EGFR amplification and outcome in a randomised phase III trial of chemotherapy alone or chemotherapy plus panitumumab for advanced gastro-oesophageal cancers

Objective Epidermal growth factor receptor (EGFR) inhibition may be effective in biomarker-selected populations of advanced gastro-oesophageal adenocarcinoma (aGEA) patients. Here, we tested the association between outcome and EGFR copy number (CN) in pretreatment tissue and plasma cell-free DNA (cfDNA) of patients enrolled in a randomised first-line phase III clinical trial of chemotherapy or chemotherapy plus the anti-EGFR monoclonal antibody panitumumab in aGEA (NCT00824785). Design EGFR CN by either fluorescence in situ hybridisation (n=114) or digital-droplet PCR in tissues (n=250) and plasma cfDNAs (n=354) was available for 474 (86%) patients in the intention-to-treat (ITT) population. Tissue and plasma low-pass whole-genome sequencing was used to screen for coamplifications in receptor tyrosine kinases. Interaction between chemotherapy and EGFR inhibitors was modelled in patient-derived organoids (PDOs) from aGEA patients. Results EGFR amplification in cfDNA correlated with poor survival in the ITT population and similar trends were observed when the analysis was conducted in tissue and plasma by treatment arm. EGFR inhibition in combination with chemotherapy did not correlate with improved survival, even in patients with significant EGFR CN gains. Addition of anti-EGFR inhibitors to the chemotherapy agent epirubicin in PDOs, resulted in a paradoxical increase in viability and accelerated progression through the cell cycle, associated with p21 and cyclin B1 downregulation and cyclin E1 upregulation, selectively in organoids from EGFR-amplified aGEA. Conclusion EGFR CN can be accurately measured in tissue and liquid biopsies and may be used for the selection of aGEA patients. EGFR inhibitors may antagonise the antitumour effect of anthracyclines with important implications for the design of future combinatorial trials.

mix. The probes and target DNA were co-denaturated by incubating at 75 o C for 2 minutes, followed by hybridization at 37 o C overnight. Post-hybridization slides were washed in 0.4x SSC/ 0.3% Igepal at 72 o C for 2 minutes, followed by a wash in 2x SSC/0.1% Igepal at room temperature for 1 minute. Nuclei were counterstained with VECTASHIELD ® Antifade Mounting Media (Vector Laboratories, Peterborough, UK) and covered with coverslip.
Microscopy and imaging were performed using an Olympus BX61 fluorescence microscope using the SMART Capture imaging software (Digital Scientific, Cambridge, UK).

Digital droplet polymerase chain reaction (ddPCR)
FAM-labelled EGFR (Hs01646307_cn) and VIC-labelled CNTNAP2 (Hs00712117_cn) probes (Thermo Fisher Scientific, Loughborough, UK) were used in a multiplex reaction to assess amplification of EGFR. Duplicate PCR reactions were prepared for each sample using the ddPCR supermix for probes without dUTP (Bio-Rad, Watford, UK) and a maximum of 5 ng of template DNA. The PCR reactions were converted into droplets using the QX200 AutoDG Droplet Generator (Bio-Rad, Watford, UK) and the PCR was conducted using the following conditions: 95°C for 10 min; 40 cycles of 94°C for 30 sec and 60°C for 1 min; 98°C for 10 min.
The droplets were then analyzed using the QX200 Droplet Reader (Bio-Rad), and the obtained data were analysed using the QuantaSoft software (Bio-Rad). Quality of individual runs was controlled with negative (range 0.6-1.2) and positive (range >20:1) controls, as well as nontemplate control on each plate. For a valid result, a minimum of 20,000 merged total droplets was required, 400 positive droplets for each assay, and ≤25% difference between replicates.

RNA sequencing and data analysis
RNA sequencing and data analysis were performed by Arraystar (Rockville, MD, USA). Library construction, sequence quality control, and data analysis were performed as previously

described.[13]
EdU incorporation assay F-014 BL and DD191 organoids were harvested and dissociated following the passaging procedure previously described.   FASTQ files were trimmed using skewer [43] for adaptor content with a minimum length allowed after trimming of 35 bp, only on reads with a minimum mean quality of 10 and with the filter to remove highly degenerative reads (-l 35 -Q 10 -n). Trimmed FASTQ files were aligned using bwa mem [44] to hg38 (GRCh38). Sam files were sorted, compressed to bam files and duplicates were marked using Picard tools (www.broadinstitute.github.io/picard/). Indexing was performed using samtools. [45] Bam files were then processed using QDNAseq [46] to convert read counts in 500kb bins across the autosomes of hg38 into log2ratio data. Data normalisation was performed in accordance to the QDNAseq workflow, except for the step which uses the smoothOutlierBins function which was seen to artificially depress signal from highly amplified bins. Bins for hg38 were also generated according to QDNAseq instructions.
Bins with a log2ratio greater than or equal to 0.58 were considered amplified. Genes present within these bins were identified using biomaRt. [47] BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s)