Medizinische Universität Graz - Research portal
Selected Publication:
Hasenleithner, S.
Identification of actionable targets in patients with advanced cancer
Doktoratsstudium der Medizinischen Wissenschaft; Humanmedizin; [ Dissertation ] Graz Medical University; 2021. pp. 196
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- Authors Med Uni Graz:
- Hasenleithner Samantha
- Advisor:
- Heitzer Ellen
- Sill Heinz
- Speicher Michael
- Altmetrics:
- Abstract:
- Precision oncology relies on the translation of molecular data into suitable therapies for patients with cancer. However, with the growing complexity of next generation sequencing-based tests, clinical interpretation of somatic genomic mutations has evolved into a formidable task. For this reason, precision oncology knowledgebases and, more recently, clinical decision support tools, have become increasingly useful in annotating complex next-generation sequencing data for identifying druggable targets. Discrepant variant interpretation among open-source knowledgebases has led to recent harmonization efforts and in this regard, we sought to evaluate the performance status quo of three prominent commercial clinical decision support tools, i.e. NAVIFY® Mutation Profiler (NAVIFY; Roche), QIAGEN Clinical Insight (QCI™) Interpret (QIAGEN) and CureMatch Bionov™ (CureMatch). In order to obtain the current status of the respective tumor genome, we analyzed cell-free DNA from patients with metastatic breast, colorectal or non-small cell lung cancer by evaluating somatic copy number alterations and in parallel applied a 77-gene 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. 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. This is the first study employing clinical decision support analysis through comprehensive genomic profiling of circulating tumor DNA, which represents a potential routine clinical application. Herein, detailed descriptions of discrepancies in pathogenicity, actionability and especially alignment of treatment matching are provided. These analyses demonstrate that at present, treatment decisions based on molecular markers appear to be arbitrary and dependent on the chosen strategy. Consequently, tumors with identical molecular profiles would be treated differently, which challenges the promising concepts of genome-informed medicine. Our analyses demonstrate the complexity of treatment matching algorithms. As these interpreted reports are central to molecular tumor board discussions, the findings are pertinent to both oncologist and patient and may greatly impact clinical care.