Novel immunohistochemistry-based signatures to predict metastatic site of triple-negative breast cancers View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-09-05

AUTHORS

Sergey Klimov, Padmashree CG Rida, Mohammed A Aleskandarany, Andrew R Green, Ian O Ellis, Emiel AM Janssen, Emad A Rakha, Ritu Aneja

ABSTRACT

BACKGROUND: Although distant metastasis (DM) in breast cancer (BC) is the most lethal form of recurrence and the most common underlying cause of cancer related deaths, the outcome following the development of DM is related to the site of metastasis. Triple negative BC (TNBC) is an aggressive form of BC characterised by early recurrences and high mortality. Athough multiple variables can be used to predict the risk of metastasis, few markers can predict the specific site of metastasis. This study aimed at identifying a biomarker signature to predict particular sites of DM in TNBC. METHODS: A clinically annotated series of 322 TNBC were immunohistochemically stained with 133 biomarkers relevant to BC, to develop multibiomarker models for predicting metastasis to the bone, liver, lung and brain. Patients who experienced metastasis to each site were compared with those who did not, by gradually filtering the biomarker set via a two-tailed t-test and Cox univariate analyses. Biomarker combinations were finally ranked based on statistical significance, and evaluated in multivariable analyses. RESULTS: Our final models were able to stratify TNBC patients into high risk groups that showed over 5, 6, 7 and 8 times higher risk of developing metastasis to the bone, liver, lung and brain, respectively, than low-risk subgroups. These models for predicting site-specific metastasis retained significance following adjustment for tumour size, patient age and chemotherapy status. CONCLUSIONS: Our novel IHC-based biomarkers signatures, when assessed in primary TNBC tumours, enable prediction of specific sites of metastasis, and potentially unravel biomarkers previously unknown in site tropism. More... »

PAGES

826

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/bjc.2017.224

DOI

http://dx.doi.org/10.1038/bjc.2017.224

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090746887

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28720841


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