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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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Differential pathway and gene expression patterns in glioblastoma subgroups. a) Alternating pathway aberration patterns for nine pathways are shown for the GBM subgroups. Highlighted are Immune response pathway (purple) that is depleted in subgroup 12, Protein kinase pathway (yellow) enriched in subgroup 6, and Wnt signalling pathway (blue) enriched in subgroup 9. Shown in panels below are mean expression levels in subgroups for the genes in three highlighted pathways: b) Immune response, c) Protein kinase cascade and d) Wnt signalling pathway. E) Expression levels of Wnt signalling pathway genes in individual samples demonstrate the consistency of gene expression patterns within an enriched subgroup.
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Figure 4: Differential pathway and gene expression patterns in glioblastoma subgroups. a) Alternating pathway aberration patterns for nine pathways are shown for the GBM subgroups. Highlighted are Immune response pathway (purple) that is depleted in subgroup 12, Protein kinase pathway (yellow) enriched in subgroup 6, and Wnt signalling pathway (blue) enriched in subgroup 9. Shown in panels below are mean expression levels in subgroups for the genes in three highlighted pathways: b) Immune response, c) Protein kinase cascade and d) Wnt signalling pathway. E) Expression levels of Wnt signalling pathway genes in individual samples demonstrate the consistency of gene expression patterns within an enriched subgroup.

Mentions: Investigating the pathway aberration differences between the subgroups, and the molecular mechanisms that cause these aberrations may offer interesting insight into the diseases. In Figure 4a, we show the mean pathway aberration profiles for nine pathways that are differentially aberrant in the 12 GBM subgroups. This analysis revealed that the tumours in the less lethal GBM subgroup 12 (Proneural/G-CIMP enriched) differed significantly on a number of pathway aberration frequencies. For example, GO category Immune response was enriched in 0% and depleted in 97% of the less lethal tumours, whereas the aberration frequencies in other tumours were 54% and 9% for enrichment and depletion, respectively. Apoptotic and haemostatic pathways were also significantly depleted compared to others. The tumours in the more lethal GBM subgroup 6 differed from the others the most radically in aberration rate of signalling cascades, and metabolic pathways. To understand the molecular mechanisms behind these pathway level changes, we investigated the mean gene expression levels in three highlighted pathways: Immune response depleted in subgroup 12 (Figure 4b), Protein kinase cascade enriched in subgroup 6 (Figure 4c), and Wnt signalling pathway enriched in subgroup 9 (Figure 4d). The genes that cause the differential pathway aberration rates in these subgroups are clearly observed: Immune response genes IFITM2-3, PTPRC, HAMP, CCR1, IL6R, and BLNK, for example, are exclusively underexpressed in subgroup 12 causing the depletion. Several protein kinase cascade genes are not underexpressed in subgroup 6, in addition to exclusive overexpression of SHC1, TRIM38, NOD1, and others, causing the enrichment. In Wnt signalling pathway, enriched only in subgroup 9, we observed especially WNT10B, FZD9, CHP2, and RAC3 overexpression and FZD7, TCF7L1, and MMP7 underexpression, whereas in other subgroups their expression changes were the opposite. To further investigate the consistency of the differential gene expression levels within subgroups, we clustered the expression ratios of the genes in Wnt signalling pathway in GBM (Figure 4e). Samples in subgroup 9 formed a separate cluster from the other samples, and from the individual gene expression pattern we observed that the most prominent genes listed above, such as WNT10B, were indeed very consistently differentially expressed in this subgroup compared to others.


Characterization of aberrant pathways across human cancers.

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

Differential pathway and gene expression patterns in glioblastoma subgroups. a) Alternating pathway aberration patterns for nine pathways are shown for the GBM subgroups. Highlighted are Immune response pathway (purple) that is depleted in subgroup 12, Protein kinase pathway (yellow) enriched in subgroup 6, and Wnt signalling pathway (blue) enriched in subgroup 9. Shown in panels below are mean expression levels in subgroups for the genes in three highlighted pathways: b) Immune response, c) Protein kinase cascade and d) Wnt signalling pathway. E) Expression levels of Wnt signalling pathway genes in individual samples demonstrate the consistency of gene expression patterns within an enriched subgroup.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Differential pathway and gene expression patterns in glioblastoma subgroups. a) Alternating pathway aberration patterns for nine pathways are shown for the GBM subgroups. Highlighted are Immune response pathway (purple) that is depleted in subgroup 12, Protein kinase pathway (yellow) enriched in subgroup 6, and Wnt signalling pathway (blue) enriched in subgroup 9. Shown in panels below are mean expression levels in subgroups for the genes in three highlighted pathways: b) Immune response, c) Protein kinase cascade and d) Wnt signalling pathway. E) Expression levels of Wnt signalling pathway genes in individual samples demonstrate the consistency of gene expression patterns within an enriched subgroup.
Mentions: Investigating the pathway aberration differences between the subgroups, and the molecular mechanisms that cause these aberrations may offer interesting insight into the diseases. In Figure 4a, we show the mean pathway aberration profiles for nine pathways that are differentially aberrant in the 12 GBM subgroups. This analysis revealed that the tumours in the less lethal GBM subgroup 12 (Proneural/G-CIMP enriched) differed significantly on a number of pathway aberration frequencies. For example, GO category Immune response was enriched in 0% and depleted in 97% of the less lethal tumours, whereas the aberration frequencies in other tumours were 54% and 9% for enrichment and depletion, respectively. Apoptotic and haemostatic pathways were also significantly depleted compared to others. The tumours in the more lethal GBM subgroup 6 differed from the others the most radically in aberration rate of signalling cascades, and metabolic pathways. To understand the molecular mechanisms behind these pathway level changes, we investigated the mean gene expression levels in three highlighted pathways: Immune response depleted in subgroup 12 (Figure 4b), Protein kinase cascade enriched in subgroup 6 (Figure 4c), and Wnt signalling pathway enriched in subgroup 9 (Figure 4d). The genes that cause the differential pathway aberration rates in these subgroups are clearly observed: Immune response genes IFITM2-3, PTPRC, HAMP, CCR1, IL6R, and BLNK, for example, are exclusively underexpressed in subgroup 12 causing the depletion. Several protein kinase cascade genes are not underexpressed in subgroup 6, in addition to exclusive overexpression of SHC1, TRIM38, NOD1, and others, causing the enrichment. In Wnt signalling pathway, enriched only in subgroup 9, we observed especially WNT10B, FZD9, CHP2, and RAC3 overexpression and FZD7, TCF7L1, and MMP7 underexpression, whereas in other subgroups their expression changes were the opposite. To further investigate the consistency of the differential gene expression levels within subgroups, we clustered the expression ratios of the genes in Wnt signalling pathway in GBM (Figure 4e). Samples in subgroup 9 formed a separate cluster from the other samples, and from the individual gene expression pattern we observed that the most prominent genes listed above, such as WNT10B, were indeed very consistently differentially expressed in this subgroup compared to others.

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