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

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-microm 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.

[1]  D. Tindall,et al.  Isolation and androgen regulation of the human homeobox cDNA, NKX3.1 , 1998, The Prostate.

[2]  L. Liotta,et al.  cDNA sequencing and analysis of POV1 (PB39): a novel gene up-regulated in prostate cancer. , 1998, Genomics.

[3]  L. Liotta,et al.  Tumor invasion and metastasis: an imbalance of positive and negative regulation. , 1991, Cancer research.

[4]  R. Veltri,et al.  Human prostate-specific transglutaminase gene: promoter cloning, tissue-specific expression, and down-regulation in metastatic prostate cancer. , 1999, Urology.

[5]  M. Loda,et al.  Prostate stem cell antigen: a cell surface marker overexpressed in prostate cancer. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[6]  P. Carroll,et al.  Genetic alterations in untreated metastases and androgen-independent prostate cancer detected by comparative genomic hybridization and allelotyping. , 1996, Cancer research.

[7]  D. Bostwick,et al.  Current evaluation of the tissue localization and diagnostic utility of prostate specific membrane antigen , 1998, Cancer.

[8]  R L Vessella,et al.  Identification of differentially expressed prostate genes: Increased expression of transcription factor ETS‐2 in prostate cancer , 1997, The Prostate.

[9]  H. Frierson,et al.  PTEN/MMAC1 is infrequently mutated in pT2 and pT3 carcinomas of the prostate , 1998, Oncogene.

[10]  J. Nelson,et al.  Prostate cancer metastasis-suppressor genes: a current perspective. , 1998, In vivo.

[11]  Fearon Er Molecular genetic studies of the adenoma-carcinoma sequence. , 1994 .

[12]  P. Abel,et al.  Molecular and cellular biology of prostate cancer , 1997, Cancer and Metastasis Reviews.

[13]  T. H. van der Kwast,et al.  Human prostate-specific transglutaminase: a new prostatic marker with a unique distribution pattern. , 1999, Laboratory investigation; a journal of technical methods and pathology.

[14]  R W Veltri,et al.  Genetically engineered neural networks for predicting prostate cancer progression after radical prostatectomy. , 1999, Urology.

[15]  Tumor invasion and metastasis. , 1982 .

[16]  R. Veltri,et al.  The role of biopsy pathology, quantitative nuclear morphometry, and biomarkers in the preoperative prediction of prostate cancer staging and prognosis. , 1998, Seminars in urologic oncology.

[17]  Taylor Murray,et al.  Cancer statistics, 1998 , 1998, CA: a cancer journal for clinicians.

[18]  Taylor Murray,et al.  Cancer statistics, 1999 , 1999, CA: a cancer journal for clinicians.

[19]  M. Miller,et al.  Observations on pathology trends in 62,537 prostate biopsies obtained from urology private practices in the United States. , 1998, Urology.

[20]  M. Kattan,et al.  Elevated expression of caveolin is associated with prostate and breast cancer. , 1998, Clinical cancer research : an official journal of the American Association for Cancer Research.

[21]  K. Kinzler,et al.  The multistep nature of cancer. , 1993, Trends in genetics : TIG.

[22]  R W Veltri,et al.  Ability to predict biochemical progression using Gleason score and a computer-generated quantitative nuclear grade derived from cancer cell nuclei. , 1996, Urology.

[23]  R. Vessella,et al.  Interleukin-8 serum levels in patients with benign prostatic hyperplasia and prostate cancer. , 1999, Urology.

[24]  D C Young,et al.  An algorithm for predicting nonorgan confined prostate cancer using the results obtained from sextant core biopsies with prostate specific antigen level. , 1996, The Journal of urology.

[25]  O. Brawley,et al.  Prostate Cancer Incidence and Mortality Rates among White and Black Men , 1997, Epidemiology.