Patent Application: US-37517707-A

Abstract:
a method of predicting the development of a cancer in a patient , comprises procuring a sample of tumour tissue from the patient , determining the expression pattern of embryonic stem cell genes in the tissue , comparing the expression pattern with the corresponding expression pattern of embryonic stem cell genes in tumour tissue of reference patients with known disease histories . also disclosed are microarrays and dna / rna probes for use in the method .

Description:
data retrieval . the method of the invention is based on published gene data such as the data sets published and deposited in the stanford microarray database ( smd ) ( http :// genome - www5 . stanford . edu /). all array experiments used the same two - dye cdna array platform with a common rna reference , which enables reliable combination of or comparison with data from different experiments . these datasets include genome - wide expression data for embryonic stem cells ( 60 ), normal tissues from most of the human organs ( 61 ), and tumors from the prostate ( 62 ), breast , lung ( 63 ), stomach ( 64 ), liver ( 65 ), blood ( 66 ), brain ( 67 ), kidney ( 68 ), soft tissue ( 69 ), ovary ( 70 ; 71 ) and pancreas ( 72 ). in total about 1000 arrays were included in the analysis . each array ( tissue ) in these datasets is denoted with corresponding basic clinical and pathological information such as histopathological type , tumor grade , clinical stage , and even survival data in a significant fraction of tumor cases . gene selection . all genes or clones on arrays are selected . control spots and empty spots are not included . data collapse / retrieval . raw data are retrieved and averaged by suid ; uid column contains name ; retrieved log ( base2 ) of r / g normalized ratio ( mean ). data filtering options : selected data filters : spot is not flagged by experimenter . data filters for genepix result sets : channel 1 mean intensity / median background intensity & gt ; 1 . 5 and channel 2 normalized ( mean intensity / median background intensity )& gt ; 1 . 5 . data centering . the es cell data set was combined with each of a number of other data sets . genes and array batches were centered separately in each combined dataset as previously described ( 61 ; 62 ). identification of es predictor genes . after centering a data set containing es cells and normal tissues from most human organs , the es data set was separated from the normal tissue data set . a one - class sam ( significant analysis of microarrays ) was carried out using the centered es dataset , by which all genes were ranked according to their expression levels in the es cells ( 73 ). using a q value equal to or less than 0 . 05 as cut - off , top 328 genes with highest level and top 313 genes with lowest level of expression in the es cells were identified ( table 1 ). these 641 es genes are named es tumor predictor genes ( estp genes ). previous studies used a small number of sample matrices to normalize the expression data of es cells ( 60 ; 74 ); this may lead to erroneous identification of estp genes . in this invention , the expression data of es genes from es cells were centered by a matrix of over 100 normal tissues from most human organs ( 62 ). this greatly reduced erroneous identification of estp genes . prediction of clinical and pathological tumor types . after centering each combined data set , a sub - dataset containing only the 641 estp genes was isolated from the original dataset . a simple hierarchical clustering was carried out based on this sub - dataset using genes with 70 % qualified data in all samples ( 78 ). the sample grouping was directly correlated with the clinical and pathological information of each individual tissue sample . prediction examples for a number of tumor types are given below . prediction in other datasets is carried out in essentially the same manner . in the one class sam analysis , numbers of genes selected is in correlation with q value . there were 201 genes selected when q value at 0 . 01 , 641 genes selected when q value at 0 . 05 , and 1368 genes selected when q value at 0 . 1 . in other words , an increased q value would result in increased number of selected genes as well as increased number of genes that would not be associated with the transcriptional regulation in the es cells . importantly , when the prediction powers were compared , the 641 genes selected by q value at 0 . 05 had best classification ( prediction ) results , as shown in the prostate cancer ( table 2 ) and lung cancer ( table 3 ) materials . the difference was particularly obvious in respect of lung cancer ( table 3 ). thus the 641 genes selected by q value at 0 . 