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Integrative analysis of survival-associated gene sets in breast cancer.

Varn FS, Ung MH, Lou SK, Cheng C - BMC Med Genomics (2015)

Bottom Line: Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested.Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression.We used this metric to identify predictive gene sets and to construct a novel gene set containing genes heavily involved in cancer progression.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755, USA. Frederick.S.Varn.Jr.GR@dartmouth.edu.

ABSTRACT

Background: Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient's cancer. Identifying robust gene sets that are consistently predictive of a patient's clinical outcome has become one of the main challenges in the field.

Methods: We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set's activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network's topology and applied the GSAS metric to characterize its role in patient survival.

Results: Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression.

Conclusions: The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used this metric to identify predictive gene sets and to construct a novel gene set containing genes heavily involved in cancer progression.

No MeSH data available.


Related in: MedlinePlus

Metastasis prediction performance using GSAS. (A) A receiver operating characteristic (ROC) curve for the Random Forest classification of metastatic versus non-metastatic samples in the van de Vijver dataset using GSASs that significantly differed between metastatic and non-metastatic samples in the van de Vijver dataset (Wilcoxon rank-sum test, FDR < 0.01) as the training data. (B) The relative importance values assigned by the Random Forest classifier used in (A) to each gene set when classifying samples. (C, D) AUC scores for the Random Forest classification of metastatic versus non-metastatic samples in different datasets when using GSASs that significantly differed between metastatic and non-metastatic samples in van de Vijver (C) and Pawitan (D) as the training data.
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Fig5: Metastasis prediction performance using GSAS. (A) A receiver operating characteristic (ROC) curve for the Random Forest classification of metastatic versus non-metastatic samples in the van de Vijver dataset using GSASs that significantly differed between metastatic and non-metastatic samples in the van de Vijver dataset (Wilcoxon rank-sum test, FDR < 0.01) as the training data. (B) The relative importance values assigned by the Random Forest classifier used in (A) to each gene set when classifying samples. (C, D) AUC scores for the Random Forest classification of metastatic versus non-metastatic samples in different datasets when using GSASs that significantly differed between metastatic and non-metastatic samples in van de Vijver (C) and Pawitan (D) as the training data.

Mentions: We next tested how informative GSASs were in metastasis classification across datasets using a Random Forest classifier. We selected gene sets that significantly differed between metastatic and non-metastatic samples as features since the full array of 4,257 gene sets contained many gene sets unrelated to metastasis and cancer (see Methods). We began this analysis in the van de Vijver dataset, where 520 gene sets qualified to be used as features. 10-fold cross validation was then performed to assess the classifier’s accuracy at predicting metastasis status for samples from the same dataset. The area under the ROC curve (AUC) was chosen to measure classifier performance in terms of specificity and sensitivity values. Our analysis on the van de Vijver dataset yielded an AUC of 0.75, which suggests relatively good performance (Figure 5A). The relative importance assigned to each gene set by the Random Forest classifier can be seen in Figure 5B.Figure 5


Integrative analysis of survival-associated gene sets in breast cancer.

Varn FS, Ung MH, Lou SK, Cheng C - BMC Med Genomics (2015)

Metastasis prediction performance using GSAS. (A) A receiver operating characteristic (ROC) curve for the Random Forest classification of metastatic versus non-metastatic samples in the van de Vijver dataset using GSASs that significantly differed between metastatic and non-metastatic samples in the van de Vijver dataset (Wilcoxon rank-sum test, FDR < 0.01) as the training data. (B) The relative importance values assigned by the Random Forest classifier used in (A) to each gene set when classifying samples. (C, D) AUC scores for the Random Forest classification of metastatic versus non-metastatic samples in different datasets when using GSASs that significantly differed between metastatic and non-metastatic samples in van de Vijver (C) and Pawitan (D) as the training data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4359519&req=5

Fig5: Metastasis prediction performance using GSAS. (A) A receiver operating characteristic (ROC) curve for the Random Forest classification of metastatic versus non-metastatic samples in the van de Vijver dataset using GSASs that significantly differed between metastatic and non-metastatic samples in the van de Vijver dataset (Wilcoxon rank-sum test, FDR < 0.01) as the training data. (B) The relative importance values assigned by the Random Forest classifier used in (A) to each gene set when classifying samples. (C, D) AUC scores for the Random Forest classification of metastatic versus non-metastatic samples in different datasets when using GSASs that significantly differed between metastatic and non-metastatic samples in van de Vijver (C) and Pawitan (D) as the training data.
Mentions: We next tested how informative GSASs were in metastasis classification across datasets using a Random Forest classifier. We selected gene sets that significantly differed between metastatic and non-metastatic samples as features since the full array of 4,257 gene sets contained many gene sets unrelated to metastasis and cancer (see Methods). We began this analysis in the van de Vijver dataset, where 520 gene sets qualified to be used as features. 10-fold cross validation was then performed to assess the classifier’s accuracy at predicting metastasis status for samples from the same dataset. The area under the ROC curve (AUC) was chosen to measure classifier performance in terms of specificity and sensitivity values. Our analysis on the van de Vijver dataset yielded an AUC of 0.75, which suggests relatively good performance (Figure 5A). The relative importance assigned to each gene set by the Random Forest classifier can be seen in Figure 5B.Figure 5

Bottom Line: Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested.Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression.We used this metric to identify predictive gene sets and to construct a novel gene set containing genes heavily involved in cancer progression.

View Article: PubMed Central - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, 03755, USA. Frederick.S.Varn.Jr.GR@dartmouth.edu.

ABSTRACT

Background: Patient gene expression information has recently become a clinical feature used to evaluate breast cancer prognosis. The emergence of prognostic gene sets that take advantage of these data has led to a rich library of information that can be used to characterize the molecular nature of a patient's cancer. Identifying robust gene sets that are consistently predictive of a patient's clinical outcome has become one of the main challenges in the field.

Methods: We inputted our previously established BASE algorithm with patient gene expression data and gene sets from MSigDB to develop the gene set activity score (GSAS), a metric that quantitatively assesses a gene set's activity level in a given patient. We utilized this metric, along with patient time-to-event data, to perform survival analyses to identify the gene sets that were significantly correlated with patient survival. We then performed cross-dataset analyses to identify robust prognostic gene sets and to classify patients by metastasis status. Additionally, we created a gene set network based on component gene overlap to explore the relationship between gene sets derived from MSigDB. We developed a novel gene set based on this network's topology and applied the GSAS metric to characterize its role in patient survival.

Results: Using the GSAS metric, we identified 120 gene sets that were significantly associated with patient survival in all datasets tested. The gene overlap network analysis yielded a novel gene set enriched in genes shared by the robustly predictive gene sets. This gene set was highly correlated to patient survival when used alone. Most interestingly, removal of the genes in this gene set from the gene pool on MSigDB resulted in a large reduction in the number of predictive gene sets, suggesting a prominent role for these genes in breast cancer progression.

Conclusions: The GSAS metric provided a useful medium by which we systematically investigated how gene sets from MSigDB relate to breast cancer patient survival. We used this metric to identify predictive gene sets and to construct a novel gene set containing genes heavily involved in cancer progression.

No MeSH data available.


Related in: MedlinePlus