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Tumor Biology |
Cancer Research Program, Garvan Institute of Medical Research, St. Vincent’s Hospital, Darlinghurst, Sydney, New South Wales 2010, Australia [S. M. H., L. G. H., D. I. Q., K. K. R., J. G. K., M. G-G., R. L. S.]; Genomics Research, Eos Biotechnology, South San Francisco, California [D. E. H. A., J. H., K. G., D. W., D. H. M.]; Department of Medical Oncology (J. J. G.), and Department of Urology, St. Vincent’s Hospital, Darlinghurst, Sydney, New South Wales 2010, Australia [P. D. S.]; Cedars-Sinai Prostate Cancer Center, Los Angeles, California [D. B. A.]; and Genitourinary Oncology Service, Memorial Sloan-Kettering Cancer Center, New York, New York [H. S.]
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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200 probesets showing strongest correlation with relapse were identified as the gene for the putative calcium channel protein, trp-p8, with loss of trp-p8 mRNA expression associated with a significantly shorter time to PSA relapse-free survival. We observed subsequently that trp-p8 is lost in the transition to androgen independence in a prostate cancer xenograft model and in prostate cancer tissue from patients treated preoperatively with antiandrogen therapy, suggesting that trp-p8 is androgen regulated, and its loss may be associated with more advanced disease. The identification of trp-p8 and other proteins implicated in the phosphatidylinositol signal transduction pathway that are associated with prostate cancer outcome, both here and in other published work, suggests an integral role for this pathway in prostate carcinogenesis. Thus, our findings demonstrate that multivariable survival analysis can be applied to gene expression profiles of prostate cancers with censored follow-up data and used to identify molecular markers of prostate cancer relapse with strong predictive power and relevance to the etiology of this disease. |
INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Although the benefits of PSA screening are widely debated, this serum marker remains one of only a few preoperative parameters of prognostic utility. To enhance the predictive value of individual parameters with outcomes, nomograms have been developed that incorporate parameters that are measured routinely in clinical practice to predict the probability of PSA relapse-free survival of individual patients both before and after therapy (2, 3, 4, 5, 6) . Models such as these currently form the basis of routine clinical decision-making, but such classification systems cannot explore differences in outcomes observed between cancers with similar histopathological features. Hence, there remains a critical need for increased accuracy in the subcategorization of prostate cancers to identify those with an aggressive phenotype. One approach is to define patterns of gene expression that correlate with disease phenotype and patient outcome. Here, we undertook a systematic search for novel biomarkers of prostate cancer prognosis by outcome-based analyses of transcript profiles.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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75% invasive cancer were used for subsequent transcript profiling. Only one biopsy per patient was analyzed.
Xenograft Model.
The androgen-dependent LuCaP-35 (7)
prostate cancer xenograft (generously provided by Robert L. Vessella, University of Washington, Seattle, WA) was grown as s.c. tumors in nude male mice. To study the androgen-withdrawal process, tumor-bearing mice were castrated and monitored for tumor regression and PSA levels. Tumors were harvested from mice before castration and at various time points (1–100 days) postcastration, and were processed for microarray analysis. For data analysis and identification of androgen-regulated genes, i.e., genes that behaved similar to PSA, the LuCaP-35 xenografts were binned into two groups (days 0–2 postcastration versus 5–100 days postcastration), because PSA levels were high at days 0–2 and dropped precipitously at day 5 postcastration. Genes that showed a significant (P < 0.01) difference in the means of each group were identified by a standard Student’s t test.
RNA Extraction and Microarray Protocols.
Preparation of total RNA from fresh-frozen prostate and xenograft tissue was performed by extraction with Trizol reagent (Life Technologies, Inc., Gaithersburg, MD) and was reverse transcribed using a primer containing oligodeoxythymidylic acid and a T7 promoter sequence. The resulting cDNAs were then in vitro transcribed in the presence of biotinylated nucleotides (Bio-11-CTP and Bio-16-UTP) using the T7 MEGAscript kit (Ambion, Austin, TX).
