title: The Cancer Stem Cell Fraction in Hierarchically Organized Tumors Can Be Estimated Using Mathematical Modeling and Patient-Specific Treatment Trajectories.
abstract: Many tumors are hierarchically organized and driven by a subpopulation of tumor-initiating cells (TIC), or cancer stem cells. TICs are uniquely capable of recapitulating the tumor and are thought to be highly resistant to radio- and chemotherapy. Macroscopic patterns of tumor expansion before treatment and tumor regression during treatment are tied to the dynamics of TICs. Until now, the quantitative information about the fraction of TICs from macroscopic tumor burden trajectories could not be inferred. In this study, we generated a quantitative method based on a mathematical model that describes hierarchically organized tumor dynamics and patient-derived tumor burden information. The method identifies two characteristic equilibrium TIC regimes during expansion and regression. We show that tumor expansion and regression curves can be leveraged to infer estimates of the TIC fraction in individual patients at detection and after continued therapy. Furthermore, our method is parameter-free; it solely requires the knowledge of a patient's tumor burden over multiple time points to reveal microscopic properties of the malignancy. We demonstrate proof of concept in the case of chronic myeloid leukemia (CML), wherein our model recapitulated the clinical history of the disease in two independent patient cohorts. On the basis of patient-specific treatment responses in CML, we predict that after one year of targeted treatment, the fraction of TICs increases 100-fold and continues to increase up to 1,000-fold after 5 years of treatment. Our novel framework may significantly influence the implementation of personalized treatment strategies and has the potential for rapid translation into the clinic. Cancer Res; 76(7); 1705-13. ©2016 AACR.
authors.affiliation: Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom. Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Plön, Germany.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Werner
authors.affiliation: Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida. Wolfson Centre for Mathematical Biology, University of Oxford, Oxford, United Kingdom.
authors.forename: Jacob G
authors.initials: JG
authors.lastname: Scott
authors.affiliation: Centre for Evolution and Cancer, The Institute of Cancer Research, London, United Kingdom.
authors.forename: Andrea
authors.initials: A
authors.lastname: Sottoriva
authors.affiliation: Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida.
authors.forename: Alexander R A
authors.initials: AR
authors.lastname: Anderson
authors.affiliation: Department of Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Plön, Germany.
authors.forename: Arne
authors.initials: A
authors.lastname: Traulsen
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. Program for Evolutionary Dynamics, Harvard University, Cambridge, Massachusetts. paltrock@jimmy.harvard.edu.
authors.forename: Philipp M
authors.initials: PM
authors.lastname: Altrock
mesh_terms: D006801:Humans; D008962:Models, Theoretical; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*; D014411:Neoplastic Stem Cells / Q000378:metabolism* / Q000473:pathology; D047368:Tumor Burden
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