Subtyping sub-Saharan esophageal squamous cell carcinoma by comprehensive molecular analysis

Abstract

Esophageal squamous cell carcinoma (ESCC) is endemic in regions of sub-Saharan Africa (SSA), where it is the third most common cancer. Here, we describe whole-exome tumor/normal sequencing and RNA transcriptomic analysis of 59 patients with ESCC in Malawi. We observed similar genetic aberrations as reported in Asian and North American cohorts, including mutations of TP53, CDKN2A, NFE2L2, CHEK2, NOTCH1, FAT1, and FBXW7. Analyses for nonhuman sequences did not reveal evidence for infection with HPV or other occult pathogens. Mutational signature analysis revealed common signatures associated with aging, cytidine deaminase activity (APOBEC), and a third signature of unknown origin, but signatures of inhaled tobacco use, aflatoxin and mismatch repair were notably absent. Based on RNA expression analysis, ESCC could be divided into 3 distinct subtypes, which were distinguished by their expression of cell cycle and neural transcripts. This study demonstrates discrete subtypes of ESCC in SSA, and suggests that the endemic nature of this disease reflects exposure to a carcinogen other than tobacco and oncogenic viruses.

Figures

Figure 1. Summary of patient characteristics and somatic point mutations.

Each column represents 1 of 59 patient samples and columns are ordered based on RNA subtype (see Figure 4). The frequency of synonymous and nonsynonymous mutations per Mb sequenced (log scale) are stacked and shown at the top of the figure. Clinical and environmental characteristics of each sample are displayed in a matrix in the center of the figure, where each row is a single characteristic. Somatic single-nucleotide polymorphisms and copy number variations of each sample are displayed in a matrix in the bottom of the figure, where each row is a single mutated gene ordered top-to-bottom by decreasing percentage of the cohort with mutations in a given gene. Each mutation type is color coded and samples with 2 or more mutation types per gene are indicated by 2 or more colors. Percentage of the cohort with mutations in a given gene are shown in the bottom right of the figure.

Figure 2. Summary of copy number alterations (CNAs).

Genomic identification of significant targets in cancer (GISTIC) analysis of 59 esophageal squamous cell carcinoma (ESCC) samples for significantly amplified or deleted regions. The genomic profile of the copy number data is displayed in a heat map in the center of the figure, where samples are ordered based on RNA subtype (see Figure 4). Significant amplifications are indicated on the left in red and deletions on the right in blue. The score at the bottom is the false discovery rate q value as calculated by GISTIC.

Figure 3. Mutational analysis of exome data and p53.

Signature stability (top) and mutational signatures (bottom) are shown for Malawian (A) and Chinese (B) exome analyses. Stability is a measure of the reproducibility of a signature and reconstruction error is a measure of the reproducibility of the original mutation catalog (25). In Chinese patients, 2 signatures (aging and apolipoprotein B mRNA editing enzyme catalytic polypeptide-like [APOBEC]) are noted, whereas in Malawian esophageal squamous cell carcinoma (ESCC), a third signature is also identified, characterized by cytosine to adenine transversions and cytosine to thymine transitions.

Figure 4. RNA expression analysis of Malawian esophageal squamous cell carcinoma (ESCC) tumors.

A heat map of transcripts identified as being dynamically expressed (red, upregulated transcripts; blue, downregulated transcripts) among the 59 patient samples. Unsupervised hierarchical clustering suggests 2 subtypes ( and 2) with significantly different expression profiles. Subtype 1 can be further divided into 2 subtypes (1a and 1b) that are distinguished by frequency of copy number alterations (CNAs), point mutations, and TP53 mutations (Figures 1 and 2). The finding of 3 subtypes by RNA expression analysis is consistent with silhouette width and principal component analysis (Supplemental Figures 6 and 7).