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

BACKGROUND Survival for patients with castration-resistant prostate cancer is highly variable. We assessed the effectiveness of a whole-blood RNA transcript-based model as a prognostic biomarker in castration-resistant prostate cancer. METHODS Peripheral blood was prospectively collected from 62 men with castration-resistant prostate cancer on various treatment regimens who were enrolled in a training set at the Dana-Farber Cancer Institute (Boston, MA, USA) from August, 2006, to June, 2008, and from 140 patients with castration-resistant prostate cancer in a validation set from Memorial Sloan-Kettering Cancer Center (New York, NY, USA) from August, 2006, to February, 2009. A panel of 168 inflammation-related and prostate cancer-related genes was assessed with optimised quantitative PCR to assess biomarkers predictive of survival. FINDINGS A six-gene model (consisting of ABL2, SEMA4D, ITGAL, and C1QA, TIMP1, CDKN1A) separated patients with castration-resistant prostate cancer into two risk groups: a low-risk group with a median survival of more than 34·9 months (median survival was not reached) and a high-risk group with a median survival of 7·8 months (95% CI 1·8-13·9; p<0·0001). The prognostic utility of the six-gene model was validated in an independent cohort. This model was associated with a significantly higher area under the curve compared with a clinicopathological model (0·90 [95% CI 0·78-0·96] vs 0·65 [0·52-0·78]; p=0·0067). INTERPRETATION Transcriptional profiling of whole blood yields crucial prognostic information about men with castration-resistant prostate cancer. The six-gene model suggests possible dysregulation of the immune system, a finding that warrants further study. FUNDING Source MDX.

[1]  P. Zipfel,et al.  Requirement for Abl Kinases in T Cell Receptor Signaling , 2004, Current Biology.

[2]  Giblin Pa,et al.  LFA-1 as a key regulator of immune function: approaches toward the development of LFA-1-based therapeutics. , 2006 .

[3]  J. McDonald,et al.  Mixture modelling of recurrent event times with long-term survivors: Analysis of Hutterite birth intervals , 2001 .

[4]  W. Hornebeck,et al.  Beneficial and detrimental influences of tissue inhibitor of metalloproteinase-1 (TIMP-1) in tumor progression. , 2005, Biochimie.

[5]  Kenneth C. Land,et al.  Discrete-Time Hazard Regression Models with Hidden Heterogeneity , 2001 .

[6]  R. Priore,et al.  Prognostic factors in patients with advanced stage prostate cancer. , 1985, Cancer research.

[7]  Jun Ma,et al.  Short Communication Gene profiling identifies secreted protein transcripts from peripheral blood cells in coronary artery disease , 2003 .

[8]  M. Kaul,et al.  Human macrophages simultaneously express membrane-C1q and Fc-receptors for IgG. , 2005, Immunology letters.

[9]  P. Comoglio,et al.  Tumor angiogenesis and progression are enhanced by Sema4D produced by tumor-associated macrophages , 2008, The Journal of experimental medicine.

[10]  R. Drouin,et al.  Transcriptional regulation of the cyclin-dependent kinase inhibitor 1A (p21) gene by NFI in proliferating human cells , 2006, Nucleic acids research.

[11]  Jinhua Lu,et al.  The regulatory roles of C1q. , 2007, Immunobiology.

[12]  L. Corrado Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models , 2005 .

[13]  J. Vermunt A general latent class approach to unobserved heterogeneity in the analysis of event history data , 2002 .

[14]  Beth Katcher,et al.  Development of an integrated prostate cancer research information system. , 2006, Clinical genitourinary cancer.

[15]  M Mazumdar,et al.  Prostate-specific antigen as a measure of disease outcome in metastatic hormone-refractory prostate cancer. , 1993, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  Xu-wen Liu,et al.  Novel functions of TIMPs in cell signaling , 2006, Cancer and Metastasis Reviews.

[17]  Nus Immunology The Classical and Regulatory Functions of C1q in Immunity and Autoimmunity , 2008 .

[18]  K. Pienta,et al.  Circulating Tumor Cells Predict Survival Benefit from Treatment in Metastatic Castration-Resistant Prostate Cancer , 2008, Clinical Cancer Research.

[19]  M. Stockler,et al.  Identification of candidate biomarkers of therapeutic response to docetaxel by proteomic profiling. , 2009, Cancer research.

[20]  S. P. Nana-Sinkam,et al.  Gene microarray analysis of peripheral blood cells in pulmonary arterial hypertension. , 2004, American journal of respiratory and critical care medicine.

[21]  J. Hagenaars,et al.  Applied Latent Class Analysis , 2003 .

[22]  J. Heckman,et al.  A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data , 1984 .

[23]  Hong Zhong,et al.  Novel Blood Biomarkers of Human Urinary Bladder Cancer , 2006, Clinical Cancer Research.

[24]  N. Hogg,et al.  T-cell integrins: more than just sticking points , 2003, Journal of Cell Science.

[25]  P. Kantoff,et al.  Prognostic model for predicting survival in men with hormone-refractory metastatic prostate cancer. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[26]  N. Hogg,et al.  The role of the integrin LFA‐1 in T‐lymphocyte migration , 2007, Immunological reviews.

[27]  J. Vermunt,et al.  Technical Guide for Latent GOLD Choice 4 . 0 : Basic and Advanced 1 , 2006 .

[28]  M Schemper,et al.  A note on quantifying follow-up in studies of failure time. , 1996, Controlled clinical trials.

[29]  Jun Ma,et al.  Gene profiling identifies secreted protein transcripts from peripheral blood cells in coronary artery disease. , 2003, Journal of molecular and cellular cardiology.

[30]  Fiona Steele,et al.  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 , 2003, Lifetime data analysis.

[31]  Jay Magidson,et al.  Qualitative variance, entropy, and correlation ratios for nominal dependent variables , 1981 .

[32]  Jay Magidson,et al.  The Role of Proxy Genes in Predictive Models: An Application to Early Detection of Prostate Cancer , 2010 .

[33]  E. Basch,et al.  End points and outcomes in castration-resistant prostate cancer: from clinical trials to clinical practice. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[34]  W. Berry,et al.  Prognostic factors in metastatic and hormonally unresponsive carcinoma of the prostate , 1979, Cancer.

[35]  P. Kantoff,et al.  Elevated plasma tissue inhibitor of metalloproteinase‐1 levels predict decreased survival in castration‐resistant prostate cancer patients , 2011, Cancer.

[36]  Blaise Hanczar,et al.  Feature construction from synergic pairs to improve microarray-based classification , 2007, Bioinform..

[37]  D. Alcorta,et al.  Microarray Studies of Gene Expression in Circulating Leukocytes in Kidney Diseases , 2002, Nephron Experimental Nephrology.

[38]  Kevin Regan,et al.  Nomogram for overall survival of patients with progressive metastatic prostate cancer after castration. , 2002, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[39]  R. Rosenfeld Patients , 2012, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.