ArticleCancer nanotechnology2016
Profiling lung adenocarcinoma by liquid biopsy: can one size fit all?
Article in Cancer nanotechnology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Prognosis of recurrence after complete resection in early-stage lung adenocarcinoma based on molecular alterations: a systematic review and meta-analysis.Scientific reports · 2023Pooled it
- Artificial intelligence and machine learning in precision and genomic medicine.Medical oncology (Northwood, London, England) · 2022Review
- Identification of Common and Subtype-Specific Mutated Sub-Pathways for a Cancer.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer is first and foremost a disease of the genome. Specific genetic signatures within a tumour are prognostic of disease outcome, reflect subclonal architecture and intratumour heterogeneity, inform treatment choices and predict the emergence of resistance to targeted therapies. Minimally invasive liquid biopsies can give temporal resolution to a tumour's genetic profile and allow the monitoring of treatment response through levels of circulating tumour DNA (ctDNA). However, the detection of ctDNA in repeated liquid biopsies is currently limited by economic and time constraints associated with targeted sequencing.
methodsHere we bioinformatically profile the mutational and copy number spectrum of The Cancer Genome Network's lung adenocarcinoma dataset to uncover recurrently mutated genomic loci.
resultsWe build a panel of 400 hotspot mutations and show that the coverage extends to more than 80% of the dataset at a median depth of 8 mutations per patient. Additionally, we uncover several novel single-nucleotide variants present in more than 5% of patients, often in genes not commonly associated with lung adenocarcinoma.
conclusionWith further optimisation, this hotspot panel could allow molecular diagnostics laboratories to build curated primer banks for 'off-the-shelf' monitoring of ctDNA by droplet-based digital PCR or similar techniques, in a time- and cost-effective manner.
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Registered trials
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