Limits...
The molecular diversity of Luminal A breast tumors.

Ciriello G, Sinha R, Hoadley KA, Jacobsen AS, Reva B, Perou CM, Sander C, Schultz N - Breast Cancer Res. Treat. (2013)

Bottom Line: We identified an atypical Luminal A subtype characterized by high genomic instability, TP53 mutations, and increased Aurora kinase signaling; these genomic alterations lead to a worse clinical prognosis.Aberrations of chromosomes 1, 8, and 16, together with PIK3CA, GATA3, AKT1, and MAP3K1 mutations drive the other subtypes.Finally, an unbiased pathway analysis revealed multiple rare, but mutually exclusive, alterations linked to loss of activity of co-repressor complexes N-Cor and SMRT.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, ciriello@cbio.mskcc.org.

ABSTRACT
Breast cancer is a collection of diseases with distinct molecular traits, prognosis, and therapeutic options. Luminal A breast cancer is the most heterogeneous, both molecularly and clinically. Using genomic data from over 1,000 Luminal A tumors from multiple studies, we analyzed the copy number and mutational landscape of this tumor subtype. This integrated analysis revealed four major subtypes defined by distinct copy-number and mutation profiles. We identified an atypical Luminal A subtype characterized by high genomic instability, TP53 mutations, and increased Aurora kinase signaling; these genomic alterations lead to a worse clinical prognosis. Aberrations of chromosomes 1, 8, and 16, together with PIK3CA, GATA3, AKT1, and MAP3K1 mutations drive the other subtypes. Finally, an unbiased pathway analysis revealed multiple rare, but mutually exclusive, alterations linked to loss of activity of co-repressor complexes N-Cor and SMRT. These rare alterations were the most prevalent in Luminal A tumors and may predict resistance to endocrine therapy. Our work provides for a further molecular stratification of Luminal A breast tumors, with potential direct clinical implications.

Show MeSH

Related in: MedlinePlus

Copy number clustering of Luminal A breast tumors. a Hierarchical clustering of copy number data from 209 Luminal A tumors from the TCGA dataset reveals four distinct patterns of alterations, plus a mixed subgroup. Chromosomes are arranged from left to right, and tumors are arranged vertically and grouped according to cluster membership. Red indicates copy number gain, blue copy number losses, with color intensity proportional to absolute copy number values. b Cluster centroids were used to classify the METABRIC dataset (721 samples). Clusters in the METABRIC dataset show similar proportions to the TCGA counterparts, and the quality of the clusters is confirmed by statistically significant IGP. c Clusters determined from the METABRIC dataset are compared with the breast cancer subtypes proposed in [12]. Lines connect clusters with non-empty overlap with a thickness proportional to the extent of overlap
© Copyright Policy - OpenAccess
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3824397&req=5

Fig2: Copy number clustering of Luminal A breast tumors. a Hierarchical clustering of copy number data from 209 Luminal A tumors from the TCGA dataset reveals four distinct patterns of alterations, plus a mixed subgroup. Chromosomes are arranged from left to right, and tumors are arranged vertically and grouped according to cluster membership. Red indicates copy number gain, blue copy number losses, with color intensity proportional to absolute copy number values. b Cluster centroids were used to classify the METABRIC dataset (721 samples). Clusters in the METABRIC dataset show similar proportions to the TCGA counterparts, and the quality of the clusters is confirmed by statistically significant IGP. c Clusters determined from the METABRIC dataset are compared with the breast cancer subtypes proposed in [12]. Lines connect clusters with non-empty overlap with a thickness proportional to the extent of overlap

Mentions: We first explored the spectrum of copy number changes across Luminal A tumors, to identify novel and subset-specific alterations. We performed hierarchical clustering of Affymetrix 6.0 SNP copy number data from 209 Luminal A tumors from the TCGA dataset. Segments of uniform copy number value for each patient were compared to compute the set of unified breakpoints across the whole dataset, and the so determined set of minimal segments of change were used as features for the clustering procedure. Hierarchical clustering of copy number changes across the whole genome on the TCGA dataset revealed a complex structure of recurrent patterns of alterations. Based on clustering results and recurrent CNA, we were able to identify four major characteristic patterns and a mixed group (Fig. 2a; Table S1).Fig. 2


The molecular diversity of Luminal A breast tumors.

