Standardization, analytical validation, and quality control of intermediate endpoint biomarkers

• Published in: Urology (Ridgewood, N.J.) Vol. 57; no. 4; pp. 164 - 170
• Main Authors: Veltri, Robert W, Miller, M.Craig, An, Gang
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 01.04.2001; Elsevier Science
• Subjects: Analysis of Variance; Area Under Curve; Biological and medical sciences; Biomarkers, Tumor - analysis; Biomarkers, Tumor - blood; Biopsy, Needle; Cell Nucleus; Chromatin - ultrastructure; Chromosome Aberrations; Disease Progression; Gene Expression; Genetic Markers; Humans; Image Processing, Computer-Assisted; Investigative techniques, diagnostic techniques (general aspects); Male; Medical sciences; Neoplasm Recurrence, Local - genetics; Nephrology. Urinary tract diseases; Neural Networks (Computer); Pathology. Cytology. Biochemistry. Spectrometry. Miscellaneous investigative techniques; Predictive Value of Tests; Prostate - chemistry; Prostate - pathology; Prostate-Specific Antigen - analysis; Prostatic Neoplasms - classification; Prostatic Neoplasms - genetics; Prostatic Neoplasms - pathology; Regression Analysis; Reverse Transcriptase Polymerase Chain Reaction; Sensitivity and Specificity; Tumors of the urinary system; Urinary system; Urinary tract. Prostate gland; Human; Urinary system disease; Prognosis; Prostate disease; Standardization; Male; Tumoral marker; Malignant tumor; Automated processing; Prostate specific antigen; Polymerase chain reaction; Image analysis; Quality assurance; Diagnostic aid; Male genital diseases; Prostate
• ISSN: 0090-4295; 1527-9995
• DOI: 10.1016/S0090-4295(00)00965-1

Standardized processes should be used in the identification and development of intermediate endpoint biomarkers (IEB) for the prediction of patient-specific disease outcomes. Using our own experiences, we outline some of our standardized processes. Using computer-assisted image analysis, we developed a new biomarker of genetic instability, termed quantitative nuclear grade (QNG). The QNG biomarker is derived using nuclear images analyzed from the tumor areas of Feulgen-stained 5-μm biopsy or radical prostatectomy tissue sections. From the variances of 41 to 60 different nuclear size, shape, and chromatin organization features, a QNG solution is computed using either logistic regression or artificial neural networks. QNG can then be used as an input for models that solve for a patient-specific probability to accurately predict disease outcomes. Preoperatively, QNG predicted both the pathologic stage and progression of prostate cancer using biopsies ( P <0.0001). Postoperatively, QNG proved extremely valuable in the prediction of biochemical progression using radical prostatectomy specimens with more than 10 years of follow-up ( P <0.0001). We also demonstrate the identification of novel, differentially expressed, prostate cancer genes using RNA fingerprinting methods and the clinical utility of testing for these genes in both blood and tissue samples. Also illustrated is the improvement of serum biomarker performance by combining molecular forms of PSA with new biomarkers. In conclusion, the development of new IEBs requires planning based upon an understanding of the molecular pathogenesis of disease. IEB selection and clinical evaluation should employ standardized methods of testing and validation, followed by publication. QNG is 1 example of a new, highly predictive, IEB for prostate cancer that has been developed using these processes.