BACKGROUND
prostate cancer is the second leading cause of cancer illness and death among men in the united states and the third most common cancer world wide  <cit> .
according to recent estimates, it accounts for 33% percent of new cancer incidences and six percent of cancer deaths in men world wide  <cit> .
in  <dig>  the number of new incidences and deaths in the united states was approximately  <dig>  and  <dig> , respectively  <cit> .
the difficulty lies, at least partly, in the heterogeneous nature of the disease.
tumor growth is initially dependent on androgen levels, which stimulate cell proliferation and inhibit apoptosis via the androgen receptor  pathway.
the prostate-specific antigen  level has been a standard screening for early diagnosis; androgen ablation is a prevalent therapy to repress the development of androgen-dependent tumors.
however, in many cases, this therapy eventually fails and patients die of the recurrent androgen independent prostate cancer , a lethal form that progresses and metastasizes .
multiple pathways permit cancer cells to escape or bypass the control of the normal ar activation to up-regulate target genes abnormally  <cit> .
although it has been reported that a number of genes are related to these pathways as well as other aspects of prostate cancer, there is still an urgent need for good biomarkers for early clinical diagnosis and treatment.
microarray technologies developed in the last decade permit monitoring of mrna abundance levels of tens of thousands of genes in parallel.
the accuracy improvement and cost reduction have made them a routine approach in looking for genes that are differentially expressed between normal and tumor samples or between different tumor types/stages  <cit> .
in a recent study, segal et al.
summarized ~ <dig> array experiments and derived a panoramic view of activated/deactivated gene expression modules for various types of tumors  <cit> .
microarrays have also been employed in prostate cancer studies.
using cdna arrays, dhanasekaran et al.
measured gene expression in  <dig> normal and neoplastic prostate specimens, as well as three prostate-cancer cell lines, and identified gene signatures characterizing androgen-dependent and aipc samples  <cit> .
nelson et al.
<cit>  and deprimo et al.
<cit>  studied gene expression in the androgen treated lncap cell line, which was known to be highly androgen responsive.
lapointe et al.
profiled  <dig> primary tumors and  <dig> normal specimens; three subclasses of tumors representing different tumor stages and risks of recurrence were obtained along with characteristic expression signatures  <cit> .
these studies demonstrated the potential of using microarray analyses in characterizing prostate cancer at the gene expression level.
while transcriptional regulation plays important roles within a cell, post-transcriptional regulation, such as alternative splicing, dramatically increases the diversity of the proteome.
alternative splicing also plays a critical role in gene expression regulation and human diseases  <cit> .
it has been reported that about 15% of point mutations that cause human genetic diseases can alter splicing patterns  <cit> .
in particular, splicing aberrations have been characterized in a number of genes and tumor types .
in a previous work, we developed a microarray-based assay called rasl™ , which can systematically monitor the abundances of unique splicing events  <cit> .
a modified version of the assay, the dasl®  assay, offers additional robustness for analyzing highly degraded mrnas, as well as an additional flexibility in probe design  <cit> .
different from other exon-junction arrays  <cit> , the dasl assay achieves high specificity and sensitivity due to the fact that both hybridization and ligation of a pair of oligos complementary to the 5' splice site of the upstream exon and the 3' splice site of the downstream exon are required .
in our recent study, this technology was applied to profile the abundances of ~ <dig> unique splice isoforms in prostate cancer cell lines, tumor specimens and normal control samples  <cit> .
this previous study led to two implications:  the splicing patterns were altered in a number of genes in response to androgen treatment in the lncap cell line;  a number of splice isoforms were differentially expressed in tumor samples.
they prioritized a list of prostate cancer marker candidates for further investigations.
in this study, we extend our previous work and perform a comprehensive analysis of using alternatively spliced isoforms to classify prostate cancer samples.
compared with our previous work, the focus of this study is to quantitatively compare isoform profiling and overall mrna profiling for cancer classification, which has not been systematically investigated before.
to be more specific, the contribution of this study lies in four key aspects:  isoform-sensitive microarrays studies have been assumed to be able to provide more information for cancer classification than conventional microarray studies because isoform abundances couple both transcriptional regulation and splicing regulation.
however, it has remained unclear how much unique information could be provided by isoform profiling.
in this paper, this assumption is examined qualitatively for the first time through differential expression analysis.
further examinations for several genes are also described.
as in a number of other microarray studies , hierarchical clustering has been used to segregate similar tissues.
this approach was not able to obtain an unbiased estimation of the predictive power for new unknown samples.
to assess the predictive power of isoform profiling and that of overall mrna profiling, a support vector machine with recursive feature elimination  was employed to build prediction models and the prediction accuracies were compared.
building a prediction model with a minimal subset of variables is one of the critical tasks in cancer classification.
we compared two different variable selection methods for sample classification and examined whether the robustness of prediction can be improved by taking the correlation among isoforms into account during variable selection.
in our previous study, two smaller datasets generated in different batches were analyzed separately.
the two lists of candidate markers selected from the two datasets had a relatively small overlap.
to achieve more robust results, all analyses in this study were based on the larger combined dataset after careful normalizations.
