Patent Application: US-201013517411-A

Abstract:
the present invention relates to a method for assessing a hypoxia phenotype of a tumour of a subject in which the gene expression of between 3 and 50 hypoxia - related genes of a sample obtained from said tumour of the subject is determined , thereby obtaining a sample expression profile of said hypoxia - related genes . the sample gene expression profile is then compared with a reference expression profile of said hypoxia - related genes . the hypoxia - related genes comprise at least slc2a1 , vegfa and pgam1 . probes , arrays and kits for use in the method are also disclosed .

Description:
the following is presented by way of example and is not to be construed as a limitation to the scope of the claims . large - meta analysis of multiple cancers reveals a common , compact and highly prognostic hypoxia metagene gene - expression studies attempt to extrapolate biologically and clinically relevant hypotheses from gene expression patterns . however , many current studies make little use of existing knowledge such as gene function within specific pathways , and prognostic signatures are often derived with no reference to the functional roles of their components . one increasingly popular method that aims to make use of prior knowledge is gene set enrichment analysis ( gsea ) ( subramanian et al , 2005 ). gsea first conducts a supervised analysis by ranking genes according to their ability to discriminate between different sample groups , and then maps them onto previously defined gene - sets , typically formed according to common function using annotation sources . the goal is to identify sets containing a statistically significant number of highly ranked genes , and then to use this information to provide functional characterizations for the samples in question . although powerful , gsea relies on stratification of the experimental samples into distinct groups , often making it unsuitable for use with heterogeneous clinical datasets . another approach often applied to microarray data involves creation of a co - expression network within which each ‘ node ’ represents a gene , and ‘ edges ’ are created between genes when their expression patterns are significantly correlated . co - expression networks have been used to formulate functional and clinical hypotheses from in vivo data ( butte & amp ; kohane , 2003 ; hahn & amp ; kern , 2005 ; wolfe et al , 2005 ). a disadvantage with the approach is that it can be susceptible to the multiple testing issues that arise due to the large number of genes represented on a typical microarray . setting a low threshold for a significant correlation between genes will result in the inclusion of many spurious links , while a high threshold will control the false positive rate at the expense of omitting many genuine edges . here we illustrate and validate a network - based approach with parallels to both gsea and co - expression networks ; for a workflow of the method see suppl . material and methods . it can be applied directly to clinical data , even when the samples cannot be partitioned in advance into distinct groups . the algorithm begins with a collection of ‘ seed ’ genes that are then used as starting point from which to build an association network . rather than simply connect gene pairs with high correlation between their expression profiles , the approach defines a “ neighborhood of co - expression ” around each seed gene , and then connects seeds that have a significant degree of overlap between their neighborhoods . this approach is relatively robust against the inclusion of spurious edges , since edges are only added when there is consistently high correlation to many intermediate genes that form the intersection between seeds . we previously used a seed - based approach successfully to predict hypoxia - related genes ( winter et al , 2007 ); the current study develops the method in a meta - analysis context to produce robust signatures requiring fewer genes , making them more suitable for clinical use , for example in quantitative rt - pcr analyses of biopsies at presentation . hypoxia plays a key role in defining the behavior of many cancers including head and neck squamous cell carcinomas ( hnscc ) ( nordsmark et al , 2005 ) and breast carcinomas ( bc ) ( fox et al , 2007 ); thus the identification of common hypoxia - regulated genes is important both for understanding of cancer evolution , and for improved prognosis or development of novel therapies . the described approach was applied to a large meta - analysis of hnsccs and bcs to successfully define a common and robust hypoxia signature . the process begins with k seed genes , π ={ π 1 , π 2 . . . π k } (‘ gene ’ is used throughout for convenience , although ‘ transcript ’ is generally more accurate ). spearman correlation , ρ , is computed between seeds and genes y ={ y 1 , y 2 . . . y m } in a dataset of n samples , x ={ x 1 , x 2 . . . x n }. for each seed / gene pair , their ‘ affinity ’ is defined as : where θ t and θ s define extent and sharpness of the cluster . when θ s → 0 , δ reduces to the step function with δ = 0 if ρ 2 & lt ; θ t , δ = 1 if ρ 2 & gt ; θ t . in this limit , the method is parameter - free , and this will be used in this study . θ t is defined objectively using a probability threshold , α , of observing a given correlation if the null hypothesis ( i . e . no association ) was true . this needs to be corrected for multiple testing ( hastie et al , 2001 ) to account for the size of y ; here , α = 0 . 