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

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/ .

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Publication Details

Journal
Scientific Reports
Published
2025-12-13
DOI
https://doi.org/10.1038/s41598-025-30752-5
Primary Topic
Breast Cancer Treatment Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Craig D. Shriver, Meixia Hu, Praveen Kumar, Boyi Chen et al.
Scientific Reports
Breast Cancer Treatment Studies
article

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

Craig D. Shriver, Meixia Hu, Praveen Kumar, Boyi Chen, Hai Hu, Jianfang Liu, Xiaoying Lin, Tyler Akers Hohenstein
article en

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/ .

Scientific ReportsVol. 16(1)
Windber Research Institute (US), Uniformed Services University of the Health Sciences (US), Pennsylvania Highlands Community College (US), Walter Reed National Military Medical Center (US)
U.S. Department of Defense, Henry M. Jackson Foundation, Uniformed Services University of the Health Sciences
Openalex Percentile: Top 51%
Breast Cancer Treatment Studies
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