Patent Application: US-201214361153-A

Abstract:
cancer marker sets consisting of particular genes differentially expressed in tumours provide improved accuracy of predicting effectiveness of paclitaxel or paclitaxel - like drug treatment against a cancer . these sets are further useful for screening drug candidates for paclitaxel - like cancer treatment activity . the cancer marker sets may be used in a clinical setting to provide information about the likelihood that a cancer patient would or would not respond to paclitaxel or paclitaxel - like drug treatment .

Description:
to develop er + 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 260 er + breast cancer samples was selected from a public metadata set ( geo gse4779 , gse20194 , gse20271 , gse22093 and gse23988 ). each patient has been treated with paclitaxel and followed - up pathologically to determine who is responsive to the treatment . the primary tumors prior to any drug treatment have been microarray profiled . the datasets contain information about gene expression profiles for patient primary tumours and the information of response / non - response for paclitaxel treatment for each patient . datasets identify whether each of these genes is up - regulated or down - regulated in tumours and correlates these genes with responsiveness to paclitaxel treatment ( i . e . “ good ” vs . “ bad ”). 100 samples from the datasets were randomly selected in which 70 were samples that did not respond to paclitaxel treatment (“ bad ”) and 30 were samples that did respond to paclitaxel treatment (“ good ”). array - wide single - gene based clustering ( using fuzzy clustering method , http :// stat . ethz . ch / r - manual / r - patched / library / cluster / html / fanny . html ) of responsive / non - responsive was conducted to obtain effectiveness genes , which are genes whose differential expression values are correlated with effective paclitaxel treatment . it is not relevant whether the expression of each gene is upregulated or downregulated so long as the differential expression is correlated to effective paclitaxel treatment . selection of samples and array - wide single - gene based clustering analyses ( using fuzzy clustering method , http :// stat . ethz . ch / r - manual / r - patched / library / cluster / html / fanny . html ) were repeated 100 times , and the effectiveness genes ( which have p value & lt ; 0 . 05 in more than 75 out of the 100 times ) from each of the 100 repetitions were merged . using the effectiveness 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 cancer , such as apoptosis , response to wounding , dna replication and transcription repair , mitosis and immune response . table 1 lists the er + cancer - related go term gene sets . two million distinct random - gene - sets were generated by randomly picking 30 genes from each er + cancer - related go term gene set . of 83 samples ( 58 with no response to paclitaxel treatment and 25 that responded to paclitaxel treatment ) selected from the dataset to form the training set , 36 random datasets were generated . for a given go term gene set , paclitaxel effectiveness screening was then conducted using the 2 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 paclitaxel effectiveness status (“ good ” or “ bad ”) was examined by fuzzy clustering analysis ( using fuzzy clustering method , http :// stat . ethz . ch / r - manual / r - patched / library / cluster / html / fanny . html ). if the p value was less than a cut - off for an effectiveness 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 2 ), 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 effectiveness screening of random - gene - sets against random datasets was performed for each of the other selected go term gene sets . only apoptosis , mitosis and immune response go term gene sets were used to generate the er + marker sets . for each go term gene set used , another 1 million distinct random - gene - sets were generated and the clustering 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 er + 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 this way , three er + cancer marker sets were generated having stable signatures , one related to apoptosis ( set 1 ), one related to mitosis ( set 2 ) and one related to immune response ( set 3 ). the genes , entrezgene id and full names of the genes in each of the three marker sets are given above . 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 /. to develop ern ( estrogen receptor negative ) 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 202 ern breast cancer samples was selected from gse25066 dataset ( hatzis 2011 ). the dataset contains information which is the same as those described above ( the er + datasets ). 