet unrecognized molecular predictors of treatment response. This suggests that the application of single-cell methods with high analytical resolution in pre-clinical models may powerfully inform clinical decision-making.

Chromatin and Epigenetic Dysregulation of Prostate Cancer Development, Progression, and Therapeutic Response

The dysregulation of chromatin and epigenetics has been defined as the overarching cancer hallmark. By disrupting transcriptional regulation in normal cells and mediating tumor progression by promoting cancer cell plasticity, this process has the ability to mediate all defined hallmarks of cancer. In this review, we collect and assess evidence on the contribution of chromatin and epigenetic dysregulation in prostate cancer. We highlight important mechanisms leading to prostate carcinogenesis, the emergence of castration-resistance upon treatment with androgen deprivation therapy, and resistance to antiandrogens. We examine in particular the contribution of chromatin structure and epigenetics to cell lineage commitment, which is dysregulated during tumorigenesis, and cell plasticity, which is altered during tumor progression.

A Personalized Genomics Approach of the Prostate Cancer

Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary—“A”, and two secondary—“B” & “C”) cancer nodules and the surrounding normal tissue (“N”) from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the “A”, “B”, “C”, and “N” regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.

Development and Validation of a 7-Gene Prognostic Signature to Improve Survival Prediction in Pancreatic Ductal Adenocarcinoma

Background: Previous prognostic signatures of pancreatic ductal adenocarcinoma (PDAC) are mainly constructed to predict the overall survival (OS), and their predictive accuracy needs to be improved. Gene signatures that efficaciously predict both OS and disease-free survival (DFS) are of great clinical significance but are rarely reported. Methods: Univariate Cox regression analysis was adopted to screen common genes that were significantly associated with both OS and DFS in three independent cohorts. Multivariate Cox regression analysis was subsequently performed on the identified genes to determine an optimal gene signature in the MTAB-6134 training cohort. The Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were employed to assess the predictive accuracy. Biological process and pathway enrichment analyses were conducted to elucidate the biological role of this signature. Results: Multivariate Cox regression analysis determined a 7-gene signature that contained ASPH, DDX10, NR0B2, BLOC1S3, FAM83A, SLAMF6, and PPM1H. The signature had the ability to stratify PDAC patients with different OS and DFS, both in the training and validation cohorts. ROC curves confirmed the moderate predictive accuracy of this signature. Mechanically, the signature was related to multiple cancer-related pathways. Conclusion: A novel OS and DFS prediction model was constructed in PDAC with multi-cohort and cross-platform compatibility. This signature might foster individualized therapy and appropriate management of PDAC patients.

The Quandary of DNA-Based Treatment Assessment in De Novo Metastatic Prostate Cancer in the Era of Precision Oncology

Guidelines for genetic testing have been established for multiple tumor types, frequently indicating the most confident molecularly targeted treatment options. However, considering the often-complex presentation of individual cancer patients, in addition to the combinatorial complexity and inherent uncertainties of molecular findings, deriving optimal treatment strategies frequently becomes very challenging. Here, we report a comprehensive analysis of a 68-year-old male with metastatic prostate cancer, encompassing pathology and MRI findings, transcriptomic results, and key genomics findings from whole-exome sequencing, both somatic aberrations and germline variants. We identify multiple somatic aberrations that are known to be enriched in prostate cancer, including a deletion of PTEN and a fusion transcript involving BRCA2. The gene expression patterns in the tumor biopsy were also strikingly similar to prostate tumor samples from TCGA. Furthermore, we detected multiple lines of evidence for homologous recombination repair deficiency (HRD), including a dominant contribution by mutational signature SBS3, which is specifically attributed to HRD. On the basis of the genomic and transcriptomic findings, and in light of the clinical case presentation, we discussed the personalized treatment options that exist for this patient and the various challenges that one faces in the process of translating high-throughput sequencing data towards treatment regimens.