ESMO Open · 2018 · 108 citations · 22 references
MedicineMolecular MedicineOncologyMolecular OncologyPrecision MedicinePrecision OncologyCancer Precision MedicineCirculating Tumor Dna AnalysisDna SequencingLiquid BiopsyNext-generation Sequencing In OncologyPersonalized Cancer TherapyCirculating Cell-free Dna AnalysisCirculating Tumor Dna DetectionLiquid Biopsy SequencingMolecular Tumor Board ReviewNext-generation Sequencing AnalysisPersonalized Cancer TreatmentPhase I Clinical TrialsPrecision Medicine Applications
BACKGROUND: The complexity of delivering precision medicine to oncology patients has led to the creation of molecular tumourboards (MTBs) for patient selection and assessment of treatment options. New technologies like the liquid biopsy are augmenting available therapeutic opportunities. This report aims to analyse the experience of our MTB in the implementation of personalised medicine in a cancer network. MATERIALS AND METHODS: Patients diagnosed with solid tumours progressing to standard treatments were referred to our Phase I unit. They underwent comprehensive next generation sequencing (NGS) of either tumour tissue or cell-free circulating tumour DNA (ctDNA) or both. The MTB expressed either a positive or negative opinion for the treatment of the patients with discovered druggable alterations inside a clinical trial, in an expanded access programme, with a compassionate use. Afterwards, discovered alterations were matched with OncoKB levels of evidence for the choice of alteration-specific treatments in order to compare MTB outcomes with a standardised set of recommendations. RESULTS: NGS was performed either on ctDNA or tumour tissue or in both of them in 204 patients. The MTB evaluated 173 of these cases. Overall, the MTB proposed alteration-specific targeted therapy to 72 patients (41.6%). 49 patients (28.3% of the total evaluated) were indicated to enter a clinical trial. In 29 patients with matched liquid biopsy NGS (lbNGS), tumour tissue NGS (ttNGS) and MTB evaluation, the MTB changed the treatment strategy coming from standardised recommendations based on lbNGS and ttNGS alone in 10 patients (34.5%), thanks to the evaluation of other clinical parameters. In our cohort, lbNGS was more likely, compared with ttNGS, to detect point mutations (OR 11, 95% CI 2.9 to 24.1, p<0.001) and all-type alterations (OR 13.6, 95% CI 5.5 to 43.2, p<0.001) from the same genes of matched patients. CONCLUSIONS: Our MTB allows patients with refractory cancer to be included in clinical trials and improves the precision of clinical decisions compared with a standardised set of mutation-driven recommendations.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
OncoKB: A Precision Oncology Knowledge Base
Debyani Chakravarty, Jianjiong Gao, Sarah Phillips et al. · JCO Precision Oncology · 2017 · 2.7K citations
Christophe Le Tourneau, Jean-Pierre Delord, Anthony Gonçalvès et al. · The Lancet Oncology · 2015 · 1.1K citations