PTU-181 Optimised response prediction in oesophagogastric adenocarcinomas (OGA) with combination of molecular biomarkers, serum cell death markers and FDG-PET. (28th May 2012)

• Record Type: Journal Article
• Authors: Bain, G H; Collie-Duguid, E; Murray, G; Gilbert, F; Denison, A; McKiddie, F; Ahearn, T; Leeds, J; Phull, P; Park, K; Welch, A; Schweiger, L; Petty, R D
• Is Part Of: Gut. Volume 61(2012)Supplement 2
• Page Start: A259
• Page End: A259
• Publication Date: 2012-05-28
• Subjects: Gastroenterology -- Periodicals; 616.33
• DOI: 10.1136/gutjnl-2012-302514c.181
• Languages: English
• ISSNs: 0017-5749

Abstract

Abstract : Introduction: Predictive biomarkers (BMs) for OGA would optimise treatment selection and avoid ineffective therapy. Metabolic response (MR) defined as >35% decrease in tumour FDG Standardised Uptake Value (SUV) between day 0 and day 14 after starting chemotherapy has a high negative predictive value (95%) for response, but limited positive predictive value (50%). Combining molecular BMs and serum cell death markers with FDG-PET may optimise response prediction. We used global gene expression profiling (GEP) and cell death ELISAs to identify molecular BMs and serum markers that when combined with FDG-PET would improve predictive accuracy. Methods: 28 patients with locally advanced/metastatic OGA received platinum based chemotherapy (PBC). FDG-PET scans were at day 0 & 14 and GEP (Affymetrix ST1.0 Exon Genechips) on day 0 tumour biopsies. A tissue microarray comprising an independent set of 154 OGA who had surgery +/-neoadjuvant PBC was used with immunohistochemistry (IHC) for qualification of GEP results. Cytokeratin 18 (CK18) M30 (apoptosis) and M65 (apoptosis+necrosis) ELISAs (Pevivia, Sweden) were used to assess cell death from serial serum samples during chemo. Radiological response was assessed after 3/4 cycles of PBC by RECISTv1.1. Results: We identified a gene expression signature (86 genes) that separated FDG-PET MR patients (>35% fall SUV day 0–14) into those that do and do not have a RECIST response. In cross validation this signature correctly predicted response in 14/14 metabolic responders (MRs). Pathway analysis on GEP data identified potential novel mechanisms of response including the Leptin pathway. Leptin mRNA was higher in FDG MRs who did not have a RECIST response compared to those that did (p=0.026). In the independent set high Leptin protein by IHC was associated with lack of histopathologic response to neoadjuvant PBC (n=64, p=0.007). High Leptin expression also had a therapy independent prognostic effect with longer survival in the absence of histopathologic response or with no neoadjuvant PBC and in low Leptin patients poor survival was mitigated to a degree by neoadjuvant PBC. Serum CK18M30 decreased from day 0–14 in MRs but in metabolic non-responders (MNRs) there was a smaller fall or a rise (p=0.021). Levels in MNRs did not change with subsequent chemo. In MRs levels continued to fall in RECIST responders but increased again in non-responders. Conclusion: Molecular biomarkers (Leptin in particular) and serum cell death markers combine with FDG-PET to optimise response prediction in OGA. Further investigation of this combined molecular, serum and imaging approach is warranted. Competing interests: None declared.