Novel strategies to identify biomarkers in tuberculosis

The more we learn about the immune response against tuberculosis (TB) and particularly about the features which distinguish protective immunity, disease susceptibility and pathology, the better we can define biomarkers which correlate with these different stages of infection. The most widely used biomarker in TB, which without a doubt is an important component of protective immunity, is IFNgamma secreted by antigen-specific CD4 T-cells. However, the complexity of the immune response against TB makes it more than likely that additional biomarkers are required for a reliable correlate of protection. As a corollary, we assume that a set of biomarkers will be required, termed a biosignature.     

Proteomics approach to identify biomarkers for upper gastrointestinal cancer

Introduction: The prognosis for patients with upper gastrointestinal cancers remains dismal despite the development of multimodality therapies that incorporate surgery, chemotherapy, and radiotherapy. Early diagnosis and personalized treatment should lead to better prognosis. Given the advances in proteomic technologies over the past decades, proteomics promises to be the most effective technique to identify novel diagnostics and therapeutic targets.

Areas covered: For this review, keywords were searched in combination with ‘proteomics’ and ‘gastric cancer’ or ‘esophageal cancer’ in PubMed. Studies that evaluated proteomics associated with upper gastrointestinal cancer were identified through reading, with several studies quoted at second hand. We summarize the proteomics involved in upper gastrointestinal cancer and discuss potential biomarkers and therapeutic targets.

Expert commentary: In particular, the development of mass spectrometry has enabled detection of multiple proteins and peptides in more biological samples over a shorter time period and at lower cost than was previously possible. In addition, more sophisticated protein databases have allowed a wider variety of proteins in samples to be quantified. Novel biomarkers that have been identified by new proteomic technologies should be applied in a clinical setting.     

A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers

Epithelial ovarian cancer is the most lethal gynecological malignancy, and disease-specific biomarkers are urgently needed to improve diagnosis, prognosis, and to predict and monitor treatment efficiency. We present an in-depth proteomic analysis of selected biochemical fractions of human ovarian cancer ascites, resulting in the stringent and confident identification of over 2500 proteins. Rigorous filter schemes were applied to objectively minimize the number of false-positive identifications, and we only report proteins with substantial peptide evidence. Integrated computational analysis of the ascites proteome combined with several recently published proteomic data sets of human plasma, urine, 59 ovarian cancer related microarray data sets, and protein-protein interactions from the Interologous Interaction Database I (2)D ( http://ophid.utoronto.ca/i2d) resulted in a short-list of 80 putative biomarkers. The presented proteomics analysis provides a significant resource for ovarian cancer research, and a framework for biomarker discovery.     

Preclinical strategies to define predictive biomarkers for therapeutically relevant cancer subtypes

Defining key driver mutations in cancer, the resulting aberrations in molecular mechanisms and the subsequent phenotype underpins the development and implementation of novel personalized medicine strategies. The literature is replete with biomarkers of prognosis and therapeutic responsiveness identified in single cohorts of patients that have not been independently validated and as a consequence, not developed. Integrating companion biomarker discovery with therapeutic development at the preclinical stage creates the opportunity to identify candidate biomarkers early, which would significantly facilitate both biomarker and therapeutic development. Advances in “-omic” technologies have led to large-scale efforts in characterizing and cataloguing the full range of aberrations in cancer. These include the International Cancer Genome Consortium and The Cancer Genome Atlas, which aim to comprehensively catalogue the range of genomic aberrations for large numbers of cancers for a progressively increasing range of cancer types and subtypes. The technical challenges associated with achieving these goals in some instances have required the generation of primary xenografts and cell lines. These extensively characterized model systems will provide an unprecedented resource for the discovery of biomarkers of therapeutic responsiveness for established therapies, and the development of companion biomarkers linked with preclinical novel therapeutic development in the future.     

