Quantifying potential confounders of panel-based tumor mutational burden (TMB) measurement
Objectives - Retrospective data including subgroup analyses in clinical studies have sparked strong interest in developing tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint blockade. While individual factors influencing panel sequencing based measurement of TMB (psTMB) ha...
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| Hauptverfasser: | , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
1 February 2020
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| In: |
Lung cancer
Year: 2020, Jahrgang: 142, Pages: 114-119 |
| ISSN: | 1872-8332 |
| DOI: | 10.1016/j.lungcan.2020.01.019 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.lungcan.2020.01.019 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S0169500220302828 
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| Verfasserangaben: | Jan Budczies, Daniel Kazdal, Michael Allgäuer, Petros Christopoulos, Eugen Rempel, Nicole Pfarr, Wilko Weichert, Stefan Fröhling, Michael Thomas, Solange Peters, Volker Endris, Peter Schirmacher, Albrecht Stenzinger |
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| 245 | 1 | 0 | |a Quantifying potential confounders of panel-based tumor mutational burden (TMB) measurement |c Jan Budczies, Daniel Kazdal, Michael Allgäuer, Petros Christopoulos, Eugen Rempel, Nicole Pfarr, Wilko Weichert, Stefan Fröhling, Michael Thomas, Solange Peters, Volker Endris, Peter Schirmacher, Albrecht Stenzinger |
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| 520 | |a Objectives - Retrospective data including subgroup analyses in clinical studies have sparked strong interest in developing tumor mutational burden (TMB) as a predictive biomarker for immune checkpoint blockade. While individual factors influencing panel sequencing based measurement of TMB (psTMB) have been discussed in the recent literature, an integrative study quantifying, comparing and combining all potential confounders is still missing. - Material and methods - We separated different potential confounders of psTMB measurement including “panel size”, “germline mutation filtering”, “biological variance” and “technical variance” and developed a specific error model for each of these factors. Published experimental psTMB data were fitted to the error models to quantify the contribution of each of the confounders. The total psTMB variance was obtained as sum over the variance contributions of each of the confounders. - Results - Using a typical large panel (size 1-1.5 Mbp) total errors of 57 %, 42 %, 34 % and 28 % were observed for tumors with psTMB of 5, 10, 20 and 40 muts/Mbp. Even for large panels, the stochastic error connected to the panel size represented the largest of all contributions to the total psTMB variance, especially for tumors with TMB up to 20 muts/Mbp. Other sources of psTMB variability could be kept under control, but rigorous quality control, best practice laboratory workflows and optimized bioinformatics pipelines are essential. - Conclusion - A statistical framework for the analysis of complex, genomic biomarkers was developed and applied to the analysis of psTMB variability. The methods developed here can support the analysis of other quantitative biomarkers and their implementation in clinical practice. | ||
| 650 | 4 | |a Confounders | |
| 650 | 4 | |a Panel sequencing | |
| 650 | 4 | |a Panel size | |
| 650 | 4 | |a Stochastic error | |
| 650 | 4 | |a TMB | |
| 650 | 4 | |a Tumor mutational burden | |
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