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

Calculation Immunotherapy Molecular Tumor Board Precision medicine Tumor mutational burden

Journal

BMC biology
ISSN: 1741-7007
Titre abrégé: BMC Biol
Pays: England
ID NLM: 101190720

Informations de publication

Date de publication:
20 Feb 2024
Historique:
received: 29 11 2022
accepted: 02 02 2024
medline: 21 2 2024
pubmed: 21 2 2024
entrez: 20 2 2024
Statut: epublish

Résumé

High 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. FFPE/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). We 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.

Sections du résumé

BACKGROUND BACKGROUND
High 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.
RESULTS RESULTS
FFPE/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).
CONCLUSIONS CONCLUSIONS
We 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.

Identifiants

pubmed: 38378561
doi: 10.1186/s12915-024-01839-8
pii: 10.1186/s12915-024-01839-8
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

43

Informations de copyright

© 2024. The Author(s).

Références

Burtness B, Harrington KJ, Greil R, et al. Pembrolizumab alone or with chemotherapy versus cetuximab with chemotherapy for recurrent or metastatic squamous cell carcinoma of the head and neck (KEYNOTE-048): a randomised, open-label, phase 3 study. Lancet Lond Engl. 2019;394(10212):1915–28. https://doi.org/10.1016/S0140-6736(19)32591-7 .
doi: 10.1016/S0140-6736(19)32591-7
Garon EB, Rizvi NA, Hui R, et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 2015;372(21):2018–28. https://doi.org/10.1056/NEJMoa1501824 .
doi: 10.1056/NEJMoa1501824 pubmed: 25891174
Herbst RS, Giaccone G, de Marinis F, et al. Atezolizumab for first-line treatment of PD-L1–selected patients with NSCLC. N Engl J Med. 2020;383(14):1328–39. https://doi.org/10.1056/NEJMoa1917346 .
doi: 10.1056/NEJMoa1917346 pubmed: 32997907
Cortes J, Cescon DW, Rugo HS, et al. Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for previously untreated locally recurrent inoperable or metastatic triple-negative breast cancer (KEYNOTE-355): a randomised, placebo-controlled, double-blind, phase 3 clinical trial. Lancet. 2020;396(10265):1817–28. https://doi.org/10.1016/S0140-6736(20)32531-9 .
doi: 10.1016/S0140-6736(20)32531-9 pubmed: 33278935
Schmid P, Cortes J, Pusztai L, et al. Pembrolizumab for early triple-negative breast cancer. N Engl J Med. 2020;382(9):810–21. https://doi.org/10.1056/NEJMoa1910549 .
doi: 10.1056/NEJMoa1910549 pubmed: 32101663
Mahoney KM, Sun H, Liao X, et al. PD-L1 Antibodies to its cytoplasmic domain most clearly delineate cell membranes in immunohistochemical staining of tumor cells. Cancer Immunol Res. 2015;3(12):1308–15. https://doi.org/10.1158/2326-6066.CIR-15-0116 .
doi: 10.1158/2326-6066.CIR-15-0116 pubmed: 26546452 pmcid: 4743889
Rimm DL, Han G, Taube JM, et al. A prospective, multi-institutional, pathologist-based assessment of 4 immunohistochemistry assays for PD-L1 expression in non-small cell lung cancer. JAMA Oncol. 2017;3(8):1051–8. https://doi.org/10.1001/jamaoncol.2017.0013 .
doi: 10.1001/jamaoncol.2017.0013 pubmed: 28278348 pmcid: 5650234
Gaule P, Smithy JW, Toki M, et al. A quantitative comparison of antibodies to programmed cell death 1 ligand 1. JAMA Oncol. 2017;3(2):256–9. https://doi.org/10.1001/jamaoncol.2016.3015 .
doi: 10.1001/jamaoncol.2016.3015 pubmed: 27541827 pmcid: 5491359
Torlakovic E, Lim HJ, Adam J, et al. “Interchangeability” of PD-L1 immunohistochemistry assays: a meta-analysis of diagnostic accuracy. Mod Pathol. 2020;33(1):4–17. https://doi.org/10.1038/s41379-019-0327-4 .
doi: 10.1038/s41379-019-0327-4 pubmed: 31383961
Bach DH, Zhang W, Sood AK. Chromosomal instability in tumor initiation and development. Cancer Res. 2019;79(16):3995–4002. https://doi.org/10.1158/0008-5472.CAN-18-3235 .