05 after bonferroni correction was considered . finally , a membership function is defined : γ ( y i , π k )= δ ( y i , π k )/ σ j = 1 k δ ( y i , π j ) ( equation 2 ) an increasing γ indicates stronger membership of a gene to a seed cluster . where γ is the membership ( eq . 2 ). two seeds are considered to carry a high degree of related information if their clusters share many genes ( high s values ). a sign function is also defined : where sgn ( x ) is the sign function :— sgn ( x )= 1 if x & gt ; 0 , sgn ( x )=− 1 if x & lt ; 0 . if two seeds are correlated with their shared features in the same direction , f = 1 ( seeds are fully concordant ); if they are correlated with their shared features in opposite direction , f =− 1 . the strength of the relationship between a gene and the whole set of seeds is estimated using the connectivity function : where γ is defined in eq . 2 and w are weights which regulate the importance of each seed . in this study , we consider w = 1 , unless y i is one of the seeds , or a probeset biding to the same transcript as the seed ; in this case , to avoid bias , for that seed w = 0 . a connectivity score , is defined as the fractional rank of c ; that is the ranking normalized between 0 ( lowest c ) and 1 ( highest c ). random sets of seeds are generated by monte - carlo sampling , clusters aggregated around them , c and s calculated . this procedure is repeated to generate null distributions and it provides an estimate of the probability of observing by chance a given value of c and s . bootstrapping is re - sampling with replacement of the original population ; it is used to provide maximum likelihood best estimates when an analytical approach is not feasible ( hastie et al , 2001 ). here , it is used to provide best estimates and confidence limits for c and s . these are used in a meta - analysis across several datasets to define a meta - connectivity score as : where r [ c ( y i )] k is the fractional rank of c ( eq . 5 ), n d is the number of datasets , σ 2 k is the variance of the ranked c , r [ c ( y i )] k , in dataset k for gene y i . a common metagene between tumours types is derived by taking the ĉ scores product , ĉ . this is effectively a rank product , as ĉ is an average rank ( eq . 6 ). a common metagene between tumours types is derived by taking the ĉ scores product , ĉ . this is effectively a rank product , as ĉ is an average rank ( eq . 6 ). a summary expression score , e , is defined in each sample as the median of the absolute expression of the genes in the signature . the median is used as summary statistics to reduce the effect of outliers . a cumulative forest plot is defined :— genes are added to the signature , one by one , in order of their connectivity , c , score so that genes that are introduced first have the highest connectivity . at each step , a summary expression , e , is derived using the new gene and genes from the previous steps . samples are then ranked by their e value ; this assigns a hypoxia score ( hs ) from lowest ( least hypoxic ) to highest ( most hypoxic ). hs is then renormalized between 0 and 1 ; introduced into a cox multivariate analysis that includes the other significant clinical covariates ; and the hazard ratio ( hr ) of the hs is calculated . ncbi gene expression omnibus ( http :// www . ncbi . nlm . nih . gov / geo /) was searched for gene expression studies in cancer , published in peer - reviewed journals , where microarray were performed on frozen material extracted before chemotherapy , radiotherapy or adjuvant treatment . eight datasets ( table 1 ) were selected that used similar platforms ( affymetrix u133a , b and plus2 ). processing was performed using simpleaffy ( wilson & amp ; miller , 2005 ); the gcrma function was used to estimate expression values , data were quantile - normalized and logged ( base2 ). other datasets were identified for validation in which different technologies were used ( table 1 ); non - affymetrix datasets were processed as described in the original publications . more details on pre - processing and annotation are given in the supplementary methods . a hypoxia expression network was built first in a dataset comprising 59 hnscc tumour samples ( vice 125 ; table 1 ) using well - characterized hypoxia - related genes identified from the literature covering a comprehensive set of hypoxia - induced pathways ( set a , table s1 ). these were adrenomedullin ( adm ), adenylate kinase 3 - like 1 ( ak3l1 ), bcl2 / adenovirus e1b 19 kda interacting protein 3 ( bnip3 ), carbonic anhydrase ix ( ca9 ), enolase 1 ( eno1 ), hexokinase 2 ( hk2 ), lactate dehydrogenase a ( ldha ), phosphoglycerate kinase 1 ( pgk1 ), solute carrier family 2 member1 ( slc2a1 ), and solute carrier family 2 ( vegfa ). the resultant network ( fig1 ) was observed to map to distinct regions of the reactome ( www . reactome . org ) network and to several hypoxia - related pathways ( fig2 and s 1 ). the method was applied to additional hnscc and bc training datasets ( table 1 ) with similar results ( table s2 ). in the resulting expression networks , high shared neighborhood , s ( equation 3 ), values between seed - pairs were generally associated with a high pair - wise correlation . however , this relationship did not always hold . an example is given in fig . s 2 , where genes in a published 245 - gene literature list ( ll ) ( winter et al , 2007 ), were used as starting seeds . many of the seeds with high pair - wise s but low correlation appeared in the same kegg ( http :// www . genome . jp / kegg /) pathway but would not be detected in a straightforward correlation analysis ( fig . s 2 ). furthermore some seeds showed markedly different in vivo and in vitro behaviors ; for example , pfkfb3 ( set b , table s1 ) did not have significant overlap with any other seeds , while ccng2 showed a consistent inverse - correlation with other seeds ( f & lt ; 0 ; equation 4 ) supporting results from previous studies ( choi & amp ; chen , 2005 ). thus , the method was able to identify seeds that behave differently from their peers ; for the rest of this study , only the conservative seed set a was used . this set showed higher pair - wise s values than any other set of randomly selected seeds ( repeated 1000 times ) from the 245 - gene ll . genes in the co - expression networks were ranked by their connectivity score , c ( equation 5 ), and compared with the hypoxia 245 - gene ll . as the latter is biased towards up - regulated genes ( harris , 2002 ), only genes showing consistent positive correlation with the initial seeds were considered . to avoid bias , the initial seeds were excluded from this comparison . the relative proportion of known hypoxia genes increased with increasing connectivity , c , score ( fig2 ), confirming its utility as a metric for predicting functional relationships . similar results were observed with different clustering and pre - processing methods ( fig . s 3 ). however , differences were observed between datasets . much of this inter - experimental variation is likely to reflect differences in both the patient populations and the processing of the biological material . for example , both datasets gse6791 and gse3494 , which showed a lower level of enrichment for hypoxia genes than others , featured samples with the highest proportions of tumour cells selected either by micro - dissection or visual scoring . next we selected a subset of ‘ hub ’ genes from the hypoxia network , with the goal of using them as a hypoxia signature . genes with high connectivity , c ( equation 5 ), score ( p & lt ; 0 . 01 , estimated by monte - carlo simulation ) were considered ( table s2 ). each of these genes had a greater - than - expected overlap with the neighborhoods of all other genes in the network ( fig . s 4 ). the seeds were only selected if they were hubs with respect to all other seeds . using the reactome database we confirmed that pathways known to be regulated by hypoxia , such as glycolysis , gluconeogenesis , glucose metabolism and cori cycle ( recycling of lactic acid ) were consistently over - represented in these genes ( fig2 and table s3 ). similarly , go analysis ( http :// genecodis . dacya . ucm . es ) found over - representation ( false discovery rate & lt ; 0 . 05 ) of pathways such as glycolysis , phosphoinositide - mediated signaling , nuclear mrna splicing , translational initiation , regulation of cell cycle , ubiquitin - dependent protein catabolism , apoptosis and regulation of cell proliferation . over - represented molecular functions included atp binding , nucleotide binding , lipoic acid binding , oxidoreductase and l - lactate dehydrogenase activity . we selected genes that showed consistent high connectivity across datasets and derived meta - signatures for hypoxia in hnscc and bc . interestingly , although some of the datasets performed poorly on their own , meta - analysis signatures were robust to their inclusion and performed well ( fig2 b , c ). we assessed the meta - signatures &# 39 ; prognostic relevance in four independent datasets ( table 1 ). samples were ranked using a summary expression score , e , of the genes in the signature ; this produced a hypoxia score , hs , which assigns a hypoxic status to the tumours in the validation datasets . multivariate cox analysis including available clinical factors was carried out using each dataset ; clinical variables were selected using backward - stepwise maximum likelihood . the hs was introduced into the reduced clinical model to estimate the prognostic significance of the meta - signatures independently from other clinical variables ( fig . s 5 and table