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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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Clustering of glioblastoma pathway aberration profile. Pathway aberration matrix of GBM shows enriched (in red) and depleted pathways (in green). The bar plot on top of the matrix shows the frequency of enrichment for each pathway, and the bar plot below shows the frequencies of depletion. Some of the most frequently aberrant pathways and processes are annotated to the figure. Hierarchical clustering of the matrix resulted in 12 subgroups (marked in alternating blue and yellow bars, first subgroup being marked with the first blue bar in the bottom), two of which were clinically relevant (subgroup 6 in red and subgroup 12 in green). Some of the subgroups corresponded to the pre-existing molecular classifications in right.
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Figure 2: Clustering of glioblastoma pathway aberration profile. Pathway aberration matrix of GBM shows enriched (in red) and depleted pathways (in green). The bar plot on top of the matrix shows the frequency of enrichment for each pathway, and the bar plot below shows the frequencies of depletion. Some of the most frequently aberrant pathways and processes are annotated to the figure. Hierarchical clustering of the matrix resulted in 12 subgroups (marked in alternating blue and yellow bars, first subgroup being marked with the first blue bar in the bottom), two of which were clinically relevant (subgroup 6 in red and subgroup 12 in green). Some of the subgroups corresponded to the pre-existing molecular classifications in right.

Mentions: Common molecular subtypes have already been identified in many of the cancers before [11,13,14,17]. To find out how these related to pathway aberrations, and to identify new subgroups based on pathway level changes, we hierarchically clustered the samples into small and homogeneous clusters characterized by a unique set of pathway aberrations (Additional File 3, Figure 2, Figures S1-S7 in Additional File 4,). We divided the eight tumour types into 62 subgroups each consisting of 1.5-65% of the samples. Subgroups were also compared to the previously established molecular subgroups or pathological grading and staging systems where available. In general, our subgroups reflected the molecular classifications, but not the tumour grades and stages. Clinical relevance of the subgroups was investigated by comparing the survival estimators; however, the combination of modest number of samples in some cancers (especially KIRC, READ, and UCEC) and very short follow-up times for many patients hindered the strength of this analysis. Associations to other potentially relevant clinical variables were omitted due to poor quality or complete lack of available metadata.


Characterization of aberrant pathways across human cancers.

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

Clustering of glioblastoma pathway aberration profile. Pathway aberration matrix of GBM shows enriched (in red) and depleted pathways (in green). The bar plot on top of the matrix shows the frequency of enrichment for each pathway, and the bar plot below shows the frequencies of depletion. Some of the most frequently aberrant pathways and processes are annotated to the figure. Hierarchical clustering of the matrix resulted in 12 subgroups (marked in alternating blue and yellow bars, first subgroup being marked with the first blue bar in the bottom), two of which were clinically relevant (subgroup 6 in red and subgroup 12 in green). Some of the subgroups corresponded to the pre-existing molecular classifications in right.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Clustering of glioblastoma pathway aberration profile. Pathway aberration matrix of GBM shows enriched (in red) and depleted pathways (in green). The bar plot on top of the matrix shows the frequency of enrichment for each pathway, and the bar plot below shows the frequencies of depletion. Some of the most frequently aberrant pathways and processes are annotated to the figure. Hierarchical clustering of the matrix resulted in 12 subgroups (marked in alternating blue and yellow bars, first subgroup being marked with the first blue bar in the bottom), two of which were clinically relevant (subgroup 6 in red and subgroup 12 in green). Some of the subgroups corresponded to the pre-existing molecular classifications in right.
Mentions: Common molecular subtypes have already been identified in many of the cancers before [11,13,14,17]. To find out how these related to pathway aberrations, and to identify new subgroups based on pathway level changes, we hierarchically clustered the samples into small and homogeneous clusters characterized by a unique set of pathway aberrations (Additional File 3, Figure 2, Figures S1-S7 in Additional File 4,). We divided the eight tumour types into 62 subgroups each consisting of 1.5-65% of the samples. Subgroups were also compared to the previously established molecular subgroups or pathological grading and staging systems where available. In general, our subgroups reflected the molecular classifications, but not the tumour grades and stages. Clinical relevance of the subgroups was investigated by comparing the survival estimators; however, the combination of modest number of samples in some cancers (especially KIRC, READ, and UCEC) and very short follow-up times for many patients hindered the strength of this analysis. Associations to other potentially relevant clinical variables were omitted due to poor quality or complete lack of available metadata.

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