ANXA1 Identified as Potential Predictor of Trastuzumab Resistance

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High tumor expression of ANXA1 could be predictive of trastuzumab resistance in patients with HER-2 positive breast cancer.
High tumor expression of ANXA1 could be predictive of trastuzumab resistance in patients with HER-2 positive breast cancer.

High tumor expression of annexin A1 (ANXA1) may be predictive of trastuzumab resistance in patients with HER2-positive breast cancer, according to a study published in Oncotarget.1

Trastuzumab is considered to be a remarkable therapy for many patients with HER2-positive breast cancer; however, primary and acquired resistance occurs.

“We developed an in silico bioinformatics approach integrating proteomic and gene expression data to uncover novel biomarkers associated with trastuzumab benefit,” the authors wrote. “For the first time, we have demonstrated an association between ANXA1 and trastuzumab resistance.”

ANXA1 is a phospholipid-binding protein involved in cell proliferation, inflammation, and the regulation of cell death.2 In addition, ANXA1 has been associated with faster breast cancer cell growth and progression in vitro.3,4

In the initial step of the study, clinicopathologic, mRNA expression, and protein expression data from the publicly-available Tumor Cancer Genomic Atlas project (TCGA) database were analyzed for metagenes, or patterns of gene expression.

Protein expression data were collected using reverse-phase protein array analysis (RPPA). Of the 139 metagenes identified in the TCGA dataset, 10 were independently validated using 97 samples of the Responsify and 432 samples of the FinHer datasets with a false discovery rate (FDR) of 0.05 or less.

The Responsify dataset was from patients with early stage HER2-positive breast cancer, whereas the FinHer dataset included patients with breast cancer, 202 of whom had HER2-positive disease.

RELATED: Women With Mucinous, Tubular/Cribriform Breast Cancer Fare Better Than Other Histotypes

Biologic relevance of the metagenes based on their pathway of origin was investigated using Gene Ontology and the Molecular Signatures Database, and a clustering algorithm was used to build a network by linking significant intersecting metagenes. Two subnetworks were identified, which was consistent across the TCGA, Responsify, and FinHer datasets.

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