Identification Of Cancer Biomarkers Research Articles

Epigenetic Biomarkers in the Management of Ovarian Cancer: Current Prospectives.

Ovarian cancer (OC) causes significant morbidity and mortality as neither detection nor screening of OC is currently feasible at an early stage. Difficulty to promptly diagnose OC in its early stage remains challenging due to non-specific symptoms in the early-stage of the disease, their presentation at an advanced stage and poor survival. Therefore, improved detection methods are urgently needed. In this article, we summarize the potential clinical utility of epigenetic signatures like DNA methylation, histone modifications, and microRNA dysregulation, which play important role in ovarian carcinogenesis and discuss its application in development of diagnostic, prognostic, and predictive biomarkers. Molecular characterization of epigenetic modification (methylation) in circulating cell free tumor DNA in body fluids offers novel, non-invasive approach for identification of potential promising cancer biomarkers, which can be performed at multiple time points and probably better reflects the prevailing molecular profile of cancer. Current status of epigenetic research in diagnosis of early OC and its management are discussed here with main focus on potential diagnostic biomarkers in tissue and body fluids. Rapid and point of care diagnostic applications of DNA methylation in liquid biopsy has been precluded as a result of cumbersome sample preparation with complicated conventional methods of isolation. New technologies which allow rapid identification of methylation signatures directly from blood will facilitate sample-to answer solutions thereby enabling next-generation point of care molecular diagnostics. To date, not a single epigenetic biomarker which could accurately detect ovarian cancer at an early stage in either tissue or body fluid has been reported. Taken together, the methodological drawbacks, heterogeneity associated with ovarian cancer and non-validation of the clinical utility of reported potential biomarkers in larger ovarian cancer populations has impeded the transition of epigenetic biomarkers from lab to clinical settings. Until addressed, clinical implementation as a diagnostic measure is a far way to go.

Identification of potential diagnostic and prognostic biomarkers for prostate cancer.

Prostate cancer (PCa) is one of the most common malignant tumors worldwide. The aim of the present study was to determine potential diagnostic and prognostic biomarkers for PCa. The GSE103512 dataset was downloaded, and the differentially expressed genes (DEGs) were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) analyses of DEGs were performed. The result of GO analysis suggested that the DEGs were mostly enriched in ‘carboxylic acid catabolic process’, ‘cell apoptosis’, ‘cell proliferation’ and ‘cell migration’. KEGG analysis results indicated that the DEGs were mostly concentrated in ‘metabolic pathways’, ‘ECM-receptor interaction’, the ‘PI3K-Akt pathway’ and ‘focal adhesion’. The PPI analysis results showed that Golgi membrane protein 1 (GOLM1), melanoma inhibitory activity member 3 (MIA3), ATP citrate lyase (ACLY) and G protein subunit β2 (GNB2) were the key genes in PCa, and the Module analysis revealed that they were associated with ‘ECM-receptor interaction’, ‘focal adhesion’, the ‘PI3K-Akt pathway’ and the ‘metabolic pathway’. Subsequently, the gene expression was confirmed using Gene Expression Profiling Interactive Analysis and the Human Protein Atlas. The results demonstrated that GOLM1 and ACLY expression was significantly upregulated (P<0.05) in PCa compared with that in normal tissues. Receiver operating characteristic and survival analyses were performed. The results showed that area under the curve values of these genes all exceeded 0.85, and high expression of these genes was associated with poor survival in patients with PCa. In conclusion, this study identified GOLM1 and ACLY in PCa, which may be potential diagnostic and prognostic biomarker of PCa.

Detection of breast cancer stem cell gene mutations in circulating free DNA during the evolution of metastases

Limited knowledge exists on the detection of breast cancer stem cell (BCSC)-related mutations in circulating free DNA (cfDNA) from patients with advanced cancers. Identification of new cancer biomarkers may allow for earlier detection of disease progression and treatment strategy modifications. We conducted a prospective study to determine the feasibility and prognostic utility of droplet digital polymerase chain reaction (ddPCR)-based BCSC gene mutation analysis of cfDNA in patients with breast cancer. Detection of quantitative BCSC gene mutation in cfDNA by ddPCR mirrors disease progression and thus may represent a valuable and cost-effective measure of tumor burden. We have previously shown that hematological and neurological expressed 1-like (HN1L), ribosomal protein L39 (RPL39), and myeloid leukemia factor 2 (MLF2) are novel targets for BCSC self-renewal, and targeting these genetic alterations could be useful for personalized genomic-based therapy. BCSC mutation detection in cfDNA may have important implications for diagnosis, prognosis, and serial monitoring.

