Cancer Biomarker Research Research Articles

The current status of cancer biomarker research using tumour-associated antigens for minimal invasive and early cancer diagnostics

Tumour-associated antigens (TAA) can be detected prior to clinical diagnosis and thus would be ideal biomarkers for early detection of cancer using only a few microliters of a patient's serum. In this article we provide a summary of TAA screening and serum-profiling conducted for breast, prostate, lung and colon cancers. Different methodological approaches, including SEREX, SERPA, and phage display for TAA identification and TAA panels are summarised, and a revision of array based techniques is provided. The most promising studies performed on these cancers (performed with 80-400 serum samples, including controls) obtained sensitivities in a range of 44-95% and specificities of 80-100%. From the various studies reviewed, only one performed cross validation (AUC=0.71) in a prostate cancer study. Thus, albeit receiver operation characteristics are very promising, cross validation of most studies is still missing. Additionally, the concerted action of research groups for standardization of serum-TAA testing and cross validation is required. Along with today's technological options, the chances of establishing TAA biomarkers are now higher than ever before. This may also be true for confirmation and validation of already existing data, which is a prerequisite for implementation of TAA biomarkers into clinical diagnostics. This article is part of a Special Issue entitled: Integrated omics.

An ovarian cancer malignancy risk index composed of HE4, CA125, ultrasonographic score, and menopausal status: use in differentiation of ovarian cancers and benign lesions

A case-control study included 83 ovarian cancer patients, 76 patients with benign ovarian tumors, and 79 healthy control subjects in the control group. Objective of the study is to analyze biomarker concentrations included in the two novel ovarian tumor differential diagnostic tests (risk of ovarian malignancy algorithm and OVA1) approved by food and drug administration in patients with ovarian tumors and to establish a new ovarian cancer risk assessment algorithm in conjunction with ultrasound score and menopausal status. Ovarian cancer diagnostic tests, developed in the training setting, were evaluated in the independent validation settings of Asian Pacific ovarian cancer biomarker research group study population and Denmark Pelvic Mass project population. Results show that mean serum concentrations of cancer antigen 125 (CA125), human epididymis secretory protein 4 (HE4), and beta-2-microglobulin were upregulated, but apolipoprotein A1, transferrin, and transthyretin were downregulated among ovarian cancer patients. When only one biomarker was introduced in the logistic regression analysis, together with ultrasonographic score and menopausal status, HE4 (area under the curve (AUC) = 0.930; 95 % confidence interval (CI) 0.891-0.969) was more accurate than CA125 (AUC = 0.902; 95 % CI 0.855-0.949) in ovarian cancer diagnostic, but when both biomarkers were included in the logistic regression analyses, ovarian cancer diagnostic accuracy was increased (AUC = 0.939; 95 % CI 0.902-0.977). In conclusions, human epididymis secretory protein 4 and CA125 in combination with ultrasonographic features and menopausal status has high accuracy in ovarian tumor differentiation.

Recent advances of biomarker research in lung cancer with special reference to new targeted therapies: Status and future challenges

Recent advances of biomarker research in lung cancer with special reference to new targeted therapies: Status and future challenges

Exhaled breath analysis for early cancer detection: principle and progress in direct mass spectrometry techniques

Volatile biomarker analysis in exhaled breath is becoming one of the desirable strategies for cancer detection because it may offer a relatively inexpensive, rapid, and non-invasive screening method for early diagnosis. Breath analysis has attracted a considerable amount of scientific and clinical interest over the past decade. However, breath is not yet used for routine medical diagnostic purposes. Challenges faced in the development of breath analysis for cancer diagnosis include developing techniques that can measure biomarkers in exhaled breath at ultratrace levels, providing definitive evidence for their presence and for the relationship between the proposed biomarker and the underlying condition. Various analytical methods are used for the detection of breath biomarkers. Gas chromatography-based methods which involve sample collection, analyte preconcentration, desorption, and separation steps are the most popular. However, direct-sampling mass spectrometry techniques have been proven more reliable for air analysis without prior sample pretreatment or chromatographic separation. This review focuses on the most commonly used direct mass spectrometry methods for the direct online analysis of endogenous cancer biomarkers in exhaled breath, with particular attention to principle of detection, method performance, advantages, shortcomings, recent advances, and applications within health-related studies for cancer biomarkers research. The principle behind the science of breath analysis for cancer diagnosis is also discussed.

