Article Figures & Data
Figures
- Figure 1.
Somatic promoters in primary gastric adenocarcinoma. A, Example of an unaltered gastric cancer (GC) promoter. The UCSC genome track of the RHOA TSS (shaded box) highlights similar H3K4me3 signals in gastric cancer and matched normal samples. Similar signals are seen in gastric cancer lines. The bottom two tracks display similar levels of RNA expression in the same gastric cancer and matched normal samples (RNA-seq). B, Example of a gained somatic promoter. The UCSC genome track of the CEACAM6 TSS (shaded box) highlights gain of H3K4me3 signals in gastric cancer samples and gastric cancer lines, compared with matched normal samples. In contrast, no changes are observed at the TSS of CEACAM5, an adjacent gene. Concordant tumor-specific gain of RNA expression is shown in the bottom two tracks displaying RNA-seq profiles of the same gastric cancer and matched normal samples. C, Example of a lost somatic promoter. The UCSC genome track of the ATP4A TSS (shaded box) highlights loss of H3K4me3 signals in gastric cancer samples and gastric cancer lines compared with matched normal samples. Concordant tumor-specific loss of RNA expression is shown in the bottom two tracks displaying RNA-seq profiles of the same gastric cancer and matched normal samples. D, Heat map of H3K4me3 read densities (row scaled) of somatic promoters (rows) in primary gastric cancer and matched normal samples. E, Correlation between H3K4me3 promoter signals and H3K27ac activity signals in primary gastric samples (r = 0.91, P < 0.001). Each data point corresponds to a single H3K4me3hi/H3K4me1lo region. Gold points, all promoters; blue points, somatic promoters. Analysis was performed using data from 16 N/T pairs (Supplementary Table S2). F, Top five gene sets associated with canonical gained and lost somatic promoters. Gene sets associated with genes upregulated and downregulated in gastric cancer are rediscovered. Also note that gene sets related to H3K27me3 and SUZ12, a PRC2 component, are enriched. ESC, embryonic stem cell; HCP, high-CpG promoter.
- Figure 2.
Association of somatic promoters with gene expression in gastric cancer (GC) and other tumor types. A, Example of a gastric cancer somatic promoter (red, H3K4me3 signal in gastric cancer; blue, H3K4me3 signal in gastric normal). Example is for illustrative purposes only. B, Changes in RNA-seq expression (top) and DNA methylation (bottom) in discovery samples between somatic promoters and all promoters. Top, box plot depicting changes in RNA-seq expression between 9 paired primary gastric cancer and gastric normal samples at genomic regions exhibiting somatic promoters (gained and lost; ***, P < 0.001, Wilcoxon test). Bottom, box plot depicting changes in DNA methylation (β-values) at regions exhibiting somatic promoters between 20 paired gastric cancer and gastric normal samples, compared with all promoters (***, P < 0.001, Wilcoxon test). C, Independent validation cohorts. Box plot depicting changes in RNA-seq expression at genomic regions exhibiting somatic promoters across 354 (321 gastric cancer, 33 normal) TCGA stomach adenocarcinoma (STAD) samples, compared with all promoters (***, P < 0.001, Wilcoxon test). D, Somatic promoters in other cancer types. Box plot depicting changes in RNA-seq expression at genomic regions exhibiting gastric cancer somatic promoters compared against all promoters, across 326 TCGA colon adenocarcinoma (COAD) samples (286 COAD, 40 normal; ***, P < 0.001, Wilcoxon test), 170 TCGA kidney ccRCC samples (98 ccRCC and 72 normal; ***, P < 0.001, Wilcoxon test), and 115 TCGA LUAD samples (58 LUAD, 57 normal; ***, P < 0.001 somatic gain vs. all promoters and somatic gain vs. somatic loss, Wilcoxon test). T/N, tumor/normal.
- Figure 3.
Alternative promoters in gastric cancer (GC). A, UCSC browser track of the HNF4A gene. Gastric cancer and matched gastric normal samples have equal H3K4me3 signals at the canonical HNF4A promoter. However, an alternative promoter, seen by H3K4me3 gain, can be observed at a downstream TSS in gastric cancers compared with matched normals. At the RNA level, both in-house and TCGA STAD samples also show gain of gene expression at the alternate promoter TSS compared with normal samples. B, UCSC browser track of the EPCAM gene. Another example of alternative promoter usage at a downstream TSS. Gain of H3K4me3 is observed at a TSS downstream of the canonical promoter, while the canonical promoter exhibits equal H3K4me3 signals in gastric cancer and gastric normal. Gain of RNA-seq expression can also be observed in gastric cancer at the alternative promoter–driven transcript in both in-house and TCGA STAD samples. C, UCSC browser track of the RASA3 gene, demonstrating H3K4me3 and RNA-seq signals highlighting gain of promoter activity at an unannotated TSS (dark gray box) corresponding to a novel N-terminal truncated RASA3 transcript. Expression of this variant transcript was validated through 5′ Rapid Amplification of cDNA Ends (RACE) in gastric cancer lines (bottom). D, Functional domains of the translated RASA3 canonical and alternate isoform. The alternate transcript is predicted to encode a RASA3 protein missing the RASGAP domain. E, Effect of overexpression of RASA3 canonical (CanT) and alternate (SomT) isoforms on the migration capability of SNU1967 (top) and GES1 (bottom) cells. Representative images of RASA3-Ctl (empty vector), RASA3-CanT, and RASA3-SomT in migration assays (n = 3). Bar plots show the percentage area of migrated cells versus the area of Transwell membrane. Data are shown as mean ± SD; n = 3 (**, P < 0.01; Student one sided t test).
