Biology subjects

Hammer, J. L.

Publications and source records attributed to Hammer, J. L..

3 recordsLinked to original sources

bioRxiv2026

A Dynamically Regulated Designed IL-2 for Tumor-localized Signaling

Cytokines mediate cell-cell communication to coordinate immune responses and hold clinical promise as immunotherapies. While natural cytokines are exquisitely regulated by immune cells, engineered cytokines have not yet matched this sophisticated regulation, limiting clinical efficacy and safety. IL-2 is an important cytokine involved in antitumor response, but its production is disrupted in the immune-suppressive tumor microenvironment. Here we designed a regulated IL-2 module, OUTSMART designed IL-2 (dIL-2), that functions robustly in solid tumors and reduces systemic activity that could trigger toxicity: we computationally redesigned IL-2 to have a novel topology that increases stability, preserves native IL-2R{beta}{gamma} interfaces, ablates IL-2R binding, and delivers IL-2 signaling to CD8+ immune-effector cells. This designed cytokine is genetically regulated by a T cell activation-responsive promoter, allowing dynamic production from chimeric antigen receptor (CAR) T cells. Like wild type IL-2, OUTSMART dIL-2 drives T cell proliferation and enhances effector function. Unlike wild type IL-2, it preferentially stimulates CD8+ T cells and Natural Killer (NK) cells, avoids T regulatory cells, and stimulates CAR-T proliferation while preserving stemness, achieving durable tumor elimination in two solid tumor animal models. These design principles can be applied to create other dynamically-regulated cytokine systems that address mechanisms of complex disease.

synthetic biology↗

bioRxiv2026

A Rationally Designed Transgene Drives CAR T Functional Persistence and Durable Regression of Solid Tumors

The eradication of solid tumors by chimeric antigen receptor (CAR) T cells requires dynamic therapies capable of outlasting an immune suppressive tumor microenvironment (TME). However, biological barriers--including rapid exhaustion, poor expansion, and loss of stem-like memory--quickly neutralize these therapies. Because single-technology interventions often introduce unacceptable tradeoffs between efficacy and safety, durable remission demands a paradigm where multiple engineered solutions work in concert. To holistically address these mechanisms, we rationally designed a single-vector transgene that intrinsically drives CAR T functional persistence. The platform integrates four synergistic technologies: a high-avidity mesothelin (MSLN)-targeting CAR optimized to resist shed decoy antigens, a T-cell activation-responsive promoter (OUTLAST OP1) resisting exhaustion, a CD8-targeted designed IL-2 cytokine (OUTSMART dIL-2) driving intratumoral CAR-T expansion, and an EGFRopt safety switch. In lung and ovarian cancer models, this rational integration was required to drive antigen-dependent T cell expansion and eradicate established tumors at extremely low CAR T doses. Furthermore, engineered cells established a self-renewing pool of stem-like memory T cells capable of rejecting tumor rechallenge months later. Ultimately, this work demonstrates that intrinsic T cell dysfunction and extrinsic tumor-derived barriers can be simultaneously overcome by integrating synergistic technologies.

synthetic biology↗

bioRxiv2024

Density-based optimization for unbiased, reproducible clustering applied to single molecule localization microscopy

Single molecule localization microscopy (SMLM) has provided insight into the spatial organization of molecules at length scales below the diffraction limit of visible light. In SMLM data, density-based clustering approaches have proven to be valuable tools for probing the nanoscale structure of biological molecules, although little guidance is available for evaluating the accuracy of these results, which are often strongly dependent on user-input parameters. Here, we develop an efficient implementation of density-based cluster validation (DBCV) that can quantitatively evaluate clustering performance in SMLM-sized datasets without ground truth knowledge. We demonstrate that maximizing DBCV scores accurately identifies ground truth clustering in noisy, simulated datasets. By coupling DBCV score maximization with Bayesian optimization, we outline an optimization method, DBOpt, that selects unbiased input parameters for density-based clustering algorithms. We demonstrate that optimal input parameters can be selected for popular algorithms (DBSCAN, HDBSCAN, OPTICS) with minimal user input. Lastly, we show that DBOpt reports accurate feature sizes in 2D and 3D experimental datasets. Taken together, we propose an analysis pipeline that can be applied to a diverse array of experimental data that will improve the integrity and quality of cluster analyses in the broader scientific community.

biophysics↗