Multi-omic machine learning predictor of breast cancer therapy response
Open accessAbstract
Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment 1 . The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy 2 . Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2 )-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery 3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.
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Sammut et al. used an ensemble machine learning model to integrate clinical, genomic, transcriptomic, and digital pathology images to predict pCR.
“In addition, Sammut et al. (19) used the ensemble machine learning model to integrate clinical, genomic, transcriptomic, and digital pathology images to predict pCR.”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [19]
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- Authors
- Sammut, S.-J., Crispin-Ortuzar, M., Chin, S.-F., Provenzano, E., Bardwell, H. A., Ma, W., Cope, W., Dariush, A., Dawson, S.-J., Abraham, J. E., Dunn, J., Hiller, L., Thomas, J., Cameron, D. A., Bartlett, J. M. S., Hayward, L., Pharoah, P. D., Markowetz, F., Rueda, O. M., Earl, H. M., & Caldas, C.
- Affiliations
- University of Cambridge (GB)
- The University of Melbourne (AU)
- University of Warwick (GB)
- Western General Hospital (GB)
- Ontario Institute for Cancer Research (CA)
- Corresponding author
- Caldas, Carlos
- ORCID
- Venue
- Nature
- Published
- 2021-12-07
- Volume
- 601
- Issue
- 7894
- Pages
- 623-629
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8791834
- OpenAlex
- W3216448540
- Cited by
- 654
- References
- 79
- Citations by year
- 2026: 133 · 2025: 186 · 2024: 153 · 2023: 132 · 2022: 50
- FWCI
- 34.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- Wellcome Trust (A29580, 106566/Z/14/Z, 20014, 106566, BRC-1215-20014)
- Cancer Research UK (A17197, 29567, 28290, A:29580, BRC-1215-20014, S_3525, A19274, A27657, 16942, 19274, 22310)
- National Institute for Health and Care Research (NF-SI-0515-10090, [IS-BRC-1215-20014, NF-SI-0611-10154, BRC1215-20014, CL-2018-14-503)
- University of Cambridge (BRC-1215-20014)
- Medical Research Council (MC_UU_00002/16, BRC-1215-20014)
- Trinity College, University of Cambridge
- NIHR Cambridge Biomedical Research Centre ((BRC-1215-20014), 1215-20014, BRC-1215, NF-SI-0515-10090)
- Cancer Research UK Cambridge Institute, University of Cambridge (A29580, A27657, A19274, A17197)
- H2020 European Research Council (694620)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- breast cancer, treatment response prediction, tumor microenvironment, multi-omics integration, pathological complete response, residual disease, tumor mutational burden, copy number alterations, tumour proliferation, immune cell infiltration, T cell dysfunction, T-cell exclusion, HER2-targeted therapy, chemotherapy, pre-treatment biopsy, digital pathology, transcriptomics, genomic profiling
- MeSH terms
- Machine Learning, Breast Neoplasms, Female, Humans, Ecosystem, Neoadjuvant Therapy, Genomics, Tumor Microenvironment
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