Understanding the organ tropism of metastatic breast cancer through the combination of liquid biopsy tools.

Journal Article (Journal Article)


Liquid biopsy provides real-time data about prognosis and actionable mutations in metastatic breast cancer (MBC). The aim of this study was to explore the combination of circulating tumour DNA (ctDNA) analysis and circulating tumour cells (CTCs) enumeration in estimating target organs more susceptible to MBC involvement.


This retrospective study analysed 88 MBC patients characterised for both CTCs and ctDNA at baseline. CTCs were isolated through the CellSearch kit, while ctDNA was analysed using the Guardant360 NGS-based assay. Sites of disease were collected on the basis of imaging. Associations were explored both through uni- and multivariate logistic regression and Fisher's exact test and the random forest machine learning algorithm.


After multivariate logistic regression, ESR1 mutation was the only significant factor associated with liver metastases (OR 8.10), while PIK3CA was associated with lung localisations (OR 3.74). CTC enumeration was independently associated with bone metastases (OR 10.41) and TP53 was associated with lymph node localisations (OR 2.98). The metastatic behaviour was further investigated through a random forest machine learning algorithm. Bone involvement was described by CTC enumeration and alterations in ESR1, GATA3, KIT, CDK4 and ERBB2, while subtype, CTC enumeration, inflammatory BC diagnosis, ESR1 and KIT aberrations described liver metastases. PIK3CA, MET, AR, CTC enumeration and TP53 were associated with lung organotropism. The model, moreover, showed that AR, CCNE1, ESR1, MYC and CTC enumeration were the main drivers in HR positive MBC metastatic pattern.


These results indicate that ctDNA and CTCs enumeration could provide useful insights regarding MBC organotropism, suggesting a possible role for future monitoring strategies that dynamically focus on high-risk organs defined by tumourbiology.

Full Text

Duke Authors

Cited Authors

  • Gerratana, L; Davis, AA; Polano, M; Zhang, Q; Shah, AN; Lin, C; Basile, D; Toffoli, G; Wehbe, F; Puglisi, F; Behdad, A; Platanias, LC; Gradishar, WJ; Cristofanilli, M

Published Date

  • January 2021

Published In

Volume / Issue

  • 143 /

Start / End Page

  • 147 - 157

PubMed ID

  • 33307492

Electronic International Standard Serial Number (EISSN)

  • 1879-0852

International Standard Serial Number (ISSN)

  • 0959-8049

Digital Object Identifier (DOI)

  • 10.1016/j.ejca.2020.11.005


  • eng