05 was the best choice of gene selection when both stem cell association and tumor classification are taken into consideration . definition of prediction . as described above , the estp genes were derived from the es cell dataset . the power of this set of genes in the classification of a broad spectrum of tumors was then validated in each independent tumor dataset . prostate cancer . published clinical data and predicted tumor subtype by estp genes of the invention for prostate cancer are listed in table 2 : gleason grade , stage , biological subtype and short term recurrence ( prostate specific antigen ( psa ) survival ) after radical surgery . of the 641 estp genes , 505 had good data in 70 % of all samples . in the gene expression profile of fig2 , the expression level ( range in log ratio between − 5 . 06 and 6 . 15 ) was transformed into a transitional color presentation , with red indicating above 0 , black equal to 0 and green for less than 0 ; in fig2 and the other figures illustrating gene expression profiles the colors are rendered in white , black , and grey ( see , description of the figures ). based on these expression data , all samples were classified by hierarchical clustering into distinct groups as normal prostate , embryonic stem ( es ) cells , prostate cancer group that contained all cases ( 66 ) with recurrence ( pca recurrent ), prostate cancer group that contained only cases without recurrence ( pca non - recurrent ), and es carcinoma cells . the classification is significantly ( fisher &# 39 ; s exact test , p = 0 . 001 ) correlated with the previous classification by using 5000 genes ( lapointe j et al ., 2004 ). it should be noted that the pca non - recurrent group predicted by the present invention is also significantly correlated with low gleason score & lt ; 6 ( fisher &# 39 ; s exact test , p = 0 . 028 ) and early stage ( t & lt ; t3 ) ( fisher &# 39 ; s exact test , p = 0 . 007 ). prediction value for choice of treatment . patients with a tumor predicted to be of a recurrent type ( pertaining to the recurrent group ) should be treated by radical surgery at a very early stage even in case of a moderate or low gleason score . patients with a very early stage tumor predicted to be of a non - recurrent type ( pertaining to the non - recurrent group ) should be kept under regular psa and other examination control , because most of the tumors in this group are in fact indolent or very slow - progressive . lung cancer . published clinical data and predicted tumor subtype by estp genes of the invention are shown in table 3 . prediction of histological type and survival in lung cancer is illustrated in fig3 , tissue clustering by estp genes . of the 641 es predictor genes , 316 had qualified data in 70 % or more of the samples . lung cancer tissue samples were predictively sorted into two major groups , an adenocarcinoma group ( a ) that mainly contained adenocarcinomas , some normal lung tissues , es cells and a few non - adenocarcinomas , and a ( b ) non - adenocarcinoma group that contained most non - adenocarcinomas including squamous cell carcinoma , large cell lung cancer and small cell lung cancer , together with a fraction of adenocarcinomas . in general , adenocarcinoma has a better prognosis than other types of lung cancer . survival analysis based on lung adenocarcinoma subtypes is illustrated in fig4 . the adenocarcinoma cases in the non - adenocarcinoma group ( b ) further showed shorter survival than adenocarcinoma cases in the adenocarcinoma group ( a ) as shown in fig3 , adenocarcinoma subtypes by es predictor genes associated with survival . predictive value for choice of treatment strategy : tumors predicted to pertain to the adenocarcinoma group seem to have a generally favorable outcome after radical surgery at a very early stage ; whereas tumors in the non - adenocarcinoma group may respond relatively better to chemotherapy such as to iressa or radiation . gastric cancer . published clinical data and tumor subtype predicted by estp genes of the invention are illustrated in table 4 . the prediction of histological types and survival in gastric cancer is illustrated in fig5 : ( a ) tissue clustering by es predictor genes ; ( b ) issue subtypes by es predictor genes associated with survival . prediction of subtypes of gastric cancer by estp genes : of the 641 estp genes 613 had qualified data in 70 % of all samples . gastric tumors were classified into two major subtypes , type 1 enriched in tumors with diffuse and mix histological types generally with poor prognosis , type 0 together with most normal gastric tissue samples . the survival time for gastric cancer patients pertaining to these groups is compared in fig6 . the subtype 0 tumors can be further divided into two sub - subtypes , one with the a subtype enriched in eb virus positive tumors , the other not . predictive value : a ) ebv infection is linked to gastric cancer via stem cell biology . preventing an ebv infection by vaccination may have preventive effect on gastric cancer ; b ) diffused type of gastric cancer has very strong hereditary tendency . one should specifically exclude gastric cancer in a relative to a patient whose tumor is predicted to pertain to this group , so that possible tumor can be treated radically at a very early stage . leukemia . published clinical data and predicted tumor subtype by estp genes of the invention are listed