doi: 10.1158/0008-5472.CAN-18-3235 pubmed: 31350294 pmcid: 7694409
Baretti M, Le DT. DNA mismatch repair in cancer. Pharmacol Ther. 2018;189:45–62. https://doi.org/10.1016/j.pharmthera.2018.04.004 .
doi: 10.1016/j.pharmthera.2018.04.004 pubmed: 29669262
Bonneville R, Krook MA, Kautto EA, et al. Landscape of microsatellite instability across 39 cancer types. JCO Precis Oncol. 2017;2017. https://doi.org/10.1200/PO.17.00073 .
Overman MJ, McDermott R, Leach JL, et al. Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): an open-label, multicentre, phase 2 study. Lancet Oncol. 2017;18(9):1182–91. https://doi.org/10.1016/S1470-2045(17)30422-9 .
doi: 10.1016/S1470-2045(17)30422-9 pubmed: 28734759 pmcid: 6207072
Le DT, Durham JN, Smith KN, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science. 2017;357(6349):409–13. https://doi.org/10.1126/science.aan6733 .
doi: 10.1126/science.aan6733 pubmed: 28596308 pmcid: 5576142
Le DT, Uram JN, Wang H, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372(26):2509–20. https://doi.org/10.1056/NEJMoa1500596 .
doi: 10.1056/NEJMoa1500596 pubmed: 26028255 pmcid: 4481136
Marabelle A, Le DT, Ascierto PA, et al. Efficacy of pembrolizumab in patients with noncolorectal high microsatellite instability/mismatch repair–deficient cancer: results from the phase II KEYNOTE-158 study. J Clin Oncol. 2020;38(1):1–10. https://doi.org/10.1200/JCO.19.02105 .
doi: 10.1200/JCO.19.02105 pubmed: 31682550
Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019;19(3):133–50. https://doi.org/10.1038/s41568-019-0116-x .
doi: 10.1038/s41568-019-0116-x pubmed: 30755690 pmcid: 6705396
Marabelle A, Fakih M, Lopez J, et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study. Lancet Oncol. 2020;21(10):1353–65. https://doi.org/10.1016/S1470-2045(20)30445-9 .
doi: 10.1016/S1470-2045(20)30445-9 pubmed: 32919526
Snyder A, Makarov V, Merghoub T, et al. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N Engl J Med. 2014;371(23):2189–99. https://doi.org/10.1056/NEJMoa1406498 .
doi: 10.1056/NEJMoa1406498 pubmed: 25409260 pmcid: 4315319
Stenzinger A, Endris V, Budczies J, et al. Harmonization and standardization of panel-based tumor mutational burden measurement: real-world results and recommendations of the quality in pathology study. J Thorac Oncol. 2020;15(7):1177–89. https://doi.org/10.1016/j.jtho.2020.01.023 .
doi: 10.1016/j.jtho.2020.01.023 pubmed: 32119917
Budczies J, Kazdal D, Allgäuer M, et al. Quantifying potential confounders of panel-based tumor mutational burden (TMB) measurement. Lung Cancer Amst Neth. 2020;142:114–9. https://doi.org/10.1016/j.lungcan.2020.01.019 .
doi: 10.1016/j.lungcan.2020.01.019
Fancello L, Gandini S, Pelicci PG, Mazzarella L. Tumor mutational burden quantification from targeted gene panels: major advancements and challenges. J Immunother Cancer. 2019;7(1):183. https://doi.org/10.1186/s40425-019-0647-4 .
doi: 10.1186/s40425-019-0647-4 pubmed: 31307554 pmcid: 6631597
Merino DM, McShane LM, Fabrizio D, et al. Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project. J Immunother Cancer. 2020;8(1):e000147. https://doi.org/10.1136/jitc-2019-000147 .
doi: 10.1136/jitc-2019-000147 pubmed: 32217756 pmcid: 7174078
Stenzinger A, Allen JD, Maas J, et al. Tumor mutational burden standardization initiatives: recommendations for consistent tumor mutational burden assessment in clinical samples to guide immunotherapy treatment decisions. Genes Chromosomes Cancer. 2019;58(8):578–88. https://doi.org/10.1002/gcc.22733 .
doi: 10.1002/gcc.22733 pubmed: 30664300 pmcid: 6618007
Chalmers ZR, Connelly CF, Fabrizio D, et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017;9(1):34. https://doi.org/10.1186/s13073-017-0424-2 .
doi: 10.1186/s13073-017-0424-2 pubmed: 28420421 pmcid: 5395719
