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Article Abstract
Breast cancer (BC) recurrence is a major concern for both patients and healthcare providers. Accurately predicting the risk of BC recurrence can help guide treatment decisions and improve patient outcomes for a disease-free survival. There are several approaches and models that have been developed to predict BC recurrence risk. These include derived clinical assays such as genetic profiling (Oncotye Dx, MammaPrint, CanAssist and others), and algorithm derived open access tools such as Magee equations (ME), CTS5 Calculator and Predict Breast cancer. All the clinical assays are well accepted, but affordability and feasibility remain the challenge due to a noteworthy price tag of USD 3000. As per The American Society of Clinical Oncology (ASCO) updates, open access tools are possible substitutes but the availability of limited information on their applicability is a concern. These tools take into consideration the histopathologic parameters and immunohistochemistry (IHC) biomarkers data for estrogen receptor/progesterone (ER/PR), human epidermal growth factor receptor 2 (HER2), and Ki67. The current study focuses on the application of these tools in a subset of 55 Indian BC patients considering the influence of the androgen receptor (AR) IHC expression profile. AR is a potent target and a close interacting neighbor protein to ER and available literature also suggests their crosstalk expression in BC clinical models. Our comparative recurrence scores (RSs) predictive data showed a statistically significant AR expression correlation with average ME scores. No significance was noted across different prediction tools. The findings are suggestive that ME predictive scores are more relevant and informative compared to other online tools and with an additional AR IHC expression analysis the recurrence prediction might prove to be beneficial and feasible to many deprived BC patients.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11622693 | PMC |
| http://dx.doi.org/10.14293/genint.15.1.001 | DOI Listing |
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