From somatic variants towards precision oncology: Evidence-driven reporting of treatment options in molecular tumor boards

Abstract

Background: A comprehensive understanding of cancer has been furthered with technological improvements and decreasing costs of next-generation sequencing (NGS). However, the complexity of interpreting genomic data is hindering the implementation of high-throughput technologies in the clinical context: increasing evidence on gene-drug interactions complicates the task of assigning clinical significance to genomic variants.

Methods: Here we present a method that automatically matches patient-specific genomic alterations to treatment options. The method relies entirely on public knowledge of somatic variants with predictive evidence on drug response. The output report is aimed at supporting clinicians in the task of finding the clinical meaning of genomic variants. We applied the method to 1) The Cancer Genome Atlas (TCGA) and Genomics Evidence Neoplasia Information Exchange (GENIE) cohorts and 2) 11 patients from the NCT MASTER trial whose treatment discussions included information on their genomic profiles.

Results: Our reporting strategy showed a substantial number of patients with actionable variants in the analyses of TCGA and GENIE samples. Notably, it was able to reproduce experts' treatment suggestions in a retrospective study of 11 patients from the NCT MASTER trial. Our results establish a proof of concept for comprehensive, evidence-based reports as a supporting tool for discussing treatment options in tumor boards.

Conclusions: We believe that a standardized method to report actionable somatic variants will smooth the incorporation of NGS in the clinical context. We anticipate that tools like the one we present here will become essential in summarizing for clinicians the growing evidence in the field of precision medicine. The R code of the presented method is provided in Additional file 6 and available at https://github.com/jperera-bel/MTB-Report .

Keywords: Actionable variants; Cancer genomics; Genomic report; Molecular tumor board; Personalized treatment; Predictive biomarkers; Targeted therapies.

Fig. 1

Overview of the pipeline to report actionable variants from tumor genomic profiles. a The algorithm uses two types of input: type of tumor (e.g., breast cancer) and its genomic profile (i.e., somatic variants). b First, the genomic profile is used to identify the actionable variants as depicted in the flowchart. A variant with an established significance will follow the central path of the flowchart (e.g., BRAF V600E). The side arms are designed to repurpose variants of unknown significance. c Then, the actionable variants are classified into clinically relevant categories using a system of six levels of evidence. d Finally, the output is in form of hand-in reports

Fig. 2

The molecular tumor board (MTB) report. First page of the report of patient MASTER-04 from the NCT MASTER dataset. General information of the patient, clinical history, and genomic data are summarized under a first header entitled “Patient information”. Under a second block called “Gene-drug predictive associations”, the user can find all the details regarding the actionable variants identified. The method is briefly described at the beginning. The number of gene–drug predictive associations found at each level are summarized in a figure and then detailed in a table. In the table, the patient’s variants are located in the left part, and the public knowledge on those variants is located in the right part. Each row details a specific association between a gene variant and a drug response in a specific cancer type

Fig. 3

Unsupervised clustering of 3184 TCGA samples based on genomic status of 312 genes. The figure displays a heatmap of the genomic status of the top 50 most altered genes (rows) on 3184 tumor samples (columns) with dendrogram from hierarchical clustering of the samples. The percentage of mutated samples of every gene is vertically displayed at the left of the heatmap (histogram). The legend Cancer types refers to the annotation of the tumor samples in the columns, the legend Genomic status describes the colors used in the heatmap and the histogram

Fig. 4

Heatmap representation of the distribution of identified actionable variants in TCGA cohort. Somatic alterations of each sample (3184 samples) were analyzed as depicted in Fig. 1 and the resulting biomarker–drug associations were assigned to one of the six levels of evidence. Evidence in wild-type variants and resistances are not included in this representation (unless if the evidence is in level A1, e.g., NRAS, KRAS wild type in colorectal cancer). a The percentage of patients with at least one actionable variant at each level of evidence. b The cumulative percentage of patients with at least one actionable variant at increasing levels of evidence (from A1 to B3, x-axis). c Average (± standard deviation (SD)) number of actionable genes per patient at each level of evidence. d Combination of the data shown in a–c panels depicted for the whole cohort, no distinction among cancer types