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Published on: October 23, 2021
Bias, randomization, and ovarian proteomic data: a reply to "producers and consumers"
Keith A Baggerly1, Kevin R Coombes, Jeffrey S Morris
1Department of Biostatistics, U.T. M.D. Anderson Cancer Center, Houston, Texas, USA. kabagg@mdanderson.org
Cancer Informatics
|March 24, 2009
Summary
Proteomic patterns show promise for early cancer diagnosis, but experimental bias may affect results. Addressing these issues in experimental design is key for reliable biomarker discovery in proteomics.
Area of Science:
- Biochemistry
- Oncology
- Analytical Chemistry
Background:
- Proteomic patterns from mass spectrometry are explored as potential early cancer diagnostic biomarkers.
- Initial studies on SELDI profiling of serum for ovarian cancer suggested high sensitivity and specificity.
- Concerns have been raised regarding experimental bias potentially influencing these proteomic findings.
Purpose of the Study:
- To address objections raised against findings of experimental bias in proteomic biomarker studies.
- To re-evaluate the validity of proteomic patterns as diagnostic markers in light of potential biases.
- To identify and clarify issues related to experimental design and processing in mass spectrometry-based biomarker discovery.
Main Methods:
- Analysis of proteomic data, specifically SELDI profiling of serum.
- Evaluation of experimental design and data processing steps.
- Rebuttal and examination of objections concerning experimental bias.
Main Results:
- Evidence of experimental bias in proteomic profiling studies was confirmed.
- The identified biases are linked to specific experimental design and data processing methodologies.
- Objections to the findings of bias were systematically addressed.
Conclusions:
- Experimental bias remains a significant concern in proteomic biomarker discovery.
- Biases in proteomic studies are attributable to experimental design and processing, not inherent limitations of the technique.
- Future studies require careful experimental design and processing to ensure the reliability of proteomic biomarkers for cancer diagnosis.
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