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Supplementary figure 5. LncRNAs and mutational signatures. IGV coverage plot of two previously characterised lncRNAs (a) AFAP1-AS1, (b) HNF1A-AS1 upregulated in EAC. Coverage is normalised as counts per million reads. (c) TCGA RNA-seq EAC data was utilised to validate neighbor lncRNA-mRNA pair correlation. Through TCGA alignment of Refseq only 15 of our differentially expressed lncRNAs-gene pairs were available in the TCGA data and then subsequently correlated using the same methods as presented in the main article. (d) SNPs in normal samples were removed from this analysis. All NDBE, LGD and EAC patients were investigated for mutational signatures as described. Only mutations covered by {greater than or equal to}20 reads were used to determine the presence of transcriptional mutation signatures (a) Five signatures were identified, which was used to (e) cluster the patients based on each signatures contribution to the patients mutations.

Supplementary figure 2. Differentially expressed transcriptions factors between LGD and NDBE, and examples of EAC specific genes and eigen-modules from WGCNA. (a) Box plots of the top transcription factors (FOSB, FOSB, EGR1, EGR3, NR4A1, ATF3) differentially expressed between LGD and BE and the corresponding expression in Nsq and EAC. y-axis is log2(cpm 1). (b) Boxplot (top) and IGV coverage plot (bottom) of two examples of highly connected, and also differentially expressed gens between both EAC and NDBE/LGD in the brown eigenmodule. (c, d ) (left) Heatmap (top), median eigenexpression (bottom), (right) protein-protein-interaction network (top), and network gene ontology enrichment (bottom) of eigenmodules found to be either over- (c) or underexpressed (d) in EAC. Genes are coloured based on differential expression, of either EAC and NDBE/LGD (red), EAC vs NDBE (blue), EAC vs LGD (green).

Supplementary figure 6. Differences between TP53mut vs TP53wt EAC gene expression. EAC patients were divided up into patients with no mutated TP53 transcripts (TP53-) and those with wild-type TP53 transcripts (e.g. no identification of SNPs in the TP53 consensus reads

Esophageal adenocarcinoma (EAC) has one of the fastest increases in incidence of any cancer, along with poor five-year survival rates. Barrett's esophagus (BE) is the main risk factor for EAC; however, the mechanisms driving EAC development remain poorly understood. Here, transcriptomic profiling was performed using RNA-sequencing (RNA-seq) on premalignant and malignant Barrett's tissues to better understand this disease. Machine-learning and network analysis methods were applied to discover novel driver genes for EAC development. Identified gene expression signatures for the distinction of EAC from BE were validated in separate datasets. An extensive analysis of the noncoding RNA (ncRNA) landscape was performed to determine the involvement of novel transcriptomic elements in Barrett's disease and EAC. Finally, transcriptomic mutational investigation of genes that are recurrently mutated in EAC was performed. Through these approaches, novel driver genes were discovered for EAC, which involved key cell cycle and DNA repair genes, such as BRCA1 and PRKDC. A novel 4-gene signature (CTSL, COL17A1, KLF4, and E2F3) was identified, externally validated, and shown to provide excellent distinction of EAC from BE. Furthermore, expression changes were observed in 685 long noncoding RNAs (lncRNA) and a systematic dysregulation of repeat elements across different stages of Barrett's disease, with wide-ranging downregulation of Alu elements in EAC. Mutational investigation revealed distinct pathways activated between EAC tissues with or without TP53 mutations compared with Barrett's disease. In summary, transcriptome sequencing revealed altered expression of numerous novel elements, processes, and networks in EAC and premalignant BE.Implications: This study identified opportunities to improve early detection and treatment of patients with BE and esophageal adenocarcinoma. Mol Cancer Res; 15(11); 1558–69. ©2017 AACR.

Supplementary figure 4. Esophageal adenocarcinoma cell line viability changes after shRNA knockdown of 21 genes identified through network and machine learning analysis, and Representative IHC staining slides of the 4-gene signature. (a) Two esophageal cancer cell lines, JHESOAD1 and OE33 were interrogated through the Broad Institute Project Achilles for changes in viability after pooled shRNA mediated gene knockdown. Y-axis measures relative change in cell viability. (b) Comparison of the effect of knockdowns of genes of interest on different GI cell types from the Achilles data base. (c) Representative slides from the IHC staining between NE, NDBE, and EAC, NDBE and EAC slides from COL17A1 and from E2F3 are the same as in Figure 3f.

