Biology subjects

Gardner, H.

Publications and source records attributed to Gardner, H..

3 recordsLinked to original sources

bioRxiv2025

The shared selection landscape of dog and human cancers

Cancers in pet dogs are prevalent, progress rapidly, and closely resemble human cancers, positioning them as powerful models for precision oncology. While genetic drivers of human cancer often transcend histologic boundaries, most comparative studies have focused on matched cancer types, leaving the broader scope of genomic similarity unresolved. We performed the first exome-wide, histology-agnostic comparison of canine and human cancers, analyzing 429 dog and 14,966 human tumors across 39 types. Mutational signatures and genes under selection are widely shared between species, and cancer types are as genomically similar between species as within species, with no greater similarity within dog breeds than between breeds. Machine-learning models identify genetic features shared by dog and human tumors of different histologies, mirroring cross-histology patterns in human cancer. These findings establish dog cancer as a powerful system for genomics-informed precision oncology and support pan-cancer approaches to discover translationally relevant models beyond histologic classification.

cancer biology↗

bioRxiv2022

Design of a systemic small molecule clinical STING agonist using physics-based simulations and artificial intelligence

The protein STING (stimulator of interferon genes) is a central regulator of the innate immune system and plays an important role in antitumor immunity by inducing the production of cytokines such as type I interferon (IFN). Activation of STING stems from the selective recognition of endogenous cyclic dinucleotides (CDNs) by the large, polar, and flexible binding site, thus posing challenges to the design of small molecule agonists with drug-like physicochemical properties. In this work we present the design of SNX281, a small molecule STING agonist that functions through a unique self-dimerizing mechanism in the STING binding site, where the ligand dimer approximates the size and shape of a cyclic dinucleotide while maintaining drug-like small molecule properties. SNX281 exhibits systemic exposure, STING-mediated cytokine release, strong induction of type I IFN, potent in vivo antitumor activity, durable immune memory, and single-dose tumor elimination in mouse models via a Cmax-driven pharmacologic response. Bespoke computational methods - a combination of quantum mechanics, molecular dynamics, binding free energy simulations, and artificial intelligence - were developed during the course of the project to design SNX281 by explicitly accounting for the unique self-dimerization mechanism and the large-scale conformational change of the STING protein upon activation. Over the course of the project, we explored millions of virtual molecules while synthesizing and testing only 208 molecules in the lab. This work highlights the value of a multifaceted computationally-driven approach anchored by methods tailored to address target-specific problems encountered along the project progression from initial hit to the clinic.

cancer biology↗

bioRxiv2017

Detecting T cell receptor rearrangements in silico from non-targeted DNA-sequencing (WGS/WES)

To better understand the composition of heterogeneous tissue samples used in generating large genomic datasets, we developed a method for estimating the abundance of T cells within the cellular population. Somatic recombination of chromosomal DNA in T cells creates a vast repertoire of structurally divergent T cell receptors (TCRs) that recognize an array of non-self proteins. It also generates a genomic signature by which TCR sequences can be distinguished from other cell types in non-targeted NGS genomic data. Here we leverage this signature to extract reads with rearranged TCR sequences from a non-targeted population, such as whole genome sequencing (WGS) or whole exome sequencing (WES) datasets. We isolate and confirm T cell rearranged reads from the remainder of the genome (99.9%), accurately estimate relative T cell abundance within a cellular population, and provide a snapshot of the T cell receptor repertoire. This approach is unique from available TCR software options that focus on examining the overall diversity of the TCR repertoire and require prior amplification or selection of this region before sequencing, and has particular utility in immunoscoring clinical patient samples in situations where genomic data exists and other approaches are unavailable.

bioinformatics↗