Predicting response to combination evofosfamide and immunotherapy under hypoxic conditions in murine models of colon cancer.

The goal of this study is to develop a mathematical model that captures the interaction between evofosfamide, immunotherapy, and the hypoxic landscape of the tumor in the treatment of tumors. Recently, we showed that evofosfamide, a hypoxia-activated prodrug, can synergistically improve treatment outcomes when combined with immunotherapy, while evofosfamide alone showed no effects in an in vivo syngeneic model of colorectal cancer. However, the mechanisms behind the interaction between the tumor microenvironment in the context of oxygenation (hypoxic, normoxic), immunotherapy, and tumor cells are not fully understood. To begin to understand this issue, we develop a system of ordinary differential equations to simulate the growth and decline of tumors and their vascularization (oxygenation) in response to treatment with evofosfamide and immunotherapy (6 combinations of scenarios). The model is calibrated to data from in vivo experiments on mice implanted with colon adenocarcinoma cells and longitudinally imaged with [18F]-fluoromisonidazole ([18F]FMISO) positron emission tomography (PET) to quantify hypoxia. The results show that evofosfamide is able to rescue the immune response and sensitize hypoxic tumors to immunotherapy. In the hypoxic scenario, evofosfamide reduces tumor burden by $ 45.07 \pm 2.55 $%, compared to immunotherapy alone, as measured by tumor volume. The model accurately predicts the temporal evolution of five different treatment scenarios, including control, hypoxic tumors that received immunotherapy, normoxic tumors that received immunotherapy, evofosfamide alone, and hypoxic tumors that received combination immunotherapy and evofosfamide. The average concordance correlation coefficient (CCC) between predicted and observed tumor volume is $ 0.86 \pm 0.05 $. Interestingly, the model values to fit those five treatment arms was unable to accurately predict the response of normoxic tumors to combination evofosfamide and immunotherapy (CCC = $ -0.064 \pm 0.003 $). However, guided by the sensitivity analysis to rank the most influential parameters on the tumor volume, we found that increasing the tumor death rate due to immunotherapy by a factor of $ 18.6 \pm 9.3 $ increases CCC of $ 0.981 \pm 0.001 $. To the best of our knowledge, this is the first study to mathematically predict and describe the increased efficacy of immunotherapy following evofosfamide.

Investigating tumor-host response dynamics in preclinical immunotherapy experiments using a stepwise mathematical modeling strategy.

Immunotherapies such as checkpoint blockade to PD1 and CTLA4 can have varied effects on individual tumors. To quantify the successes and failures of these therapeutics, we developed a stepwise mathematical modeling strategy and applied it to mouse models of colorectal and breast cancer that displayed a range of therapeutic responses. Using longitudinal tumor volume data, an exponential growth model was utilized to designate response groups for each tumor type. The exponential growth model was then extended to describe the dynamics of the quality of vasculature in the tumors via [18F] fluoromisonidazole (FMISO)-positron emission tomography (PET) data estimating tumor hypoxia over time. By calibrating the mathematical system to the PET data, several biological drivers of the observed deterioration of the vasculature were quantified. The mathematical model was then further expanded to explicitly include both the immune response and drug dosing, so that model simulations are able to systematically investigate biological hypotheses about immunotherapy failure and to generate experimentally testable predictions of immune response. The modeling results suggest elevated immune response fractions (> 30 %) in tumors unresponsive to immunotherapy is due to a functional immune response that wanes over time. This experimental-mathematical approach provides a means to evaluate dynamics of the system that could not have been explored using the data alone, including tumor aggressiveness, immune exhaustion, and immune cell functionality.

18F-FMISO PET Imaging Identifies Hypoxia and Immunosuppressive Tumor Microenvironments and Guides Targeted Evofosfamide Therapy in Tumors Refractory to PD-1 and CTLA-4 Inhibition.

