Rules of evidence for cancer molecular-marker discovery and validation

Scientific frontiers: emerging technologies for salivary diagnostics

Saliva, a biofluid historically well-studied biochemically and physiologically, has entered the post-genomic 'omics' era, where its proteomic, genomic, and microbiome constituents have been comprehensively deciphered. The translational path of these salivary constituents has begun toward a variety of personalized individual medical applications, including early detection of cancer. Salivary diagnostics is a late-comer, but it is catching up where dedicated resources, like the Salivaomics Knowledge Base (SKB), now have taken center stage in the dissemination of the diagnostic potentials of salivary biomarkers and other translational and clinical utilities.

Discovery and validation of molecular biomarkers for colorectal adenomas and cancer with application to blood testing

Background & Aims

Colorectal cancer incidence and deaths are reduced by the detection and removal of early-stage, treatable neoplasia but we lack proven biomarkers sensitive for both cancer and pre-invasive adenomas. The aims of this study were to determine if adenomas and cancers exhibit characteristic patterns of biomarker expression and to explore whether a tissue-discovered (and validated) biomarker is differentially expressed in the plasma of patients with colorectal adenomas or cancer.

Methods

Candidate RNA biomarkers were identified by oligonucleotide microarray analysis of colorectal specimens (222 normal, 29 adenoma, 161 adenocarcinoma and 50 colitis) and validated in a previously untested cohort of 68 colorectal specimens using a custom-designed oligonucleotide microarray. One validated biomarker, KIAA1199, was assayed using qRT-PCR on plasma extracted RNA from 20 colonoscopy-confirmed healthy controls, 20 patients with adenoma, and 20 with cancer.

Results

Genome-wide analysis uncovered reproducible gene expression signatures for both adenomas and cancers compared to controls. 386/489 (79%) of the adenoma and 439/529 (83%) of the adenocarcinoma biomarkers were validated in independent tissues. We also identified genes differentially expressed in adenomas compared to cancer. KIAA1199 was selected for further analysis based on consistent up-regulation in neoplasia, previous studies and its interest as an uncharacterized gene. Plasma KIAA1199 RNA levels were significantly higher in patients with either cancer or adenoma (31/40) compared to neoplasia-free controls (6/20).

Conclusions

Colorectal neoplasia exhibits characteristic patterns of gene expression. KIAA1199 is differentially expressed in neoplastic tissues and KIAA1199 transcripts are more abundant in the plasma of patients with either cancer or adenoma compared to controls.

Why Hasn't Genomic Testing Changed the Landscape in Clinical Oncology?

The "omics" revolution produced great optimism that tumor biomarker tests based on high-order analysis of multiple (sometimes thousands) of factors would result in truly personalized oncologic care. Unfortunately, 10 years into the revolution, the promise of omics-based research has not yet been realized. The factors behind the slow progress in omics-based clinical care are many. First, over the last 15 years, there has been a gradual recognition of the importance of conducting tumor biomarker science with the kind of rigor that has traditionally been used for therapeutic research. However, this recognition has only recently been applied widely, and therefore most tumor biomarkers have insufficiently high levels of evidence to determine clinical utility. Second, omics-based research offers its own particular set of concerns, especially in regard to overfitting computational models and false discovery rates. Researchers and clinicians need to understand the importance of analytic validity, and the difference between clinical/biologic validity and clinical utility. The latter is required to introduce a tumor biomarker test of any kind (single analyte or omics-based), and are ideally generated by carefully planned and properly conducted "prospective retrospective" or truly prospective clinical trials. Only carefully planned studies, which take all three of these into account and in which the investigators are aware and recognize the enormous risk of unintended bias and overfitting inherent in omics-based test development, will ultimately result in translation of the exciting new technologies into better care for patients with cancer.

STrengthening the Reporting of OBservational studies in Epidemiology - Molecular Epidemiology (STROBE-ME): an extension of the STROBE statement

Advances in laboratory techniques have led to a rapidly increasing use of biomarkers in epidemiological studies. Biomarkers of internal dose, early biological change, susceptibility and clinical outcomes are used as proxies for investigating interactions between external and/or endogenous agents and body components or processes. The need for improved reporting of scientific research led to influential statements of recommendations such as the STrengthening Reporting of OBservational studies in Epidemiology (STROBE) statement. The STROBE initiative established in 2004 aimed to provide guidance on how to report observational research. Its guidelines provide a user-friendly checklist of 22 items to be reported in epidemiological studies, with items specific to the three main study designs: cohort studies, case-control studies and cross-sectional studies. The present STrengthening the Reporting of OBservational studies in Epidemiology -Molecular Epidemiology (STROBE-ME) initiative builds on the STROBE statement implementing nine existing items of STROBE and providing 17 additional items to the 22 items of STROBE checklist. The additions relate to the use of biomarkers in epidemiological studies, concerning collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations. The STROBE-ME recommendations are intended to complement the STROBE recommendations.

