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

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.

Technical Evaluation of Commercial Mutation Analysis Platforms and Reference Materials for Liquid Biopsy Profiling.

Molecular profiling from liquid biopsy, in particular cell-free DNA (cfDNA), represents an attractive alternative to tissue biopsies for the detection of actionable targets and tumor monitoring. In addition to PCR-based assays, Next Generation Sequencing (NGS)-based cfDNA assays are now commercially available and are being increasingly adopted in clinical practice. However, the validity of these products as well as the clinical utility of cfDNA in the management of patients with cancer has yet to be proven. Within framework of the Innovative Medicines Initiative (IMI) program CANCER-ID we evaluated the use of commercially available reference materials designed for ctDNA testing and cfDNA derived from Diagnostic Leukaphereses (DLA) for inter- and intra-assay as well as intra- and inter-laboratory comparisons. In three experimental setups, a broad range of assays including ddPCR, MassARRAY and various NGS-based assays were tested. We demonstrate that both reference materials with predetermined VAFs and DLA samples are extremely useful for the performance assessment of mutation analysis platforms. Moreover, our data indicate a substantial variability of NGS assays with respect to sensitivity and specificity highlighting the importance of extensive validation of the test performance before offering these tests in clinical routine practice.

Cell-free DNA analysis reveals POLR1D-mediated resistance to bevacizumab in colorectal cancer.

BACKGROUND: Bevacizumab, a monoclonal antibody against soluble VEGFA, is an approved and commonly administered anti-angiogenic drug in patients with metastasized colorectal cancer (mCRC). The survival benefit of anti-VEGF therapy in mCRC patients is limited to a few months, and acquired resistance mechanisms are largely unknown. Here, we employed whole-genome sequencing of plasma DNA to evaluate the tumor genome of patients undergoing treatment with bevacizumab to determine novel aberrations associated with resistance. METHODS: Using longitudinal plasma analyses, we studied the evolution of tumor genomes in a mCRC cohort (n = 150) and conducted analyses of CRC cases from The Cancer Genome Atlas (TCGA) database (n = 619) to identify associations between genomic aberrations and clinical features. We employed whole-genome sequencing to identify the most frequently occurring focal somatic copy number alterations (SCNAs). Using the TCGA data as a comparative and supporting dataset, we defined the minimally amplified overlapping region and studied the mechanistic consequences of copy number gain of the involved genes in this segment. In addition, we established an in vitro cell model and conducted downstream gene expression and cell viability assays to confirm our findings from the patient dataset. RESULTS: We observed a recurrent focal amplification (8.7% of cases) on chromosome 13q12.2. Analysis of CRC cases from the TCGA database suggested that this amplicon is associated with more advanced stages. We confirmed that this 13q12.2 amplicon frequently emerges later during the clinical course of disease. After defining the minimally amplified region, we observed that the amplification and expression of one gene, POLR1D, impacted cell proliferation and resulted in upregulation of VEGFA, an important regulator of angiogenesis which has been implicated in the resistance to bevacizumab treatment. In fact, in several patients, we observed the emergence of this 13q12.2 amplicon under bevacizumab treatment, which was invariably associated with therapy resistance. CONCLUSIONS: Non-invasive analyses of cell-free DNA from patients undergoing treatment with bevacizumab enabled the tracking of evolving tumor genomes and helped identify a recurrent focal SCNA of clinical relevance. Here, we describe a novel resistance mechanism against a widely applied treatment in patients with mCRC which will impact the clinical management of patients.

Non-coding RNAs Enabling Prognostic Stratification and Prediction of Therapeutic Response in Colorectal Cancer Patients.

Colorectal cancer (CRC) is a heterogeneous disease and current treatment options for patients are associated with a wide range of outcomes and tumor responses. Although the traditional TNM staging system continues to serve as a crucial tool for estimating CRC prognosis and for stratification of treatment choices and long-term survival, it remains limited as it relies on macroscopic features and cases of surgical resection, fails to incorporate new molecular data and information, and cannot perfectly predict the variety of outcomes and responses to treatment associated with tumors of the same stage. Although additional histopathologic features have recently been applied in order to better classify individual tumors, the future might incorporate the use of novel molecular and genetic markers in order to maximize therapeutic outcome and to provide accurate prognosis. Such novel biomarkers, in addition to individual patient tumor phenotyping and other validated genetic markers, could facilitate the prediction of risk of progression in CRC patients and help assess overall survival. Recent findings point to the emerging role of non-protein-coding regions of the genome in their contribution to the progression of cancer and tumor formation. Two major subclasses of non-coding RNAs (ncRNAs), microRNAs and long non-coding RNAs, are often dysregulated in CRC and have demonstrated their diagnostic and prognostic potential as biomarkers. These ncRNAs are promising molecular classifiers and could assist in the stratification of patients into appropriate risk groups to guide therapeutic decisions and their expression patterns could help determine prognosis and predict therapeutic options in CRC.