RESULTS
in our previous work  <cit> , the two datasets of prostate tumors and normal samples were analyzed separately by hierarchical clustering because they were generated in two different batches and there were significant heterogeneities between them .
in both datasets, splice isoforms could be used to separate tumor samples and normal samples.
however, the sample size in each dataset was limited and the overlap between the two lists of differentially expressed isoforms selected from the two datasets was relatively small.
in this paper, the two datasets were combined after careful normalizations to achieve more robust results and statistical power .
the combined datasets included  <dig> cases of prostate tumors and  <dig> matched normal samples.
splice isoforms reveal distinct signatures of prostate cancer
we first examined whether the global distinction between tumors and normal samples still exists in the combined dataset by unsupervised methods.
as expected, tumors can be readily separated from normal samples by average-linkage hierarchical clustering   <cit> .
compared with cluster c <dig>  the majority of tissues in cluster c <dig> are normal prostate and stroma, with the average tumor percentage being  <dig> % , and stromal percentage being  <dig> % .
of the three tumors segregated with normal samples in cluster c <dig>  two have low tumor content.
additional analysis reveals that c <dig> cases in general have a significantly higher percentage of more advanced stages  and more patients die of prostate cancer compared to c <dig> cases.
specifically, 100% of the cases in c <dig> were from patients with organ confined tumors , whereas 50% of the cases in c <dig> were from metastasized patients .
at the time of analysis, none of the c <dig> patients died of prostate cancer while14% of the c <dig> patients died of prostate cancer.
interestingly, the cluster c <dig> enriched by tumors was further segregated into two sub-clusters, reflecting different percentage in tumor and stromal content .
singular value decomposition  was used to identify an orthogonal low dimensional space which preserves the maximal variation of the original high dimensional space.
the first two principal components capture 17% and 9% of the total variation, respectively .
remarkably, the first principal component alone shows a strong separation of tumor and normal samples.
the clusters and sub-clusters derived from hierarchical clustering are also reflected in the 3d space spanned by the first three principal components , which confirms the results of clustering.
further examination of the gene clustering results shows distinct molecular signatures of different tissue clusters, including both well known marker genes and less studied marker candidates .
figure 1c shows isoforms up-regulated in cluster tumor sub-cluster c <dig> , including isoforms from genes rps <dig>  xbp <dig>  u1af <dig> and atp5a <dig>  all of which were known to be up-regulated in tumors.
figure 1d shows isoforms down-regulated in normal tissues and up-regulated in tumor tissues, including isoforms from genes u2af <dig>  cln <dig> and hpn.
figure 1e shows isoforms with high expression levels in normal tissues and down-regulated in tumor tissues, especially in sub-cluster c <dig> .
several genes in this cluster are known to be involved in the tgf-beta signaling pathway, such as tgfb <dig>  ltbp <dig> and tgfbr <dig>
differentially expressed splice isoforms
a two sided t-test was used to identify genes with statistically significant changes in expression between tumors and normal samples.
a false discovery rate  or q-value was calculated as described previously  <cit> , to correct for multiple testing.
as a result,  <dig> isoforms  representing  <dig> genes  are reported as being significant  .
the high proportion of differentially expressed isoforms reflects the fact that the genes profiled are potentially related to prostate cancer according to existing evidence.
top isoforms among them include amacr- <dig>  fgfr2- <dig>  fgfr2- <dig>  fgfr2- <dig>  clu- <dig>  pgr- <dig>  etc.
profiling of splice isoforms provides additional information to overall mrna abundances
in theory, profiling individual splice isoforms can provide more information than profiling overall mrna levels as in conventional microarrays.
this is because isoform profiling detects the combinatorial effects of both transcriptional regulation and splicing regulation.
consider the simplest case of a gene with two alternatively spliced isoforms.
if one isoform is up-regulated in tumors whereas the other is down-regulated, the overall mrna abundance may not change.
on the contrary, if the overall mrna level is differentially expressed, there is at least one isoform exhibiting differential expression.
however, how much additional information can be obtained for cancer classification by isoform profiling has not been systematically evaluated.
to address this question, we compared individual isoforms and overall mrnas for differential expression.
due to the costs and array capacity, the original array design did not include probes targeting common regions of all isoforms.
therefore, the overall mrna expression level can not be obtained directly.
however, since the probed exon junctions target unique major isoforms and hybridization efficiencies of different probes are comparable  <cit> , we reason that the overall expression level can be estimated by summing up the abundances of individual isoforms.
to examine the validity of this idea, two well-known prostate cancer cell lines lncap and pc- <dig> were profiled using the same dasl assay .
for comparison,  <dig> genes were arbitrarily selected for gene expression profiling in the same cell lines .
an independent oligo pool targeting common regions of all isoforms in each of the  <dig> genes were used in the expression array.
therefore, the log expression ratio of each gene in the two cell lines can be obtained from the estimation based on the splicing array and from the direct measurement in the expression array independently.
to our satisfaction, the two quantities are highly correlated , suggesting a reasonable accuracy of the estimation .
having validated the approach, the overall mrna abundances of each gene in prostate tissues were estimated.
a t-test was similarly applied to identify genes with significant differential expression in tumors at the overall mrna level.
in total,  <dig> genes  are reported as being significant .
again, the high proportion of significant genes reflects the fact that they are potentially relevant to prostate cancer according to previous studies.
strikingly, more genes are called significant by examining individual isoforms than by examining overall mrnas .
among the  <dig> genes that are called significant,  <dig> genes  have at least one isoform that is reported as significant .
in contrast, only 68% of genes with significant isoforms can be detected at the overall mrna level.
the remaining 32% of the genes have significant isoforms but do not exhibit significant differential expression at the overall mrna level.
it is important to note that these genes represent the unique information that is provided by splice isoform sensitive microarrays and cannot be obtained from conventional microarrays.
from the perspective of isoforms, 78% of significant isoforms are from those genes that are also called significant whereas 22% of significant isoforms are from those genes that do not show overall mrna differential expression  .
