Spatial intra-tumour heterogeneity and treatment-induced genomic evolution in oesophageal adenocarcinoma: implications for prognosis and therapy

Abstract

Background: Oesophageal adenocarcinoma (OAC) is a highly heterogeneous cancer with poor survival. Standard curative treatment is chemotherapy with or without radiotherapy followed by oesophagectomy. Genomic heterogeneity is a feature of OAC and has been linked to treatment resistance.

Methods: Whole-genome sequencing data from 59 treatment-naïve and 18 post-treatment samples from 29 OAC patients was analysed. Twenty-seven of these were enrolled in the DOCTOR trial, sponsored by the Australasian Gastro-Intestinal Trials Group. Two biopsies from each treatment-naïve tumour were assessed to define 'shared' (between both samples) and 'private' (present in one sample) mutations.

Results: Mutational signatures SBS2/13 (APOBEC) and SBS3 (BRCA) were almost exclusively detected in private mutation populations of treatment-naïve tumours. Patients presenting these signatures had significantly worse disease specific survival. Furthermore, mutational signatures associated with platinum-based chemotherapy treatment as well as high platinum enrichment scores were only detected in post-treatment samples. Additionally, clones with high putative neoantigen binding scores were detected in some treatment-naïve samples suggesting immunoediting of clones.

Conclusions: This study demonstrates the high intra-tumour heterogeneity in OAC, as well as indicators for treatment-induced changes during tumour evolution. Intra-tumour heterogeneity remains a problem for successful treatment strategies in OAC.

Keywords: Genetics; Oesophageal adenocarcinoma; Treatment impact; Tumour evolution; Whole-genome sequencing.

Fig. 1

Swimmer plot of patient cohort. Visualisation of the disease specific survival (DSS) and status for each patient as well as clinical events. DSS has been censored after 60 months. Clinical stage (cTNM), pathological stage (ypTNM), tumour regression at surgery, allocated treatments and data availability per patient are displayed on the left. CF, cisplatin and 5-fluorouracil; DCF, CF and docetaxel; RT, 45 Gy radiotherapy; WGS, whole-genome sequencing

Fig. 2

Mutational signatures in the shared and private mutation populations of the multi-region treatment-naïve samples. Shared mutations are shared across both treatment-naïve samples; private mutations were only detected in one sample. A The top panel of bar plots shows the proportion of shared (grey) and private mutations. The private mutations are split into private to sample T1 (green) and private to sample T2 (blue). The middle panel shows the absolute number of mutations used in the mutational signature analysis. The bottom panel of stacked bar plots shows the mutational signatures identified in the shared (first bar) and private (second bar) mutation populations of each tumour. Signatures SBS2 and SBS13 as well as SBS20, SBS26 and SBS44 are combined to an APOBEC and an MMR/MSI signature, respectively. Patients are ordered by survival status and time. B–D Violin plots of B the proportions of shared and private mutations per tumour, C the proportions of signatures SBS17a and SBS17b in the shared and private mutation populations and D signature SBS2/13 (APOBEC) and SBS3 (BRCA) proportions in the shared and private mutation populations. q-values represent the fdr corrected p-values. E–F Stacked bar plots showing the contingency table of the presence of the E APOBEC associated signatures (SBS2/13) and F BRCA associated signature (SBS3) in the shared and private mutation populations. G–H Disease specific survival (DSS) of patients stratified by presence of mutational signatures SBS2/13 (APOBEC) in the private mutations G in a Kaplan-Meier plot (log-rank test) and H a forest plot of the associated hazard ratios after adjusting for clinical cTNM stage (cox regression). I–J DSS for patients with signature SBS3 (BRCA) present or absent in the private mutation populations in I a Kaplan-Meier plot and J a forest plot of the hazard ratios after adjustment for clinical stage. SBS, single base substitution; OAC, oesophageal adenocarcinoma; ROS, reactive oxygen species; MMR, mismatch repair; MSI, microsatellite instability

Fig. 3

Clonal compositions detected in multi-region treatment-naïve samples. A Bar plot of number of clones detected per sample (T1/T2) and per tumour (union of clones identified in T1 and T2). The patients are grouped by tumour clone number above average (5 clones). B Kaplan-Meier plot for disease specific survival (DSS; log-rank test) stratified by high (> 5) and low (≤ 5) clone numbers. C Schematic map of sample collection. D–H Circle plots, clonal evolution trees and mutational signature bar plots. The circle plots visualise the clonal composition and proportions of identified clones in each sample. Each box contains the patient ID, two circle plots (one per sample), a phylogenetic tree showing the relationship between clones, and the mutational signatures per clone per patient. Tumours with D the same clones detected in both samples, E similar mutational signature profile between all clones, and F different mutational signatures in the founder clone (C1) and the subclones (C2-7) of the treatment-naïve samples. G APOBEC signatures detected in subclonal mutation populations were traced back to individual clones. H Additional cases of APOBEC signatures in unique subclones

Fig. 4

Mutational signatures in multi-region and multi-timepoint samples. A Mutation populations were divided into shared (shared across all samples), unique pre (present in any treatment-naïve sample but no post-treatment sample) and unique post (present in any post-treatment sample but no treatment-naïve sample). The top panel shows bar plots of the number of mutations, while the bottom panel shows the proportion of mutational signatures detected in each mutation population as stacked bar plots. B–C Violin plots of B the platinum enrichment scores in the unique pre- and post-treatment mutation populations and C the number of mutations in each subset

Fig. 5

Clonal evolution over time. A Number of clones detected in each sample and the tumour (union of clones identified in all samples of the tumour). B Violin plot of clone numbers in pre- and post-treatment samples. C Scatter plot of clone numbers and tumour cellularity per sample including linear model and Spearman’s rank correlation. D–G Fishplots visualising the clonal evolution. Each fishplot shows the average clone proportions in the pre- and post-treatment tumour. D Tumour case showing a relatively stable clonal composition over time. E–F A clonal sweep was detected either E by itself or F in addition to clonal expansion. G Cases showing that treatment pressure can affect different parts of the tumour differently, leading to complex evolution patterns. The clone IDs are used to distinguish clones of each patient but cannot be used for comparison between patients

Fig. 6

Treatment impact on tumour evolution. Each box contains the fishplot of the clonal composition of each pre- and post-treatment tumour. Below each fishplot are A the platinum enrichment score and mutational signatures for each individual clone and B the differential agretopicity index (DAI) scores of the predicted neoantigens found in each clone. The clones and their corresponding scores are colour-matched per patient. The crossbars indicate the mean per group