407P - Integrating histologic and genomic characteristics to predict tumour mutation burden of early-stage non-small cell lung cancer

Background

Tumor mutation burden (TMB) served as an effective biomarker predicting efficacy of mono-immunotherapy for non-small cell lung cancer (NSCLC). While, establishing a precise TMB predicting model is essential to monitor which populations are likely to respond to immunotherapy or prognosis and to maximize the benefits of treatment.

Methods

Available Formalin-fixed paraffin embedded tumor tissues were collected from 499 patients with NSCLC. Targeted sequencing of 636 cancer related genes were performed and TMB was calculated.

Results

Distribution of TMB was significantly (p < 0.001) correlated with sex, clinical features (pathological /histological subtype, pathological stage, lymph node metastasis and lympho-vascular invasion). It was also significantly (p < 0.001) associated with mutations in genes like TP53, EGFR, PIK3CA, KRAS, EPHA3, TSHZ3, FAT3, NAV3, KEAP1, NFE2L2, PTPRD, LRRK2, STK11, NF1, KMT2D and GRIN2A. No significant correlations were found between TMB and age, neuro-invasion (p=0.125), and tumor location (p= 0.696). Patients with KRAS p.G12 mutations and FAT3 missense mutations were associated (p< 0.001) with TMB. TP53 mutations also influence TMB distribution (P<0.001). TMB is reversely related to EGFR mutations (P<0.001) but is not differed by mutation types. According to multivariate logistic regression model, genomic parameters can effectively construct model predicting TMB, which may be improved by introducing clinical information.

Conclusions

Our study demonstrates genomic together with clinical features yielded a better reliable model predicting TMB-high status. A simplified model consisting of less than 20 genes and couples of clinical parameters sought to be useful to provide TMB status with less cost and waiting time.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

D. Shao: Full/Part-time employment: BGI Genomics. All other authors have declared no conflicts of interest.