multiple testing has been appropriately accounted for, so the additional significant calls using splice isoforms are not due to the different stringencies of thresholds, but reflect additional information provided by including splicing regulation.
for many genes, only one isoform is specifically altered in tumors.
in these cases, the addition of other isoforms to the total mrna level simply introduces random noise.
notably, there are  <dig> genes with one isoform being up-regulated in tumors and another isoform being down-regulated.
among them,  <dig> genes are not significant at the overall mrna level: cd <dig> , itgb <dig>  and mapt .
cd <dig> is a multifunctional receptor involved in cell-cell interactions and cell trafficking.
deregulated expression of a number of variants is correlated with tumor metastasis .
itgb <dig> is a protein involved in extra-cellular matrix interactions and is also related to many tumor types, including prostate cancer  <cit> .
there are relatively fewer studies discussing the role of mapt in cancer.
mapt encodes the microtubule-associated protein tau mainly expressed in the central nervous system.
mutations in the mapt gene disrupt the normal binding of tau to tubulin.
this in turn results in pathological deposits of hyperphosphorylated tau in the brain, which is a pathological hallmark of several neurodegenerative disorders .
previously, sangrajrang et al.
found that mapt was also expressed in the du <dig> cell line using rt-pcr and the expression at the protein level was validated by western blotting  <cit> .
the expression was elevated after estramustine treatment and the authors suggested that the protein may be positively related to drug resistance.
this was consistent with a recent report demonstrating that the up-regulation of the protein tau was correlated to the decrease of paclitaxel sensitivity in breast cancer  <cit> .
in our data, mapt- <dig>  has a two fold increase in tumors relative to normal tissues, whereas mapt- <dig>  has a two fold decrease in tumors relative to normal tissues .
it is likely that exon 4a is uniquely skipped in prostate cancer cells.
this hypothesis is further supported by the following evidence.
exon 4a harbors a c/t single nucleotide polymorphism  near the 5' splice site .
this snp was assayed from  <dig> individuals and the c/t ratio is  <dig> / <dig> .
in the major c allele, a putative exonic splicing enhancer  cagccgg encompassing the snp is predicted by esefinder and resembles the specific rna binding site of sf2/asf, a critical serine rich  protein that helps to recruit the splicing apparatus   <cit> .
this putative ese is disrupted in the minor t allele for all four sr proteins in esefinder including sf2/asf, sc <dig>  srp <dig> and srp <dig>  however, further experimental studies and confirmation of the splicing alteration may be required to validate this hypothesis.
profiling of splice isoforms improves predictive power
a robust prediction model to classify unknown samples is essential for early cancer detection and diagnosis.
having demonstrated that a large fraction of genes show differential expression at the splice isoform level but not at the overall mrna level, a key question is how much additional predictive power can be achieved by isoform profiling.
another related problem is to select minimal subsets of variables with the best performance.
like many other types of tumors, a single molecular marker is usually not robust enough for prostate cancer detection, as is the case for the widely used psa level for early stage screening.
at the other extreme, including all variables from a genome-wide profiling is not justifiable either, due to the noise introduced by a huge number of uninformative variables and the difficulty in the interpretation of the resulting model.
a support vector machine  was used here to build the classifier because of its excellent performance in many previous studies with small sample sizes  <cit> .
an recursive feature elimination  algorithm was integrated as described previously with minor adaptations  <cit> .
leave-one-out cross validation  with external variable selection was used to give an unbiased evaluation of the prediction accuracy .
svm-classifiers were built using the individual splice isoforms and estimated overall mrna abundances.
the results of loocv are shown in figure 3a.
for the classifiers using isoform abundances, the best performance,  <dig> correct predictions out of  <dig> samples , is achieved when  <dig> isoforms are included for classification.
for the classifiers using overall mrna abundances, the best performance  is achieved when  <dig> genes are used.
the additional information provided by splicing regulation gives rise to an improvement of about 5% in predictive power.
importantly, the difference persists in the whole range of different sizes of selected variable subsets, which is unlikely by random chance.
with an independent method, this demonstrates that isoform profiling can provide valuable information for cancer classification.
also, the classification performance deteriorates when the subset of selected variables is too small in size .
this is consistent with the previous observation that a robust cancer prediction model should use a reasonable number of molecular signatures  <cit> .
comparison of different variable selection methods
both t-tests and svm-rfe can generate lists of candidate markers.
these two approaches represent univariate variable selection and multivariate variable selection, respectively.
they have different assumptions and may characterize different yet overlapping perspectives of the molecular mechanisms underlying the data.
for example, variables are assumed to be independent in a t-test but there is no assumption of independence in svm-rfe.
comparing the multiple outputs of selected signatures by different methods may shed further insights into the data and the methods.
therefore, the two different variable selection approaches, t-test and svm-rfe, were applied to select marker candidates and their performances in building linear svm models were compared.
the results of loocv are shown in figure 3b.
the best performance of t-test selection is achieved with a similar number of variables as svm-rfe.
both methods result in an accuracy of 92%.
the similar best performance by t-test and svm-rfe is likely due to the distinct features of tumors and normal tissues.
the information to classify the two groups is largely redundant.
however, the curve of prediction accuracy by the svm-rfe selection is smoother than that by the t-test selection as the size of selected variable subset decreases.
this smaller variation suggests that svm-rfe is more robust than t-test in variable selection for cancer classification.
the  <dig> isoforms selected by t-test  and the  <dig> isoforms selected by svm-rfe  share  <dig> isoforms .
the common list includes amacr- <dig>  amacr- <dig>  amacr- <dig>  fgfr2- <dig>  fgfr2- <dig>  pgr- <dig> and pgr- <dig> among others.
