Multi-omic machine learning predictor of breast cancer therapy response

Stephen-John Sammut, Mireia Crispin-Ortuzar, Suet-Feung Chin, Elena Provenzano, Helen A. Bardwell, Wenxin Ma, Wei Cope, Ali Dariush, Sarah-Jane Dawson, Jean E. Abraham, Janet Dunn, Louise Hiller, Jeremy Thomas, David A. Cameron, John M. S. Bartlett, Larry Hayward, Paul D. Pharoah, Florian Markowetz, Oscar M. Rueda, Helena M. Earl and Carlos Caldas ()
Additional contact information
Stephen-John Sammut: Li Ka Shing Centre
Mireia Crispin-Ortuzar: Li Ka Shing Centre
Suet-Feung Chin: Li Ka Shing Centre
Elena Provenzano: University of Cambridge and Cambridge University Hospitals NHS Foundation Trust
Helen A. Bardwell: Li Ka Shing Centre
Wenxin Ma: University of Cambridge
Wei Cope: Li Ka Shing Centre
Ali Dariush: Li Ka Shing Centre
Sarah-Jane Dawson: Peter MacCallum Cancer Centre
Jean E. Abraham: University of Cambridge
Janet Dunn: University of Warwick
Louise Hiller: University of Warwick
Jeremy Thomas: Western General Hospital
David A. Cameron: Western General Hospital
John M. S. Bartlett: Western General Hospital
Larry Hayward: Western General Hospital
Paul D. Pharoah: University of Cambridge and Cambridge University Hospitals NHS Foundation Trust
Florian Markowetz: Li Ka Shing Centre
Oscar M. Rueda: Li Ka Shing Centre
Helena M. Earl: University of Cambridge
Carlos Caldas: Li Ka Shing Centre

Nature, 2022, vol. 601, issue 7894, 623-629

Abstract: Abstract Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. 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 surgery3 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.

Date: 2022
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DOI: 10.1038/s41586-021-04278-5

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