PURPOSE: Hypoxia is a common characteristic of many tumor microenvironments, and it has been shown to promote suppression of antitumor immunity. Despite strong biological rationale, longitudinal correlation of hypoxia and response to immunotherapy has not been investigated. EXPERIMENTAL DESIGN: In this study, we probed the tumor and its surrounding microenvironment with 18F-FMISO PET imaging to noninvasively quantify tumor hypoxia in vivo prior to and during PD-1 and CTLA-4 checkpoint blockade in preclinical models of breast and colon cancer. RESULTS: Longitudinal imaging identified hypoxia as an early predictive biomarker of therapeutic response (prior to anatomic changes in tumor volume) with a decreasing standard uptake value (SUV) ratio in tumors that effectively respond to therapy. PET signal correlated with ex vivo markers of tumor immune response including cytokines (IFNγ, GZMB, and TNF), damage-associated molecular pattern receptors (TLR2/4), and immune cell populations (macrophages, dendritic cells, and cytotoxic T cells). Responding tumors were marked by increased inflammation that were spatially distinct from hypoxic regions, providing a mechanistic understanding of the immune signaling pathways activated. To exploit image-guided combination therapy, hypoxia signal from PET imaging was used to guide the addition of a hypoxia targeted treatment to nonresponsive tumors, which ultimately provided therapeutic synergy and rescued response as determined by longitudinal changes in tumor volume. CONCLUSIONS: The results generated from this work provide an immediately translatable paradigm for measuring and targeting hypoxia to increase response to immune checkpoint therapy and using hypoxia imaging to guide combinatory therapies.

Imaging for Response Assessment in Cancer Clinical Trials.

The use of biomarkers is integral to the routine management of cancer patients, including diagnosis of disease, clinical staging and response to therapeutic intervention. Advanced imaging metrics with computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are used to assess response during new drug development and in cancer research for predictive metrics of response. Key components and challenges to identifying an appropriate imaging biomarker are selection of integral vs integrated biomarkers, choosing an appropriate endpoint and modality, and standardization of the imaging biomarkers for cooperative and multicenter trials. Imaging biomarkers lean on the original proposed quantified metrics derived from imaging such as tumor size or longest dimension, with the most commonly implemented metrics in clinical trials coming from the Response Evaluation Criteria in Solid Tumors (RECIST) criteria, and then adapted versions such as immune-RECIST (iRECIST) and Positron Emission Tomography Response Criteria in Solid Tumors (PERCIST) for immunotherapy response and PET imaging, respectively. There have been many widely adopted biomarkers in clinical trials derived from MRI including metrics that describe cellularity and vascularity from diffusion-weighted (DW)-MRI apparent diffusion coefficient (ADC) and Dynamic Susceptibility Contrast (DSC) or dynamic contrast enhanced (DCE)-MRI (Ktrans, relative cerebral blood volume (rCBV)), respectively. Furthermore, Fluorodexoyglucose (FDG), fluorothymidine (FLT), and fluoromisonidazole (FMISO)-PET imaging, which describe molecular markers of glucose metabolism, proliferation and hypoxia have been implemented into various cancer types to assess therapeutic response to a wide variety of targeted- and chemotherapies. Recently, there have been many functional and molecular novel imaging biomarkers that are being developed that are rapidly being integrated into clinical trials (with anticipation of being implemented into clinical workflow in the future), such as artificial intelligence (AI) and machine learning computational strategies, antibody and peptide specific molecular imaging, and advanced diffusion MRI. These include prostate-specific membrane antigen (PSMA) and trastuzumab-PET, vascular tumor burden extracted from contrast-enhanced CT, diffusion kurtosis imaging, and CD8 or Granzyme B PET imaging. Further excitement surrounds theranostic procedures such as the combination of 68Ga/111In- and 177Lu-DOTATATE to use integral biomarkers to direct care and personalize therapy. However, there are many challenges in the implementation of imaging biomarkers that remains, including understand the accuracy, repeatability and reproducibility of both acquisition and analysis of these imaging biomarkers. Despite the challenges associated with the biological and technical validation of novel imaging biomarkers, a distinct roadmap has been created that is being implemented into many clinical trials to advance the development and implementation to create specific and sensitive novel imaging biomarkers of therapeutic response to continue to transform medical oncology.