they may represent robust marker candidates.
significant isoforms in each list were further divided into two groups according to whether the corresponding genes also exhibit significant differential expression at the overall mrna level.
interestingly, among those  <dig> isoforms included only in the svm <dig> list,  <dig> of the isoforms are in the category that the corresponding genes do not show significant differential expression at the overall mrna level.
in contrast, among the  <dig> isoforms included only in the t-test <dig> list, only  <dig> isoforms lie in this category.
therefore, svm-rfe captures more information uniquely provided by considering splicing regulation .
this demonstrates the advantage of a variable selection method taking the correlation between variables into account.
discussion
the diagnosis and treatment of prostate cancer are fields with long histories.
various efforts have led to the progressive understanding of the disease.
however, the present criteria of diagnosis and prognosis, as well as the approaches of treatment and surgery, are not sufficiently reliable.
previous gene expression profiling studies on prostate tumors and normal tissues demonstrated the feasibility in characterizing the molecular alterations at the overall mrna transcript level.
however, these transcriptome analyses were based on the old central dogma of "one gene, one mrna", which may underestimate the complexity of tumorigenesis  <cit> .
previously, we carried out a study of prostate cancer by exon-junction microarray-based assay and demonstrated the power of this integrated technology in detecting both transcriptional and splicing regulation  <cit> .
in this paper, we present systematic analyses with the focus on using splice isoform profiling for prostate cancer classification.
isoform-sensitive microarrays have been used in several recent studies  <cit>  .
these studies demonstrated that isoform-sensitive microarray is a reliable, high throughput approach to detecting splicing alterations in various tissues and conditions.
although more and more data are expected to be generated in the near future, the dataset used in this study is the only dataset currently available which screened a relatively large sample of cancer and normal tissues.
as far as we know, this is the first systematic comparison of isoform-sensitive microarrays and conventional microarrays for cancer classification.
previous studies have used a "splice index", which is the fraction of each isoform, to remove the effect of transcriptional regulation  <cit> .
this is not desired for cancer classification because as much information as possible should be incorporated.
therefore the abundance of each isoform, which couples both transcriptional regulation and splicing regulation, was used for classification.
the performance was compared with that of using overall mrna abundances.
one has to note a caveat of the current dasl assay: it does not include probes complementary to the common regions of all mrna transcripts for each gene due to the current limit in array capacity.
therefore, the overall mrna level was estimated indirectly by summing up all the isoforms targeted.
the estimation is not ideal due to the fact that not all isoforms were included in the array and the probes target splicing events that are not mutually exclusive in several cases.
however, the estimation is reasonably good and highly correlated with the direct measurement by an expression array.
various other methods were tried to estimate the overall mrna abundances, but the method used here is the most accurate and simplest.
among the ~ <dig> isoforms from putative prostate cancer-related genes, a large fraction of them exhibit differential expression in cancer cells.
tumors and normal tissues can be readily separated by both unsupervised and supervised methods.
by comparing individual isoforms and overall mrnas for differential expression, we arrived at the conclusion that an isoform-sensitive microarray, which detects coupled transcription and splicing regulation, can provide about 30% more information than conventional microarrays.
this value may still be underestimated due to the following reasons.
the current dasl assay included only  <dig> genes potentially relevant with prostate cancer derived from previous studies.
till now, a large body of literature, especially those in the genomic scale, focused more on transcriptional regulation.
therefore, the selection of genes may be biased to those exhibiting aberrant transcriptional regulation.
the optimal prediction model was built by svm with variable selection integrated, a powerful machine learning approach.
with around  <dig> isoforms, the best classification performance can be achieved at a correct prediction rate of 92%.
compared with the optimal svm classifier built with overall mrna abundances, this represents an improvement of five percent.
therefore, both differential expression analysis and classification analysis quantitatively demonstrated the advantage of isoform-sensitive microarrays.
we also compared the effect of different variable selection approaches on classification performance.
by taking the correlation between isoforms into account, isoforms selected by svm-rfe are more robust for classification than isoforms selected by a t-test.
although univariate two-sample comparisons such as t-test are widely used to identify differentially expressed genes, the assumption of independence between genes or isoforms is not biologically justifiable.
in cancer signal transduction pathways, a group of genes in the same pathway are interacting with each other; cross-talks often exist between pathways as well .
variables are more convoluted in the dasl data due to the coupling of transcription and splicing.
the multi-loci nature of the disease also makes it difficult to use a single or few molecular markers to build a sufficiently robust prediction model.
this study identified a number of known prostate cancer markers as well as less studied marker candidates, which span a wide spectrum of biological functional roles.
some are related to signal transduction , as well as extracellular matrix and cytoskeleton .
others appear to be involved in epidermal differentiation and proliferation , cell growth and development , apoptosis , lipid metabolism , etc.
very significantly, multiple isoforms from amacr, a key player in catalyzing the isomerization of alpha-methyl-branched fatty acid and a recently reported good prostate cancer marker, show the strongest signal in our data  <cit> .
several genes encoding splicing factors, such as u2af <dig>  u2af <dig> and dhx <dig>  also show significant differential expression.
this is consistent with our observation that a large fraction of splicing factors are deregulated in tumors .
another interesting observation obtained by examining the panel of potential marker candidates selected by one or more methods is that a number of genes are normally expressed specifically in neuronal cells .
the relationship between abnormal expression of neuronal genes and tumors is not completely clear.
however, it is believed that there is a link between diverse neurodegenerative diseases and cancers via the induction of antitumor immunity, known as paraneoplastic neurological degenerations  .
alternative splicing is also prevalent for neuronal genes.
