Evaluating optimal therapy robustness by virtual expansion of a sample population, with a case study in cancer immunotherapy

Abstract

Cancer is a highly heterogeneous disease, exhibiting spatial and temporal variations that pose challenges for designing robust therapies. Here, we propose the VEPART (Virtual Expansion of Populations for Analyzing Robustness of Therapies) technique as a platform that integrates experimental data, mathematical modeling, and statistical analyses for identifying robust optimal treatment protocols. VEPART begins with time course experimental data for a sample population, and a mathematical model fit to aggregate data from that sample population. Using nonparametric statistics, the sample population is amplified and used to create a large number of virtual populations. At the final step of VEPART, robustness is assessed by identifying and analyzing the optimal therapy (perhaps restricted to a set of clinically realizable protocols) across each virtual population. As proof of concept, we have applied the VEPART method to study the robustness of treatment response in a mouse model of melanoma subject to treatment with immunostimulatory oncolytic viruses and dendritic cell vaccines. Our analysis (i) showed that every scheduling variant of the experimentally used treatment protocol is fragile (nonrobust) and (ii) discovered an alternative region of dosing space (lower oncolytic virus dose, higher dendritic cell dose) for which a robust optimal protocol exists.robust therapies | cancer treatment | mathematical modeling | virotherapy | immunotherapy H eterogeneity is a defining feature of cancer (1, 2). Interpatient heterogeneity manifests clinically in variable disease progression and treatment response between patients with the same diagnosis, whereas intrapatient heterogeneity describes variations that exist between tumor cells in a single patient. Intrapatient heterogeneity can be broken down further into intratumor heterogeneity, intrametastatic heterogeneity, intermetastatic heterogeneity, and temporal heterogeneity (2, 3). Intratumor heterogeneity is evident through the presence of multiple genetic subclones within a primary tumor (2), which have even been shown to exist in spatially distinct regions of the primary tumor (4). Intrametastatic heterogeneity is similar to intratumor heterogeneity, but describes heterogeneity within a single metastatic lesion instead of within the primary tumor (2). Intermetastatic heterogeneity, on the other hand, describes variations in subclones between different metastases in the same patient (2). Finally, temporal heterogeneity is defined as changes that take place in the tumor over time, whether they are a result of genomic instability, natural selection, non-Darwinian evolution, or selective pressures imposed by treatment (4-6). Note that, in each case, heterogeneity need not be genetic but may also be epigenetic, phenotypic, or microenvironmental (2, 7, 8).For decades, cancer patients have been treated using standard of care, meaning they receive the best known treatment that has been deemed as efficacious and safe in epidemiological studies (9). However, in the face of such int...

Modeling vaccine-induced immunotherapy: treatment scheduling and robustness with virtual mice cohorts

“…al. [2]. Three key model parameters were chosen: tumor cell net proliferation (r), tumor cell transfection rate (β), and tumor cell killing rate by the T cells (k), and the generated distributions for each of these parameters are shown in Figure 2.…”

“…The model flowchart and equations are shown in Figure 4, and model parameters are listed in Table 1. [1], transfected cells dI/dt [2], the vaccine dV/dt [3], and T-cells dT/dt [4].…”

Optimising Hydrogel Release Profiles for Viro-Immunotherapy Using Oncolytic Adenovirus Expressing IL-12 and GM-CSF with Immature Dendritic Cells

“…For combined immunotherapy and chemotherapy, de Pillis et al [32] developed an ODE system that predicted which treatment protocols would result in tumour elimination. There are also a handful of examples where deterministic models have also been used to improve chemotherapy delivery from sustained-release systems [33,34], Mathematical modelling has also been used to determine optimal dosage protocols for oncolytic virotherapy and immunotherapy [35][36][37][38]. Using ODEs calibrated with experimental data, Barish et al [38] demonstrated that lower oncolytic virus doses combined with higher dendritic cell doses resulted in an optimal robust therapy.…”

Modeling three-dimensional invasive solid tumor growth in heterogeneous microenvironment under chemotherapy

“…For example, the effects of spatial heterogeneity in drug concentration 42 , vascular structure and heterogeneous host environment 7,43 , cell packing density 44 , intrinsic heterogeneity in cell phenotypes and cell cycles [45][46][47][48][49][50] on the effectiveness of treatment and acquired drug resistance 51,52 have been systematically studied. Computational tools for treatment optimization have been devised [53][54][55][56] and data-based platform has been developed to assess robustness of treatment 57 .…”