The biotinylated targets were hybridized to the Eos Hu03, a customized Affymetrix GeneChip (Affymetrix, Santa Clara, CA) oligonucleotide array comprising 59,619 probesets representing 46,000 unique sequences including both known and FGENESH predicted exons that were based on the first draft of the human genome. The Hu03 probesets consist of perfect match probes only, most probesets having 6 or 7 probes. Hybridization signals were visualized using phycoerythrin-conjugated streptavidin (Molecular Probes, Eugene, OR).
Normalization of the gene expression data was performed as follows. The probe-level intensity data from each array were fitted to a fixed 
distribution, using an inverse function to map the empirical cumulative distribution of intensities to the desired distribution. This procedure is akin to other per-chip normalization procedures, such as fixing the mean and SD of each chip to a standard value, except it is more stringent in that it fixes the entire distribution rather than one or two parameters. The purpose of per-chip normalization is to remove between-chip variation, on the assumption that it is attributable to nonbiological factors, i.e., technical noise. The scale parameter for the distribution was chosen to yield a distribution with an arbitrary mean value of 300, and the shape parameter of 0.81 was chosen to reproduce the typical shape of the empirical distribution seen in good samples.
A single measure of average intensity was calculated for each probeset using Tukey’s trimean of the intensity of the constituent probes (8) . The trimean is a measure of central tendency that is resistant to the effects of outliers. Finally, a background subtraction was applied to each average intensity measure to correct for nonspecific hybridization. The average intensity measure of a "null" probeset consisting of 491 probes with scrambled sequence was subtracted from all of the other probesets on the chip.
Statistical Methods.
Before survival analysis, a screen was applied to the expression data to eliminate probesets without meaningful variation. For each probeset, the ratio of the 90th percentile to the 15th percentile intensity measure was required to be at least 2, and the minimum expression level was required to be at least 150 average intensity units. This screen reduced the initial set of 59,619 probesets to a subset of 8,521 probesets for additional examination. Cox proportional hazards analyses with pretreatment PSA concentration dichotomized at 20 ng/ml and gene expression modeled as a continuous variable were computed for each probeset, to identify gene expression that predicts PSA recurrence (9)
. To assist interpretation, we next calculated the IQR HR for each probeset. Because the expression data are treated here as continuous covariates, HRs expressed in their natural scale illustrate only the change in risk of relapse associated with a change of 1 unit on the expression scale, a change too small to be comprehended easily. To put the HRs and associated confidence limits on a more interpretable scale, we present here the HR associated with a change in expression values equivalent to 1 IQR of the sample data for each probeset. The IQR is simply the 75th percentile minus the 25th percentile, and thus contains the middle 50 percent of observations. The IQR HR was computed by multiplying the regression coefficient for each probeset by its own IQR before exponentiation.
The multiple hypothesis testing problem has been recognized as an important issue to address in microarray research. The many tests that are performed simultaneously on thousands of probesets greatly increases the chances of making type 1 errors (or false-positive findings). To assess the effect of multiple hypothesis testing, we adapted a method developed by Storey and Tibshirani (10) for calculating the pFDR, an estimate of the proportion of false positives present in a set of findings. This technique was developed explicitly for use with microarray data, for which the usual assumption of independence among tests is untenable. The procedure involves simulation of null data by randomly permutating the relapse status of subjects and reperforming the survival analyses. In each simulation, the number of relapsers and nonrelapsers (17 and 55, respectively) remains constant, but these designations are shuffled and assigned to patients at random. By performing the permutation many times and noting the number of findings at a given level of significance each time, an estimate of the expected number of false positives under null conditions is obtained. This figure is then divided by the number of actual findings to obtain an estimate of the proportion of false-positive findings.
Variables of clinical relevance were also modeled in univariate analyses for their ability to predict disease-free survival in the 72 prostate cancers using the Cox proportional hazards model. Trp-p8 mRNA expression assessed by ISH, was reported as proportions within histological groups and compared between groups using a Fisher’s Exact test.
Cluster analysis was used to explore and visualize the findings from the survival analyses. The distance metric used was the square root of (1 - r), where r is the standard Pearson product-moment correlation, and the clustering algorithm used was Ward’s minimum variance method (11) .
All of the statistical analyses were performed using SAS (SAS Institute Inc., Cary, NC).
