Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma

Abstract

Neoadjuvant therapy followed by surgery is the standard of care for locally advanced esophageal adenocarcinoma (EAC). Unfortunately, response to neoadjuvant chemotherapy (NAC) is poor (20-37%), as is the overall survival benefit at five years (9%). The EAC genome is complex and heterogeneous between patients, and it is not yet understood whether specific mutational patterns may result in chemotherapy sensitivity or resistance. To identify associations between genomic events and response to NAC in EAC, a comparative genomic analysis was performed in 65 patients with extensive clinical and pathological annotation using whole-genome sequencing (WGS). We defined response using Mandard Tumor Regression Grade (TRG), with responders classified as TRG1-2 (n = 27) and non-responders classified as TRG4-5 (n =38). We report a higher non-synonymous mutation burden in responders (median 2.08/Mb vs. 1.70/Mb, p = 0.036) and elevated copy number variation in non-responders (282 vs. 136/patient, p < 0.001). We identified copy number variants unique to each group in our cohort, with cell cycle (CDKN2A, CCND1), c-Myc (MYC), RTK/PIK3 (KRAS, EGFR) and gastrointestinal differentiation (GATA6) pathway genes being specifically altered in non-responders. Of note, NAV3 mutations were exclusively present in the non-responder group with a frequency of 22%. Thus, lower mutation burden, higher chromosomal instability and specific copy number alterations are associated with resistance to NAC.

Keywords: NAV3; chemotherapy; esophageal adenocarcinoma; genomics; mutation; response.

Figure 1

Outline of the cohort and analyses performed. (A) Description of the study design. (B) Kaplan–Meier of overall survival (n = 64) for responders (blue line) and non-responders (red line). Number of cases at risk are detailed in the table.

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 Secrier 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.

Figure 3

Non-responders have less stable genomes and unique patterns of copy number change in EAC driver genes. (A) Proportions of the genome affected by copy-number changes (Genomic Instability Index, GII). Non-responders showed a higher level of genomic instability (p = 2.5 × 10⁻¹³). (B) Violin plots depicting the frequency of all amplifications and deletions in responders and non-responders. (C) Amplified peak regions across the genome plotted for responders vs non-responders (n = 63) using GISTIC2.0 (FDR < 0.1). Amplifications unique to each group are labeled. (D) Oncoplot of recurrently amplified/deleted EAC drivers among responders and non-responders identified by GISTIC2.0 (FDR < 0.1).

Figure 4

Amplified EAC driver genes are overexpressed in non-responders and copy number alterations associate with poor survival. (A) Violin plots comparing mRNA expression levels (FPKM) in matched RNA-Seq data (n = 9 responders, n = 21 non-responders) for copy number altered EAC driver genes in responders and non-responders. p-values were based on one-tailed Wilcoxon rank-sum test. (B) Kaplan–Meier plot comparing overall survival of patients with CDK6, CCND1, CDKN2A, GATA4 and MYC copy number changes (red) vs. neutral patients (blue). (C) Gene Set Enrichment Analysis (GSEA) of MYC target genes in available RNA-Seq data (n = 30).

Figure 5

Non-responders have more EAC driver alterations of all types, including exclusive mutation of the tumor suppressor NAV3. (A) Oncoplot of SNVs, indels and CNVs combined in responders vs. non-responders (n = 63). Genes shown are the subset of the 76 EAC driver genes described in Frankell et al. [25] that were mutated in at least 5% of either group. Percentages of responders or non-responders with driver gene mutations are shown next to the corresponding row. (B) Protein-level diagram of mutations in the coding sequence of NAV3, which was exclusively mutated in non-responders. Domains are labeled as follows: CH—calponin homology; CC—coiled coil; AAA—ATPase associated with diverse cellular activities. Mutational sites are shown as lollipops color-coded according to the type of mutation. (C) Violin plots comparing frequency of all alterations (SNVs, indels and CNVs) in EAC driver genes per sample in responders vs. non-responders. (D) Oncoplot of SNVs, indels and CNVs combined in TARGET database genes, which are associated with a clinical action in cancer (n = 63). Genes that were mutated in at least 5% of either group are shown.