s4 ). to address whether smaller signatures with equal prognostic ability could be derived by using a more stringent c - score , cumulative forest plots were generated in which genes were introduced into the hs calculation one - by - one , in decreasing order of their meta - c score ( fig . s 5 ). only a few genes were needed before the hazard ratio stabilized and a reduced signature was found to be at least as prognostic as a larger one ( fig . s 5 ). interestingly , when genes were introduced into the cumulative plots in random order , rather than by their ranked c - score , more genes were needed to reach equivalent prognostic significance ( fig . s 5 ). common hubs in hnscc and bc were selected by considering , for each gene , the product , c , of the c - scores between the hnscc and bc meta - analyses . a common metagene was derived by considering genes with c & gt ; 0 . 5 ( table 2 and s5 ). this hard cut - off was chosen since a gene with a c score approaching that which would be expected by chance ( c ≈ 0 . 5 ) in one tumour site , would have to achieve a maximal score in the other tumour site to be included . we investigated in cell lines potential regulation of genes in the common metagene by hypoxia and by hif1a , the main mediator of the hypoxia response in cancer . we considered two datasets : a hypoxia time course in a panel of epithelial and endothelial non - malignant cells ( chi et al , 2006 ), and a hif1a and hif2a sirna experiment in mcf7 bc cells ( elvidge et al , 2006 ) exposed to hypoxia . for details of these data we refer to the original publications . although differences between cell lines and bc in vivo are expected , a high proportion of genes in the common metagene ( 38 / 51 ) showed either regulation in the hypoxia time course or in the sirna experiment ( fig3 a , b and table s5 ). several of these genes were also predicted as hif1a targets and showed potential hif1a binding sites ( table s5 ). furthermore , 22 had already been found hypoxia - regulated by previous published work ( table s5 ). overall approximately 80 % ( 42 / 51 ) of genes in the common metagene were confirmed by at least one validation , several of them by more than one . the common hypoxia metagene ( 51 genes ) was prognostic in independent datasets of different cancer types ( table 3 ) and showed greater prognostic power than ( i ) an in - vitro derived hypoxia signature ( chi et al , 2006 ); ( ii ) the initial seeds and ( iii ) our 99 - gene hnscc hypoxia metagene derived previously ( winter et al , 2007 ) ( table 3 ). a signature derived by selecting genes co - expressed with vegf in bc ( desmedt et al , 2008 ) had no independent prognostic significance ( data not shown ), in agreement with the published study . in a further validation using oncomine ( http :// www . oncomine . org ), all but one of the fifteen top - ranked ( by hc score ) genes showed prognostic significance in at least one tumour site ( p & lt ; 0 . 0001 ). the only top gene for which prognostic significance was not reported in oncomine , slc2a1 ( glut1 ), is prognostic in other studies ( oliver et al , 2004 ). finally , cumulative forest plots based on connectivity score ( fig3 ) showed no further improvement in hazard ratio after addition of a small number of genes . although differences were observed between hnscc , bc and lung cancers , we found in all cases that a common signature reduced to a small number of c score top - ranked genes was at least as prognostic as the full signature ( fig3 c , d and table 3 ). hypoxia is a frequent feature of poor - prognosis tumours , and the identification of common in vivo hypoxia - related genes is desirable both for prognostic stratification of patients , and development of novel therapies . although prognostic markers of hypoxia have been identified , there are discrepancies between studies and powerful methods used in large - meta analyses are needed to define generally applicable signatures . a method is described for defining a hypoxia signature that combines previous knowledge derived from in vitro experiments , with co - expression data produced from in vivo samples . we demonstrate that by constructing a gene expression network and then extracting core ‘ hub ’ ( high connectivity ) genes it is possible to define signatures that are significantly enriched for phenotype - specific genes , and pathways . while we have used this method to derive a compact and clinically relevant signature of hypoxia in cancer , the approach is likely to have broader applicability . specifically , we used the described method in a meta - analysis of a total of 1136 hnscc and bcs to derive tissue - specific and common signatures of hypoxia by including only genes that are consistently useful across multiple experiments or tissue types respectively . the ability of the method to derive highly prognostic hypoxia signatures despite differences between datasets highlights its robustness . the gene expression network used to construct the signature was found to be biologically relevant and to map to a discrete