Autoantibodies in cancer: prognostic biomarkers and immune activation

The development of proteomic technologies that display a wide variety of antigenic structures has led to the identification of autoantibodies to cancer-derived tumor antigens. These autoantibodies have been detected in sera from patients with multiple cancer types, and are being evaluated as biomarkers for early cancer detection. It is not known whether these antibodies also contribute to active immune surveillance or even tumorigenicity of developing tumors. Here, we review which tumor antigen-specific antibodies are prognostic biomarkers of cancer outcome, and emerging proteomic methods for the isolation and cloning of these antibodies for potential molecular diagnostics and therapeutics.     

Predictive and prognostic biomarkers in colorectal cancer

Colorectal cancer is the third most common cancer and the second leading cause of cancer-related death in the United States.Three quarters of patients diagnosed with colorectal cancer will have early s...     

Apoptosis signaling proteins as prognostic biomarkers in colorectal cancer: A review

Colorectal cancer is a leading cause of cancer related mortality in the Western world. In recent years, combination 5-fluorouracil based adjuvant chemotherapy as first line treatment of this disease has led to improved disease free and overall survival. However drug resistance, both innate and acquired, remains an obstacle in the effective treatment of this disease. Apoptotic pathways are frequently altered in both tumor progression and drug resistance; therefore proteins associated with this pathway may have potential as prognostic biomarkers for this disease. Identification of clinical biomarkers that are able to identify patients who are more likely to respond to specific chemotherapy will lead to more personalized, effective, and less toxic therapy. This review focuses on the current status of apoptosis related proteins as biomarkers for colorectal cancer and discusses the possible application of systems approaches in this context.     

The sentinel within: exploiting the immune system for cancer biomarkers

The release of proteins from tumors triggers an immune response in cancer patients. These tumor antigens arise from several mechanisms including tumor-specific alterations in protein expression, mutation, folding, degradation, or intracellular localization. Responses to most tumor antigens are rarely observed in healthy individuals, making the response itself a biomarker that betrays the presence of underlying cancer. Antibody immune responses show promise as clinical biomarkers because antibodies have long half-lives in serum, are easy to measure, and are stable in blood samples. However, our understanding of the specificity and the impact of the immune response in early stages of cancer is limited. The immune response to cancer, whether endogenous or driven by vaccines, involves highly specific T lymphocytes (which target tumor-derived peptides bound to self-MHC proteins) and B lymphocytes (which generate antibodies to tumor-derived proteins). T cell target antigens have been identified either by expression cloning from tumor cDNA libraries, or by prediction based on patterns of antigen expression ("reverse immunology"). B cell targets have been similarly identified using the antibodies in patient sera to screen cDNA libraries derived from tumor cell lines. This review focuses on the application of recent advances in proteomics for the identification of tumor antigens. These advances are opening the door for targeted vaccine development, and for using immune response signatures as biomarkers for cancer diagnosis and monitoring.     

Tissue and serum proteomic profiling for diagnostic and prognostic bladder cancer biomarkers

A panel of biomarkers for the early detection of bladder cancer has not yet been identified. Many different molecules, including DNA, RNA or proteins have been reported but none have provided adequate sensitivity for a single-tier screening test or a test to replace cystoscopy. Therefore, multimarker panels are discussed at present to give a more-precise answer to the biomarker quest. Mass spectrometry or 2D gel-electrophoresis have evolved greatly within recent years and are capable of analyzing multiple proteins or peptides in parallel with high sensitivity and specificity. However, transmission of screening results from one laboratory to another is still the main pitfall of those methods; a fact that emphasizes the need for consistent and standardized procedures as suggested by the Human Proteome Organization (HUPO). In this article, recent results in screening approaches and other proteomic techniques used for biomarker evaluation in bladder cancer are discussed with a focus on serum and tissue biomarkers.     

