Sascha Abramson

We previously developed human CAR macrophages (CAR-M) and demonstrated redirection of macrophage anti-tumor function leading to tumor control in immunodeficient xenograft models. Here, we develop clinically relevant fully immunocompetent syngeneic models to evaluate the potential for CAR-M to remodel the tumor microenvironment (TME), induce T cell anti-tumor immunity, and sensitize solid tumors to PD1/PDL1 checkpoint inhibition. In vivo, anti-HER2 CAR-M significantly reduce tumor burden, prolong survival, remodel the TME, increase intratumoral T cell and natural killer (NK) cell infiltration, and induce antigen spreading. CAR-M therapy protects against antigen-negative relapses in a T cell dependent fashion, confirming long-term anti-tumor immunity. In HER2+ solid tumors with limited sensitivity to anti-PD1 (aPD1) monotherapy, the combination of CAR-M and aPD1 significantly improves tumor growth control, survival, and remodeling of the TME in pre-clinical models. These results demonstrate synergy between CAR-M and T cell checkpoint blockade and provide a strategy to potentially enhance response to aPD1 therapy for patients with non-responsive tumors. Anti-PD1 monotherapy shows limited efficacy against HER2+ tumors. Here, the authors show that murine CAR macrophages (CAR-M) induce tumor microenvironment remodeling, T-cell mediated immunity and synergy with PD1 blockade, improving survival in immunocompetent female-mouse models of HER2+ solid tumors.

Abstract Summary: The advent of high‐throughput combinatorial synthesis techniques in drug discovery has stimulated efforts to apply these techniques to the discovery of biomaterials. To be of practical utility, combinatorial approaches to biomaterials design require (i) the availability of parallel synthesis techniques to generate libraries of polymers, (ii) efficient assays for the rapid characterization of biorelevant material properties, and (iii) computational methods to efficiently model different biological responses in the presence of polymers. Here we report the integration of these methodologies and illustrate the potential of this approach to accelerate the development of new biomaterials. The parallel synthesis of a library of 112 biodegradable polyarylates has been reported previously. This library was used to develop efficient screening techniques to determine biorelevant polymer properties (fibrinogen adsorption, gene expression in macrophages, growth of fetal rat lung fibroblasts (RLFs)). A Surrogate (semiempirical) Model was developed (i) to determine molecular‐scale polymer properties that correlate to various biological responses, and (ii) to predict fibrinogen adsorption and RLF growth on polymeric surfaces. For 38 out of 45 polymers, the model predicted the amount of fibrinogen adsorbed correctly within the error of the experimental measurements. The growth of rat lung fibroblasts was correctly predicted by the model for 41 out of 48 polymers. The correlation factor between the model's predicted values and the experimentally determined data was 0.54 ± 0.09 and 0.69 ± 0.12 for fibrinogen adsorption and RLF growth, respectively. The results presented here demonstrate the utility of combinatorial and computational approaches for the rational design of polymers for biomedical applications. Design of the library of polyarylates, which are copolymers of a diacid and a diphenol. Chemical diversity was created by variations in the structure of the diacid (marked as “Y”) and the pendent chain (marked as “R”). magnified image Design of the library of polyarylates, which are copolymers of a diacid and a diphenol. Chemical diversity was created by variations in the structure of the diacid (marked as “Y”) and the pendent chain (marked as “R”).

We present a Surrogate (semi-empirical) model for prediction of cellular response to the surfaces of biodegradable polymers that have been designed for tissue engineering applications. The predictions of our model, when tested against experimental results, show a high degree of accuracy that is sufficient for rational design of polymeric materials for biomedical applications. The model was determined by fitting experimental data for a series of 62 polyarylates to a small number of polymer structure-based ‘molecular descriptors’ using the technique of partial least squares (PLS) regression. While PLS is commonly applied in quantitative structure activity relationship (QSAR) analysis employed in the pharmaceutical industry, this study marks the first time the technique has been extended to the problem of biomaterials discovery/design. Quantitative predictions of cellular response to six polymers (untested prior to model building) concurred with experiment within 15.8% on average. This performance compares quite favorably with the overall variation in experimental values for the library of polyarylates. Examination of the PLS ‘loadings’ reveals those structure-based features most associated with variations in the polymer performance properties, thereby providing direct guidance to the synthetic chemist in biomaterials design.

A predictive model that can correlate the chemical composition of a biomaterial with the biological response of cells that are in contact with that biomaterial would represent a major advance and would facilitate the rational design of new biomaterials. As a first step toward this goal, we report here on the use of Logical Analysis of Data (LAD) to model the effect of selected polymer properties on the growth of two different cell types, rat lung fibroblasts (RLF, a transformed cell line), and normal foreskin fibroblasts (NFF, nontransformed human cells), on 112 surfaces obtained from a combinatorially designed library of polymers. LAD is a knowledge extraction methodology, based on using combinatorics, optimization, and Boolean logic. LAD was trained on a subset of 62 polymers and was then used to predict cell growth on 50 previously untested polymers. Experimental validation indicated that LAD correctly predicted the high and low cell growth polymers and found optimal ranges for polymer chemical composition, surface chemistry, and bulk properties. Particularly noteworthy is that LAD correctly identified high-performing polymer surfaces, which surpassed commercial tissue culture polystyrene as growth substratum for normal foreskin fibroblasts. Our results establish the feasibility of using computational modeling of cell growth on flat polymeric surfaces to identify promising "lead" polymers for applications that require either high or low cell growth.