Predictive biomarkers for drug response in bladder cancer.

Bladder cancer is a heterogeneous disease. Interpatient heterogeneity in response to a drug limits treatment options and impairs improvement of patient survival. For example, approximately half of patients do not respond to cisplatin-based combination chemotherapy, although it is the standard of care for muscle-invasive and metastatic bladder cancer. The development of robust predictive biomarkers is expected to improve outcomes by enabling clinicians to use chemotherapy only in the patients who will benefit from it. Recent advances in the molecular characterization of bladder cancer showed that the basal subtype of bladder cancer and tumors with inactivating mutations inDNA damage repair genes were associated with greater benefit from cisplatin-based chemotherapy. The present review summarizes current efforts to develop predictive biomarkers for drug response in bladder cancer, focusing on those that predict the response to cisplatin-based chemotherapy for advanced bladder cancer. We also review the current situation with regard to the identification of predictive biomarkers for response to intravesical therapy, immune checkpoint inhibitors and molecularly-targeted drugs. We also discuss the future applications of new technologies, including liquid biopsies and patient-derived organoids that will also serve as resources for the identification of biomarkers in bladder cancer.

Computational identification and analysis of early diagnostic biomarkers for kidney cancer.

Renal cell carcinoma is one of the most common urinary system tumors in adults, it is usually asymptomatic in its early stage and the patients are often diagnosed late. MicroRNA has a higher diagnostic accuracy than traditional markers and may become a new type of early diagnostic biomarker for kidney cancer. Three computational methods and several bioinformatic methods including PPI network, overall survival analysis and enrichment analysis were used to identify the significant differentially expressed miRNAs. Thirteen miRNAs that were significantly differentially expressed in RCC patients were identified, 10 of them have been proved to be associated with kidney cancer in other studies, miR-576, miR-616 and miR-133a-2 are three newly discovered biomarkers of RCC in this study. We found that the target genes of miR-576 (CUL3 and RAC1) are involved in the regulation of multiple cancer-related biological pathways, and the target gene of miR-616 (ASB13 and FBXW2) has been reported to be associated with the development of other cancers. Our findings may have guiding significance for the early diagnosis of renal cell carcinoma.

Proteomics for the Identification of Biomarkers in Testicular Cancer-Review.

A large number of biomarkers have been proposed for the diagnosis of testicular cancer, representing putative molecular targets for anticancer treatments. However, no conclusive data have been provided. Proteomics represents a research field recently developed. It evaluates the large-scale analysis of the full protein components of a single cell, of a specific tissue, or of biological fluids. In the last decades, proteomics has been applied in clinical fields, thanks to modern technology and new bioinformatic tools, to identify novel molecular markers of diseases. The aim of this review is to argue the findings of recent studies in the discoveries of putative prognostic and diagnostic markers of testis cancer by proteomic techniques. We present here a panel of proteins identified by proteomics which might be used after validation for early detection and the prognostic evaluation of testicular tumors. In addition, the molecular mechanisms revealed by these proteomic studies might also guide the development of novel treatments in future.

Abstract 3165: Identification of candidate biomarkers for aggressive prostate cancer using targeted proteomics and FFPE tissue samples with outcomes data

