Correcting common errors in identifying cancer-specific serum peptide signatures Journal Article
| Authors: | Villanueva, J.; Philip, J.; Chaparro, C. A.; Li, Y.; Toledo-Crow, R.; DeNoyer, L.; Fleisher, M.; Robbins, R. J.; Tempst, P. |
| Article Title: | Correcting common errors in identifying cancer-specific serum peptide signatures |
| Abstract: | 'Molecular signatures' are the qualitative and quantitative patterns of groups of biomolecules (e.g., mRNA, proteins, peptides, or metabolites) in a cell, tissue, biological fluid, or an entire organism. To apply this concept to biomarker discovery, the measurements should ideally be noninvasive and performed in a single read-out. We have therefore developed a peptidomics platform that couples magnetics-based, automated solid-phase extraction of small peptides with a high-resolution MALDI-TOF mass spectrometric readout (Villanueva, J.; Philip, J.; Entenberg, D.; Chaparro, C. A.; Tanwar, M. K.; Holland, E. C.; Tempst, P. Anal. Chem. 2004, 76, 1560-1570). Since hundreds of peptides can be detected in microliter volumes of serum, it allows to search for disease signatures, for instance in the presence of cancer. We have now evaluated, optimized, and standardized a number of clinical and analytical chemistry variables that are major sources of bias; ranging from blood collection and clotting, to serum storage and handling, automated peptide extraction, crystallization, spectral acquisition, and signal processing. In addition, proper alignment of spectra and user-friendly visualization tools are essential for meaningful, certifiable data mining. We introduce a minimal entropy algorithm, 'Entropycal', that simplifies alignment and subsequent statistical analysis and increases the percentage of the highly distinguishing spectral information being retained after feature selection of the datasets. Using the improved analytical platform and tools, and a commercial statistics program, we found that sera from thyroid cancer patients can be distinguished from healthy controls based on an array of 98 discriminant peptides. With adequate technological and computational methods in place, and using rigorously standardized conditions, potential sources of patient related bias (e.g., gender, age, genetics, environmental, dietary, and other factors) may now be addressed. © 2005 American Chemical Society. |
| Keywords: | controlled study; genetics; mass spectrometry; biomarkers; biological marker; neoplasm proteins; tumor markers, biological; protein; peptide; algorithms; proteomics; age; diet; chemistry; statistical analysis; messenger rna; blood sampling; measurement; peptides; thyroid neoplasms; gender; crystallization; matrix assisted laser desorption ionization time of flight mass spectrometry; environmental factor; signal processing; serum; spectroscopy; metabolite; tissue; body fluid; magnetics; blood clotting; spectrometry, mass, matrix-assisted laser desorption-ionization; bias; patterns; cell; molecular diagnostic techniques; blood storage; entropy; specimen collection; peak alignment; sample processing; solid phase extraction; curatella americana |
| Journal Title: | Journal of Proteome Research |
| Volume: | 4 |
| Issue: | 4 |
| ISSN: | 1535-3893 |
| Publisher: | American Chemical Society |
| Date Published: | 2005-07-01 |
| Start Page: | 1060 |
| End Page: | 1072 |
| Language: | English |
| DOI: | 10.1021/pr050034b |
| PUBMED: | 16083255 |
| PROVIDER: | scopus |
| PMCID: | PMC1852495 |
| DOI/URL: | |
| Notes: | --- - "Cited By (since 1996): 136" - "Export Date: 24 October 2012" - "CODEN: JPROB" - "Source: Scopus" |
Altmetric
Citation Impact
BMJ Impact Analytics
MSK Authors
-
54Robbins -
24
Villanueva -
324Tempst -
25Toledo-Crow -
312Fleisher -
20Li -
48Philip -
4
Chaparro
Related MSK Work