STrengthening the Reporting of OBservational studies in Epidemiology--Molecular Epidemiology STROBE-ME: an extension of the STROBE statement

Advances in laboratory techniques have led to a rapidly increasing use of biomarkers in epidemiological studies. Biomarkers of internal dose, early biological change susceptibility and clinical outcomes are used as proxies for investigating the interactions between external and/or endogenous agents and body components or processes. The need for improved reporting of scientific research led to influential statements of recommendations such as the STrengthening Reporting of OBservational studies in Epidemiology (STROBE) statement. The STROBE initiative established in 2004 aimed to provide guidance on how to report observational research. Its guidelines provide a user-friendly checklist of 22 items to be reported in epidemiological studies, with items specific to the three main study designs: cohort studies, case-control studies and cross-sectional studies. The present STrengthening the Reporting of OBservational studies in Epidemiology -Molecular Epidemiology (STROBE-ME) initiative builds on the STROBE statement implementing 9 existing items of STROBE and providing 17 additional items to the 22 items of STROBE checklist. The additions relate to the use of biomarkers in epidemiological studies, concerning collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations. The STROBE-ME recommendations are intended to complement the STROBE recommendations.

Serum profiling to distinguish early- and late-stage ovarian cancer patients from disease-free individuals

Sera mass spectrometry (MS) peak differences were analyzed from 35 ovarian cancer patients and 16 disease-free individuals. "Leave one out" cross validation was used to assign "% cancer peaks" in control and ovarian cancer sera samples. Sera MS discriminated stage I/II and stage III/V ovarian cancer patients versus controls with ROC curve area values of 0.82 and 0.92. Test sensitivities for ovarian cancer stage I/II and III/V were 80% and 93% respectively. These results indicate that MS is useful for distinguishing sera from early-stage ovarian cancer patients, and has potential as a test for early detection of this disease.

Distinguishing non-small cell lung adenocarcinoma patients from squamous cell carcinoma patients and control individuals using serum profiling

Goals of this study were to analyze the ability of mass spectrometry serum profiling to distinguish non-small cell lung adenocarcinoma from squamous cell carcinoma patients and healthy controls. Sera were obtained from 19 adenocarcinoma patients, 24 squamous cell carcinoma patients, and 21 controls. Identifications of significant mass-to-charge ratio (m/z) peak differences between these groups were performed using t-tests. A "leave one out" cross-validation procedure yielded discriminatory lung adenocarcinoma versus squamous cell carcinoma p and ROC curve values of <.0001 and 0.92, respectively. Test sensitivity and specificity were 84% and 79%, respectively. This approach could aid in lung cancer diagnosis and sub-typing.

STrengthening the Reporting of OBservational studies in Epidemiology - Molecular Epidemiology (STROBE-ME): an extension of the STROBE statement

Advances in laboratory techniques have led to a rapidly increasing use of biomarkers in epidemiological studies. Biomarkers of internal dose, early biological change, susceptibility and clinical outcomes are used as proxies for investigating the interactions between external and/or endogenous agents and the body components or processes. The need for improved reporting of scientific research led to influential statements of recommendations such as the STrenghtening Reporting of Observational studies in Epidemiology (STROBE) statement. The STROBE initiative established in 2004 aimed to provide guidance on how to report observational research. Its guidelines provide a user-friendly checklist of 22 items to be reported in epidemiological studies, with items specific to the three main study designs: cohort studies, case-control studies and cross-sectional studies. The present STrengthening the Reporting of OBservational studies in Epidemiology - Molecular Epidemiology (STROBE-ME) initiative builds on the STROBE Statement implementing 9 existing items of STROBE and providing 17 additional items to the 22 items of STROBE checklist. The additions relate to the use of biomarkers in epidemiological studies, concerning collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations. The STROBE-ME recommendations are intended to complement the STROBE recommendations.

STrengthening the reporting of OBservational studies in Epidemiology-Molecular Epidemiology (STROBE-ME): an extension of the STROBE statement

Advances in laboratory techniques have led to a rapidly increasing use of biomarkers in epidemiological studies. Biomarkers of internal dose, early biological change, susceptibility, and clinical outcomes are used as proxies for investigating the interactions between external and/or endogenous agents and the body components or processes. The need for improved reporting of scientific research led to influential statements of recommendations such as STrengthening Reporting of Observational studies in Epidemiology (STROBE) statement. The STROBE initiative established in 2004 aimed to provide guidance on how to report observational research. Its guidelines provide a user-friendly checklist of 22 items to be reported in epidemiological studies, with items specific to the three main study designs: cohort studies, case-control studies and cross-sectional studies. The present STrengthening the Reporting of OBservational studies in Epidemiology-Molecular Epidemiology (STROBE-ME) initiative builds on the STROBE Statement implementing 9 existing items of STROBE and providing 17 additional items to the 22 items of STROBE checklist. The additions relate to the use of biomarkers in epidemiological studies, concerning collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations. The STROBE-ME recommendations are intended to complement the STROBE recommendations.