Abstract Introduction: Although many (~40%) of the screen-detected prostate cancers are indolent (Gleason Score &amp;lt;= 6) and pose minimal risk for progression, advanced stage prostate cancer is a lethal disease with 5-year survival rates around 29%. The challenge is to identify biomarkers for early detection of aggressive disease, when the cancer is still organ confined. Such markers would be also used to better select patients with indolent and low risk cancers for active surveillance. Material and Methods: To identify a panel of protein markers that could predict prostate cancer progression, we developed ultra-sensitive, high-pressure, high-resolution separations coupled with intelligent selection and multiplexing-selected reaction monitoring (PRISM-SRM) assays for 52 protein markers. Candidate protein markers were identified and selected from existing prostate cancer genomics data sets and validated lists of known prostate cancer drivers. The PRISM-SRM assays used heavy isotope-labeled synthetic peptides as internal standards for quantitative proteomics analysis. Study comprised of a prostate cancer patient cohort with organ confined primary tumors (N=338) presenting following post-surgery features: 53 (15.7%) metastatic progression, 124 (36.7%) biochemical recurrence (BCR), and 161 (47.6%) no progression after more than ten years of follow-up after radical prostatectomy. Index tumor region for each case was scraped from representative 10-m sections of formalin-fixed paraffin embedded (FFPE)-whole-mounted prostatectomy specimens and processed for PRISM-SRM analysis. Results: Overall, PRISM-SRM analysis of the FFPE tissue samples enabled the detection of 42 (80.8%) out of 52 biomarker candidates; in comparison regular LC-SRM without the front-end chromatographic enrichment could detect only 21 (40.4%) of these candidates at the protein level. Kruskal-Wallis testing was used for statistical evaluation of the PRISM-SRM results and comparison of relative protein levels between the “no progression”, BCR and “metastatic progression” groups. Several prostate differentiation/androgen receptor signaling related proteins (FOLH1, PSA and NCOA) and tumor progression-related proteins (TGFB1, CCND1 and SPRC) had significantly different expression levels between the three groups, and showed initial promise in predicting progression to invasive cancer, BCR and metastasis. Conclusion: Ultra-sensitive targeted proteomics can be used to select and verify performance of early prognostic markers based on the analysis of prostatectomy specimens. The top performing markers appeared able to predict progression from organ confined cancer to BCR and metastasis. Citation Format: Yuqian Gao, Yi-Ting Wang, Hui Wang, Denise Young, Jennifer Cullen, Yingjie Song, Yongmei Chen, Athena Schepmoes, Gyorgy Petrovics, Thomas Fillmore, Tujin Shi, Wei-Jun Qian, Richard Smith, Sudhir Srivastava, Jacob Kagan, Albert Dobi, Inger Rosner, Karin Rodland, Isabell Sesterhenn, Shiv Srivastava, Tao Liu. Identification of candidate biomarkers for aggressive prostate cancer using targeted proteomics and FFPE tissue samples with outcomes data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3165.

Abstract 3989: Identification of biomarkers for UM-164 in triple-negative breast cancer

Abstract Triple negative breast cancer (TNBC) is one of the deadliest forms of breast cancer due to limited treatment options beyond conventional chemotherapy. While making up approximately 10-20% of total breast cancer cases in the US, TNBC carries a serious burden of disease as it tends to be more aggressive, higher grade, and have a poorer prognosis than other forms of breast cancer. Though new therapies, such as PARP inhibitors, have shown promise in clinical trials in very select patients (e.g. BRCA1 mutation carriers), there is a general lack of predictive markers of therapeutic efficacy to identify subsets of patients most likely to positively respond to a given targeted or conventional therapy. Thus, there is a critical need for biomarkers of drug efficacy in TNBC to help design precision medicine clinical trials based on systematically derived predictive biomarkers. To fill this gap, we devised a strategy to identify biomarkers of drug efficacy in TNBC. As a paradigm of our approach, we treated a panel of 23 TNBC cell lines with our potent c-Src/p38 inhibitor, UM-164, and determined a drug sensitivity score (DSS) in each line. We then calculated correlations of DSS with a variety of molecular readouts, including RNA sequencing, reverse-phase protein array, DNA sequencing array, and Nanostring RNA and miRNA arrays. From these correlations, we have identified expression DJ1, MAPK11, and PCM1 as strong predictors of UM-164 efficacy in TNBC. Building on this, we used our results to identify companion drugs that lead to synergy or test others that could instead result in antagonism when used in combination. To verify our results, independently targeting the DJ1 pathway, we show that AKT inhibition results in synergy when combined with UM-164. The outcome of this research yields a robust and generalizable methodology for the identification of biomarkers in cancer to design Phase I-III trials and for generating the mechanistic evidence of effective rational combinations. Citation Format: Nathan M. Merrill, Nathalie M. Vandecan, John P. Lloyd, Eric J. Lachacz, Peter J. Ulintz, Sofia D. Merajver, Matthew B. Soellner. Identification of biomarkers for UM-164 in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3989.

