Integration of Clinical Trial Spatial Multi-omics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery.

Computational methods that simulate tumors mathematically to describe cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico, potentially greatly accelerating the delivery of new therapeutics to patients. To facilitate the design of dosing regimens and identification of potential biomarkers for immunotherapy, we developed a new computational model to track tumor progression at the organ scale while capturing the spatial heterogeneity of the tumor in HCC. This computational model of spatial quantitative systems pharmacology (spQSP) was designed to simulate the effects of combination immunotherapy. The model was initiated using literature-derived parameter values and fitted to the specifics of HCC. Model validation was done through comparison to spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. Validation using spatial proteomics data from Imaging Mass Cytometry (IMC) demonstrated that closer proximity between CD8 T cells and macrophages correlated with non-response. We also compared the model output with Visium spatial transcriptomics (ST) profiling of samples from post-treatment tumor resections in the clinical trial and from another independent study of anti-PD1 monotherapy. ST data confirmed simulation results, suggesting the importance of spatial patterns of tumor vasculature and TGFß in tumor and immune cell interactions. Our findings demonstrate that incorporating mathematical modeling and computer simulations with high-throughput spatial multi-omics data provides a novel approach for patient outcome prediction and biomarker discovery.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade.

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Natural killer cells associate with epithelial cells in the pancreatic ductal adenocarcinoma tumor microenvironment.

Background: Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer. PDAC's poor prognosis and resistance to immunotherapy are attributed in part to its dense, fibrotic tumor microenvironment (TME), which is known to inhibit immune cell infiltration. We recently demonstrated that PDAC patients with higher natural killer (NK) cell content and activation have better survival rates. However, NK cell interactions in the PDAC TME have yet to be deeply studied. We show here that NK cells are present and active in the human PDAC TME. Methods: We used imaging mass cytometry (IMC) to assess NK cell content, function, and spatial localization in human PDAC samples. Then, we used CellChat, a tool to infer ligand-receptor interactions, on a human PDAC scRNAseq dataset to further define NK cell interactions in PDAC. Results: Spatial analyses showed for the first time that active NK cells are present in the PDAC TME, and both associate and interact with malignant epithelial cell ducts. We also found that fibroblast-rich, desmoplastic regions limit NK cell infiltration in the PDAC TME. CellChat analysis identified that the CD44 receptor on NK cells interacts with PDAC extracellular matrix (ECM) components such as collagen, fibronectin and laminin expressed by fibroblasts and malignant epithelial cells. This led us to hypothesize that these interactions play roles in regulating NK cell motility in desmoplastic PDAC TMEs. Using 2D and 3D in vitro assays, we found that CD44 neutralization significantly increased NK cell invasion through matrix. Conclusions: Targeting ECM-immune cell interactions may increase NK cell invasion into the PDAC TME.

Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence.

BACKGROUND: Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients. Little is known about the inter- and intra-tumor heterogeneity in cellular signaling networks within the HCC tumor microenvironment (TME) that underlie responses to modern systemic therapy. METHODS: We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a prospective clinical trial of neoadjuvant cabozantinib, a multi-tyrosine kinase inhibitor that primarily blocks VEGF, and nivolumab, a PD-1 inhibitor in which 5 out of 15 patients were found to have a pathologic response at the time of resection. RESULTS: ST profiling demonstrated that the TME of responding tumors was enriched for immune cells and cancer-associated fibroblasts (CAF) with pro-inflammatory signaling relative to the non-responders. The enriched cancer-immune interactions in responding tumors are characterized by activation of the PAX5 module, a known regulator of B cell maturation, which colocalized with spots with increased B cell marker expression suggesting strong activity of these cells. HCC-CAF interactions were also enriched in the responding tumors and were associated with extracellular matrix (ECM) remodeling as there was high activation of FOS and JUN in CAFs adjacent to the tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic responses, a single patient experienced early HCC recurrence. ST analysis of this clinical outlier demonstrated marked tumor heterogeneity, with a distinctive immune-poor tumor region that resembles the non-responding TME across patients and was characterized by HCC-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient. CONCLUSIONS: These data show that responses to modern systemic therapy in HCC are associated with distinctive molecular and cellular landscapes and provide new targets to enhance and prolong responses to systemic therapy in HCC.

