Patent Application: US-201214004507-A

Abstract:
prostate cancer marker sets consisting of particular genes differentially expressed in prostate tumours provide improved accuracy of prostate cancer prognosis . the prostate cancer marker sets of the present invention , one of which consists of 30 genes related to apoptosis , one of which consists of 22 genes related to cell cycle and one of which consists of 30 genes related to response to external stimulus , may be used in a clinical setting to provide information about the likelihood of a prostate cancer patient to survive without treatment .

Description:
to develop the prostate cancer marker sets of the present invention , the multiple survival screening ( mss ) method ( li 2010 ; wang 2010 ) was used . in applying this method , a training set of 189 samples was selected from the gse10645 geo dataset ( nakagawa 2008 ). this prostate cancer gene expression datatset is from the population - based swedish - watchful waiting cohort . the cohort consists of men with localized prostate cancer ( clinical stage t1 - t2 , mx , no ). the gse10645 geo dataset contains information about genes that are differentially expressed in prostate tumours . the dataset identifies whether each of these genes is up - regulated or down - regulated in tumours and correlates these genes to patient survival ( i . e . “ good ” vs . “ bad ” tumours ). the 189 samples from gse10645 were randomly divided into three groups of 63 samples , each group retaining the same proportion of “ good ” vs . “ bad ” tumours that was identified in the original gse10645 dataset . array - wide screening of the genes was performed on each of the three groups as described in the art ( li 2010 ; wang 2010 ) to obtain survival genes , which are genes whose differential expression values are correlated with prostate cancer patient survivals . it is not relevant whether the expression of each gene is upregulated or downregulated so long as the differential expression is correlated to patient survival . merging the results from each of the three groups yielded a survival gene set , which includes 133 survival genes . using the survival gene set , gene ontology ( go ) analysis ( using go annotation software , david , http :// david . abcc . ncifcrf . gov /) was performed to identify only those genes that belong to go terms that are known to be associated with prostate cancer , such as apoptosis ( cell death ), cell adhesion , cell cycle , phosphorylation , response to external stimulus , cell motility and cell assembly . table 1 lists the cancer - related go term gene sets . one million distinct random - gene - sets were generated by randomly picking 30 genes from each cancer - related go term gene set . of the 189 samples selected from the gse10645 geo dataset to form the training set , 36 random datasets were generated by randomly picking 60 samples from the training set while retaining in each random dataset the same proportion of “ good ” vs . “ bad ” tumours that was identified in the original gse10645 dataset . for a given go term gene set , survival screening was then conducted using the 1 million random - gene - sets against all the 36 random datasets . for each random dataset , the statistical significance of the correlation between the expression values of each random - gene - set ( 30 genes ) and patient survival status (“ good ” or “ bad ”) was examined by kaplan - meier analysis by implementing the cox - mantel log - rank test ( cui 2007 ). if the p value was less than a cut - off for a survival screening using one random - gene - set against one random dataset , that random - gene - set was said to have passed . when a few thousands of random - gene - sets had passed 32 or more random datasets ( the detailed parameters are shown in table 5 ), the random - gene - sets that had passed were retained for further analysis . the genes in the retained random - gene - sets were then ranked based on their frequency of appearance in the passed random - gene - sets . the top 30 genes were chosen as a potential - marker - set . a similar survival screening of random - gene - sets against random datasets was performed for each of the other selected go term gene sets . for each go term gene set another 1 million distinct random - gene - sets were generated and the survival screening process using the random datasets mentioned above was repeated . if the gene members for the top 30 were substantially the same as those in the potential - marker - set generated by the first screening , then the potential - marker - set is stable and can be used as a real prostate cancer marker set . if the genes for the two potential marker sets were not substantially the same , then these go term genes are unsuitable for finding a real marker set and the potential marker set was dropped from further analysis . in some cases somewhat fewer than 30 genes may be the same in the two potential marker sets , in which case the smaller set may be designated as a marker set . in this way , three prostate cancer marker sets were generated having stable signatures , one related to apoptosis , one related to cell cycle and one related to response to external stimulus . the genes , entrezgene id and full names of the genes in each of the three marker sets are given in the tables 2 - 4 below . more details of each gene , including the nucleotide sequence of each gene , are known in the art and may be conveniently found in the national center for biotechnology information ( ncbi ) databases at http :// www . ncbi . nlm . nih . gov /. the effectiveness of the three marker sets generated in example 1 was validated against three separate geo datasets containing prostate cancer gene expression data from sample populations . one of the three datasets against which the markers were validated was the gse16560 dataset described above except that 261 samples from that dataset were used . the other two test datasets were geo datasets gse21034 ( taylor 2010 ) and gse10645 ( nakagawa 2008 , the validation samples marked by the authors ). in all three cases , test datasets were constructed by selecting samples from the geo datasets so that the test datasets contained 90 % “ good ” tumours and 10 % “ bad ” tumours , based on ultimate patient survival outcomes , in order to simulate the suggestion that over 90 % of prostate cancer patients do not actually need to be treated . to perform the validation for a given test dataset containing ‘ n ’ samples , the gene expression profile of the marker set was extracted . for each gene expression value its marker - factor was multiplied to obtain a modified gene expression profile of the testing sample . standardized centroids were computed for both “ good ” and “ bad ” classes from n − 1 samples for the marker set using the prediction analysis for microarrays ( pam ) method ( tibshirani 2002 ). the marker - factor of each gene was multiplied to the class centroids to get modified class centroids of the marker set . for predicting the recurrence of the targeted testing sample using the marker set the modified gene expression profile of the sample was compared to each of these modified class centroids . the class whose centroid that it is closest to , in pearson correlation distance , is the predicted class for that sample . if the sample is predicted to be a “ good ” tumour , it is denoted as 0 , otherwise it is denoted as 1 . if all three marker sets predict that a particular prostate cancer sample is “ good ” ( i . e . denoted as 0 for all 3 marker sets ), the sample is assigned to low - risk group . if all three marker sets predict that a particular prostate cancer sample is “ bad ” ( i . e . denoted as 1 for all 3 marker sets ), the sample is assigned to high - risk group . if a sample is not assigned to low - risk or high - risk group , it is assigned to intermediate - risk group . this validation process was carried out in all three of the test datasets . table 6 shows the results for the low - risk group in comparison to the gse16560 training set originally used to generate the three marker sets ( see example 1 ). as would be expected , the accuracy of the marker sets against the training set is 100 %. the accuracy of the marker sets against the test datasets derived from the three geo datasets is remarkably high . the accuracy of the present marker sets can be compared to the prior art . table 7 provides the performance of several markers and marker sets of the prior art . table 7 is derived from table 5 of nakagawa 2008 . the clinical models used and the nature of the various markers and marker sets listed in table 7 below are explained in nakagawa 2008 . it is clear comparing table 6 to table 7 that the prognostic accuracy of the present marker sets for determining the expected survival of a prostate cancer patient is substantially greater than the prior art markers and marker sets . andriole g l , crawford e d , grubb iii r l , et al . 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( 2004 ) gene expression alterations in prostate cancer predicting tumour aggression and preceding development of malignancy . j clin oncol . 22 , 2790 - 9 . other advantages that are inherent to the structure are obvious to one skilled in the art . the embodiments are described herein illustratively and are not meant to limit the scope of the invention as claimed . variations of the foregoing embodiments will be evident to a person of ordinary skill and are intended by the inventor to be encompassed by the following claims .