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Evolutionary analysis of primary tumors and metastatic lesions from 20 breast cancer patients (99 samples in total) using exome sequencing data.

Metastatic breast cancers are still incurable. Characterizing their evolutionary landscape including the role of metastatic axillary lymph nodes to seed distant organ metastasis can provide rational basis for effective treatments. Here, we describe the genomic analyses of the primary tumors and metastatic lesions from 20 breast cancer patients (99 samples). Our evolutionary analyses revealed diverse spreading and seeding patterns governing tumor progression. Although linear evolution to successive metastatic sites was common, parallel evolution from the primary tumor to multiple distant sites was also evident. Metastatic spreading was frequently coupled with polyclonal seeding, where multiple metastatic subclones originated from primary tumor and/or other distant metastases. Synchronous axillary lymph node metastasis, a well-established prognosticator of breast cancer, was not involved in seeding distant metastasis, suggesting haematogenous route for cancer dissemination. Clonal evolution coincided frequently with emerging driver alterations and evolving mutational processes, notably a significant increase in apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC) associated mutagenesis. Our data provide the first genomic evidence regarding the role of axillary lymph node metastasis in seeding distant organ metastasis and elucidates the evolving mutational landscape during cancer progression.

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00001003837 Illumina HiSeq 2500 125
Publications Citations
Evolutionary history of metastatic breast cancer reveals minimal seeding from axillary lymph nodes.
J Clin Invest 128: 2018 1355-1370
83
Clonal replacement and heterogeneity in breast tumors treated with neoadjuvant HER2-targeted therapy.
Nat Commun 10: 2019 657
31