in table 5 . fig7 illustrates the prediction of subtypes of acute mononucleocyte leukemia associated with chromosome aberration and survival : ( a ) classification by estp genes ; ( b ) aml subtypes associated with survival . prediction of acute myeloid leukemia ( aml ) by estp genes : of the 641 es predictor genes , 324 had qualified data in 70 % of all samples . aml cases were classified into two major subtypes , type 1 enriched in cases with t ( 8 ; 21 ) and del7q chromosomal aberrations , and type 0 , which was further divided into two sub - subtypes a and b the first with a subtype enriched with inv ( 16 ), the second enriched with t ( 15 ; 17 ). type 1 cases showed shorter overall survival than type 0 as presented in fig8 . survival analysis was based on aml subtypes predicted in fig4 a and the published clinical data in table 5 . predictive value for treatment choices : aml with different chromosomal aberrations responds to different chemotherapies ; in particular all - trans retinoic acid can induce differentiation of aml with t ( 15 ; 17 ) translocation . it is suggested that aml in the group enriched with t ( 15 ; 17 ) but without the translocation detected by cytogenetic diagnostic method may show good response to all - trans retinoic acid due to the same stem cell biological alteration . case history and retrospective cancer treatment strategy suggested by the method of the invention . ( a ) prostate cancer patient # pc007 ( table 5 ) aged 56 y at diagnosis . gleason score of prostate cancer was 3 + 3 = 6 ; tumor stage was t2b , suggesting a well differentiated tumor at an early stage by conventional clinical pathological examination . in spite of this the tumor recurred as diagnosed by a re - increased psa level 27 . 7 months after radical surgery . according to the predictive method of the invention , the tumor is predicted to be of es type 1 with poor prognosis . this case illustrates a typical situation in which es type prediction can outperform conventional clinical pathological methods in predicting clinical outcome . a similar case is patient pc250 ( table 5 ). ( b ) prostate cancer patient # pc037 ( table 5 ). this 57 year - old patient had a gleason 4 + 3 tumor , a high grade tumor that would have a poor prognosis according to conventional clinical concepts . but , according to the predictive method of the invention , the tumor is classified as being of es type 0 and thus would have had a better prognosis . the patient had a radical surgery without any signs of recurrence after 16 . 2 months . this case provides also an example for the situation that the es typing in the present invention is superior to conventional gleason grading . ( c ) prostate cancer patient # pc092 ( table 5 ). this patient was aged 68 y at diagnosis . his tumor had gleason 3 + 3 = 6 and staged t2b , suggesting a well differentiated tumor at an early stage . by the method of the present invention the tumor is classified as being of es type 0 with good prognosis . the patient was treated by radical surgery . no signs of recurrence were observed 13 . 7 months post surgery . there is good agreement between gleason grading and es typing according to the present invention . the es typing result also suggests that the patient could have been safely kept under regular psa control instead of immediate radical surgery . prognosis of lung adenocarcinoma . in addition to the prostate cancer cases from table 5 elucidated above , it is seen that es typing according to the present invention is significantly better than conventional histological grading in the prognosis of lung adenocarcinoma . for example , cases # 222 - 97 and # 226 - 97 were of grade 3 that would be poorly differentiated with poor outcome according to conventional clinical prognostic methods . by the method of the present invention the cases are classified as being of es type 0 that would have a relatively good outcome . the patients were recurrence - free more than 48 months after radical surgery . again es typing by the method of the invention is more accurate than by conventional histological grading . fig1 . identification of estp genes by a one - class sam ranking test . there were 24361 genes with qualified expression data in 75 % of the 6 embryonic stem ( es ) cell lines . these 24361 genes were ranked according to their homogenous expression levels in the es cells by a one - class sam ( significant analysis of microarrays ) method as shown in this figure . at delta 0 . 23 , q value & lt ; 0 . 05 , 328 genes with highest expression levels and 313 genes with lowest expression levels were identified . the expression changes of these 641 genes in different tumor samples showed also strongest classification power as compared to genes located within the cut - off lines . increasing the delta value ( decreasing the q value ) can increase the specificity in selecting genes representing the transcriptional regulation in the es cells whereas it can decrease the number of selected genes . a decrease in significant genes selected could result in a decrease in the corresponding tumor classification power . by successively changing the cut - off line it was shown that the 641 genes selected at delta 0 . 