Abstract 5036: MicroRNA expression profiling potential for human breast cancer diagnosis using a bead-based microRNA-specific capturing method

Abstract MicroRNAs (miRNAs), a family of small, endogenous, non-coding (∼17-24 nucleotides) nucleic acids, were initially discovered in 1993, but the discovery of a second miRNA led to their use as molecular markers of disease and potential therapeutic targets. MiRNAs regulate RNA translation, play a role in cell proliferation, control timing of organism development, and are associated with carcinogenesis. Because of their regulatory function in cell differentiation and renewal during physiologic and malignant conditions, miRNAs are emerging as a new class of effective biomarkers for cancer research. The miRNA expression profiling of tumor cells will allow for the identification of prognostic markers to indicate potential targets for patient- or disease-specific therapies. Three criteria important for profiling miRNAs are ease of use, simplicity and sensitivity. We have designed and developed a new method to quickly extract and purify specific miRNAs for detection using our current reverse transcription and real-time quantitative PCR workflows. The method involved 1μm-diameter magnetic beads conjugated with anti-miRNA oligoncleotides to capture these select miRNA targets from blood, plasma, and other tissues. The miRNAs were then transcribed into cDNA and subsequently used as templates for TaqMan® qRT-PCR analysis. We used this method to evaluate the miRNA signatures from normal and cancerous human breast modeled by cell lines and represented by formalin-fixed, paraffin-embedded tissues. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5036. doi:1538-7445.AM2012-5036

Abstract 2100: Cancer biomarker research using castPCR technology

Abstract Cancer Biomarkers have applications in the diagnosis, staging, prognosis and monitoring of disease progression, as well as in the predication and monitoring of drug response. Profiling and validation research tools are needed that exhibit the combined features of high sensitivity and high specificity for cancers. However, the sensitivity of molecular methods such as DNA sequencing and conventional genotyping in tumor samples is limited, typically ranging from 5-20%. We have recently developed TaqMan® Mutation Detection Assays using our competitive allele specific TaqMan® PCR (castPCR) technology for cancer biomarker research. castPCR assays were tested with >300 tumor research samples (either fresh/frozen or formalin-fixed, paraffin-embedded samples) and cell lines to assess mutation status. The results showed that castPCR technology can robustly detect mutations as low as 0.1% and has >99% concordance to other technologies including PCR-based technology and sequencing. In this study, a large panel of castPCR assays for AKT1, APC, BRAF, CTNNB1, HRAS, KRAS, NRAS, PIK3CA, PTEN and TP53 genes were used for investigating somatic mutations in breast tumor research samples. Initially, 4 model FFPE cell lines were used to validate the assays. Mutant DNAs were titrated in the wild type DNAs from 50% to 0.1%. Mutations were identified down to 0.1% titration with high reproducibility. No false positives were found in non-tumor samples. The results obtained by castPCR assays for 20 breast tumor samples were concordant to those reported by other methods. Our data showed that castPCR technology provides an excellent tool for identifying cancer biomarkers or confirming potential cancer markers such as those obtained by next-generation sequencing and other technologies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2100. doi:1538-7445.AM2012-2100

