Tumour mutation burden analysis in a 5660-cancer-patient cohort reveals cancer type-specific mechanisms for high mutation burden

Zang, Yuan‐Sheng; Jiao, Xiao‐Dong; Zhang, X.-C.; Qin, Bao‐Dong; Liu, D.; Liu, L.; Ni, Jianjiao; Zhou, Ning; Chen, L.; Zhu, Lingjun; Qin, Simeng; Zhou, Jianying; Ying, Shenpeng; Chen, X.; Li, A.; Hou, Ting; Lizaso, Analyn; Zhang, H.-H.; Liu, K.; Wang, Z.

doi:10.1093/annonc/mdz431.007

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“…The alterations of EGFR, KRAS, and BRAF genes proved to be more frequent in micropapillary LUAD (24,25). The studies have suggested that the molecular pathogenesis of micropapillary component may differ from other types of LUAD (26). Pre-invasive LUAD displayed distinct mutation profiles.…”

Section: Discussionmentioning

confidence: 99%

Integrating Histologic and Genomic Characteristics to Predict Tumor Mutation Burden of Early-Stage Non-Small-Cell Lung Cancer

et al. 2021

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Tumor mutation burden (TMB) serves as an effective biomarker predicting efficacy of mono-immunotherapy for non-small cell lung cancer (NSCLC). Establishing a precise TMB predicting model is essential to select which populations are likely to respond to immunotherapy or prognosis and to maximize the benefits of treatment. In this study, available Formalin-fixed paraffin embedded tumor tissues were collected from 499 patients with NSCLC. Targeted sequencing of 636 cancer related genes was performed, and TMB was calculated. 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 was reversely related to EGFR mutations (P < 0.001) but did not differ by mutation types. According to multivariate logistic regression model, genomic parameters could effectively construct model predicting TMB, which may be improved by introducing clinical information. Our study demonstrates that 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 were sought to be useful to provide TMB status with less cost and waiting time.

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Section: Discussionmentioning

confidence: 99%