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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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Pathway and gene expression level comparison of subgroups across all cancers. Polar dendrogram in the centre shows how subgroups of different cancers hierarchically cluster together. GBM subgroups (olive branch) all cluster together, but there are also mixed clusters, such as the lime, green, red and blue branches, containing subgroups of few different cancers. Some of the key differences between subgroups in mixed branches are highlighted for these four branches. For example, TNFa/NFkB signalling is enriched in the green branch (upper right corner) subgroups COAD1 and BRCA11 in contrast to other BRCA and COAD tumours. Few of the most differentially expressed genes in this pathway between these subgroups and other tumours of the same type were GNA11, GNAI1, and AKAP12 shown in upper right corner.
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Figure 5: Pathway and gene expression level comparison of subgroups across all cancers. Polar dendrogram in the centre shows how subgroups of different cancers hierarchically cluster together. GBM subgroups (olive branch) all cluster together, but there are also mixed clusters, such as the lime, green, red and blue branches, containing subgroups of few different cancers. Some of the key differences between subgroups in mixed branches are highlighted for these four branches. For example, TNFa/NFkB signalling is enriched in the green branch (upper right corner) subgroups COAD1 and BRCA11 in contrast to other BRCA and COAD tumours. Few of the most differentially expressed genes in this pathway between these subgroups and other tumours of the same type were GNA11, GNAI1, and AKAP12 shown in upper right corner.

Mentions: Fuelled by discovery of similar pathway level characteristics between subgroups of different cancers, and previous results indicating that there are relevant similarities between cancer subtypes, such as those between Basal-like breast cancer and high-grade serous ovarian cancer [14], we further clustered all subgroups together based on the pathway aberration frequencies in each group (Figure 5). We wanted to find subgroups of different cancers that shared common pathway aberration profiles. The clustering indicated that all GBM subgroups (olive branch) are alike, and none of them share considerable amount of pathway aberrations with subgroups of any other cancer. This may also be due to enrichment and depletion of cell-type specific pathways. Most of the colon and rectal tumours were clustered together (purple branch) with the exception of COAD subgroup 1 that clustered with kidney cancers and BRCA subgroup 11. Other mixed type branches included the red, blue and lime branches which consisted of BRCA, OV, and LUSC subgroups. We did not include the three KIRC subgroups in further analysis of the green branch, because there were no other KIRC samples to compare with in other branches, neither did we not consider the yellow branch with two endometrial subgroups a mixed branch.


Characterization of aberrant pathways across human cancers.

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

Pathway and gene expression level comparison of subgroups across all cancers. Polar dendrogram in the centre shows how subgroups of different cancers hierarchically cluster together. GBM subgroups (olive branch) all cluster together, but there are also mixed clusters, such as the lime, green, red and blue branches, containing subgroups of few different cancers. Some of the key differences between subgroups in mixed branches are highlighted for these four branches. For example, TNFa/NFkB signalling is enriched in the green branch (upper right corner) subgroups COAD1 and BRCA11 in contrast to other BRCA and COAD tumours. Few of the most differentially expressed genes in this pathway between these subgroups and other tumours of the same type were GNA11, GNAI1, and AKAP12 shown in upper right corner.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Pathway and gene expression level comparison of subgroups across all cancers. Polar dendrogram in the centre shows how subgroups of different cancers hierarchically cluster together. GBM subgroups (olive branch) all cluster together, but there are also mixed clusters, such as the lime, green, red and blue branches, containing subgroups of few different cancers. Some of the key differences between subgroups in mixed branches are highlighted for these four branches. For example, TNFa/NFkB signalling is enriched in the green branch (upper right corner) subgroups COAD1 and BRCA11 in contrast to other BRCA and COAD tumours. Few of the most differentially expressed genes in this pathway between these subgroups and other tumours of the same type were GNA11, GNAI1, and AKAP12 shown in upper right corner.
Mentions: Fuelled by discovery of similar pathway level characteristics between subgroups of different cancers, and previous results indicating that there are relevant similarities between cancer subtypes, such as those between Basal-like breast cancer and high-grade serous ovarian cancer [14], we further clustered all subgroups together based on the pathway aberration frequencies in each group (Figure 5). We wanted to find subgroups of different cancers that shared common pathway aberration profiles. The clustering indicated that all GBM subgroups (olive branch) are alike, and none of them share considerable amount of pathway aberrations with subgroups of any other cancer. This may also be due to enrichment and depletion of cell-type specific pathways. Most of the colon and rectal tumours were clustered together (purple branch) with the exception of COAD subgroup 1 that clustered with kidney cancers and BRCA subgroup 11. Other mixed type branches included the red, blue and lime branches which consisted of BRCA, OV, and LUSC subgroups. We did not include the three KIRC subgroups in further analysis of the green branch, because there were no other KIRC samples to compare with in other branches, neither did we not consider the yellow branch with two endometrial subgroups a mixed branch.

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