Hamaneh Mehdi, Yu Yi-Kuo
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Cancer Inform. 2023 Mar 25;22:11769351231159893. doi: 10.1177/11769351231159893. eCollection 2023.
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.
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.
MPAM50 is a robust, simple, and accurate single-sample classifier of intrinsic subtypes of breast cancer.
PAM50特征/方法被广泛用于乳腺癌样本的内在亚型分类。然而，根据队列中所包含样本的数量和组成，该方法可能会给同一个样本分配不同的亚型。这种缺乏稳健性的主要原因在于，PAM50在分类前会从每个样本中减去一个参考概况，该参考概况是使用队列中的所有样本计算得出的。在本文中，我们提出对PAM50进行修改，以开发一种简单且稳健的单样本分类器，称为MPAM50，用于乳腺癌的内在亚型分类。与PAM50一样，修改后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，并且到质心的距离使用另一种方法确定。此外，MPAM50使用未归一化的表达值进行分类，并且不从样本中减去参考概况。换句话说，MPAM50独立地对每个样本进行分类，从而避免了上述稳健性问题。
使用一个训练集来找到新的MPAM50质心。然后在包含9637个样本的19个独立数据集（使用各种表达谱技术获得）上对MPAM50进行测试。观察到PAM50和MPAM50分配的亚型之间总体一致性良好，中位数准确率为0.792，（我们表明）这与PAM50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告的临床亚型具有相当的一致性。生存分析还表明，MPAM50保留了内在亚型的预后价值。这些观察结果表明，MPAM50可以替代PAM50而不会损失性能。另一方面，将MPAM50与2个先前发表的单样本分类器以及3种替代的修改后的PAM50方法进行了比较。结果表明MPAM50具有卓越的性能。
MPAM50是一种用于乳腺癌内在亚型的稳健、简单且准确的单样本分类器。