Breaking the Bottleneck in the Protein Biomarker Pipeline

The process for discovery and development of biomarkers of solid tumors presents exceptional challenges. The major technical obstacle is the disconnection between the site of their generation (tissue, proximal fluid) and the source of detection (body fluids, predominantly serum and plasma). This single factor limits, in practical terms, the potential pool of biomarkers to those that are secreted, shed, or leaked from the cell surface. On reaching the circulation, a biomarker undergoes “dilution” into a mixture of thousands of proteins that are present at concentrations spanning at least 10 orders of magnitude. Furthermore, the integrity of a circulating biomarker may be compromised by proteolytic degradation or distortion of its posttranslational modifications. Other levels of complexity involve the inherent variability of biological systems (intraindividual and across a population) and ambiguities in defining disease phenotypes. Finally, fundamental and technological difficulties involved in biomarker studies are exacerbated by preanalytical variables associated with sample collection, handling, and storage (1, 2). An ideal cancer biomarker would be detectable in body fluids at an early stage of disease in a highly specific and selective fashion and be measurable for an inexpensive price. Development of such a biomarker is not a trivial endeavor. To promote efficiency and rigor in cancer biomarker research, Pepe et al. (3) introduced guidelines for a generally appropriate process that could be applied to many other diseases and classes of biomarker (e.g., a protein or a metabolite). These guidelines propose specific aims and measures of success for each of the 5 phases of a biomarker discovery pipeline in the context of progress being made in the field and relevant published studies. It is now evident that the majority of proteomics and genomics studies published to date do not progress far beyond the discovery stage (phase 1), because the development of clinical …

Expression Analysis on Archival Material

Formalin-fixed paraffin-embedded (FFPE) tissue is the most common tissue specimen widely available. Moreover, long clinical follow-up is on hand. Therefore, FFPE material is a precious source of material for identifying predictive and/or prognostic biomarkers in cancer research on the basis of gene expression. However, the main drawback of FFPE tissue is the significant reduction in quantity and quality of the extracted RNA. The aim of this study is the comparison of different commercially available kits for the RNA isolation in FFPE tissue material. Five commercially available RNA isolation kits were tested and the concentration, purity, integrity, and raw cycle threshold values were determined. The mean total RNA concentrations were as follows: Qiagen 25957±19417 ng, Ambion 8249±2898 ng, SA Biosciences 8070±3700 ng, and Macherey-Nagel 622±394 ng. The mean A260/A280 ratios were as follows: Qiagen: 1.81, SA Biosciences: 0.66, Ambion: 1.03, and Macherey-Nagel: 1.04. The mean A260/A230 ratios were as follows: Qiagen: 1.88, SA Biosciences: 1.61, Ambion: 1.54, and Macherey-Nagel: 1.88. The RNA extractions from Epicentre could not be measured by the Nanodrop and, therefore, were excluded from further analysis. The mean RNA integrity number (range, 2.09 to 2.47) and the mean raw cycle threshold values (range, 33.43 to 35.37) were more or less the same for all the tested RNA isolation kits. Altogether, on the basis of the number of adequate isolations, the kit from Qiagen seems to be the most appropriate kit to be used in our further studies that require RNA isolation from FFPE material.

Bioinformatics: Biomarkers of early detection

Capturing, sharing, and publishing cancer biomarker research data are all fundamental challenges of enabling new opportunities to research and understand scientific data. Informatics experts from the National Cancer Institute's (NCI) Early Detection Research Network (EDRN) have pioneered a principled informatics infrastructure to capture and disseminate data from biomarker validation studies, in effect, providing a national-scale, real-world successful example of how to address these challenges. EDRN is a distributed, collaborative network and it requires its infrastructure to support research across cancer research institutions and across their individual laboratories. The EDRN informatics infrastructure is also referred to as the EDRN Knowledge Environment, or EKE. EKE connects information about biomarkers, studies, specimens and resulting scientific data, allowing users to search, download and compare each of these disparate sources of cancer research information. EKE's data is enriched by providing annotations that describe the research results (biomarkers, protocols, studies) and that link the research results to the captured information within EDRN (raw instrument datasets, specimens, etc.). In addition EKE provides external links to public resources related to the research results and captured data. EKE has leveraged and reused data management software technologies originally developed for planetary and earth science research results and has infused those capabilities into biomarker research. This paper will describe the EDRN Knowledge Environment, its deployment to the EDRN enterprise, and how a number of these challenges have been addressed through the capture and curation of biomarker data results.

Cancer Biomarkers: Closer to Delivering on their Promise

Taguchi etal. describe, in this issue of Cancer Cell, a quantitative comparative biomarker discovery approach that integrates animal lung cancer models with validation in well-controlled human clinical study sets. This approach overcomes many of the major barriers that have held back the field of cancer biomarkers in the past.

