Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data.

Chimeric antigen receptor (CAR) T-cell therapy has shown promise in the treatment of haematological cancers and is currently being investigated for solid tumours, including high-grade glioma brain tumours. There is a desperate need to quantitatively study the factors that contribute to the efficacy of CAR T-cell therapy in solid tumours. In this work, we use a mathematical model of predator-prey dynamics to explore the kinetics of CAR T-cell killing in glioma: the Chimeric Antigen Receptor T-cell treatment Response in GliOma (CARRGO) model. The model includes rates of cancer cell proliferation, CAR T-cell killing, proliferation, exhaustion, and persistence. We use patient-derived and engineered cancer cell lines with an in vitro real-time cell analyser to parametrize the CARRGO model. We observe that CAR T-cell dose correlates inversely with the killing rate and correlates directly with the net rate of proliferation and exhaustion. This suggests that at a lower dose of CAR T-cells, individual T-cells kill more cancer cells but become more exhausted when compared with higher doses. Furthermore, the exhaustion rate was observed to increase significantly with tumour growth rate and was dependent on level of antigen expression. The CARRGO model highlights nonlinear dynamics involved in CAR T-cell therapy and provides novel insights into the kinetics of CAR T-cell killing. The model suggests that CAR T-cell treatment may be tailored to individual tumour characteristics including tumour growth rate and antigen level to maximize therapeutic benefit

Quantitative Evaluation of Intraventricular Delivery of Therapeutic Neural Stem Cells to Orthotopic Glioma

Neural stem cells (NSCs) are inherently tumor-tropic, which allows them to migrate through normal tissue and selectively localize to invasive tumor sites in the brain. We have engineered a clonal, immortalized allogeneic NSC line (HB1.F3.CD21; CD-NSCs) that maintains its stem-like properties, a normal karyotype and is HLA Class II negative. It is genetically and functionally stable over time and multiple passages, and has demonstrated safety in phase I glioma trials. These properties enable the production of an “off-the-shelf” therapy that can be readily available for patient treatment. There are multiple factors contributing to stem cell tumor-tropism, and much remains to be elucidated. The route of NSC delivery and the distribution of NSCs at tumor sites are key factors in the development of effective cell-based therapies. Stem cells can be engineered to deliver and/or produce many different therapeutic agents, including prodrug activating enzymes (which locally convert systemically administered prodrugs to active chemotherapeutic agents); oncolytic viruses; tumor-targeted antibodies; therapeutic nanoparticles; and extracellular vesicles that contain therapeutic oligonucleotides. By targeting these therapeutics selectively to tumor foci, we aim to minimize toxicity to normal tissues and maximize therapeutic benefits. In this manuscript, we demonstrate that NSCs administered via intracerebral/ventricular (IVEN) routes can migrate efficiently toward single or multiple tumor foci. IVEN delivery will enable repeat administrations for patients through an Ommaya reservoir, potentially resulting in improved therapeutic outcomes. In our preclinical studies using various glioma lines, we have quantified NSC migration and distribution in mouse brains and have found robust migration of our clinically relevant HB1.F3.CD21 NSC line toward invasive tumor foci, irrespective of their origin. These results establish proof-of-concept and demonstrate the potential of developing a multitude of therapeutic options using modified NSCs

Targeting Wnt signaling for improved glioma immunotherapy

IntroductionDespite aggressive standard-of-care therapy, including surgery, radiation, and chemotherapy, glioblastoma recurrence is almost inevitable and uniformly lethal. Activation of glioma-intrinsic Wnt/β-catenin signaling is associated with a poor prognosis and the proliferation of glioma stem-like cells, leading to malignant transformation and tumor progression. Impressive results in a subset of cancers have been obtained using immunotherapies including anti-CTLA4, anti-PD-1, and anti-PD-L1 or chimeric antigen receptor (CAR) T cell therapies. However, the heterogeneity of tumors, low mutational burden, single antigen targeting, and associated antigen escape contribute to non-responsiveness and potential tumor recurrence despite these therapeutic efforts. In the current study, we determined the effects of the small molecule, highly specific Wnt/CBP (CREB Binding Protein)/β-catenin antagonist ICG-001, on glioma tumor cells and the tumor microenvironment (TME)–including its effect on immune cell infiltration, blood vessel decompression, and metabolic changes.MethodsUsing multiple glioma patient-derived xenografts cell lines and murine tumors (GL261, K-Luc), we demonstrated in vitro cytostatic effects and a switch from proliferation to differentiation after treatment with ICG-001.ResultsIn these glioma cell lines, we further demonstrated that ICG-001 downregulated the CBP/β-catenin target gene Survivin/BIRC5–a hallmark of Wnt/CBP/β-catenin inhibition. We found that in a syngeneic mouse model of glioma (K-luc), ICG-001 treatment enhanced tumor infiltration by CD3+ and CD8+ cells with increased expression of the vascular endothelial marker CD31 (PECAM-1). We also observed differential gene expression and induced immune cell infiltration in tumors pretreated with ICG-001 and then treated with CAR T cells as compared with single treatment groups or when ICG-001 treatment was administered after CAR T cell therapy.DiscussionWe conclude that specific Wnt/CBP/β-catenin antagonism results in pleotropic changes in the glioma TME, including glioma stem cell differentiation, modulation of the stroma, and immune cell activation and recruitment, thereby suggesting a possible role for enhancing immunotherapy in glioma patients

Comparison of CD38-Targeted α- Versus β-Radionuclide Therapy of Disseminated Multiple Myeloma in an Animal Model.

