Aligning tumor mutational burden (TMB) quantification across diagnostic platforms: phase II of the Friends of Cancer Research TMB Harmonization Project

被引：115

作者：

Vega, D. M. ^{[1
]}

Yee, L. M. ^{[2
]}

McShane, L. M. ^{[2
]}

Williams, P. M. ^{[3
]}

Chen, L. ^{[3
]}

Vilimas, T. ^{[3
]}

Fabrizio, D. ^{[4
]}

Funari, V. ^{[5
]}

Newberg, J. ^{[4
]}

Bruce, L. K. ^{[5
]}

Chen, S. -j. ^{[6
]}

Baden, J. ^{[7
]}

Barrett, J. Carl ^{[8
]}

Beer, P. ^{[9
]}

Butler, M. ^{[10
]}

Cheng, J. -h. ^{[6
]}

Conroy, J. ^{[11
]}

Cyanam, D. ^{[12
]}

Eyring, K. ^{[13
]}

Garcia, E. ^{[14
]}

Green, G. ^{[7
]}

Gregersen, V. R. ^{[15
]}

Hellmann, M. D. ^{[16
]}

Keefer, L. A. ^{[17
]}

Lasiter, L. ^{[1
]}

Lazar, A. J. ^{[18
]}

Li, M. -c. ^{[2
]}

MacConaill, L. E. ^{[14
]}

Meier, K. ^{[19
]}

Mellert, H. ^{[20
]}

Pabla, S. ^{[11
]}

Pallavajjalla, A. ^{[21
]}

Pestano, G. ^{[20
]}

Salgado, R. ^{[9
]}

Samara, R. ^{[15
]}

Sokol, E. S. ^{[4
]}

Stafford, P. ^{[22
]}

Budczies, J. ^{[23
]}

Stenzinger, A. ^{[23
]}

Tom, W. ^{[12
]}

Valkenburg, K. C. ^{[17
]}

Wang, X. Z. ^{[24
]}

Weigman, V. ^{[25
]}

Xie, M. ^{[8
]}

Xie, Q. ^{[26
]}

Zehir, A. ^{[16
]}

Zhao, C. ^{[19
]}

Zhao, Y. ^{[2
]}

Stewart, M. D. ^{[1
]}

Allen, J. ^{[1
]}

机构：

[1] Friends Canc Res, 1800 M St NW,Suite 1050 South, Washington, DC 20036 USA

[2] NCI, Bethesda, MD 20892 USA

[3] Leidos Biomed Res Inc, Mol Characterizat Lab, Frederick Natl Lab Canc Res, Frederick, MD USA

[4] Fdn Med Inc, Cambridge, MA USA

[5] NeoGen Labs, Aliso Viejo, CA USA

[6] ACT Genom, Taipei, Taiwan

[7] Bristol Myers Squibb Co, Princeton, NJ USA

[8] AstraZeneca Pharmaceut LP, Waltham, MA USA

[9] European Org Res Treatment Canc, Brussels, Belgium

[10] LGC Clin Diagnost, Gaithersburg, MD USA

[11] OmniSeq Inc, Buffalo, NY USA

[12] Thermo Fisher Sci, Clin Sequencing Div, Ann Arbor, MI USA

[13] Intermt Precis Genom, St George, UT USA

[14] Brigham & Womens Hosp, 75 Francis St, Boston, MA 02115 USA

[15] QIAGEN Inc, Aarhus, Denmark

[16] Mem Sloan Kettering Canc Ctr, 1275 York Ave, New York, NY 10021 USA

[17] Personal Genome Diagnost, Baltimore, MD USA

[18] Univ Texas MD Anderson Canc Ctr, Houston, TX 77030 USA

[19] Illumina Inc, Clin Genom, San Diego, CA USA

[20] Biodesix Inc, Boulder, CO USA

[21] Johns Hopkins Univ, Baltimore, MD USA

[22] Caris Life Sci Inc, Phoenix, AZ USA

[23] Univ Hosp Heidelberg, Inst Pathol, Heidelberg, Germany

[24] EMD Serono Res & Dev Inst Inc, Billerica, MA USA

[25] Q Squared Solut, Durham, NC USA

[26] Gen Dynam Informat Technol Inc, Columbia, MD USA

来源：

ANNALS OF ONCOLOGY | 2021年 / 32卷 / 12期

关键词：

precision medicine; biomarker; tumor mutational burden; immunotherapy; cancer; PD-L1; IHC; ASSAY; VALIDATION; NSCLC;

D O I：

10.1016/j.annonc.2021.09.016

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

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 assaywere 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.

引用

页码：1626 / 1636

页数：11

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