Somatic whole genome dynamics of precancer in Barrett’s Esophagus

Abstract

While the genomes of normal tissues undergo dynamic changes over time, little is understood about the temporal-spatial dynamics of genomes in premalignant tissues that progress to cancer compared to those that remain cancer-free. Here we use whole genome sequencing to contrast genomic alterations in 427 longitudinal samples from 40 patients with stable Barrett’s esophagus compared to 40 Barrett’s patients who progressed to esophageal adenocarcinoma (ESAD). We show the same somatic mutational processes were active in Barrett’s tissue regardless of outcome, with high levels of mutation, ESAD gene and focal chromosomal alterations, and similar mutational signatures. The critical distinction between stable Barrett’s versus those who progress to cancer is acquisition and expansion of TP53-/- cell populations having complex structural variants and high-level amplifications, which were detectable up to six years prior to a cancer diagnosis. These findings reveal the timing of common somatic genome dynamics in stable Barrett’s esophagus and define key genomic features specific to progression to esophageal adenocarcinoma, both of which are critical for cancer prevention and early detection strategies.

Extrachromosomal DNA in the cancerous transformation of Barrett’s esophagus

“…Prior loss of TP53 enables genomic instability 7,8,24,25 , and we found a strong association in both FHCC and Cambridge cohorts between TP53 disruption (Supplementary Appendix) and ecDNA-positive status (Supplemental Figure 6a-b). In the FHCC cohort, all eight samples in which ecDNA was found prior to cancer diagnosis (TP-1) showed biallelic disruption of TP53 .…”

“…We analyzed WGS data from esophageal biopsies in an independent, prospectively collected case-control study conducted at the Fred Hutchinson Cancer Center (FHCC) of patients with BE (Figure 1b.) 8 , including 40 patients with a cancer outcome (CO) and 40 patients who did not develop cancer (NCO) during the study period. At least two biopsies for WGS were obtained by isolating epithelial tissue (Figure 1c) of the BE at each of the two primary study time points - timepoint 1 (TP-1) and timepoint 2 (TP-2).…”

Unravelling the tumour genome: The evolutionary and clinical impacts of structural variants in tumourigenesis

“…In multiple myeloma, Oben et al [165] showed that the presence of myeloma‐defining genomic events, including chromothripsis in the rarely progressing precursor state, monoclonal gammopathy of undetermined significance (MGUS), can define a more progressive subset. In oesophageal cancer, complex SVs and CNAs are the most accurate predictors of malignant transformation from the Barrett's oesophagus state [166–168].…”

“…In the last decade, many studies have demonstrated proof-ofconcept data highlighting the potential that SVs can play in every stage of cancer, from screening to treatment to prognostication (Figure 3). Premalignancy, driver SVs can exist decades before the onset of symptoms, generating opportunities for screening and early detection [160,[164][165][166][167][168]. The TRACERx group [160] showed that chromosome 3p loss is often the initiating driver in clear cell renal cell carcinoma and is predicted to arise 30-50 years before diagnosis (Figure 3A).…”

“…In multiple myeloma, Oben et [165] showed that the presence of myeloma-defining genomic events, including chromothripsis in the rarely progressing precursor state, monoclonal gammopathy of undetermined significance (MGUS), can define a more progressive subset. In oesophageal cancer, complex SVs and CNAs are the most accurate predictors of malignant transformation from the Barrett's oesophagus state [166][167][168]. Following malignant transformation, the presence of specific SVs is pathognomonic of certain cancer types [157,158] demonstrated that malignant mesothelioma harbours neoantigens that are generated by complex SVs, and that their generation was associated with the clonal expansion of T cells, indicating that complex SV burden can be used to predict response to immune checkpoint inhibitors.…”