Buchhalter I, Rempel E, Endris V, et al. Size matters: dissecting key parameters for panel-based tumor mutational burden analysis. Int J Cancer. 2019;144(4):848–58. https://doi.org/10.1002/ijc.31878 .
doi: 10.1002/ijc.31878 pubmed: 30238975
Srinivasan M, Sedmak D, Jewell S. Effect of fixatives and tissue processing on the content and integrity of nucleic acids. Am J Pathol. 2002;161(6):1961–71. https://doi.org/10.1016/S0002-9440(10)64472-0 .
doi: 10.1016/S0002-9440(10)64472-0 pubmed: 12466110 pmcid: 1850907
Jennings LJ, Arcila ME, Corless C, et al. Guidelines for validation of next-generation sequencing-based oncology panels: a joint consensus recommendation of the Association for Molecular Pathology and College of American Pathologists. J Mol Diagn JMD. 2017;19(3):341–65. https://doi.org/10.1016/j.jmoldx.2017.01.011 .
doi: 10.1016/j.jmoldx.2017.01.011 pubmed: 28341590
Hong J, Gresham D. Incorporation of unique molecular identifiers in TruSeq adapters improves the accuracy of quantitative sequencing. Biotechniques. 2017;63(5):221–6. https://doi.org/10.2144/000114608 .
doi: 10.2144/000114608 pubmed: 29185922 pmcid: 7359820
Zhou W, Chen T, Zhao H, et al. Bias from removing read duplication in ultra-deep sequencing experiments. Bioinformatics. 2014;30(8):1073–80. https://doi.org/10.1093/bioinformatics/btt771 .
doi: 10.1093/bioinformatics/btt771 pubmed: 24389657 pmcid: 3982159
Ebbert MTW, Wadsworth ME, Staley LA, et al. Evaluating the necessity of PCR duplicate removal from next-generation sequencing data and a comparison of approaches. BMC Bioinformatics. 2016;17(7):239. https://doi.org/10.1186/s12859-016-1097-3 .
doi: 10.1186/s12859-016-1097-3 pubmed: 27454357 pmcid: 4965708
Endris V, Buchhalter I, Allgäuer M, et al. Measurement of tumor mutational burden (TMB) in routine molecular diagnostics: in silico and real-life analysis of three larger gene panels. Int J Cancer. 2019;144(9):2303–12. https://doi.org/10.1002/ijc.32002 .
doi: 10.1002/ijc.32002 pubmed: 30446996
Vega DM, Yee LM, McShane LM, et al. Aligning tumor mutational burden (TMB) quantification across diagnostic platforms: phase II of the Friends of Cancer Research TMB Harmonization Project. Ann Oncol. 2021;32(12):1626–36. https://doi.org/10.1016/j.annonc.2021.09.016 .
doi: 10.1016/j.annonc.2021.09.016 pubmed: 34606929
Chen G, Mosier S, Gocke CD, Lin MT, Eshleman JR. Cytosine deamination is a major cause of baseline noise in next generation sequencing. Mol Diagn Ther. 2014;18(5):587–93. https://doi.org/10.1007/s40291-014-0115-2 .
doi: 10.1007/s40291-014-0115-2 pubmed: 25091469 pmcid: 4175022
Guo Q, Lakatos E, Bakir IA, Curtius K, Graham TA, Mustonen V. The mutational signatures of formalin fixation on the human genome. Nat Commun. 2022;13(1):4487. https://doi.org/10.1038/s41467-022-32041-5 .
doi: 10.1038/s41467-022-32041-5 pubmed: 36068219 pmcid: 9448750
Berra CM, Torrezan GT, de Paula CA, Hsieh R, Lourenço SV, Carraro DM. Use of uracil-DNA glycosylase enzyme to reduce DNA-related artifacts from formalin-fixed and paraffin-embedded tissues in diagnostic routine. Appl Cancer Res. 2019;39(1):7. https://doi.org/10.1186/s41241-019-0075-2 .
doi: 10.1186/s41241-019-0075-2
Alexandrov LB, Nik-Zainal S, Wedge DC, et al. Signatures of mutational processes in human cancer. Nature. 2013;500(7463):415–21. https://doi.org/10.1038/nature12477 .
doi: 10.1038/nature12477 pubmed: 23945592 pmcid: 3776390
Sun JX, He Y, Sanford E, et al. A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal. PLoS Comput Biol. 2018;14(2):e1005965. https://doi.org/10.1371/journal.pcbi.1005965 .
doi: 10.1371/journal.pcbi.1005965 pubmed: 29415044 pmcid: 5832436
Karczewski KJ, Francioli LC, Tiao G, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434–43. https://doi.org/10.1038/s41586-020-2308-7 .
