Evidence map›Paper›PMID 31470908›Full record

ArticleHuman genomics2019

Novel analytical methods to interpret large sequencing data from small sample sizes.

Florence Lichou, Sébastien Orazio, Stéphanie Dulucq, Gabriel Etienne, Michel Longy, Christophe Hubert, Alexis Groppi, Alain Monnereau, François-Xavier Mahon, Béatrice Turcq

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

Article in Human genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 37% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

10 authors at 6 institutions in 1 country.

Florence LichouLaboratory of Mammary and Leukaemic Oncogenesis, Inserm U1218 ACTION, Bergonié Cancer Institute, University of Bordeaux, 146 rue Léo Saignat, bâtiment TP 4ème étage, case 50, 33076, Bordeaux, France.
Sébastien OrazioTeam EPICENE, Inserm U1219 BPH, Bergonié Cancer Institute, University of Bordeaux, Bordeaux, France.
Stéphanie DulucqLaboratory of Mammary and Leukaemic Oncogenesis, Inserm U1218 ACTION, Bergonié Cancer Institute, University of Bordeaux, 146 rue Léo Saignat, bâtiment TP 4ème étage, case 50, 33076, Bordeaux, France.
Gabriel EtienneLaboratory of Mammary and Leukaemic Oncogenesis, Inserm U1218 ACTION, Bergonié Cancer Institute, University of Bordeaux, 146 rue Léo Saignat, bâtiment TP 4ème étage, case 50, 33076, Bordeaux, France.
Michel LongyLaboratory of Mammary and Leukaemic Oncogenesis, Inserm U1218 ACTION, Bergonié Cancer Institute, University of Bordeaux, 146 rue Léo Saignat, bâtiment TP 4ème étage, case 50, 33076, Bordeaux, France.
Christophe HubertInserm U1211 MRGM, University of Bordeaux, Bordeaux, France.
Alexis GroppiThe Bordeaux Bioinformatics Center (CBiB), University of Bordeaux, Bordeaux, France.
Alain MonnereauTeam EPICENE, Inserm U1219 BPH, Bergonié Cancer Institute, University of Bordeaux, Bordeaux, France.
François-Xavier MahonLaboratory of Mammary and Leukaemic Oncogenesis, Inserm U1218 ACTION, Bergonié Cancer Institute, University of Bordeaux, 146 rue Léo Saignat, bâtiment TP 4ème étage, case 50, 33076, Bordeaux, France.
Béatrice TurcqLaboratory of Mammary and Leukaemic Oncogenesis, Inserm U1218 ACTION, Bergonié Cancer Institute, University of Bordeaux, 146 rue Léo Saignat, bâtiment TP 4ème étage, case 50, 33076, Bordeaux, France. beatrice.turcq@u-bordeaux.fr.
Inserm · FRUniversité de Bordeaux · FRCentre Hospitalier Universitaire de Bordeaux · FRInstitut Bergonié · FRInstitut de Mathématiques de Bordeaux · FRLaboratoire Hubert Curien · FR

Funding

La Fondation ARC PGA120140200913
6 · The paper itself

Abstract

backgroundTargeted therapies have greatly improved cancer patient prognosis. For instance, chronic myeloid leukemia is now well treated with imatinib, a tyrosine kinase inhibitor. Around 80% of the patients reach complete remission. However, despite its great efficiency, some patients are resistant to the drug. This heterogeneity in the response might be associated with pharmacokinetic parameters, varying between individuals because of genetic variants. To assess this issue, next-generation sequencing of large panels of genes can be performed from patient samples. However, the common problem in pharmacogenetic studies is the availability of samples, often limited. In the end, large sequencing data are obtained from small sample sizes; therefore, classical statistical analyses cannot be applied to identify interesting targets. To overcome this concern, here, we described original and underused statistical methods to analyze large sequencing data from a restricted number of samples.

resultsTo evaluate the relevance of our method, 48 genes involved in pharmacokinetics were sequenced by next-generation sequencing from 24 chronic myeloid leukemia patients, either sensitive or resistant to imatinib treatment. Using a graphical representation, from 708 identified polymorphisms, a reduced list of 115 candidates was obtained. Then, by analyzing each gene and the distribution of variant alleles, several candidates were highlighted such as UGT1A9, PTPN22, and ERCC5. These genes were already associated with the transport, the metabolism, and even the sensitivity to imatinib in previous studies.

conclusionsThese relevant tests are great alternatives to inferential statistics not applicable to next-generation sequencing experiments performed on small sample sizes. These approaches permit to reduce the number of targets and find good candidates for further treatment sensitivity studies.

Indexed as

AdultAgedAged, 80 and overAllelesDNA-Binding ProteinsDNA Excision Repair Protein ERCC-5Drug Resistance, NeoplasmEndonucleasesFemaleGlucuronosyltransferaseHumansImatinib MesylateLeukemia, Myelogenous, Chronic, BCR-ABL PositiveMaleMiddle AgedMutationDNA-Binding ProteinsDNA Excision Repair Protein ERCC-5EndonucleasesGlucuronosyltransferaseImatinib MesylateNuclear ProteinsProtein Kinase InhibitorsProtein Tyrosine Phosphatase, Non-Receptor Type 22PTPN22 protein, humanTranscription FactorsUDP-Glucuronosyltransferase 1A9UGT1A9 protein, humanChronic myeloid leukemiaFactorial correspondence analysisHierarchical clustering on principal componentsNext-generation sequencingPharmacogeneticsRank productsSmall sample sizeStatistics

Identifiers

PMID31470908
PMCPMC6717342
OpenAlexW2970026683

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.