Urinary proteomic profiling for diagnostic bladder cancer biomarkers

The ability to detect and monitor bladder cancer in noninvasively obtained urine samples is a major goal. While a number of protein biomarkers have been identified and commercially developed, none have greatly improved the accuracy of sample evaluation over invasive cystoscopy. The ongoing development of high-throughput proteomic profiling technologies will facilitate the identification of molecular signatures that are associated with bladder disease. The appropriate use of these approaches has the potential to provide efficient biomarkers for the early detection and monitoring of recurrent bladder cancer. Identification of disease-associated proteins will also advance our knowledge of tumor biology, which, in turn, will enable development of targeted therapeutics aimed at reducing morbidity from bladder cancer. In this article, we focus on the accumulating proteomic signatures of urine in health and disease, and discuss expected future developments in this field of research.     

子宫颈癌细胞学新型生物标志、诊断标志物的筛选和开发现状

子宫颈癌至今仍是全球范围内一个重要的公共卫生问题，是妇女疾病死亡的主要原因之一。因此，子宫颈癌细胞学的筛查、早期诊断和治疗越来越受到重视，虽然新的技术不断推出使子宫颈癌的早期筛查及诊治水平有了很大提高，但仍缺乏新型的特异性生物学标志物。本文从新的子宫颈癌相关蛋白生物标志物和诊断靶标的发现，治疗子宫颈癌的药物作用、治疗靶标和作用机制的评估，子宫颈癌相关微小核糖核酸作为诊断和治疗靶标的筛选等方面对子宫颈脱落细胞筛查方法的开发和研究进展进行综述，为子宫颈癌的早期筛查和诊断寻找新的生物学标志物。     

Large-scale proteomics analysis of human ovarian cancer for biomarkers

Ovarian cancer is usually found at a late stage when the prognosis is often bad. Relative survival rates decrease with tumor stage or grade, and the 5-year survival rate for women with carcinoma is only 38%. Thus, there is a great need to find biomarkers that can be used to carry out routine screening, especially in high-risk patient groups. Here, we present a large-scale study of 64 tissue samples taken from patients at all stages and show that we can identify statistically valid markers using nonsupervised methods that distinguish between normal, benign, borderline, and malignant tissue. We have identified 217 of the significantly changing protein spots. We are expressing and raising antibodies to 35 of these. Currently, we have validated 5 of these antibodies for use in immunohistochemical analysis using tissue microarrays of healthy and diseased ovarian, as well as other, human tissues.     

Proteomics discovery of chemoresistant biomarkers for ovarian cancer therapy

Introduction: Chemoresistance is a major challenge to current ovarian cancer chemotherapy. It is important to identify biomarkers to distinguish chemosensitive and chemoresistant patients.

Areas covered: We review the medical literature, discuss MS-based technologies with respect to chemoresistant ovarian cancer and summarize the promising chemoresistant biomarkers identified. In addition, the challenges and future perspectives of biomarker discovery research are explored. With the employment of mass spectrometry-based (MS-based) proteomics, biomarker discovery of ovarian cancer has made great progress in the last decade. Many potential biomarkers were identified by MS-based proteomics technologies, some of which have been validated for further extensive studies in clinical settings.

Expert commentary: The discovery of chemoresistant biomarkers is a newly developing area and may provide a clue for predicting chemotherapeutic response and discover therapeutic targets for paving the way of personalized medicine. Multiple complementary MS-based proteomics approaches hold promise for finding novel therapeutic targets in ovarian cancer treatment.     

Fine-needle aspiration of thyroid nodules: proteomic analysis to identify cancer biomarkers

