18 02 Metastasis Base Architecture Triple-Negative Breast TNBC

Triple-negative breast cancer (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR), and HER2 amplification, represents 15-20% of breast cancers but accounts for disproportionate mortality due to aggressive metastatic dissemination and limitedtargeted therapy options . Metastatic organotropism—the preferential colonization of specific distant organs—emerges from complex interactions between circulating tumor cell (CTC)phenotypes and organ microenvironments.Current understanding of TNBC metastasis remains largely descriptive, lacking quantitativeframeworks to predict site-specific progression dynamics, identify critical metabolic vulnerabilities, or optimize therapeutic timing. Itry to adress this gap with this model that takes a snapshot of how metastasis sites corelate annd evolve along with identifying the base architecture for each of the major metastatic sites derived from this carcinoma

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-07
DOI
https://doi.org/10.5281/zenodo.18167799
Primary Topic
Cancer Cells and Metastasis
Type
article
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article

18 02 Metastasis Base Architecture Triple-Negative Breast TNBC

Ionut Alexandru Stefan
Zenodo (CERN European Organization for Nuclear Research)
Cancer Cells and Metastasis
article

18 02 Metastasis Base Architecture Triple-Negative Breast TNBC

Ionut Alexandru Stefan
article en

Abstract

Triple-negative breast cancer (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR), and HER2 amplification, represents 15-20% of breast cancers but accounts for disproportionate mortality due to aggressive metastatic dissemination and limitedtargeted therapy options . Metastatic organotropism—the preferential colonization of specific distant organs—emerges from complex interactions between circulating tumor cell (CTC)phenotypes and organ microenvironments.Current understanding of TNBC metastasis remains largely descriptive, lacking quantitativeframeworks to predict site-specific progression dynamics, identify critical metabolic vulnerabilities, or optimize therapeutic timing. Itry to adress this gap with this model that takes a snapshot of how metastasis sites corelate annd evolve along with identifying the base architecture for each of the major metastatic sites derived from this carcinoma

Zenodo (CERN European Organization for Nuclear Research)
Good health and well-being
Openalex Percentile: Top 94%
Cancer Cells and Metastasis
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