Elucidating the molecular drivers of human breast cancers requires a strategy that is capable of integrating multiple forms of data and an ability to interpret the functional consequences of a given genetic aberration. Here we present an integrated genomic strategy based on the use of gene expression signatures of oncogenic pathway activity (n = 52) as a framework to analyze DNA copy number alterations in combination with data from a genome-wide RNA-mediated interference screen. We identify specific DNA amplifications and essential genes within these amplicons representing key genetic drivers, including known and new regulators of oncogenesis. The genes identified include eight that are essential for cell proliferation (FGD5, METTL6, CPT1A, DTX3, MRPS23, EIF2S2, EIF6 and SLC2A10) and are uniquely amplified in patients with highly proliferative luminal breast tumors, a clinical subset of patients for which few therapeutic options are effective. This general strategy has the potential to identify therapeutic targets within amplicons through an integrated use of genomic data sets.
At a glance
Gene Expression Omnibus
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- Supplementary Figure 1: Patterns of pathway activity correspond with molecular subtypes of breast cancer. (1,210 KB)
Analysis of molecular subtypes of breast cancer based on 52 gene expression signature scores. Euclidean distance was used to calculate the relationship between samples based on scores of 52 gene express signatures. Samples are commonly ordered on the X and Y axis according to molecular subtype. These results demonstrate high concordance within a subtype (dark blue), and lower concordance across subtypes; each sample versus itself is the blue diagonal line.
- Supplementary Figure 2: Correlation between calculated pathway activity. (578 KB)
A Pearson correlation matrix of each signature versus all other signatures (including itself as the diagonal line) demonstrates a high degree of concordance amongst independently developed gene expression signatures measuring similar or associated pathways. Red indicates high positive correlation and blue a strong anti-correlation.
- Supplementary Figure 3: Identification of pathway-specific copy number alterations by Spearman rank correlation. (443 KB)
A Spearman rank correlation, both positive (red) and negative (blue) were used to identify associations between predicted genomic signature pathway activity and gene-level DNA copy number content (n=476). The negative log10 Bonferroni adjust p-values are plotted according to chromosomal position. Chromosomal borders are delineated by vertical black lines.
- Supplementary Figure 4: Identification of pathway-specific copy number alterations based on frequency of gains or losses calculated by Fisher’s exact test. (461 KB)
A Fisher’s exact test was used to calculate the statistical significance of the frequency of copy number gains (red) or losses (blue) in samples with the highest (top quartile) pathway signature activity relative to all other samples (n=476). The negative log10 Bonferroni adjust p-values are plotted according to chromosomal position. Vertical black lines indicate chromosomal borders.
- Supplementary Figure 5: Patterns of pathway activity in human breast cancer cell lines. (615 KB)
The scored pathway activity for a panel of 51 breast cancer cell lines (GSE12777) was calculated for the 52 pathway signatures. Of these cell lines, 27 which are denoted by black squares in lower panel were subjected to a genome-wide RNAi screen.
- Supplementary Figure 6: Identification of essential genes in proliferative breast cancer cell lines. (101 KB)
Identification of genes essential for cell viability in vitro in the context of the 11-gene PAM50 Proliferation signature in (A) all cell line samples and (B) in luminal and HER2+ breast cancer cell lines. The negative log10 Spearman rank correlation p values are plotted for each gene relative to chromosomal position.
- Supplementary Figure 7: Correlation between candidate gene mRNA expression and DNA copy number status in TCGA samples. (234 KB)
The mRNA expression levels of the 21 identified candidate genes that are required for cell viability and are uniquely amplified in highly proliferative luminal breast tumors. In each plot, the mRNA levels from the TCGA data are compared in those tumors with amplifications versus all others. Of the 21 genes, two (ANKRD56 and TMEM189) were not present on the mRNA expression array and are not included here. Of the remaining 19 genes, 12 had a significant relationship (p<0.05) between copy number status and mRNA expression levels.
- Supplementary Figure 8: Correlation between candidate gene mRNA expression and DNA copy number status in METABRIC samples. (152 KB)
The mRNA expression levels of the 12 identified candidate genes that are required for cell viability and are uniquely amplified in highly proliferative luminal breast tumors were analyzed within the context of copy number level in the METABRIC dataset. Of these 12 genes, three (SNX21, ZBTB46 and DNAJC5) were not present on both of the METABRIC data platforms (mRNA expression and copy number). Of the remaining 9 genes, all had a significant relationship (p<0.05) between copy number status and mRNA expression levels.
- Supplementary Figure 9: Validation of increased candidate gene copy number status in highly proliferative luminal breast tumors in METABRIC samples. (125 KB)
The relationship between amplification of each candidate gene within the context of highly proliferative luminal breast tumors was examined in the METABRIC dataset. Of the nine candidate genes, eight showed a significant enrichment in highly proliferative (top quartile) luminal breast tumors.
- Supplementary Figure 10: Candidate gene expression correlation with PAM50 proliferation score independent of copy number status. (393 KB)
The relationship between mRNA expression and the PAM50 proliferation signature was determined independent of copy number status (t-test) in the TCGA (n=388) and METABRIC (n=1,333) luminal/ ER+ subset of patients. Three classes of genes were identified (top rows) those that have a positive correlation with the signature score irrespective of CN status (EIF2S2, EIF6, MRPS23, CPT1A), those that have an inverse correlation (DTX3) and those that do not show a consistent pattern between datasets (FGD5, METTL6, SLC2A10).
- Supplementary Figure 11: Amplification status of a subset of candidate genes has no reproducible effect on prognosis. (320 KB)
Kaplan-Meier survival analysis based upon the amplification status of highly proliferative luminal tumor genes. No consistent difference in disease specific survival was observed for EIF2S2 (A, D), EIF6 (B, E) or SLC2A10 (C, F) when comparing luminal tumors characterized by amplification of each candidate gene relative to luminal tumors without an amplification (log rank p>0.05) in the TCGA (A-C) and METABRIC (D-F) datasets.
- Supplementary Text and Figures (6,105 KB)
Supplementary Figures 1–11
- Supplementary Tables 1–5 (520 KB)
Supplementary Tables 1–5
- Supplementary Tables 6–18 (17,165 KB)
Supplementary Tables 6–18
- Supplementary Tables 19–31 (16,705 KB)
Supplementary Tables 19–31
- Supplementary Tables 32–44 (17,417 KB)
Supplementary Tables 32–44
- Supplementary Tables 45–57 (17,124 KB)
Supplementary Tables 45–57
- Supplementary Tables 58–67 (9,444 KB)
Supplementary Tables 58–67