Targeted therapies for multiple myeloma (MM) include the anti-CD38 antibody daratumumab, which, in addition to its inherent cytotoxicity, can be radiolabeled with tracers for imaging and with β- and α-emitter radionuclides for radioimmunotherapy. Methods: We have compared the potential therapeutic efficacy of β- versus α-emitter radioimmunotherapy using radiolabeled DOTA-daratumumab in a preclinical model of disseminated multiple myeloma. Multiple dose levels were investigated to find the dose with the highest efficacy and lowest toxicity. Results: In a dose–response study with the β-emitter 177Lu-DOTA-daratumumab, the lowest tested dose, 1.85 MBq, extended survival from 37 to 47 d but did not delay tumor growth. Doses of 3.7 and 7.4 MBq extended survival to 55 and 58 d, respectively, causing a small equivalent delay in tumor growth, followed by regrowth. The higher dose, 11.1 MBq, eradicated the tumor but had no effect on survival compared with untreated controls, because of whole-body toxicity. In contrast, the α-emitter 225Ac-DOTA-daratumumab had a dose-dependent effect, in which 0.925, 1.85, and 3.7 kBq increased survival, compared with untreated controls (35 d), to 47, 52, and 73 d, respectively, with a significant delay in tumor growth for all 3 doses. Higher doses of 11.1 and 22.2 kBq resulted in equivalent survival to 82 d but with significant whole-body toxicity. Parallel studies with untargeted 225Ac-DOTA-trastuzumab conferred no improvement over untreated controls and resulted in whole-body toxicity. Conclusion: We conclude, and mathematic modeling confirms, that maximal biologic doses were achieved by targeted α-therapy and demonstrated 225Ac to be superior to 177Lu in delaying tumor growth and decreasing whole-body toxicity

Mathematical modeling of therapeutic neural stem cell migration in mouse brain with and without brain tumors.

Neural stem cells (NSCs) offer a potential solution to treating brain tumors. This is because NSCs can circumvent the blood-brain barrier and migrate to areas of damage in the central nervous system, including tumors, stroke, and wound injuries. However, for successful clinical application of NSC treatment, a sufficient number of viable cells must reach the diseased or damaged area(s) in the brain, and evidence suggests that it may be affected by the paths the NSCs take through the brain, as well as the locations of tumors. To study the NSC migration in brain, we develop a mathematical model of therapeutic NSC migration towards brain tumor, that provides a low cost platform to investigate NSC treatment efficacy. Our model is an extension of the model developed in Rockne et al. (PLoS ONE 13, e0199967, 2018) that considers NSC migration in non-tumor bearing naive mouse brain. Here we modify the model in Rockne et al. in three ways: (i) we consider three-dimensional mouse brain geometry, (ii) we add chemotaxis to model the tumor-tropic nature of NSCs into tumor sites, and (iii) we model stochasticity of migration speed and chemosensitivity. The proposed model is used to study migration patterns of NSCs to sites of tumors for different injection strategies, in particular, intranasal and intracerebral delivery. We observe that intracerebral injection results in more NSCs arriving at the tumor site(s), but the relative fraction of NSCs depends on the location of injection relative to the target site(s). On the other hand, intranasal injection results in fewer NSCs at the tumor site, but yields a more even distribution of NSCs within and around the target tumor site(s)

Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer

While targeted therapies exist for human epidermal growth factor receptor 2 positive (HER2 +) breast cancer, HER2 + patients do not always respond to therapy. We present the results of utilizing a biophysical mathematical model to predict tumor response for two HER2 + breast cancer patients treated with the same therapeutic regimen but who achieved different treatment outcomes. Quantitative data from magnetic resonance imaging (MRI) and 64Cu-DOTA-trastuzumab positron emission tomography (PET) are used to estimate tumor density, perfusion, and distribution of HER2-targeted antibodies for each individual patient. MRI and PET data are collected prior to therapy, and follow-up MRI scans are acquired at a midpoint in therapy. Given these data types, we align the data sets to a common image space to enable model calibration. Once the model is parameterized with these data, we forecast treatment response with and without HER2-targeted therapy. By incorporating targeted therapy into the model, the resulting predictions are able to distinguish between the two different patient responses, increasing the difference in tumor volume change between the two patients by > 40%. This work provides a proof-of-concept strategy for processing and integrating PET and MRI modalities into a predictive, clinical-mathematical framework to provide patient-specific predictions of HER2 + treatment response

Data driven model discovery and interpretation for CAR T-cell killing using sparse identification and latent variables

In the development of cell-based cancer therapies, quantitative mathematical models of cellular interactions are instrumental in understanding treatment efficacy. Efforts to validate and interpret mathematical models of cancer cell growth and death hinge first on proposing a precise mathematical model, then analyzing experimental data in the context of the chosen model. In this work, we present the first application of the sparse identification of non-linear dynamics (SINDy) algorithm to a real biological system in order discover cell-cell interaction dynamics in in vitro experimental data, using chimeric antigen receptor (CAR) T-cells and patient-derived glioblastoma cells. By combining the techniques of latent variable analysis and SINDy, we infer key aspects of the interaction dynamics of CAR T-cell populations and cancer. Importantly, we show how the model terms can be interpreted biologically in relation to different CAR T-cell functional responses, single or double CAR T-cell-cancer cell binding models, and density-dependent growth dynamics in either of the CAR T-cell or cancer cell populations. We show how this data-driven model-discovery based approach provides unique insight into CAR T-cell dynamics when compared to an established model-first approach. These results demonstrate the potential for SINDy to improve the implementation and efficacy of CAR T-cell therapy in the clinic through an improved understanding of CAR T-cell dynamics