Article

Proteomic Approaches to the Discovery of Cancer Biomarkers for Early Detection and Personalized Medicine

Department of Chemotherapy and Clinical Research, National Cancer Center Research Institute, Tokyo, Japan.
Japanese Journal of Clinical Oncology (Impact Factor: 1.75). 12/2012; 43(2). DOI: 10.1093/jjco/hys200
Source: PubMed

ABSTRACT Cancer biomarkers for the early detection of malignancies and selection of therapeutic strategies have been requested in the clinical field. Accurate and informative cancer biomarkers hold significant promise for improvements in the early detection of disease and in the selection of the most effective therapeutic strategies. Recently, significant progress in the comprehensive analysis of the human genome, epigenome, transcriptome, proteome and metabolome has led to revolutionary changes in the discovery of cancer biomarkers. The Human Proteome Organization has launched a global Human Proteome Project to map the entire human protein set. The Human Proteome Project research group has focused on three working proteomic pillars-mass spectrometry-based, antibody-based and knowledge-based proteomics-and each of these technologies is advancing rapidly. In this review, we introduce the proteomic platforms that are currently being used for cancer biomarker discovery, and describe examples of novel cancer biomarkers that were identified with each proteomic technology.

Full-text

Available from: Mari Masuda, Jun 16, 2015
2 Followers
 · 
1,621 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Purpose: Cancer of the upper digestive tract (uGI) is a major contributor to cancer-related death worldwide. Due to a rise in occurrence, together with poor survival rates and a lack of diagnostic or prognostic clinical assays, there is a clear need to establish molecular biomarkers.Experimental design: Initial assessment was performed on urine samples from 60 control and 60 uGI cancer patients using MS to establish a peak-pattern or fingerprint model, which was validated by a further set of 59 samples.Results: We detected 86 cluster-peaks by MS above frequency- and detection-thresholds. Statistical testing and model building resulted in a peak-profiling model of 5 relevant peaks with 88% overall sensitivity and 91% specificity, and overall correctness of 90%. High resolution MS of 40 samples in the 2–10 kDa range resulted in 646 identified proteins, and pattern matching identified 4 of the 5 model-peaks within significant parameters, namely Programmed cell death 6-interacting protein (PDCD6IP/Alix/AIP1), Rabenosyn-5 (ZFYVE20), Protein S100A8 and Protein S100A9, of which the first two were validated by Western blotting.Conclusions and clinical relevance: We demonstrate that MS analysis of human urine can identify lead biomarker candidates in uGI cancers which makes this technique potentially useful in defining and consolidating biomarker patterns for uGI cancer screening.This article is protected by copyright. All rights reserved
    PROTEOMICS - CLINICAL APPLICATIONS 01/2015; DOI:10.1002/prca.201400111 · 2.68 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Lumbar spinal stenosis (LSS) is a syndromic degenerative spinal disease and is characterized by spinal canal narrowing with subsequent neural compression causing gait disturbances. Although LSS is a major age-related musculoskeletal disease that causes large decreases in the daily living activities of the elderly, its molecular pathology has not been investigated using proteomics. Thus, we used several proteomic technologies to analyze the ligamentum flavum (LF) of individuals with LSS. Using comprehensive proteomics with SCX fractionation, we detected 1288 proteins in these LF samples. A GO analysis of the comprehensive proteome revealed that more than 30% of the identified proteins were extracellular. Next we used 2-dimensional image converted analysis of LC/MS (2DICAL) to compare LF obtained from individuals with LSS to that obtained from individuals with disc herniation (non-degenerative control). We detected 64781 MS peaks and identified 1675 differentially expressed peptides derived from 286 proteins. We verified four differentially expressed proteins (fibronectin, serine protease HTRA1, tenascin, and asporin) by quantitative proteomics using SRM/MRM. The present proteomic study is the first to identify proteins from degenerated and hypertrophied LF in LSS, which will help in studying LSS.This article is protected by copyright. All rights reserved
    Proteomics 01/2015; 15(9). DOI:10.1002/pmic.201400442 · 3.97 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: As for many other cancers, metastasis is the leading cause of death of patients with ovarian cancer. Vigorous basic and clinical research is being performed to initiate more efficacious treatment strategies to improve the poor outcome of women with this cancer. Current treatment for ovarian cancer includes advanced cyto-reductive surgery and traditional platinum and taxane combined chemotherapy. Clinical trials using novel cytotoxic reagents and tyrosine kinase inhibitors have also been progressing. In parallel, the application of robust unbiased high throughput research platforms using transcriptomic and proteomic approaches has identified that not only individual cell signalling pathways, but a network of molecular pathways, play an important role in the biology of ovarian cancer. Furthermore, intensive genomic and epigenetic analyses have also revealed single nucleotide polymorphisms associated with risk and/or aetiology of this cancer including patient response to treatment. Taken together, these approaches, that are advancing our understanding, will have an impact on the generation of new therapeutic approaches and strategies for improving the outcome and quality of life of patients with ovarian cancer in the near future.