doi: 10.1038/s41586-020-2308-7 pubmed: 32461654 pmcid: 7334197
Fancello L, Guida A, Frige G, et al. TMBleR, a bioinformatic tool to optimize TMB estimation and predictive power. Bioinforma Oxf Engl. 2021:btab836. https://doi.org/10.1093/bioinformatics/btab836 . Published online December 20.
Goodman AM, Sokol ES, Frampton GM, Lippman SM, Kurzrock R. Microsatellite-stable tumors with high mutational burden benefit from immunotherapy. Cancer Immunol Res. 2019;7(10):1570–3. https://doi.org/10.1158/2326-6066.CIR-19-0149 .
doi: 10.1158/2326-6066.CIR-19-0149 pubmed: 31405947 pmcid: 6774837
Barroso-Sousa R, Keenan TE, Pernas S, et al. Tumor mutational burden and PTEN alterations as molecular correlates of response to PD-1/L1 blockade in metastatic triple-negative breast cancer. Clin Cancer Res Off J Am Assoc Cancer Res. 2020;26(11):2565–72. https://doi.org/10.1158/1078-0432.CCR-19-3507 .
doi: 10.1158/1078-0432.CCR-19-3507
Okamura R, Kato S, Lee S, Jimenez RE, Sicklick JK, Kurzrock R. ARID1A alterations function as a biomarker for longer progression-free survival after anti-PD-1/PD-L1 immunotherapy. J Immunother Cancer. 2020;8(1):e000438. https://doi.org/10.1136/jitc-2019-000438 .
doi: 10.1136/jitc-2019-000438 pubmed: 32111729 pmcid: 7057434
Assoun S, Theou-Anton N, Nguenang M, et al. Association of TP53 mutations with response and longer survival under immune checkpoint inhibitors in advanced non-small-cell lung cancer. Lung Cancer. 2019;132:65–71. https://doi.org/10.1016/j.lungcan.2019.04.005 .
doi: 10.1016/j.lungcan.2019.04.005 pubmed: 31097096
Basse C, Morel C, Alt M, et al. Relevance of a molecular tumour board (MTB) for patients’ enrolment in clinical trials: experience of the Institut Curie. ESMO Open. 2018;3(3):e000339. https://doi.org/10.1136/esmoopen-2018-000339 .
doi: 10.1136/esmoopen-2018-000339 pubmed: 29636991 pmcid: 5890857
Moreira A, Poulet A, Masliah-Planchon J, et al. Prognostic value of tumor mutational burden in patients with oral cavity squamous cell carcinoma treated with upfront surgery. ESMO Open. 2021;6(4):100178. https://doi.org/10.1016/j.esmoop.2021.100178 .
doi: 10.1016/j.esmoop.2021.100178 pubmed: 34118772 pmcid: 8207209
O’Leary NA, Wright MW, Brister JR, et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44(D1):D733-745. https://doi.org/10.1093/nar/gkv1189 .
doi: 10.1093/nar/gkv1189 pubmed: 26553804
Sherry ST, Ward MH, Kholodov M, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–11. https://doi.org/10.1093/nar/29.1.308 .
doi: 10.1093/nar/29.1.308 pubmed: 11125122 pmcid: 29783
Tate JG, Bamford S, Jubb HC, et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 2019;47(D1):D941–7. https://doi.org/10.1093/nar/gky1015 .
doi: 10.1093/nar/gky1015 pubmed: 30371878
1000 Genomes Project Consortium, Auton A, Brooks LD, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. https://doi.org/10.1038/nature15393 .
SciCrunch | Research Resource Resolver. https://scicrunch.org/resolver/SCR_012761 . Accessed 8 Feb 2022.
Zhang J, Bajari R, Andric D, et al. The International Cancer Genome Consortium Data Portal. Nat Biotechnol. 2019;37(4):367–9. https://doi.org/10.1038/s41587-019-0055-9 .
doi: 10.1038/s41587-019-0055-9 pubmed: 30877282
Dong C, Wei P, Jian X, et al. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Hum Mol Genet. 2015;24(8):2125–37. https://doi.org/10.1093/hmg/ddu733 .
doi: 10.1093/hmg/ddu733 pubmed: 25552646
Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164. https://doi.org/10.1093/nar/gkq603 .
doi: 10.1093/nar/gkq603 pubmed: 20601685 pmcid: 2938201
Karczewski KJ, Weisburd B, Thomas B, et al. The ExAC browser: displaying reference data information from over 60 000 exomes. Nucleic Acids Res. 2017;45(D1):D840–5. https://doi.org/10.1093/nar/gkw971 .
doi: 10.1093/nar/gkw971 pubmed: 27899611