153 samples from the dataset were randomly selected in which 100 were samples that did not respond to paclitaxel treatment (“ bad ”) and 53 were samples that did respond to paclitaxel treatment (“ good ”). array - wide single - gene based fuzzy clustering ( using fuzzy clustering method , http :// stat . ethz . ch / r - manual / r - patched / library / cluster / html / fanny . html ) screening of responsive / non - responsive samples was performed to obtain effectiveness genes , which are genes whose differential expression values are correlated with effective paclitaxel treatment . it is not relevant whether the expression of each gene is upregulated or downregulated so long as the differential expression is correlated to effective paclitaxel treatment . selection of samples and array - wide screening were repeated 3 times , and effectiveness genes ( p value & lt ; 0 . 05 ) from each of the 3 repetitions were merged . using the effectiveness 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 cancer , such as apoptosis , cell cycle , cell adhesion , response , dna repair & amp ; replication and mitosis . table 3 lists the ern cancer - related go term gene sets . two million distinct random - gene - sets were generated by randomly picking 30 genes from each ern cancer - related go term gene set . of 152 samples ( 99 with no response to paclitaxel treatment and 53 that responded to paclitaxel treatment ) selected from the dataset to form the training set , 36 random datasets were generated . for a given go term gene set , paclitaxel effectiveness 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 paclitaxel effectiveness status (“ good ” or “ bad ”) was examined by fuzzy clustering analysis ( using fuzzy clustering method , http :// stat . ethz . ch / r - manual / r - patched / library / cluster / html / fanny . html ). if the p value was less than a cut - off for an effectiveness 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 4 ), 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 effectiveness screening of random - gene - sets against random datasets was performed for each of the other selected go term gene sets . only apoptosis , cell adhesion and response go term gene sets were used to generate the ern marker sets . for each go term gene set used , 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 ern 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 this way , three ern cancer marker sets were generated having stable signatures , one related to apoptosis ( set 4 ), one related to cell adhesion ( set 5 ) and one related to response to stimulus ( set 6 ). the genes , entrezgene id and full names of the genes in each of the three marker sets are given above . 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 /. validating effectiveness of the marker sets in predicting paclitaxel effectiveness for treating breast cancer the effectiveness of the marker sets generated in examples 1 and 2 was validated against datasets containing breast cancer gene expression data from sample populations . sets 1 , 2 and 3 from example 1 were validated against metadata from public data ( gse4779 , gse20194 , gse20271 , gse22093 and gse23988 ) and against the gse25066 dataset ( hatzis 2011 ). sets 4 , 5 and 6 from example 2 were validated against the gse25066 dataset ( ern , 87 % triple negative ) ( hatzis 2011 ), the gse20174 dataset ( triple negative ) ( zeidler - erdely 2010 ), and the gse20194 dataset ( triple negative ) ( popovici 2010 ; shi 2010 ). 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 paclitaxel response 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 unresponsive to paclitaxel treatment ( i . e . “ bad ”), it is denoted as 0 , otherwise it is denoted as 1 . if all three marker sets ( sets 1 , 2 and 3 , or sets 4 , 5 and 6 ) predict that a particular sample is unresponsive to paclitaxel ( i . e . denoted as 0 for all 3 marker sets ), the sample is assigned to a paclitaxel unresponsive group ( i . e . “ bad ”). if all three marker sets predict that a particular sample is responsive to paclitaxel ( i . e . denoted as 1 for all 3 marker sets ), the sample is assigned to a paclitaxel responsive group ( i . e . “ good ”). if a sample is not assigned to either of these groups , it is assigned to an indeterminate group . this validation process was carried out in each of the test datasets . table 5 shows the accuracy for sets 1 , 2 and 3 in predicting the paclitaxel unresponsive group in the metadata from public data dataset and the gse25066 dataset . table 6 shows the accuracy for sets 4 , 5 and 6 in predicting the paclitaxel unresponsive group in the gse25066 dataset , the gse20174 dataset and the gse20194 dataset . the accuracy of the marker sets against the test datasets is remarkably high , and much higher than the 50 - 60 % that can be achieved using current prior art marker sets ( hatzis 2011 ). cui q , ma y , jaramillo m , bari h , awan a , yang s , zhang s , liu l , lu m , o &# 39 ; 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( 2010 ) response of the mouse lung transcriptome to welding fume : effects of stainless and mild steel fumes on lung gene expression in nj and c57bl / 6j mice . respir res . 11 ( 1 ), 70 ( 18 pages ). 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 .