Abstract 3169: Development of an engineered T cell receptor-based system for the rapid detection of cancer biomarkers

Abstract Biomarkers play a central role in cancer management by facilitating detection and characterization and are key to the development of personalized therapeutic strategies. We have developed a novel Surface Programmable Activation Receptor (SPAR) diagnostic platform which uses T cell-based technology to achieve the rapid and sensitive identification of cancer biomarkers. SPAR cells are T cells modified to express both an engineered T-cell receptor (TCR) and the luminescent reporter aequorin. Jurkat T cells are first transfected with the aequorin expression vector pEF1-Aeq introduced via random insertion, and stable transfectants selected using G418. The TCR complex is functionally modified via the gene fusion of enhanced monoavidin (eMA) with the CD3ϵ subunit of the human T cell receptor complex to form eMA-CD3ϵ and introduced as a homozygous insert into Jurkat/pEF1-Aeq T cells via electroporation, using CRISPR/Cas9 targeting to replace endogenous CD3ϵ. The modified TCR complex efficiently binds biotinylated target detection molecules (TDMs), which target specific biomarkers via an engineered protein binding domain and a biotinylated protein tag, and act as a ligand to program the SPAR TCR. Simultaneous binding of the TDM to the target and to the modified TCR causes receptor aggregation. Aggregation signals are amplified via the native T-cell signal cascade and result in rapid calcium release from the endoplasmic reticulum. Increased calcium concentrations activate aequorin, triggering the emission of detectible light. Initially developed for programmable pathogen detection, the system has demonstrated reliable and specific detection of the lymphoma marker CD19. Cultured human Burkitt’s lymphoma (Raji B) cells abundantly expressing the CD19 surface receptor were combined with SPAR cells programmed with a biotinylated anti-CD19 antibody as TDM. Binding of the anti-CD19 SPAR cells to cell surface CD19 resulted in TCR aggregation, signal transduction, and the generation of reporter signal. Repeat studies conducted in the presence of blood demonstrated equivalent sensitivity. We achieve similar results using the human melanoma markers GD2 and CD133, and mouse melanoma markers TRP1 and CD44 in cell lines known to express these markers. In these studies, a variant of the SPAR system was used to screen commercial monoclonal antibodies to identify those with biologically relevant activity against the cell surface biomarkers, thus demonstrating the utility of this system in optimizing useful antibody/ target combinations without requiring target protein purification. With optimized TDMs, no significant light signal could be detected with K562 cells used as control. The SPAR diagnostic platform is a rapid and effective method for the detection of known cancer biomarkers and may provide a direct screening tool for the optimization of interacting proteins used to guide cell therapies. Citation Format: Joseph D. Kittle, Joel Lwande, M Russell Williams, Shengwen Liang, Kyle McQuaid, Melissa Frenchmeyer, Yuanyuan Tang, Allison Neese, Jiangzhou Hua, Charles McBrairty. Development of an engineered T cell receptor-based system for the rapid detection of cancer biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3169.

Abstract 820: Enzymatic Methyl-Seq: methylome analysis of challenging DNA samples

Abstract DNA isolated from blood draws (cell-free DNA (cfDNA)) or from archival material like formalin fixed paraffin embedded (FFPE) tissues have advanced the field of cancer genetics. DNA methylation (5-methylcytosines (5mC) and 5-hydroxymethylcytosines (5hmC)) is a key epigenetic factor that plays an important role in cellular processes and it’s misregulation results in diseased states like cancer. Advances in the field of sample preparation from biological matrices and genomics have enabled cancer biomarker identification based on methylation profiling. Bisulfite sequencing is the standard method to detect methylation and has been employed for both targeted and whole genome methylation analysis. However, the chemical based bisulfite conversion of cytosines to uracils also results in DNA damage which subsequently results in shorter DNA insert sizes as well as introducing bias into the data. Robust biomarker detection relies primarily on the ability to profile methylation accurately. Analysis of DNA methylation from cfDNA and FFPE DNA is challenging as the DNA is typically of low quality and quantity. To overcome the drawbacks of bisulfite sequencing, we developed an enzyme based methylation detection technology, called NEBNext Enzymatic Methyl-Seq (EM-Seq). DNA damage is minimized enabling longer insert sizes, lower duplication rates and minimal GC bias resulting in more accurate quantification of methylation in the sample DNA. Using EM-Seq, we profiled cfDNA and FFPE DNA from multiple tissue types. Results for these challenging DNA types showed that the EM-Seq libraries had longer inserts, lower duplication rates, higher percentages of mapped reads and less GC bias compared to WGBS libraries. These libraries also identified a higher number of CpG’s and the estimated global methylation levels were in good agreement with the absolute levels quantified using LC/MS. In conclusion, EM-Seq libraries have superior sequencing metrics resulting in robust methylation profiling for these types of challenging DNA samples. Citation Format: Louise Williams, V K Chaithanya Ponnaluri, Brittany S. Sexton, Lana Saleh, Katherine Marks, Mala Samaranayake, Laurence Ettwiller, Shengxi Guan, Heidi E. Church, Nan Dai, Esta Tamanaha, Erbay Yigit, Bradley Langhorst, Zhiyi Sun, Thomas C. Evans, Romualdas Vaisvila, Eileen Dimalanta, Theodore B. Davis. Enzymatic Methyl-Seq: methylome analysis of challenging DNA samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 820.

