Recent advances in biomarkers for cancer diagnosis and treatment

Logic Forest: an ensemble classifier for discovering logical combinations of binary markers

MOTIVATION

Highly sensitive and specific screening tools may reduce disease -related mortality by enabling physicians to diagnose diseases in asymptomatic patients or at-risk individuals. Diagnostic tests based on multiple biomarkers may achieve the needed sensitivity and specificity to realize this clinical gain.

RESULTS

Logic regression, a multivariable regression method predicting an outcome using logical combinations of binary predictors, yields interpretable models of the complex interactions in biologic systems. However, its performance degrades in noisy data. We extend logic regression for classification to an ensemble of logic trees (Logic Forest, LF). We conduct simulation studies comparing the ability of logic regression and LF to identify variable interactions predictive of disease status. Our findings indicate LF is superior to logic regression for identifying important predictors. We apply our method to single nucleotide polymorphism data to determine associations of genetic and health factors with periodontal disease.

AVAILABILITY

LF code is publicly available on CRAN, http://cran.r-project.org/.

Measurement of biomarker proteins for point-of-care early detection and monitoring of cancer

This critical review evaluates progress toward viable point-of-care protein biomarker measurements for cancer detection and diagnostics. The ability to measure panels of specific, selective cancer biomarker proteins in physicians' surgeries and clinics has the potential to revolutionize cancer detection, monitoring, and therapy. The dream envisions reliable, cheap, automated, technically undemanding devices that can analyze a patient's serum or saliva in a clinical setting, allowing on-the-spot diagnosis. Existing commercial products for protein assays are reliable in laboratory settings, but have limitations for point-of-care applications. A number of ultrasensitive immunosensors and some arrays have been developed, many based on nanotechnology. Multilabel detection coupled with high capture molecule density in immunosensors and arrays seems to be capable of detecting a wide range of protein concentrations with sensitivity ranging into the sub pg mL(-1) level. Multilabel arrays can be designed to detect both high and ultralow abundance proteins in the same sample. However, only a few of the newer ultrasensitive methods have been evaluated with real patient samples, which is key to establishing clinical sensitivity and selectivity.

Biomarkers in oncology research and treatment: early detection research network: a collaborative approach

Several important criteria are essential for the development of biomarkers in clinical oncology. First, the biomarkers should be easily measured using standardized and cost-efficient methods. Second, biomarkers should be easily attainable from clinical materials such as body fluids and cells. Third, biomarkers should have clearly defined cutoff values with high sensitivity and specificity. Lastly, the predictive value of biomarkers should be possible in strata as large as possible. Single biomarkers may not be able to meet all of these criteria, which necessitates the development of biomarker panels. High-throughput technologies will be necessary for measuring these biomarker sets and translation of these methods into a clinical setting will be necessary in order to employ these biomarkers in a healthcare setting. One of the most important aspects of biomarker development will be standardization and statistical evaluation of biomarker studies. Guidelines for biomarker studies need to be developed that will enable standardization to take place. The Early Detection Research Network has been in the forefront of this objective. Early detection of cancer through appropriately validated biomarkers will provide for decreased morbidity and mortality and allow for the development of new therapeutic tools targeted specifically toward eradication of these early malignancies, hopefully increasing the survival rate of patients diagnosed with early-stage cancer.

The evolution of biomarkers in thyroid cancer-from mass screening to a personalized biosignature

Thyroid cancer is the most common malignancy of the endocrine system. The diagnosis of thyroid nodules, made by neck examination and ultrasonography, is a common event occurring in over 50% of the patient population over the age of 50. Yet, only 5% of these patients will be diagnosed with cancer. Fine needle aspiration biopsy is the gold standard for diagnosing thyroid nodules. However, 10–15% of these biopsies are inconclusive, ultimately requiring a diagnostic thyroid lobectomy. Consequently, research in thyroid biomarkers has become an area of active interest. In the 40 years since calcitonin was first described as the biomarker for medullary thyroid cancer, new biomarkers in thyroid cancer have been discovered. Advances in genomic and proteomic technologies have defined many of these novel thyroid biomarkers. The purpose of this article is to provide a comprehensive literature review of how these biomarkers have evolved from simple screening tests into a complex array of multiple markers to help predict the malignant potential and genetic signature of thyroid neoplasms.