Ciriello G, Sinha R, Hoadley KA, Jacobsen AS, Reva B, Perou CM, Sander C, Schultz N - Breast Cancer Res. Treat. (2013)

Copy number clustering of Luminal A breast tumors. a Hierarchical clustering of copy number data from 209 Luminal A tumors from the TCGA dataset reveals four distinct patterns of alterations, plus a mixed subgroup. Chromosomes are arranged from left to right, and tumors are arranged vertically and grouped according to cluster membership. Red indicates copy number gain, blue copy number losses, with color intensity proportional to absolute copy number values. b Cluster centroids were used to classify the METABRIC dataset (721 samples). Clusters in the METABRIC dataset show similar proportions to the TCGA counterparts, and the quality of the clusters is confirmed by statistically significant IGP. c Clusters determined from the METABRIC dataset are compared with the breast cancer subtypes proposed in [12]. Lines connect clusters with non-empty overlap with a thickness proportional to the extent of overlap
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3824397&req=5

Fig2: Copy number clustering of Luminal A breast tumors. a Hierarchical clustering of copy number data from 209 Luminal A tumors from the TCGA dataset reveals four distinct patterns of alterations, plus a mixed subgroup. Chromosomes are arranged from left to right, and tumors are arranged vertically and grouped according to cluster membership. Red indicates copy number gain, blue copy number losses, with color intensity proportional to absolute copy number values. b Cluster centroids were used to classify the METABRIC dataset (721 samples). Clusters in the METABRIC dataset show similar proportions to the TCGA counterparts, and the quality of the clusters is confirmed by statistically significant IGP. c Clusters determined from the METABRIC dataset are compared with the breast cancer subtypes proposed in [12]. Lines connect clusters with non-empty overlap with a thickness proportional to the extent of overlap
Mentions: We first explored the spectrum of copy number changes across Luminal A tumors, to identify novel and subset-specific alterations. We performed hierarchical clustering of Affymetrix 6.0 SNP copy number data from 209 Luminal A tumors from the TCGA dataset. Segments of uniform copy number value for each patient were compared to compute the set of unified breakpoints across the whole dataset, and the so determined set of minimal segments of change were used as features for the clustering procedure. Hierarchical clustering of copy number changes across the whole genome on the TCGA dataset revealed a complex structure of recurrent patterns of alterations. Based on clustering results and recurrent CNA, we were able to identify four major characteristic patterns and a mixed group (Fig. 2a; Table S1).Fig. 2

Bottom Line: We identified an atypical Luminal A subtype characterized by high genomic instability, TP53 mutations, and increased Aurora kinase signaling; these genomic alterations lead to a worse clinical prognosis.Aberrations of chromosomes 1, 8, and 16, together with PIK3CA, GATA3, AKT1, and MAP3K1 mutations drive the other subtypes.Finally, an unbiased pathway analysis revealed multiple rare, but mutually exclusive, alterations linked to loss of activity of co-repressor complexes N-Cor and SMRT.

View Article: PubMed Central - PubMed

Affiliation: Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY, USA, ciriello@cbio.mskcc.org.

ABSTRACT
Breast cancer is a collection of diseases with distinct molecular traits, prognosis, and therapeutic options. Luminal A breast cancer is the most heterogeneous, both molecularly and clinically. Using genomic data from over 1,000 Luminal A tumors from multiple studies, we analyzed the copy number and mutational landscape of this tumor subtype. This integrated analysis revealed four major subtypes defined by distinct copy-number and mutation profiles. We identified an atypical Luminal A subtype characterized by high genomic instability, TP53 mutations, and increased Aurora kinase signaling; these genomic alterations lead to a worse clinical prognosis. Aberrations of chromosomes 1, 8, and 16, together with PIK3CA, GATA3, AKT1, and MAP3K1 mutations drive the other subtypes. Finally, an unbiased pathway analysis revealed multiple rare, but mutually exclusive, alterations linked to loss of activity of co-repressor complexes N-Cor and SMRT. These rare alterations were the most prevalent in Luminal A tumors and may predict resistance to endocrine therapy. Our work provides for a further molecular stratification of Luminal A breast tumors, with potential direct clinical implications.

Show MeSH
Related in: MedlinePlus