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Characterization of aberrant pathways across human cancers.

Ylipää A, Yli-Harja O, Zhang W, Nykter M - BMC Syst Biol (2013)

Bottom Line: Especially in molecular level, tumours of the same category can appear significantly dissimilar due to complex combinations of genetic aberrations leading to a similar malignancy.Clustering analysis revealed five clinically relevant subgroups of tumours in four cancers that exhibited significant differences in survival compared to others.The cross-cancer analysis of the subgroups resulted in the identification of tumours that shared potentially significant alterations.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Cancer is a broad group of genetic diseases which account for millions of deaths worldwide each year. Cancers are classified by various clinical, pathological and molecular methods, but even within a well-characterized disease, there is a significant inter-patient variability in survival, response to treatment, and other parameters. Especially in molecular level, tumours of the same category can appear significantly dissimilar due to complex combinations of genetic aberrations leading to a similar malignancy. We extended the current classification methods by studying tumour heterogeneity at pathway level.

Methods: We computed the rate of alterations in 1994 pathways and 2210 tumours consisting of eight different cancers. Using gene set enrichment analysis, each sample was computed a pathway aberration profile that reflected its molecular state. The profiles were analysed together to infer the characteristic aberration rates for each pathway within each cancer. Subgroups of tumours defined by similar pathway aberrations were identified using clustering analyses. The pathway aberration and gene expression profiles of the subgroups were consecutively compared across all eight cancer types to search for similar tumours crossing the standard classification.

Results: We identified pathways and processes that were common to all cancers as well as traits that are unique to a cancer type or closely related cancers. Studying the gene expression patterns within the pathway context suggested potential alteration mechanisms. Clustering analysis revealed five clinically relevant subgroups of tumours in four cancers that exhibited significant differences in survival compared to others. The cross-cancer analysis of the subgroups resulted in the identification of tumours that shared potentially significant alterations.

Conclusions: This study represents the first effort to extend the molecular characterizations towards pathway level descriptions across the family of cancers. In addition to providing a proof-of-concept for single sample pathway aberration analysis in this context, we present a comprehensive pathway aberration dataset that can be used to study pathway aberration patterns within or across cancers. Significant similarities between subgroups of different cancers on pathway and gene expression levels provide interesting hypotheses for understanding variable drug response, or transferring treatments across diseases by identifying common druggable pathways or genes, for example.

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Similarities and differences of tumour types in gene expression and pathway levels. a) Two first principal components of the gene expression data from all the tumours are plotted. Colours denote the eight different cancer types. Blow-up panel highlights a distinct group of samples from almost all cancer types b) Hierarchical clustering of the pathway aberration profiles indicates that colon and rectal cancers (blue branch) are alike. Gynaecological cancers (red branch) are also similar on pathway level c) Nervous system development is enriched in nearly all GBM, but rarely in any other cancer type. There is a considerable variation in enrichment and depletion frequency in d) Platelet activation pathway e) Drug metabolism through cytochrome p450 and f) Irinotecan pathway.
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Figure 1: Similarities and differences of tumour types in gene expression and pathway levels. a) Two first principal components of the gene expression data from all the tumours are plotted. Colours denote the eight different cancer types. Blow-up panel highlights a distinct group of samples from almost all cancer types b) Hierarchical clustering of the pathway aberration profiles indicates that colon and rectal cancers (blue branch) are alike. Gynaecological cancers (red branch) are also similar on pathway level c) Nervous system development is enriched in nearly all GBM, but rarely in any other cancer type. There is a considerable variation in enrichment and depletion frequency in d) Platelet activation pathway e) Drug metabolism through cytochrome p450 and f) Irinotecan pathway.

Mentions: Using TCGA data portal, we downloaded (on 3/8/2012) all of the dual-channel mRNA expression array data (Agilent 244K TCGA Custom 1-3) (n = 2365) from the eight cancer types that have been characterized by TCGA project (see Table 1). We used "level 3" data which corresponds to pre-processed and interpreted expression signals for each gene. These data had been normalized against Stratagene Universal Reference RNA and Lowess normalization had been applied on a per-gene basis by the TCGA. Log ratios of gene expressions were finally obtained for 17,814 genes. Expression values were quantile normalized for cross-sample variation for principal component analysis (PCA). After checking for consistency of gene expression profiles PCA analysis, we removed all duplicate samples so that each sample represents a unique case. We also removed data from patient IDs 'TCGA-07-0249' and 'TCGA-AV-A03E' that were found in many duplicates, had a distinct gene expression signature, had no meta data, and most alarmingly were annotated to many different cancers (see blow-up in Figure 1a). Removing the data from 155 duplicate or bad samples, we arrived at gene expression profiles of 2210 unique samples.


Characterization of aberrant pathways across human cancers.