Tissue Microarray and ISH.
Tissue microarrays were constructed as described previously (12)
, and were comprised of prostate cancer samples from 95 patients that are part of a previously published cohort of patients treated for localized prostate cancer by RP alone at St. Vincent’s Hospital (13)
. Forty-seven of the 95 prostate cancer samples were independent of the cohort used for microarray analysis. In addition, 13 prostate cancer specimens were collected from patients treated for localized prostate cancer by RP who had received at least 3 months (range, 3–10 months) of preoperative neoadjuvant hormonal treatment (5 with antiandrogens alone, 6 with a combination of a Gn-RH analogue and antiandrogens and 2 with a Gn-RH analogue alone). Trp-p8 expression in these 13 samples was assessed on conventional tissue sections.
For ISH, a 424-bp probe for trp-p8 was derived from the 3' end of the trp-p8 gene and transcribed to produce a DIG-labeled riboprobe using an RNA DIG-labeling kit (Roche, Mannheim, Germany). ISH was performed on the Ventana Discovery instrument (Ventana Medical Systems, Tucson, AZ) using the RiboMap kit with protease P2 for 2 min (Ventana Medical Systems) and hybridization for 8 h at 65°C. Chromogenic detection was achieved with the BlueMap detection system as described by the manufacturer (Ventana Medical Systems). Independent readings of the pathology and the ISH measurements were performed.
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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). The overall relapse rate of 23.6% and median time to relapse of 14 months in this group of 72 patients was similar to that observed in a cohort of 732 patients treated for localized prostate cancer by RP at the same institution between 1986 and 1999 (13)
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The pFDR (10)
was calculated to estimate the number of false positives present among the 266 findings obtained at the selected 0.01 significance level. On the basis of 500 random permutations of the class labels, the final estimate for the pFDR was 23%. Thus, we can expect that 61 of the 266 findings are false positives.
Another way to address the issue of significance across the entire dataset is to estimate the likelihood of obtaining 266 findings significant at P < 0.01. This can be accomplished using the 500 permuted simulations described above. Among the 500 permutations, only 20, or 4%, had 266 or more findings significant at P < 0.01. Thus, under simulated null conditions, it is estimated that the probability of finding at least 266 differentially expressed probesets is 4%.
Identification of the Candidate Marker Genes.
The 266 probesets identified by survival analysis included both known genes and hypothetical genes of unknown function, as well as ESTs (Table 2
; Supplementary Data). Cluster analysis performed in both dimensions on the 72 RP samples and these 266 probesets using Ward’s minimum variance method (11)
identified two gene expression subgroups (Fig. 1)
. Sixteen of the 17 patients known to have experienced a PSA relapse were clustered in one gene expression group characterized by a relative increase in expression of 85 genes (cluster 1) and loss of expression of 181 genes (cluster 2; Fig. 1
). An additional 22 patients that were disease-free at the time of censoring were located in this expression cluster, and may suggest that these patients have an increased propensity for relapse in the future. Thirty-two patients who were disease-free at the time of censoring constituted the second expression group, which also included 1 patient who had experienced a PSA relapse.