Robust classification method of tumor subtype by using correlation filters

Tumor classification based on gene expression profiles, which is of great benefit to the accurate diagnosis and personalized treatment for different types of tumor, has drawn a great attention in recent years. This paper proposes a novel tumor classification method based on correlation filters to identify the overall pattern of tumor subtype hidden in differentially expressed genes. Concretely, two correlation filters, i.e., Minimum Average Correlation Energy (MACE) and Optimal Tradeoff Synthetic Discriminant Function (OTSDF), are introduced to determine whether a test sample matches the templates synthesized for each subclass. The experiments on six publicly available datasets indicate that the proposed method is robust to noise, and can more effectively avoid the effects of dimensionality curse. Compared with many model-based methods, the correlation filter based method can achieve better performance when balanced training sets are exploited to synthesize the templates. Particularly, the proposed method can detect the similarity of overall pattern while ignoring small mismatches between test sample and the synthesized template. And it performs well even if only few training samples are available. More importantly, the experimental results can be visually represented, which is helpful for the further analysis of results.

Transcriptional modulator H2A histone family, member Y (H2AFY) marks Huntington disease activity in man and mouse

Huntington disease (HD) is a progressive neurodegenerative disease that affects 30,000 individuals in North America. Treatments that slow its relentless course are not yet available, and biomarkers that can reliably measure disease activity and therapeutic response are urgently needed to facilitate their development. Here, we interrogated 119 human blood samples for transcripts associated with HD. We found that the dynamic regulator of chromatin plasticity H2A histone family, member Y (H2AFY) is specifically overexpressed in the blood and frontal cortex of patients with HD compared with controls. This association precedes the onset of clinical symptoms, was confirmed in two mouse models, and was independently replicated in cross-sectional and longitudinal clinical studies comprising 142 participants. A histone deacetylase inhibitor that suppresses neurodegeneration in animal models reduces H2AFY levels in a randomized phase II clinical trial. This study identifies the chromatin regulator H2AFY as a potential biomarker associated with disease activity and pharmacodynamic response that may become useful for enabling disease-modifying therapeutics for HD.

STrengthening the Reporting of OBservational studies in Epidemiology--Molecular Epidemiology (STROBE-ME): an extension of the STROBE Statement

Valentina Gallo and colleagues provide detailed guidance to authors to help more accurately report the findings of epidemiological studies involving biomarkers. Their guidance covers issues regarding collection, handling and storage of biological samples; laboratory methods, validity and reliability of biomarkers; specificities of study design; and ethical considerations.

Biomarkers in prostate cancer epidemiology

Understanding the etiology of a disease such as prostate cancer may help in identifying populations at high risk, timely intervention of the disease, and proper treatment. Biomarkers, along with exposure history and clinical data, are useful tools to achieve these goals. Individual risk and population incidence of prostate cancer result from the intervention of genetic susceptibility and exposure. Biochemical, epigenetic, genetic, and imaging biomarkers are used to identify people at high risk for developing prostate cancer. In cancer epidemiology, epigenetic biomarkers offer advantages over other types of biomarkers because they are expressed against a person's genetic background and environmental exposure, and because abnormal events occur early in cancer development, which includes several epigenetic alterations in cancer cells. This article describes different biomarkers that have potential use in studying the epidemiology of prostate cancer. We also discuss the characteristics of an ideal biomarker for prostate cancer, and technologies utilized for biomarker assays. Among epigenetic biomarkers, most reports indicate GSTP1 hypermethylation as the diagnostic marker for prostate cancer; however, NKX2-5, CLSTN1, SPOCK2, SLC16A12, DPYS, and NSE1 also have been reported to be regulated by methylation mechanisms in prostate cancer. Current challenges in utilization of biomarkers in prostate cancer diagnosis and epidemiologic studies and potential solutions also are discussed.

Predictive Power Estimation Algorithm (PPEA)--a new algorithm to reduce overfitting for genomic biomarker discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.

Multimarker panels in sepsis

Biomarkers differentiate between 2 or more biologic states. The complexity of diseases like sepsis makes it unlikely that any single marker will allow for precise disease specification. Combining several biomarkers into a single classification rule should help to improve their accuracy and, therefore, their usefulness. This article reviews several studies using multimarker panels, and highlights the potential of more sophisticated diagnostic and prognostic techniques in future multimarker panels. More complex algorithms should accelerate the adoption of multimarker panels into the routine management of patients with sepsis, provided that clinicians understand the multimarker approach.

Microarrays in the 2010s: the contribution of microarray-based gene expression profiling to breast cancer classification, prognostication and prediction

Breast cancer comprises a collection of diseases with distinctive clinical, histopathological, and molecular features. Importantly, tumors with similar histological features may display disparate clinical behaviors. Gene expression profiling using microarray technologies has improved our understanding of breast cancer biology and has led to the development of a breast cancer molecular taxonomy and of multigene 'signatures' to predict outcome and response to systemic therapies. The use of these prognostic and predictive signatures in routine clinical decision-making remains controversial. Here, we review the clinical relevance of microarray-based profiling of breast cancer and discuss its impact on patient management.

