Model-based Data Analysis of Radio-sensitization by Hyperthermia in Combination with Radiotherapy
In the last decades, many improvements concerning equipment and treatment planning tools have driven radiation therapy (RT) towards precise applications of radiation doses. Remarkable progress has been made regarding geometrical precision. In contrast to these more engineering – related aspects, the biological knowledge about growth dynamics and therapy response of tumours seems to remain behind technology development. This may be a reason for the upcoming discussion of a biologically adapted RT (Thorwarth, D., 2017, Biologically adapted radiation therapy. Z Med Phys, 28, 177-183). To a certain extent, the situation for mild loco-regional hyperthermia (HT) seems to be similar. In radiation therapy, radiobiological research and extended clinical experience and trials resulted in standardized dose prescriptions and fractionation schemes, whereas for hyperthermia, the thermal dose concept is unclear. Biological treatment planning would require a quantitative framework for estimating or predicting the radio-sensitizing or supportive effects of HT. To compare different RT fractionation schemes, concepts such as Biological Equivalent Dose BED (Thorwarth, D. (2017). Biologically adapted radiation therapy. Z Med Phys, 28:177-183.) or EQD2 (which is comparing a fractionation using 2 Gy per fractions with different doses per fraction) have been developed. These handy descriptions assume a linear quadratic (LQ) relationship between the logarithm of survival and dose. Although the LQ model is questionable (especially for large doses pre fractions and large variations of dose rates; see Scheidegger S, Lutters G, and Bodis S, 2011, A LQ-based kinetic model formulation for exploring dynamics of treatment response of tumours in patients. Z. Med. Phys 21(3), 164–173.), these concepts may work for small variations of the dose per fraction around 2 Gy and a comparable range of dose rate. Following this idea, Van Leeuwen et al. (CM Van Leeuwen, J Crezee, AL Oei, NAP Franken, LJA Stalpers, A Bel, and HP Kok, 2017: 3D radiobiological evaluation of combined radiotherapy and hyperthermia treatments. Int J Hyperthermia 33(2) 160–169; CM Van Leeuwen, AL Oei, R Ten Cate, NAP Franken, A Bel, LJA Stalpers, J Crezee, and HP Kok, 2018: Measurement and analysis of the impact of time-interval, temperature and radiation dose on tumour cell survival and its application in thermoradiotherapy plan evaluation.” Int J Hyperthermia 34(1), 30–38.) have developed a similar approach by introducing an equivalent dose EQDRT which is comparing a combined hyperthermia – radiation therapy (HT-RT) with RT solely. This approach could be integrated in a treatment planning system but does not describe the dynamics of the (cellular and systemic) response outside the range for calibration. In contrast to this, the Multi-Hit Repair (MHR) Model (Scheidegger S, Fuchs HU, Zaugg K, Bodis S, Füchslin RM, 2013: Using State Variables to Model the Response of Tumour Cells to Radiation and Heat: A Novel Multi-Hit-Repair (MHR-) Approach. Computational and Mathematical Methods in Medicine, 2013, http://dx.doi.org/10.1155/2013/587543) is a fully dynamic model describing the synergistic effect of heat and radiation. This model is able to describe a large variety of observed radiobiological effects including time gap or time interval and dose rate – effects and could be used to investigate the dynamic processes leading to radio-sensitization by HT. Unfortunately, a full model calibration is still missing. An important aim of this project is the development of a framework allowing the comparison between the different models and approaches.
Previous researched demonstrated the ability of the MHR model to describe survival and Comet assay data simultaneously (Weyland, SM, Thumser-Henner P, Nytko, KJ, Rohrer Bley C, Ulzega S, Petri-Fink A, Lattuada M, Füchslin RM, Scheidegger S, 2020: Holistic View on Cell Survival and DNA Damage: How Model-Based Data Analysis Supports Exploration of Dynamics in Biological Systems, Computational and Mathematical Methods in Medicine, https://dx.doi.org/10.1155/5972594; Chaachouay H, Scheidegger S, Schulz N, Grosse N, Füchslin RM, van Loon B, Rohrer Bley C, 2015: Monitoring of DNA damage and repair kinetics during radiotherapy in vivo: a minimally invasive approach using the dog as a model. Molecular Radiation Biology / Oncology, 11, 22). This ability to reproduce the cellular response on different levels (survival as a emergent cellular response and DNA damage generation and repair as a result of interacting molecular pathways) is a remarkable achievement. One reason for this may be the fact that the MHR model is describing the biological processes on a dynamic level. However, a full calibration oft this model would require a large amount of experimental data, acquired under different dynamic conditions (dose rates, time gaps between HT and RT etc.) and by different assays (survival data alone or Comet data alone does not contain the full information needed for model calibration).
The translation of data derived by the work package WP3 and WP5 into mathematical relations valid for all dynamic combinations of RT + HT doses (possible in clinical applications) is an important step forward to biological treatment planning. Incremental complexity in dose distributions from 1) uniform, 2) heterogeneous, 3) distributions changing during sessions and during treatment series should be addressed. Key processes and dynamic patterns leading to the therapeutic effects observed by ESR1-4 and ESR15 are identified using dynamic models (Multi-Hit-Repair (MHR) or repair-pathway-models previously applied at different scales of biological systems, e.g. survival- and comet-data. The MHR-, repair pathway, and immunological response dynamic models will be used for model-based analysis of preclinical thermal enhancement data and to provide insight into dynamic tumor characteristics (WP3). This knowledge directly contributes to clinical treatment strategies or serves as precursor for descriptive models used for treatment planning (WP4) and supports monitoring strategies for HT effects and clinical evaluation in a systems medicine framework (WP5).