Quality control patterns in comprehensive genomic testing of malignancies.

Abstract

456 Background: Molecular genetic testing is a crucial tool to detect genetic alterations in tumor samples and can aid in identifying targeted therapies for patients. Next generation sequencing (NGS) testing is a comprehensive technology to detect a broad spectrum of targetable biomarkers with a high sensitivity, while providing a cost- and time-effective strategy compared to other molecular genetic tests such as Sanger sequencing. NGS requires a minimum sufficient genetic material to detect a true mutation and report a significant result. Methods: In our study, we have analyzed NGS results from clinical samples from different organs or malignancies, obtained from 278 patients (median age 55, average age of 64, range 20-89) were analyzed. The specimens were collected between September 2005 and December 2022 from adenocarcinomas, not otherwise specified (NOS, 34.95%), metastatic adenocarcinomas, NOS (13.42%); metastatic carcinomas, NOS (4.49%), and others (47.14%). A total of 5127 individual results were reported. NGS testing (DNA-seq and RNA-seq) was performed and reported by Caris Life Sciences. Results: From our molecular genetic results analysis, 28 specimens (n= 12 female, 16 male) were reported to be ‘Quantity Not Sufficient’ (QNS), representing 9.45% of all specimens tested. To further understand the patterns of reported QNS, we identified the prevalence of QNS results in various tumor types. The majority of QNS results occurred in adenocarcinomas (NOS) representing 45.16%; followed by metastatic adenocarcinoma (NOS), 16.33%; and glioblastoma, 6.45%. The majority of specimen sites with QNS reported were the liver 15.12%, lung (NOS) 13.71%, and the prostate (NOS) 12.10%. Conclusions: Understanding patterns of QNS can be helpful to optimize testing and sampling strategies specifically for tissues with a high prevalence of insufficient quantity of genetic material. Additionally, analyzing and comparing biomarkers and their QNS pattern can be supportive to identify testing errors and improve testing methodologies.