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior.

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial.

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFß in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces.

Recent advances in spatial transcriptomics (STs) enable gene expression measurements from a tissue sample while retaining its spatial context. This technology enables unprecedented in situ resolution of the regulatory pathways that underlie the heterogeneity in the tumor as well as the tumor microenvironment (TME). The direct characterization of cellular co-localization with spatial technologies facilities quantification of the molecular changes resulting from direct cell-cell interaction, as it occurs in tumor-immune interactions. We present SpaceMarkers, a bioinformatics algorithm to infer molecular changes from cell-cell interactions from latent space analysis of ST data. We apply this approach to infer the molecular changes from tumor-immune interactions in Visium spatial transcriptomics data of metastasis, invasive and precursor lesions, and immunotherapy treatment. Further transfer learning in matched scRNA-seq data enabled further quantification of the specific cell types in which SpaceMarkers are enriched. Altogether, SpaceMarkers can identify the location and context-specific molecular interactions within the TME from ST data.

Effective band gap engineering in multi-principal oxides (CeGdLa-Zr/Hf)Ox by temperature-induced oxygen vacancies.

Oxygen vacancy control has been one of the most efficient methods to tune the physicochemical properties of conventional oxide materials. A new conceptual multi-principal oxide (MPO) is still lacking a control approach to introduce oxygen vacancies for tuning its inherent properties. Taking multi-principal rare earth-transition metal (CeGdLa-Zr/Hf) oxides as model systems, here we report temperature induced oxygen vacancy generation (OVG) phenomenon in MPOs. It is found that the OVG is strongly dependent on the composition of the MPOs showing different degrees of oxygen loss in (CeGdLaZr)Ox and (CeGdLaHf)Ox under identical high temperature annealing conditions. The results revealed that (CeGdLaZr)Ox remained stable single phase with a marginal decrease in the band gap of about 0.08 eV, whereas (CeGdLaHf)Ox contained two phases with similar crystal structure but different oxygen vacancy concentrations causing semiconductor-to-metal like transition. Due to the intrinsic high entropy, the metallic atoms sublattice in (CeGdLaHf)Ox remains rather stable, regardless of the interstitial oxygen atoms ranging from almost fully occupied (61.84 at%) to almost fully empty (8.73 at%) state in the respective crystal phases. Such highly tunable oxygen vacancies in (CeGdLa-Zr/Hf) oxides show a possible path for band gap engineering in MPOs for the development of efficient photocatalysts.

Spatial transcriptomics analysis of neoadjuvant cabozantinib and nivolumab in advanced hepatocellular carcinoma identifies independent mechanisms of resistance and recurrence.

Novel immunotherapy combination therapies have improved outcomes for patients with hepatocellular carcinoma (HCC), but responses are limited to a subset of patients and recurrence can also occur. Little is known about the inter- and intra-tumor heterogeneity in cellular signaling networks within the HCC tumor microenvironment (TME) that underlie responses to modern systemic therapy. We applied spatial transcriptomics (ST) profiling to characterize the tumor microenvironment in HCC resection specimens from a clinical trial of neoadjuvant cabozantinib, a multi-tyrosine kinase inhibitor that primarily blocks VEGF, and nivolumab, a PD-1 inhibitor in which 5 out of 15 patients were found to have a pathologic response. ST profiling demonstrated that the TME of responding tumors was enriched for immune cells and cancer associated fibroblasts (CAF) with pro-inflammatory signaling relative to the non-responders. The enriched cancer-immune interactions in responding tumors are characterized by activation of the PAX5 module, a known regulator of B cell maturation, which colocalized with spots with increased B cell markers expression suggesting strong activity of these cells. Cancer-CAF interactions were also enriched in the responding tumors and were associated with extracellular matrix (ECM) remodeling as there was high activation of FOS and JUN in CAFs adjacent to tumor. The ECM remodeling is consistent with proliferative fibrosis in association with immune-mediated tumor regression. Among the patients with major pathologic response, a single patient experienced early HCC recurrence. ST analysis of this clinical outlier demonstrated marked tumor heterogeneity, with a distinctive immune-poor tumor region that resembles the non-responding TME across patients and was characterized by cancer-CAF interactions and expression of cancer stem cell markers, potentially mediating early tumor immune escape and recurrence in this patient. These data show that responses to modern systemic therapy in HCC are associated with distinctive molecular and cellular landscapes and provide new targets to enhance and prolong responses to systemic therapy in HCC.