Molecular classification and clonal differentiation of hepatocellular carcinoma: the step forward for patient selection for liver transplantation

Liver transplantation is a successful treatment for hepatocellular carcinoma (HCC). However, advanced stages are not selected for transplant, based on the United Network for Organ Sharing selection criteria’s. Nowadays, molecular biology-based techniques constitute an excellent option to better understand HCC origin differentiation and biological behavior. Moreover, microarray technology is a powerful tool to address a variety of tumor tissues at molecular level and is actively used for the discovery of biomarkers in cancer research. This article will discuss published data in the field of HCC origin differentiation and its potential impact on outcomes following liver transplantation. Although preliminary results are presented, these findings encourage the use of gene-expression profiling microarrays for studying HCC biology and behavior and ultimately optimizing treatment success.

Gel-based proteomics of liver cancer progression in rat

A significant challenge in proteomics biomarker research is to identify the changes that are of highest diagnostic interest, among the many unspecific aberrations associated with disease burden and inflammation. In the present study liver tissue specimens (n = 18) from six experimental stages were collected from the resistant hepatocyte (RH) rat model of liver cancer and analyzed by 2D DIGE. The study included triplicates of regenerating liver, control “sham-operated” liver, three distinct premalignant stages and hepatomas. Out of 81 identified proteins two-thirds were differentially abundant in rat hepatomas compared to control rat liver and, secondly, the majority of proteins were also changed in precursor stages. This underscores the importance of adequate control samples in explorative cancer biomarker research. We confirm several proteomic changes previously identified in human hepatocellular carcinoma (HCC) and we identify novel candidate proteomic aberrations for further analysis in human HCC. In particular, increased levels of HSP70, HSP90, AKR1B1, AKR7A3, GCLM, ANXA5, VDBP, RGN and SULT1E1 were associated specifically with rat hepatomas, or with liver cancer progression in rat. In addition, we examine an integrated gel-based workflow for analysis of protein post-translational modifications (PTMs) and microtubule-association. We highlight differential PTM and localization of HSP60 as an interesting target for further analysis in liver cancer.

Improving Detection Accuracy of Lung Cancer Serum Proteomic Profiling via Two-Stage Training Process

BackgroundSurface-Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-TOF-MS) is a frequently used technique for cancer biomarker research. The specificity of biomarkers detected by SELDI can be influenced by concomitant inflammation. This study aimed to increase detection accuracy using a two-stage analysis process.MethodsSera from 118 lung cancer patients, 72 healthy individuals, and 31 patients with inflammatory disease were randomly divided into training and testing groups by 3:2 ratio. In the training group, the traditional method of using SELDI profile analysis to directly distinguish lung cancer patients from sera was used. The two-stage analysis of distinguishing the healthy people and non-healthy patients (1st-stage) and then differentiating cancer patients from inflammatory disease patients (2nd-stage) to minimize the influence of inflammation was validated in the test group.ResultsIn the test group, the one-stage method had 87.2% sensitivity, 37.5% specificity, and 64.4% accuracy. The two-stage method had lower sensitivity (> 70.1%) but statistically higher specificity (80%) and accuracy (74.7%). The predominantly expressed protein peak at 11480 Da was the primary splitter regardless of one- or two-stage analysis. This peak was suspected to be SAA (Serum Amyloid A) due to the similar m/z countered around this area. This hypothesis was further tested using an SAA ELISA assay.ConclusionsInflammatory disease can severely interfere with the detection accuracy of SELDI profiles for lung cancer. Using a two-stage training process will improve the specificity and accuracy of detecting lung cancer.

Abstract LB-178: Analysis of restored FFPE samples on Infinium methylation arrays

