Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards.
Clinical interpretation
Molecular tumor board
Precision oncology
RNA-sequencing
Targeted therapy
Whole-exome sequencing
Journal
BMC medicine
ISSN: 1741-7015
Titre abrégé: BMC Med
Pays: England
ID NLM: 101190723
Informations de publication
Date de publication:
24 10 2022
24 10 2022
Historique:
received:
04
02
2022
accepted:
09
09
2022
entrez:
24
10
2022
pubmed:
25
10
2022
medline:
26
10
2022
Statut:
epublish
Résumé
Structured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards. High-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0-1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically. A median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician's choice. Reproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts.
Sections du résumé
BACKGROUND
Structured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards.
METHODS
High-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0-1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically.
RESULTS
A median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician's choice.
CONCLUSIONS
Reproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts.
Identifiants
pubmed: 36274133
doi: 10.1186/s12916-022-02560-5
pii: 10.1186/s12916-022-02560-5
pmc: PMC9590222
doi:
Substances chimiques
Poly(ADP-ribose) Polymerase Inhibitors
0
RNA
63231-63-0
Protein-Tyrosine Kinases
EC 2.7.10.1
Nucleotides
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
367Informations de copyright
© 2022. The Author(s).
Références
Eur J Cancer. 2020 Mar;127:41-51
pubmed: 31982633
Oncoscience. 2014 Jul 23;1(7):504-9
pubmed: 25594052
J Natl Cancer Inst. 2015 Apr 11;107(7):
pubmed: 25863335
Ann Oncol. 2014 Dec;25(12):2295-2303
pubmed: 25344359
Nat Genet. 2017 Jan 31;49(2):170-174
pubmed: 28138153
J Mol Diagn. 2017 Jan;19(1):4-23
pubmed: 27993330
Int J Cancer. 2019 Dec 1;145(11):2996-3010
pubmed: 31008532
Genome Biol. 2014 Aug 27;15(8):438
pubmed: 25222080
Nat Commun. 2020 Oct 2;11(1):4965
pubmed: 33009371
N Engl J Med. 2015 Feb 26;372(9):793-5
pubmed: 25635347
Genome Med. 2016 Nov 4;8(1):117
pubmed: 27814769
Lancet Oncol. 2015 Oct;16(13):1324-34
pubmed: 26342236
Oncologist. 2021 Aug;26(8):e1347-e1358
pubmed: 33111480
Genome Med. 2020 Jan 14;12(1):8
pubmed: 31937368
Cancer Discov. 2021 Nov;11(11):2780-2795
pubmed: 34112699
N Engl J Med. 2015 Jun 25;372(26):2509-20
pubmed: 26028255
Nat Med. 2019 May;25(5):744-750
pubmed: 31011206
JCO Precis Oncol. 2017 Jul;2017:
pubmed: 28890946
N Engl J Med. 2011 Jun 30;364(26):2507-16
pubmed: 21639808
N Engl J Med. 2020 Jan 2;382(1):41-50
pubmed: 31751012
Nat Med. 2019 May;25(5):751-758
pubmed: 31011205
BMC Bioinformatics. 2019 Aug 16;20(1):429
pubmed: 31419935
N Engl J Med. 2018 Feb 22;378(8):731-739
pubmed: 29466156
Genet Med. 2022 May;24(5):986-998
pubmed: 35101336
Cancer Discov. 2017 Jun;7(6):586-595
pubmed: 28365644
JCO Precis Oncol. 2019 Jul 24;3:
pubmed: 32914021
Nat Genet. 2020 Apr;52(4):448-457
pubmed: 32246132
JCO Precis Oncol. 2018 Nov;2:1-14
pubmed: 35135153