Evidence map›Paper›PMID 27409166›Full record

ArticleOncotarget2016

Comprehensive profiling and quantitation of oncogenic mutations in non small-cell lung carcinoma using single molecule amplification and re-sequencing technology.

Shirong Zhang, Bing Xia, Hong Jiang, Limin Wang, Rujun Xu, Yanbin Shi, Jianguang Zhang, Mengnan Xu, David S Cram, Shenglin Ma

Abstract read
In one paragraph

Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. PlasmaAnnals of translational medicine · 2021
    Article
  3. Article
  4. Cell-Free Circulating Tumour DNA Blood Testing to DetectOntario health technology assessment series · 2020
    Article
  5. Observational
  6. [Detection and Clinical Significance of Abundance of EGFR Mutation].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2017
    Review
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

10 authors.

Shirong ZhangDepartment of Oncology, Hangzhou First People's Hospital, Nanjing Medical University, Zhejiang, Hangzhou 310006, China.
Bing XiaDepartment of Oncology, Hangzhou First People's Hospital, Nanjing Medical University, Zhejiang, Hangzhou 310006, China.
Hong JiangDepartment of Oncology, Hangzhou First People's Hospital, Nanjing Medical University, Zhejiang, Hangzhou 310006, China.
Limin WangDepartment of Oncology, Hangzhou First People's Hospital, Nanjing Medical University, Zhejiang, Hangzhou 310006, China.
Rujun XuDepartment of Oncology, Hangzhou First People's Hospital, Nanjing Medical University, Zhejiang, Hangzhou 310006, China.
Yanbin ShiBerry Genomics Corporation, Beijing 100015, China.
Jianguang ZhangBerry Genomics Corporation, Beijing 100015, China.
Mengnan XuBerry Genomics Corporation, Beijing 100015, China.
David S CramBerry Genomics Corporation, Beijing 100015, China.
Shenglin MaDepartment of Oncology, Hangzhou First People's Hospital, Nanjing Medical University, Zhejiang, Hangzhou 310006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Activating and resistance mutations in the tyrosine kinase domain of several oncogenes are frequently associated with non-small cell lung carcinoma (NSCLC). In this study we assessed the frequency, type and abundance of EGFR, KRAS, BRAF, TP53 and ALK mutations in tumour specimens from 184 patients with early and late stage disease using single molecule amplification and re-sequencing technology (SMART). Based on modelling of EGFR mutations, the detection sensitivity of the SMART assay was at least 0.1%. Benchmarking EGFR mutation detection against the gold standard ARMS-PCR assay, SMART assay had a sensitivity and specificity of 98.7% and 99.0%. Amongst the 184 samples, EGFR mutations were the most prevalent (59.9%), followed by KRAS (16.9%), TP53 (12.7%), EML4-ALK fusions (6.3%) and BRAF (4.2%) mutations. The abundance and types of mutations in tumour specimens were extremely heterogeneous, involving either monoclonal (51.6%) or polyclonal (12.6%) mutation events. At the clinical level, although the spectrum of tumour mutation(s) was unique to each patient, the overall patterns in early or advanced stage disease were relatively similar. Based on these findings, we propose that personalized profiling and quantitation of clinically significant oncogenic mutations will allow better classification of patients according to tumour characteristics and provide clinicians with important ancillary information for treatment decision-making.

Indexed as

Gene Expression ProfilingGene Expression Regulation, NeoplasticAgedAnaplastic Lymphoma KinaseCarcinoma, Non-Small-Cell LungDecision Support Systems, ClinicalDNA Mutational AnalysisErbB ReceptorsFemaleHumansLung NeoplasmsMaleMiddle AgedMutationProto-Oncogene Proteins B-rafProto-Oncogene Proteins p21(ras)ALK protein, humanAnaplastic Lymphoma KinaseBRAF protein, humanEGFR protein, humanErbB ReceptorsKRAS protein, humanProto-Oncogene Proteins B-rafProto-Oncogene Proteins p21(ras)Receptor Protein-Tyrosine KinasesTP53 protein, humanTumor Suppressor Protein p53allele-specific amplification refractory mutation systemnon small-cell lung carcinomaoncogenic mutationssingle molecule amplification and re-sequencing technology

Identifiers

PMID27409166
PMCPMC5226597

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.