Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients

Xiaohui Ni(Harvard University), Minglei Zhuo, Zhe Su(Peking University), Jianchun Duan, Yan Gao(Peking University), Zhijie Wang, Chenghang Zong(Harvard University), Hua Bai, Alec R. Chapman(Harvard University), Jun Zhao, Liya Xu(Peking University), Tongtong An, Qi Ma(Peking University), Yuyan Wang, Meina Wu, Yu Sun, Shuhang Wang, Zhenxiang Li, Xiaodan Yang, Jun Yong(Harvard University), Xiaodong Su(Peking University), Youyong Lu(Peking University), Fan Bai(Peking University), X. Sunney Xie(Harvard University), Jie Wang
Proceedings of the National Academy of Sciences
December 9, 2013
Cited by 481Open Access
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Abstract

Circulating tumor cells (CTCs) enter peripheral blood from primary tumors and seed metastases. The genome sequencing of CTCs could offer noninvasive prognosis or even diagnosis, but has been hampered by low single-cell genome coverage of scarce CTCs. Here, we report the use of the recently developed multiple annealing and looping-based amplification cycles for whole-genome amplification of single CTCs from lung cancer patients. We observed characteristic cancer-associated single-nucleotide variations and insertions/deletions in exomes of CTCs. These mutations provided information needed for individualized therapy, such as drug resistance and phenotypic transition, but were heterogeneous from cell to cell. In contrast, every CTC from an individual patient, regardless of the cancer subtypes, exhibited reproducible copy number variation (CNV) patterns, similar to those of the metastatic tumor of the same patient. Interestingly, different patients with the same lung cancer adenocarcinoma (ADC) shared similar CNV patterns in their CTCs. Even more interestingly, patients of small-cell lung cancer have CNV patterns distinctly different from those of ADC patients. Our finding suggests that CNVs at certain genomic loci are selected for the metastasis of cancer. The reproducibility of cancer-specific CNVs offers potential for CTC-based cancer diagnostics.


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