CONCLUSIONS
profiling of individual isoforms can provide unique and important additional insights into prostate cancer classification.
robust prediction models can be built with a subset of isoforms selected by multivariate variable selection method.

 prostate cancer is the second leading cause of cancer illness and death among men in the united states and the third most common cancer world wide. according to recent estimates, it accounts for 33% percent of new cancer incidences and six percent of cancer deaths in men world wide. in the number of new incidences and deaths in the united states was approximately and, respectively. the difficulty lies, at least partly, in the heterogeneous nature of the disease. tumor growth is initially dependent on androgen levels, which stimulate cell proliferation and inhibit apoptosis via the androgen receptor pathway. the prostate-specific antigen level has been a standard screening for early diagnosis; androgen ablation is a prevalent therapy to repress the development of androgen-dependent tumors. however, in many cases, this therapy eventually fails and patients die of the recurrent androgen independent prostate cancer, a lethal form that progresses and metastasizes. multiple pathways permit cancer cells to escape or bypass the control of the normal ar activation to up-regulate target genes abnormally. although it has been reported that a number of genes are related to these pathways as well as other aspects of prostate cancer, there is still an urgent need for good biomarkers for early clinical diagnosis and treatment. microarray technologies developed in the last decade permit monitoring of mrna abundance levels of tens of thousands of genes in parallel. the accuracy improvement and cost reduction have made them a routine approach in looking for genes that are differentially expressed between normal and tumor samples or between different tumor types/stages. in a recent study, segal et al. summarized ~ array experiments and derived a panoramic view of activated/deactivated gene expression modules for various types of tumors. microarrays have also been employed in prostate cancer studies. using cdna arrays, dhanasekaran et al. measured gene expression in normal and neoplastic prostate specimens, as well as three prostate-cancer cell lines, and identified gene signatures characterizing androgen-dependent and aipc samples. nelson et al. and deprimo et al. studied gene expression in the androgen treated lncap cell line, which was known to be highly androgen responsive. lapointe et al. profiled primary tumors and normal specimens; three subclasses of tumors representing different tumor stages and risks of recurrence were obtained along with characteristic expression signatures. these studies demonstrated the potential of using microarray analyses in characterizing prostate cancer at the gene expression level. while transcriptional regulation plays important roles within a cell, post-transcriptional regulation, such as alternative splicing, dramatically increases the diversity of the proteome. alternative splicing also plays a critical role in gene expression regulation and human diseases. it has been reported that about 15% of point mutations that cause human genetic diseases can alter splicing patterns. in particular, splicing aberrations have been characterized in a number of genes and tumor types. in a previous work, we developed a microarray-based assay called rasl™, which can systematically monitor the abundances of unique splicing events. a modified version of the assay, the dasl® assay, offers additional robustness for analyzing highly degraded mrnas, as well as an additional flexibility in probe design. different from other exon-junction arrays, the dasl assay achieves high specificity and sensitivity due to the fact that both hybridization and ligation of a pair of oligos complementary to the 5' splice site of the upstream exon and the 3' splice site of the downstream exon are required. in our recent study, this technology was applied to profile the abundances of ~ unique splice isoforms in prostate cancer cell lines, tumor specimens and normal control samples. this previous study led to two implications: the splicing patterns were altered in a number of genes in response to androgen treatment in the lncap cell line; a number of splice isoforms were differentially expressed in tumor samples. they prioritized a list of prostate cancer marker candidates for further investigations. in this study, we extend our previous work and perform a comprehensive analysis of using alternatively spliced isoforms to classify prostate cancer samples. compared with our previous work, the focus of this study is to quantitatively compare isoform profiling and overall mrna profiling for cancer classification, which has not been systematically investigated before. to be more specific, the contribution of this study lies in four key aspects: isoform-sensitive microarrays studies have been assumed to be able to provide more information for cancer classification than conventional microarray studies because isoform abundances couple both transcriptional regulation and splicing regulation. however, it has remained unclear how much unique information could be provided by isoform profiling. in this paper, this assumption is examined qualitatively for the first time through differential expression analysis. further examinations for several genes are also described. as in a number of other microarray studies, hierarchical clustering has been used to segregate similar tissues. this approach was not able to obtain an unbiased estimation of the predictive power for new unknown samples. to assess the predictive power of isoform profiling and that of overall mrna profiling, a support vector machine with recursive feature elimination was employed to build prediction models and the prediction accuracies were compared. building a prediction model with a minimal subset of variables is one of the critical tasks in cancer classification. we compared two different variable selection methods for sample classification and examined whether the robustness of prediction can be improved by taking the correlation among isoforms into account during variable selection. in our previous study, two smaller datasets generated in different batches were analyzed separately. the two lists of candidate markers selected from the two datasets had a relatively small overlap. to achieve more robust results, all analyses in this study were based on the larger combined dataset after careful normalizations.  