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200 probesets showing strongest correlation with relapse in our model were identified as the gene for the putative Ca2+ channel protein, trp-p8 (Table 2
; Supplementary Data; Ref. 14
). For all three of the probesets, loss of expression of trp-p8 mRNA was associated with a significantly shorter time to PSA relapse-free survival. The trp-p8 probeset having the strongest association with relapse had an IQR HR of 0.26 (95% confidence interval, 0.12–0.54; P < 0.001), when PSA was included in the analysis (Fig. 2)
. To describe the magnitude of this effect, consider two hypothetical patients with identical PSA levels, but expression of trp-p8 as measured by this probeset differing by 118 expression units (which is the IQR for this probeset). The patient with lower trp-p8 expression would have an 4-fold increased risk of relapse. Loss of trp-p8 remained a significant predictor of PSA relapse even when controlling for pretreatment PSA, Gleason score, and clinical stage simultaneously in a multivariate Cox proportional hazards model (P = 0.0008). Additional analysis of trp-p8 was pursued subsequently, as this was the only gene within the top 10 ranked by P of which the expression was primarily restricted to the prostate (Fig. 3A)
. Low-level expression was detected in normal liver, and no detectable expression was seen in 32 distinct other normal tissues examined by oligonucleotide microarray analysis (Fig. 3A)
. These data confirm the findings of a recent study that also showed that trp-p8 expression was prostate-specific (14)
. Analysis of 23 cancer cell lines showed that trp-p8 is only expressed at very low levels in the androgen-dependent prostate cancer cell line LNCaP, but not in the androgen-independent prostate cancer cell lines, PC-3 and DU-145 (data not shown), consistent with previous data (14)
. Because this observation alone is not conclusive evidence that trp-p8 expression is androgen regulated, we next used the androgen-dependent LuCaP-35 prostate cancer xenograft model to assess changes in trp-p8 expression that occur during transition from androgen dependence to androgen independence of prostate cancer (7)
. Male LuCaP-35 mice were castrated, and tumors were harvested at several time points (0–100 days) after castration. High levels of trp-p8 expression were detected on days 0–2 after castration, but not on days 5–100 after castration, and correlated significantly with PSA expression in the same mice (Pearson P = 0.80; Fig. 3, B and C
). Because the mRNA expression of housekeeping genes such as ß-actin and glyceraldehyde-3-phosphate dehydrogenase showed only minor changes throughout the experiment, these data suggest that trp-p8 may be an androgen-regulated gene.
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. These latter patients formed part of a large RP cohort described previously (13)
. Whereas trp-p8 mRNA was detected in 80 of 95 (84%) prostate cancers from patients treated with RP alone, those patients who underwent NHT before RP demonstrated significantly less expression of trp-p8, with only 4 of 13 (31%) samples positive for trp-p8 mRNA (Fisher’s exact test, P < 0.001; Fig. 4
). Taken together, these data from prostate cancer xenografts and clinical specimens are consistent with trp-p8 being an androgen-regulated gene. We will now use a monoclonal antibody to trp-p8 to assess protein expression by immunohistochemistry in an independent cohort of formalin-fixed, paraffin-embedded prostate cancer specimens with known prostate cancer outcome that includes patients treated with antiandrogens before RP (13)
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200 probesets that were significantly associated with relapse in the study presented here. |
DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Here we describe for the first time the application of classical multivariable survival analysis to a prostate cancer microarray dataset incorporating the expression profiles of >46,000 genes to identify markers of disease outcome. This technique provides several significant advances over previous methods of analyses that have been used to discover markers of disease outcome from microarray data. In contrast to statistical methods described previously that rely on the classification of tumors based on known outcome (17)
or known classifiers of patient outcome (e.g., estrogen receptor status; Refs. 18
, 19
), this technique provides for censored data. This enables these analyses to proceed before the occurrence of all of the events, in this case, PSA relapse. Moreover, this survival analysis incorporates the time taken to PSA relapse and may also include covariates (e.g., preoperative serum PSA levels) to identify genes that provide additional predictive value above conventional markers of outcome. The statistical analyses described herein have also incorporated a stringent method of estimating the pFDR that was described recently (10)
. This method is designed specifically for the analysis of microarray data where general dependence between hypotheses or "clumpy dependence" exists, where 50 genes interact in common pathways to produce some overall process (10)
. However, this is, to our knowledge, the first instance that it has been applied to microarray data from a survival analysis.