Identification of a novel prostate cancer biomarker, caveolin-1: Implications and potential clinical benefit

While prostate cancer is a common disease in men, it is uncommonly life-threatening. To better understand this phenomenon, tumor biologists have sought to elucidate the mechanisms that contribute to the development of virulent prostate cancer. The recent discovery that caveolin-1 (Cav-1) functions as an important oncogene involved in prostate cancer progression reflects the success of this effort. Cav-1 is a major structural coat protein of caveolae, specialized plasma membrane invaginations involved in multiple cellular functions, including molecular transport, cell adhesion, and signal transduction. Cav-1 is aberrantly overexpressed in human prostate cancer, with higher levels evident in metastatic versus primary sites. Intracellular Cav-1 promotes cell survival through activation of Akt and enhancement of additional growth factor pro-survival pathways. Cav-1 is also secreted as a biologically active molecule that promotes cell survival and angiogenesis within the tumor microenvironment. Secreted Cav-1 can be reproducibly detected in peripheral blood using a sensitive and specific immunoassay. Cav-1 levels distinguish men with prostate cancer from normal controls, and preoperative Cav-1 levels predict which patients are at highest risk for relapse following radical prostatectomy for localized disease. Thus, secreted Cav-1 is a promising biomarker in identifying clinically significant prostate cancer.

Identification of a novel prostate cancer biomarker, caveolin-1: Implications and potential clinical benefit

While prostate cancer is a common disease in men, it is uncommonly life-threatening. To better understand this phenomenon, tumor biologists have sought to elucidate the mechanisms that contribute to the development of virulent prostate cancer. The recent discovery that caveolin-1 (Cav-1) functions as an important oncogene involved in prostate cancer progression reflects the success of this effort. Cav-1 is a major structural coat protein of caveolae, specialized plasma membrane invaginations involved in multiple cellular functions, including molecular transport, cell adhesion, and signal transduction. Cav-1 is aberrantly overexpressed in human prostate cancer, with higher levels evident in metastatic versus primary sites. Intracellular Cav-1 promotes cell survival through activation of Akt and enhancement of additional growth factor pro-survival pathways. Cav-1 is also secreted as a biologically active molecule that promotes cell survival and angiogenesis within the tumor microenvironment. Secreted Cav-1 can be reproducibly detected in peripheral blood using a sensitive and specific immunoassay. Cav-1 levels distinguish men with prostate cancer from normal controls, and preoperative Cav-1 levels predict which patients are at highest risk for relapse following radical prostatectomy for localized disease. Thus, secreted Cav-1 is a promising biomarker in identifying clinically significant prostate cancer.

Evaluation of the QIAsymphony SP Workstation for Magnetic Particle—Based Nucleic Acid Purification from Different Sample Types for Demanding Downstream Applications

We evaluated automated nucleic acid (NA) extraction from a variety of different biological specimens using the QIAsymphony SP instrument. QIAsymphony DNA kits were used for DNA purification from human blood and from diverse human and animal tissue specimens. RNA was isolated from human blood stabilized in PAXgene Blood RNA tubes with the QIAsymphony PAXgene Blood RNA kit, and from human colon and bladder carcinoma biopsies using the QIAsymphony RNA kit. Photometric measurement, gel electrophoresis, and LabChip analysis on an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, California) showed that the purified NAs were highly pure and intact, and that excellent yields were obtained. The DNA purified from blood and tissues performed well in single nucleotide polymorphism (SNP) array analysis, shown by call rates for the Affymetrix Genome-Wide Human 6.0 SNP arrays of > 99%. No significant differences were observed when array results of DNA purified either with magnetic particle technology or silica membrane technology were compared. The quality of the DNA allowed accurate allelic discrimination by TaqMan SNP PCR. Gene expression analyses of purified RNA either by “Human Endogenous Control Panel” TaqMan low-density array or on Affymetrix HG UI33 plus 2.0 GeneChips revealed high concordance between manually purified samples and those extracted on the QIAsymphony SP.