in our previous work, the two datasets of prostate tumors and normal samples were analyzed separately by hierarchical clustering because they were generated in two different batches and there were significant heterogeneities between them. in both datasets, splice isoforms could be used to separate tumor samples and normal samples. however, the sample size in each dataset was limited and the overlap between the two lists of differentially expressed isoforms selected from the two datasets was relatively small. in this paper, the two datasets were combined after careful normalizations to achieve more robust results and statistical power. the combined datasets included cases of prostate tumors and matched normal samples. splice isoforms reveal distinct signatures of prostate cancer we first examined whether the global distinction between tumors and normal samples still exists in the combined dataset by unsupervised methods. as expected, tumors can be readily separated from normal samples by average-linkage hierarchical clustering. compared with cluster c the majority of tissues in cluster c are normal prostate and stroma, with the average tumor percentage being %, and stromal percentage being %. of the three tumors segregated with normal samples in cluster c two have low tumor content. additional analysis reveals that c cases in general have a significantly higher percentage of more advanced stages and more patients die of prostate cancer compared to c cases. specifically, 100% of the cases in c were from patients with organ confined tumors, whereas 50% of the cases in c were from metastasized patients. at the time of analysis, none of the c patients died of prostate cancer while14% of the c patients died of prostate cancer. interestingly, the cluster c enriched by tumors was further segregated into two sub-clusters, reflecting different percentage in tumor and stromal content. singular value decomposition was used to identify an orthogonal low dimensional space which preserves the maximal variation of the original high dimensional space. the first two principal components capture 17% and 9% of the total variation, respectively. remarkably, the first principal component alone shows a strong separation of tumor and normal samples. the clusters and sub-clusters derived from hierarchical clustering are also reflected in the 3d space spanned by the first three principal components, which confirms the results of clustering. further examination of the gene clustering results shows distinct molecular signatures of different tissue clusters, including both well known marker genes and less studied marker candidates. figure 1c shows isoforms up-regulated in cluster tumor sub-cluster c, including isoforms from genes rps xbp u1af and atp5a all of which were known to be up-regulated in tumors. figure 1d shows isoforms down-regulated in normal tissues and up-regulated in tumor tissues, including isoforms from genes u2af cln and hpn. figure 1e shows isoforms with high expression levels in normal tissues and down-regulated in tumor tissues, especially in sub-cluster c. several genes in this cluster are known to be involved in the tgf-beta signaling pathway, such as tgfb ltbp and tgfbr differentially expressed splice isoforms a two sided t-test was used to identify genes with statistically significant changes in expression between tumors and normal samples. a false discovery rate or q-value was calculated as described previously, to correct for multiple testing. as a result, isoforms representing genes are reported as being significant. the high proportion of differentially expressed isoforms reflects the fact that the genes profiled are potentially related to prostate cancer according to existing evidence. top isoforms among them include amacr- fgfr2- fgfr2- fgfr2- clu- pgr- etc. profiling of splice isoforms provides additional information to overall mrna abundances in theory, profiling individual splice isoforms can provide more information than profiling overall mrna levels as in conventional microarrays. this is because isoform profiling detects the combinatorial effects of both transcriptional regulation and splicing regulation. consider the simplest case of a gene with two alternatively spliced isoforms. if one isoform is up-regulated in tumors whereas the other is down-regulated, the overall mrna abundance may not change. on the contrary, if the overall mrna level is differentially expressed, there is at least one isoform exhibiting differential expression. however, how much additional information can be obtained for cancer classification by isoform profiling has not been systematically evaluated. to address this question, we compared individual isoforms and overall mrnas for differential expression. due to the costs and array capacity, the original array design did not include probes targeting common regions of all isoforms. therefore, the overall mrna expression level can not be obtained directly. however, since the probed exon junctions target unique major isoforms and hybridization efficiencies of different probes are comparable, we reason that the overall expression level can be estimated by summing up the abundances of individual isoforms. to examine the validity of this idea, two well-known prostate cancer cell lines lncap and pc- were profiled using the same dasl assay. for comparison, genes were arbitrarily selected for gene expression profiling in the same cell lines. an independent oligo pool targeting common regions of all isoforms in each of the genes were used in the expression array. therefore, the log expression ratio of each gene in the two cell lines can be obtained from the estimation based on the splicing array and from the direct measurement in the expression array independently. to our satisfaction, the two quantities are highly correlated, suggesting a reasonable accuracy of the estimation. having validated the approach, the overall mrna abundances of each gene in prostate tissues were estimated. a t-test was similarly applied to identify genes with significant differential expression in tumors at the overall mrna level. in total, genes are reported as being significant. again, the high proportion of significant genes reflects the fact that they are potentially relevant to prostate cancer according to previous studies. strikingly, more genes are called significant by examining individual isoforms than by examining overall mrnas. among the genes that are called significant, genes have at least one isoform that is reported as significant. in contrast, only 68% of genes with significant isoforms can be detected at the overall mrna level. the remaining 32% of the genes have significant isoforms but do not exhibit significant differential expression at the overall mrna level. it is important to note that these genes represent the unique information that is provided by splice isoform sensitive microarrays and cannot be obtained from conventional microarrays. from the perspective of isoforms, 78% of significant isoforms are from those genes that are also called significant whereas 22% of significant isoforms are from those genes that do not show overall mrna differential expression. multiple testing has been appropriately accounted for, so the additional significant calls using splice isoforms are not due to the different stringencies of thresholds, but reflect additional information provided by including splicing regulation. for many genes, only one isoform is specifically altered in tumors. in these cases, the addition of other isoforms to the total mrna level simply introduces random noise. notably, there are genes with one isoform being up-regulated in tumors and another isoform being down-regulated. among them, genes are not significant at the overall mrna level: cd, itgb and mapt. cd is a multifunctional receptor involved in cell-cell interactions and cell trafficking. deregulated expression of a number of variants is correlated with tumor metastasis. itgb is a protein involved in extra-cellular matrix interactions and is also related to many tumor types, including prostate cancer. there are relatively fewer studies discussing the role of mapt in cancer. mapt encodes the microtubule-associated protein tau mainly expressed in the central nervous system. mutations in the mapt gene disrupt the normal binding of tau to tubulin. this in turn results in pathological deposits of hyperphosphorylated tau in the brain, which is a pathological hallmark of several neurodegenerative disorders. previously, sangrajrang et al. found that mapt was also expressed in the du cell line using rt-pcr and the expression at