A robust blood gene expression-based prognostic model for castration-resistant prostate cancer

Gene expression signature of castrate resistant prostate cancer

Prostate gland is a highly androgen dependent gland and hence the first line of treatment for metastatic prostate cancer happens to be androgen ablation. This is achieved by multiple non-surgical methods. However, most of these cancers although respond well initially, become resistant to androgen ablation sooner or later. These cancers then become extremely aggressive and difficult to treat, thereby drastically affect the patient prognosis. Identification of a gene expression signature for castrate resistant prostate cancer may aid in identification of mechanisms responsible for castrate resistance, which in turn would help in better management of the disease. METHODS: Patient samples belonging to a. Control group; b. Castrate Sensitive group and c. Castrate Resistant group were collected. Gene expression profiling was performed on these samples using RNA-seq. Differentially expressed genes between control and castrate sensitive as well as control and castrate resistant groups were identified. This data was compared with data from The Cancer Genome Atlas (TCGA) in order to get relevance in prognosis. RESULTS: We have identified 481 differentially expressed between control and castrate sensitive groups; and 446 genes differentially expressed between control and castrate resistant groups. We have also identified 364 genes which are expressed in the castrate resistant group alone, which is of interest since these may have an implication in evolution of castrate resistance and also prognosis. When compared to prostate cancer data from TCGA, 763 genes were found in common to our dataset. With this, a CaS and CaR signature was defined. Using criteria such as overall survival, disease-free survival, progression-free survival and biochemical recurrence, we have identified genes that may have relevance in progression to castrate resistance and in prognosis. Functional annotation of these genes may give an insight into the mechanism of development of castrate resistance.

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.

Targeting myeloid chemotaxis to reverse prostate cancer therapy resistance

Inflammation is a hallmark of cancer1. In patients with cancer, peripheral blood myeloid expansion, indicated by a high neutrophil-to-lymphocyte ratio, associates with shorter survival and treatment resistance across malignancies and therapeutic modalities2-5. Whether myeloid inflammation drives progression of prostate cancer in humans remain unclear. Here we show that inhibition of myeloid chemotaxis can reduce tumour-elicited myeloid inflammation and reverse therapy resistance in a subset of patients with metastatic castration-resistant prostate cancer (CRPC). We show that a higher blood neutrophil-to-lymphocyte ratio reflects tumour myeloid infiltration and tumour expression of senescence-associated mRNA species, including those that encode myeloid-chemoattracting CXCR2 ligands. To determine whether myeloid cells fuel resistance to androgen receptor signalling inhibitors, and whether inhibiting CXCR2 to block myeloid chemotaxis reverses this, we conducted an investigator-initiated, proof-of-concept clinical trial of a CXCR2 inhibitor (AZD5069) plus enzalutamide in patients with metastatic CRPC that is resistant to androgen receptor signalling inhibitors. This combination was well tolerated without dose-limiting toxicity and it decreased circulating neutrophil levels, reduced intratumour CD11b+HLA-DRloCD15+CD14- myeloid cell infiltration and imparted durable clinical benefit with biochemical and radiological responses in a subset of patients with metastatic CRPC. This study provides clinical evidence that senescence-associated myeloid inflammation can fuel metastatic CRPC progression and resistance to androgen receptor blockade. Targeting myeloid chemotaxis merits broader evaluation in other cancers.

Evaluation of an RNAseq-Based Immunogenomic Liquid Biopsy Approach in Early-Stage Prostate Cancer

The primary objective of this study is to detect biomarkers and develop models that enable the identification of clinically significant prostate cancer and to understand the biologic implications of the genes involved. Peripheral blood samples (1018 patients) were split chronologically into independent training (n = 713) and validation (n = 305) sets. Whole transcriptome RNA sequencing was performed on isolated phagocytic CD14+ and non-phagocytic CD2+ cells and their gene expression levels were used to develop predictive models that correlate to adverse pathologic features. The immune-transcriptomic model with the highest performance for predicting adverse pathology, based on a subtraction of the log-transformed expression signals of the two cell types, displayed an area under the curve (AUC) of the receiver operating characteristic of 0.70. The addition of biomarkers in combination with traditional clinical risk factors (age, serum prostate-specific antigen (PSA), PSA density, race, digital rectal examination (DRE), and family history) enhanced the AUC to 0.91 and 0.83 for the training and validation sets, respectively. The markers identified by this approach uncovered specific pathway associations relevant to (prostate) cancer biology. Increased phagocytic activity in conjunction with cancer-associated (mis-)regulation is also represented by these markers. Differential gene expression of circulating immune cells gives insight into the cellular immune response to early tumor development and immune surveillance.

