Topological Polymer Networks-Enabled Mechanically Strong Polyamide-Imide Aerogel Fibers for Thermal Insulation in Harsh Environments.

Aerogel fibers have sparked substantial interest as attractive candidates for thermal insulation materials. Developing aerogel fibers with the desired porous structure, good knittability, flame retardancy, and high- and low-temperature resistance is of great significance for practical applications; however, that is very challenging, especially by using an efficient method. Herein, mechanically strong and flexible aerogel fibers with remarkable thermal insulation performance are reported, which are achieved by constructing stiff-soft topological polymer networks and a multilevel hollow porous structure. The combination of polyamide-imide (PAI) with stiff chains and polyurethane (PU) with soft chains is first found to be able to form a topological entanglement architecture. More importantly, multilevel hollow pores can be constructed synchronously through just a one-step and green wet-spinning process. The resultant PAI/PU@340 aerogel fibers show an ultrahigh breaking strength of 94.5 MPa and superelastic property with a breaking strain of 20%. Furthermore, they can be knitted into fabrics with a low thermal conductivity of 25 mW/(m·K) and exhibit attractive thermal insulation property under extremely high (300 °C) and low temperatures (-191 °C), implying them as promising candidates for next-generation thermal insulation materials.

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.

480†nm InGaN-based cyan laser diode grown on Si by interface engineering of active region.

InGaN-based long wavelength laser diodes (LDs) grown on Si are highly desirable for expanding the applications in laser display and lighting. Proper interface engineering of high In-content InGaN multi-quantum wells (MQWs) is urgently required for the epitaxial growth of InGaN-based long wavelength LD on Si, because the deteriorated interfaces and crystalline quality of InGaN MQWs can severely increase the photon scattering and further exacerbate the internal absorption loss of LDs, which prevents the lasing wavelength of InGaN-based LDs from extending. In this work, a significantly improved morphology and sharp interface of the InGaN active region are obtained by using a graded-compositional InGaN lower waveguide (LWG) capped with a 10-nm-thick Al0.1Ga0.9N layer. The V-pits density of the InGaN LWG was one order of magnitude reduction from 4.8 × 108 to 3.6 × 107 cm-2 along with the root-mean-square surface roughness decreasing from 0.3 to 0.1 nm. Therefore, a room-temperature electrically injected 480 nm InGaN-based cyan LD grown on Si under pulsed current operation was successfully achieved with a threshold current density of 18.3 kA/cm2.

Harnessing Disparities in Magnetic Microswarms: From Construction to Collaborative Tasks.

Individual differences in size, experience, and task specialization in natural swarms often result in heterogeneity and hierarchy, facilitating efficient and coordinated task accomplishment. Drawing inspiration from this phenomenon, a general strategy is proposed for organizing magnetic micro/nanorobots (MNRs) with apparent differences in size, shape, and properties into cohesive microswarms with tunable heterogeneity, controlled spatial hierarchy, and collaborative tasking capability. In this strategy, disparate magnetic MNRs can be manipulated to show reversible transitions between synchronization and desynchronization by elaborately regulating parameter sets of the rotating magnetic field. Utilizing these transitions, alongside local robust hydrodynamic interactions, diverse heterospecific pairings of disparate magnetic MNRs can be organized into heterogeneous microswarms, and their spatial organization can be dynamically adjusted from egalitarian to leader-follower-like hierarchies on the fly, both in open space and complex microchannels. Furthermore, when specializing the disparate MNRs with distinct functions ("division of labor") such as sensing and drug carrying, they can execute precise drug delivery targeting unknown sites in a collaborative sensing-navigating-cargo dropping sequence, demonstrating significant potential for precise tumor treatment. These findings highlight the critical roles of attribute differences and hierarchical organization in designing efficient swarming micro/nanorobots for biomedical applications.

The complete plastome and phylogenetic analysis of Commelina benghalensis L.1753 (Commelinaceae).

Commelina benghalensis L. 1753, a member of the Commelinaceae family, holds significant medicinal and culinary value. This study represents the first documentation of the sequencing and assembly of the entire plastome of C. benghalensis. The genome spans a total length of 160,663 bp, exhibiting a conventional quadripartite architecture that comprises a large single-copy (LSC) region (87,750 bp), a small single-copy (SSC) region (18,417 bp), and two inverted repeats (IR) regions (both 27,248 bp). In its entirety, the C. benghalensis plastome encompasses 129 genes (with 108 being unique), incorporating 77 individual protein-coding genes, 37 unique tRNA genes, and four unique rRNA genes. Phylogenetic analysis revealed a close resemblance between C. benghalensis and C. communis. The sequencing of this plastome stands to expedite the development of molecular markers and significantly contribute to genetic assays involving this distinctive plant.

Stimulus-Induced Dynamic Behavior Regulation of Flexible Crystals through the Tuning of Module Rigidity.

Introducing dynamic behavior into periodic frameworks has borne fruit in the form of flexible porous crystals. The detailed molecular design of frameworks in order to control their collective dynamics is of particular interest, for example, to achieve stimulus-induced behavior. Herein, by varying the degree of rigidity of ditopic pillar linkers, two isostructural flexible metal-organic frameworks (MOFs) with common rigid supermolecular building bilayers were constructed. The subtle substitution of single (in bibenzyl-4,4'-dicarboxylic acid; H2BBDC) with double (in 4,4'-stilbenedicarboxylic acid; H2SDC) C-C bonds in pillared linkers led to markedly different flexible behavior of these two MOFs. Upon the removal of guest molecules, both frameworks clearly show reversible single-crystal-to-single-crystal transformations involving the cis-trans conformation change and a resulting swing of the corresponding pillar linkers, which gives rise to Flex-Cd-MOF-1a and Flex-Cd-MOF-2a, respectively. Strikingly, a more favorable gas-induced dynamic behavior in Flex-Cd-MOF-2a was verified in detail by stepwise C3H6/C3H8 sorption isotherms and the corresponding in situ powder X-ray diffraction experiments. These insights are strongly supported by molecular modeling studies on the sorption mechanism that explores the sorption landscape. Furthermore, a consistency between the macroscopic elasticity and microscopic flexibility of Flex-Cd-MOF-2 was observed. This work fuels a growing interest in developing MOFs with desired chemomechanical functions and presents detailed insights into the origins of flexible MOFs.

