Metadata supporting data files of the related manuscript: LobSig is a multigene predictor of outcome in Invasive Lobular Carcinoma

The related study presents an integrative analysis of gene expression and DNA copy number to identify novel drivers and prognostic biomarkers for Invasive Lobular Carcinoma (ILC). A 194-gene set (LobSig) was derived that is highly prognostic in ILC.

Study design and methodology: In silico integrative analysis of gene expression and DNA copy number was carried out using single nucleotide polymorphism (SNP) array data from ILC tumors of three cohorts: the "in house" UQCCR (n=25), the METABRIC (n=125) and the TCGA (n=146) cohorts. Fresh frozen tumors were accessed from the Brisbane Breast Bank at the University of Queensland Centre for Clinical Research and from the Australian Breast Cancer Tissue Bank. These cases constituted the ‘UQCCR’ cohort. DNA and RNA were extracted from frozen tissue and gene expression profiling of the “in house” UQCCR samples was performed. Please read the related manuscript for more details on the methodology.

Participant consent: All patients provided written, informed consent to the use of their tissues for research and the study had ethics approval from the Human Research Ethics Committee and Royal Brisbane and Women’s Hospital.

Data files: The .xlsx file provides a description of the manuscript-related datasets that can be found in data repositories and the persistent links to these datasets.

Data supporting figures:

Figure 1 shows data in Supplementary Table 2 including gene copy number landscape of 303 ILC tumors and GISTIC significant focal alterations in each chromosome across the genome. 1C (Excel files): heatmap of frequency of recurrent amplifications in ILC tumors. 1D, E, F and G (.jpg files) show FISH analysis using gene-specific probes for FGFR1 and CCND1 in tumor (D, F, G) and normal cells (E). 1H (Prism file): Boxplot of copy number versus mRNA expression z-scores of FGFR1 and CCND1. 1I (Excel file): Spearman genes plotted as rho across chromosomal location and ANOVA genes plotted as -log P Value across chromosomes.

Figure 2: 2A (Excel file) corresponds to Supplementary Table 4 and shows a Manhattan plot of the prognostic grade 2 ILC differentially expressed genes across all chromosomes. 2B-E (Prism files): Survival plots where high and low LobSig scores have been associated with patient survival in different types of ILCs. 2F-J (Prism files): survival plots where scores of various gene expression prognostic tests have been associated with survival in patients with grade 2 ILC. 2K (Prism file): Survival plot for the LobSig stratification in cases of the RATHER cohort. ROC curves comparing performances of prognostic gene signatures in ILC tumors (Figure 2L, jpg file) and Grade 2 ILC tumors (Figure 2M, jpg file).

Figure 3 (Prism file): Heatmap comparing LobSig risk predictions to the risk scores generated by NPI, GGI, PAM50 ROR and OncotypeDx.

Figure 4: 4A (Prism files): Survival plot of the LobSig stratified NPI moderate grade 2 ILC population. 4B (Excel file): Heatmap showing histopathological characteristics of NPI moderate LobSig stratified tumors. 4C (Prism and Excel files): Scatterplot showing the genomic alterations that are enriched in the LobSig stratified NPI moderate ILC cohort. 4D (jpg images): ROC curve comparing the prognostic performance of different prognostic gene signatures in the NPI moderate grade 2 ILC cases.

Figure 5: 5A (Prism and Excel files): Genomic alterations and their enrichment in the LobSig high risk group from grade 2 ILC tumors. 5B (GeneGo csv and tiff files): Gene Ontology analysis of the differentially expressed genes between LobSig high and low tumors.

Data supporting Supplementary figures:

Figure 1 (Excel files): Corresponds to data shown in Supplementary Table 2 and shows global recurrent alterations plotted along the genome across the three different cohorts.

Figure 2 (Excel files): Shows breast cancer specific survival (BCSS) for patients in the TCGA cohort and its association with specific genomic alterations.

Figure 3 (Excel files): Shows BCSS for patients in the TCGA cohort and its association with specific focal co-amplifications.

