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Mathematical Oncology to Integrate Multimodal Clinical and Liquid Biopsy Data for the Prediction of Survival

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Circulating Tumor Cells

Part of the book series: Current Cancer Research ((CUCR))

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Abstract

The complex multimodality in clinical and liquid biopsy data generated by multiparametric diagnostic and tumor profiling technologies presents an exciting opportunity for developing innovative predictive mathematical models by harnessing large datasets across studies and institutional data repositories. Further, comprehensive liquid biopsy analysis with single-cell profiling provides multiscale data on the morphology, genomics, and proteomics of circulating tumor cells (CTCs) and tumor microenvironment cells with deeper resolution on spatiotemporal tumor biology. However, clinical datasets are frequently sparse, inconsistent, and incomplete, due to the lack of standardization and interoperability across cancer centers and healthcare systems. In liquid biopsies where specimens are oftentimes samples of convenience, datasets are often of limited scale and lacking paired clinical data. Methods for integrating multimodal oncology data are paramount for building robust models for outcomes prediction. This chapter presents methodological challenges and advances in handling missingness and sparsity, integrating multidimensional clinical and multi-omic liquid biopsy data for improving the accuracy and robustness of machine and deep learning survival prediction models. We highlight the critical role that the convergence of artificial intelligence and liquid biopsies holds for the future of therapy response and survival prediction in precision oncology.

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Acknowledgments

The authors would like to thank all the patients who participated in this study. This work is supported fully or partially by the Adelson Medical Research Foundation Multiple Myeloma Research Program No. 04-7023433 (L.N., J.M., P.K.); Breast Cancer Research Foundation No. 20-089; Novartis Pharmaceuticals Corporation (L.N., J.M., P.K.); the USC Institute of Urology (J.M.); and NCI’s USC Norris Comprehensive Cancer Center (CORE) Support 5P30CA014089-40 (P.K.).

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Authors and Affiliations

  1. Convergent Science Institute in Cancer, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA, USA

    Libere J. Ndacayisaba, Jeremy Mason & Peter Kuhn

  2. Programs in Biomedical and Biological Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Libere J. Ndacayisaba & Peter Kuhn

  3. USC Institute of Urology, Catherine & Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Jeremy Mason

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Correspondence to Peter Kuhn .

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  1. Department of Pathology and Immunology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA

    Richard J. Cote

  2. ACTC Lab, Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Athens, Greece

    Evi Lianidou

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Ndacayisaba, L.J., Mason, J., Kuhn, P. (2023). Mathematical Oncology to Integrate Multimodal Clinical and Liquid Biopsy Data for the Prediction of Survival. In: Cote, R.J., Lianidou, E. (eds) Circulating Tumor Cells. Current Cancer Research. Springer, Cham. https://doi.org/10.1007/978-3-031-22903-9_7

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