the protein level was validated by western blotting. the expression was elevated after estramustine treatment and the authors suggested that the protein may be positively related to drug resistance. this was consistent with a recent report demonstrating that the up-regulation of the protein tau was correlated to the decrease of paclitaxel sensitivity in breast cancer. in our data, mapt- has a two fold increase in tumors relative to normal tissues, whereas mapt- has a two fold decrease in tumors relative to normal tissues. it is likely that exon 4a is uniquely skipped in prostate cancer cells. this hypothesis is further supported by the following evidence. exon 4a harbors a c/t single nucleotide polymorphism near the 5' splice site. this snp was assayed from individuals and the c/t ratio is /. in the major c allele, a putative exonic splicing enhancer cagccgg encompassing the snp is predicted by esefinder and resembles the specific rna binding site of sf2/asf, a critical serine rich protein that helps to recruit the splicing apparatus. this putative ese is disrupted in the minor t allele for all four sr proteins in esefinder including sf2/asf, sc srp and srp however, further experimental studies and confirmation of the splicing alteration may be required to validate this hypothesis. profiling of splice isoforms improves predictive power a robust prediction model to classify unknown samples is essential for early cancer detection and diagnosis. having demonstrated that a large fraction of genes show differential expression at the splice isoform level but not at the overall mrna level, a key question is how much additional predictive power can be achieved by isoform profiling. another related problem is to select minimal subsets of variables with the best performance. like many other types of tumors, a single molecular marker is usually not robust enough for prostate cancer detection, as is the case for the widely used psa level for early stage screening. at the other extreme, including all variables from a genome-wide profiling is not justifiable either, due to the noise introduced by a huge number of uninformative variables and the difficulty in the interpretation of the resulting model. a support vector machine was used here to build the classifier because of its excellent performance in many previous studies with small sample sizes. an recursive feature elimination algorithm was integrated as described previously with minor adaptations. leave-one-out cross validation with external variable selection was used to give an unbiased evaluation of the prediction accuracy. svm-classifiers were built using the individual splice isoforms and estimated overall mrna abundances. the results of loocv are shown in figure 3a. for the classifiers using isoform abundances, the best performance, correct predictions out of samples, is achieved when isoforms are included for classification. for the classifiers using overall mrna abundances, the best performance is achieved when genes are used. the additional information provided by splicing regulation gives rise to an improvement of about 5% in predictive power. importantly, the difference persists in the whole range of different sizes of selected variable subsets, which is unlikely by random chance. with an independent method, this demonstrates that isoform profiling can provide valuable information for cancer classification. also, the classification performance deteriorates when the subset of selected variables is too small in size. this is consistent with the previous observation that a robust cancer prediction model should use a reasonable number of molecular signatures. comparison of different variable selection methods both t-tests and svm-rfe can generate lists of candidate markers. these two approaches represent univariate variable selection and multivariate variable selection, respectively. they have different assumptions and may characterize different yet overlapping perspectives of the molecular mechanisms underlying the data. for example, variables are assumed to be independent in a t-test but there is no assumption of independence in svm-rfe. comparing the multiple outputs of selected signatures by different methods may shed further insights into the data and the methods. therefore, the two different variable selection approaches, t-test and svm-rfe, were applied to select marker candidates and their performances in building linear svm models were compared. the results of loocv are shown in figure 3b. the best performance of t-test selection is achieved with a similar number of variables as svm-rfe. both methods result in an accuracy of 92%. the similar best performance by t-test and svm-rfe is likely due to the distinct features of tumors and normal tissues. the information to classify the two groups is largely redundant. however, the curve of prediction accuracy by the svm-rfe selection is smoother than that by the t-test selection as the size of selected variable subset decreases. this smaller variation suggests that svm-rfe is more robust than t-test in variable selection for cancer classification. the isoforms selected by t-test and the isoforms selected by svm-rfe share isoforms. the common list includes amacr- amacr- amacr- fgfr2- fgfr2- pgr- and pgr- among others. they may represent robust marker candidates. significant isoforms in each list were further divided into two groups according to whether the corresponding genes also exhibit significant differential expression at the overall mrna level. interestingly, among those isoforms included only in the svm list, of the isoforms are in the category that the corresponding genes do not show significant differential expression at the overall mrna level. in contrast, among the isoforms included only in the t-test list, only isoforms lie in this category. therefore, svm-rfe captures more information uniquely provided by considering splicing regulation. this demonstrates the advantage of a variable selection method taking the correlation between variables into account. discussion the diagnosis and treatment of prostate cancer are fields with long histories. various efforts have led to the progressive understanding of the disease. however, the present criteria of diagnosis and prognosis, as well as the approaches of treatment and surgery, are not sufficiently reliable. previous gene expression profiling studies on prostate tumors and normal tissues demonstrated the feasibility in characterizing the molecular alterations at the overall mrna transcript level. however, these transcriptome analyses were based on the old central dogma of "one gene, one mrna", which may underestimate the complexity of tumorigenesis. previously, we carried out a study of prostate cancer by exon-junction microarray-based assay and demonstrated the power of this integrated technology in detecting both transcriptional and splicing regulation. in this paper, we present systematic analyses with the focus on using splice isoform profiling for prostate cancer classification. isoform-sensitive microarrays have been used in several recent studies. these studies demonstrated that isoform-sensitive microarray is a reliable, high throughput approach to detecting splicing alterations in various tissues and conditions. although more and more data are expected to be generated in the near future, the dataset used in this study is the only dataset currently available which screened a relatively large sample of cancer and normal tissues. as far as we know, this is the first systematic comparison of isoform-sensitive microarrays and conventional