- Figure 4.
Somatic promoters correlate with immunoediting signatures. A, Schematic outlining alternative promoter usage [H3K4me3 box, overlapping gastric cancer (GC) in red and normal gastric tissue in blue] leading to alternative transcript usage (transcript box) and N-terminally truncated protein isoforms (protein box). B, Bar plot showing the average percentage of peptides with predicted high-affinity binding to MHC class I (HLA-A, B, and C, IC50 ≤ 50 nmol/L). N-terminal peptides associated with recurrent somatic promoters (alternative promoters) show significantly enriched predicted MHC I binding compared with canonical gastric cancer peptides (P < 0.01, Fisher test), random peptides from the human proteome (P < 0.001), and C-terminal peptides (P < 0.01) derived from the same genes exhibiting the N-terminal alterations. Canonical peptides refer to peptides derived from protein-coding genes overexpressed in gastric cancer through nonalternative promoters. C, Percentage (%) of high-affinity peptides predicted to bind different patient-specific HLA alleles categorized by somatic gain or loss. Most alleles have a greater number of N-terminal lost peptides predicted to have high binding affinity. The percentage of patients bearing specific HLA alleles is denoted inside the brackets. D, Quantification of somatic promoter expression using NanoString profiling. Top, distinct NanoString probes were designed to measure the expression of alternate and canonical promoter–driven transcripts. Two probes were designed for each gene, a canonical probe at the 5′ transcript marked by unaltered H3K4me3, and an alternate probe at the 5′ transcript of the somatic promoter. Bottom, heat map of alternative promoter expression from 95 gastric cancer and matched normal samples. Gastric cancer samples have been ordered left to right by their levels of somatic promoter usage. E, Association between somatic promoters and T-cell immune correlates. NS, not significant. Samples with high somatic promoter usage are in red, whereas those with low usage are in blue. Top left, expression of T-cell markers CD8A (P = 0.1443) and the T-cell cytolytic markers GZMA (P = 0.0001) and PRF1 (P = 0. 00806) in gastric cancer samples with either high or low somatic promoter usage (SG cohort). Samples with high alternative promoter usage show lower expression of immune markers. All P values are from Wilcoxon one-sided test. Top right, Kaplan–Meier analysis comparing overall survival curves between validation samples with high somatic promoter usage (top 25%) and low somatic promoter usage (bottom 25%; HR = 2.56, P = 0.02). Bottom left, expression of T-cell markers CD8A (P = 0.02), GZMA (P = 0.01), and PRF1 (P = 0.03) in TCGA STAD with either high or low somatic promoter usage. T-cell markers were evaluated by RNA-seq [transcripts per million (TPM)]. Bottom right, expression of T-cell markers CD8A (P = 0.035), GZMA (P = 0.001), and PRF1 (P = 0.025) in Asian Cancer Research Group (ACRG) gastric cancer samples with either high or low somatic promoter usage. All P values are from Wilcoxon one-sided test. F, EPIMAX heat map of total cytokine responses (fold change relative to actin) for 15 peptide pools against 9 donors. G, Individual cytokine responses against 15 peptides for two individual donors (donor 2 and donor 3) showing complex cytokine responses (FC ≥ 2). *, P < 0.05; **, P < 0.01; ***, P < 0.001.
- Figure 5.
Somatic promoters are associated with EZH2 occupancy. A, Binding enrichment of ReMap-defined transcription factor–binding sites at genomic regions exhibiting somatic promoters. Transcription factors were sorted according to their binding frequency at all H3K4me3-defined promoter regions. EZH2 and SUZ12 binding sites significantly overlap regions exhibiting somatic promoters (gained and lost; P < 0.01, empirical distribution test). B, Proportion of RNA transcripts associated with somatic promoters changing upon GSK126 treatment in IM95 cells, compared with RNA transcripts associated with unaltered promoters. The top somatic promoter figure is for illustrative purposes only. Unaltered promoters were defined as all gene promoters except the somatic promoters. The proportion of genes changing upon treatment, as a proportion of all genes, is also shown. Somatic promoters are more likely to change expression after GSK126 treatment relative to unaltered promoters (OR = 1.46, P < 0.001) or all GSK126-regulated genes (OR = 9.21, P < 0.001, Fisher test). ***, P < 0.001. C, UCSC browser track of the SLC9A9 TSS, a gene with loss of promoter activity [overlapping gastric cancer (GC; red) and normal gastric tissue (blue) H3K4me3]. Gain of expression is seen after inhibition of EZH2 using GSK126 in IM95 cells at both day 6 (D6) and day 9 (D9) treatment. D, UCSC browser track of the PSCA TSS, with loss of promoter activity [GC (red) and normal gastric tissue (blue) H3K4me3]. Gain of expression is seen after inhibition of EZH2 using GSK126 in IM95 cells at both day 6 (D6) and day 9 (D9) treatment.
