A whole-blood RNA transcript-based prognostic model in men with castration-resistant prostate cancer: A prospective study

The Lancet Oncology

Volumn 13, Issue 11, 2012, Pages 1105-1113

  Ross, Robert W ^a,b   Galsky, Matthew D ^c   Scher, Howard I ^d,e   Magidson, Jay ^f   Magidson, Karl ^g   Lee, Gwo Shu Mary ^a,b   Katz, Leah ^a,b   Subudhi, Sumit K ^d,e   Anand, Aseem ^d,e   Fleisher, Martin ^d   Kantoff, Philip W ^a,b   Oh, William K ^c  

^a DANA FARBER CANCER INSTITUTE   (United States)

^b HARVARD MEDICAL SCHOOL   (United States)

^c MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^d MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

^e WEILL CORNELL MEDICAL COLLEGE   (United States)

^f Statistical Innovations   (United States)

^g GeneNews   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOLOGICAL MARKER; COMPLEMENT COMPONENT C1Q; CYCLIN DEPENDENT KINASE INHIBITOR 1A; RNA; TISSUE INHIBITOR OF METALLOPROTEINASE 1;

ABL2 GENE; ADULT; AGED; ARTICLE; BLOOD; BLOOD ANALYSIS; CANCER CENTER; CANCER PATIENT; CANCER PROGNOSIS; CANCER SURVIVAL; CASTRATION RESISTANT PROSTATE CANCER; CONTROLLED STUDY; GENE; GENETIC TRANSCRIPTION; HUMAN; IMMUNE DYSREGULATION; INFLAMMATION; ITGAL GENE; MAJOR CLINICAL STUDY; MALE; PATIENT CODING; POLYMERASE CHAIN REACTION; PRIORITY JOURNAL; PROGNOSIS; PROSPECTIVE STUDY; RISK ASSESSMENT; SEMA4D GENE; SURVIVAL TIME;

AGED; AGED, 80 AND OVER; CASTRATION; HUMANS; INFLAMMATION; MALE; MIDDLE AGED; PROGNOSIS; PROSPECTIVE STUDIES; PROSTATIC NEOPLASMS; RISK FACTORS; RNA; SURVIVAL ANALYSIS; TUMOR MARKERS, BIOLOGICAL;

EID: 84868208177     PISSN: 14702045     EISSN: 14745488     Source Type: Journal    
DOI: 10.1016/S1470-2045(12)70263-2     Document Type: Article

References (39)