Abstract Millions of Formalin Fixed Paraffin Embedded (FFPE) cancer tissue samples currently present in the US and worldwide provide an enormous, invaluable repository to the discovery of biomarkers in cancer research, drug development, diagnosis and treatment of diseases. However, the processes of fixation and storage of FFPE samples leads to degradation and base modification with chemical residues. This degraded FFPE DNA is inefficiently amplified by the Infinium® whole genome amplification (WGA) step leading to poor performance in genome wide methylation analysis. In this study, purified FFPE DNA or crude lysates (200–500 ng input) were bisulfite converted and restored through Illumina's Infinium HD DNA Restoration kit consisting of a combination of treatment with a DNA polymerase, a repair enzyme, and a ligase. Results of methylation analysis on high density HumanMethylation450 BeadChip®, featuring over 450,000 assays indicated that even highly degraded samples which show initial call rates (genes detected p =0.01) of 50–70% can be successfully restored to a state that achieved call rates of greater than 90%. Direct comparison of frozen and FFPE samples from the same tumors showed an R2 correlation of beta values of greater than 95.0% between fresh frozen (FF) and FFPE samples. We also developed a method to assess FFPE sample quality based on a 90 bp qPCR assay (Infinium HD FFPE QC kit). In this assay FFPE samples exhibiting a Ct (Cq) difference of less than six relative to an intact standard are considered to pass the QC test and generally perform well in the assay. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr LB-178. doi:10.1158/1538-7445.AM2011-LB-178

Abstract 280: RNA isolation from formalin-fixed paraffin-embedded material: A comparison of five commercially available RNA isolation kits

Abstract Formalin-fixed paraffin-embedded (FFPE) tissue is the most common tissue specimen widely available. Moreover, long clinical follow-up is on hand. Therefore, FFPE material is a precious source of material for identifying predictive and/or prognostic biomarkers in cancer research on the basis of gene expression. However, the main drawback of FFPE tissue is the significant reduction in quantity and quality of the extracted RNA. The aim of this study is the comparison of different commercially available kits for the RNA isolation in FFPE tissue material. Five commercially available kits were compared and evaluated to identify the most optimal RNA isolation procedure for FFPE material. The FFPE sections used in this study derived from cervical cancer patients treated by surgery. Three paraffin sections (10 μm) per patient were used to isolate RNA, according to the manufacturer's protocol. The following RNA isolation kits were used: Rneasy® FFPE isolation kit (Qiagen), RecoverAllTM Total Nucleic Acid isolation kit (Ambion), ArrayGradeTM FFPE RNA isolation kit (SA Biosciences), NucleoSpin® FFPE RNA isolation kit (Macherey-Nagel) and QuickExtractTM FFPE RNA Extraction Kit (Epicentre® Biotechnologies). RNA extraction was carried out in RNAse free environment. The concentration and purity (A260/A280 ratio and A260/A230 ratio) were measured using a Nanodrop ND-1000 spectrophotometer. The integrity of the isolated RNA was assessed by capillary electrophoresis with an Agilent 2100 Bioanalyzer using Agilent RNA 6000 Series Nano kits, expressed in RNA integrity numbers (RIN). The mean total RNA eluated by the different kits were as follows: Qiagen 25957 ± 19417ng, Ambion 8249 ± 2898ng, SA Biosciences 8070 ± 3700ng and Machery-Nagel 622 ± 394ng. The A260/A280 and A260/A230 ratio was used to evaluate the purity of the samples. The mean A260/A280 ratios were as follows: Qiagen: 1.81 ± 0.23, SA Biosciences: 0.66 ± 0.36, Ambion: 1.03 ± 0.37 and Machery-Nagel: 1.04 ± 0.61 and for the mean A260/A230 ratios: Qiagen: 1.88 ± 0.09, SA Biosciences: 1.61 ± 0.32, Ambion: 1.54 ± 0.30 and Machery-Nagel: 1.88 ± 0.61. The RNA extractions from Epicentre® could not be measured by Nanodrop and, therefore, were excluded from further analysis. The mean RIN values were as follows: Qiagen: 2.19 ± 0,37, SA Biosciences: 2.09 ± 0.38, Ambion: 2.44 ± 0.12 and Macherey-Nagel: 2.30 ± 0.44. Based on the Nanodropdata, the most consistent results were obtained with the Rneasy® FFPE Isolation Kit (Qiagen). However, RNA isolated with RecoverAllTM Total Nucleic Acid isolation kit (Ambion) gave slightly higher RIN values. Taken these results together, the kit from Qiagen appears the most appropriate kit to be used in our further studies that require RNA isolation from FFPE material. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 280. doi:10.1158/1538-7445.AM2011-280

Biomarker research in prostate cancer—towards utility, not futility

The identification of an appropriate clinical question is critical for any biomarker project. Despite rapid advances in technology, few biomarkers have been forthcoming for prostate cancer. This could be because the clinical questions under investigation have not actually originated from clinical practice. These clinical questions are difficult to identify in the complex and heterogeneous pathogenesis of prostate cancer. In this Review, we have developed a prostate cancer 'roadmap' to identify the aspects of prostate cancer that may be amenable to biomarker discovery and serve as a guide for future projects in prostate cancer biomarker research.

