SwissMTB: establishing comprehensive molecular cancer diagnostics in Swiss clinics

Abstract

Background: Molecular precision oncology is an emerging practice to improve cancer therapy by decreasing the risk of choosing treatments that lack efficacy or cause adverse events. However, the challenges of integrating molecular profiling into routine clinical care are manifold. From a computational perspective these include the importance of a short analysis turnaround time, the interpretation of complex drug-gene and gene-gene interactions, and the necessity of standardized high-quality workflows. In addition, difficulties faced when integrating molecular diagnostics into clinical practice are ethical concerns, legal requirements, and limited availability of treatment options beyond standard of care as well as the overall lack of awareness of their existence.

Methods: To the best of our knowledge, we are the first group in Switzerland that established a workflow for personalized diagnostics based on comprehensive high-throughput sequencing of tumors at the clinic. Our workflow, named SwissMTB (Swiss Molecular Tumor Board), links genetic tumor alterations and gene expression to therapeutic options and clinical trial opportunities. The resulting treatment recommendations are summarized in a clinical report and discussed in a molecular tumor board at the clinic to support therapy decisions.

Results: Here we present results from an observational pilot study including 22 late-stage cancer patients. In this study we were able to identify actionable variants and corresponding therapies for 19 patients. Half of the patients were analyzed retrospectively. In two patients we identified resistance-associated variants explaining lack of therapy response. For five out of eleven patients analyzed before treatment the SwissMTB diagnostic influenced treatment decision.

Conclusions: SwissMTB enables the analysis and clinical interpretation of large numbers of potentially actionable molecular targets. Thus, our workflow paves the way towards a more frequent use of comprehensive molecular diagnostics in Swiss hospitals.

Keywords: Cancer diagnostics; Molecular diagnostics; Molecular tumor board; NGS; Personalized medicine.

Fig. 1

SwissMTB molecular diagnostics workflow. DNA (and RNA) is extracted from a tumor biopsy (and paired control tissue, e.g., blood) and sequenced. The resulting data is analyzed to detect genetic alterations (only in the tumor sample), which are associated with potential therapy options. Suitable therapies and clinical trial options are summarized in a clinical report, which is returned to the clinician and discussed in the molecular tumor board

Fig. 2

Overview of the SwissMTB bioinformatics analysis workflow. The reads generated by the sequencer are first mapped to the human reference genome. Afterwards, somatic variant and copy number variant calling is performed. Variants are annotated and then prioritized according to clinical relevance. RNA-seq based gene expression levels are compared to publicly available tumor sample cohorts. The findings are summarized in a clinical report. All steps from mapping to prioritization are fully automatized using a Snakemake-based pipeline. Selecting variants and therapies for the report is currently mainly manual work. All steps are documented and quality controlled, partly based on built-in routines in the analysis pipelines

Fig. 3

The overview page of an example clinical report including the categorization of therapies into cancer type specific, off-label (non cancer type specific), investigational, and possibly contraindicated therapies. We indicate the mutation status of commonly mutated genes, visualize the mutational burden of the patient, and inform on the patient’s HLA type

Fig. 4

Report section for therapies potentially lacking benefit. Gene name and variant type, as well as observed frequency, copy number, or gene expression are presented to indicate resistance-causing events. Furthermore, details on the affected therapy, as well as a brief description of the finding and literature support are provided

Fig. 5

Therapy recommendation confidence levels, based on categorization by the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists [67]

Fig. 6

Example for therapy categories for a melanoma patient presenting (among other mutations) several copy number mutations, an FGFR4 overexpression, and a BRAF V600E point mutation, identified based on WES, WGS, and RNA-seq data. Each table is structured as follows: Column “Gene” shows the name of the affected gene. Column “Variant” either contains the exact amino acid change resulting from a point mutation, or states the copy number change (amplification or deletion), or presents the change observed by the RNA-seq analysis (e.g. overexpression). Column “Frequency or Copy number” presents the variant frequency of point mutations (in percent), or presents the copy number observed for the affected gene. Column “Relative gene expression” includes the boxplot that shows the expression of the particular gene in comparison to the TCGA cohort of the same cancer type. For ease of interpretation, the different types of boxplot are explained in the “Guide section” of the clinical report, Additional file 1. Column “Pathway/Function” gives details on the functions of a gene. Column “Therapy” shows the name of the drug with a potential drug-gene interaction, while columns “Confidence” and “References” present the confidence level and the literature support of the drug-gene interaction, respectively

Fig. 7

Example for clinical trial options presented in a clinical report