Ylipää A, Yli-Harja O, Zhang W, Nykter M - BMC Syst Biol (2013)

Similarities and differences of tumour types in gene expression and pathway levels. a) Two first principal components of the gene expression data from all the tumours are plotted. Colours denote the eight different cancer types. Blow-up panel highlights a distinct group of samples from almost all cancer types b) Hierarchical clustering of the pathway aberration profiles indicates that colon and rectal cancers (blue branch) are alike. Gynaecological cancers (red branch) are also similar on pathway level c) Nervous system development is enriched in nearly all GBM, but rarely in any other cancer type. There is a considerable variation in enrichment and depletion frequency in d) Platelet activation pathway e) Drug metabolism through cytochrome p450 and f) Irinotecan pathway.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750561&req=5

Figure 1: Similarities and differences of tumour types in gene expression and pathway levels. a) Two first principal components of the gene expression data from all the tumours are plotted. Colours denote the eight different cancer types. Blow-up panel highlights a distinct group of samples from almost all cancer types b) Hierarchical clustering of the pathway aberration profiles indicates that colon and rectal cancers (blue branch) are alike. Gynaecological cancers (red branch) are also similar on pathway level c) Nervous system development is enriched in nearly all GBM, but rarely in any other cancer type. There is a considerable variation in enrichment and depletion frequency in d) Platelet activation pathway e) Drug metabolism through cytochrome p450 and f) Irinotecan pathway.
Mentions: Using TCGA data portal, we downloaded (on 3/8/2012) all of the dual-channel mRNA expression array data (Agilent 244K TCGA Custom 1-3) (n = 2365) from the eight cancer types that have been characterized by TCGA project (see Table 1). We used "level 3" data which corresponds to pre-processed and interpreted expression signals for each gene. These data had been normalized against Stratagene Universal Reference RNA and Lowess normalization had been applied on a per-gene basis by the TCGA. Log ratios of gene expressions were finally obtained for 17,814 genes. Expression values were quantile normalized for cross-sample variation for principal component analysis (PCA). After checking for consistency of gene expression profiles PCA analysis, we removed all duplicate samples so that each sample represents a unique case. We also removed data from patient IDs 'TCGA-07-0249' and 'TCGA-AV-A03E' that were found in many duplicates, had a distinct gene expression signature, had no meta data, and most alarmingly were annotated to many different cancers (see blow-up in Figure 1a). Removing the data from 155 duplicate or bad samples, we arrived at gene expression profiles of 2210 unique samples.

Bottom Line: Especially in molecular level, tumours of the same category can appear significantly dissimilar due to complex combinations of genetic aberrations leading to a similar malignancy.Clustering analysis revealed five clinically relevant subgroups of tumours in four cancers that exhibited significant differences in survival compared to others.The cross-cancer analysis of the subgroups resulted in the identification of tumours that shared potentially significant alterations.

View Article: PubMed Central - HTML - PubMed

ABSTRACT

Background: Cancer is a broad group of genetic diseases which account for millions of deaths worldwide each year. Cancers are classified by various clinical, pathological and molecular methods, but even within a well-characterized disease, there is a significant inter-patient variability in survival, response to treatment, and other parameters. Especially in molecular level, tumours of the same category can appear significantly dissimilar due to complex combinations of genetic aberrations leading to a similar malignancy. We extended the current classification methods by studying tumour heterogeneity at pathway level.

Methods: We computed the rate of alterations in 1994 pathways and 2210 tumours consisting of eight different cancers. Using gene set enrichment analysis, each sample was computed a pathway aberration profile that reflected its molecular state. The profiles were analysed together to infer the characteristic aberration rates for each pathway within each cancer. Subgroups of tumours defined by similar pathway aberrations were identified using clustering analyses. The pathway aberration and gene expression profiles of the subgroups were consecutively compared across all eight cancer types to search for similar tumours crossing the standard classification.

Results: We identified pathways and processes that were common to all cancers as well as traits that are unique to a cancer type or closely related cancers. Studying the gene expression patterns within the pathway context suggested potential alteration mechanisms. Clustering analysis revealed five clinically relevant subgroups of tumours in four cancers that exhibited significant differences in survival compared to others. The cross-cancer analysis of the subgroups resulted in the identification of tumours that shared potentially significant alterations.

Conclusions: This study represents the first effort to extend the molecular characterizations towards pathway level descriptions across the family of cancers. In addition to providing a proof-of-concept for single sample pathway aberration analysis in this context, we present a comprehensive pathway aberration dataset that can be used to study pathway aberration patterns within or across cancers. Significant similarities between subgroups of different cancers on pathway and gene expression levels provide interesting hypotheses for understanding variable drug response, or transferring treatments across diseases by identifying common druggable pathways or genes, for example.

Show MeSH
Related in: MedlinePlus