Serum discrimination of early-stage lung cancer patients using electrospray-ionization mass spectrometry

The goal of this study was to evaluate the usefulness of electrospray ionization-mass spectrometry (ESI-MS) technology to distinguish sera of early-stage lung cancer patients from control individuals. ESI-MS m/z (mass divided by charge) data were generated from sera of 43 non-small cell lung cancer patients (pathological stages I and II) and 21 control individuals. Identifications of m/z peak area significances between cancer and control ESI-MS sera spectra were performed using t-tests. A "leave one out" cross validation procedure, which mimics blinded sera analysis and corrects for "over-fitting" of data, yielded discriminatory cancer versus control distribution p value and ROC curve area value of <0.001 and 0.87, respectively. Analysis without the "leave one out" cross validation procedure yielded a ROC curve area of 0.99 for discrimination of sera from lung cancer patients versus control individuals. Predictive value measurements revealed overall test efficiency and sensitivity for distinguishing sera from lung cancer patients from controls (using "leave one out" cross validation) of 80% and 84%, respectively. ESI-MS serum analysis between control individuals and lung cancer patients who smoked or did not smoke had p values in ranges indicating that smoking effects are not pronounced in our analysis. These studies indicate that ESI-MS analyses of sera from early stage non-small cell lung cancer patients were helpful in distinguishing these patients from control individuals.

Revisiting the technical validation of tumour biomarker assays: how to open a Pandora's box

A tumour biomarker is a characteristic that is objectively measured and evaluated in tumour samples as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. The development of a biomarker contemplates distinct phases, including discovery by hypothesis-generating preclinical or exploratory studies, development and qualification of the assay for the identification of the biomarker in clinical samples, and validation of its clinical significance. Although guidelines for the development and validation of biomarkers are available, their implementation is challenging, owing to the diversity of biomarkers being developed. The term 'validation' undoubtedly has several meanings; however, in the context of biomarker research, a test may be considered valid if it is 'fit for purpose'. In the process of validation of a biomarker assay, a key point is the validation of the methodology. Here we discuss the challenges for the technical validation of immunohistochemical and gene expression assays to detect tumour biomarkers and provide suggestions of pragmatic solutions to address these challenges.

Evaluating the consistency of differential expression of microRNA detected in human cancers

Differential expression of microRNA (miRNA) is involved in many human diseases and could potentially be used as a biomarker for disease diagnosis, prognosis, and therapy. However, inconsistency has often been found among differentially expressed miRNAs identified in various studies when using miRNA arrays for a particular disease such as a cancer. Before broadly applying miRNA arrays in a clinical setting, it is critical to evaluate inconsistent discoveries in a rational way. Thus, using data sets from 2 types of cancers, our study shows that the differentially expressed miRNAs detected from multiple experiments for each cancer exhibit stable regulation direction. This result also indicates that miRNA arrays could be used to reliably capture the signals of the regulation direction of differentially expressed miRNAs in cancer. We then assumed that 2 differentially expressed miRNAs with the same regulation direction in a particular cancer play similar functional roles if they regulate the same set of cancer-associated genes. On the basis of this hypothesis, we proposed a score to assess the functional consistency between differentially expressed miRNAs separately extracted from multiple studies for a particular cancer. We showed although lists of differentially expressed miRNAs identified from different studies for each cancer were highly variable, they were rather consistent at the level of function. Thus, the detection of differentially expressed miRNAs in various experiments for a certain disease tends to be functionally reproducible and capture functionally related differential expression of miRNAs in the disease.

Involvement of the TGF-beta and beta-catenin pathways in pelvic lymph node metastasis in early-stage cervical cancer

PURPOSE

Presence of pelvic lymph node metastases is the main prognostic factor in early-stage cervical cancer patients, primarily treated with surgery. Aim of this study was to identify cellular tumor pathways associated with pelvic lymph node metastasis in early-stage cervical cancer.

EXPERIMENTAL DESIGN

Gene expression profiles (Affymetrix U133 plus 2.0) of 20 patients with negative (N(0)) and 19 with positive lymph nodes (N(+)), were compared with gene sets that represent all 285 presently available pathway signatures. Validation immunostaining of tumors of 274 consecutive early-stage cervical cancer patients was performed for representatives of the identified pathways.

RESULTS

Analysis of 285 pathways resulted in identification of five pathways (TGF-β, NFAT, ALK, BAD, and PAR1) that were dysregulated in the N(0), and two pathways (β-catenin and Glycosphingolipid Biosynthesis Neo Lactoseries) in the N(+) group. Class comparison analysis revealed that five of 149 genes that were most significantly differentially expressed between N(0) and N(+) tumors (P < 0.001) were involved in β-catenin signaling (TCF4, CTNNAL1, CTNND1/p120, DKK3, and WNT5a). Immunohistochemical validation of two well-known cellular tumor pathways (TGF-β and β-catenin) confirmed that the TGF-β pathway (positivity of Smad4) was related to N(0) (OR: 0.20, 95% CI: 0.06-0.66) and the β-catenin pathway (p120 positivity) to N(+) (OR: 1.79, 95%CI: 1.05-3.05).

CONCLUSIONS

Our study provides new, validated insights in the molecular mechanism of lymph node metastasis in cervical cancer. Pathway analysis of the microarray expression profile suggested that the TGF-β and p120-associated noncanonical β-catenin pathways are important in pelvic lymph node metastasis in early-stage cervical cancer.

