Molecular pathology selected abstracts

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Correspondence: Dr. Jun J. Yang at jun.yang@stjude.org

Use of genomic landscape of esophageal adenocarcinoma to define biomarkers

Esophageal cancer is the eighth most common cancer worldwide and the sixth most common cause of cancer-related death. Esophageal adenocarcinoma (EAC), the predominant subtype in the West, is highly aggressive and generally resistant to chemotherapy, and it has an overall five-year survival rate of less than 15 percent. In comparison to other cancer types, EAC is characterized by very high mutation rates but, paradoxically, a paucity of recurrently mutated genes. Knowledge of which genetic events drive the development of EAC is limited. Consequently, there are few molecular biomarkers for prognosis or targeted therapeutics. A study by Frankell, et al., accumulated a cohort of 551 newly sequenced and previously characterized EACs. Cases had high quality clinical annotation, associated whole genome sequencing (WGS), and RNA sequencing (RNA-seq) data. From these 551 samples, the authors identified 11,813,333 single-nucleotide variants (SNVs) and small insertions or deletions (indels), 286,965 copy number alterations (CNAs), and 134,697 structural variants. They used an armamentarium of bioinformatic tools to assess recurrent mutations within a gene (dNdScv, ActivedriverWGS, and MutSigCV2), high-functional-impact mutations (OncodriveFM and ActivedriverWGS), mutation clustering (OncodriveClust, eDriver, and eDriver3D), and recurrent amplification or deletions of genes (GISTIC; genomic identification of significant targets in cancer) undergoing concurrent over- or underexpression. Seventy-six EAC driver genes were discovered, 71 percent of which had not been detected in EAC and 69 percent of which are known drivers based on published pancancer analyses. The authors discovered 21 noncoding driver elements in the study cohort, including known elements, such as the enhancer on chromosome 7, which is linked to TP53TG1, and new elements found in the 5′ untranslated region of MMP24. Using GISTIC, they identified 149 recurrently deleted or amplified loci. To determine which genes within these loci confer a selective advantage, the authors correlated cases with matched RNA-seq to detect changes in expression. Although the study may have been underpowered to detect small expression changes, the authors were able to identify significant changes in 17 cancer genes, including ERBB2, KRAS, and SMAD4. Some loci also showed extremely high copy number amplification, commonly more than 100 copies. In one example, circularization and amplification initially occurred around MYC but subsequently incorporated ERBB2 from a different chromosome. Such a pattern of extrachromosomal amplification via double minutes has been previously noted in EAC. The authors also detected several cases of overexpression or complete expression loss without associated copy number changes, reflecting nongenetic mechanisms for driver dysregulation. Novel drivers of particular interest included B2M, which encodes a core component of the MHC class I complex and is a marker of acquired resistance to immunotherapy, and ABCB1, which encodes a channel pump protein associated with multiple instances of drug resistance. TP53 was found to be a critical tumor suppressor in EAC, although 28 percent of cases remain TP53 wild type. Amplification and overexpression of MDM2, an E3 ubiquitin ligase that targets p53 for degradation, is mutually exclusive with TP53 mutation, suggesting that its degradation can functionally substitute for the effect of TP53 mutation. Mutually exclusive relationships were observed among KRAS and ERBB2, GATA4 and GATA6, and cyclin genes (CCNE1, CCND1, and CCND3). The authors also identified co-occurring relationships between TP53 and MYC, GATA6 and SMAD4, and Wnt and immune pathways. In univariate analysis, events in two genes were associated with significantly poorer prognosis after multiple-hypothesis correction—GATA4 amplification and SMAD4 mutation or homozygous deletion. The authors presented a detailed catalog of genomic events that have been selected for during the evolution of EAC. This catalog of biologically important gene alterations was used to identify prognostic biomarkers and actionable genomic events. This study should help pave the way for evidence-based clinical trials for esophageal adenocarcinoma.

Frankell AM, Jammula S, Li X, et al. The landscape of selection in 551 esophageal adenocarcinomas defines genomic biomarkers for the clinic. Nat Genet. 2019;51(3):506–516.

Correspondence: Dr. Rebecca C. Fitzgerald at rcf29@mrc-cu.cam.ac.uk

Secrier M, Li X, de Silva N, et al. Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance. Nat Genet. 2016;48(10):​1131–1141.

Correspondence: Dr. Rebecca C. Fitzgerald at rcf29@mrc-cu.cam.ac.uk