Rustamadji Primariadewi, Wiyarta Elvan, Bethania Kristina Anna
Department of Anatomic Pathology, Faculty of Medicine Universitas Indonesia, Dr. Cipto Mangunkusumo National Hospital, Jakarta, Indonesia.
Department of Medical Science, Faculty of Medicine Universitas Indonesia, Dr. Cipto Mangunkusumo National Hospital, Jakarta, Indonesia.
Iran J Pathol. 2022 Fall;17(4):480-490. doi: 10.30699/IJP.2022.551244.2866. Epub 2022 Oct 5.
BACKGROUND & OBJECTIVE: Invasive breast carcinoma of no special type (IBC-NST) is the most common type of breast cancer, which mainly causes axillary lymph-node metastasis (ALNM). Building on our previous research, we wanted to explore the optimal combination of AKT2, CD44v6, and MT1-MMP for the ALNM prediction.
The presence or absence of ALNM was used to separate 46 paraffin blocks containing IBC-NST primary tumors into two groups. Age, tumor grade, tumor size, receptor status (ER, PR, HER2, Ki-67, TOP2A), and test biomarker expression were evaluated. Biomarker expressions were assessed by IHC staining and categorized according to their respective cut-offs from our previous study, while other data were collected from archives. Data was gathered and analyzed using univariate, multivariate, and AUROC models.
The expression of CD44v6 (OR: 12.77, 95% CI: 2.18-87.12, =0.005) was identified as the independent variable for ALNM. Meanwhile, AKT2 expression (OR: 3.22, 95% CI: 0.36-22.41, =0.237) and MT1-MMP expression (OR: 5.35, 95% CI: 0.83-34.54, =0.078) did not demonstrate a statistically significant independent association in respect to ALNM. Combining AKT2 and MT1-MMP on CD44v6 increased overall accuracy by 4% compared to CD44v6 alone (AUROC 0.89 vs. 0.85).
The combined usage of AKT2, CD44v6, and MT1-MMP revealed no significant change compared to CD44v6 alone. Due to the cost and practicality, we propose using CD44v6 as a predictor biomarker of ALNM in IBC-NST.
非特殊类型浸润性乳腺癌(IBC-NST)是最常见的乳腺癌类型,主要导致腋窝淋巴结转移(ALNM)。基于我们之前的研究,我们想探索AKT2、CD44v6和MT1-MMP用于ALNM预测的最佳组合。
根据是否存在ALNM,将46个含有IBC-NST原发性肿瘤的石蜡块分为两组。评估年龄、肿瘤分级、肿瘤大小、受体状态(ER、PR、HER2、Ki-67、TOP2A)以及检测生物标志物的表达。生物标志物表达通过免疫组化染色评估,并根据我们之前研究各自的临界值进行分类,而其他数据从档案中收集。使用单变量、多变量和AUROC模型收集并分析数据。
CD44v6的表达(OR:12.77,95%CI:2.18 - 87.12,P = 0.005)被确定为ALNM的独立变量。同时,AKT2表达(OR:3.22,95%CI:0.36 - 22.41,P = 0.237)和MT1-MMP表达(OR:5.35,95%CI:0.83 - 34.54,P =