Evidence map›Paper›PMID 26848768›Full record

ArticleOncotarget2016

Detection rate of actionable mutations in diverse cancers using a biopsy-free (blood) circulating tumor cell DNA assay.

Maria Schwaederle, Hatim Husain, Paul T Fanta, David E Piccioni, Santosh Kesari, Richard B Schwab, Kimberly C Banks, Richard B Lanman, AmirAli Talasaz, Barbara A Parker and 1 more

Open access · diamondAbstract 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 97 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed, 1 pooled it
10.3field-weighted citation impact, top 1% of its field
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

97 citing papers in PubMed, 1 synthesis or guideline pooled it, 145 citations in OpenAlex.

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  16. The coming of age of liquid biopsy in neuro-oncology.Brain : a journal of neurology · 2023
    Article
  17. Review
  18. Article
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  20. Biomaterial-basedCancer pathogenesis and therapy · 2023
    Review

37 more citing papers are in PubMed but not listed here.

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

11 authors at 2 institutions in 1 country.

Maria SchwaederleCenter for Personalized Cancer Therapy and Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, CA, USA.
Hatim HusainCenter for Personalized Cancer Therapy and Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, CA, USA.
Paul T FantaCenter for Personalized Cancer Therapy and Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, CA, USA.
David E PiccioniCenter for Personalized Cancer Therapy and Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, CA, USA.
Santosh KesariCenter for Personalized Cancer Therapy and Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, CA, USA.
Richard B SchwabCenter for Personalized Cancer Therapy and Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, CA, USA.
Kimberly C BanksGuardant Health, Inc., Redwood City, CA, USA.
Richard B LanmanGuardant Health, Inc., Redwood City, CA, USA.
AmirAli TalasazGuardant Health, Inc., Redwood City, CA, USA.
Barbara A ParkerCenter for Personalized Cancer Therapy and Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, CA, USA.
Razelle KurzrockCenter for Personalized Cancer Therapy and Division of Hematology and Oncology, UCSD Moores Cancer Center, La Jolla, CA, USA.
Moores Cancer CenterGuardant (United States) · US

Funding

VIRAL MALIGNANCYP30CA023100 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Jesse Nodora · 1985 to 2026
$124.9M
NCI NIH HHS P30 CA023100
6 · The paper itself

Abstract

Analysis of cell-free DNA using next-generation sequencing (NGS) is a powerful tool for the detection/monitoring of alterations present in circulating tumor DNA (ctDNA). Plasma extracted from 171 patients with a variety of cancers was analyzed for ctDNA (54 genes and copy number variants (CNVs) in three genes (EGFR, ERBB2 and MET)). The most represented cancers were lung (23%), breast (23%), and glioblastoma (19%). Ninety-nine patients (58%) had at least one detectable alteration. The most frequent alterations were TP53 (29.8%), followed by EGFR (17.5%), MET (10.5%), PIK3CA (7%), and NOTCH1 (5.8%). In contrast, of 222 healthy volunteers, only one had an aberration (TP53). Ninety patients with non-brain tumors had a discernible aberration (65% of 138 patients; in 70% of non-brain tumor patients with an alteration, the anomaly was potentially actionable). Interestingly, nine of 33 patients (27%) with glioblastoma had an alteration (6/33 (18%) potentially actionable). Overall, sixty-nine patients had potentially actionable alterations (40% of total; 69.7% of patients (69/99) with alterations); 68 patients (40% of total; 69% of patients with alterations), by a Food and Drug Administration (FDA) approved drug. In summary, 65% of diverse cancers (as well as 27% of glioblastomas) had detectable ctDNA aberration(s), with the majority theoretically actionable by an approved agent.

Indexed as

AdultAgedAged, 80 and overBiomarkers, TumorBiopsyClass I Phosphatidylinositol 3-KinasesDNA Copy Number VariationsDNA, NeoplasmErbB ReceptorsFemaleHigh-Throughput Nucleotide SequencingHumansMaleMiddle AgedMutationNeoplasmsBiomarkers, TumorClass I Phosphatidylinositol 3-KinasesDNA, NeoplasmEGFR protein, humanErbB ReceptorsMET protein, humanNOTCH1 protein, humanPhosphatidylinositol 3-KinasesPIK3CA protein, humanProto-Oncogene Proteins c-metReceptor, Notch1TP53 protein, humanTumor Suppressor Protein p53actionable alterationcancerctDNAliquid biopsypersonalized therapy

Identifiers

PMID26848768
PMCPMC4891078
OpenAlexW2288310435

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.