Supplementary figure 3. Work flow and filtering steps for gene selection following machine learning to discover EAC driver genes. (a) For cross validation we used all normalised reads from EdgeR as cpm. These were put through the R package CMA using four cross-validation methods to find driver genes between EAC and NDBE. Loodat (leave-one-out), Bootstrap, Monte Carlo, Five-cross validation was used. All four methods were compared and genes present in {greater than or equal to}2 methods were kept (n=52). These 52 genes were then investigated for differential expression in EAC vs NDBE (n=39). Gene ontology enrichment was run on this filtered set. Furthermore, genes from all 4 cross validations methods were used as a gene panel and validated in 2 independent micro array data sets comparing EAC vs NDBE (GSE37203 and GSE26886). Random forest analysis was conducted to identify the most important genes of the 12-gene panel using the combined microarray datasets, and subsequently tested on two separate, public microarrays of benign and malignant gastric and colonic tissues. (b) The RNA-seq expression of the selected 12-gene panel of MSMO1, ACAA1, COL17A1, CXCL2, MKNK2, AQP9, E2F3, DHCR24, CTSL, KLF4, PM20D2, CREB5 for EAC and NDBE. Y-axis is log2(cpm 1). (c) AUROC curves for the 4-gene signature on RNA-seq data (left) the two individual microarray data sets (right, bottom left) comparing EAC and NDBE that were used to identify the 4-gene signature. (bottom right) Independent microarray data set used to validate the 4-gene signature on (d) Decision tree of the 12-gene signature on colonic (left) and gastric (right) normal and tumor tissues identifying E2F3 and KLF4 as main classification drivers.

Supplementary table 1 describes patient demographics and sample sequencing information Supplementary table 2 describes the top 20 differentially expressed genes, and the 12-gene signature. Supplementary table 3 shows the Discriminative performance of EAC driver genes in predicting the presence of EAC compared to BE with or without dysplasia. Supplementary table 4-5 describes the top 20 differentially expressed lncRNAs and repeat elements, respectively. Supplementary table 6 shows the association of genes in 12-signature with malignancies. Searched Pubmed: "Gene & Cancer" Supplementary table 7 shows the primers for lncRNA expression validation.

Tissue purity, RNA-seq based correlation analysis, and gene ontology enrichment. ESTIMATE (see methods) was used for in silico assessment of the (a) tissue purity, (c) stromal contamination, and (b) immune infiltration of each patient from the Normal esophagus (NE), Non-dysplastic Barrett's esophagus (NDBE), Low-grade dysplasia (LGD) and Esophageal adenocarcinoma (EAC) group. (d) Scatterplot comparing the mean expression (log2(cpm 1)) of all Gencode genes between each condition. Spearman correlation (R) shows the correlation score. (e) Venn diagram of the differentially expressed (absolute log2FC{greater than or equal to}1, FDR

Esophageal adenocarcinoma (EAC) has one of the fastest increases in incidence of any cancer, along with poor five-year survival rates. Barrett's esophagus (BE) is the main risk factor for EAC; however, the mechanisms driving EAC development remain poorly understood. Here, transcriptomic profiling was performed using RNA-sequencing (RNA-seq) on premalignant and malignant Barrett's tissues to better understand this disease. Machine-learning and network analysis methods were applied to discover novel driver genes for EAC development. Identified gene expression signatures for the distinction of EAC from BE were validated in separate datasets. An extensive analysis of the noncoding RNA (ncRNA) landscape was performed to determine the involvement of novel transcriptomic elements in Barrett's disease and EAC. Finally, transcriptomic mutational investigation of genes that are recurrently mutated in EAC was performed. Through these approaches, novel driver genes were discovered for EAC, which involved key cell cycle and DNA repair genes, such as BRCA1 and PRKDC. A novel 4-gene signature (CTSL, COL17A1, KLF4, and E2F3) was identified, externally validated, and shown to provide excellent distinction of EAC from BE. Furthermore, expression changes were observed in 685 long noncoding RNAs (lncRNA) and a systematic dysregulation of repeat elements across different stages of Barrett's disease, with wide-ranging downregulation of Alu elements in EAC. Mutational investigation revealed distinct pathways activated between EAC tissues with or without TP53 mutations compared with Barrett's disease. In summary, transcriptome sequencing revealed altered expression of numerous novel elements, processes, and networks in EAC and premalignant BE. Implications: This study identified opportunities to improve early detection and treatment of patients with BE and esophageal adenocarcinoma. Mol Cancer Res; 15(11); 1558–69. ©2017 AACR.