Yusuke Suita

Background: Bispecific T-cell Engagers (TCEs) targeting B7H3 (CD276) show promise for solid tumors but are limited by systemic toxicities and poor tumor penetration. Intratumoral (IT) delivery is proposed as a solution, but the safety and spatial pharmacodynamics (PD) remain poorly defined in these malignancies. Spontaneous canine tumors serve as a highly translatable model for human therapeutic development due to its clinical, genetic, and immunological similarities to human patients. This study evaluates the feasibility of an IT-delivered B7H3:CD3 TCE in a trial that enrolls companion dogs with solid tumors. Methods: We engineered a canine B7H3:CD3 TCE and validated its ability to induce T-cell activation and T-cell mediated cytotoxicity in vitro on several B7H3-expressing canine tumor cell lines. Two STS canine patients received intratumoral columnar injections of the TCE and saline (internal control) at fixed distance of 1.5cm using a custom-engineered multi-needle assembly. Safety was evaluated by physical examinations and hematological and biochemical changes in peripheral blood. PD response was analyzed by H&E and immunohistochemistry. Results: In vitro assays validated the cytotoxicity of the B7H3:CD3 TCE on B7H3+ canine tumor cell lines. TCE IT administration (7.83 μg/ 148.2 pmol) was well tolerated with no adverse events greater than Grade 1 and no evidence of systemic cytokine release or organ toxicity. Immunohistochemistry of tumors collected 7 days after TCE administration revealed a significant five-fold increase in CD3+ T-cell density at the TCE injection site (within 0.5 cm radius) compared to internal saline controls. Conclusions: This study demonstrated the feasibility of evaluating pharmacodynamic response to IT delivery of B7H3:CD3 TCE, namely local T-cell accumulation. T-cell localization around the TCE injection site supports our hypothesis that effective IT immunotherapy might require enhanced volumetric coverage using multi-needle injections and/or co-stimulatory strategies to convert T-cell localization into a robust, sustained anti-tumor response.

Hydrogel biomaterials offer great promise for three-dimensional cell culture and therapeutic delivery. Despite many successes, challenges persist in that gels formed from natural proteins are only marginally tunable whereas those derived from synthetic polymers lack intrinsic bioinstructivity. Toward the creation of biomaterials with both excellent biocompatibility and customizability, recombinant protein–based hydrogels have emerged as molecularly defined and user-programmable platforms that mimic the proteinaceous nature of the extracellular matrix. Here, we introduce PhoCoil, a dynamically tunable recombinant hydrogel formed from a single protein component with unique multistimuli responsiveness. Physical cross-linking through coiled-coil interactions promotes rapid shear-thinning and self-healing behavior, rendering the gel injectable, whereas an included photodegradable motif affords on-demand network dissolution via visible light. PhoCoil gel photodegradation can be spatiotemporally and lithographically controlled in a dose-dependent manner, through complex tissue, and without harm to encapsulated cells. We anticipate that PhoCoil will further enable applications in tissue engineering and regenerative medicine.

Epigenetic mechanisms play a crucial role in driving transcript expression and shaping the phenotypic plasticity of glioblastoma stem cells (GSCs), contributing to tumor heterogeneity and therapeutic resistance. These mechanisms dynamically regulate the expression of key oncogenic and stemness-associated genes, enabling GSCs to adapt to environmental cues and evade targeted therapies. Importantly, epigenetic reprogramming allows GSCs to transition between cellular states, including therapy-resistant mesenchymal-like phenotypes, underscoring the need for epigenetic-targeting strategies to disrupt these adaptive processes. Understanding these epigenetic drivers of gene expression provides a foundation for novel therapeutic interventions aimed at eradicating GSCs and improving glioblastoma outcomes. Using machine learning (ML), we employ cross-patient prediction of transcript expression in GSCs by combining epigenetic features from various sources, including ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq. We investigate different ML and deep learning (DL) models for this task and ultimately build our final pipeline using XGBoost. The model trained on one patient generalizes to other 11 patients with high performance. Notably, H3K27Ac alone from a single patient is sufficient to predict gene expression in all 11 patients. Furthermore, the distribution of H3K27Ac peaks across the genomes of all patients is remarkably similar. These findings suggest that GSCs share a common distributional pattern of enhancer activity characterized by H3K27Ac, which can be utilized to predict gene expression in GSCs across patients. In summary, while GSCs are known for their transcriptomic and phenotypic heterogeneity, we propose that they share a common epigenetic pattern of enhancer activation that defines their underlying transcriptomic expression pattern. This pattern can predict gene expression across patient samples, providing valuable insights into the biology of GSCs.

