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JCO Precis Oncol. 2019;3. doi: 10.1200/PO.19.00171. Epub 2019 Nov 12.

Harmonization of Tumor Mutational Burden Quantification and Association With Response to Immune Checkpoint Blockade in Non-Small-Cell Lung Cancer.

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Dana-Farber Cancer Institute, Boston, MA.
Broad Institute of Harvard and MIT, Cambridge, MA.
University Hospital 12 de Octubre, Madrid, Spain.
Memorial Sloan Kettering Cancer Center, New York, NY.
Harvard Graduate Program in Biophysics, Boston, MA.
Brigham and Women's Hospital, Boston, MA.
Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Boston, MA.
Massachusetts Institute of Technology, Cambridge, MA.
Weill Cornell Medical College, New York, NY.



Heterogeneity in tumor mutational burden (TMB) quantification across sequencing platforms limits the application and further study of this potential biomarker of response to immune checkpoint inhibitors (ICI). We hypothesized that harmonization of TMB across platforms would enable integration of distinct clinical datasets to better characterize the association between TMB and ICI response.


Cohorts of NSCLC patients sequenced by one of three targeted panels or by whole exome sequencing (WES) were compared (total n=7297). TMB was calculated uniformly and compared across cohorts. TMB distributions were harmonized by applying a normal transformation followed by standardization to z-scores. In sub-cohorts of patients treated with ICIs (DFCI n=272; MSKCC n=227), the association between TMB and outcome was assessed. Durable clinical benefit (DCB) was defined as responsive/stable disease lasting ≥6 months.


TMB values were higher in the panel cohorts than the WES cohort. Average mutation rates per gene were highly concordant across cohorts (Pearson coefficient 0.842-0.866). Subsetting the WES cohort by gene panels only partially reproduced the observed differences in TMB. Standardization of TMB into z-scores harmonized TMB distributions and enabled integration of the ICI-treated sub-cohorts. Simulations indicated that cohorts >900 are necessary for this approach. TMB did not associate with response in never smokers or patients harboring targetable driver alterations, although these analyses were under-powered. Increasing TMB thresholds increased DCB rate, but DCB rates within deciles varied. Receiver operator curves yielded an area under the curve of 0.614 with no natural inflection point.


Z-score conversion harmonizes TMB values and enables integration of datasets derived from different sequencing panels. Clinical and biologic features may provide context to the clinical application of TMB, and warrant further study.

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