23 , q value & lt ; 0 . 05 was the best choice for both stem cell association and tumor classification . fig2 . prediction of prostate cancer — gleason grade , stage , biological subtype and short term recurrence ( prostate specific antigen ( psa ) survival ) after radical surgery . of the 641 estp genes , 505 had good data in 70 % of all samples . in this gene expression profile , the expression level ( range in log ratio between − 5 . 06 and 6 . 15 ) was transformed into a transitional gray - black scale presentation , with black indicating above 1 , median gray indicate equal to 1 and green for less than 1 . based on these expression data , all samples were classified by hierarchical clustering into distinct groups as normal prostate , prostate cancer aggressive group type 1 that contained all cases with recurrence , prostate cancer non - aggressive group type 0 that contained only cases without recurrence . the classification significantly ( fisher &# 39 ; s exact test , p = 0 . 001 ) correlated with the previous classification by using 5000 genes ( lapointe j et al ., 2004 ). the non - aggressive group predicted by the present invention was also significantly correlated with low gleason score & lt ; 6 ( fisher &# 39 ; s exact test , p = 0 . 028 ) and early stage ( t & lt ; t3 ) ( fisher &# 39 ; s exact test , p = 0 . 007 ). one tumor sample was provided for each prostate cancer patient . for some prostate cancer patients also a healthy (“ normal ”) tissue sample was provided from an unaffected prostate area . these normal samples formed the “ normal ” cluster in fig1 . there were 6 embryonic stem ( es ) cell lines from non - prostate cancer subjects . in addition 10 embryonic carcinoma ( ec ) cell lines from patients with embryonic carcinoma were included . these es and ec cell lines were used as reference to illustrate different patterns of gene expression . importance of this prediction for treatment choices : patients whose tumor is predicted in the aggressive group type 1 should be treated by radical surgery at very early stage even if the tumor gleason score is not high ; whereas patients whose tumor is predicted in the non - aggressive group type 0 should be under regular psa and other examination control if the tumor is at very early stage , because most of the tumors in this group are in fact indolent or progress very slowly . fig3 . prediction of lung cancer tissue type . of the 641 estp genes , 316 had qualified data in 70 % or more of the samples . lung cancer tissue samples were predicted into two major groups , adenocarcinoma group type 0 that mainly contained adenocarcinomas , some normal lung tissues , es cells and a few non - adenocarcinomas , and non - adenocarcinoma group type 1 that contained most non - adenocarcinomas including squamous cell carcinoma , large cell lung cancer and small cell lung cancer , together with a fraction of adenocarcinomas . in general , adenocarcinoma has relatively better prognosis than other types of lung cancer . in this invention , the adenocarcinoma cases in the non - adenocarcinoma group type 1 further showed shorter survival than adenocarcinoma cases in the adenocarcinoma group type 0 as shown in fig4 . all lung cancer patients had a tumor sample . a few patients had also a normal sample from the unaffected lung areas . these a few normal samples clustered together as shown in this figure . there were 6 embryonic stem ( es ) cell lines from non - prostate cancer subjects . in addition 10 embryonic carcinoma ( ec ) cell lines from patients with embryonic carcinoma were also included . these es and ec cell lines were used as reference to indicate different patterns of gene expression . importance of the prediction for treatment strategy : tumors predicted in the adenocarcinoma group may have favourable outcome after radical surgery at very early stage . fig4 . lund adenocarcinoma survival analysis . the analysis is based on lung adenocarcinoma subtypes predicted in fig3 and the published clinical data reproduced in table 3 . time unit : months . fig5 . prediction of subtypes of gastric cancer by estp genes . of the 641 estp genes , 613 had qualified measuring in 70 % of all samples . gastric tumors were classified into two major subtypes , type 1 enriched with diffuse type and mix type tumors generally with poor prognosis , type 0 together with most normal gastric tissue samples . type 0 tumors was further divided into two subtypes with the a subtype enriched with tumors with eb virus - positive . one tumor sample was provided from each gastric cancer patient . from some of the patients also a normal sample was taken from an unaffected stomach area . these “ normal ” samples formed the normal cluster in fig5 . there were 6 embryonic stem ( es ) cell lines from non - prostate cancer subjects . in addition 10 embryonic carcinoma ( ec ) cell lines from patients with embryonic carcinoma were also included . these