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Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization.

Kao KJ, Chang KM, Hsu HC, Huang AT - BMC Cancer (2011)

Bottom Line: Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients.When molecular subtypes were correlated with recurrence risk predicted by genes of Oncotype and MammaPrint predictors, a significant degree of heterogeneity within the same risk group was noted.Our results indicate that the molecular subtypes established in this study can be utilized for customization of breast cancer treatment.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Research, Koo Foundation SYS Cancer Center, Taipei, Taiwan. kuojang.kao@gmail.com

ABSTRACT

Background: Optimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization.

Methods: Gene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied.

Results: We identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was consistent with a unique subset of luminal A intrinsic type. When molecular subtypes were correlated with recurrence risk predicted by genes of Oncotype and MammaPrint predictors, a significant degree of heterogeneity within the same risk group was noted. This heterogeneity was distributed over several subtypes, suggesting that patients in the same risk groups require different treatment approaches.

Conclusions: Our results indicate that the molecular subtypes established in this study can be utilized for customization of breast cancer treatment.

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Related in: MedlinePlus

Validation of molecular subtypes of breast cancer established in this study. One-way hierarchical clustering analysis was performed on 327 samples in our dataset using genes associated with cell cycle/proliferation, wound-response [9], stromal reaction [21], and tumor vascular endothelial normalization [22,23]. Breast cancer samples were arranged according to their subtype as shown at the top of each panel. Dendrograms of signature genes are shown on the left. The identities of genes in all four dendrograms are listed in the Additional file 3, Figure S4. None of the genes used in this study were part of the 783 probe-sets used for molecular subtyping. The same gene clusters generated from our dataset were used to draw heat maps for the other three independent datasets. The heat maps from top to bottom for each signature were KFSYSCC, EMC [10], Uppsala [19], and TRANSBIG [20]. Each molecular subtype shared the same distinctive gene expression pattern among all four datasets. Subtypes I, II and IV showed increased expressions of cell cycle/proliferation genes. Subtypes I and II showed higher expression of stromal genes known to associate with poorer survival [21]. Subtypes III and VI had elevated expression of genes associated with vascular endothelial normalization. The concordance of differential gene expression for the six molecular subtypes between the KFSYSCC dataset and each of the other three independent datasets [10,19,20] was analyzed by Pearson correlation. The p value for each correlation coefficient was determined by comparing with  distribution based on 10,000 permutations of each independent dataset at subtype level. The Pearson correlation coefficient between the KFSYSCC dataset and that of EMC, Uppsala or TRANSBIG was 0.94, 0.92 or 0.87 for cell cycle/proliferation, 0.85, 0.84 or 0.78 for wound response, 0.94, 0.91 or 0.87 for stromal reaction, and 0.86, 0.86 or 0.83 for tumor vascular endothelial normalization. All p values were < 0.0001.
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Figure 3: Validation of molecular subtypes of breast cancer established in this study. One-way hierarchical clustering analysis was performed on 327 samples in our dataset using genes associated with cell cycle/proliferation, wound-response [9], stromal reaction [21], and tumor vascular endothelial normalization [22,23]. Breast cancer samples were arranged according to their subtype as shown at the top of each panel. Dendrograms of signature genes are shown on the left. The identities of genes in all four dendrograms are listed in the Additional file 3, Figure S4. None of the genes used in this study were part of the 783 probe-sets used for molecular subtyping. The same gene clusters generated from our dataset were used to draw heat maps for the other three independent datasets. The heat maps from top to bottom for each signature were KFSYSCC, EMC [10], Uppsala [19], and TRANSBIG [20]. Each molecular subtype shared the same distinctive gene expression pattern among all four datasets. Subtypes I, II and IV showed increased expressions of cell cycle/proliferation genes. Subtypes I and II showed higher expression of stromal genes known to associate with poorer survival [21]. Subtypes III and VI had elevated expression of genes associated with vascular endothelial normalization. The concordance of differential gene expression for the six molecular subtypes between the KFSYSCC dataset and each of the other three independent datasets [10,19,20] was analyzed by Pearson correlation. The p value for each correlation coefficient was determined by comparing with distribution based on 10,000 permutations of each independent dataset at subtype level. The Pearson correlation coefficient between the KFSYSCC dataset and that of EMC, Uppsala or TRANSBIG was 0.94, 0.92 or 0.87 for cell cycle/proliferation, 0.85, 0.84 or 0.78 for wound response, 0.94, 0.91 or 0.87 for stromal reaction, and 0.86, 0.86 or 0.83 for tumor vascular endothelial normalization. All p values were < 0.0001.

