Multi-omics prediction of axillary treatment response and tumour microenvironment alterations in lymph node-positive luminal breast cancer

Luminal breast cancer (BC) with axillary lymph node (ALN) metastasis is typically treated with neoadjuvant chemotherapy (NAC). Theoretically, patients who achieve pathological lymph node complete response after NAC can be exempted from ALN dissection and even have the possibility of being spared axillary surgery. However, there is no effective way to preoperatively assess whether a metastatic ALN achieved pathological lymph node complete response (pLCR) after NAC. Therefore, we retrospectively collected imaging, clinical, and pathological data from two centres, built a multi-omic model to predict pLCR, and validated its accuracy and clinical applicability. We identified 12 radiomic and four clinicopathological features for model construction; the areas under the curve for training and validation cohorts were 0.853 and 0.805, respectively. Subsequently, single-cell RNA sequencing analysis was conducted on patients with different efficacy and its association with the tumour immune microenvironment was investigated. Eleven cell clusters in 14 samples from five patients were identified with differing NAC responses; comparative analysis indicated that those with poor responses had immunosuppressive features, which provided a theoretical basis for elucidating the resistance mechanism of NAC in axillary metastatic lymph nodes. The multi-omics prediction model demonstrated good performance in predicting ALN status after NAC, offering the possibility of reducing unnecessary axillary surgery.

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Publication Details

Journal
Cell Death and Disease
Published
2025-08-04
DOI
https://doi.org/10.1038/s41419-025-07877-6
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Multi-omics prediction of axillary treatment response and tumour microenvironment alterations in lymph node-positive luminal breast cancer

Haibo Wang, Teng Ma, Zhe Zhou, Xiangping Liu et al.
Cell Death and Disease
Radiomics and Machine Learning in Medical Imaging
article

Multi-omics prediction of axillary treatment response and tumour microenvironment alterations in lymph node-positive luminal breast cancer

Haibo Wang, Teng Ma, Zhe Zhou, Xiangping Liu, Tianyi Ma, Zirui Wang, Yongmei Wang, Yan Mao
article en

Abstract

Luminal breast cancer (BC) with axillary lymph node (ALN) metastasis is typically treated with neoadjuvant chemotherapy (NAC). Theoretically, patients who achieve pathological lymph node complete response after NAC can be exempted from ALN dissection and even have the possibility of being spared axillary surgery. However, there is no effective way to preoperatively assess whether a metastatic ALN achieved pathological lymph node complete response (pLCR) after NAC. Therefore, we retrospectively collected imaging, clinical, and pathological data from two centres, built a multi-omic model to predict pLCR, and validated its accuracy and clinical applicability. We identified 12 radiomic and four clinicopathological features for model construction; the areas under the curve for training and validation cohorts were 0.853 and 0.805, respectively. Subsequently, single-cell RNA sequencing analysis was conducted on patients with different efficacy and its association with the tumour immune microenvironment was investigated. Eleven cell clusters in 14 samples from five patients were identified with differing NAC responses; comparative analysis indicated that those with poor responses had immunosuppressive features, which provided a theoretical basis for elucidating the resistance mechanism of NAC in axillary metastatic lymph nodes. The multi-omics prediction model demonstrated good performance in predicting ALN status after NAC, offering the possibility of reducing unnecessary axillary surgery.

Cell Death and DiseaseVol. 16(1)
Qingdao University (CN), Shandong University (CN), China Institute of Finance and Capital Markets (CN), Affiliated Hospital of Qingdao University (CN), Affiliated Hospital of Jining Medical University (CN)
National Natural Science Foundation of China, Qingdao University
No poverty, Good health and well-being
Openalex Percentile: Top 72%
Radiomics and Machine Learning in Medical Imaging
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