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Multi-omic machine learning predictor of breast cancer therapy response

Author

Listed:
  • Stephen-John Sammut

    (Li Ka Shing Centre
    University of Cambridge
    University of Cambridge and Cambridge University Hospitals NHS Foundation Trust)

  • 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
    University of Cambridge)

  • Sarah-Jane Dawson

    (Peter MacCallum Cancer Centre
    University of Melbourne)

  • Jean E. Abraham

    (University of Cambridge
    University of Cambridge and Cambridge University Hospitals NHS Foundation Trust)

  • Janet Dunn

    (University of Warwick)

  • Louise Hiller

    (University of Warwick)

  • Jeremy Thomas

    (Western General Hospital
    Q2 Laboratory Solutions)

  • David A. Cameron

    (Western General Hospital)

  • John M. S. Bartlett

    (Western General Hospital
    Ontario Institute for Cancer Research
    University of Toronto)

  • Larry Hayward

    (Western General Hospital)

  • Paul D. Pharoah

    (University of Cambridge and Cambridge University Hospitals NHS Foundation Trust
    University of Cambridge)

  • Florian Markowetz

    (Li Ka Shing Centre)

  • Oscar M. Rueda

    (Li Ka Shing Centre
    University of Cambridge)

  • Helena M. Earl

    (University of Cambridge
    University of Cambridge and Cambridge University Hospitals NHS Foundation Trust)

  • Carlos Caldas

    (Li Ka Shing Centre
    University of Cambridge
    University of Cambridge and Cambridge University Hospitals NHS Foundation Trust)

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.

Suggested Citation

  • 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 H, 2022. "Multi-omic machine learning predictor of breast cancer therapy response," Nature, Nature, vol. 601(7894), pages 623-629, January.
  • Handle: RePEc:nat:nature:v:601:y:2022:i:7894:d:10.1038_s41586-021-04278-5
    DOI: 10.1038/s41586-021-04278-5
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    Cited by:

    1. Umberto Perron & Elena Grassi & Aikaterini Chatzipli & Marco Viviani & Emre Karakoc & Lucia Trastulla & Lorenzo M. Brochier & Claudio Isella & Eugenia R. Zanella & Hagen Klett & Ivan Molineris & Julia, 2024. "Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer patient-derived xenografts," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Mattia Rediti & Aranzazu Fernandez-Martinez & David Venet & Françoise Rothé & Katherine A. Hoadley & Joel S. Parker & Baljit Singh & Jordan D. Campbell & Karla V. Ballman & David W. Hillman & Eric P. , 2023. "Immunological and clinicopathological features predict HER2-positive breast cancer prognosis in the neoadjuvant NeoALTTO and CALGB 40601 randomized trials," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. James M. Dolezal & Andrew Srisuwananukorn & Dmitry Karpeyev & Siddhi Ramesh & Sara Kochanny & Brittany Cody & Aaron S. Mansfield & Sagar Rakshit & Radhika Bansal & Melanie C. Bois & Aaron O. Bungum & , 2022. "Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Joyce V. Lee & Filomena Housley & Christina Yau & Rachel Nakagawa & Juliane Winkler & Johanna M. Anttila & Pauliina M. Munne & Mariel Savelius & Kathleen E. Houlahan & Daniel Mark & Golzar Hemmati & G, 2022. "Combinatorial immunotherapies overcome MYC-driven immune evasion in triple negative breast cancer," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    5. Khoa A. Tran & Venkateswar Addala & Rebecca L. Johnston & David Lovell & Andrew Bradley & Lambros T. Koufariotis & Scott Wood & Sunny Z. Wu & Daniel Roden & Ghamdan Al-Eryani & Alexander Swarbrick & E, 2023. "Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    6. Yuan Gao & Sofia Ventura-Diaz & Xin Wang & Muzhen He & Zeyan Xu & Arlene Weir & Hong-Yu Zhou & Tianyu Zhang & Frederieke H. Duijnhoven & Luyi Han & Xiaomei Li & Anna D’Angelo & Valentina Longo & Zaiyi, 2024. "An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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