A Robust Approach for Identification of Cancer Biomarkers and Candidate Drugs.

Background and objectives: Identification of cancer biomarkers that are differentially expressed (DE) between two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, those are not suitable for paired samples. Furthermore, the traditional methods use p-values or fold change (FC) values to detect the DE genes. However, sometimes, p-value based results do not comply with FC based results due to the smaller pooled variance of gene expressions, which occurs when variance of each individual condition becomes smaller. There are some methods that combine both p-values and FC values to solve this problem. But, those methods also show weak performance for small sample cases in the presence of outlying expressions. To overcome this problem, in this paper, an attempt is made to propose a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values computed by SAM statistic using minimum β-divergence method, which is designed for paired samples. Materials and Methods: The proposed method introduces a weight function known as β-weight function. This weight function produces larger weights corresponding to usual and smaller weights for unusual expressions. The β-weight function plays the significant role on the performance of the proposed method. The proposed method uses β-weight function as a measure of outlier detection by setting β = 0.2. We unify both classical and robust estimates using β-weight function, such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum β-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. Results: We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP, and FCROS) using both simulated and real gene expression profiles for both small and large sample cases. From the simulation and a real spike in data analysis results, we observed that the proposed method outperforms other methods for small sample cases in the presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP, and FCROS) otherwise. From the head and neck cancer (HNC) gene expression dataset, the proposed method identified two additional genes (CYP3A4 and NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan–Meier curve revealed that combined effect of these two genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank literature based gene associations. Conclusions: Using pathway analysis, disease association study, protein–protein interactions and survival analysis we found that our proposed two additional genes might be involved in the critical pathways of cancer. Furthermore, the identified drugs showed statistical significance which indicates that proteins associated with these genes might be therapeutic target in cancer.

Identification of new targets and biomarkers in lung cancer.

e14656 Background: Lung cancer is the second most common type of cancer in both genders; however is the leading cause of cancer deaths. Adenocarcinoma constitutes more than 60% of non-small cell lung cancer (NSCLC), and accounts for approximately 40% of lung cancers. Therefore, early prediction, prognosis of the disease and treatment strategy is of primary importance. Molecular biology assays, including microarrays, enable identification of new targets. The above targets can be used either as biomarkers, useful for early prediction and prognosis of the disease, or as drugable targets, upon proof of their biological action. The present study aimed to identify new biomarkers and/or targets in lung cancer. Methods: Whole genome gene expression microarrays have been performed for 12 lung cancer cell lines (provided be ECACC, ATCC), representing non-small cell lung (adenocarcinoma, squamous, large cell) and small cell lung cancer. Real-time experiments were followed for validation of thirty genes that were overexpressed. The experiments were performed including other cell lines, representing breast, colon, prostate, hepatocellular, melanoma and pancreatic cancer. Sample from healthy donors, as well as normal lung epithelial cell were used. Results: Among the genes that were studied, there was observed downregulation or none expression in normal donors as well as in normal lung epithelia cells. VSIG10 overexpressed in adenocarcinoma and large cells, while no expression observed in breast, prostate, melanoma and pancreatic cancer. DCBLD2 overexpressed in all NSCLC samples, but not in small cells. LIFR was overexpressed mainly in squamous cells, while no expression or downregulation observed in the other types of cancer. The same expression profile was mentioned for PDGFC. Conclusions: Taking everything into consideration, it is obvious that the identification of new biomarkers and/or targets for lung cancer is of primary importance. The present study revealed different genes, with different biological function that might be used primary as biomarkers. Their role need to be clarified also in clinical samples as to be used as drugable targets. However, the above data are encouraging for identification of new therapeutic models in lung cancer.