es and ec cell lines were used as reference to indicate different patterns of gene expression . importance of the prediction : a ) ebv infection is linked to gastric cancer via stem cell biology . preventing ebv infection by vaccination may have preventing effect on gastric cancer ; b ) diffused type of gastric cancer has a very strong hereditary tendency . one should specifically exclude gastric cancer in a relative to a patient , whose tumor is predicted in this group , so that a tumor , if detected , can be treated radically at very early stage . fig6 . gastric cancer survival analysis . the analysis was based on gastric cancer subtypes predicted in fig5 and on the published clinical data reproduced in table 4 . time unit : months . fig7 . prediction of acute myeloid leukemia ( aml ) by estp genes . of the 641 es predictor genes , 324 had qualified data in 70 % of all samples . aml cases were classified into two major subtypes , type 1 enriched in cases with t ( 8 ; 21 ) and del7q chromosomal aberrations , type 0 that was further divided into two subtypes a and b with a subtype enriched inv ( 16 ) and b subtype enriched with t ( 15 ; 17 ). type 1 cases showed shorter overall survival than type 0 as presented in fig5 . from each patient one leukocyte sample was harvested . there were 6 embryonic stem ( es ) cell lines from non - prostate cancer subjects . in addition 10 embryonic carcinoma ( ec ) cell lines from patients with embryonic carcinoma were also included . these es and ec cell lines were used as reference to indicate different patterns of gene expression . importance of the prediction for treatment choices : aml with different chromosomal aberrations respond to different chemotherapies , in particular all - trans retinoic acid can induce differentiation of aml with t ( 15 ; 17 ) translocation . it is highly possible that aml in the group enriched with t ( 15 ; 17 ) but without the translocation detected by cytogenetic diagnostic method can show good response to all - trans retinoic acid due to the same stem cell biological alteration . fig8 . leukemia survival analysis . the analysis was based on aml subtypes predicted in fig7 and on the published clinical data reproduced in table 5 . time unit : months . 1 . lapointe j et al ., gene expression profiling identifies clinically relevant subtypes of prostate cancer . proc natl acad sci usa , 2004 ; 101 ( 3 ): 811 - 816 . 2 . perou c m , et al ., molecular portraits of human breast tumours . nature , 2000 ; 406 ( 6797 ): 747 - 752 . 3 . singh r et al ., microarray based comparison of three amplification methods for nanogram amounts of total rna . am j physiol cell physiol , 2004 . 4 . sorlie t et al ., gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications . proc natl acad sci usa , 2001 ; 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( a ) all patients hade one tumor sample analyzed . a fraction of patients hade also normal tissues from unaffected areas of the prostate analyzed ; they are presented as the “ normal ” cluster in fig2 . ( b ) increasing the q value in the one - class sam ( significant analysis of microarrays ) ranking test gave a list of increased number of significant es genes as shown in fig1 . by choosing different q value cut - off at 0 . 01 , 0 . 05 and 0 . 1 , there were 201 , 641 and 1386 significant es genes selected respectively . using the expression profile of these three gene lists to predict the tumor aggressiveness gave some slight different results as shown in this table . the result by the gene list at q ≦ 0 . 05 gave the best prediction . ( a ) table 3 presents clinical data from lung adenocarcinoma cases only . in fig3 cases with non - adenocarcinoma are included , comprising large cell lung cancer , small cell lung cancer , and squamous cell lung cancer . the non - adenocarcinoma cases were analyzed by gene expression profiling in the original publication but lacked clinical follow - up data . ( b ) by choosing different q value cut - off at 0 . 01 , 0 . 05 and 0 . 1 , 201 , 641 , and 1386 , respectively , significant es genes were selected . using the expression profile of the corresponding gene lists for tumor aggressiveness prediction provided slightly different results as shown table 3 . the q ≦ 0 . 05 gene list gave the best prediction . ( a ) only tumor sample id was indicated in table 4 . some cases had both a tumor sample and a normal sample from respective stomach areas analyzed by gene expression profiling . the normal samples formed a normal cluster as shown in fig5 . ( b ) the es type was determined by using the gene list of 641 es predictor genes selected at q ≦ 0 . 05 in the one - class sam . the gene symbols for all genes in this invention are given according to their standard symbol in the national center for biotechnology information &# 39 ; s gene database ( http :// www . ncbi . nlm . nih . gov / entrez / querv . fcgi ? db = gene & amp ; cmd = search & amp ; term ). for expressed sequence tag ( est ) without gene symbol , the image clone id or the unigene cluster id are given