Evidence map›Paper›PMID 28196074›Full record

ArticlePLoS medicine2017

Clinical applicability and cost of a 46-gene panel for genomic analysis of solid tumours: Retrospective validation and prospective audit in the UK National Health Service.

Angela Hamblin, Sarah Wordsworth, Jilles M Fermont, Suzanne Page, Kulvinder Kaur, Carme Camps, Pamela Kaisaki, Avinash Gupta, Denis Talbot, Mark Middleton and 7 more

Abstract readValidation Study
In one paragraph

Article in PLoS medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

30 citing papers in PubMed.

  1. Article
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  4. Observational
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  7. Article
  8. Differences in Willingness to UndergoJournal of personalized medicine · 2022
    Article
  9. Review
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  12. Review
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  14. Article
  15. Article
  16. Article
  17. Article
  18. Identification of a Combined RNA Prognostic Signature in Adenocarcinoma of the Lung.Medical science monitor : international medical journal of experimental and clinical research · 2019
    Article
  19. Bioinformatics for precision oncology.Briefings in bioinformatics · 2019
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Angela HamblinMolecular Diagnostics Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Sarah WordsworthNational Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom.
Jilles M FermontExperimental Medicine and Immunotherapeutics, Department of Medicine, University of Cambridge, Cambridge, United Kingdom.
Suzanne PageMolecular Diagnostics Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.ORCID 0000-0002-3117-4156
Kulvinder KaurNational Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom.
Carme CampsNational Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom.
Pamela KaisakiNational Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom.
Avinash GuptaDepartment of Oncology, University of Oxford, Oxford, United Kingdom.
Denis TalbotNational Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom.
Mark MiddletonNational Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom.
Shirley HendersonMolecular Diagnostics Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Anthony CuttsMolecular Diagnostics Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Dimitrios V VavoulisMolecular Diagnostics Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Nick HousbyMolecular Diagnostics Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Ian TomlinsonNational Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom.ORCID 0000-0003-3037-1470
Jenny C TaylorNational Institute for Health Research Biomedical Research Centre, Oxford, United Kingdom.
Anna SchuhMolecular Diagnostics Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.

Funding

Cancer Research UK 14189Cancer Research UK 16459Cancer Research UK 8466Wellcome TrustWellcome Trust 090532/Z/09/Z
6 · The paper itself

Abstract

backgroundSingle gene tests to predict whether cancers respond to specific targeted therapies are performed increasingly often. Advances in sequencing technology, collectively referred to as next generation sequencing (NGS), mean the entire cancer genome or parts of it can now be sequenced at speed with increased depth and sensitivity. However, translation of NGS into routine cancer care has been slow. Healthcare stakeholders are unclear about the clinical utility of NGS and are concerned it could be an expensive addition to cancer diagnostics, rather than an affordable alternative to single gene testing. METHODS AND

findingsWe validated a 46-gene hotspot cancer panel assay allowing multiple gene testing from small diagnostic biopsies. From 1 January 2013 to 31 December 2013, solid tumour samples (including non-small-cell lung carcinoma [NSCLC], colorectal carcinoma, and melanoma) were sequenced in the context of the UK National Health Service from 351 consecutively submitted prospective cases for which treating clinicians thought the patient had potential to benefit from more extensive genetic analysis. Following histological assessment, tumour-rich regions of formalin-fixed paraffin-embedded (FFPE) sections underwent macrodissection, DNA extraction, NGS, and analysis using a pipeline centred on Torrent Suite software. With a median turnaround time of seven working days, an integrated clinical report was produced indicating the variants detected, including those with potential diagnostic, prognostic, therapeutic, or clinical trial entry implications. Accompanying phenotypic data were collected, and a detailed cost analysis of the panel compared with single gene testing was undertaken to assess affordability for routine patient care. Panel sequencing was successful for 97% (342/351) of tumour samples in the prospective cohort and showed 100% concordance with known mutations (detected using cobas assays). At least one mutation was identified in 87% (296/342) of tumours. A locally actionable mutation (i.e., available targeted treatment or clinical trial) was identified in 122/351 patients (35%). Forty patients received targeted treatment, in 22/40 (55%) cases solely due to use of the panel. Examination of published data on the potential efficacy of targeted therapies showed theoretically actionable mutations (i.e., mutations for which targeted treatment was potentially appropriate) in 66% (71/107) and 39% (41/105) of melanoma and NSCLC patients, respectively. At a cost of £339 (US$449) per patient, the panel was less expensive locally than performing more than two or three single gene tests. Study limitations include the use of FFPE samples, which do not always provide high-quality DNA, and the use of "real world" data: submission of cases for sequencing did not always follow clinical guidelines, meaning that when mutations were detected, patients were not always eligible for targeted treatments on clinical grounds.

conclusionsThis study demonstrates that more extensive tumour sequencing can identify mutations that could improve clinical decision-making in routine cancer care, potentially improving patient outcomes, at an affordable level for healthcare providers.

Indexed as

GenomicsAdolescentAdultAgedAged, 80 and overCarcinoma, Non-Small-Cell LungChildClinical Decision-MakingColorectal NeoplasmsFemaleHigh-Throughput Nucleotide SequencingHumansMaleMelanomaMiddle AgedNational Health Programs

Identifiers

PMID28196074
PMCPMC5308858

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.