set of biochemical pathways , that is significantly enriched for hypoxia - regulated genes and pathways . this finding highlights that not only can in vitro data assist understanding of clinical data , but also the reverse , that clinical data can be used to formulate specific biological hypotheses . remarkably , a reduced common hypoxia metagene containing as few as three genes , namely vegfa , slc2a1 and pgam1 , was as prognostic as a large signature in independent bc and hnscc series . furthermore , it was more prognostic than several published signatures when tested in a set of independent datasets , suggesting a level of general applicability . specifically , genes with highest connectivity were also the most prognostic across a panel of cancers . this further validates the method , as prognosis was not used to select genes which were only ranked by their connectivity ; and this ranking was derived in independent datasets . although a reduced signature was prognostic in all tumour sites tested , the number of genes before convergence was lower in hnscc and bc than lung cancer . this offers another positive control as this was a common signature between hnscc and bc , thus it is expected to reflect their biology to a better extent ; however , it also indicates a degree of tumour specificity . the common signature and the tumour - type specific signatures are being evaluated in prospective prognostic and predictive studies in hnscc and breast cancer . in summary , this study uses knowledge from in vitro experiments regarding function of multiple genes combined with in vivo co - expression patterns to derive a common hypoxia metagene in multiple cancers that is highly prognostic , whilst being compact and robust . & amp ; reduced models of clinical covariates are derived using backward stepwise likelihood . signature scores are entered into the reduced model ; hazard - ratio , 95 % confidence limits and significance ( model with and without the signature ) are shown . δ these two datasets were used to develop the signature but no training on outcome was done . μ summary score , e , is calculated for the signature including only the initial seeds . data were normalized using gcrma in bioconductor ( http :// www . bioconductor or 0 and log 2 expression was considerd . the nbc ! database , biomart and matchminer were used to retrieve other aliases and previous ids for the seeds . filtering was performed based on expression levels and coefficient of variation :— gene were selected for the clustering if their expression level was above the 0 . 55 quantile , and their coefficient of variation was above the 0 . 10 quantile , of the global array distribution for expression and cv respectively . to avoid noise arising from cross - contamination in some of the arrays ; filtering of unspecific probestes was done using array information provided by affymetrix . specifically , probesets with termination x at in the u133 plus2 array , and probesets with termination s at and g at in the u95 arrays , were not used to calculate the seeds &# 39 ; expression levels ( for definition of “ seed ” see clustering section below ). 10 genes known to be related to hypoxia in previous studies were used as seeds . set a in the table below was used in this study : when more than one probeset mapped to the same gene , the ‘ best candidate ’ probeset was used :— after filtering was performed to select highly expressed probesets that showed significant variation ( see 5 above ); a ‘ best candidate ’ seed was selected as the seed on which most evidence have been accumulated in previous studies ; in this case , ca9 was selected as the “ gold ”- candidate seed . the median expression was computed for this seed if more than one probesets are present ( in the case of ca9 only 1 probeset present on the array ); for the other seeds , the probeset with expression showing the highest correlation to the expression of the “ gold ”- candidate seed was selected . the process begins with k seed genes , π ={ π 1 , π 2 . . . π k } (‘ gene ’ is used throughout for convenience , although ‘ transcript ’ is generally more accurate ). spearman correlation , ρ , is computed between seeds and genes y ={ y 1 , y 2 . . . y m } in a dataset of n samples , x ={ x 1 , x 2 . . . x n }. for each seed / gene pair , their ‘ affinity ’ is defined as : where θ t and θ s define extent and sharpness of the cluster . when θ s → 0 , δ reduces to the step function with δ = 0 if ρ 2 & lt ; η t , δ = 1 if ρ 2 & gt ; θ t . this was the limit used for this study as it is parameter - free . this needs to be corrected for multiple testing to account for the size of y ; here , α = 0 . 05 after bonferroni correction was considered . finally , a membership function is defined : γ ( y i , π k )= δ ( y i , π k )/ σ j = 1 k δ ( y i , π j ) ( equation 2 ) an increasing γ indicates stronger membership of a gene to a seed cluster . where γ is the membership ( eq . 