Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy Journal Article
| Authors: | Stephenson, A. J.; Smith, A.; Kattan, M. W.; Satagopan, J.; Reuter, V. E.; Scardino, P. T.; Gerald, W. L. |
| Article Title: | Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy |
| Abstract: | BACKGROUND. Gene expression profiling of prostate carcinoma offers an alternative means to distingui0sh aggressive tumor biology and may improve the accuracy of outcome prediction for patients with prostate carcinoma treated by radical prostatectomy. METHODS. Gene expression differences between 37 recurrent and 42 nonrecurrent primary prostate tumor specimens were analyzed by oligonucleotide microarrays. Two logistic regression modeling approaches were used to predict prostate carcinoma recurrence after radical prostatectomy. One approach was based exclusively on gene expression differences between the two classes. The second approach integrated prognostic gene variables with a validated postoperative predictive model based on standard variables (nomogram). The predictive accuracy of these modeling approaches was evaluated by leave-one-out cross-validation (LOOCV) and compared with the nomogram. RESULTS. The modeling approach using gene variables alone accurately classified 59 (75%) tissue samples in LOOCV, a classification rate substantially higher than expected by chance. However, this predictive accuracy was inferior to the nomogram (concordance index, 0.75 vs. 0.84, P = 0.01). Models combining clinical and gene variables accurately classified 70 (89%) tissue samples and the predictive accuracy using this approach (concordance index, 0.89) was superior to the nomogram (P = 0.009) and models based on gene variables alone (P < 0.001). Importantly, the combined approach provided a marked improvement for patients whose nomogram-predicted likelihood of disease recurrence was in the indeterminate range (7-year disease progression-free probability, 30-70%; concordance index, 0.83 vs. 0.59, P = 0.01). CONCLUSIONS. Integration of gene expression signatures and clinical variables produced predictive models for prostate carcinoma recurrence that perform significantly better than those based on either clinical variables or gene expression information alone. © 2005 American Cancer Society. |
| Keywords: | adult; clinical article; controlled study; human tissue; treatment outcome; aged; middle aged; clinical feature; cancer recurrence; validation process; accuracy; reproducibility of results; neoplasm recurrence, local; gene expression profiling; logistic models; prediction; prostatic neoplasms; oligonucleotide array sequence analysis; prostatectomy; prostate tumor; carcinoma; predictive value of tests; intermethod comparison; dna microarray; logistic regression analysis; nomogram; prostate carcinoma; prostatic neoplasms/pathology/surgery |
| Journal Title: | Cancer |
| Volume: | 104 |
| Issue: | 2 |
| ISSN: | 0008-543X |
| Publisher: | Wiley Blackwell |
| Date Published: | 2005-07-15 |
| Start Page: | 290 |
| End Page: | 298 |
| Language: | English |
| DOI: | 10.1002/cncr.21157 |
| PUBMED: | 15948174 |
| PROVIDER: | scopus |
| PMCID: | PMC1852494 |
| DOI/URL: | |
| Notes: | --- - "Cited By (since 1996): 64" - "Export Date: 24 October 2012" - "CODEN: CANCA" - "Source: Scopus" |
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