Genetic diversity of tumors with mismatch repair deficiency influences anti-PD-1 immunotherapy response

Optimizing panel-based tumor mutational burden (TMB) measurement

BACKGROUND

Panel sequencing based estimates of tumor mutational burden (psTMB) are increasingly replacing whole exome sequencing (WES) tumor mutational burden as predictive biomarker of immune checkpoint blockade (ICB).

DESIGN

A mathematical law describing psTMB variability was derived using a random mutation model and complemented by the contributions of non-randomly mutated real-world cancer genomes and intratumoral heterogeneity through simulations in publicly available datasets.

RESULTS

The coefficient of variation (CV) of psTMB decreased inversely proportional with the square root of the panel size and the square root of the TMB level. In silico simulations of all major commercially available panels in the TCGA pan-cancer cohort confirmed the validity of this mathematical law and demonstrated that the CV was 35% for TMB = 10 muts/Mbp for the largest panels of size 1.1-1.4 Mbp. Accordingly, misclassification rates (gold standard: WES) to separate 'TMBhigh' from 'TMBlow' using a cut-point of 199 mutations were 10%-12% in TCGA-LUAD and 17%-19% in TCGA-LUSC. A novel three-tier psTMB classification scheme which accounts for the likelihood of misclassification is proposed. Simulations in two WES datasets of immunotherapy treated patients revealed that small gene panels were poor predictors of ICB response. Moreover, we noted substantial intratumoral variance of psTMB scores in the TRACERx 100 cohort and identified indel burden as independent marker complementing missense mutation burden.

CONCLUSIONS

A universal mathematical law describes accuracy limitations inherent to psTMB, which result in substantial misclassification rates. This scenario can be controlled by two measures: (i) a panel design that is based on the mathematical law described in this article: halving the CV requires a fourfold increase in panel size, (ii) a novel three-tier TMB classification scheme. Moreover, inclusion of indel burden can complement TMB reports. This work has substantial implications for panel design, TMB testing, clinical trials and patient management.

Clinical and Immunological Implications of Frameshift Mutations in Lung Cancer

INTRODUCTION

Presently, programmed death ligand 1 is the most commonly used biomarker to predict response to immune checkpoint inhibitors (ICIs) in NSCLC. Owing to its several limitations, there is continuous search for more precise and reliable markers. Frameshift mutations by insertion or deletion (fsindels) are suggested to induce more immunogenic tumor-specific neoantigens, conferring better response to ICIs. Positive correlation of fsindels with ICI response has been studied in melanoma and renal cell carcinoma. We investigated the implication of fsindels in the clinical outcomes and immune landscape of patients with NSCLC treated with ICIs.

METHODS

We utilized The Cancer Genome Atlas data set to analyze tumor mutational burden, neoantigen burden, and immune landscape in relation to fsindel status. In addition, utilizing the clinical data from 122 patients treated with ICIs, we evaluated the influence of fsindels on disease response rates and survival outcomes.

RESULTS

A positive correlation between fsindel burden and tumor mutational burden and activated CD4/CD8 T-cell infiltration was shown. Presence of fsindels was also associated with significant prolongation of progression-free survival in patients treated with ICIs (median 6.2 versus 2.7 months [p = 0.01]). In addition, significant differences in the overall response rates (26% versus 12% [p = 0.04]) and disease control rates (68% versus 48% [p = 0.02]) were observed in patients with fsindels.

CONCLUSION

Our findings suggest that fsindels may have a predictive role for ICI response in NSCLC.