Spatial oncology: Translating contextual biology to the clinic.

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Spatially resolved analysis of pancreatic cancer identifies therapy-associated remodeling of the tumor microenvironment.

In combination with cell-intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with neoadjuvant chemotherapy and radiotherapy. We developed spatially constrained optimal transport interaction analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid coculture system. We identified enrichment in interleukin-6 family signaling that functionally confers resistance to chemotherapy. Overall, this study demonstrates that characterization of the tumor microenvironment using single-cell spatial transcriptomics allows for the identification of molecular interactions that may play a role in the emergence of therapeutic resistance and offers a spatially based analysis framework that can be broadly applied to other contexts.

Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis.

In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

Machine learning guided structure function predictions enable in silico nanoparticle screening for polymeric gene delivery.

Developing highly efficient non-viral gene delivery reagents is still difficult for many hard-to-transfect cell types and, to date, has mostly been conducted via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development of devices or therapeutics by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a dataset of synthetic biodegradable polymers, poly(beta-amino ester)s (PBAEs), which have shown exciting promise for therapeutic gene delivery in vitro and in vivo. The data set includes polymer properties as inputs as well as polymeric nanoparticle transfection performance and nanoparticle toxicity in a range of cells as outputs. This data was used to train and evaluate several state-of-the-art machine learning algorithms for their ability to predict transfection and understand structure-function relationships. By developing an encoding scheme for vectorizing the structure of a PBAE polymer in a machine-readable format, we demonstrate that a random forest model can satisfactorily predict DNA transfection in vitro based on the chemical structure of the constituent PBAE polymer in a cell line dependent manner. Based on the model, we synthesized PBAE polymers and used them to form polymeric gene delivery nanoparticles that were predicted in silico to be successful. We validated the computational predictions in two cell lines in vitro, RAW 264.7 macrophages and Hep3B liver cancer cells, and found that the Spearman's R correlation between predicted and experimental transfection was 0.57 and 0.66 respectively. Thus, a computational approach that encoded chemical descriptors of polymers was able to demonstrate that in silico computational screening of polymeric nanomedicine compositions had utility in predicting de novo biological experiments. STATEMENT OF SIGNIFICANCE: Developing highly efficient non-viral gene delivery reagents is difficult for many hard-to-transfect cell types and, to date, has mostly been explored via brute force screening routines. High throughput in silico methods of evaluating biomaterials can enable accelerated optimization and development for therapeutic or biomanufacturing purposes by exploring large chemical design spaces quickly and at low cost. This work reports application of state-of-the-art machine learning algorithms to a large compiled PBAE DNA gene delivery nanoparticle dataset across many cell types to develop predictive models for transfection and nanoparticle cytotoxicity. We develop a novel computational pipeline to encode PBAE nanoparticles with chemical descriptors and demonstrate utility in a de novo experimental context.

The utility of a genetic progression risk test for Barrett esophagus.

This study sought to characterize the utility of a gene methylation-based biomarker test that has been validated to predict progression towards esophageal adenocarcinoma. Barrett esophagus (BE) is a precursor condition for esophageal adenocarcinoma (EAC) with somewhat variable approaches among gastroenterologists toward managing neoplastic progression risk. Capsulomics has developed a validated multigene DNA methylation-based biomarker assay performed on BE biopsies designed to address this variability by classifying BE patients into progression risk groups. In the current study, a survey was administered to practicing gastroenterologists in order to assess the potential impact of this assay on clinical practice. In this context, 89% (95% Cl: 85.4-92.6%) of surveyed physicians felt strongly that the multigene Barrett Esophagus test helped resolve uncertainties and optimize care of patients with BE by impacting their decisions on surveillance intervals and use of active treatments, such as ablation. The assay significantly impacted surveillance intervals for both high-risk (22.0 no assay vs 12.3 months with assay; P = 1.7E-8) and low-risk (7.9 no assay vs 11.4 months with assay, P = 8.8E-4) stratified case results. Finally, the assay also significantly impacted decisions to pursue active ablation treatments in both high-risk (5% recommending ablation without assay vs 42% with assay; P = 3.7E-11) and low-risk (42% recommending ablation without assay vs 29% with assay; P = .049) stratified case results. Results demonstrated a strong effect of the assay on clinical decision making, even in conjunction with established clinical guidelines.

Non-viral gene delivery of HIF-1α promotes angiogenesis in human adipose-derived stem cells.

Stable and mature vascular formation is a current challenge in engineering functional tissues. Transient, non-viral gene delivery presents a unique platform for delivering genetic information to cells for tissue engineering purposes and to restore blood flow to ischemic tissue. The formation of new blood vessels can be induced by upregulation of hypoxia-inducible factor-1α (HIF-1), among other factors. We hypothesized that biodegradable polymers could be used to efficiently deliver the HIF-1α gene to human adipose-derived stromal/stem cells (hASCs) and that this treatment could recruit an existing endogenous endothelial cell population to induce angiogenesis in a 3D cell construct in vitro. In this study, end-modified poly(ß-amino ester) (PBAE) nanocomplexes were first optimized for transfection of hASCs and a new biodegradable polymer with increased hydrophobicity and secondary amine structures, N'-(3-aminopropyl)-N,N-dimethylpropane-1,3-diamine end-modified poly(1,4-butanediol diacrylate-co-4-amino-1-butanol), was found to be most effective. Optimal PBAE nanocomplexes had a hydrodynamic diameter of approximately 140 nm and had a zeta potential of 30 mV. The PBAE polymer self-assembled with HIF-1α plasmid DNA and treatment of hASCs with these nanocomplexes induced 3D vascularization. Cells transfected with this polymer-DNA complex were found to have 106-fold upregulation HIF-1α expression, an approximately 2-fold increase in secreted VEGF, and caused the formation of vessel tubules compared to an untransfected control. These gene therapy biomaterials may be useful for regenerative medicine. STATEMENT OF SIGNIFICANCE: Not only is the formation of stable vasculature a challenge for engineering human tissues in vitro, but it is also of valuable interest to clinical applications such as peripheral artery disease. Previous studies using HIF-1α to induce vascular formation have been limited by the necessity of hypoxic chambers. It would be advantageous to simulate endogenous responses to hypoxia without the need for physical hypoxia. In this study, 3D vascular formation was shown to be inducible through non-viral gene delivery of HIF-1α with new polymeric nanocomplexes. A biodegradable polymer N'-(3-aminopropyl)-N,N-dimethylpropane-1,3-diamine end-modified poly(1,4-butanediol diacrylate-co-4-amino-1-butanol) demonstrates improved transfection of human adipose-derived stem cells. This nanobiotechnology could be a promising strategy for the creation of vasculature for tissue engineering and clinical applications.