Is there a correlation between clinic-pathological features, MSI, PD-L1 and survival outcomes in resected gastric cancer? Looking for optimal biomarkers.

e15566 Background: Identification of prognostic biomarkers for gastric cancer (GC) patient selection is compelling to improve survival outcomes. Microsatellite instability (MSI) is related with a positive prognostic effect in GC, whereas perioperative chemotherapy resulted detrimental in this subgroup. In metastatic GC, immunotherapy with anti-PD1/PD-L1 drugs has shown promising results. Nevertheless, in early stages, data on the relation between MSI, clinic-pathological features, PD-L1 expression and overall survival (OS) remains sparse, especially in Western population. In our study, the prognostic role of MSI, clinic-pathological features and PD-L1 expression in a cohort of Italian GC patients was examined. Methods: CP data of 148 consecutive stage I-III GC pts resected in Cremona Institute between 2010 and 2014 (mostly chemo and/or radio-naive) were collected. MSI analysis was performed on tissue samples for all cases by polymerase chain reaction. PDL-1 expression, evaluated by immunohistochemistry, was assessed in MSI group. Differences between subgroups were evaluated with Chi-square test; Kaplan-Meier method and Long Rank test were used to calculate OS. Results: Female sex (p=0.012), earlier TNM stages (p=0.011) and limited nodal involvement (p=0.29) significantly correlated with MSI status. MSI is significantly associated with better prognosis, exhibiting an advantage of 28.6 months in OS compared with microsatellite stable subgroup (p&lt;0.001). Most MSI patients expressed PD-L1. MSI patients without PD-L1 expression showed higher percentage of clinical features correlated with better prognosis compared with PD-L1 expressing MSI patients and MSS subgroup. Conclusions: MSI is an independent prognostic biomarker in GC and identifies a subset of patients with better OS and specific clinic-pathological features, including high percentage of PD-L1 expression. MSI could represent a promising biomarker to select patients for chemotherapy versus immunotherapy in non-metastatic disease.

Identification of candidate diagnostic and prognostic biomarkers for human prostate cancer: RPL22L1 and RPS21.

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. This study was designed to investigate the potential of Ribosomal Protein L22-like1 (RPL22L1) and Ribosomal Protein S21 (RPS21) as diagnostic and prognostic biomarkers for PCa. First, RPL22L1 and RPS21 were screened as the key molecular of PCa by bioinformatics analysis. Subsequently, the prostate tissue samples were stained for antibodies against RPL22L1 and RPS21. The unbiased signal quantification was performed by ImageJ software, and the results showed that the expression of RPL22L1 and RPS21 exhibited significant differences between the PCa tissues and the normal prostate tissues. Receiver-operating characteristics (ROC) curves were prepared, and then the area under the curve (AUC) values of RPL22L1 and RPS21 were calculated as 0.798 and 0.768, and the likelihood ratio (LR) values of RPL22L1 and RPS21 were calculated as 2.86 and 2.53. These data implied that the over-expression of RPL22L1 and RPS21 is associated with the presence of PCa. The further analysis suggested that the expression of RPL22L1 and RPS21 were significantly higher in high Gleason grade than they were in low Gleason grade. In addition, in vitro studies were undertaken to evaluate the roles of RPL22L1 and RPS21 in PCa. The results revealed that these genes promote PCa cell proliferation, migration and invasion, and inhibit PCa cell apoptosis. Taken together, these data showed that RPL22L1 and RPS21 exhibited higher expression in human prostate cancer tissue, and involved in PCa cell proliferation and invasion. This research provided a novel insight into diagnostic and prognostic biomarkers for PCa.

Identification of Novel MicroRNAs and Their Diagnostic and Prognostic Significance in Oral Cancer.

Background: Oral cancer is one of the most prevalent cancers worldwide. Despite that the oral cavity is easily accessible for clinical examinations, oral cancers are often not promptly diagnosed. Furthermore, to date no effective biomarkers are available for oral cancer. Therefore, there is an urgent need to identify novel biomarkers able to improve both diagnostic and prognostic strategies. In this context, the development of innovative high-throughput technologies for molecular and epigenetics analyses has generated a huge amount of data that may be used for the identification of new cancer biomarkers. Methods: In the present study, GEO DataSets and TCGA miRNA profiling datasets were analyzed in order to identify miRNAs with diagnostic and prognostic significance. Furthermore, several computational approaches were adopted to establish the functional roles of these miRNAs. Results: The analysis of datasets allowed for the identification of 11 miRNAs with a potential diagnostic role for oral cancer. Additionally, eight miRNAs associated with patients’ prognosis were also identified; six miRNAs predictive of patients’ overall survival (OS) and one, hsa-miR-let.7i-3p, associated with tumor recurrence. Conclusions: The integrated analysis of different miRNA expression datasets allows for the identification of a set of miRNAs that, after validation, may be used for the early detection of oral cancers.

