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Published in: BMC Medicine 1/2022

Open Access 01-12-2022 | Biomarkers | Research article

Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards

Authors: Damian T. Rieke, Till de Bortoli, Peter Horak, Mario Lamping, Manuela Benary, Ivan Jelas, Gina Rüter, Johannes Berger, Marit Zettwitz, Niklas Kagelmann, Andreas Kind, Falk Fabian, Dieter Beule, Hanno Glimm, Benedikt Brors, Albrecht Stenzinger, Stefan Fröhling, Ulrich Keilholz

Published in: BMC Medicine | Issue 1/2022

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Abstract

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.
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Literature
1.
go back to reference Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793–5.CrossRef Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372:793–5.CrossRef
2.
go back to reference Drilon A, Laetsch TW, Kummar S, DuBois SG, Lassen UN, Demetri GD, et al. Efficacy of larotrectinib in TRK fusion-positive cancers in adults and children. N Engl J Med. 2018;378:731–9.CrossRef Drilon A, Laetsch TW, Kummar S, DuBois SG, Lassen UN, Demetri GD, et al. Efficacy of larotrectinib in TRK fusion-positive cancers in adults and children. N Engl J Med. 2018;378:731–9.CrossRef
3.
go back to reference Ramalingam SS, Vansteenkiste J, Planchard D, Cho BC, Gray JE, Ohe Y, et al. Overall survival with osimertinib in untreated, EGFR-mutated advanced NSCLC. N Engl J Med. 2020;382:41–50.CrossRef Ramalingam SS, Vansteenkiste J, Planchard D, Cho BC, Gray JE, Ohe Y, et al. Overall survival with osimertinib in untreated, EGFR-mutated advanced NSCLC. N Engl J Med. 2020;382:41–50.CrossRef
4.
go back to reference Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, Larkin J, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med. 2011;364:2507–16.CrossRef Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, Larkin J, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med. 2011;364:2507–16.CrossRef
5.
go back to reference Malone ER, Oliva M, Sabatini PJB, Stockley TL, Siu LL. Molecular profiling for precision cancer therapies. Genome Med. 2020;12:8.CrossRef Malone ER, Oliva M, Sabatini PJB, Stockley TL, Siu LL. Molecular profiling for precision cancer therapies. Genome Med. 2020;12:8.CrossRef
7.
go back to reference Ritter DI, Roychowdhury S, Roy A, Rao S, Landrum MJ, Sonkin D, et al. Somatic cancer variant curation and harmonization through consensus minimum variant level data. Genome Med. 2016;8:117.CrossRef Ritter DI, Roychowdhury S, Roy A, Rao S, Landrum MJ, Sonkin D, et al. Somatic cancer variant curation and harmonization through consensus minimum variant level data. Genome Med. 2016;8:117.CrossRef
8.
go back to reference Li MM, Datto M, Duncavage EJ, Kulkarni S, Lindeman NI, Roy S, et al. Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn. 2017;19:4–23.CrossRef Li MM, Datto M, Duncavage EJ, Kulkarni S, Lindeman NI, Roy S, et al. Standards and guidelines for the interpretation and reporting of sequence variants in cancer: a joint consensus recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn. 2017;19:4–23.CrossRef
9.
go back to reference Andre F, Mardis E, Salm M, Soria J-C, Siu LL, Swanton C. Prioritizing targets for precision cancer medicine. Ann Oncol. 2014;25:2295–303.CrossRef Andre F, Mardis E, Salm M, Soria J-C, Siu LL, Swanton C. Prioritizing targets for precision cancer medicine. Ann Oncol. 2014;25:2295–303.CrossRef
11.
go back to reference Leichsenring J, Horak P, Kreutzfeldt S, Heining C, Christopoulos P, Volckmar A-L, et al. Variant classification in precision oncology. Int J Cancer. 2019;145:2996–3010.CrossRef Leichsenring J, Horak P, Kreutzfeldt S, Heining C, Christopoulos P, Volckmar A-L, et al. Variant classification in precision oncology. Int J Cancer. 2019;145:2996–3010.CrossRef
13.
go back to reference Griffith M, Spies NC, Krysiak K, McMichael JF, Coffman AC, Danos AM, et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet. 2017;49:170–4.CrossRef Griffith M, Spies NC, Krysiak K, McMichael JF, Coffman AC, Danos AM, et al. CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer. Nat Genet. 2017;49:170–4.CrossRef
14.
go back to reference Seva J, Wiegandt DL, Gotze J, Lamping M, Rieke D, Schafer R, et al. VIST - a Variant-Information Search Tool for precision oncology. BMC Bioinformatics. 2019;20:429.CrossRef Seva J, Wiegandt DL, Gotze J, Lamping M, Rieke D, Schafer R, et al. VIST - a Variant-Information Search Tool for precision oncology. BMC Bioinformatics. 2019;20:429.CrossRef
15.
go back to reference Pallarz S, Benary M, Lamping M, Rieke D, Starlinger J, Sers C, et al. Comparative analysis of public knowledge bases for precision oncology. JCO Prec Oncol. 2019;3:1–8. Pallarz S, Benary M, Lamping M, Rieke D, Starlinger J, Sers C, et al. Comparative analysis of public knowledge bases for precision oncology. JCO Prec Oncol. 2019;3:1–8.
16.
go back to reference Wagner AH, Walsh B, Mayfield G, Tamborero D, Sonkin D, Krysiak K, et al. A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer. Nat Genet. 2020;52:448–57.CrossRef Wagner AH, Walsh B, Mayfield G, Tamborero D, Sonkin D, Krysiak K, et al. A harmonized meta-knowledgebase of clinical interpretations of somatic genomic variants in cancer. Nat Genet. 2020;52:448–57.CrossRef