Tissue and serum proteomic profiling for diagnostic and prognostic bladder cancer biomarkers

A panel of biomarkers for the early detection of bladder cancer has not yet been identified. Many different molecules, including DNA, RNA or proteins have been reported but none have provided adequate sensitivity for a single-tier screening test or a test to replace cystoscopy. Therefore, multimarker panels are discussed at present to give a more-precise answer to the biomarker quest. Mass spectrometry or 2D gel-electrophoresis have evolved greatly within recent years and are capable of analyzing multiple proteins or peptides in parallel with high sensitivity and specificity. However, transmission of screening results from one laboratory to another is still the main pitfall of those methods; a fact that emphasizes the need for consistent and standardized procedures as suggested by the Human Proteome Organization (HUPO). In this article, recent results in screening approaches and other proteomic techniques used for biomarker evaluation in bladder cancer are discussed with a focus on serum and tissue biomarkers.     

The prognostic value of biomarkers in stroke

Background

Ischemic injury triggers inflammatory cascades and changes in the protein synthesis, neurotransmitters and neuro-hormones in the brain parenchyma that may further amplify the tissue damage. The “Triage® Stroke Panel”, a biochemical multimarker assay, detects Brain Natriuretic Peptide (BNP), D-Dimers (DD), Matrix-Metalloproteinase-9 (MMP-9), and S100β protein generating a Multimarker index of these values (MMX). The aims of this prospective study in consecutive patients with ischemic or hemorrhagic stroke were to assess: 1) the rate of an increase of biomarkers (BNP, D-dimer, MMP-9 and S-100β) tested with the Triage Stroke Panel; 2) the correlation between the increase of these biomarkers and functional outcome at 4 months; 3) the risk factors for the increase of biomarkers.

Methods

The outcome of the study was 120-day mortality and it was compared in patients with Stroke Panel >4 and ≤4. Multiple logistic regression analyses were performed to identify independent predictors for death and for the increase of biomarkers.

Results

244 consecutive patients (mean age 73.02 years; 53.7 % males) were included in the study; 210 ischemic strokes and 34 hemorrhagic strokes. 161/244 (66.0 %) had an increase of biomarkers. At 120 days, 85 patients had died (34.8 %). Death was seen in 68/161 patients with an increase of biomarkers (42.2 %) compared with 17/83 patients without (20.5 %). Regression logistic analysis found that a Stroke Panel >4 (OR 3.1; 95 % CI 1.5–6.2, p?=?0.002) was associated with mortality. The increase of biomarkers was independently predicted by an increase of PCR on admission (OR 2.9, 95 CI 1.4–6.0, p?=?0.003).

Conclusions

An increase of biochemical markers such as BNP, D-Dimers, MMP-9, and S100β tested with a Triage Stroke Panel (>4) was correlated with mortality at 120 days from stroke onset.

     

The search for novel diagnostic and prognostic biomarkers in cholangiocarcinoma

The poor prognosis of cholangiocarcinoma (CCA) is in part due to late diagnosis, which is currently achieved by a combination of clinical, radiological and histological approaches. Available biomarkers determined in serum and biopsy samples to assist in CCA diagnosis are not sufficiently sensitive and specific. Therefore, the identification of new biomarkers, preferably those obtained by minimally invasive methods, such as liquid biopsy, is important. The development of innovative technologies has permitted to identify a significant number of genetic, epigenetic, proteomic and metabolomic CCA features with potential clinical usefulness in early diagnosis, prognosis or prediction of treatment response. Potential new candidates must be rigorously evaluated prior to entering routine clinical application. Unfortunately, to date, no such biomarker has achieved validation for these purposes. This review is an up-to-date of currently used biomarkers and the candidates with promising characteristics that could be included in the clinical practice in the next future. This article is part of a Special Issue entitled: Cholangiocytes in Health and Disease edited by Jesus Banales, Marco Marzioni, Nicholas LaRusso and Peter Jansen.     

Epigenetic biomarkers in colorectal cancer: premises and prospects

Context: Colorectal cancer is one of the most common cancers worldwide. Epigenetic alterations play an important role in the pathogenesis of the colorectal cancer.

Objective: This review has focused on the most recent investigations, which has suggested potential epigenetic biomarkers in colorectal cancer.

Methods: Evidences were achieved by searching online medical databases including Google scholar, Pubmed, Scopus and Science Direct.

Results: Extensive studies have indicated that aberrant epigenetic modifications could serve as potential biomarkers for diagnosis, prognosis and prediction of colorectal cancer.