High Monocyte Count and Expression of S100A9 and S100A12 in Peripheral Blood Mononuclear Cells Are Associated with Poor Outcome in Patients with Metastatic Prostate Cancer

Increasing evidence indicates calcium-binding S100 protein involvement in inflammation and tumor progression. In this prospective study, we evaluated the mRNA levels of two members of this family, S100A9 and S100A12, in peripheral blood mononuclear cells (PBMCs) in a cohort of 121 prostate cancer patients using RT-PCR. Furthermore, monocyte count was determined by flow cytometry. By stratifying patients into different risk groups, according to TNM stage, Gleason score and PSA concentration at diagnosis, expression of S100A9 and S100A12 was found to be significantly higher in patients with metastases compared to patients without clinically detectable metastases. In line with this, we observed that the protein levels of S100A9 and S100A12 in plasma were higher in patients with advanced disease. Importantly, in patients with metastases at diagnosis, high monocyte count and high levels of S100A9 and S100A12 were significantly associated with short progression free survival (PFS) after androgen deprivation therapy (ADT). High monocyte count and S100A9 levels were also associated with short cancer-specific survival, with monocyte count providing independent prognostic information. These findings indicate that circulating levels of monocytes, as well as S100A9 and S100A12, could be biomarkers for metastatic prostate cancer associated with particularly poor prognosis.

Identification of Key Modules and Hub Genes of Annulus Fibrosus in Intervertebral Disc Degeneration

Background: Intervertebral disc degeneration impairs the quality of patients lives. Even though there has been development of many therapeutic strategies, most of them remain unsatisfactory due to the limited understanding of the mechanisms that underlie the intervertebral disc degeneration. Questions/purposes: This study is meant to identify the key modules and hub genes related to the annulus fibrosus in intervertebral disc degeneration (IDD) through: (1) constructing a weighted gene co-expression network; (2) identifying key modules and hub genes; (3) verifying the relationships of key modules and hub genes with IDD; and (4) confirming the expression pattern of hub genes in clinical samples. Methods: The Gene Expression Omnibus provided 24 sets of annulus fibrosus microarray data. Differentially expressed genes between the annulus fibrosus of degenerative and non-degenerative intervertebral disc samples have gone through the Gene Ontology (GO) and pathway analysis. The construction of a gene network and classification of genes into different modules were conducted through performing Weighted Gene Co-expression Network Analysis. The identification of modules and hub genes that were most related to intervertebral disc degeneration was proceeded. In order to verify the relationships of the module and hub genes with intervertebral disc degeneration, Ingenuity Pathway Analysis was operated. Clinical samples were adopted to help verify the hub gene expression profile. Results: One thousand one hundred ninety differentially expressed genes were identified. Terms and pathways associated with intervertebral disc degeneration were presented by GO and pathway analysis. The construction of a Weighted Gene Coexpression Network was completed and clustering differentially expressed genes into four modules was also achieved. The module with the lowest P-value and the highest absolute correlation coefficient was selected and its relationship with intervertebral disc degeneration was confirmed by Ingenuity Pathway Analysis. The identification of hub genes and the confirmation of their expression profile were also realized. Conclusions: This study generated a comprehensive overview of the gene networks underlying annulus fibrosus in intervertebral disc degeneration. Clinical Relevance: Modules and hub genes identified in this study are highly associated with intervertebral disc degeneration, and may serve as potential therapeutic targets for intervertebral disc degeneration.

Comprehensive analysis of biomarkers for prostate cancer based on weighted gene co-expression network analysis

BACKGROUND

Prostate cancer (PCa) is one of the leading causes of cancer-related death. In the present research, we adopted a comprehensive bioinformatics method to identify some biomarkers associated with the tumor progression and prognosis of PCa.

METHODS

Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were applied for exploring gene modules correlative with tumor progression and prognosis of PCa. Clinically Significant Modules were distinguished, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to Annotation, Visualization and Integrated Discovery (DAVID). Protein-protein interaction (PPI) networks were used in selecting potential hub genes. RNA-Seq data and clinical materials of prostate cancer from The Cancer Genome Atlas (TCGA) database were used for the identification and validation of hub genes. The significance of these genes was confirmed via survival analysis and immunohistochemistry.

RESULTS

2688 DEGs were filtered. Weighted gene co-expression network was constructed, and DEGs were divided into 6 modules. Two modules were selected as hub modules which were highly associated with the tumor grades. Functional enrichment analysis was performed on genes in hub modules. Thirteen hub genes in these hub modules were identified through PPT networks. Based on TCGA data, 4 of them (CCNB1, TTK, CNN1, and ACTG2) were correlated with prognosis. The protein levels of CCNB1, TTK, and ACTG2 had a degree of differences between tumor tissues and normal tissues.

CONCLUSION

Four hub genes were identified as candidate biomarkers and potential therapeutic targets for further studies of exploring molecular mechanisms and individual therapy on PCa.