Aurora kinase inhibitors: identification and preclinical validation of their biomarkers

Aurora kinases are key regulators of mitosis and inhibitors being developed by a wide range of pharmaceutical and biotechnology companies for the treatment of cancer. Tumor cells respond differentially on inhibition of different Aurora kinase family members and these differences have to be considered in the clinical development of small-molecule inhibitors with respect to the chosen indications, the schedules or the selection of appropriate end points and they should also guide the development of biomarkers. Preclinical validation of potential biomarkers for Aurora kinase inhibitors led to a first application in clinical trials, as exemplified for the phosphorylation of histone H3 to follow Aurora-B inhibition. This review discusses the criteria for translation into the clinic and the value of pharmacodynamic biomarkers and their potential, but also their limitations to be used as surrogate markers for clinical end points.

Isolation of microarray-grade total RNA, microRNA, and DNA from a single PAXgene blood RNA tube

We have developed a procedure for isolation of microRNA and genomic DNA in addition to total RNA from whole blood stabilized in PAXgene Blood RNA tubes. The procedure is based on automatic extraction on a BioRobot MDx and includes isolation of DNA from a fraction of the stabilized blood and recovery of small RNA species that are otherwise lost. The procedure presented here is suitable for large-scale experiments and is amenable to further automation. Procured total RNA and DNA was tested using Affymetrix Expression and single-nucleotide polymorphism GeneChips, respectively, and isolated microRNA was tested using spotted locked nucleic acid-based microarrays. We conclude that the yield and quality of total RNA, microRNA, and DNA from a single PAXgene blood RNA tube is sufficient for downstream microarray analysis.

Conference report and review: current status of biomarkers potentially associated with prostate cancer outcomes

PURPOSE

The use of prostate specific antigen screening to diagnose and monitor prostate cancer is associated with well-known shortcomings. A 2-day Prostate Cancer Biomarker Conference was convened to identify promising areas of research and focus efforts on the most critical needs.

MATERIALS AND METHODS

The conference provided a forum for the presentation and discussion of ongoing prostate cancer biomarker research. This meeting also sought to identify a range of critical issues in the development and validation of biomarkers, foster research collaboration between groups representing government, academic and industry initiatives, and coordinate efforts with planned and ongoing clinical trials.

RESULTS

Taken collectively the conference presentations offered various new technologies for biomarker discovery and pathological assessment of clinical disease as well as the promise of biomarkers for improving prostate cancer diagnosis and treatment decisions. However, research efforts focused on biomarker validation and implementation clearly lag behind those directed toward initial biomarker discovery. It is apparent that guidelines are desperately needed to ensure the consistency of sample collection across institutions.

CONCLUSIONS

Several ongoing and planned adjuvant prostate cancer trials will provide a tremendous opportunity for biological sample collection along with the potential to validate many biomarkers. Practicing urologists have an opportunity to have a critical role in the successful accrual of patients into these trials.

Biomarkers in Diagnosis of pancreatic carcinoma in fine-needle aspirates

This study was undertaken to determine whether recently identified proteins could be translated to clinical practice as markers to distinguish pancreatic adenocarcinoma from chronic pancreatitis on fine-needle aspirate (FNA) samples. Resected pancreatic tissue sections (n = 40) and FNA samples (n = 65) were stained for clusterin-beta, MUC4, survivin, and mesothelin. For each biomarker, the staining patterns in adenocarcinoma and in reactive ductal epithelium were evaluated and compared. Clusterin-beta stained reactive ductal epithelium significantly more frequently than pancreatic adenocarcinoma (P < .001). In comparison, MUC4 and mesothelin were expressed more frequently in pancreatic adenocarcinoma on tissue sections. Positive staining for MUC4 (91% vs 0%; P < .001) and mesothelin (62% vs 0%; P = .01) and absence of staining for clusterin-beta (90% vs 7%; P < .001) were noted significantly more frequently in adenocarcinoma cells than in reactive cells in FNA samples. Clusterin-beta and MUC4 can help distinguish reactive ductal epithelial cells from the cells of pancreatic adenocarcinoma in FNA samples.

