Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards

Abstract

Background Structured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards. Methods High-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0–1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically. Results A median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician’s choice. Conclusions Reproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts.

Real-world performance analysis of a novel computational method in the precision oncology of pediatric tumors

“…Standardization is a universal need in precision oncology [ 53 ]. Notably, MTBs generally have low concordance rates, 40%–63%, from the same input data [ 44 , 45 , 54 ]. The use of DDA can overcome this discordance and accelerate and standardize variant interpretation and decision support.…”

“…The limitation of this simplistic approach is that most cancers are driven by a complexity of multiple driver alterations, making standardized decisions difficult [ 44 ]. Two molecular tumor boards (MTBs), located in the same country, were shown to have an agreement rate of just 44% on high-dimensional data [ 45 ]. Another possibility is to use combination therapies to match more than half of drivers.…”

Leveraging Large Language Models for Decision Support in Personalized Oncology

“…Each case vignette was assigned to 1 expert physician of the Charité MTB for manual clinical interpretation of molecular findings, following previously described workflows . Additionally, 4 different LLMs were tasked to generate treatment options: BioMed LM (MosaicML; Stanford University) (LLM 1), Perplexity.ai (University of California, Berkeley) (LLM 2), ChatGPT (OpenAI) (LLM 3), and Galactica (Meta) (LLM 4) .…”

“…Each case vignette was assigned to 1 expert physician of the Charité MTB for manual clinical interpretation of molecular findings, following previously described workflows. 5 Additionally, 4 different LLMs were tasked to generate treatment options: BioMed LM (MosaicML; Stanford University) (LLM 1), 14 Perplexity.ai (University of California, Berkeley) (LLM 2), 15 ChatGPT (OpenAI) (LLM 3), 16 and Galactica (Meta) (LLM 4). 17 These 4 were selected to compare across 4 different criteria: type of usage (local installation vs online, important regarding data privacy requirements), model size (in terms of computational resources required), openness (whether an integrated retrieval engine is used, impact on up-to-datedness), and pretraining domain (general or medical, impact on result quality) (eTable 2 in Supplement 1).…”

“…However, the identification of uncommon and complex molecular alterations or defined biomarkers falling outside currently established guidelines and recommendations creates challenges for clinical decision-making. These findings are frequently discussed in specialized and interdisciplinary molecular tumor boards (MTB) . Especially in these settings, the clinical interpretation of molecular alterations remains manual work based on search engines and specialized curated databases .…”