- Figure 6.
Somatic promoters reveal novel cancer-associated transcripts. A, Distribution of distances for different promoter categories to the nearest annotated TSSs. Left, the first bar plot shows distance distributions for promoters present in gastric normal tissues, the second for promoters present in gastric cancer (GC) samples, and the third for promoters exhibiting somatic alterations (i.e., different in tumor vs. normal). Right, the bar plots present distance distributions associated with either lost or gained somatic promoters. A substantial proportion of gained somatic promoters occupy locations distant from previously annotated TSSs (red, green, purple, blue, orange). B, Median functional scores of unannotated promoters as predicted by GenoSkyline across 7 different tissues. Unannotated promoters exhibited high functional scores for gastrointestinal, fetal, and embryonic stem cell (ESC) tissues. C, Box plot depicting average RNA-seq reads for CAGE-validated promoters, comparing either all promoters or somatic promoters and also supported by CAGE data (***, P < 0.001, Wilcoxon one-sided test). Somatic promoters are observed to have lower levels of RNA-seq expression. D, Cartoon depicting proposed effects of dynamic range on Nano-ChIP-seq and RNA-seq sensitivity in detecting lowly expressed transcripts. NGS, next-generation sequencing. Because of a more restricted dynamic range, epigenomic profiling may detect active promoters missed by RNA-seq, due to the random sampling of abundantly expressed genes by RNA-seq. E, Down-sampling and up-sampling analysis. The y-axis depicts the number of transcripts detected that overlap either all promoters (blue line) or somatic promoters (red line) at varying RNA-seq depths. Original primary sample RNA-seq data were sequenced at approximately 106 M reads, which were down-sampled to 20, 40, and 60 M reads. Deep RNA-seq data were additionally generated at approximately 139 M read depth. F, Cancer-associated transcripts detected at deep but not regular RNA-seq depth. The UCSC genome browser track for ABCA13 shows an example of a novel transcript detected by Nano-ChIP-seq at a read depth of 20 M but detected by RNA-seq only at a read depth of approximately 139 M (deep sequencing GC). This transcript is not detected by regular-depth RNA-seq (GC).
Additional Files
Supplementary Data
- Supplementary Text, Supplementary Figures 1 through 13, Supplementary Methods - Supplementary text S1. Supplementary Figure 1: Chromatin Profiles of Primary GC. Supplementary Figure 2: Epithelial features of GC promoters. Supplementary Figure 3: GC Somatic Promoter Features. Supplementary Figure 4: Association of Somatic Promoters with Gene Expression in GC and Other Tumor Types. Supplementary Figure 5: Changes in DNA methylation at CpG island containing promoters. Supplementary Figure 6: Expression distribution of alternative and canonical isoforms. Supplementary Figure 7: Characterization of RASA3 Isoform. Supplementary Figure 8: Characterization of MET Isoforms. Supplementary Figure 9: Immunogenicity of N-terminal peptides. Supplementary Figure 10: Immunogenicity Assay and Nanostring Profiling. Supplementary Figure 11: Functional Assessment of Peptide Immunogenicity. Supplementary Figure 12: EZH2 Inhibition. Supplementary Figure 13: Unannotated somatic promoters.
- Supplementary Tables 1 through 13 - Supplementary Table 1: Clinicopathological Parameters of samples used Supplementary Table 2: Read Mapping Statistics of NanoChIP-seq Libraries Supplementary Table 3: Non coding RNAs associated with Somatic Promoters Supplementary Table 4: Alternative Promoters Supplementary Table 5: Spectral Counts from CRC samples of N terminal peptides predicted to be gained in GC Supplementary Table 6: HLA prediction of GC samples Supplementary Table 7: Recurrent N terminal sequences with high affinity to MHC Class I Supplementary Table 8: P values of Wilcoxon test between ACRG samples with high and low somatic promoter usage Supplementary Table 9: HLA types of healthy PBMC donors Supplementary Table 10: Peptide pools for alternative promoters Supplementary Table 11: Cytokine Responses of N terminal Peptides Supplementary Table 12: Somatic Promoters Overlapping EZH2/SUZ12 Binding Sites Supplementary Table 13: RACE Primers
Volume 7, Issue 6