There is a discrepancy between our results and those reported by Singh et al. (15)
: in the previous study, no probesets were significantly associated with relapse in univariate analyses, whereas we identified 200 probesets that predicted prostate cancer relapse, when pretreatment PSA levels were controlled. None of the probesets we identified overlapped with the 11 probesets used by Singh et al. (15)
in their multivariate classifiers. This discrepancy may be because of several factors. First, we used the custom-designed Eos Hu03 Affymetrix array, rather than the Affymetrix U95Av2 arrays used in the previous study. The Eos Hu03 arrays have approximately five times as many probesets as the U95Av2 arrays; trp-p8 is an example of a gene that was not present in the oligonucleotide array used in the previous study. Also, even the probesets thought to measure the same gene on each array are often substantially different, in terms of the specific probe sequences and also the number of probes representing each probeset. Second, by using a statistical method that applies to censored data, we were able to take into account the various times to prostate cancer relapse in this model. Therefore, we were able to use our full data set in the analysis, rather than restricting the analysis to those patients with a specified length of follow-up. The larger data set and concomitant increase in statistical power may also contribute to our results differing from those of Singh et al. (15)
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We have noted with interest the potential functional link between trp-p8 and calnexin identified in this study, and chromogranin A and IP3R identified by Singh et al. (15)
, because TRP channels are linked to the phosphatidylinositol signal transduction pathway (20)
. The TRP channels are subunits with six membrane-spanning domains with both COOH and NH2 termini located intracellularly that probably form into tetramers to form nonselective cationic channels, which allow for the influx of Ca2+ ions into the cell. Trp-p8 or TRPM8 is a member of the TRPM (Transient Receptor Potential, Melastatin) subfamily of TRP ion channels that have potential roles in Ca2+-dependent signaling, and control of cell proliferation, cell division, and cell migration (Fig. 5)
. Ligand binding to some membrane receptors initiates a sequence of events that lead to the activation of phospholipase C, generating inositol-1,4,5-triphosphate, which opens the intracellular ion channel IP3R and liberates Ca2+ from the endoplasmic reticulum. Activation of the TRP channels accompanies this chain of events, allowing the influx of Ca2+ ions into the cells, although their activation is not necessarily directly linked to Ca2+ depletion from internal stores (20)
. Chromogranin A, which is contained in large amounts in the secretory granules of some cell types, is believed to complex with the IP3R resulting in a conformational change that promotes inositol-1,4,5-triphosphate binding and subsequent Ca2+ release. Serum chromogranin A concentration has also been shown to be associated with poor prognosis after endocrine therapy for prostate cancer (21)
. Calnexin, also identified in this analysis as a marker of potential prognostic utility (P = 0.004), is believed to be a key chaperone involved in the folding, assembly, and oligomerization of newly synthesized IP3R receptors (22)
. Thus, both studies implicate an important role for this pathway in prostate cancer progression.
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In summary, our analyses have identified a statistically significant group of genes that strongly correlate with prostate cancer relapse and contribute unique information to relapse prediction above preoperative PSA. The annotation and clinical validation of these genes will likely identify prognostic indicators of clinical utility in identifying prostate cancer patients with significant disease as well as potential targets for therapeutic intervention. Moreover, the functional characterization of genes that cosegregate with phenotype and disease progression is expected to lead to a much deeper understanding of the underlying biology of prostate cancer.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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1 Supported by grants from the National Health and Medical Research Council of Australia, The Cancer Council New South Wales, the R. T. Hall Trust, Freedman Foundation, Royal Australasian College of Surgeons, Australasian Urological Foundation, Prostate Cancer Foundation of Australia, and David Wilson Trust. 
2 Present address: Protein Design Labs Inc., Fremont, CA 94555.
3 Supplementary data are submitted for review as part of this manuscript.
4 To whom correspondence should be addressed, at Cancer Research Program, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, New South Wales, 2010 Australia. Phone: 612-9295-8322; Fax: 612-9295-8321; E-mail: r.sutherland{at}garvan.org.au
5 The abbreviations: PSA, prostate-specific antigen; Ca2+, calcium; HR, hazard ratio; IQR, interquartile range; IP3R, inositol triphosphate receptor; ISH, in situ hybridization; NHT, neoadjuvant hormone therapy; pFDR, positive false discovery rate; RP, radical prostatectomy; EST, expressed sequence tag; Gn-RH, gonadotrophin-releasing hormone; DIG, digoxigenin.
Received 10/25/02. Accepted 5/15/03.
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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0.001) with prostate cancer relapse in analyses using dichotomous preoperative serum PSA levels as a covariate. The survival curve represents the predicted survivorship function for a hypothetical patient who is PSA positive, and has a probeset intensity measure exactly at the 25th percentile of the observed data or at the 75th percentile. The survival curve indicates that loss of trp-p8 expression is associated with an increased risk of PSA relapse.