Mentions: To demonstrate the biologically distinctive nature of six different subtypes of breast cancer, we studied the differential expressions of genes associated with cell cycle/proliferation, wound-response [9], stromal reaction [21] and vascular endothelial normalization [22,23] using one-way clustering analysis. Genes used in this study were not used for molecular subtyping. As shown in Figure 3, all six molecular subtypes demonstrated distinct gene expression characteristics. The dendrograms of the probe-sets and the probe-set IDs are summarized in Figure S4 of Additional file 3. For validation, we used our classifier genes with centroid analysis to determine molecular subtypes of breast cancer samples in three independent datasets [10,19,20]. We then compared differential gene expression patterns associated with cell cycle/proliferation, wound-response, stromal reaction and vascular endothelial normalization for the same molecular subtypes between our dataset and the other three independent datasets. The same molecular subtypes in all four datasets were shown to share the same differential gene expression patterns (Figure 3). For further validation, we employed a different approach. We selected five genes (CAV1, DHFR, TYMS, VIM, ZEB1) known to be associated with drug sensitivity and the epithelial-mesenchymal transition of breast cancer [25-29]. The intensity of expression of these genes was plotted according to molecular subtypes. Again, each molecular subtype shared the same unique molecular characteristics across all four datasets (Additional file 3, Figure S5).


Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization.

Kao KJ, Chang KM, Hsu HC, Huang AT - BMC Cancer (2011)

