The PAM50 classifier is widely used for breast tumor intrinsic subtyping based on gene expression. Clinical subtyping, however, is based on immunohistochemistry assays of 3–4 biomarkers. Subtype calls by these two methods do not completely match even on comparable subtypes. Nevertheless, the estrogen receptor (ER)-balanced subset for gene-centering in PAM50 subtyping, is selected based on clinical ER status. Here we present a new method called Principle Component Analysis-based iterative PAM50 subtyping (PCA-PAM50) to perform intrinsic subtyping in ER status unbalanced cohorts. This method leverages PCA and iterative PAM50 calls to derive the gene expression-based ER status and a subsequent ER-balanced subset for gene centering. Applying PCA-PAM50 to three different breast cancer study cohorts, we observed improved consistency (by 6–9.3%) between intrinsic and clinical subtyping for all three cohorts. Particularly, a more aggressive subset of luminal A (LA) tumors as evidenced by higher MKI67 gene expression and worse patient survival outcomes, were reclassified as luminal B (LB) increasing the LB subtype consistency with IHC by 25–49%. In conclusion, we show that PCA-PAM50 enhances the consistency of breast cancer intrinsic and clinical subtyping by reclassifying an aggressive subset of LA tumors into LB. PCA-PAM50 code is available at ftp://ftp.wriwindber.org/.

PAM50 分类器广泛用于基于基因表达的乳腺肿瘤内在亚型。然而，临床亚型基于 3-4 种生物标志物的免疫组织化学分析。即使在可比较的子类型上，这两种方法的子类型调用也不完全匹配。然而，基于临床 ER 状态选择用于 PAM50 亚型中基因中心的雌激素受体 (ER) 平衡子集。在这里，我们提出了一种称为基于主成分分析的迭代 PAM50 亚型 (PCA-PAM50) 的新方法，用于在 ER 状态不平衡队列中执行内在亚型。该方法利用 PCA 和迭代 PAM50 调用来导出基于基因表达的 ER 状态和随后的 ER 平衡子集用于基因中心。将 PCA-PAM50 应用于三个不同的乳腺癌研究队列，我们​​观察到一致性的提高（6-9. 3%) 在所有三个队列的内在和临床亚型之间。特别是，管腔 A (LA) 肿瘤更具侵袭性的子集，如更高的MKI67基因表达和较差的患者生存结果被重新归类为 luminal B (LB)，将 LB 亚型与 IHC 的一致性提高了 25-49%。总之，我们表明 PCA-PAM50 通过将 LA 肿瘤的侵袭性子集重新分类为 LB，增强了乳腺癌内在和临床亚型的一致性。PCA-PAM50 代码可从 ftp://ftp.wriwindber.org/ 获得。