Proteomics and Down syndrome screening: a validation study

OBJECTIVE

In a previous discovery study, we identified seven potential screening markers for Down syndrome (DS). Here, we report on an extended study to validate the discriminative potential of these markers.

METHODS

Concentrations of the seven analytes were measured using bead-based multiplexed immunoassays in maternal serum from 27 DS pregnancies and 27 matched controls. Control samples were matched to the cases by gestational age (exact day), maternal weight ( ± 5 kg), and maternal age ( ± 1 year) and by closest sample date. Prediction values were obtained for current screening markers [pregnancy-associated plasma protein A (PAPP-A), free beta human chorionic gonadotrophin (fβ-hCG) and nuchal translucency (NT)] and seven markers identified before based on concentration fold ratios between DS and controls. Models were fitted based on data of the discovery study or this study and also tested on both datasets.

RESULTS

A significantly higher fold ratio was only found for epidermal growth factor (EGF) (-1.96; p = 0.006). In the prediction model for the current dataset, EGF improved the detection rate (DR) of DS by 5.7% [at a fixed 5% false-positive rate (FPR)] when added to the currently used screening markers.

CONCLUSIONS

Validation of previously identified biomarkers only confirmed EGF for further consideration as a DS screening marker. This underlines the importance of validating biomarkers; in this study, limiting the range of plausible biomarkers to only one suitable biomarker.

[Clinical utility of serum tumor markers in lung cancer]

Lung cancer shows a tendency of higher incidence and higher mortality in recent years, but the overall 5-year survival rate is less than 15%. Serum tumor markers of lung cancer play an important role in early diagnosis, determining of pathology types, staging, evaluation of response, and prognosis of lung cancer. In this review, 6 most important markers were reviewed, including neuron-specific enolase (NSE), pro-gastrin-releasing peptide (ProGRP), cytokeratin 19 fragments (Cyfra 21-1), tissue polypeptide antigen (TPA), squamous cell carcinoma associated antigen (SCC-Ag), carcinoembryonic antigen (CEA).

Diagnostic accuracy and receiver-operating characteristics curve analysis in surgical research and decision making

In surgical research, the ability to correctly classify one type of condition or specific outcome from another is of great importance for variables influencing clinical decision making. Receiver-operating characteristic (ROC) curve analysis is a useful tool in assessing the diagnostic accuracy of any variable with a continuous spectrum of results. In order to rule a disease state in or out with a given test, the test results are usually binary, with arbitrarily chosen cut-offs for defining disease versus health, or for grading of disease severity. In the postgenomic era, the translation from bench-to-bedside of biomarkers in various tissues and body fluids requires appropriate tools for analysis. In contrast to predetermining a cut-off value to define disease, the advantages of applying ROC analysis include the ability to test diagnostic accuracy across the entire range of variable scores and test outcomes. In addition, ROC analysis can easily examine visual and statistical comparisons across tests or scores. ROC is also favored because it is thought to be independent from the prevalence of the condition under investigation. ROC analysis is used in various surgical settings and across disciplines, including cancer research, biomarker assessment, imaging evaluation, and assessment of risk scores.With appropriate use, ROC curves may help identify the most appropriate cutoff value for clinical and surgical decision making and avoid confounding effects seen with subjective ratings. ROC curve results should always be put in perspective, because a good classifier does not guarantee the expected clinical outcome. In this review, we discuss the fundamental roles, suggested presentation, potential biases, and interpretation of ROC analysis in surgical research.

Multidimensionality of microarrays: statistical challenges and (im)possible solutions

A typical array experiment yields at least tens of thousands of measurements on often not more than a hundred patients, a situation often denoted as the curse of dimensionality. With a focus on prognostic multi-biomarker scores derived from microarrays, we highlight the multidimensionality of the problem and the issues in the multidimensionality of the data. We go over several statistical challenges raised by this curse occurring in each step of microarray analysis on patient data, from the hypothesis and the experimental design to the analysis methods, interpretation of results and clinical utility. Different analytical tools and solutions to answer these challenges are provided and discussed.

Plasma TIMP-1 and CEA in detection of primary colorectal cancer: a prospective, population based study of 4509 high-risk individuals

OBJECTIVE

The combination of plasma tissue inhibitor of metalloproteinases-1 (TIMP-1) and carcinoembryonic antigen (CEA) may be valuable biomarkers for early detection of colorectal cancer (CRC). A prospective, population based study was performed to validate this hypothesis.

MATERIAL AND METHODS

Individuals (n = 4509) referred for large bowel endoscopy due to symptoms of CRC were prospectively included. Baseline data and concurrent diseases were recorded. The primary endpoint was detection of CRC and findings at examinations were recorded using International Classification of Diseases-10 codes. Plasma was obtained before endoscopy and TIMP-1 and CEA levels were determined after the inclusion of all individuals.

