Evidence map›Paper›PMID 38378561›Full record

ArticleBMC biology2024

Tumor mutational burden assessment and standardized bioinformatics approach using custom NGS panels in clinical routine.

Célia Dupain, Tom Gutman, Elodie Girard, Choumouss Kamoun, Grégoire Marret, Zahra Castel-Ajgal, Marie-Paule Sablin, Cindy Neuzillet, Edith Borcoman, Ségolène Hescot and 20 more

Open access · goldAbstract read
In one paragraph

Article in BMC biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
4.2field-weighted citation impact, top 5% 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

17 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Observational
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. 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

30 authors at 5 institutions in 1 country.

Célia Dupain *Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Tom Gutman *Bioinformatics Core Facility, INSERM U900, Mines Paris Tech, Institut Curie, Paris, France.
Elodie GirardBioinformatics Core Facility, INSERM U900, Mines Paris Tech, Institut Curie, Paris, France.
Choumouss KamounBioinformatics Core Facility, INSERM U900, Mines Paris Tech, Institut Curie, Paris, France.
Grégoire MarretDepartment of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Zahra Castel-AjgalDepartment of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Marie-Paule SablinDepartment of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Cindy NeuzilletDepartment of Medical Oncology, Institut Curie, Paris & Saint Cloud, France.
Edith BorcomanDepartment of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Ségolène HescotDepartment of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Céline CallensDepartment of Genetics, Institut Curie, Paris, France.
Olfa Trabelsi-GratiDepartment of Genetics, Institut Curie, Paris, France.
Samia MelaabiDepartment of Genetics, Institut Curie, Paris, France.
Roseline VibertDepartment of Genetics, Institut Curie, Paris, France.
Samantha AntonioDepartment of Genetics, Institut Curie, Paris, France.
Coralie FranckDepartment of Genetics, Institut Curie, Paris, France.
Michèle GalutDepartment of Pathology, Institut Curie, PSL Research University, Paris, France.
Isabelle GuillouDepartment of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Maral HalladjianDepartment of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Yves AlloryDepartment of Pathology, Université Paris-Saclay, UVSQ, Institut Curie, Saint-Cloud, France.
Joanna CyrtaDepartment of Pathology, Institut Curie, PSL Research University, Paris, France.
Julien RomejonBioinformatics Core Facility, INSERM U900, Mines Paris Tech, Institut Curie, Paris, France.
Eleonore FrouinDepartment of Genetics, Institut Curie, Paris, France.
Dominique Stoppa-LyonnetDepartment of Genetics, Institut Curie, Paris, France.
Jennifer WongDepartment of Genetics, Institut Curie, Paris, France.
Christophe Le TourneauDepartment of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Ivan BiècheDepartment of Genetics, Institut Curie, Paris, France.
Nicolas Servant *Bioinformatics Core Facility, INSERM U900, Mines Paris Tech, Institut Curie, Paris, France.
Maud Kamal *Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Julien Masliah-Planchon *Department of Genetics, Institut Curie, Paris, France. julien.masliahplanchon@curie.fr.
Institut Curie · FRCancer et génome: Bioinformatique, biostatistiques et épidémiologie des systèmes complexesGénomique Bioinformatique et Applications · FRUniversité Paris Cité · FRÉcole Nationale Supérieure des Mines de Paris · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHigh tumor mutational burden (TMB) was reported to predict the efficacy of immune checkpoint inhibitors (ICIs). Pembrolizumab, an anti-PD-1, received FDA-approval for the treatment of unresectable/metastatic tumors with high TMB as determined by the FoundationOne®CDx test. It remains to be determined how TMB can also be calculated using other tests.

resultsFFPE/frozen tumor samples from various origins were sequenced in the frame of the Institut Curie (IC) Molecular Tumor Board using an in-house next-generation sequencing (NGS) panel. A TMB calculation method was developed at IC (IC algorithm) and compared to the FoundationOne® (FO) algorithm. Using IC algorithm, an optimal 10% variant allele frequency (VAF) cut-off was established for TMB evaluation on FFPE samples, compared to 5% on frozen samples. The median TMB score for MSS/POLE WT tumors was 8.8 mut/Mb versus 45 mut/Mb for MSI/POLE-mutated tumors. When focusing on MSS/POLE WT tumor samples, the highest median TMB scores were observed in lymphoma, lung, endometrial, and cervical cancers. After biological manual curation of these cases, 21% of them could be reclassified as MSI/POLE tumors and considered as "true TMB high." Higher TMB values were obtained using FO algorithm on FFPE samples compared to IC algorithm (40 mut/Mb [10-3927] versus 8.2 mut/Mb [2.5-897], p < 0.001).

conclusionsWe herein propose a TMB calculation method and a bioinformatics tool that is customizable to different NGS panels and sample types. We were not able to retrieve TMB values from FO algorithm using our own algorithm and NGS panel.

Indexed as

NeoplasmsHigh-Throughput Nucleotide SequencingHumansMutationCalculationImmunotherapyMolecular Tumor BoardPrecision medicineTumor mutational burden

Identifiers

PMID38378561
PMCPMC10880437
OpenAlexW4391958649

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.