1. Scher HI, Morris MJ, Basch E, Heller G End points and outcomes in castration-resistant prostate cancer: from clinical trials to clinical practice. J Clin Oncol 2011, 29:3695-3704.
2. Berry WR, Laszlo J, Cox E, Walker A, Paulson D Prognostic factors in metastatic and hormonally unresponsive carcinoma of the prostate. Cancer 1979, 44:763-775.
3. Emrich LJ, Priore RL, Murphy GP, Brady MF Prognostic factors in patients with advanced stage prostate cancer. Cancer Res 1985, 45:5173-5179.
4. Kelly WK, Scher HI, Mazumdar M, Vlamis V, Schwartz M, Fossa SD Prostate-specific antigen as a measure of disease outcome in metastatic hormone-refractory prostate cancer. J Clin Oncol 1993, 11:607-615.
5. Halabi S, Small EJ, Kantoff PW, et al. Prognostic model for predicting survival in men with hormone-refractory metastatic prostate cancer. J Clin Oncol 2003, 21:1232-1237.
6. Smaletz O, Scher HI, Small EJ, et al. Nomogram for overall survival of patients with progressive metastatic prostate cancer after castration. J Clin Oncol 2002, 20:3972-3982.
7. Alcorta D, Preston G, Munger W, et al. Microarray studies of gene expression in circulating leukocytes in kidney diseases. Exp Nephrol 2002, 10:139-149.
8. Bull TM, Coldren CD, Moore M, et al. Gene microarray analysis of peripheral blood cells in pulmonary arterial hypertension. Am J Respir Crit Care Med 2004, 170:911-919.
9. Ma J, Liew CC Gene profiling identifies secreted protein transcripts from peripheral blood cells in coronary artery disease. J Mol Cell Cardiol 2003, 35:993-998.
10. Osman I, Bajorin DF, Sun TT, et al. Novel blood biomarkers of human urinary bladder cancer. Clin Cancer Res 2006, 12:3374-3380.
11. Oh WK, Hayes J, Evan C, et al. Development of an integrated prostate cancer research information system. Clin Genitourin Cancer 2006, 5:61-66.
12. Rosenberg JE, Ross RW, Wassmann K, et al. Gene expression similarities in whole-blood RNA transcript analysis in geographically disparate prostate cancer populations. ASCO Genitourinary Cancers Symposium 2010; San Francisco, CA, USA; March 5-7, 2010: abstr 181.
13. Ross RW, Bankaitis-Davis D, Siconolfi L, et al. Sensitivity and specificity of a whole-blood RNA transcript-based diagnostic test for the diagnosis of prostate cancer (CaP) compared with prostate-specific antigen (PSA) alone. 44th annual meeting of the American Society of Clinical Oncology; Orlando, FL, USA; May 29-June 2, 2009: abstr 5052.
14. Magidson J, Wassmann K The role of proxy genes in predictive models: an application to early detection of prostate cancer (accessed June 6, 2012). http://www.statisticalinnovations.com/technicalsupport/Suppressor.AMSTAT.pdf.
15. Hanczar B, Zucker JD, Henegar C, Saitta L Feature construction from synergic pairs to improve microarray-based classification. Bioinformatics 2007, 23:2866-2872.
16. Magidson J Qualitative variance, entropy, and correlation ratios for nominal dependent variables. Soc Sci Res 1981, 10:177-194.
17. Heckman JJ, Singer B A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 1984, 52:271-320.
18. Land K, Nagin D, McCall P Discrete-time hazard regression models with hidden heterogeneity: The semiparametric mixed poisson regression approach. Sociol Methods Res 2001, 29:342-373.
19. A general latent class approach to unobserved heterogeneity analysis of event history data. Applied latent class analysis 2002, 383-407. Cambridge University Press, Cambridge. J.K. Vermunt, J. Hagenaars, A. McCutcheon (Eds.).
20. McDonald JD, Alessandro R Mixture modeling of recurrent event times with long-term survivors: analysis of Hutterite birth intervals. Stat Methods Appl 2001, 10:257-272.
21. Steele F A discrete-time multilevel mixture model for event history data with long-term survivors, with an application to an analysis of contraceptive sterilization in Bangladesh. Lifetime Data Anal 2003, 9:155-174.
22. Skrondal A, Rabe-Hesketh S Generalized latent variable modeling: Multilevel, longitudinal and structural equation models 2004, Chapman & Hall/CRC Press, Boca Raton.
23. Vermunt JK, Magidson J Technical Guide for Latent GOLD 4.0: Basic and Advanced 2005, Statistical Innovations Inc, Belmont Massachusetts.
24. Schemper M, Smith TL A note on quantifying follow-up in studies of failure time. Control Clin Trials 1996, 17:343-346.
25. Giblin PA, Lemieux RM LFA-1 as a key regulator of immune function: approaches toward the development of LFA-1-based therapeutics. Curr Pharm Des 2006, 12:2771-2795.
26. Hogg N, Laschinger M, Giles K, McDowall A T-cell integrins: more than just sticking points. J Cell Sci 2003, 116:4695-4705.
27. Sierra JR, Corso S, Caione L, et al. Tumor angiogenesis and progression are enhanced by Sema4D produced by tumor-associated macrophages. J Exp Med 2008, 205:1673-1685.
28. Smith A, Stanley P, Jones K, Svensson L, McDowall A, Hogg N The role of the integrin LFA-1 in T-lymphocyte migration. Immunol Rev 2007, 218:135-146.
29. Zipfel PA, Zhang W, Quiroz M, Pendergast AM Requirement for Abl kinases in T cell receptor signaling. Curr Biol 2004, 14:1222-1231.
30. Chirco R, Liu XW, Jung KK, Kim HR Novel functions of TIMPs in cell signaling. Cancer Metastasis Rev 2006, 25:99-113.
31. Hornebeck W, Lambert E, Petitfrère E, Bernard P Beneficial and detrimental influences of tissue inhibitor of metalloproteinase-1 (TIMP-1) in tumor progression. Biochimie 2005, 87:377-383.
32. Lu J, Wu X, Teh BK The regulatory roles of C1q. Immunobiology 2007, 212:245-252.
33. Lu JH, Teh BK, Wang L, et al. The classical and regulatory functions of C1q in immunity and autoimmunity. Cell Mol Immunol 2008, 5:9-21.
34. Ouellet S, Vigneault F, Lessard M, Leclerc S, Drouin R, Guérin SL Transcriptional regulation of the cyclin-dependent kinase inhibitor 1A (p21) gene by NFI in proliferating human cells. Nucleic Acids Res 2006, 34:6472-6487.
35. Terheyden P, Loos M, Störkel S, Kaul M Human macrophages simultaneously express membrane-C1q and Fc-receptors for IgG. Immunol Lett 2005, 101:202-209.
36. Oh WK, Vargas R, Jacobus S, et al. Elevated plasma tissue inhibitor of metalloproteinase-1 levels predict decreased survival in castration-resistant prostate cancer patients. Cancer 2011, 117:517-525.
37. Olmos D, Clark J, Brewer D, et al. An evaluation of blood mRNA expression array signatures derived from unsupervised analyses in the identification of prostate cancers with poor outcome. Proc Am Soc Clin Oncol 2011, 29(suppl):4577.
38. Zhao L, Lee BY, Brown DA, et al. Identification of candidate biomarkers of therapeutic response to docetaxel by proteomic profiling. Cancer Res 2009, 69:7696-7703.
39. de Bono JS, Scher HI, Montgomery RB, et al. Circulating tumor cells predict survival benefit from treatment in metastatic castration-resistant prostate cancer. Clin Cancer Res 2008, 14:6302-6309.