# Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma
## 3 Results
### Mutational Profiles Associated with Response to NAC  
To establish an overview of the mutational landscape associated with response to NAC, we compared the genomes of responders and non-responders. We performed assessment of somatic copy number variations (CNVs), small insertions/deletions (indels) and variant calling of somatic single nucleotide variants (SNVs) as previously described [24; 25; 28; 45]. Two non-responders were found to have microsatellite instability(MSI) (Patient 27, score = 5.63 and Patient 29, score = 3.44) and these were excluded to avoid potentially confounding effects on statistical analyses, which could compromise assessment of the other tumors [24; 46]. MSI-high tumors are known to respond to immune checkpoint blockade, providing a potential treatment pathway for these patients [47]. Initially, we investigated overall mutation burden in responders and non-responders on a genome-wide level. We identified a median of 104 (3-286) non-synonymous mutations per tumor genome in responders, compared to 85 (1-171) mutations in non-responders and the mutation frequency per megabase (Mb) was higher in the responder group (2.08 (range: 0.14-3.66) vs. 1.7 (range: 0.02-3.42); Figure 2A, Wilcoxon rank-sum test, \(p\) = 0.036). To investigate the mutational profile of SNVs in the trinucleotide context in our cohort, we performed mutational signature extraction using SigProfiler (version 1.1.0; San Diego, CA, USA, 2020.) [35]. Nine mutational signatures were defined in our cohort (Figure 2B). We hierarchically clustered our cases, and in agreement with prior studies [24], three main subgroups were observed corresponding to predominant signatures: these are classified as C > A/T dominant (SBS1/5 and SBS18); DDR impa  
Figure 2: Mutational landscape of NAC response. Responders have a higher mutation burden and neoantigen recognition potential. (**A**) Group dot plot of mutation per megabase. Each dot represents a patient with the red line marking the group median. (**B**) Clustering of the nine mutational signatures in our patient samples as previously described by [PERSON] et al. [24]. (**C**) Pathway deregulation scores (PDS) calculated using gene expression values (log2 normalized) of available RNA-seq samples for DDR pathways. (**D**) Neoantigen recognition potential scores. Only neoantigens with recognition potential above 1 are shown. (SBS17A/B) subgroups. There was no significant difference in the proportion of responders and non-responders between subgroups (Chi-squared test, \(p\) = 0.4). However, the DDR impaired subgroup is defined by signature 3 mutations, and the majority of these tumors (3/4) were non-responders. Signature 3 is associated with failure of DNA double-strand break-repair by homologous recombination, which could lead to chromosomal instabilities. Consequently, we assessed the dysregulation of DDR pathways using gene expression data of available RNA-seq samples (\(n\) = 9 responders, \(n\) = 21 non-responders) and created a pathways dysregulation score (PDS) using Pathifier (version v1.0; Rehovot, Israel, 2013) [39]. Non-responders exhibited greater dysregulation in DDR pathways compared to responders (Figure 2C, Wilcoxon rank-sum test, \(p\) = 0.002). Having observed higher mutational burden in responders, we hypothesized that this would also be correlated with a greater neoantigen load. We used NeoPredPipe (version 1.1; Tampa, FL, USA, 2019) [48], a predictive tool, to identify tumor neoantigens using binding affinity for patient-specific class I human lymphocyte antigen (HLA) alleles. We next calculated the neoantigen recognition potential by quantifying the peptides that displayed high affinity binding in tumors but had no predicted binding in the matched normal sample [36].