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

Date

23 Nov 2019

Session

Poster display session

Presenters

Yuan-Sheng Zang

Citation

Annals of Oncology (2019) 30 (suppl_9): ix122-ix130. 10.1093/annonc/mdz431

Authors

Y. Zang1, X. Jiao1, X. Zhang2, B. Qin1, D. Liu2, L. Liu3, J. Ni4, Z. Ning4, L. Chen5, L. Zhu5, S. Qin6, J. Zhou7, S. Ying8, X. Chen9, A. Li10, T. Hou11, A. Lizaso11, H. Zhang11, K. Liu1, Z. Wang1

Author affiliations

  • 1 Department Of Medical Oncology, Shanghai Changzheng Hospital, 200003 - Shanghai/CN
  • 2 Department Of Medical Oncology, The Affiliated Hospital of Qingdao University, 250012 - Qingdao/CN
  • 3 Department Of Radiation Oncology, Fudan University Shanghai Cancer Center, 200032 - Shanghai/CN
  • 4 Department Of Integrative Oncology, Fudan University Shanghai Cancer Center, 200032 - Shanghai/CN
  • 5 Jiangsu Cancer Hospital & Jiangsu Institute Of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing/CN
  • 6 Department Of Tumor Radiotherapy, The First Affiliated Hospital of Suzhou University, Suzhou/CN
  • 7 Department Of Respiratory, First Affiliated Hospital of Zhejing University School of Medicine, Hangzhou/CN
  • 8 Department Of Radiation Oncology, Taizhou Central Hospital, Taizhou/CN
  • 9 Department Of Medical Oncology, Hangzhou Cancer Hospital, Hangzhou/CN
  • 10 Department Of Medical Oncology, Shanghai Eastern Hepatobiliary Surgery Hospital, Shanghai/CN
  • 11 Burning Rock Biotech, Burning Rock Biotech, Shanghai/CN
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Resources

Background

In this study, we examined the TMB landscape of 5,660 pan-cancer cases in Chinese population, using NGS panel. We established cancer-specific and histology-specific biological pathways associated with the TMB status. In addition, as a proof on concept, an unsupervised algorithm was conducted using stepwise logistic regression to generate TMB-predicting signatures from both lung adenocarcinoma and lung squamous cell carcinoma.

Methods

Patients: 5,660 Chinese cancer patients across 11 cancer types Panel: Targeted sequencing was performed on tissue samples using a panel consisting of 295 or 520 cancer-related genes, spanning 1.4 and 1.6Mb of human genome, respectively. An average sequencing depth of 1,000X and 10,000x were achieved for tissue and plasma samples, respectively. Tumor mutation burden: calculated as the ratio of mutation count to the size of coding region of the panel, excluding CNV, fusions, large genomic rearrangements and mutations occurring on the kinase domain of EGFR and ALK.

Results

Across the 11 cancer types included in the analysis, lung squamous cell carcinoma had the highest average TMB, whereas sarcoma has the lowest TMB. High microsatellite instability, DNA damage response deficiency, and homologous recombination repair deficiency indicated significantly higher TMB.The independent predictive power for TMB 26 biological pathways was tested in 11 cancer types. Mismatch repair, DNA damage response, homologous recombination repair, and PI3K-AKT signaling pathway were most commonly correlated with high-TMB. In contrast, ERBB signaling pathway, and adhesion-related pathways were most commonly correlated with low-TMB. Moreover, we developed a 23- and 16-gene signature for TMB prediction for LUAD and LUSC, respectively, with 12 genes shared by both signatures, indicating a histology- specific mechanism for driving high- TMB in lung cancer.

Conclusions

The findings extended the knowledge of the underlying biological mechanisms for high TMB and might be helpful for developing more precise and accessible TMB assessment panels and algorithms in more cancer types.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Burning Rock Biotech.

Disclosure

All authors have declared no conflicts of interest.

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