Auteurs

Célia Dupain (C)

Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.

Tom Gutman (T)

Bioinformatics Core Facility, INSERM U900, Mines Paris Tech, Institut Curie, Paris, France.

Elodie Girard (E)

Bioinformatics Core Facility, INSERM U900, Mines Paris Tech, Institut Curie, Paris, France.

Choumouss Kamoun (C)

Bioinformatics Core Facility, INSERM U900, Mines Paris Tech, Institut Curie, Paris, France.

Grégoire Marret (G)

Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.

Zahra Castel-Ajgal (Z)

Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.

Marie-Paule Sablin (MP)

Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.

Cindy Neuzillet (C)

Department of Medical Oncology, Institut Curie, Paris & Saint Cloud, France.

Edith Borcoman (E)

Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.

Ségolène Hescot (S)

Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.

Céline Callens (C)

Department of Genetics, Institut Curie, Paris, France.

Olfa Trabelsi-Grati (O)

Department of Genetics, Institut Curie, Paris, France.

Samia Melaabi (S)

Department of Genetics, Institut Curie, Paris, France.

Roseline Vibert (R)

Department of Genetics, Institut Curie, Paris, France.

Samantha Antonio (S)

Department of Genetics, Institut Curie, Paris, France.

Coralie Franck (C)

Department of Genetics, Institut Curie, Paris, France.

Michèle Galut (M)

Department of Pathology, Institut Curie, PSL Research University, Paris, France.

Isabelle Guillou (I)

Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.

Maral Halladjian (M)

Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.

Yves Allory (Y)

Department of Pathology, Université Paris-Saclay, UVSQ, Institut Curie, Saint-Cloud, France.

Joanna Cyrta (J)

Department of Pathology, Institut Curie, PSL Research University, Paris, France.

Julien Romejon (J)

Bioinformatics Core Facility, INSERM U900, Mines Paris Tech, Institut Curie, Paris, France.

Eleonore Frouin (E)

Department of Genetics, Institut Curie, Paris, France.

Dominique Stoppa-Lyonnet (D)

Department of Genetics, Institut Curie, Paris, France.
Paris-Cité University, Paris, France.
INSERM U830, Paris, France.

Jennifer Wong (J)

Department of Genetics, Institut Curie, Paris, France.

Christophe Le Tourneau (C)

Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.
Inserm U900 Research Unit, Saint Cloud, France.
Paris-Saclay University, Paris, France.

Ivan Bièche (I)

Department of Genetics, Institut Curie, Paris, France.
Paris-Cité University, Paris, France.
Faculty of Pharmaceutical and Biological Sciences, INSERM U1016, Paris Descartes University, Paris, France.

Nicolas Servant (N)

Bioinformatics Core Facility, INSERM U900, Mines Paris Tech, Institut Curie, Paris, France.

Maud Kamal (M)

Department of Drug Development and Innovation (D3i), Institut Curie, Paris, France.

Julien Masliah-Planchon (J)

Department of Genetics, Institut Curie, Paris, France. julien.masliahplanchon@curie.fr.

Classifications MeSH