Circulating Tumor Cell Enumeration and Characterization in Metastatic Castration-Resistant Prostate Cancer Patients Treated with Cabazitaxel

(1) Background: Markers identifying which patients with metastatic, castration-resistant prostate cancer (mCRPC) will benefit from cabazitaxel therapy are currently lacking. Therefore, the aim of this study was to identify markers associated with outcome to cabazitaxel therapy based on counts and gene expression profiles of circulating tumor cells (CTCs). (2) Methods: From 120 mCRPC patients, CellSearch enriched CTCs were obtained at baseline and after 6 weeks of cabazitaxel therapy. Furthermore, 91 genes associated with prostate cancer were measured in mRNA of these CTCs. (3) Results: In 114 mCRPC patients with an evaluable CTC count, the CTC count was independently associated with poor progression-free survival (PFS) and overall survival (OS) in multivariable analysis with other commonly used variables associated with outcome in mCRPC (age, prostate specific antigen (PSA), alkaline phosphatase, lactate dehydrogenase (LDH), albumin, hemoglobin), together with alkaline phosphatase and hemoglobin. A five-gene expression profile was generated to predict for outcome to cabazitaxel therapy. However, even though this signature was associated with OS in univariate analysis, this was not the case in the multivariate analysis for OS nor for PFS. (4) Conclusion: The established five-gene expression profile in CTCs was not independently associated with PFS nor OS. However, along with alkaline phosphatase and hemoglobin, CTC-count is independently associated with PFS and OS in mCRPC patients who are treated with cabazitaxel.

Peripheral monocyte count: an independent diagnostic and prognostic biomarker for prostate cancer - a large Chinese cohort study

Increasing evidence indicates that inflammation may play important roles in tumorigenesis and progression, and an elevated peripheral monocyte count predicts a poor prognosis in various types of malignancies. Here, we evaluate the roles of peripheral monocyte count in the diagnosis and prognosis for prostate cancer in Chinese patients. A total of 1107 consecutive patients who had undergone prostate biopsy and 290 prostate cancer patients receiving androgen deprivation therapy as first-line therapy were retrospectively analyzed. The parameters were measured at the time of diagnosis. Univariate and multivariate logistic regression analyses were performed to identify the independent predictors of a positive biopsy. Patients were categorized in two groups using a cutoff point of 0.425 × 10⁹ l⁻¹ as calculated by the receiver-operating curve analysis for prognosis. Univariate and multivariate Cox regression analyses were performed to determine the associations of monocyte count with progression-free survival, cancer-specific survival, and overall survival. Multivariate logistic regression analyses showed that monocyte count, age, prostate-specific antigen (PSA), free/total PSA, and prostate volume were independent predictors for prostate cancer. Multivariate Cox regression analyses identified an elevated monocyte count as an independent prognostic factor for worse cancer-specific survival (hazard ratio = 2.244, P < 0.05) and overall survival (hazard ratio = 1.995, P < 0.05), but not progression-free survival (P = 0.117). Our results indicated that an elevated monocyte count was an independent diagnostic biomarker for prostate cancer, and pretreatment peripheral monocyte count might play a significant role in the prognosis of prostate cancer patients treated with androgen deprivation therapy.

Integrative (epi) Genomic Analysis to Predict Response to Androgen-Deprivation Therapy in Prostate Cancer

Therapeutic resistance is a central problem in clinical oncology. We have developed a systematic genome-wide computational methodology to allow prioritization of patients with favorable and poor therapeutic response. Our method, which integrates DNA methylation and mRNA expression data, uncovered a panel of 5 differentially methylated sites, which explain expression changes in their site-harboring genes, and demonstrated their ability to predict primary resistance to androgen-deprivation therapy (ADT) in the TCGA prostate cancer patient cohort (hazard ratio = 4.37). Furthermore, this panel was able to accurately predict response to ADT across independent prostate cancer cohorts and demonstrated that it was not affected by Gleason, age, or therapy subtypes. We propose that this panel could be utilized to prioritize patients who would benefit from ADT and patients at risk of resistance that should be offered an alternative regimen. Such approach holds a long-term objective to build an adaptable accurate platform for precision therapeutics.

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Integrative DNA methylation and mRNA expression analysis discovers a panel of markers of treatment resistance.

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This panel can predict patients with predisposition to resistance and those who would benefit from the therapy.

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Our approach is applicable to a wide range of therapeutic regimens.

Therapeutic resistance is an emerging clinical problem, with detrimental implications in oncology. Here, we propose a computational approach that integrates genomic and epigenomic data to prioritize patients at risk of treatment resistance. We have integrated DNA methylation and mRNA expression patient profiles, which defined a comprehensive panel of markers of therapeutic response. We have demonstrated that this panel predicts patients with predisposition to resistance and those who would benefit from the therapy. Even though driven by a critical need to investigate resistance to androgen-deprivation therapy in prostate cancer, our approch is applicable to a wide range of therapeutic regimens.

