Amit Roy Chowdhury, Somya Saswati Swain, Sandip Kumar Mohanty, Birendranath Banerjee
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引用次数: 0
摘要
乳腺癌(BC)复发是患者和医疗保健提供者关注的主要问题。准确预测BC复发的风险可以帮助指导治疗决策并改善患者无病生存的结果。目前已有几种方法和模型用于预测BC复发风险。这些包括衍生的临床分析,如基因分析(Oncotye Dx、MammaPrint、CanAssist等),以及算法衍生的开放获取工具,如Magee equation (ME)、CTS5 Calculator和Predict Breast cancer。所有的临床检测都被广泛接受,但由于3000美元的价格标签,可负担性和可行性仍然是一个挑战。根据美国临床肿瘤学会(ASCO)的更新,开放获取工具是可能的替代品,但关于其适用性的有限信息的可用性是一个问题。这些工具考虑了雌激素受体/孕激素(ER/PR)、人表皮生长因子受体2 (HER2)和Ki67的组织病理学参数和免疫组织化学(IHC)生物标志物数据。目前的研究重点是考虑雄激素受体(AR) IHC表达谱的影响,将这些工具应用于55名印度BC患者。AR是ER的有效靶点和密切相互作用的邻居蛋白,现有文献也表明它们在BC临床模型中的串扰表达。我们的比较复发评分(RSs)预测数据显示AR表达与平均ME评分有统计学意义。不同预测工具间无显著性差异。研究结果提示,与其他在线工具相比,ME预测评分更具相关性和信息性,并且通过额外的AR IHC表达分析,复发预测可能对许多贫困BC患者有益和可行。
Androgen Receptor Influenced Recurrence Score Correlation in Hormone Positive and HER2 Negative Breast Cancer Indian Patients: A Comparative Approach.
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.