MALDI Imaging Mass Spectrometry (MALDI-IMS)-application of spatial proteomics for ovarian cancer classification and diagnosis.

MALDI imaging mass spectrometry (MALDI-IMS) allows acquisition of mass data for metabolites, lipids, peptides and proteins directly from tissue sections. IMS is typically performed either as a multiple spot profiling experiment to generate tissue specific mass profiles, or a high resolution imaging experiment where relative spatial abundance for potentially hundreds of analytes across virtually any tissue section can be measured. Crucially, imaging can be achieved without prior knowledge of tissue composition and without the use of antibodies. In effect MALDI-IMS allows generation of molecular data which complement and expand upon the information provided by histology including immuno-histochemistry, making its application valuable to both cancer biomarker research and diagnostics. The current state of MALDI-IMS, key biological applications to ovarian cancer research and practical considerations for analysis of peptides and proteins on ovarian tissue are presented in this review.

Recent developments in prostate cancer biomarker research: therapeutic implications

This review aims to present an overview of recent clinical trials targeting biomarkers in advanced prostate cancer. We searched ClinicalTrials.gov for early phase clinical trials on treatments of prostate cancer that have been recently completed, are ongoing or are actively recruiting participants. Drug targets and their mechanism of actions were assessed and summarized. Trials were categorized according to prostate cancer biomarkers that have potential as therapeutic targets. A total of 19 new therapeutic agents for the treatment of prostate cancer are included in this review. Trials are summarized according to the targeted biomarkers and are categorized into five therapeutic approaches: prostate cancer vaccine, epigenetic therapy, pro-apoptotic agents, prostate cancer antibodies and anti-angiogenesis approach. Some of the therapeutic agents reviewed showed promising results, warranting further investigation in late phase clinical trials. Recent novel prostate cancer biomarkers that made it through clinical trials and their relevance as drug targets are summarized. This review emphasizes the importance of specific prostate cancer biomarkers and their potentials as targets of the disease. Some clinical trials of targeted treatments in prostate cancer show promising results. Better understanding of disease mechanisms should potentially lead to more specific treatments for individual patients.

Biomarkers in cancer research and treatment: promises and challenges

International Agency for Research on Cancer, Lyon, France Correspondence to Pierre Hainaut, International Agency for Research on Cancer, 150 Cours Albert Thomas, 69372 Lyon, France Tel: +33 4 72 73 85 32; e-mail: [email protected]

Outlier-Based Differential Expression Analysis in Proteomics Studies

An active area in cancer biomarker research is the development of statistical methods to identify expression signatures reflecting the heterogeneity of cancer across affected individuals. Tomlins et al. [5] observed heterogeneous patterns of oncogene activation within several cancer types, and introduced a statistical method called Cancer Outlier Profile Analysis (COPA) to identify "cancer outlier genes". Several related statistical approaches have since been developed, but the operating characteristics of these procedures (e.g. power, false positive rate), have not yet been fully characterized, especially in a proteomics setting. Here, we use simulation to identify the degree to which an outlier pattern of differential expression must hold in order for outlier-based approaches to be more effective than mean-based approaches. We also propose a diagnostic procedure that characterizes the potentially unequal levels of differential expression in the tails and in the center of a distribution of expression values. We find that for sample sizes and effect sizes typical of proteomics studies, the outlier pattern must be strong in order for outlier-based analysis to provide a meaningful benefit. This is corroborated by analysis of proteomics data from a melanoma study, in which the differential expression is most often present throughout the distribution, rather than being concentrated in the tails, albeit with a few proteins showing expression patterns consistent with outlier expression.