Cancer cells show remarkable plasticity and can switch lineages in response to the tumor microenvironment. Cellular plasticity drives invasiveness and metastasis and helps cancer cells to evade therapy by developing resistance to radiation and cytotoxic chemotherapy. Increased understanding of cell fate determination through epigenetic reprogramming is critical to discover how cancer cells achieve transcriptomic and phenotypic plasticity. Glioblastoma is a perfect example of cancer evolution where cells retain an inherent level of plasticity through activation or maintenance of progenitor developmental programs. However, the principles governing epigenetic drivers of cellular plasticity in glioblastoma remain poorly understood. Here, using machine learning (ML) we employ cross-patient prediction of transcript expression using a combination of epigenetic features (ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq) of glioblastoma stem cells (GSCs). We investigate different ML and deep learning (DL) models for this task and build our final pipeline using XGBoost. The model trained on one patient generalizes to another one suggesting that the epigenetic signals governing gene transcription are consistent across patients even if GSCs can be very different. We demonstrate that H3K27Ac is the epigenetic feature providing the most significant contribution to cross-patient prediction of gene expression. In addition, using H3K27Ac signals from patients-derived GSCs, we can predict gene expression of human neural crest stem cells suggesting a shared developmental epigenetic trajectory between subpopulations of these malignant and benign stem cells. Our cross-patient ML/DL models determine weighted patterns of influence of epigenetic marks on gene expression across patients with glioblastoma and between GSCs and neural crest stem cells. We propose that broader application of this analysis could reshape our view of glioblastoma tumor evolution and inform the design of new epigenetic targeting therapies.

Nanomedicines have shown great potential in cancer therapy; in particular, the combination of chemotherapy and immunotherapy (namely chemoimmunotherapy) that is revolutionizing cancer treatment. Currently, most nanomedicines for chemoimmunotherapy are still in preclinical and clinical trials. Lipid-based nanoparticles, the most widely used nanomedicine platform in cancer therapy, is a promising delivery platform for chemoimmunotherapy. In this review, we introduce the commonly used immunotherapy agents and discuss the opportunities for chemoimmunotherapy mediated by lipid-based nanoparticles. We summarize the clinical trials involving lipid-based nanoparticles for chemoimmunotherapy. We also highlight different chemoimmunotherapy strategies based on lipid-based nanoparticles such as liposomes, nanodiscs, and lipid-based hybrid nanoparticles in preclinical research. Finally, we discuss the challenges that have hindered the clinical translation of lipid-based nanoparticles for chemoimmunotherapy, and their future perspectives.

Chemical sun filters are commonly used as active ingredients in sunscreens due to their efficient absorption of ultraviolet (UV) radiation. Yet, it is known that these compounds can photochemically react with UV light and generate reactive oxygen species and oxidative stress in vitro, though this has yet to be validated in vivo. One label-free approach to probe oxidative stress is to measure and compare the relative endogenous fluorescence generated by cellular coenzymes nicotinamide adenine dinucleotides and flavin adenine dinucleotides. However, chemical sun filters are fluorescent, with emissive properties that contaminate endogenous fluorescent signals. To accurately distinguish the source of fluorescence in ex vivo skin samples treated with chemical sun filters, fluorescence lifetime imaging microscopy data were processed on a pixel-by-pixel basis using a non-Euclidean separation algorithm based on Mahalanobis distance and validated on simulated data. Applying this method, ex vivo samples exhibited a small oxidative shift when exposed to sun filters alone, though this shift was much smaller than that imparted by UV irradiation. Given the need for investigative tools to further study the clinical impact of chemical sun filters in patients, the reported methodology may be applied to visualize chemical sun filters and measure oxidative stress in patients’ skin.

Ectopic expression of lineage master regulators induces transdifferentiation. Whether cell fate transitions can be induced during various developmental stages has not been systemically examined. Here we discover that amongst different developmental stages, mouse embryonic stem cells (mESCs) are resistant to cell fate conversion induced by the melanocyte lineage master regulator MITF. By generating a transgenic system we exhibit that in mESCs, the pluripotency master regulator Oct4, counteracts pro-differentiation induced by Mitf by physical interference with MITF transcriptional activity. We further demonstrate that mESCs must be released from Oct4-maintained pluripotency prior to ectopically induced differentiation. Moreover, Oct4 induction in various differentiated cells represses their lineage identity in vivo. Alongside, chromatin architecture combined with ChIP-seq analysis suggest that Oct4 competes with various lineage master regulators for binding promoters and enhancers. Our analysis reveals pluripotency and transdifferentiation regulatory principles and could open new opportunities in the field of regenerative medicine.