# Genomic Analysis of Response to Neoadjuvant Chemotherapy in Esophageal Adenocarcinoma
## 3 Results
### Mutational Profiles Associated with Response to NAC
#### 3.2.4 Potentially Targetable Alterations in Non-Responders  
To make sense of the complex genomic aberrations observed in this study and in EAC in general, we combined recurrent CNVs and non-synonymous mutations with the aim of identifying unique genomic aberrations in responders or non-responders. Overall, the mean number of EAC drivers carrying any alteration (SNVs, CNVs and Indels) was higher in non-responders (6.4/patient vs 4.4/patient, Wilcoxon rank-sum test, \(p=0.007\), Figure 5C). We observed that the majority of differentially altered genes were found in non-responders (Figure 5A). Many of these genomic lesions are potentially targetable, and to investigate this further we focused on somatically altered cancer genes which are directly linked to a clinical action in the TARGET database ([[https://software.broadinstitute.org/cancer/cga/target](https://software.broadinstitute.org/cancer/cga/target)]([https://software.broadinstitute.org/cancer/cga/target](https://software.broadinstitute.org/cancer/cga/target)); date accessed: 1 December 2020.) (Tables S13 and S14). Non-responders displayed exclusive focal alterations of genes in this list (Figure 5D), including: amplification of _Atikka_ (16/36 non-responders, 44%); _GNAS_ (20/36 non-responders, 56%) and _RARA_ (12/36 non-responders, 33%); and deletion of _ERFFII_ (12/36 non-responders, 33%), in addition to the previously identified EAC drivers _CDKN2A_, _CCND1_, _EGFR_ and _KRAS_ (Table S14). However, we observed chromosome arm amplifications of 20q, containing GNAS and AURKA, in responders. We also found potentially targetable genes exclusively amplified at the focal level in responders: _CEBPA_ (13/36 responders, 37%) and _AKT3_ (13/36 responders, 37%) were amplified in addition to EAC drivers _ERBB2_ and _CCNE1_ (Figure 5D). These findings are in contrast to chromosome arm deletions at 19q containing CCNE1 and CEBPA, which were found in non-responders. We examined the levels of evidence for biomarker-drug associations for our targets using the OncoKB database ([[https://www.oncokb.org/](https://www.oncokb.org/)]([https://www.oncokb.org/](https://www.oncokb.org/)); date accessed: 1 January 2021.) [41]. _ERBB2_, _EGFR_ and _KRAS_ have FDA-approved drugs for use in cancer therapy, whereas _CDKN2A_ has biological evidence for targetability, but associated drugs are not yet standard-of-care.  
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 [PERSON] et al. (2019) 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.