At present, the clinical and pathological analysis used in the diagnosis of papillary thyroid cancer (PTC) are insufficient to discern tumor behavior, and new diagnostic and prognostic markers need to be identified. In this study, we performed a comparative proteome analysis to examine the global changes of fine needle aspiration fluid (FNA) protein patterns of two variants of malignant PTC (classical variant PTC (cPTC) and tall cell variant PTC (TCV)) with respect to the controls. Changes in protein expression were identified using two-dimensional electrophoresis (2DE) and peptide mass fingerprinting via MALDI-TOF mass spectrometry (MS), as well as Western blot analysis. A statistical significant up-regulation of 17 protein spots in cPTC and/or TCV with respect to controls was demonstrated. These proteins included transthyretin precursor (TTR), ferritin light chain (FLC), proteasome activator complex subunit 1 and 2, alpha-1-antitrypsin precursor, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), lactate dehydrogenase chain B (LDH-B), apolipoprotein A1 precursor (Apo-A1), annexin A1, DJ-1 protein and cofilin-1. In addition, 12 protein spots were found exclusively in cPTC and three exclusively in TCV. These latter proteins (ferritin heavy chain (FHC), peroxiredoxin 1 (PRX1) and 6-phosphogluconate dehydrogenase (6-PDGH)) correspond to stress response proteins and, until now, had not been described in thyroid tumors. These findings illustrate the potential use of FNA proteomics to identify protein changes associated with thyroid cancer and to advance potential protein biomarkers in the diagnostic classification of the disease.     

Improving diagnostic strategies for ovarian cancer in Filipino women using ultrasound imaging and a multivariate index assay

ObjectiveTo evaluate the clinical performance and overall utility of imaging and biomarker assays in discriminating between benign and malignant ovarian masses in a Filipino population.MethodsThis is a prospective cohort study among Filipino women undergoing assessment for an ovarian mass in a tertiary center. All included patients underwent a physical examination before level III specialist ultrasonographic and Doppler evaluation, multivariate index assay (MIA2G), and surgery for an adnexal mass. Ovarian tumors were classified as high-risk for malignancy based on the International Ovarian Tumour Analysis (IOTA) – Logistic Regression 2 (LR2) score. The ovarian imaging and biomarker results were correlated with the reference standard: histological findings.ResultsAmong the 379 women with adnexal masses enrolled in this study, 291 were evaluable with ultrasound imaging, biomarker assays, and histopathological results. The risk of malignancy was higher for women classified as high-risk based on IOTA-LR2 (≥10%). The sensitivity, specificity, and diagnostic accuracy for the prediction of malignancy were 81.2%, 81%, and 0.81 (95% CI: 0.77–0.86) for IOTA-LR2; 77.5%, 66.7%, and 0.72 (95% CI: 0.67–0.77) for CA-125; and 91.3%, 41.2%, and 0.66 (95% CI: 0.62–0.71) for MIA2G. A combination of IOTA-LR2 and MIA2G significantly influenced the diagnostic performance and the result. When MIA2G was combined with IOTA-LR2 in parallel, the sensitivity (94.2%) and NPV (87.7%) increased, but the specificity (37.3%) decreased. When combined with IOTA-LR2 in series, there were fewer false positives, which resulted in improved specificity (85%).ConclusionThis study determined the utility of ovarian imaging and a second-generation multivariate index assay in predicting the risk of ovarian malignancy. IOTA-LR2 and MIA2G were useful in classifying patients with a high risk for ovarian malignancy.     

A multiplex platform for the identification of ovarian cancer biomarkers

Background

Currently, there are no FDA approved screening tools for detecting early stage ovarian cancer in the general population. Development of a biomarker-based assay for early detection would significantly improve the survival of ovarian cancer patients.

Methods

We used a multiplex approach to identify protein biomarkers for detecting early stage ovarian cancer. This new technology (Proseek^® Multiplex Oncology Plates) can simultaneously measure the expression of 92 proteins in serum based on a proximity extension assay. We analyzed serum samples from 81 women representing healthy, benign pathology, early, and advanced stage serous ovarian cancer patients.

