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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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Comparison of Kaplan-Meier survival estimators for clinically significant subgroups of patients. Kaplan-Meier survival estimators for subgroups that have a significant survival difference compared to the rest of the cohort a) patients with tumours in GBM cluster 12 (green) have a significantly increased survival estimate compared to other GBM samples (blue) (p = 0.047) b) patients in GBM cluster 6 (red) have a significantly worse survival estimate compared to others (blue) (p = 0.047) c) BRCA cluster 14 (red) contains patients with worse survival (p = 0.012) d) COAD cluster 6 (red) contains patients with worse survival (p = 0.026) e) OV cluster 12 (red) contains patients with worse survival (p = 0.029)
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Figure 3: Comparison of Kaplan-Meier survival estimators for clinically significant subgroups of patients. Kaplan-Meier survival estimators for subgroups that have a significant survival difference compared to the rest of the cohort a) patients with tumours in GBM cluster 12 (green) have a significantly increased survival estimate compared to other GBM samples (blue) (p = 0.047) b) patients in GBM cluster 6 (red) have a significantly worse survival estimate compared to others (blue) (p = 0.047) c) BRCA cluster 14 (red) contains patients with worse survival (p = 0.012) d) COAD cluster 6 (red) contains patients with worse survival (p = 0.026) e) OV cluster 12 (red) contains patients with worse survival (p = 0.029)

Mentions: Interestingly, based on the clustering of GBM pathway aberration profile (Figure 2), we found one subgroup that was significantly less lethal (p <0.05) (Figure 3a) than the others, and one that was more lethal (p <0.05) (Figure 3b). Tumours in the less lethal subgroup were enriched of Proneural subtype [11] (p <4.5e-5), and concordantly with IDH mutations (p <5.1e-5) and glioma-CpG Island Methylator Phenotype (G-CIMP) [17] (p <1.7-e6). Since TCGA's GBM cohort currently is considerably larger compared to the one used in the previous studies, not all of tumours were annotated to these groups. Our results encourage investigating the remainder of the tumours in this subgroup for the associated features of Proneural and G-CIMP tumours. The most common molecular subtype in the more lethal subgroup was Mesenchymal [11] (p <0.02). Additionally, from BRCA pathway aberration profile (Figure S1 in Additional File 4), we discovered a more aggressive subgroup (p <0.05) (Figure 3c) that was enriched in Her2 positive tumours [14] (p <1.4e-5). Some of the other subgroups were also enriched in tumours annotated to specific molecular subgroups (Basal-like, LuminalA and LuminalB) underlining the differences of these subtypes not only on mutation and gene expression level but also functionally. For example, subgroups 3 and 4 that consisted of a significant portion of the Basal-like tumours (p <1.8e-9 and p <2.4e-7, respectively) were not more aggressive than others, in agreement with previous findings of Basal-like tumours [14]. Based on pathway aberration profiles of colon (FigureS2inAdditional File 4) and ovarian (Figure S3 in Additional File 4) cancers, we identified two additional aggressive (p <0.05) subgroups (Figure 3d-e). Neither group were associated to the tumour stages. No subgroups with significantly different survival estimators were found in READ (FigureS4 inAdditional File 4), LUSC (FigureS5 inAdditional File 4), KIRC (FigureS6 inAdditional File 4), and UCEC (FigureS7 inAdditional File 4), probably also due to the smaller sample sizes and shorter patient follow-up periods.


Characterization of aberrant pathways across human cancers.

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

Comparison of Kaplan-Meier survival estimators for clinically significant subgroups of patients. Kaplan-Meier survival estimators for subgroups that have a significant survival difference compared to the rest of the cohort a) patients with tumours in GBM cluster 12 (green) have a significantly increased survival estimate compared to other GBM samples (blue) (p = 0.047) b) patients in GBM cluster 6 (red) have a significantly worse survival estimate compared to others (blue) (p = 0.047) c) BRCA cluster 14 (red) contains patients with worse survival (p = 0.012) d) COAD cluster 6 (red) contains patients with worse survival (p = 0.026) e) OV cluster 12 (red) contains patients with worse survival (p = 0.029)
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3750561&req=5

Figure 3: Comparison of Kaplan-Meier survival estimators for clinically significant subgroups of patients. Kaplan-Meier survival estimators for subgroups that have a significant survival difference compared to the rest of the cohort a) patients with tumours in GBM cluster 12 (green) have a significantly increased survival estimate compared to other GBM samples (blue) (p = 0.047) b) patients in GBM cluster 6 (red) have a significantly worse survival estimate compared to others (blue) (p = 0.047) c) BRCA cluster 14 (red) contains patients with worse survival (p = 0.012) d) COAD cluster 6 (red) contains patients with worse survival (p = 0.026) e) OV cluster 12 (red) contains patients with worse survival (p = 0.029)
Mentions: Interestingly, based on the clustering of GBM pathway aberration profile (Figure 2), we found one subgroup that was significantly less lethal (p <0.05) (Figure 3a) than the others, and one that was more lethal (p <0.05) (Figure 3b). Tumours in the less lethal subgroup were enriched of Proneural subtype [11] (p <4.5e-5), and concordantly with IDH mutations (p <5.1e-5) and glioma-CpG Island Methylator Phenotype (G-CIMP) [17] (p <1.7-e6). Since TCGA's GBM cohort currently is considerably larger compared to the one used in the previous studies, not all of tumours were annotated to these groups. Our results encourage investigating the remainder of the tumours in this subgroup for the associated features of Proneural and G-CIMP tumours. The most common molecular subtype in the more lethal subgroup was Mesenchymal [11] (p <0.02). Additionally, from BRCA pathway aberration profile (Figure S1 in Additional File 4), we discovered a more aggressive subgroup (p <0.05) (Figure 3c) that was enriched in Her2 positive tumours [14] (p <1.4e-5). Some of the other subgroups were also enriched in tumours annotated to specific molecular subgroups (Basal-like, LuminalA and LuminalB) underlining the differences of these subtypes not only on mutation and gene expression level but also functionally. For example, subgroups 3 and 4 that consisted of a significant portion of the Basal-like tumours (p <1.8e-9 and p <2.4e-7, respectively) were not more aggressive than others, in agreement with previous findings of Basal-like tumours [14]. Based on pathway aberration profiles of colon (FigureS2inAdditional File 4) and ovarian (Figure S3 in Additional File 4) cancers, we identified two additional aggressive (p <0.05) subgroups (Figure 3d-e). Neither group were associated to the tumour stages. No subgroups with significantly different survival estimators were found in READ (FigureS4 inAdditional File 4), LUSC (FigureS5 inAdditional File 4), KIRC (FigureS6 inAdditional File 4), and UCEC (FigureS7 inAdditional File 4), probably also due to the smaller sample sizes and shorter patient follow-up periods.

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