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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

KARAKAS_TGFB1_SIGNALING as a predictor of survival in four datasets. Patients with positive GSASs (red curve) versus negative GSASs (green curve) for the gene set KARAKAS_TGFB1_SIGNALING in four different datasets. The GSASs for this gene set are significantly predictive of survival in the van de Vijver and Wang datasets (p < 0.05), however when applied to the Schmidt and Sotiriou datasets, the GSASs for this gene set are no longer significant (p > 0.1).
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Fig2: KARAKAS_TGFB1_SIGNALING as a predictor of survival in four datasets. Patients with positive GSASs (red curve) versus negative GSASs (green curve) for the gene set KARAKAS_TGFB1_SIGNALING in four different datasets. The GSASs for this gene set are significantly predictive of survival in the van de Vijver and Wang datasets (p < 0.05), however when applied to the Schmidt and Sotiriou datasets, the GSASs for this gene set are no longer significant (p > 0.1).

Mentions: Several gene sets available on MSigDB have been reported to be significantly associated with breast cancer survival, while others represent pathways that may be significantly associated with patient survival. However, many of these signatures and pathways are not robust in predicting clinical outcome across large groups of patients, and their predictive value can be highly dependent on the dataset from which they are derived [32,33]. Figure 2 shows an example of this, with samples from four publicly available datasets [8,10,27,28] dichotomized based on their GSAS from a gene set containing genes upregulated by transforming growth factor-beta 1, a protein involved in the regulation of cell proliferation [34]. While the gene set activity was significantly correlated with distant metastasis-free survival in the van de Vijver and Wang datasets (p = 4.4e-6 and 0.02, respectively), there appeared to be little separation between samples with high and low gene set activity in the Schmidt and Sotiriou datasets (p > 0.1 for both).Figure 2


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

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

KARAKAS_TGFB1_SIGNALING as a predictor of survival in four datasets. Patients with positive GSASs (red curve) versus negative GSASs (green curve) for the gene set KARAKAS_TGFB1_SIGNALING in four different datasets. The GSASs for this gene set are significantly predictive of survival in the van de Vijver and Wang datasets (p < 0.05), however when applied to the Schmidt and Sotiriou datasets, the GSASs for this gene set are no longer significant (p > 0.1).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: KARAKAS_TGFB1_SIGNALING as a predictor of survival in four datasets. Patients with positive GSASs (red curve) versus negative GSASs (green curve) for the gene set KARAKAS_TGFB1_SIGNALING in four different datasets. The GSASs for this gene set are significantly predictive of survival in the van de Vijver and Wang datasets (p < 0.05), however when applied to the Schmidt and Sotiriou datasets, the GSASs for this gene set are no longer significant (p > 0.1).
Mentions: Several gene sets available on MSigDB have been reported to be significantly associated with breast cancer survival, while others represent pathways that may be significantly associated with patient survival. However, many of these signatures and pathways are not robust in predicting clinical outcome across large groups of patients, and their predictive value can be highly dependent on the dataset from which they are derived [32,33]. Figure 2 shows an example of this, with samples from four publicly available datasets [8,10,27,28] dichotomized based on their GSAS from a gene set containing genes upregulated by transforming growth factor-beta 1, a protein involved in the regulation of cell proliferation [34]. While the gene set activity was significantly correlated with distant metastasis-free survival in the van de Vijver and Wang datasets (p = 4.4e-6 and 0.02, respectively), there appeared to be little separation between samples with high and low gene set activity in the Schmidt and Sotiriou datasets (p > 0.1 for both).Figure 2

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