O-OGC06 Genomic analysis of response to neoadjuvant chemotherapy in oesophageal adenocarcinoma. (16th December 2021) - Breininger, Stella; Izadi, Fereshteh; Sharpe, Benjamin; Secrier, Maria; Gibson, Jane; Walker, Robert; Rahman, Saqib; Devonshire, Ginny; Lloyd, Megan; Walters, Zoë; Fitzgerald, Rebecca; Rose-Zerilli, Matthew; et al.
Abstract: Background: Oesophageal adenocarcinoma (OAC) is the ninth most common cancer worldwide, with an estimated mortality of over 500, 000 deaths yearly. Neoadjuvant chemotherapy (NAC) followed by surgery is the standard of care (SOC) for locally advanced OAC. Although almost all patients receive chemotherapy as SOC, fewer than 20% obtain a clinically meaningful response and benefit before surgery. The OAC genome is complex and heterogeneous between patients, and it is not yet understood whether specific mutational patterns may result in chemotherapy sensitivity or resistance. Methods: To identify associations between genomic events and response to NAC in OAC, a comparative genomic analysis was performed in 65 patients using whole-genome sequencing.  We defined response to NAC using Mandard Tumour Regression Grade (TRG), with responders classified as TRG1-2 (n = 27) and non-responders classified as TRG4-5 (n = 38). Results: 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 (CNV) in non-responders (282 vs 136/patient, P <0.001). We identified CNVs unique to each group, 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 particular interest was the identification of the Neuron Navigator-3 ( NAV3 ), a known tumour suppressor downstream of EGFR, which was mutated exclusively in non-responders with a frequency of 22%. Conclusions: Our work characterises genetic features and mutations that are uniquely associated with response to NAC. We envision a treatment pipeline that incorporates driver mutation profiling in OAC, combining response prediction with targeted therapies to enhance response to NAC and improve survival outcomes.