D M Vega1, L M Yee2, L M McShane2, P M Williams3, L Chen3, T Vilimas3, D Fabrizio4, V Funari5, J Newberg4, L K Bruce5, S-J Chen6, J Baden7, J Carl Barrett8, P Beer9, M Butler10, J-H Cheng6, J Conroy11, D Cyanam12, K Eyring13, E Garcia14, G Green7, V R Gregersen15, M D Hellmann16, L A Keefer17, L Lasiter1, A J Lazar18, M-C Li2, L E MacConaill14, K Meier19, H Mellert20, S Pabla11, A Pallavajjalla21, G Pestano20, R Salgado9, R Samara15, E S Sokol4, P Stafford22, J Budczies23, A Stenzinger23, W Tom12, K C Valkenburg17, X Z Wang24, V Weigman25, M Xie8, Q Xie26, A Zehir16, C Zhao19, Y Zhao2, M D Stewart27, J Allen1. 1. Friends of Cancer Research, Washington, USA. 2. National Cancer Institute, Bethesda, USA. 3. Molecular Characterization Laboratory, Frederick National Lab for Cancer Research, Leidos Biomedical Research Inc., Frederick, USA. 4. Foundation Medicine Inc., Cambridge, USA. 5. NeoGenomics Laboratories, Aliso Viejo, USA. 6. ACT Genomics, Taipei, Taiwan. 7. Bristol Myers Squibb Co., Princeton, USA. 8. AstraZeneca Pharmaceuticals LP, Waltham, USA. 9. European Organisation for Research and Treatment of Cancer, Brussels, Belgium. 10. LGC Clinical Diagnostics, Gaithersburg, USA. 11. OmniSeq Inc., Buffalo, USA. 12. Clinical Sequencing Division, Thermo Fisher Scientific, Ann Arbor, USA. 13. Intermountain Precision Genomics, St. George, USA. 14. Brigham and Women's Hospital, Boston, USA. 15. QIAGEN Inc, Aarhus, Denmark. 16. Memorial Sloan Kettering Cancer Center, New York, USA. 17. Personal Genome Diagnostics, Baltimore, USA. 18. The University of Texas MD Anderson Cancer Center, Houston, USA. 19. Illumina Inc, Clinical Genomics, San Diego, USA. 20. Biodesix, Inc., Boulder, USA. 21. Johns Hopkins University, Baltimore, USA. 22. Caris Life Sciences Inc, Phoenix, USA. 23. Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany. 24. EMD Serono Research and Development Institute, Inc., Billerica, USA. 25. Q Squared Solutions, Durham, USA. 26. General Dynamics Information Technology, Inc., Columbia, USA. 27. Friends of Cancer Research, Washington, USA. Electronic address: mstewart@focr.org.
Abstract
BACKGROUND: Tumor mutational burden (TMB) measurements aid in identifying patients who are likely to benefit from immunotherapy; however, there is empirical variability across panel assays and factors contributing to this variability have not been comprehensively investigated. Identifying sources of variability can help facilitate comparability across different panel assays, which may aid in broader adoption of panel assays and development of clinical applications. MATERIALS AND METHODS: Twenty-nine tumor samples and 10 human-derived cell lines were processed and distributed to 16 laboratories; each used their own bioinformatics pipelines to calculate TMB and compare to whole exome results. Additionally, theoretical positive percent agreement (PPA) and negative percent agreement (NPA) of TMB were estimated. The impact of filtering pathogenic and germline variants on TMB estimates was assessed. Calibration curves specific to each panel assay were developed to facilitate translation of panel TMB values to whole exome sequencing (WES) TMB values. RESULTS: Panel sizes >667 Kb are necessary to maintain adequate PPA and NPA for calling TMB high versus TMB low across the range of cut-offs used in practice. Failure to filter out pathogenic variants when estimating panel TMB resulted in overestimating TMB relative to WES for all assays. Filtering out potential germline variants at >0% population minor allele frequency resulted in the strongest correlation to WES TMB. Application of a calibration approach derived from The Cancer Genome Atlas data, tailored to each panel assay, reduced the spread of panel TMB values around the WES TMB as reflected in lower root mean squared error (RMSE) for 26/29 (90%) of the clinical samples. CONCLUSIONS: Estimation of TMB varies across different panels, with panel size, gene content, and bioinformatics pipelines contributing to empirical variability. Statistical calibration can achieve more consistent results across panels and allows for comparison of TMB values across various panel assays. To promote reproducibility and comparability across assays, a software tool was developed and made publicly available.
BACKGROUND: Tumor mutational burden (TMB) measurements aid in identifying patients who are likely to benefit from immunotherapy; however, there is empirical variability across panel assays and factors contributing to this variability have not been comprehensively investigated. Identifying sources of variability can help facilitate comparability across different panel assays, which may aid in broader adoption of panel assays and development of clinical applications. MATERIALS AND METHODS: Twenty-nine tumor samples and 10 human-derived cell lines were processed and distributed to 16 laboratories; each used their own bioinformatics pipelines to calculate TMB and compare to whole exome results. Additionally, theoretical positive percent agreement (PPA) and negative percent agreement (NPA) of TMB were estimated. The impact of filtering pathogenic and germline variants on TMB estimates was assessed. Calibration curves specific to each panel assay were developed to facilitate translation of panel TMB values to whole exome sequencing (WES) TMB values. RESULTS: Panel sizes >667 Kb are necessary to maintain adequate PPA and NPA for calling TMB high versus TMB low across the range of cut-offs used in practice. Failure to filter out pathogenic variants when estimating panel TMB resulted in overestimating TMB relative to WES for all assays. Filtering out potential germline variants at >0% population minor allele frequency resulted in the strongest correlation to WES TMB. Application of a calibration approach derived from The Cancer Genome Atlas data, tailored to each panel assay, reduced the spread of panel TMB values around the WES TMB as reflected in lower root mean squared error (RMSE) for 26/29 (90%) of the clinical samples. CONCLUSIONS: Estimation of TMB varies across different panels, with panel size, gene content, and bioinformatics pipelines contributing to empirical variability. Statistical calibration can achieve more consistent results across panels and allows for comparison of TMB values across various panel assays. To promote reproducibility and comparability across assays, a software tool was developed and made publicly available.
Authors: Ramaswamy Govindan; Charu Aggarwal; Scott J Antonia; Marianne Davies; Steven M Dubinett; Andrea Ferris; Patrick M Forde; Edward B Garon; Sarah B Goldberg; Raffit Hassan; Matthew D Hellmann; Fred R Hirsch; Melissa L Johnson; Shakun Malik; Daniel Morgensztern; Joel W Neal; Jyoti D Patel; David L Rimm; Sarah Sagorsky; Lawrence H Schwartz; Boris Sepesi; Roy S Herbst Journal: J Immunother Cancer Date: 2022-05 Impact factor: 12.469
Authors: Ryon P Graf; Virginia Fisher; Janick Weberpals; Ole Gjoerup; Marni B Tierno; Richard S P Huang; Nicolas Sayegh; Douglas I Lin; Kira Raskina; Alexa B Schrock; Eric Severson; James F Haberberger; Jeffrey S Ross; James Creeden; Mia A Levy; Brian M Alexander; Geoffrey R Oxnard; Neeraj Agarwal Journal: JAMA Netw Open Date: 2022-03-01