A Simple Method for Robust and Accurate Intrinsic Subtyping of Breast Cancer.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2023-01-01 DOI:10.1177/11769351231159893
Mehdi Hamaneh, Yi-Kuo Yu
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引用次数: 0

Abstract

Motivation: The PAM50 signature/method is widely used for intrinsic subtyping of breast cancer samples. However, depending on the number and composition of the samples included in a cohort, the method may assign different subtypes to the same sample. This lack of robustness is mainly due to the fact that PAM50 subtracts a reference profile, which is computed using all samples in the cohort, from each sample before classification. In this paper we propose modifications to PAM50 to develop a simple and robust single-sample classifier, called MPAM50, for intrinsic subtyping of breast cancer. Like PAM50, the modified method uses a nearest centroid approach for classification, but the centroids are computed differently, and the distances to the centroids are determined using an alternative method. Additionally, MPAM50 uses unnormalized expression values for classification and does not subtract a reference profile from the samples. In other words, MPAM50 classifies each sample independently, and so avoids the previously mentioned robustness issue.

Results: A training set was employed to find the new MPAM50 centroids. MPAM50 was then tested on 19 independent datasets (obtained using various expression profiling technologies) containing 9637 samples. Overall good agreement was observed between the PAM50- and MPAM50-assigned subtypes with a median accuracy of 0.792, which (we show) is comparable with the median concordance between various implementations of PAM50. Additionally, MPAM50- and PAM50-assigned intrinsic subtypes were found to agree comparably with the reported clinical subtypes. Also, survival analyses indicated that MPAM50 preserves the prognostic value of the intrinsic subtypes. These observations demonstrate that MPAM50 can replace PAM50 without loss of performance. On the other hand, MPAM50 was compared with 2 previously published single-sample classifiers, and with 3 alternative modified PAM50 approaches. The results indicated a superior performance by MPAM50.

Conclusions: MPAM50 is a robust, simple, and accurate single-sample classifier of intrinsic subtypes of breast cancer.

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一种简便、可靠、准确的乳腺癌固有亚型分型方法。
动机:PAM50特征/方法被广泛用于乳腺癌样本的内在亚型分型。然而,根据队列中样本的数量和组成,该方法可能为同一样本分配不同的亚型。这种鲁棒性的缺乏主要是由于PAM50在分类前从每个样本中减去了参考概况,该参考概况是使用队列中的所有样本计算的。在本文中,我们提出修改PAM50,以开发一个简单而稳健的单样本分类器,称为MPAM50,用于乳腺癌的内在亚型。与PAM50一样,改进的方法使用最近质心方法进行分类,但质心的计算方式不同,并且使用替代方法确定到质心的距离。此外,MPAM50使用非规范化表达式值进行分类,并且不会从样本中减去参考配置文件。换句话说,MPAM50对每个样本进行独立分类,从而避免了前面提到的鲁棒性问题。结果:利用训练集找到新的MPAM50质心。然后在包含9637个样本的19个独立数据集(使用各种表达谱分析技术获得)上测试MPAM50。在PAM50和mpam50分配的亚型之间观察到总体上良好的一致性,中位数准确性为0.792,(我们表明)与PAM50的各种实现之间的中位数一致性相当。此外,发现MPAM50和pam50分配的内在亚型与报道的临床亚型相当一致。此外,生存分析表明MPAM50保留了内在亚型的预后价值。这些观察结果表明,MPAM50可以代替PAM50而不损失性能。另一方面,将MPAM50与先前发表的2个单样本分类器以及3个可选的修改PAM50方法进行比较。结果表明,MPAM50具有优异的性能。结论:MPAM50是一种强大、简单、准确的乳腺癌固有亚型单样本分类器。
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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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