Identification of prognostic biomarkers for breast cancer based on miRNA and mRNA co-expression network.

Breast cancer (BC) remains a serious health threat for women due to its high incidence and the trend of rejuvenation. Accumulating evidence has highlighted that microRNAs (miRNAs) and messenger RNAs (mRNAs) could play important roles in various biological processes involved in the pathogenesis of BC. The present study aimed to identify potentialprognostic biomarkers associated with BC. Here, original gene expression profiles of patients with BC was downloaded from The Cancer Genome Atlas (TCGA) database. TargetScan, miRDB, and miRTarBase databases were used to predict the target genes of prognostic-related differentially expressed miRNAs (DEMs). Subsequently, functional enrichment analysis and topological analysis were performed on the overlaps of target genes and differentially expressed mRNAs (DEGs), and Kaplan-Meier analysis was used to predict prognosis-related target genes to identify prognostic biomarkers. A total of 218 DEMs and 2222 DEGs were extracted in which eight miRNAs were associated with prognosis, and 278 target DEGs were screened out incorporated into functional enrichment analysis and protein-protein interaction network visualization studies. Additionally, five hub genes (CXCL12, IGF1, LEF1, MMP1, and RACGAP1) were observed as potential biomarkers for BC prognosis through survival analysis. We performed a distinctive correlation analysis of miRNA-mRNA in BC patients, and identified eight miRNAs and five hub genes may be effective biomarkers for the prognosis of BC patients.

Quantitative microimmunohistochemistry for the grading of immunostains on tumour tissues.

Immunohistochemistry is the gold-standard method for cancer-biomarker identification and patient stratification. Yet, owing to signal saturation, its use as a quantitative assay is limited as it cannot distinguish tumours with similar biomarker-expression levels. Here, we introduce a quantitative microimmunochemistry assay that enables the acquisition of dynamic information, via a metric of the evolution of the immunohistochemistry signal during tissue staining, for the quantification of relative antigen density on tissue surfaces. We used the assay to stratify 30 patient-derived breast-cancer samples into conventional classes and to determine the proximity of each sample to the other classes. We also show that the assay enables the quantification of multiple biomarkers (human epidermal growth factor receptor, oestrogen receptor and progesterone receptor) in a standard breast-cancer panel. The integration of quantitative microimmunohistochemistry into current pathology workflows may lead to improvements in the precision of biomarker quantification.

Identification of prothymosin alpha (PTMA) as a biomarker for esophageal squamous cell carcinoma (ESCC) by label-free quantitative proteomics and Quantitative Dot Blot (QDB)

BackgroundEsophageal cancer (EC) is one of the malignant tumors with a poor prognosis. The early stage of EC is asymptomatic, so identification of cancer biomarkers is important for early detection and clinical practice.MethodsIn this study, we compared the protein expression profiles in esophageal squamous cell carcinoma (ESCC) tissues and adjacent normal esophageal tissues from five patients through high-resolution label-free mass spectrometry. Through bioinformatics analysis, we found the differentially expressed proteins of ESCC. To perform the rapid identification of biomarkers, we adopted a high-throughput protein identification technique of Quantitative Dot Blot (QDB). Meanwhile, the QDB results were verified by classical immunohistochemistry.ResultsIn total 2297 proteins were identified, out of which 308 proteins were differentially expressed between ESCC tissues and normal tissues. By bioinformatics analysis, the four up-regulated proteins (PTMA, PAK2, PPP1CA, HMGB2) and the five down-regulated proteins (Caveolin, Integrin beta-1, Collagen alpha-2(VI), Leiomodin-1 and Vinculin) were selected and validated in ESCC by Western Blot. Furthermore, we performed the QDB and IHC analysis in 64 patients and 117 patients, respectively. The PTMA expression was up-regulated gradually along the progression of ESCC, and the PTMA expression ratio between tumor and adjacent normal tissue was significantly increased along with the progression. Therefore, we suggest that PTMA might be a potential candidate biomarker for ESCC.ConclusionIn this study, label-free quantitative proteomics combined with QDB revealed that PTMA expression was up-regulated in ESCC tissues, and PTMA might be a potential candidate for ESCC. Since Western Blot cannot achieve rapid and high-throughput screening of mass spectrometry results, the emergence of QDB meets this demand and provides an effective method for the identification of biomarkers.