Results

Principle component analysis and unsupervised hierarchical clustering separated patients into cancer versus non-cancer subgroups. Data from the Proseek^® plate for CA125 levels exhibited a strong correlation with current clinical assays for CA125 (correlation coefficient of 0.89, 95% CI 0.83, 0.93). CA125 and HE4 were present at very low levels in healthy controls and benign cases, while higher levels were found in early stage cases, with highest levels found in the advanced stage cases. Overall, significant trends were observed for 38 of the 92 proteins (p < 0.001), many of which are novel candidate serum biomarkers for ovarian cancer. The area under the ROC curve (AUC) for CA125 was 0.98 and the AUC for HE4 was 0.85 when comparing early stage ovarian cancer versus healthy controls. In total, 23 proteins had an estimated AUC of 0.7 or greater. Using a naïve Bayes classifier that combined 12 proteins, we improved the sensitivity corresponding to 95% specificity from 93 to 95% when compared to CA125 alone. Although small, a 2% increase would have a significant effect on the number of women correctly identified when screening a large population.

Conclusions

These data demonstrate that the Proseek^® technology can replicate the results established by conventional clinical assays for known biomarkers, identify new candidate biomarkers, and improve the sensitivity and specificity of CA125 alone. Additional studies using a larger cohort of patients will allow for validation of these biomarkers and lead to the development of a screening tool for detecting early stage ovarian cancer in the general population.

     

Grade-specific diagnostic and prognostic biomarkers in breast cancer

An integrative approach is presented to identify grade-specific biomarkers for breast cancer. Grade-specific molecular interaction networks were constructed with differentially expressed genes (DEGs) of cancer grade 1, 2, and 3. We observed that the molecular network of grade3 is predominantly associated with cancer-specific processes. Among the top ten connected DEGs in the grade3, the increase in the expression of UBE2C and CCNB2 genes was statistically significant across different grades. Along with UBE2C and CCNB2 genes, the CDK1, KIF2C, NDC80, and CCNB2 genes are also profoundly expressed in different grades and reduce the patient's survival. Gene set enrichment analysis of these six genes reconfirms their role in metastatic phenotype. Moreover, the coexpression network shows a strong association of these six genes promotes cancer specific biological processes and possibly drives cancer from lower to a higher grade. Collectively the identified genes can act as potential biomarkers for breast cancer diagnosis and prognosis.     

Characterization of serum biomarkers for detection of early stage ovarian cancer

We have previously reported the identification of three ovarian cancer biomarker panels comprised of SELDI-TOF-MS peaks representing 14 differentially expressed serum proteins for the diagnosis of ovarian cancer. Using micro-LC-MS/MS, we identified five m/z peaks as transthyretin (TTR 13.9 kDa, TTR fragment 12.9 kDa), beta-hemoglobin (Hb, 15.9 kDa), apolipoprotein AI (ApoAI, 28 kDa) and transferrin (TF, 79 kDa). Western and/or ELISA methods confirmed the differential expression of TTR, Hb, and TF, and multivariate analyses resulted in improving the detection of early stage ovarian tumors (low malignant potential and malignant; receiver operating characteristic, ROC 0.933) as compared to cancer antigen CA125 alone (ROC 0.833). Interestingly, when CA125 was included with our markers in the multivariate analysis, the ROC increased to 0.959. Furthermore, multivariate analysis with only the mucinous subtype of early stage ovarian tumors, showed our markers to greatly improve the detection of disease (ROC 0.959) as compared to CA125 alone (ROC 0.613). Interestingly, the combination of CA125 with our markers did not seem to further improve the detection of mucinous tumors (ROC 0.955). We conclude that TTR, Hb, ApoAI and TF, when combined with CA125 should significantly improve the detection of early stage ovarian cancer.     

Comprehensive serum profiling for the discovery of epithelial ovarian cancer biomarkers

