Enhanced PAM50 subtyping of breast cancer implemented in the PCAPAM50 R package

Praveen Kumar Raj-Kumar*, Boyi Chen, Ming Wen Hu, Tyler Akers Hohenstein, Jianfang Liu, Craig D. Shriver, Xiaoying Lin, Hai Hu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Breast cancer subtyping is commonly performed using two distinct approaches: immunohistochemistry (IHC) for clinical decision-making and PAM50 gene expression-based intrinsic subtyping, widely applied in research. While both methods are well-established, discrepancies in subtype classification between them highlight the need for improved concordance. To address this, we previously developed PCA-PAM50, a method that was able to enhance the concordance between the two approaches by reclassifying an aggressive subset of PAM50 Luminal A tumors as Luminal B, with clinical significance. Here, we introduce PCA-PAM50 as a reengineered, open-source R package PCAPAM50 available on CRAN. The package features optimized performance with user-friendly functionality requiring minimal input, providing ease of integration in genomic studies. We demonstrate the package application on TCGA breast cancer cohort, where PCAPAM50 achieved the highest agreement with IHC-defined subtypes, outperforming both original and robust implementations in the genefu package. It also produced the fewest Normal-like calls and maintained stable agreement across ER-imbalanced subsets, highlighting robustness to a common source of instability in PAM50 classification. Comprehensive documentation, including a vignette and user manual, supports effective application, while a dedicated tools portal provides installation instructions, frequently asked questions, and updates. The package is accessible at https://CRAN.R-project.org/package=PCAPAM50, with additional resources available at https://www.wriwindber.org/tools-portal/pcapam50/.

Original languageEnglish
Article number1112
JournalScientific Reports
Volume16
Issue number1
DOIs
StatePublished - Dec 2026

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