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Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

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  • Joshua E. Lewis

    (Georgia Institute of Technology and Emory University)

  • Melissa L. Kemp

    (Georgia Institute of Technology and Emory University)

Abstract

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

Suggested Citation

  • Joshua E. Lewis & Melissa L. Kemp, 2021. "Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22989-1
    DOI: 10.1038/s41467-021-22989-1
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    1. Yun-Tsan Chang & Pacôme Prompsy & Susanne Kimeswenger & Yi-Chien Tsai & Desislava Ignatova & Olesya Pavlova & Christoph Iselin & Lars E. French & Mitchell P. Levesque & François Kuonen & Malgorzata Bo, 2024. "MHC-I upregulation safeguards neoplastic T cells in the skin against NK cell-mediated eradication in mycosis fungoides," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    2. Cemal Erdem & Sean M. Gross & Laura M. Heiser & Marc R. Birtwistle, 2023. "MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    3. Linlin Yang & Qian Tang & Mingzhi Zhang & Yuan Tian & Xiaoxing Chen & Rui Xu & Qian Ma & Pei Guo & Chao Zhang & Da Han, 2024. "A spatially localized DNA linear classifier for cancer diagnosis," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. Senhu Wang & Lambert Zixin Li & Zhuofei Lu & Shuanglong Li & David Rehkopf, 2022. "Work Schedule Control and Allostatic Load Biomarkers: Disparities Between and Within Gender," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 163(3), pages 1249-1267, October.

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