RESULTS

Findings were based on sigmoidoscopy in 1766 and colonoscopy in 2743 individuals. Colon cancer (CC) was detected in 184 and rectal cancer in 110 individuals. Ten individuals with other cancers, 856 with adenomas and 1176 with non-neoplastic findings were also detected. The biomarker levels were increased in a variety of diseases including CRC compared to individuals without any findings at endoscopy. A multivariable analysis demonstrated that both markers were significant and independent detectors of CRC. Combining both biomarkers, independent contributions from each (TIMP-1, odds ratio (OR) = 1.8 (95% confidence interval (CI): 1.4-2.2), p < 0.0001; CEA < 5 ng/ml, OR = 1.6, 1.3-1.9, or ≥ 5 ng/ml, OR = 2.3, 95% CI: 1.9-2.7 (p < 0.0001)) were obtained. Subgroup analysis of individuals examined by colonoscopy with CC as the endpoint showed that combining both biomarkers, independent contributions from each (TIMP-1, OR = 2.5, 95% CI: 1.8-3.4, p < 0.0001; CEA < 5 ng/ml, OR = 1.4, 95% CI: 1.1-1.8, and CEA ≥ 5 ng/ml, OR = 2.3, 95% CI: 1.8-3.0 (p < 0.0001)) were obtained.

CONCLUSIONS

This prospective validation study supports the use of the combination of plasma TIMP-1 and CEA protein measurements as a potential aid in early detection of CRC and specifically of CC.

Radiogenomics predicting tumor responses to radiotherapy in lung cancer

The recently developed ability to interrogate genome-wide data arrays has provided invaluable insights into the molecular pathogenesis of lung cancer. These data have also provided information for developing targeted therapy in lung cancer patients based on the identification of cancer-specific vulnerabilities and set the stage for molecular biomarkers that provide information on clinical outcome and response to treatment. In addition, there are now large panels of lung cancer cell lines, both non-small-cell lung cancer and small-cell lung cancer, that have distinct chemotherapy and radiation response phenotypes. We anticipate that the integration of molecular data with therapy response data will allow for the generation of biomarker signatures that predict response to therapy. These signatures will need to be validated in clinical studies, at first retrospective analyses and then prospective clinical trials, to show that the use of these biomarkers can aid in predicting patient outcomes (eg, in the case of radiation therapy for local control and survival). This review highlights recent advances in molecular profiling of tumor responses to radiotherapy and identifies challenges and opportunities in developing molecular biomarker signatures for predicting radiation response for individual patients with lung cancer.

Systems medicine and the integration of bioinformatic tools for the diagnosis of Alzheimer's disease

Because of the changes in demographic structure, the prevalence of Alzheimer's disease is expected to rise dramatically over the next decades. The progression of this degenerative and terminal disease is gradual, with the subclinical stage of illness believed to span several decades. Despite this, no therapy to prevent or cure Alzheimer's disease is currently available. Early disease detection is still important for delaying the onset of the disease with pharmacological treatment and/or lifestyle changes, assessing the efficacy of potential therapeutic agents, or monitoring disease progression more closely using medical imaging. Sensitive cerebrospinal-fluid-derived marker candidates exist, but given the invasiveness of sample collection their use in routine diagnostics may be limited. The pathogenesis of Alzheimer's disease is complex and poorly understood. There is thus a strong case for integrating information across multiple physiological levels, from molecular profiling (metabolomics, lipidomics, proteomics and transcriptomics) and brain imaging to cognitive assessments. To facilitate the integration of heterogeneous data, such as molecular and image data, sophisticated statistical approaches are needed to segment the image data and study their dependencies on molecular changes in the same individuals. Molecular profiling, combined with biophysical modeling of molecular assemblies associated with the disease, offer an opportunity to link the molecular pathway changes with cell- and tissue-level physiology and structure. Given that data acquired at different levels can carry complementary information about early Alzheimer's disease pathology, it is expected that their integration will improve early detection as well as our understanding of the disease.

Clinical application of gene expression profiling in breast cancer

Breast cancer is a heterogeneous disease associated with variable clinical outcomes and response to therapy. Classic clinicopathologic factors associated with outcome include anatomic features associated with prognosis (eg, tumor size, number of positive regional lymph nodes) and biologic features associated with prognosis and/or predictive of response to specific therapies, usually by evaluating protein expression by immunohistochemistry (eg, estrogen and/or progesterone receptors) or amplification of a single gene (eg, HER2/neu). Gene expression profiling evaluating thousands of genes is now feasible, and has facilitated the development of multiparameter assays that may identify breast cancer subtypes associated with distinct clinical outcomes that were not previously recognized, or provide more accurate information about prognosis or response to specific therapies than may be provided by classic clinicopathologic features alone. Several multiparameter gene expression assays are commercially available, and additional assays are being developed that will facilitate more accurate therapeutic individualization.