Generating colorblind-friendly scatter plots for single-cell data.

Reduced-dimension or spatial in situ scatter plots are widely employed in bioinformatics papers analyzing single-cell data to present phenomena or cell-conditions of interest in cell groups. When displaying these cell groups, color is frequently the only graphical cue used to differentiate them. However, as the complexity of the information presented in these visualizations increases, the usefulness of color as the only visual cue declines, especially for the sizable readership with color-vision deficiencies (CVDs). In this paper, we present scatterHatch, an R package that creates easily interpretable scatter plots by redundant coding of cell groups using colors as well as patterns. We give examples to demonstrate how the scatterHatch plots are more accessible than simple scatter plots when simulated for various types of CVDs.

Network inference with Granger causality ensembles on single-cell transcriptomics.

Cellular gene expression changes throughout a dynamic biological process, such as differentiation. Pseudotimes estimate cells' progress along a dynamic process based on their individual gene expression states. Ordering the expression data by pseudotime provides information about the underlying regulator-gene interactions. Because the pseudotime distribution is not uniform, many standard mathematical methods are inapplicable for analyzing the ordered gene expression states. Here we present single-cell inference of networks using Granger ensembles (SINGE), an algorithm for gene regulatory network inference from ordered single-cell gene expression data. SINGE uses kernel-based Granger causality regression to smooth irregular pseudotimes and missing expression values. It aggregates predictions from an ensemble of regression analyses to compile a ranked list of candidate interactions between transcriptional regulators and target genes. In two mouse embryonic stem cell differentiation datasets, SINGE outperforms other contemporary algorithms. However, a more detailed examination reveals caveats about poor performance for individual regulators and uninformative pseudotimes.

Neoadjuvant Cabozantinib and Nivolumab Converts Locally Advanced HCC into Resectable Disease with Enhanced Antitumor Immunity.

A potentially curative hepatic resection is the optimal treatment for hepatocellular carcinoma (HCC), but most patients are not candidates for resection and most resected HCCs eventually recur. Until recently, neoadjuvant systemic therapy for HCC has been limited by a lack of effective systemic agents. Here, in a single arm phase 1b study, we evaluated the feasibility of neoadjuvant cabozantinib and nivolumab in patients with HCC including patients outside of traditional resection criteria (NCT03299946). Of 15 patients enrolled, 12 (80%) underwent successful margin negative resection, and 5/12 (42%) patients had major pathologic responses. In-depth biospecimen profiling demonstrated an enrichment in T effector cells, as well as tertiary lymphoid structures, CD138+ plasma cells, and a distinct spatial arrangement of B cells in responders as compared to non-responders, indicating an orchestrated B-cell contribution to antitumor immunity in HCC.

Integrating single cell sequencing with a spatial quantitative systems pharmacology model spQSP for personalized prediction of triple-negative breast cancer immunotherapy response.

Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of cancer cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient response nor implicate the mechanisms that underlie immunotherapy resistance. Recent advances in single-cell RNA sequencing technology measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are developed to predict treatment response. Incorporating single-cell data from tumors to parameterize these computational models provides deeper insights into prediction of clinical outcome in individual patients. Here, we integrate whole-exome sequencing and scRNA-seq data from Triple-Negative Breast Cancer patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology model. The model comprises a four-compartmental Quantitative System Pharmacology sub-model to represent a whole patient and a spatial agent-based sub-model to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. We demonstrate how this integrated modeling and single-cell analysis framework can be used to relate neoantigen heterogeneity to immunotherapy treatment outcomes. Our results demonstrate feasibility of merging single-cell data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of clinical outcomes to immunotherapy.

Antimicrobial surfaces: a review of synthetic approaches, applicability and outlook.

The rapid spread of microorganisms such as bacteria, fungi, and viruses can be extremely detrimental and can lead to seasonal epidemics or even pandemic situations. In addition, these microorganisms may bring about fouling of food and essential materials resulting in substantial economic losses. Typically, the microorganisms get transmitted by their attachment and growth on various household and high contact surfaces such as doors, switches, currency. To prevent the rapid spread of microorganisms, it is essential to understand the interaction between various microbes and surfaces which result in their attachment and growth. Such understanding is crucial in the development of antimicrobial surfaces. Here, we have reviewed different approaches to make antimicrobial surfaces and correlated surface properties with antimicrobial activities. This review concentrates on physical and chemical modification of the surfaces to modulate wettability, surface topography, and surface charge to inhibit microbial adhesion, growth, and proliferation. Based on these aspects, antimicrobial surfaces are classified into patterned surfaces, functionalized surfaces, superwettable surfaces, and smart surfaces. We have critically discussed the important findings from systems of developing antimicrobial surfaces along with the limitations of the current research and the gap that needs to be bridged before these approaches are put into practice. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10853-021-06404-0.

From bench to bedside: Single-cell analysis for cancer immunotherapy.

Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.

Wood-Derived Carbon Fibers Embedded with SnO x Nanoparticles as Anode Material for Lithium-Ion Batteries.

Carbon-SnO x composites are obtained by impregnating acetylacetone-treated, delignified wood fibers with tin precursor and successively carbonizing at 1000 °C in 95% argon and 5% oxygen. Scanning electron microscopy and nitrogen sorption studies (Brunauer-Emmett-Teller) show that acetylacetone treatment stabilizes the wood fiber structure during carbonization at 1000 °C and preserves the porous structural features. X-ray diffraction, transmission electron microscopy, and X-ray photoelectron spectroscopy studies show that the small amount of oxygen introduced in inert atmosphere passivates the surface of tin nanoparticles. The passivation process yields thermally and electrochemically stable SnO x particles embedded in carbon matrix. The resultant carbon-SnO x material with 16 wt% SnO x shows excellent electrochemical performance of rate capability from 0.1 to 10 A g-1 and cycling stability for 1000 cycles with Li-ion storage capacity of 280 mAh g-1 at a current density of 10 A g-1. The remarkable electrochemical performance of wood-derived carbon-SnO x composite is attributed to the reproduction of structural featured wood fibers to nanoscale in carbon-SnO x composite and controlled passivation of tin nanoparticles to yield SnO x nanoparticles.

Low temperature synthesis and characterization of single phase multi-component fluorite oxide nanoparticle sols.

We report for the first time a simple, scalable approach for the synthesis of single-phase multi-component fluorite oxide nanoparticle sols: Gd0.2La0.2Y0.2Hf0.2Zr0.2O2 (GLYHZ) and Gd0.2La0.2Ce0.2Hf0.2Zr0.2O2 (GLCHZ) using chemical co-precipitation followed by peptization in acidic medium under mild conditions (≤80 °C). High resolution transmission electron microscopy (HRTEM) along with selected area electron diffraction (SAED) studies confirm fully crystalline single-phase cubic fluorite nanoparticles having a particle size of about 2-3 nm with a narrow size distribution was obtained. The powder X-ray diffraction (XRD) and Rietveld refinement studies of samples calcined at 500 °C for 4 hours confirm a single phase solid solution and a lack of secondary phases.