Evidence map›Paper›PMID 33570999›Full record

ArticleJCO clinical cancer informatics2021

FrenchFISH: Poisson Models for Quantifying DNA Copy Number From Fluorescence In Situ Hybridization of Tissue Sections.

Geoff Macintyre, Anna M Piskorz, Adam Berman, Edith Ross, David B Morse, Ke Yuan, Darren Ennis, Jeremy A Pike, Teodora Goranova, Iain A McNeish and 2 more

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JCO clinical cancer informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 58% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Article
  2. 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

12 authors at 4 institutions in 1 country.

Geoff MacintyreCancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
Anna M PiskorzCancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
Adam BermanCancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
Edith RossCancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
David B MorseCancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
Ke YuanUniversity of Glasgow, Glasgow, UK.
Darren EnnisInstitute of Cancer Sciences, University of Glasgow, Glasgow, UK.
Jeremy A PikeCancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
Teodora GoranovaCancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
Iain A McNeishInstitute of Cancer Sciences, University of Glasgow, Glasgow, UK.
James D BrentonCancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
Florian MarkowetzCancer Research UK, Cambridge Institute, University of Cambridge, Cambridge, UK.
University of Cambridge · GBCancer Research UK · GBImperial College London · GBUniversity of Glasgow · GB

Funding

Cancer Research UK 19274Cancer Research UK 28290Cancer Research UK A18072Cancer Research UK A19274Cancer Research UK C14303/A17197
6 · The paper itself

Abstract

purposeChromosomal aberration and DNA copy number change are robust hallmarks of cancer. The gold standard for detecting copy number changes in tumor cells is fluorescence in situ hybridization (FISH) using locus-specific probes that are imaged as fluorescent spots. However, spot counting often does not perform well on solid tumor tissue sections due to partially represented or overlapping nuclei. MATERIALS AND

methodsTo overcome these challenges, we have developed a computational approach called FrenchFISH, which comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes or a homogeneous Poisson point process model for automated spot counting.

resultsWe benchmarked the performance of FrenchFISH against previous approaches using a controlled simulation scenario and tested it experimentally in 12 ovarian carcinoma FFPE-tissue sections for copy number alterations at three loci (c-Myc, hTERC, and SE7). FrenchFISH outperformed standard spot counting with 74% of the automated counts having < 1 copy number difference from the manual counts and 17% having < 2 copy number differences, while taking less than one third of the time of manual counting.

conclusionFrenchFISH is a general approach that can be used to enhance clinical diagnosis on sections of any tissue by both speeding up and improving the accuracy of spot count estimates.

Indexed as

Chromosome AberrationsDNA Copy Number VariationsComputer SimulationDNAHumansIn Situ Hybridization, FluorescenceDNA

Identifiers

PMID33570999
PMCPMC8140799
OpenAlexW3127905008

What Socratic 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 Socratic graph.