Clinical protein science in translational medicine targeting malignant melanoma

Melanoma of the skin is the sixth most common type of cancer in Europe and accounts for 3.4% of all diagnosed cancers. More alarming is the degree of recurrence that occurs with approximately 20% of patients lethally relapsing following treatment. Malignant melanoma is a highly aggressive skin cancer and metastases rapidly extend to the regional lymph nodes (stage 3) and to distal organs (stage 4). Targeted oncotherapy is one of the standard treatment for progressive stage 4 melanoma, and BRAF inhibitors (e.g. vemurafenib, dabrafenib) combined with MEK inhibitor (e.g. trametinib) can effectively counter BRAFV600E-mutated melanomas. Compared to conventional chemotherapy, targeted BRAFV600E inhibition achieves a significantly higher response rate. After a period of cancer control, however, most responsive patients develop resistance to the therapy and lethal progression. The many underlying factors potentially causing resistance to BRAF inhibitors have been extensively studied. Nevertheless, the remaining unsolved clinical questions necessitate alternative research approaches to address the molecular mechanisms underlying metastatic and treatment-resistant melanoma. In broader terms, proteomics can address clinical questions far beyond the reach of genomics, by measuring, i.e. the relative abundance of protein products, post-translational modifications (PTMs), protein localisation, turnover, protein interactions and protein function. More specifically, proteomic analysis of body fluids and tissues in a given medical and clinical setting can aid in the identification of cancer biomarkers and novel therapeutic targets. Achieving this goal requires the development of a robust and reproducible clinical proteomic platform that encompasses automated biobanking of patient samples, tissue sectioning and histological examination, efficient protein extraction, enzymatic digestion, mass spectrometry–based quantitative protein analysis by label-free or labelling technologies and/or enrichment of peptides with specific PTMs. By combining data from, e.g. phosphoproteomics and acetylomics, the protein expression profiles of different melanoma stages can provide a solid framework for understanding the biology and progression of the disease. When complemented by proteogenomics, customised protein sequence databases generated from patient-specific genomic and transcriptomic data aid in interpreting clinical proteomic biomarker data to provide a deeper and more comprehensive molecular characterisation of cellular functions underlying disease progression. In parallel to a streamlined, patient-centric, clinical proteomic pipeline, mass spectrometry–based imaging can aid in interrogating the spatial distribution of drugs and drug metabolites within tissues at single-cell resolution. These developments are an important advancement in studying drug action and efficacy in vivo and will aid in the development of more effective and safer strategies for the treatment of melanoma. A collaborative effort of gargantuan proportions between academia and healthcare professionals has led to the initiation, establishment and development of a cutting-edge cancer research centre with a specialisation in melanoma and lung cancer. The primary research focus of the European Cancer Moonshot Lund Center is to understand the impact that drugs have on cancer at an individualised and personalised level. Simultaneously, the centre increases awareness of the relentless battle against cancer and attracts global interest in the exceptional research performed at the centre.

The identification of new biomarkers for bladder cancer: A study based on TCGA and GEO datasets.

Bladder cancer (BC) is one of the most common neoplastic diseases worldwide. With the highest recurrence rate among all cancers, treatment of BC only improved a little in the last 30 years. Available biomarkers are not sensitive enough for the diagnosis of BC, whereas the standard diagnostic method, cystoscopy, is an invasive test and expensive. Hence, seeking new biomarkers of BC is urgent and challenging. With that order, we screened the overlapped differentially expressed genes (DEGs) of GSE13507 and TCGA BLCA datasets. Subsequent protein-protein interactions network analysis recognized the hub genes among these DEGs. Further functional analysis including Gene Ontology and KEGG pathway analysis and gene set enrichment analysis were processed to investigate the role of these genes and potential underlying mechanisms in BC. Kaplan-Meier analysis and Cox hazard ratio analysis were carried out to clarify the diagnostic and prognostic role of these genes. In conclusion, our present study demonstrated that ACTA2, CDC20, MYH11, TGFB3, TPM1, VIM, and DCN are all potential diagnostic biomarkers for BC. And may also be potential treatment targets for clinical implication in the future.