Figure 4 (Excel and Prism files): 4A: Flow chart showing a summary of the experimental design. 4B: Correlation of CCND1 copy number state and gene expression data; the Spearman analysis. 4C: Relationship between CCND1 gene expression and gene copy number state; ANOVA analysis.

Figure 5 (Excel and Prism files): Boxplots showing the relationship between gene expression and gene copy number state for the top 6 ANOVA genes.

Figure 6 (Excel and Prism files): Scatterplots showing the correlation of copy number state and mRNA expression data for the top 6 Spearman genes.

Figure 7 (Prism files): Survival plots of LobSig stratified groups in the three different cohorts, with the Logrank p-value, hazard ratio and confidence intervals reported.

Data supporting supplementary tables:

Table 1 (Excel files): Table of clinical characteristics of all the ILC cases in all 4 cohorts (METABRIC, TCGA, CCR and RATHER).

Table 2 (Excel files): Putative driver genes identified by GISTIC analysis of the ILC cases in TCGA and METABRIC cohorts, respectively.

Table 3 (Excel files): GISTIC focal alterations associated with BCSS data in regions that are highly prognostic in ILC tumors.

Table 4 (Excel files): Supervised analysis of differential gene expression profiling of ‘good‘, and ‘poor’ BCSS outcome groups.

Table 5 (Prism files): Clinical characteristics of two differential gene expression clusters. Chi-squared analysis was performed to determine whether the two sample subgroups were significantly associated with clinical/histopathological characteristics.

Table 6 (csv files): Panel of genes that were analyzed using the MetaCore which led to the identification of molecular pathways in the poor outcome and good outcome groups, respectively.

Table 7 (R files and Excel files): List of genes that were identified from the ANOVA analysis of ILC tumors from the three cohorts.

Tables 8 (R and Excel files) and 9 (R, Excel and Venn text files): Genes that were identified from the Spearman analysis and genes that were common between the two methods (Spearman analysis and ANOVA), respectively, in ILC tumors from the three cohorts.

Table 10 (R and Excel files): List of the significant, prognostic ILC genes, with Logrank p<0.05.

Table 11 (Excel files): Commonality of the 194-gene signature of LobSig with other prognostic gene signatures.

Tables 12, 13 and 14 (R and Excel files) show the univariate and multivariate models to assess the prognostic significance of LobSig grade 2 ILCs, LobSig grade 1, 2 and 3 ILCs and LobSig grade 3 ILCs, respectively, compared to other clinical and experimental indicators.

Table 15 (Excel files): Unique molecular subgroups (that were prevalent among LobSig stratified tumors.

Table 16 (Excel files): The table shows a list of pathways that were identified using GeneGo for both LobSig High and LobSig Low stratified tumors.

Table 17 (Excel files): The table shows a list of pathways that were identified using GeneGo in LobSig High and LobSig Low stratified tumors.

Data access and terms of use:

The datasets generated during the current study are available in the GEO repository (accession GSE98528), or are included in this published article (and its supplementary information files). Additional datasets analysed during this study are available from TCGA data portal (http://cancergenome.nih.gov/), data status as at May 15, 2014)15; n= 125 ILC from the METABRIC cohort (EGAS00000000083). Specific datasets (such as those in Prism, R and Venn txt file formats) used to generate figures 1-5, supplementary figures 2-7 and supplementary tables 1-10 and 12-17, are available upon request from the corresponding author. Access to datasets will be provided for non-commercial use only and will be granted upon the completion of a Data Usage Agreement (DUA).

Requests for data access should be directed to Dr Peter Simpson PhD, FFSc RCPA Senior Lecturer: Discipline of Molecular & Cellular Pathology, Faculty of Medicine and Group Leader: UQ Centre for Clinical Research, at the University of Queensland | Building 71/918 | Royal Brisbane & Women's Hospital | Herston | Qld 4029 | Australia, email: p.simpson@uq.edu.au