Understanding the interaction between radiation and systemic therapies

(e.g. immune therapies)

Modeling radiation therapy in combination with systemic therapies

Modeling radiation therapy in combination with tyrosine kinase inhibitors

**Model Predictions for Variable Induction Periods:** Model predicted freedom from local failure (A), freedom from distant failure (B), and progression free survival (C) K-M curves for various induction lengths. The simulated treatment regimen is TKI induction, chemoradiotherapy, and adjuvant TKI maintenance. The local and distant tumor volume trajectory of the median simulated patient with an initial persistent fraction of 0.1 and initial resistant fraction of 0.01, stratified by TKI response cell subtypes, are shown in D and E respectively. Hazard ratios corresponding to the K-M curves (A, B, and C) are shown in F.

**Simulated ****in-silico**** Induction Trial:** (A) Illustration of the differential evolution of the TKI-resistant and sensitive populations for a given tumor burden between a short 2 wk. and long 12 wk. induction length. When CRT is done after a significant TKI induction period, the tumor shrinks with the targeted drug killing the TKI sensitive cells (blue), but with more TKI resistant (red) cells at the time of CRT, increasing the chance of a late TKI resistant recurrence if CRT isn’t curative. (B) Simulated FFDF K-M curves for 2 wk. versus 12 wk. induction lengths with increasing number of simulated patients. Each curve corresponds to the iteration with the median log-rank p-vale. (C) A heatmap of log rank p-values testing statistical difference between the 2 wk. versus 12 wk. FFDF K-M curves for 1000 iterations of the simulation at each sample size. (D) Histograms of the median FFDF for the 1000 iterations of the 2 wk. and 12 wk. induction simulations at each sample size. (E) Histogram of the log rank p-value between the 2 wk. versus 12 wk. induction simulations at each sample size. (F) Estimated statistical power as a function of sample size. Here statistical power was estimated as the fraction of iteration resulting in a p-value<0.05.

Modeling radiation therapy in combination with immune therapies

Simulated trial outcomes for ICI monotherapy, ICI-RT with 50% of visible disease irradiated, and ICI-RT with 90% of visible disease irradiated. RT was delivered with 8 Gy × 3, similar to NCT03482102. The observed 6, 9 and 12 months PFS in the clinical trial for ICI monotherapy are shown as green circles. Results are shown for (A) 32 patients per arm and (B) 256 patients per arm. The shaded areas signify the expected 95% confidence intervals. The sharp fall-off step after 3 months stems from the fact we assumed follow-up scans every 3 months and so no progression events are simulated before the first follow-up scan.

*Abbreviations: ICI: immune checkpoint inhibitor RT: radiation therapy*

Modeling dose to the circulating blood

We have developed a time-dependent computational method to estimate dose to circulating blood cells from radiation therapy treatment fields for any treatment site. Two independent dynamic models were implemented: one describing the spatiotemporal distribution of blood particles (BPs) in organs and the second describing the time-dependent radiation field delivery. A whole-body blood flow network based on blood volumes and flow rates from ICRP Publication 89 was simulated to produce the spatiotemporal distribution of BPs in organs across the entire body using a discrete-time Markov process. Constant or time-varying transition probabilities were applied to the Markov process and their impact on transition time was investigated. Applying this framework, the impact of treatment time and anatomical site were investigated using imaging data and dose distributions from a liver cancer and a brain cancer patient. The impact on the blood dose-volume histograms (bDVHs) was assessed using bDVH metrics (V_{0Gy}, V_{0.125Gy}, V_{0.5Gy}, D_{2%}). The simulations revealed different dose levels to the circulating blood for brain irradiation compared to liver irradiation even for similar field sizes due to the different blood flow properties of the two organs. The volume of blood receiving any dose (V_{0Gy}) after a single radiation fraction increases from 1.2% for a 1-second delivery time to 20.9% for 120-second delivery time for the brain cancer treatment, and from 10% (1s) to 48.7% (120s) for a liver cancer treatment. Other measures of the low-dose bath to the circulating blood such as the dose to small volumes of blood (D_{2%}) decreases with longer delivery time. Furthermore, we demonstrate that the bDVH is highly sensitive to changes in the treatment time, indicating that dynamic modeling of blood flow and radiation fields is necessary to evaluate dose to circulating blood cells for the assessment of radiation induced lymphopenia. The developed framework is publicly available and allows for the estimation of patient-specific dose to circulating blood cells based on organ DVHs, thus enabling the study of the impact of different treatment plans, dose rates, and fractionation schemes.