17.
go back to reference Rieke DT, Lamping M, Schuh M, le Tourneau C, Basté N, Burkard ME, et al. Comparison of treatment recommendations by molecular tumor boards worldwide. JCO Prec Oncol. 2018;2:1–14. Rieke DT, Lamping M, Schuh M, le Tourneau C, Basté N, Burkard ME, et al. Comparison of treatment recommendations by molecular tumor boards worldwide. JCO Prec Oncol. 2018;2:1–14.
18.
go back to reference Rodon J, Soria J-C, Berger R, Miller WH, Rubin E, Kugel A, et al. Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nat Med. 2019;25:751–8.CrossRef Rodon J, Soria J-C, Berger R, Miller WH, Rubin E, Kugel A, et al. Genomic and transcriptomic profiling expands precision cancer medicine: the WINTHER trial. Nat Med. 2019;25:751–8.CrossRef
19.
go back to reference Massard C, Michiels S, Ferté C, Le Deley M-C, Lacroix L, Hollebecque A, et al. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: results of the MOSCATO 01 trial. Cancer Discov. 2017;7:586 LP–595.CrossRef Massard C, Michiels S, Ferté C, Le Deley M-C, Lacroix L, Hollebecque A, et al. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: results of the MOSCATO 01 trial. Cancer Discov. 2017;7:586 LP–595.CrossRef
20.
go back to reference Lamping M, Benary M, Leyvraz S, Messerschmidt C, Blanc E, Kessler T, et al. Support of a molecular tumour board by an evidence-based decision management system for precision oncology. Eur J Cancer. 2020;127:41–51.CrossRef Lamping M, Benary M, Leyvraz S, Messerschmidt C, Blanc E, Kessler T, et al. Support of a molecular tumour board by an evidence-based decision management system for precision oncology. Eur J Cancer. 2020;127:41–51.CrossRef
21.
go back to reference Horak P, Heining C, Kreutzfeldt S, Hutter B, Mock A, Hullein J, et al. Comprehensive genomic and transcriptomic analysis for guiding therapeutic decisions in patients with rare cancers. Cancer Discov. 2021:candisc.0126.2021;11. Horak P, Heining C, Kreutzfeldt S, Hutter B, Mock A, Hullein J, et al. Comprehensive genomic and transcriptomic analysis for guiding therapeutic decisions in patients with rare cancers. Cancer Discov. 2021:candisc.0126.2021;11.
22.
go back to reference Le Tourneau C, Delord J-P, Goncalves A, Gavoille C, Dubot C, Isambert N, et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol. 2015;16:1324–34.CrossRef Le Tourneau C, Delord J-P, Goncalves A, Gavoille C, Dubot C, Isambert N, et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol. 2015;16:1324–34.CrossRef
23.
go back to reference Sicklick JK, Kato S, Okamura R, Schwaederle M, Hahn ME, Williams CB, et al. Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study. Nat Med. 2019;25:744–50.CrossRef Sicklick JK, Kato S, Okamura R, Schwaederle M, Hahn ME, Williams CB, et al. Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study. Nat Med. 2019;25:744–50.CrossRef
25.
go back to reference Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372:2509–20.CrossRef Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372:2509–20.CrossRef
27.
go back to reference Klauschen F, Andreeff M, Keilholz U, Dietel M, Stenzinger A. The combinatorial complexity of cancer precision medicine. Oncoscience. 2014;1:504–9.CrossRef Klauschen F, Andreeff M, Keilholz U, Dietel M, Stenzinger A. The combinatorial complexity of cancer precision medicine. Oncoscience. 2014;1:504–9.CrossRef
28.
go back to reference Horak P, Griffith M, Danos AM, Pitel BA, Madhavan S, Liu X, et al. Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC). Genet Med. 2022. https://doi.org/10.1016/j.gim.2022.01.001. Horak P, Griffith M, Danos AM, Pitel BA, Madhavan S, Liu X, et al. Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC). Genet Med. 2022. https://​doi.​org/​10.​1016/​j.​gim.​2022.​01.​001.
29.
go back to reference Kato S, Kim KH, Lim HJ, Boichard A, Nikanjam M, Weihe E, et al. Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-One strategy. Nat Commun. 2020;11:1–9.CrossRef Kato S, Kim KH, Lim HJ, Boichard A, Nikanjam M, Weihe E, et al. Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-One strategy. Nat Commun. 2020;11:1–9.CrossRef
30.
go back to reference Pezo RC, Bedard PL. Definition: Translational and personalised medicine, biomarkers, pharmacodynamics. In: ESMO Handbook of Translational Research; 2015. Pezo RC, Bedard PL. Definition: Translational and personalised medicine, biomarkers, pharmacodynamics. In: ESMO Handbook of Translational Research; 2015.
31.
go back to reference Vijaymeena MK, Kavitha K. A survey on similarity measures in text mining. Machine Learn Appl Int J (MLAIJ). 2016;3. Vijaymeena MK, Kavitha K. A survey on similarity measures in text mining. Machine Learn Appl Int J (MLAIJ). 2016;3.
Metadata
Title
Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards
Authors
Damian T. Rieke
Till de Bortoli
Peter Horak
Mario Lamping
Manuela Benary
Ivan Jelas
Gina Rüter
Johannes Berger
Marit Zettwitz
Niklas Kagelmann
Andreas Kind
Falk Fabian
Dieter Beule
Hanno Glimm
Benedikt Brors
Albrecht Stenzinger
Stefan Fröhling
Ulrich Keilholz
Publication date
01-12-2022
Publisher
BioMed Central
Keyword
Biomarkers
Published in
BMC Medicine / Issue 1/2022
Electronic ISSN: 1741-7015
DOI
https://doi.org/10.1186/s12916-022-02560-5

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