Increasing conclusiveness of metabonomic studies by chem-informatic preprocessing of capillary electrophoretic data on urinary nucleoside profiles

Nowadays, bioinformatics offers advanced tools and procedures of data mining aimed at finding consistent patterns or systematic relationships between variables. Numerous metabolites concentrations can readily be determined in a given biological system by high-throughput analytical methods. However, such row analytical data comprise noninformative components due to many disturbances normally occurring in analysis of biological samples. To eliminate those unwanted original analytical data components advanced chemometric data preprocessing methods might be of help. Here, such methods are applied to electrophoretic nucleoside profiles in urine samples of cancer patients and healthy volunteers. The electrophoretic nucleoside profiles were obtained under following conditions: 100 mM borate, 72.5 mM phosphate, 160 mM SDS, pH 6.7; 25 kV voltage, 30 degrees C temperature; untreated fused silica capillary 70 cm effective length, 50 microm I.D. Different most advanced preprocessing tools were applied for baseline correction, denoising and alignment of electrophoretic data. That approach was compared to standard procedure of electrophoretic peak integration. The best results of preprocessing were obtained after application of the so-called correlation optimized warping (COW) to align the data. The principal component analysis (PCA) of preprocessed data provides a clearly better consistency of the nucleoside electrophoretic profiles with health status of subjects than PCA of peak areas of original data (without preprocessing).

Gastric Cancer (Biomarkers) Knowledgebase (GCBKB): A Curated and Fully Integrated Knowledgebase of Putative Biomarkers Related to Gastric Cancer

The Gastric Cancer (Biomarkers) Knowledgebase (GCBKB) ( http://biomarkers.bii.a-star.edu.sg/background/gastricCancerBiomarkersKb.php ) is a curated and fully integrated knowledgebase that provides data relating to putative biomarkers that may be used in the diagnosis and prognosis of gastric cancer. It is freely available to all users. The data contained in the knowledgebase was derived from a large literature source and the putative biomarkers therein have been annotated with data from the public domain. The knowledgebase is maintained by a curation team who update the data from a defined source. As well as mining data from the literature, the knowledgebase will also be populated with unpublished experimental data from investigators working in the gastric cancer biomarker discovery field. Users can perform searches to identify potential markers defined by experiment type, tissue type and disease state. Search results may be saved, manipulated and retrieved at a later date. As far as the authors are aware this is the first open access database dedicated to the discovery and investigation of gastric cancer biomarkers.

Practical proteomic biomarker discovery: taking a step back to leap forward

There is a pressing need for radically improved proteomic screening methods that allow for earlier diagnosis of disease, for systematic monitoring of physiological responses and for uncovering the fundamental mechanisms of drug action. Recent developments in proteomic technology offer tremendous, yet untapped, potential to yield novel biomarkers that are translatable to routine clinical use. Despite the significant conceptual promise of comparative proteomic profiling as a research platform for biomarker discovery, however, major hurdles remain for practical and clinical implementation. In particular, there is growing recognition that rigorous experimental design principles are urgently required to validate conclusively the unproven methodologies currently being touted. Debate and confusion persist about where the burden of proof lies: statistically, biologically or clinically? Moreover, there is no consensus about what constitutes a meaningful benchmark. An important question is how to achieve a scientifically rigorous, and therefore convincing, proof-of-concept that can be accepted by the field. Key analytical challenges related to these issues that must be addressed by the burgeoning biomarker community are discussed here.