2 ). two seeds are considered to carry a high degree of related information if their clusters share many genes ( high s values ). a sign function is also defined : where sgn ( x ) is the sign function :— sgn ( x )= 1 if x & gt ; 0 , sgn ( x )=− 1 if x & lt ; 0 . if two seeds are correlated with their shared features in the same direction , f = 1 ( seeds are fully concordant ); if they are correlated with their shared features in opposite direction , f =− 1 . the strength of the relationship between a gene and the whole set of seeds is estimated using the connectivity function : where γ is defined in eq . 2 and w are weights which regulate the importance of each seed . in this study , we consider w = 1 , unless y i is one of the seeds , or a probeset biding to the same transcript as the seed ; in this case , to avoid bias , for that seed w = 0 . a connectivity score , is defined as the fractional rank of c ; that is the ranking normalized between 0 ( lowest c ) and 1 ( highest c ). random sets of seeds are generated by monte - carlo sampling , clusters aggregated around them , c and s calculated . this procedure is repeated to generate null distributions and it provides an estimate of the probability of observing by chance a given value of c and s . bootstrapping was used to provide best estimates and confidence limits for c and s . these are used in a meta - analysis across several datasets to define a meta - connectivity score as : where r [ c ( y i )] k is the fractional rank of c ( eq . 5 ), n d is the number of datasets , σ 2 k is the variance of the ranked c , r [ c ( y i )] k , in dataset k for gene y i . exactly the same procedure ( described above ) was applied first to the head and neck datasets and then to the breast cancer datasets . datasets are listed below : note : the procedure described above was applied in the same way to the head and neck datasets , and then to the breast datasets and two meta - signatures , one in head - and neck , and another in breast were obtained . the head and neck cancer metagene set , containing the top 100 genes in the hn meta - signature , is shown in the following table : finally a common hypoxia signature ( or common metagene as referred to herein ) between head and neck , and breast cancer , was derived by taking the c scores product , ec . this is effectively a rank product , as c is an average rank ( eq . 6 ). so the meta - c score for the hn ( as calculated by eq . 6 ) was multiplied by the meta - c score for the breast cancer signature ( as calculated by eq . 6 ). the results for this give the common signature which is the common metagene , and which is shown in the following table : to check if a reduced signature was as prognostic as a full signature we used cumulative forest plots based on connectivity score — this was not used to train the signatures but just to understand their performance as prognostic markers in independent datasets . a summary expression score , e , is defined in each sample as the median of the absolute expression of the genes in the signature . the median is used as summary statistics to reduce the effect of outliers . a cumulative forest plot is defined :— genes are added to the signature , one by one , in order of their connectivity , c , score so that genes that are introduced first have the highest connectivity . at each step , a summary expression , e , is derived using the new gene and genes from the previous steps . samples are then ranked by their e value ; this assigns a hypoxia score ( hs ) from lowest ( least hypoxic ) to highest ( most hypoxic ). hs is then renormalized between 0 and 1 ; introduced into a cox multivariate analysis that includes the other significant clinical covariates ; and the hazard ratio ( hr ) of the hs is calculated . prognostic validation ( without further training ): this was applied in the same way to the hn , bc and common signatures . results for these validations are provided in example 1 table 3 for the common signature ; and in the supplementary table s4 for the hn and bc meta - signatures . a refined and reduced signature of 26 genes was selected for the development of a pcr card for use to assess a hypoxia phenotype of a tumour . after the bioinformatics derivation described above ( points 1 - 8 ) more practical filters were applied to the meta - hn signature to select genes which would go on a preferred pcr card to be validated prospectically : top 26 genes from the above meta - analysis ( highest meta - c score as calculated by eq . 5 , and as given the head and neck metagene set ) which also fulfilled : showed a log 2 fold change & gt ; 0 . 4 in a small subsets of 5 high and 5 low hypoxia score hn patients ( this hypoxia score was based on our first publication in cancer research , winter et al , 2007 ) were also present in at least two datasets in the meta - analysis sufficiently adequate performance in pcr experiments if one of the top 26 genes was found not to fulfill these criteria , the next one down in order of meta - c score was selected and so on until 26 genes were selected that fulfilled all of the above . this gave the preferred 26 - 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