Conclusion: Advances in aberrant epigenetic modifications can open new avenues for exploration of reliable and robust biomarkers to improve the management of CRC patients.     

Identification of prognostic biomarkers for breast cancer brain metastases based on the bioinformatics analysis

PurposeThe prognosis of breast cancer (BC) patients who develop into brain metastases (BMs) is very poor. Thus, it is of great significance to explore the etiology of BMs in BC and identify the key genes involved in this process to improve the survival of BC patients with BMs.Patients and methodsThe gene expression data and the clinical information of BC patients were downloaded from TCGA and GEO database. Differentially expressed genes (DEGs) in TCGA-BRCA and GSE12276 were overlapped to find differentially expressed metastatic genes (DEMGs). The protein-protein interaction (PPI) network of DEMGs was constructed via STRING database. ClusterProfiler R package was applied to perform the gene ontology (GO) enrichment analysis of DEMGs. The univariate Cox regression analysis and the Kaplan-Meier (K-M) curves were plotted to screen DEMGs associated with the overall survival and the metastatic recurrence survival, which were identified as the key genes associated with the BMs in BC. The immune infiltration and the expressions of immune checkpoints for BC patients with brain relapses and BC patients with other relapses were analyzed respectively. The correlations among the expressions of key genes and the differently infiltrated immune cells or the differentially expressed immune checkpoints were calculated. The gene set enrichment analysis (GSEA) of each key gene was conducted to investigate the potential mechanisms of key genes involved in BC patients with BMs. Moreover, CTD database was used to predict the drug-gene interaction network of key genes.ResultsA total of 154 DEGs were identified in BC patients at M0 and M1 in TCGA database. A total of 667 DEGs were identified in BC patients with brain relapses and with other relapses. By overlapping these DEGs, 17 DEMGs were identified, which were enriched in the cell proliferation related biological processes and the immune related molecular functions. The univariate Cox regression analysis and the Kaplan-Meier curves revealed that CXCL9 and GPR171 were closely associated with the overall survival and the metastatic recurrence survival and were identified as key genes associated with BMs in BC. The analyses of immune infiltration and immune checkpoint expressions showed that there was a significant difference of the immune microenvironment between brain relapses and other relapses in BC. GSEA indicated that CXCL9 and GPR171 may regulate BMs in BC via the immune-related pathways.ConclusionOur study identified the key genes associated with BMs in BC patients and explore the underlying mechanisms involved in the etiology of BMs in BC. These findings may provide a promising approach for the treatments of BC patients with BMs.     

Evaluating prognostic accuracy of biomarkers in nested case-control studies

Nested case-control (NCC) design is used frequently in epidemiological studies as a cost-effective subcohort sampling strategy to conduct biomarker research. Sampling strategy, on the other hoand, creates challenges for data analysis because of outcome-dependent missingness in biomarker measurements. In this paper, we propose inverse probability weighted (IPW) methods for making inference about the prognostic accuracy of a novel biomarker for predicting future events with data from NCC studies. The consistency and asymptotic normality of these estimators are derived using the empirical process theory and convergence theorems for sequences of weakly dependent random variables. Simulation and analysis using Framingham Offspring Study data suggest that the proposed methods perform well in finite samples.     

Soluble MUC1 and serum MUC1-specific antibodies are potential prognostic biomarkers for platinum-resistant ovarian cancer

