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Using prognostic signatures and machine learning to identify core features associated with response to CDK4/6 inhibitor-based therapy in metastatic breast cancer

Oncogene volume 44, pages 1387–1399 (2025)Cite this article

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Abstract

CDK4/6 inhibitors in combination with endocrine therapy are widely used to treat HR+/HER2− metastatic breast cancer leading to improved progression-free survival (PFS) compared to single agent endocrine therapy. Over 300 patients receiving standard-of-care CDK4/6 inhibitor combination therapy for metastatic disease were enrolled at a single institution. Clinical, pathological, and gene expression data were employed to define determinants for PFS duration. Visceral disease (HR 1.55, p = 0.0013), prior endocrine therapy (HR 2.34, p < 0.001), and the type of endocrine therapy (HR 2.16, p < 0.001) were highly associated with PFS duration. Multiple pre-defined gene expression signatures were employed to determine association with response to CDK4/6 inhibitor-based therapy. Random survival forest was applied to define key gene expression and clinical features associated with PFS and develop a predictive model. The time to progression predicted by this model was related to the median PFS observed in PALOMA-2/3 and PEARL studies. Interrogating genes identified as highly significant across all studies indicated common enrichment of gene networks associated with cell cycle and estrogen receptor signaling. These findings indicate that there are common features from real-world use of CDK4/6 inhibitors that could be used to infer time to progression and better inform treatment.

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Fig. 1: Clinical HR+/HER2− breast cancer cohort treated with CDK4/6 inhibitor combination therapy: study design and overview.
Fig. 2: Cell cycle signatures are highly conserved.
Fig. 3: Assessment of CDK4/6, RBsig cell cycle signatures with progression-free survival.
Fig. 4: Assessment of EndoPredict, OncotypeDX, and MammaPrint signatures with progression-free survival.
Fig. 5: Iterative random survival forest model development and performance for patient stratification.
Fig. 6: Final random survival forest model assessment of predicted mortality.

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Data availability

The data discussed in this publication have been deposited in NCBI’s Gene Expression Omnibus [56] and are accessible through GEO Series accession number GSE285861. Other patient-level data is managed by Roswell Park Comprehensive Cancer Center and is made available through structured data use agreements via their Technology Transfer Office. Investigators may contact the corresponding author to assist with the process of establishing a data use agreement through the Technology Transfer Office.

Code availability

The underlying code for this study is available in the GitHub repository and can be accessed via this link: https://github.com/jianxinwang/ciclib_rsf_predictor.

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Acknowledgements

The authors would like to thank the patients for their participation in this study. We acknowledge the entire breast oncology group at Roswell Park for their assistance in carrying out this study and Ms. Deanna Hamilton for her assistance in coordinating and collecting data for the protocol. This study was supported by funding from the Roswell Park Alliance Foundation and grants to ESK and AKW from the NCI (CA247362 and CA247362-S1).

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Authors and Affiliations

  1. Department of Molecular and Cellular Biology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Street, Buffalo, NY, 14263, USA

    Agnieszka K. Witkiewicz, Jianxin Wang, Emily Schultz, Thomas N. O’Connor & Erik S. Knudsen

  2. Department of Pathology, Roswell Park Comprehensive Cancer Center, Elm and Carlton Street, Buffalo, NY, 14263, USA

    Agnieszka K. Witkiewicz

  3. Department of Medicine, Roswell Park Comprehensive Cancer Center, Elm and Carlton Street, Buffalo, NY, 14263, USA

    Tracey O’Connor & Ellis Levine

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  1. Agnieszka K. Witkiewicz
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Contributions

The study was designed by AKW and ESK. Data was collected by ES, TO, EL, and AKW. Coordination of tissue collection and analysis for this study was performed by AKW and ES. Data was analyzed by JW, AKW, ESK, ES, and TNO. The manuscript was assembled and written by ESK, TNO, ES, JW, and AKW. All authors read, edited, and approved the final manuscript. Funding for this study was attained by AKW and ESK.

Corresponding authors

Correspondence to Agnieszka K. Witkiewicz or Erik S. Knudsen.

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Competing interests

ESK and AKW have sponsored research funded by Blueprint Medicine and Bristol Myers Squibb, in addition to funding received from the Roswell Park Alliance Foundation and National Cancer Institute. ESK is also a member of the Cancer Cell Cycle-LLC consulting enterprise. All other authors have no competing interests to declare.

Ethics approval and consent to participate

All patient data collection, protocols, and methods of this specific study were conducted in accordance with the principles outlined in the Declaration of Helsinki. All methods used in this study were approved by the RPCCC Institutional Review Board under the Roswell Park Remnant Tissue Protocol and through written informed consent received from all participating patients under clinical trial NCT04526587.

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Witkiewicz, A.K., Wang, J., Schultz, E. et al. Using prognostic signatures and machine learning to identify core features associated with response to CDK4/6 inhibitor-based therapy in metastatic breast cancer. Oncogene 44, 1387–1399 (2025). https://doi.org/10.1038/s41388-025-03308-0

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