Comparison of the clinical interpretation of high-dimensional molecular data by two molecular tumor boards.

Abstract

3564 Background: The clinical interpretation of molecular data is a bottleneck of precision oncology. High-dimensional molecular data, such as RNA sequencing (RNA-seq) and whole-exome sequencing (WES), will likely increase the complexity of clinical interpretation. We compared the recommendations by two molecular tumor boards (MTBs) that independently interpreted the same high-dimensional molecular profiles. Methods: Patients with advanced solid tumors, no available standard therapy, an ECOG performance status of 0-1, and available fresh-frozen tissue underwent WES of tumor tissue and normal blood as well as RNA-seq of tumor tissue within the MASTER (Molecularly Aided Stratification for Tumor Eradication Research) precision oncology program of the German Cancer Consortium (DKTK). Data from 46 patients (WES and RNA-seq, n = 41; WES alone, n = 5) were independently discussed by two MTBs. Treatment recommendations were compared with regard to levels of evidence, therapeutic baskets, and types of biomarkers. Results: A total of 51,610 aberrations (median, 393 per patient) were considered for clinical interpretation (34,314 mutations/single-nucleotide variants, 7,115 mRNA expression changes, 6,144 DNA copy number variations, 4,037 gene fusions). 110 and 132 treatment options were identified by the two MTBs, respectively, with an overall agreement rate of 44.1%. The highest agreement rates were identified for treatment options based on clinical levels of evidence (Level 1, 60%; Level 2, 49.6%) and for poly(ADP-ribose)-polymerase inhibition (57.1%). The lowest agreement rates were identified when MTBs opted for traditional chemotherapy (0%), combination therapies (6.9%), therapies based on preclinical levels of evidence (Level 3, 35.9%; Level 4, 32%), and MAPK inhibition (35%). Similar agreement rates, ranging from 39% (gene fusions) to 54% (loss of heterozygosity), were observed for different types of biomarkers. Conclusions: Reproducible, evidence-based annotation of high-dimensional molecular data is feasible. Our experience provides a basis for ongoing harmonization and standardization efforts within the MTBs of the DKTK.