Evidence map›Paper›PMID 35347250›Full record

ArticleModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2022

Rapid and highly sensitive approach for multiplexed somatic fusion detection.

Samuel Abbou, Sarah Finstuen-Magro, Brigit McDannell, Michelle Feenstra, Abigail Ward, David S Shulman, Birgit Geoerger, Joadly Duplan, Hannah Comeau, Katherine A Janeway and 2 more

Open access · hybridAbstract read
In one paragraph

Article in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 29% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. 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

12 authors at 3 institutions in 2 countries.

Samuel AbbouDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA.ORCID 0000-0002-7159-8869
Sarah Finstuen-MagroDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA.
Brigit McDannellDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA.
Michelle FeenstraDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA.
Abigail WardDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA.
David S ShulmanDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA.
Birgit GeoergerGustave Roussy Cancer Campus, Department of Pediatric and Adolescent Oncology, INSERM U1015, Université Paris-Saclay, Villejuif, France.ORCID 0000-0003-4361-3643
Joadly DuplanDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA.
Hannah ComeauDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA.
Katherine A JanewayDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA.
Kelly KlegaDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA.
Brian D CromptonDana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA, USA. Briand_crompton@dfci.harvard.edu.
Dana-Farber/Boston Children's Cancer and Blood Disorders Center · USBroad Institute · USInserm · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Somatic gene translocations are key to making an accurate diagnosis in many cancers including many pediatric sarcomas. Currently available molecular diagnostic approaches to identifying somatic pathognomonic translocations have limitations such as minimal multiplexing, high cost, complex computational requirements, or slow turnaround times. We sought to develop a new fusion-detection assay optimized to mitigate these challenges. To accomplish this goal, we developed a highly sensitive multiplexed digital PCR-based approach that can identify the gene partners of multiple somatic fusion transcripts. This assay was validated for specificity with cell lines and synthetized DNA fragments. Assay sensitivity was optimized using a tiered amplification approach for fusion detection from low input and/or degraded RNA. The assay was then tested for the potential application of fusion detection from FFPE tissue and liquid biopsy samples. We found that this multiplexed PCR approach was able to accurately identify the presence of seven different targeted fusion transcripts with a turnaround time of 1 to 2 days. The addition of a tiered amplification step allowed the detection of targeted fusions from as little as 1 pg of RNA input. We also identified fusions from as little as two unstained slides of FFPE tumor biopsy tissue, from circulating tumor cells collected from tumor-bearing mice, and from liquid biopsy samples from patients with known fusion-positive cancers. We also demonstrated that the assay could be easily adapted for additional fusion targets. In summary, this novel assay detects multiple somatic fusion partners in biologic samples with low tumor content and low-quality RNA in less than two days. The assay is inexpensive and could be applied to surgical and liquid biopsies, particularly in places with inadequate resources for more expensive and expertise-dependent assays such as next-generation sequencing.

Indexed as

Gene FusionSarcomaAnimalsHigh-Throughput Nucleotide SequencingHumansMiceMultiplex Polymerase Chain ReactionOncogene Proteins, FusionRNAOncogene Proteins, FusionRNA

Identifiers

PMID35347250
PMCPMC9314249
OpenAlexW4220721327

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.