Anti-CD47 antibody synergizes with rituximab to promote phagocytosis and eradicate non-Hodgkin lymphoma

Monoclonal antibodies are standard therapeutics for several cancers including the anti-CD20 antibody rituximab for B cell non-Hodgkin lymphoma (NHL). Rituximab and other antibodies are not curative and must be combined with cytotoxic chemotherapy for clinical benefit. Here we report the eradication of human NHL solely with a monoclonal antibody therapy combining rituximab with a blocking anti-CD47 antibody. We identified increased expression of CD47 on human NHL cells and determined that higher CD47 expression independently predicted adverse clinical outcomes in multiple NHL subtypes. Blocking anti-CD47 antibodies preferentially enabled phagocytosis of NHL cells and synergized with rituximab. Treatment of human NHL-engrafted mice with anti-CD47 antibody reduced lymphoma burden and improved survival, while combination treatment with rituximab led to elimination of lymphoma and cure. These antibodies synergized through a mechanism combining Fc receptor (FcR)-dependent and FcR-independent stimulation of phagocytosis that might be applicable to many other cancers.

Metabolic signatures of exercise in human plasma

Exercise provides numerous salutary effects, but our understanding of how these occur is limited. To gain a clearer picture of exercise-induced metabolic responses, we have developed comprehensive plasma metabolite signatures by using mass spectrometry to measure >200 metabolites before and after exercise. We identified plasma indicators of glycogenolysis (glucose-6-phosphate), tricarboxylic acid cycle span 2 expansion (succinate, malate, and fumarate), and lipolysis (glycerol), as well as modulators of insulin sensitivity (niacinamide) and fatty acid oxidation (pantothenic acid). Metabolites that were highly correlated with fitness parameters were found in subjects undergoing acute exercise testing and marathon running and in 302 subjects from a longitudinal cohort study. Exercise-induced increases in glycerol were strongly related to fitness levels in normal individuals and were attenuated in subjects with myocardial ischemia. A combination of metabolites that increased in plasma in response to exercise (glycerol, niacinamide, glucose-6-phosphate, pantothenate, and succinate) up-regulated the expression of nur77, a transcriptional regulator of glucose utilization and lipid metabolism genes in skeletal muscle in vitro. Plasma metabolic profiles obtained during exercise provide signatures of exercise performance and cardiovascular disease susceptibility, in addition to highlighting molecular pathways that may modulate the salutary effects of exercise.

US FDA and personalized medicine: in vitro diagnostic regulatory perspective

Personalized medicine has captured the attention of the public, including patients, healthcare providers, scientists, medical product manufacturers and many others. The US FDA will evaluate many of the products that will allow personalized medicine to be successfully implemented in the USA. This article addresses the FDA's approach to regulation of one component of personalized medicine, in vitro diagnostic devices. It also describes the FDA's efforts to integrate the various medical product regulatory authorities provided by Congress in the Federal Food, Drug and Cosmetic Act to develop effective mechanisms for oversight of medical products used to personalize treatment. Finally, it presents some of the current challenges in in vitro diagnostics oversight that may be of interest for personalized medicine.

Prediction and prognosis: impact of gene expression profiling in personalized treatment of breast cancer patients

Breast cancer is a complex disease, whose heterogeneity is increasingly recognized. Despite considerable improvement in breast cancer treatment and survival, a significant proportion of patients seems to be over- or undertreated. To date, single clinicopathological parameters show limited success in predicting the likelihood of survival or response to endocrine therapy and chemotherapy. Consequently, new gene expression based prognostic and predictive tests are emerging that promise an improvement in predicting survival and therapy response. Initial evidence has emerged that this leads to allocation of fewer patients into high-risk groups allowing a reduction of chemotherapy treatment. Moreover, pattern-based approaches have also been developed to predict response to endocrine therapy or particular chemotherapy regimens. Irrespective of current pitfalls such as lack of validation and standardization, these pattern-based biomarkers will prove useful for clinical decision making in the near future, especially if more patients get access to this form of personalized medicine.

Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy

The study of biological systems in a holistic manner (systems biology) is increasingly being viewed as a necessity to provide qualitative and quantitative descriptions of the emergent properties of the complete system. Systems biology performs studies focussed on the complex interactions of system components; emphasising the whole system rather than the individual parts. Many perturbations to mammalian systems (diet, disease, drugs) are multi-factorial and the study of small parts of the system is insufficient to understand the complete phenotypic changes induced. Metabolomics is one functional level tool being employed to investigate the complex interactions of metabolites with other metabolites (metabolism) but also the regulatory role metabolites provide through interaction with genes, transcripts and proteins (e.g. allosteric regulation). Technological developments are the driving force behind advances in scientific knowledge. Recent advances in the two analytical platforms of mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have driven forward the discipline of metabolomics. In this critical review, an introduction to metabolites, metabolomes, metabolomics and the role of MS and NMR spectroscopy will be provided. The applications of metabolomics in mammalian systems biology for the study of the health-disease continuum, drug efficacy and toxicity and dietary effects on mammalian health will be reviewed. The current limitations and future goals of metabolomics in systems biology will also be discussed (374 references).

Clinical, laboratory and molecular factors predicting chemotherapy efficacy and toxicity in colorectal cancer

Colorectal cancer (CRC) treatment has evolved significantly over the last ten years with the use of active chemotherapeutic agents including fluoropyrimidines, oxaliplatin and irinotecan plus targeted monoclonal antibodies bevacizumab, cetuximab and panitumumab. The addition of newer chemotherapeutic agents and targeted therapies has improved patient outcomes at the cost of increased toxicity with not all patients benefiting from these treatments. It is necessary for clinicians to more accurately predict clinical outcomes particularly in the predominantly elderly CRC patient population. This review aims to summarise existing data regarding the use of clinical and laboratory variables plus molecular markers in predicting response, survival and toxicity to chemotherapy agents and targeted monoclonal antibodies currently used in the treatment of CRC.

