Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer

Abstract

Objective: Precision oncology depends on translating molecular data into therapy recommendations. However, with the growing complexity of next-generation sequencing-based tests, clinical interpretation of somatic genomic mutations has evolved into a formidable task. Here, we compared the performance of three commercial clinical decision support tools, that is, NAVIFY Mutation Profiler (NAVIFY; Roche), QIAGEN Clinical Insight (QCI) Interpret (QIAGEN) and CureMatch Bionov (CureMatch).

Methods: In order to obtain the current status of the respective tumour genome, we analysed cell-free DNA from patients with metastatic breast, colorectal or non-small cell lung cancer. We evaluated somatic copy number alterations and in parallel applied a 77-gene panel (AVENIO ctDNA Expanded Panel). We then assessed the concordance of tier classification approaches between NAVIFY and QCI and compared the strategies to determine actionability among all three platforms. Finally, we quantified the alignment of treatment suggestions across all decision tools.

Results: Each platform varied in its mode of variant classification and strategy for identifying druggable targets and clinical trials, which resulted in major discrepancies. Even the frequency of concordant actionable events for tier I-A or tier I-B classifications was only 4.3%, 9.5% and 28.4% when comparing NAVIFY with QCI, NAVIFY with CureMatch and CureMatch with QCI, respectively, and the obtained treatment recommendations differed drastically.

Conclusions: Treatment decisions based on molecular markers appear at present to be arbitrary and dependent on the chosen strategy. As a consequence, tumours with identical molecular profiles would be differently treated, which challenges the promising concepts of genome-informed medicine.

Keywords: circulating tumour DNA; clinical decision support; molecular profiling; next-generation sequencing; variant interpretation.

Figure 1

Steps involved in precision oncology and summary of molecular profiling data. (A) Cascade of individual steps involved in precision oncology: due to the increasingly extensive sequencing involved in precision oncology, genetic counselling may be required and an informed consent needs to be obtained in every case. Sequencing can be conducted with a tissue sample and/or from blood after isolation of plasma DNA, the latter being the specimen used in our study. Subsequently, various sequencing strategies can be applied to the sample to determine tumour AF and to detect alterations such as SNVs (point mutations) and indels, SCNAs and fusions. A decisive step in precision oncology is variant annotation, the molecular and clinical interpretation of targets and matching these with drugs. In this study, the data were subjected to interpretation by three clinical decision support tools (NAVIFY, QCI, CureMatch) to obtain treatment possibilities. The multidisciplinary MTB evaluates the data and gives advice to the treating physician. Afterwards, the results are disclosed to the patient and matched to their clinical status and, if applicable, the treatment is started. These last two steps were not within the scope of our retrospective study. (B) Oncoplots showing top 40 genes affected by mutation (ie, SNV or indel) and/or SCNA identified in plasma DNA for patients with BC (left), CRC (centre) and NSCLC (right). SCNAs are represented by either focal amplification (Amp) or focal deletion (Del). For patients with BC, hormone receptor status is shown as a clinical feature below the plot for each patient (ER, PR, HER2) as either positive (Pos) or negative (Neg). Similarly, if evaluated, PD-L1 expression status is shown as a clinical feature below the plot for patients with NSCLC as a per cent. AF, allele fraction; ER, oestrogen receptor; HER2, human epidermal growth factor receptor 2; ND, not detected; NSCLC, non-small-cell lung cancer; PR, progesterone receptor; QCI, QIAGEN Clinical Insight; SNV, single nucleotide variant.

Figure 2

Concordant and discordant classifications of aberrations using NAVIFY and QCI. (A) Venn diagrams displaying the number of concordant and discordant tier I-A (top) and tier I-B (bottom) classifications as annotated by NAVIFY and QCI across all 48 patients. (B) Venn diagrams displaying the number of concordant and discordant tier II-C (top) and tier II-D (middle) and tier III (bottom) classifications as annotated by NAVIFY and QCI across all 48 patients. QCI, QIAGEN Clinical Insight.

Figure 3

Reported markers, actionability and number of treatment suggestions. (A–C) Stacked bar charts from NAVIFY (A: top), QCI (B: centre) and CureMatch (C: bottom) per patient and per tumour entity (left, BC; centre, CRC; right, NSCLC). Reported markers (orange) represent all alterations reported to the clinical decision support tool, whereas actionable targets (turquoise) are those alterations which were successfully matched to a therapy. Matching drugs (dark blue) correspond to the total number of drugs identified for each patient's molecular profile. In the CureMatch analysis, the actionable targets in the BC and NSCLC samples could be categorised as either a genome (light blue) or protein variant (red). BC, breast cancer; CRC, colorectal cancer; NSCLC, non-small-cell lung cancer.QCI, QIAGEN Clinical Insight.

Figure 4

Treatment suggestions per decision support platform. (A) Frequency of on compendia or off-label indications per tumour type for NAVIFY (left) and CureMatch (right). (B) Frequency of overlapping suggested therapies per platform comparison and per number of drugs. The platform recommendations were compared side-by-side with each other and all three together (x-axis). The number of drugs overlapping from the recommendations is listed on the y-axis. The numbers inside the boxes represent the number of events fitting each category. (C) Treatment alignment among all three platforms was limited to common predictive biomarkers. Here, the most frequently overlapping drugs are shown along with how each decision support platform justified the drug, which sometimes varied. BC, breast cancer; CM, CureMatch; CRC, colorectal cancer; NAVIFY, NAVIFY Mutation Profiler; NSCLC, non-small-cell lung cancer;- QCI, QIAGEN Clinical Insight Interpret.

Figure 5

Clinical trials identified per decision support platform. (A) Total number of matching clinical trials per tumour entity for NAVIFY (left), QCI (centre) and CureMatch (right). (B) Boxplot showing distribution of the highest and lowest calculated matching scores per patient across tumour types from CureMatch analysis. BC, breast cancer; CRC, colorectal cancer; NSCLC, non-small-cell lung cancer.QCI, QIAGEN Clinical Insight.