microarrays for cancer classification. previous studies have used a "splice index", which is the fraction of each isoform, to remove the effect of transcriptional regulation. this is not desired for cancer classification because as much information as possible should be incorporated. therefore the abundance of each isoform, which couples both transcriptional regulation and splicing regulation, was used for classification. the performance was compared with that of using overall mrna abundances. one has to note a caveat of the current dasl assay: it does not include probes complementary to the common regions of all mrna transcripts for each gene due to the current limit in array capacity. therefore, the overall mrna level was estimated indirectly by summing up all the isoforms targeted. the estimation is not ideal due to the fact that not all isoforms were included in the array and the probes target splicing events that are not mutually exclusive in several cases. however, the estimation is reasonably good and highly correlated with the direct measurement by an expression array. various other methods were tried to estimate the overall mrna abundances, but the method used here is the most accurate and simplest. among the ~ isoforms from putative prostate cancer-related genes, a large fraction of them exhibit differential expression in cancer cells. tumors and normal tissues can be readily separated by both unsupervised and supervised methods. by comparing individual isoforms and overall mrnas for differential expression, we arrived at the conclusion that an isoform-sensitive microarray, which detects coupled transcription and splicing regulation, can provide about 30% more information than conventional microarrays. this value may still be underestimated due to the following reasons. the current dasl assay included only genes potentially relevant with prostate cancer derived from previous studies. till now, a large body of literature, especially those in the genomic scale, focused more on transcriptional regulation. therefore, the selection of genes may be biased to those exhibiting aberrant transcriptional regulation. the optimal prediction model was built by svm with variable selection integrated, a powerful machine learning approach. with around isoforms, the best classification performance can be achieved at a correct prediction rate of 92%. compared with the optimal svm classifier built with overall mrna abundances, this represents an improvement of five percent. therefore, both differential expression analysis and classification analysis quantitatively demonstrated the advantage of isoform-sensitive microarrays. we also compared the effect of different variable selection approaches on classification performance. by taking the correlation between isoforms into account, isoforms selected by svm-rfe are more robust for classification than isoforms selected by a t-test. although univariate two-sample comparisons such as t-test are widely used to identify differentially expressed genes, the assumption of independence between genes or isoforms is not biologically justifiable. in cancer signal transduction pathways, a group of genes in the same pathway are interacting with each other; cross-talks often exist between pathways as well. variables are more convoluted in the dasl data due to the coupling of transcription and splicing. the multi-loci nature of the disease also makes it difficult to use a single or few molecular markers to build a sufficiently robust prediction model. this study identified a number of known prostate cancer markers as well as less studied marker candidates, which span a wide spectrum of biological functional roles. some are related to signal transduction, as well as extracellular matrix and cytoskeleton. others appear to be involved in epidermal differentiation and proliferation, cell growth and development, apoptosis, lipid metabolism, etc. very significantly, multiple isoforms from amacr, a key player in catalyzing the isomerization of alpha-methyl-branched fatty acid and a recently reported good prostate cancer marker, show the strongest signal in our data. several genes encoding splicing factors, such as u2af u2af and dhx also show significant differential expression. this is consistent with our observation that a large fraction of splicing factors are deregulated in tumors. another interesting observation obtained by examining the panel of potential marker candidates selected by one or more methods is that a number of genes are normally expressed specifically in neuronal cells. the relationship between abnormal expression of neuronal genes and tumors is not completely clear. however, it is believed that there is a link between diverse neurodegenerative diseases and cancers via the induction of antitumor immunity, known as paraneoplastic neurological degenerations. alternative splicing is also prevalent for neuronal genes.  profiling of individual isoforms can provide unique and important additional insights into prostate cancer classification. robust prediction models can be built with a subset of isoforms selected by multivariate variable selection method. 
these results demonstrate that profiling of splice isoforms is able to provide unique and important information which cannot be detected by conventional microarrays. there is an urgent need to discover good biomarkers for early clinical diagnosis and treatment. prostate cancer is one of the leading causes of cancer illness and death among men in the united states and world wide. previously, we developed an exon-junction microarray-based assay and profiled mrna splice isoforms from potential prostate cancer related genes in prostate tissues. here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification. as many as splice isoforms from more than genes are differentially regulated in tumors at a false discovery rate of. remarkably, about 30% of genes have isoforms that are called significant but do not exhibit differential expression at the overall mrna level. it is also observed that the classification performance can be improved using multivariate variable selection methods, which take correlation among variables into account. a support vector machine classifier trained on signature isoforms can correctly predict 92% of the cases, which outperforms the classifier using overall mrna abundance by about 5%.
 prostate cancer is one of the leading causes of cancer illness and death among men in the united states and world wide. there is an urgent need to discover good biomarkers for early clinical diagnosis and treatment. previously, we developed an exon-junction microarray-based assay and profiled mrna splice isoforms from potential prostate cancer related genes in prostate tissues. here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification.  as many as splice isoforms from more than genes are differentially regulated in tumors at a false discovery rate of. remarkably, about 30% of genes have isoforms that are called significant but do not exhibit differential expression at the overall mrna level. a support vector machine classifier trained on signature isoforms can correctly predict 92% of the cases, which outperforms the classifier using overall mrna abundance by about 5%. it is also observed that the classification performance can be improved using multivariate variable selection methods, which take correlation among variables into account.  these results demonstrate that profiling of splice isoforms is able to provide unique and important information which cannot be detected by conventional microarrays. 