Analysis of Factors Determining Success in FFPE Based NGS Panel Testing For Lung & Ovarian Cancer

The identification of oncogenic variants in both lung and ovarian cancer is central to personalised treatment. NGS approaches using formalin-fixed paraffin-embedded (FFPE) tissue samples are routinely implemented for variant detection, but optimisation of pre-analytic factors is critical for success. We performed a large multi-cohort retrospective audit assessing pre-analytic factors related to Qiagen in house custom designed NGS panel testing for lung (n=801 from 23 referring hospitals) and ovarian (n=882 from 85 referring hospitals) FFPE cancer samples, sequenced at the NHS Northwest Genomic Laboratory Hub (NWGLH). A further detailed analysis of a cohort of lung samples (n=461) submitted from a single high-volume referral centre was also undertaken. Overall NGS cohort success ranged from 74-85% with large variation amongst referring laboratories. Multivariate logistic regression analysis revealed DNA yield and quality to be significant predictors of NGS success (p<0.001) alongside sample type for lung (p=0.035) and use of macrodissection for ovarian (p=0.025). Univariate analysis revealed specific poor lung performance within biopsy samples and associated with number and length of core biopsy samples. Furthermore, excessive fixation time for lung cytology and ovarian samples was associated with NGS failure (p<0.05), with only 49.5% of lung endoscopic bronchial ultrasound (EBUS) cytology samples meeting existing local guidelines for fixation time <24 hours, with 2/3 of prolonged fixation samples being received in the lab the following day. Variation in key identified pre-analytic factors amongst referring centres and variable adherence to best practice guidelines is likely to be responsible for the wide variation in NGS success. Improved collaboration between NHS genomic hubs and referring pathology laboratories through the establishment of a regional interactive collaborative network, facilitating guideline sharing and assessing adherence to pre-analytic optimisation of sample collection and processing for genomic testing, is crucial to improve future NGS genomic cancer testing.