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Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer
OBJECTIVE: Precision oncology depends on translating molecular data into therapy recommendations. However, with the growing complexity of next-generation sequencing-based tests, clinical interpretation of somatic genomic mutations has evolved into a formidable task. Here, we compared the performance...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513637/ https://www.ncbi.nlm.nih.gov/pubmed/32967919 http://dx.doi.org/10.1136/esmoopen-2020-000872 |
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author | Perakis, Samantha O Weber, Sabrina Zhou, Qing Graf, Ricarda Hojas, Sabine Riedl, Jakob M Gerger, Armin Dandachi, Nadia Balic, Marija Hoefler, Gerald Schuuring, Ed Groen, Harry J M Geigl, Jochen B Heitzer, Ellen Speicher, Michael R |
author_facet | Perakis, Samantha O Weber, Sabrina Zhou, Qing Graf, Ricarda Hojas, Sabine Riedl, Jakob M Gerger, Armin Dandachi, Nadia Balic, Marija Hoefler, Gerald Schuuring, Ed Groen, Harry J M Geigl, Jochen B Heitzer, Ellen Speicher, Michael R |
author_sort | Perakis, Samantha O |
collection | PubMed |
description | OBJECTIVE: Precision oncology depends on translating molecular data into therapy recommendations. However, with the growing complexity of next-generation sequencing-based tests, clinical interpretation of somatic genomic mutations has evolved into a formidable task. Here, we compared the performance of three commercial clinical decision support tools, that is, NAVIFY Mutation Profiler (NAVIFY; Roche), QIAGEN Clinical Insight (QCI) Interpret (QIAGEN) and CureMatch Bionov (CureMatch). METHODS: In order to obtain the current status of the respective tumour genome, we analysed cell-free DNA from patients with metastatic breast, colorectal or non-small cell lung cancer. We evaluated somatic copy number alterations and in parallel applied a 77-gene panel (AVENIO ctDNA Expanded Panel). We then assessed the concordance of tier classification approaches between NAVIFY and QCI and compared the strategies to determine actionability among all three platforms. Finally, we quantified the alignment of treatment suggestions across all decision tools. RESULTS: Each platform varied in its mode of variant classification and strategy for identifying druggable targets and clinical trials, which resulted in major discrepancies. Even the frequency of concordant actionable events for tier I-A or tier I-B classifications was only 4.3%, 9.5% and 28.4% when comparing NAVIFY with QCI, NAVIFY with CureMatch and CureMatch with QCI, respectively, and the obtained treatment recommendations differed drastically. CONCLUSIONS: Treatment decisions based on molecular markers appear at present to be arbitrary and dependent on the chosen strategy. As a consequence, tumours with identical molecular profiles would be differently treated, which challenges the promising concepts of genome-informed medicine. |
format | Online Article Text |
id | pubmed-7513637 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-75136372020-10-05 Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer Perakis, Samantha O Weber, Sabrina Zhou, Qing Graf, Ricarda Hojas, Sabine Riedl, Jakob M Gerger, Armin Dandachi, Nadia Balic, Marija Hoefler, Gerald Schuuring, Ed Groen, Harry J M Geigl, Jochen B Heitzer, Ellen Speicher, Michael R ESMO Open Original Research OBJECTIVE: Precision oncology depends on translating molecular data into therapy recommendations. However, with the growing complexity of next-generation sequencing-based tests, clinical interpretation of somatic genomic mutations has evolved into a formidable task. Here, we compared the performance of three commercial clinical decision support tools, that is, NAVIFY Mutation Profiler (NAVIFY; Roche), QIAGEN Clinical Insight (QCI) Interpret (QIAGEN) and CureMatch Bionov (CureMatch). METHODS: In order to obtain the current status of the respective tumour genome, we analysed cell-free DNA from patients with metastatic breast, colorectal or non-small cell lung cancer. We evaluated somatic copy number alterations and in parallel applied a 77-gene panel (AVENIO ctDNA Expanded Panel). We then assessed the concordance of tier classification approaches between NAVIFY and QCI and compared the strategies to determine actionability among all three platforms. Finally, we quantified the alignment of treatment suggestions across all decision tools. RESULTS: Each platform varied in its mode of variant classification and strategy for identifying druggable targets and clinical trials, which resulted in major discrepancies. Even the frequency of concordant actionable events for tier I-A or tier I-B classifications was only 4.3%, 9.5% and 28.4% when comparing NAVIFY with QCI, NAVIFY with CureMatch and CureMatch with QCI, respectively, and the obtained treatment recommendations differed drastically. CONCLUSIONS: Treatment decisions based on molecular markers appear at present to be arbitrary and dependent on the chosen strategy. As a consequence, tumours with identical molecular profiles would be differently treated, which challenges the promising concepts of genome-informed medicine. BMJ Publishing Group 2020-09-23 /pmc/articles/PMC7513637/ /pubmed/32967919 http://dx.doi.org/10.1136/esmoopen-2020-000872 Text en © Author (s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. Published by BMJ on behalf of the European Society for Medical Oncology. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, any changes made are indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Original Research Perakis, Samantha O Weber, Sabrina Zhou, Qing Graf, Ricarda Hojas, Sabine Riedl, Jakob M Gerger, Armin Dandachi, Nadia Balic, Marija Hoefler, Gerald Schuuring, Ed Groen, Harry J M Geigl, Jochen B Heitzer, Ellen Speicher, Michael R Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer |
title | Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer |
title_full | Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer |
title_fullStr | Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer |
title_full_unstemmed | Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer |
title_short | Comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer |
title_sort | comparison of three commercial decision support platforms for matching of next-generation sequencing results with therapies in patients with cancer |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513637/ https://www.ncbi.nlm.nih.gov/pubmed/32967919 http://dx.doi.org/10.1136/esmoopen-2020-000872 |
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