MUC1 (CA15-3) and MUC16 (CA125) tumor-associated antigens are upregulated in ovarian cancer and can be detected in patients’ sera by standardized tests. We postulated that increased MUC1 and MUC16 antigens augment antibody responses in platinum-resistant ovarian cancer patients and that the frequency and intensity of these responses can be used as immune biomarkers of treatment response and disease outcome. We measured MUC1 and MUC16 tumor expression by immunohistochemistry (IHC), assessed serum antigenic levels and quantitated circulating antibodies by ELISA in a cohort of 28 ovarian cancer patients with platinum-resistant or platinum-refractory ovarian cancer, and treated with intraperitoneal (IP) interleukin-2 (IL-2). MUC1 and MUC16 were overexpressed in tumor samples and showed differential distribution profiles. Serum MUC1 (CA15-3) measurements were elevated in all patients and significantly correlated with increased risk of death (P = 0.003). MUC1-specific IgM and IgG anitbodies were found in 92 and 50% of cases, respectively. Patients with progressive disease had higher mean anti-MUC1 IgG than responders at both early (P = 0.025) and late (P = 0.022) time points during IP IL-2 treatment. Anti-MUC1 IgM antibodies inversely correlated with overall survival at both early (P = 0.052) and late (P = 0.009) time points. In contrast to MUC1, neither soluble MUC16 nor MUC16-specific antibodies were significantly associated with clinical response or overall survival in this study. Increased serum MUC1 and high anti-MUC1 antibody levels are prognostic for poor clinical response and reduced overall survival in platinum-resistant or platinum-refractory ovarian cancer.     

Construction of an N6-methyladenosine lncRNA- and immune cell infiltration-related prognostic model in colorectal cancer

The present paper aims to shed light on the influence of N6-methyladenosine (m6A) long non-coding RNAs (lncRNAs) and immune cell infiltration on colorectal cancer (CRC). We downloaded workflow-type data and xml-format clinical data on CRC from The Cancer Genome Atlas project. The relationship between lncRNA and m6A was identified by using Perl and R software. Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed. Lasso regression was utilized to construct a prognostic model. Survival analysis was used to explore the relationship between clusters of m6A lncRNAs and clinical survival data. Differential analysis of the tumor microenvironment and an immune correlation analysis were used to determine immune cell infiltration levels in different clusters and their correlation with clinical prognosis. The expression of lncRNA was tightly associated with m6A. The univariate Cox regression analysis showed that lncRNA was a risk factor for the prognosis. Differential expression analysis demonstrated that m6A lncRNAs were partially highly expressed in tumor tissue. m6A lncRNA-related prognostic model could predict the prognosis of CRC independently. “ECM_RECEPTOR_INTERACTION” was the most significantly enriched gene set. PARP8 was overexpressed in tumor tissue and high-risk cluster. CD4 memory T cells, activated resting NK cells, and memory B cells were highly clustered in the high-risk cluster. All of the scores were higher in the low-risk group. m6A lncRNA is closely related to the occurrence and progression of CRC. The corresponding prognostic model can be utilized to evaluate the prognosis of CRC. m6A lncRNA and related immune cell infiltration in the tumor microenvironment can provide novel therapeutic targets for further research.

     

Genome-wide promoter methylation analysis in neuroblastoma identifies prognostic methylation biomarkers

Background

Accurate outcome prediction in neuroblastoma, which is necessary to enable the optimal choice of risk-related therapy, remains a challenge. To improve neuroblastoma patient stratification, this study aimed to identify prognostic tumor DNA methylation biomarkers.

Results

To identify genes silenced by promoter methylation, we first applied two independent genome-wide methylation screening methodologies to eight neuroblastoma cell lines. Specifically, we used re-expression profiling upon 5-aza-2''-deoxycytidine (DAC) treatment and massively parallel sequencing after capturing with a methyl-CpG-binding domain (MBD-seq). Putative methylation markers were selected from DAC-upregulated genes through a literature search and an upfront methylation-specific PCR on 20 primary neuroblastoma tumors, as well as through MBD- seq in combination with publicly available neuroblastoma tumor gene expression data. This yielded 43 candidate biomarkers that were subsequently tested by high-throughput methylation-specific PCR on an independent cohort of 89 primary neuroblastoma tumors that had been selected for risk classification and survival. Based on this analysis, methylation of KRT19, FAS, PRPH, CNR1, QPCT, HIST1H3C, ACSS3 and GRB10 was found to be associated with at least one of the classical risk factors, namely age, stage or MYCN status. Importantly, HIST1H3C and GNAS methylation was associated with overall and/or event-free survival.

Conclusions

This study combines two genome-wide methylation discovery methodologies and is the most extensive validation study in neuroblastoma performed thus far. We identified several novel prognostic DNA methylation markers and provide a basis for the development of a DNA methylation-based prognostic classifier in neuroblastoma.