Identification of prognostic markers of high grade prostate cancer through an integrated bioinformatics approach

PURPOSE

Prostate cancer is one of the leading causes of cancer death for male. In the present study, we applied an integrated bioinformatics approach to provide a novel perspective and identified some hub genes of prostate cancer.

METHOD

Microarray data of fifty-nine prostate cancer were downloaded from Gene Expression Omnibus. Gene Ontology and pathway analysis were applied for differentially expressed genes between high and low grade prostate cancer. Weighted gene coexpression network analysis was applied to construct gene network and classify genes into different modules. The most related module to high grade prostate cancer was identified and hub genes in the module were revealed. Ingenuity pathway analysis was applied to check the chosen module's relationship to high grade prostate cancer. Hub gene's expression profile was verified with clinical samples and a dataset from The Cancer Genome Atlas project.

RESULT

3193 differentially expressed genes were filtered and gene ontology and pathway analysis revealed some cancer- and sex hormone-related results. Weighted gene coexpression network was constructed and genes were classified into six modules. The red module was selected and ingenuity pathway analysis confirmed its relationship with high grade prostate cancer. Hub genes were identified and their expression profile was also confirmed.

CONCLUSION

The present study applied integrate bioinformatics approaches to generate a holistic view of high grade prostate cancer and identified hub genes could serve as prognosis markers and potential treatment targets.

Neuropilin-1 Associated Molecules in the Blood Distinguish Poor Prognosis Breast Cancer: A Cross-Sectional Study

Circulating plasma and peripheral blood mononuclear (PBMCs) cells provide an informative snapshot of the systemic physiological state. Moreover, they provide a non-invasively accessible compartment to identify biomarkers for personalized medicine in advanced breast cancer. The role of Neuropilin-1 (NRP-1) and its interacting molecules in breast tumor tissue was correlated with cancer progression; however, the clinical impact of their systemic levels was not extensively evaluated. In this cross-sectional study, we found that circulating and tumor tissue expression of NRP-1 and circulating placental growth factor (PlGF) increase in advanced nodal and metastatic breast cancer compared with locally advanced disease. Tumor tissue expression of NRP-1 and PlGF is also upregulated in triple negative breast cancer (TNBC) compared to other subtypes. Conversely, in PBMCs, NRP-1 and its interacting molecules SEMA4A and SNAI1 are significantly downregulated in breast cancer patients compared to healthy controls, indicating a protective role. Moreover, we report differential PBMC expression profiles that correlate inversely with disease stage (SEMA4A, SNAI1, PLXNA1 and VEGFR3) and can differentiate between the TNBC and non-TNBC tumor subtypes (VEGFR3 and PLXNA1). This work supports the importance of NRP-1-associated molecules in circulation to characterize poor prognosis breast cancer and emphasizes on their role as favorable drug targets.

Disease-specific classification using deconvoluted whole blood gene expression

Blood-based biomarker assays have an advantage in being minimally invasive. Diagnostic and prognostic models built on peripheral blood gene expression have been reported for various types of disease. However, most of these studies focused on only one disease type, and failed to address whether the identified gene expression signature is disease-specific or more widely applicable across diseases. We conducted a meta-analysis of 46 whole blood gene expression datasets covering a wide range of diseases and physiological conditions. Our analysis uncovered a striking overlap of signature genes shared by multiple diseases, driven by an underlying common pattern of cell component change, specifically an increase in myeloid cells and decrease in lymphocytes. These observations reveal the necessity of building disease-specific classifiers that can distinguish different disease types as well as normal controls, and highlight the importance of cell component change in deriving blood gene expression based models. We developed a new strategy to develop blood-based disease-specific models by leveraging both cell component changes and cell molecular state changes, and demonstrate its superiority using independent datasets.

Blood-based gene expression profiling in castrate-resistant prostate cancer

Castrate-resistant prostate cancer (CRPC), the most life-threatening form of prostate cancer, has recently been the focus of many successful new treatments. Contemporary trials highlight the heterogeneous prognosis of CRPC as overall survival times vary greatly across different patient sub-groups. As presented in BMC Medicine, Wang et al. identify a blood-based prognostic signature in CRPC. Their approach is notable for discovery and validation of a four-gene model based on a whole-blood expression signature sampled from three distinct clinical cohorts. Further, the marker selection process incorporates an understanding of biological pathways expressed in myeloid or lymphoid cells which may provide some insight into host-tumor interactions as reflected in the peripheral blood. While the study includes a multivariate analysis accounting for many important clinical variables, larger datasets with more complete clinical information and sufficient follow-up are needed to confirm the independent significance of the four-gene expression model in a way which may better inform the care of CRPC patients.Please see related article: http://www.biomedcentral.com/1741-7015/13/201 .