Assessing the quality and reproducibility of a proteomic platform for clinical stroke biomarker discovery

The aim of this study was to investigate the quality and reproducibility of mass spectra derived from a matrix-assisted laser desorption ionisation time-of-flight mass spectrometry (MALDI-TOF MS) platform in a patient population undergoing carotid endarterectomy. Plasma samples were either digested with trypsin or left undigested, fractionated with either C18 or weak cation exchange (WCX) columns and analysed by MALDI-TOF MS. Quality of mass spectra for each method was assessed by baseline correction (lower area under the curve ratio indicating higher quality) and signal-to-noise ratio. Mean coefficient of variation (CV%) assessed reproducibility between repeated experiments and methods. Identified mass peak intensity differences were assessed for consistency across repeated experiments. Plasma from six patients was analysed. The quality of mass spectra was significantly better when derived from digested plasma fractionated by either WCX or C18 methods compared to undigested plasma fractionated by WCX (analysis of variance, p < 0.05). Mean CV% for repeated experiments was 18% and 28% for WCX and C18 fractionated digested plasma, respectively. A small number of differences in mass peak intensities were consistently observed in repeated experiments. Repeated experiments are required to confidently identify non-random mass peak intensity differences as putative plasma biomarkers that merit further investigation.

Is it prime time for personalized medicine in cancer treatment?

Over the last decade, with rapidly advancing biotechnology, the understanding of cancer has changed. The genomic era has resulted in an explosion of targeted therapies and prognostic and predictive biomarkers. This article aims to illustrate the advances made in the practice of oncology as well as the potential and limitations of personalized medicine in cancer.

Cytoplasmic beta-catenin accumulation is a good prognostic marker in upper and lower gastrointestinal adenocarcinomas

AIMS

beta-Catenin is an important molecule in cancer biology. Membranous beta-catenin enhances cellular differentiation and inhibits invasion by its action on E-cadherin. The aim was to ascertain whether the cellular expression of these molecules in colorectal and oesophageal cancer specimens is associated with survival in patients with gastrointestinal cancer.

METHODS AND RESULTS

Tumour samples from 149 patients undergoing resection for colorectal adenocarcinoma and 147 patients undergoing resection for oesophageal adenocarcinoma were retrospectively analysed using immunohistochemical techniques to assess beta-catenin expression. Increasing beta-catenin expression in the cytoplasm was associated with improved survival for colorectal cancer cases on both univariate (P = 0.003) and multivariate (P = 0.01) analysis. In addition, increased expression in the most recent cohort of oesophageal adenocarcinoma patients was associated with improved TNM staging (P = 0.007). Membrane expression was weakly associated with survival in colorectal cancer on univariate analysis (P = 0.09), but not on multivariate analysis (P = 0.21). Complete absence of beta-catenin expression at all three sites was associated with reduced 5-year survival in colorectal cancer.

CONCLUSIONS

This is one of the largest prognostic studies of beta-catenin in gastrointestinal adenocarcinoma. It shows that low levels of cytoplasmic beta-catenin expression are associated with reduced survival in patients with colorectal cancer as well as worse TNM staging in oesophageal adenocarcinoma (a recognized surrogate end-point for survival). We believe this is the first time that this has been reported. This finding should be tested prospectively in oncological trials to validate whether the presence of cytoplasmic beta-catenin could be used as a prognostic marker for less aggressive disease.

Why most gene expression signatures of tumors have not been useful in the clinic

Omics technologies are expected to enhance our understanding of a variety of diseases and to open the door to patient-specific personalized medicine. Despite the extensive literature on the use of gene expression arrays to predict prognosis in cancer patients, poor progress has been made in the translation of gene expression signatures for use in the clinics. Breast cancer provides a ripe arena for an analysis of why such signatures have failed to fulfill their promise.

RNA biomarkers of Parkinson's disease: developing tools for novel therapies

By 2030 the number of individuals with Parkinson's disease (PD) will nearly double to approximately 9.3 million because of aging populations. No medications have been approved that address the progressive neurodegeneration that underlies the disease and existing symptomatic treatments are only partially effective. Reliance on insensitive and confounded clinical assessments has obstructed the development of novel therapeutics designed to prevent, delay or slow the disease. While PD symptoms reflect preferential neuronal death, DNA, RNA and biochemical traits of the disease are detectable in blood cells. To systematically search for lead RNA biomarkers of PD, genome-wide expression changes in the blood of patients with early-stage PD and controls have been probed by microarray. This scan identified a candidate gene signature, as well as lead single gene biomarkers associated with PD. Efforts are underway to refine and develop these hits into biomarkers that will enable risk-modifying therapies. This development process will progress through discovery, cross-sectional and prospective clinical biomarker studies, to Phase III clinical trials.

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.