FDA-cleared ovarian cancer biomarkers are limited to CA-125 and HE4 for monitoring and recurrence and OVA1, a multivariate panel consisting of CA-125 and four additional biomarkers, for referring patients to a specialist. Due to relatively poor performance of these tests, more accurate and broadly applicable biomarkers are needed. We evaluated the dysregulation of 259 candidate cancer markers in serum samples from 499 patients. Sera were collected prospectively at 11 monitored sites under a single well-defined protocol. All stages of ovarian cancer and common benign gynecological conditions were represented. To ensure consistency and comparability of biomarker comparisons, all measurements were performed on a single platform, at a single site, using a panel of rigorously calibrated, qualified, high-throughput, multiplexed immunoassays and all analyses were conducted using the same software. Each marker was evaluated independently for its ability to differentiate ovarian cancer from benign conditions. A total of 175 markers were dysregulated in the cancer samples. HE4 (AUC=0.933) and CA-125 (AUC=0.907) were the most informative biomarkers, followed by IL-2 receptor α, α1-antitrypsin, C-reactive protein, YKL-40, cellular fibronectin, CA-72-4 and prostasin (AUC>0.800). To improve the discrimination between cancer and benign conditions, a simple multivariate combination of markers was explored using logistic regression. When combined into a single panel, the nine most informative individual biomarkers yielded an AUC value of 0.950, significantly higher than obtained when combining the markers in the OVA1 panel (AUC 0.912). Additionally, at a threshold sensitivity of 90%, the combination of the top 9 markers gave 88.9% specificity compared to 63.4% specificity for the OVA1 markers. Although a blinded validation study has not yet been performed, these results indicate that alternative biomarker combinations might lead to significant improvements in the detection of ovarian cancer.     

Serum protein profiling to identify biomarkers for small renal cell carcinoma

Diagnostic biomarkers for early detection of renal cell carcinoma (RCC) are in great need. In the present study, we compared the serum protein profiles of patients with small RCC to those of healthy individuals to identify the differentially expressed proteins with potential to serve as biomarkers. Serum samples were collected from 10 patients with small RCC and 10 healthy individuals. The serum protein expression profiles were analyzed by two-dimensional (2-D) gel electrophoresis. Twenty-seven proteins with differences in expression levels between RCC patients and healthy volunteers were identified. Of these, 19 were expressed at different levels and eight were expressed in serum from the RCC group, but not from the control group. Six differentially expressed proteins identified by using mass spectrometry included coagulation factor XIII B, complement C3 and its precursor, misato homolog 1 (isoform CRA_b), hemopexin, and alpha-1-B-glycoprotein. Some of these serum proteins are known regulators of tumor progression in human malignancies. In conclusion, we successfully applied 2-D gel electrophoresis and identified six serum proteins differentially expressed between patients with small RCC and healthy volunteers. These proteins may provide novel biomarkers for early detection and diagnosis of human RCC.     

Immunoproteomics of the active degradome to identify biomarkers for Trichomonas vaginalis

Trichomonas vaginalis, a sexually transmitted parasite, has many cysteine proteinases (CPs); some are involved in trichomonal pathogenesis, express during infection, and antibodies against CPs have been detected in patient sera. The goal of this study was to identify the antigenic proteinases of T. vaginalis as potential biomarkers for trichomonosis. The proteases detected when T. vaginalis protein extracts are incubated without protease inhibitors, the trichomonad‐active degradome, and the immunoproteome were obtained by using 2‐DE, 2‐D‐zymograms, 2‐D‐Western blot (WB) assays with trichomonosis patient sera, and MS analysis. Forty‐nine silver‐stained spots were detected in the region of 200–21 kDa of parasite protease‐resistant extracts. A similar proteolytic pattern was observed in the 2‐D zymograms. Nine CPs were identified in the 30 kDa region (TvCP1, TvCP2, TvCP3, TvCP4, TvCP4‐like, TvCP12, TvCPT, TvLEGU‐1, and another legumain‐like CP). The major reactive spots to T. vaginalis‐positive patient sera by 2‐D‐WB corresponded to four papain‐like (TvCP2, TvCP4, TvCP4‐like, TvCPT), and one legumain‐like (TvLEGU‐1) CPs. The genes of TvCP4, TvCPT, and TvLEGU‐1 were cloned, sequenced, and expressed in Escherichia coli. Purified recombinant CPs were recognized by culture‐positive patient sera in 1‐D‐WB assays. These data show that some CPs could be potential biomarkers for serodiagnosis of trichomonosis.