Validation of molecular subtypes of breast cancer established in this study. One-way hierarchical clustering analysis was performed on 327 samples in our dataset using genes associated with cell cycle/proliferation, wound-response [9], stromal reaction [21], and tumor vascular endothelial normalization [22,23]. Breast cancer samples were arranged according to their subtype as shown at the top of each panel. Dendrograms of signature genes are shown on the left. The identities of genes in all four dendrograms are listed in the Additional file 3, Figure S4. None of the genes used in this study were part of the 783 probe-sets used for molecular subtyping. The same gene clusters generated from our dataset were used to draw heat maps for the other three independent datasets. The heat maps from top to bottom for each signature were KFSYSCC, EMC [10], Uppsala [19], and TRANSBIG [20]. Each molecular subtype shared the same distinctive gene expression pattern among all four datasets. Subtypes I, II and IV showed increased expressions of cell cycle/proliferation genes. Subtypes I and II showed higher expression of stromal genes known to associate with poorer survival [21]. Subtypes III and VI had elevated expression of genes associated with vascular endothelial normalization. The concordance of differential gene expression for the six molecular subtypes between the KFSYSCC dataset and each of the other three independent datasets [10,19,20] was analyzed by Pearson correlation. The p value for each correlation coefficient was determined by comparing with  distribution based on 10,000 permutations of each independent dataset at subtype level. The Pearson correlation coefficient between the KFSYSCC dataset and that of EMC, Uppsala or TRANSBIG was 0.94, 0.92 or 0.87 for cell cycle/proliferation, 0.85, 0.84 or 0.78 for wound response, 0.94, 0.91 or 0.87 for stromal reaction, and 0.86, 0.86 or 0.83 for tumor vascular endothelial normalization. All p values were < 0.0001.
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Figure 3: Validation of molecular subtypes of breast cancer established in this study. One-way hierarchical clustering analysis was performed on 327 samples in our dataset using genes associated with cell cycle/proliferation, wound-response [9], stromal reaction [21], and tumor vascular endothelial normalization [22,23]. Breast cancer samples were arranged according to their subtype as shown at the top of each panel. Dendrograms of signature genes are shown on the left. The identities of genes in all four dendrograms are listed in the Additional file 3, Figure S4. None of the genes used in this study were part of the 783 probe-sets used for molecular subtyping. The same gene clusters generated from our dataset were used to draw heat maps for the other three independent datasets. The heat maps from top to bottom for each signature were KFSYSCC, EMC [10], Uppsala [19], and TRANSBIG [20]. Each molecular subtype shared the same distinctive gene expression pattern among all four datasets. Subtypes I, II and IV showed increased expressions of cell cycle/proliferation genes. Subtypes I and II showed higher expression of stromal genes known to associate with poorer survival [21]. Subtypes III and VI had elevated expression of genes associated with vascular endothelial normalization. The concordance of differential gene expression for the six molecular subtypes between the KFSYSCC dataset and each of the other three independent datasets [10,19,20] was analyzed by Pearson correlation. The p value for each correlation coefficient was determined by comparing with distribution based on 10,000 permutations of each independent dataset at subtype level. The Pearson correlation coefficient between the KFSYSCC dataset and that of EMC, Uppsala or TRANSBIG was 0.94, 0.92 or 0.87 for cell cycle/proliferation, 0.85, 0.84 or 0.78 for wound response, 0.94, 0.91 or 0.87 for stromal reaction, and 0.86, 0.86 or 0.83 for tumor vascular endothelial normalization. All p values were < 0.0001.
Mentions: To demonstrate the biologically distinctive nature of six different subtypes of breast cancer, we studied the differential expressions of genes associated with cell cycle/proliferation, wound-response [9], stromal reaction [21] and vascular endothelial normalization [22,23] using one-way clustering analysis. Genes used in this study were not used for molecular subtyping. As shown in Figure 3, all six molecular subtypes demonstrated distinct gene expression characteristics. The dendrograms of the probe-sets and the probe-set IDs are summarized in Figure S4 of Additional file 3. For validation, we used our classifier genes with centroid analysis to determine molecular subtypes of breast cancer samples in three independent datasets [10,19,20]. We then compared differential gene expression patterns associated with cell cycle/proliferation, wound-response, stromal reaction and vascular endothelial normalization for the same molecular subtypes between our dataset and the other three independent datasets. The same molecular subtypes in all four datasets were shown to share the same differential gene expression patterns (Figure 3). For further validation, we employed a different approach. We selected five genes (CAV1, DHFR, TYMS, VIM, ZEB1) known to be associated with drug sensitivity and the epithelial-mesenchymal transition of breast cancer [25-29]. The intensity of expression of these genes was plotted according to molecular subtypes. Again, each molecular subtype shared the same unique molecular characteristics across all four datasets (Additional file 3, Figure S5).

Bottom Line: Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients.When molecular subtypes were correlated with recurrence risk predicted by genes of Oncotype and MammaPrint predictors, a significant degree of heterogeneity within the same risk group was noted.Our results indicate that the molecular subtypes established in this study can be utilized for customization of breast cancer treatment.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Research, Koo Foundation SYS Cancer Center, Taipei, Taiwan. kuojang.kao@gmail.com

ABSTRACT

Background: Optimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization.

Methods: Gene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied.

Results: We identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was consistent with a unique subset of luminal A intrinsic type. When molecular subtypes were correlated with recurrence risk predicted by genes of Oncotype and MammaPrint predictors, a significant degree of heterogeneity within the same risk group was noted. This heterogeneity was distributed over several subtypes, suggesting that patients in the same risk groups require different treatment approaches.

Conclusions: Our results indicate that the molecular subtypes established in this study can be utilized for customization of breast cancer treatment.

Show MeSH
Related in: MedlinePlus