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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

Performance of the core gene set in classification and survival analysis. (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 the genes of the core gene set whose expression significantly differed between metastatic and non-metastatic samples in the van de Vijver dataset (Wilcoxon rank-sum, FDR < 0.01) as the features. (B) Patients from the van de Vijver dataset with a positive GSAS for the core gene set (red curve) show significantly shorter survival times than those with a negative GSAS (green curve). Vertical hash marks indicate points of censored data. (C) In a Cox PH model, the GSAS for the core gene set significantly predicts patient survival even after adjusting for traditional clinical features. (D) The core gene set is effective in predicting patient survival in ER+ samples but not in ER- samples.
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Fig7: Performance of the core gene set in classification and survival analysis. (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 the genes of the core gene set whose expression significantly differed between metastatic and non-metastatic samples in the van de Vijver dataset (Wilcoxon rank-sum, FDR < 0.01) as the features. (B) Patients from the van de Vijver dataset with a positive GSAS for the core gene set (red curve) show significantly shorter survival times than those with a negative GSAS (green curve). Vertical hash marks indicate points of censored data. (C) In a Cox PH model, the GSAS for the core gene set significantly predicts patient survival even after adjusting for traditional clinical features. (D) The core gene set is effective in predicting patient survival in ER+ samples but not in ER- samples.

Mentions: To characterize the role of the core gene set in breast cancer prognosis, we first evaluated the gene set’s ability to classify samples in the van de Vijver dataset as metastatic or non-metastatic. We repeated our Random Forest analysis as described previously, but instead used the core gene set’s component gene expression values as features. Our analysis achieved an AUC score of 0.65 (Figure 7A), a relatively good score for a single gene set, which suggests that the core gene set is ably capturing metastasis information.Figure 7


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

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

Performance of the core gene set in classification and survival analysis. (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 the genes of the core gene set whose expression significantly differed between metastatic and non-metastatic samples in the van de Vijver dataset (Wilcoxon rank-sum, FDR < 0.01) as the features. (B) Patients from the van de Vijver dataset with a positive GSAS for the core gene set (red curve) show significantly shorter survival times than those with a negative GSAS (green curve). Vertical hash marks indicate points of censored data. (C) In a Cox PH model, the GSAS for the core gene set significantly predicts patient survival even after adjusting for traditional clinical features. (D) The core gene set is effective in predicting patient survival in ER+ samples but not in ER- samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig7: Performance of the core gene set in classification and survival analysis. (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 the genes of the core gene set whose expression significantly differed between metastatic and non-metastatic samples in the van de Vijver dataset (Wilcoxon rank-sum, FDR < 0.01) as the features. (B) Patients from the van de Vijver dataset with a positive GSAS for the core gene set (red curve) show significantly shorter survival times than those with a negative GSAS (green curve). Vertical hash marks indicate points of censored data. (C) In a Cox PH model, the GSAS for the core gene set significantly predicts patient survival even after adjusting for traditional clinical features. (D) The core gene set is effective in predicting patient survival in ER+ samples but not in ER- samples.
Mentions: To characterize the role of the core gene set in breast cancer prognosis, we first evaluated the gene set’s ability to classify samples in the van de Vijver dataset as metastatic or non-metastatic. We repeated our Random Forest analysis as described previously, but instead used the core gene set’s component gene expression values as features. Our analysis achieved an AUC score of 0.65 (Figure 7A), a relatively good score for a single gene set, which suggests that the core gene set is ably capturing metastasis information.Figure 7

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