Real-world clinical multi-omics analyses reveal bifurcation of ER-independent and ER-dependent drug resistance to CDK4/6 inhibitors
Abstract To better understand drug resistance mechanisms to CDK4/6 inhibitors and inform precision medicine, we analyze real-world multi-omics data from 400 HR+/HER2- metastatic breast cancer patients treated with CDK4/6 inhibitors plus endocrine therapies, including 200 pre-treatment and 227 post-p...
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-55914-x |
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| Summary: | Abstract To better understand drug resistance mechanisms to CDK4/6 inhibitors and inform precision medicine, we analyze real-world multi-omics data from 400 HR+/HER2- metastatic breast cancer patients treated with CDK4/6 inhibitors plus endocrine therapies, including 200 pre-treatment and 227 post-progression samples. The prevalences of ESR1 and RB1 alterations significantly increase in post-progression samples. Integrative clustering analysis identifies three subgroups harboring different resistance mechanisms: ER driven, ER co-driven and ER independent. The ER independent subgroup, growing from 5% pre-treatment to 21% post-progression, is characterized by down-regulated estrogen signaling and enrichment of resistance markers including TP53 mutations, CCNE1 over-expression and Her2/Basal subtypes. Trajectory inference analyses identify a pseudotime variable strongly correlated with ER independence and disease progression; and revealed bifurcated evolutionary trajectories for ER-independent vs. ER-dependent drug resistance mechanisms. Machine learning models predict therapeutic dependency on ESR1 and CDK4 among ER-dependent tumors and CDK2 dependency among ER-independent tumors, confirmed by experimental validation. |
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| ISSN: | 2041-1723 |