Unlocking biomarker discovery: Large scale application of aptamer proteomic technology for early detection of lung cancer

Lung cancer is the leading cause of cancer deaths, because ~84% of cases are diagnosed at an advanced stage. Worldwide in 2008, ~1.5 million people were diagnosed and ~1.3 million died – a survival rate unchanged since 1960. However, patients diagnosed at an early stage and have surgery experience an 86% overall 5-year survival. New diagnostics are therefore needed to identify lung cancer at this stage. Here we present the first large scale clinical use of aptamers to discover blood protein biomarkers in disease with our breakthrough proteomic technology. This multi-center case-control study was conducted in archived samples from 1,326 subjects from four independent studies of non-small cell lung cancer (NSCLC) in long-term tobacco-exposed populations. We measured >800 proteins in 15uL of serum, identified 44 candidate biomarkers, and developed a 12-protein panel that distinguished NSCLC from controls with 91% sensitivity and 84% specificity in a training set and 89% sensitivity and 83% specificity in a blinded, independent verification set. Performance was similar for early and late stage NSCLC. This is a significant advance in proteomics in an area of high clinical need

Evidence for an elevated frequency of in vivo somatic cell mutations in ataxia telangiectasia

Somatic cell mutation frequency in vivo was measured in individuals with high cancer risk who were from ataxia telangiectasia (A-T) families. The assay for somatic mutation measures the frequency of variant erythrocytes which are progeny of erythroid precursor cells with mutations that result in a loss of gene expression at the polymorphic glycophorin A (GPA) locus. Samples from 14 of 15 A-T homozygotes showed high frequencies of GPA gene expression-loss variant cells with normal expression of only one of the two alleles at the GPA locus (i.e., GPA hemizygous variant cells). The mean elevation of the frequency of hemizygous variant cells over those in normal controls and unaffected family members was 7-14-fold. A-T homozygotes also showed an increase in the frequency of cells in which one allele at the GPA locus had lost expression and in which the remaining allele was expressed at a homozygous level (i.e., GPA homozygous variant cells). Family members who are obligate A-T heterozygotes did not appear to have a significantly elevated frequency of GPA hemizygous or homozygous variant cells. These indications of elevated in vivo frequencies of variant erythrocytes in A-T homozygotes support a causal link between susceptibility to somatic mutation and susceptibility to cancer

A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI. © 2011 Li et al

Serum protein profile in systemic-onset juvenile idiopathic arthritis differentiates response versus nonresponse to therapy

Systemic-onset juvenile idiopathic arthritis (SJIA) is a disease of unknown etiology with an unpredictable response to treatment. We examined two groups of patients to determine whether there are serum protein profiles reflective of active disease and predictive of response to therapy. The first group (n = 8) responded to conventional therapy. The second group (n = 15) responded to an experimental antibody to the IL-6 receptor (MRA). Paired sera from each patient were analyzed before and after treatment, using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Despite the small number of patients, highly significant and consistent differences were observed before and after response to therapy in all patients. Of 282 spectral peaks identified, 23 had mean signal intensities significantly different (P < 0.001) before treatment and after response to treatment. The majority of these differences were observed regardless of whether patients responded to conventional therapy or to MRA. These peaks represent potential biomarkers of active disease. One such peak was identified as serum amyloid A, a known acute-phase reactant in SJIA, validating the SELDI-TOF MS platform as a useful technology in this context. Finally, profiles from serum samples obtained at the time of active disease were compared between the two patient groups. Nine peaks had mean signal intensities significantly different (P < 0.001) between active disease in patients who responded to conventional therapy and in patients who failed to respond, suggesting a possible profile predictive of response. Collectively, these data demonstrate the presence of serum proteomic profiles in SJIA that are reflective of active disease and suggest the feasibility of using the SELDI-TOF MS platform used as a tool for proteomic profiling and discovery of novel biomarkers in autoimmune diseases

Criteria for the use of omics-based predictors in clinical trials.

The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to 'omics'-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests for guiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy

Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration

Abstract High-throughput ‘omics’ technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.http://deepblue.lib.umich.edu/bitstream/2027.42/134536/1/12916_2013_Article_1104.pd

Criteria for the use of omics-based predictors in clinical trials: Explanation and elaboration

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well. © 2013 McShane et al.; licensee BioMed Central Ltd

In vivo Somatic Mutation and Segregation at the Human Glycophorin A (GPA) Locus: Phenotypic Variation Encompassing both Gene-specific and Chromosomal Mechanisms

The human in vivo GPA assay uses immunolabelling and flow cytometry to directly detect and quantitate somatic variation in erythrocytes expressing glycophorin A (GPA) allele-loss phenotypes in peripheral blood samples from individuals heterozygous for the MN blood type. The assay distinguishes two independent classes of variant cells: those that have lost expression of one allelic form of the GPA cell surface protein (the antigen responsible for the MN blood type), and a second class that, in addition to this allele loss now express the remaining homologue at twice the level of the heterozygote. This assay has been widely applied in human populations; both classes of variant appear at frequencies of approximately 10(-5) in unexposed individuals. There is considerable inter-individual variation, however, as well as an increase in variant cell frequency with age. Exposure to genotoxic agents such as ionizing radiation or chemical mutagens cause a dose-dependent increase in the frequency of variants, and the assay has been proposed as a quantitative cumulative biodosimeter for accidental, environmental, occupational and medical exposures to these agents. Variants arising by such molecular mechanisms as recombination, gene inactivation and chromosome missegregation, as well as classical mutation are detectable by this assay, hence the term somatic segregation rather than simply somatic mutation. Indeed, the spectrum of molecular events contributing to the two classes of GPA variants are identical to those involved in the etiology of recessive cancer, and largely representative of the activating events occurring at proto-oncogenes. The GPA assay has therefore also been proposed as an intermediate biomarker of carcinogenesis and other human diseases characterized by somatic mosaicism

Bone Marrow Somatic Mutation after Genotoxic Cancer Therapy

The glycophorin A (GPA) somatic mutation assay has been shown to act as cumulative biodosimeter of chemotherapy in pediatric patients, but to show no response to localized high-dose radiotherapy. These results are consistent with studies in adult populations treated with cyclophosphamide and doxorubicin or cisplatin, but inconsistent with results showing induction of somatic mutation at the GPA locus in patients treated with localized or implant radiation. The GPA assay directly measures in-vivo somatic mutation and segregation occurring by any of several molecular mechanisms implicated in oncogenesis. It has the advantage of integrating environmental and genetic factors when evaluating the long-term health effects of medical exposures. Ultimately, application of this and other intermediate biomarkers of carcinogenesis will permit individualized design and modification of therapeutic regimen and better prognostic assessment in all types of cancer