Actionable mutational profiling in solid tumors using hybrid-capture-based next-generation sequencing in a real-world...

This study aimed to describe the performance of a next-generation sequencing (NGS) panel for the detection of precise genomic alterations in cancer in Spanish clinical practice. The impact of tumor characteristics was evaluated on informative NGS and actionable mutation rates.

A cross-sectional study was conducted at the Fundación Jiménez Díaz University Hospital (May 2021-March 2022) where molecular diagnostic of 537 Formalin-Fixed Paraffin-Embedded (FFPE) tissue samples of diverse solid tumors (lung, colorectal, melanoma, gastrointestinal stromal, among others) was performed using AVENIO Tumor Tissue Targeted Kit. A descriptive analysis of the features of all samples was carried out. Multivariable logistic analysis was conducted to assess the impact of sample characteristics on NGS performance defined by informative results rate (for all tumors and for lung tumors), and on actionable mutations rate (for lung tumors only).

AVENIO performance rate was 75.2% in all tumor samples and 75.3% in lung cancer samples, and the multivariable analysis showed that surgical specimens are most likely to provide informative results than diagnostic biopsies. Regarding the mutational findings, 727 pathogenic, likely pathogenic, or variant of unknown significance mutations were found in all tumor samples. Single nucleotide variant was the most common genomic alteration, both for all tumor samples (85.3% and 81.9% for all solid tumors and lung samples, respectively). In lung tumors, multivariable analysis showed that it is more likely to find actionable mutations from non-smokers and patients with adenocarcinoma, large cell, or undifferentiated histologies.

This is the largest cohort-level study in Spain to profile the analyses of biopsy samples of different tumors using NGS in routine clinical practice. Our findings showed that the use of NGS routinely provides good rates of informative results and can improve tumor characterization and identify a greater number of actionable mutations.