Heterogeneous Sensor-Carrier Microswarms for Collaborative Precise Drug Delivery toward Unknown Targets with Localized Acidosis.

Micro/nanorobots hold the potential to revolutionize biomedicine by executing diverse tasks in hard-to-reach biological environments. Nevertheless, achieving precise drug delivery to unknown disease sites using swarming micro/nanorobots remains a significant challenge. Here we develop a heterogeneous swarm comprising sensing microrobots (sensor-bots) and drug-carrying microrobots (carrier-bots) with collaborative tasking capabilities for precise drug delivery toward unknown sites. Leveraging robust interspecific hydrodynamic interactions, the sensor-bots and carrier-bots spontaneously synchronize and self-organize into stable heterogeneous microswarms. Given that the sensor-bots can create real-time pH maps employing pH-responsive structural-color changes and the doxorubicin-loaded carrier-bots exhibit selective adhesion to acidic targets via pH-responsive charge reversal, the sensor-carrier microswarm, when exploring unknown environments, can detect and localize uncharted acidic targets, guide itself to cover the area, and finally deploy therapeutic carrier-bots precisely there. This versatile platform holds promise for treating diseases with localized acidosis and inspires future theranostic microsystems with expandability, task flexibility, and high efficiency.

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology.

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

A linker selective retention strategy to construct hierarchical porous metal-organic frameworks with high catalytic activity for oxidative desulfurization.

In order to improve the oxidative desulfurization (ODS) performance of MOF materials, an effective way is to convert a microporous MOF into a hierarchical porous MOF (HP-MOF) by utilizing the linker selective retention strategy. Herein, UiO-66 with the introduction of an unstable linker ligand (dihydro-1,2,4,5-tetrazine-3,6-dicarboxylate, dhtz) can selectively remove dhtz ligands to form HP-MOF (HP-UiO-66-dhtz) through heat treatment at high temperature. While maintaining the original structure of UiO-66, HP-UiO-66-dhtz features mesopores and abundant Lewis acid sites, showing excellent ODS performance for diphenylthiophene (DBT).

A Dual Fluorescence Turn-On Sensor Array Formed by Poly(para-aryleneethynylene) and Aggregation-Induced Emission Fluorophores for Sensitive Multiplexed Bacterial Recognition.

Bacterial infections have emerged as the leading causes of mortality and morbidity worldwide. Herein, we developed a dual-channel fluorescence "turn-on" sensor array, comprising six electrostatic complexes formed from one negatively charged poly(para-aryleneethynylene) (PPE) and six positively charged aggregation-induced emission (AIE) fluorophores. The 6-element array enabled the simultaneous identification of 20 bacteria (OD600=0.005) within 30s (99.0 % accuracy), demonstrating significant advantages over the array constituted by the 7 separate elements that constitute the complexes. Meanwhile, the array realized different mixing ratios and quantitative detection of prevalent bacteria associated with urinary tract infection (UTI). It also excelled in distinguishing six simulated bacteria samples in artificial urine. Remarkably, the limit of detection for E. coli and E. faecalis was notably low, at 0.000295 and 0.000329 (OD600), respectively. Finally, optimized by diverse machine learning algorithms, the designed array achieved 96.7 % accuracy in differentiating UTI clinical samples from healthy individuals using a random forest model, demonstrating the great potential for medical diagnostic applications.

Regioselective Hydroxylation of Unsymmetrical Ketones Using Cu, H2O2, and Imine Directing Groups via Formation of an Electrophilic Cupric Hydroperoxide Core.

Herein, we describe the regioselective functionalization of unsymmetrical ketones using imine directing groups, Cu, and H2O2. The C-H hydroxylation of the substrate-ligands derived from 2-substituted benzophenones occurred exclusively at the γ-position of the unsubstituted ring due to the formation of only one imine stereoisomer. Conversely, the imines derived from 4-substituted benzophenones produced E/Z mixtures that upon reacting with Cu and H2O2 led to two γ-C-H hydroxylation products. Contrary to our initial hypothesis, the ratio of the hydroxylation products did not depend on the ratio of the E/Z isomers but on the electrophilicity of the reactive [LCuOOH]1+. A detailed mechanistic analysis suggests a fast isomerization of the imine substrate-ligand binding the CuOOH core before the rate-determining electrophilic aromatic hydroxylation. Varying the benzophenone substituents and/or introducing electron-donating and electron-withdrawing groups on the 4-position of pyridine of the directing group allowed for fine-tuning of the electrophilicity of the mononuclear [LCuOOH]1+ to reach remarkable regioselectivities (up to 91:9 favoring the hydroxylation of the electron-rich arene ring). Lastly, we performed the C-H hydroxylation of alkyl aryl ketones, and like in the unsymmetrical benzophenones, the regioselectivity of the transformations (sp3 vs sp2) could be controlled by varying the electronics of the substrate and/or the directing group.

Mechanism of p-Type Heteroatom Doping of Lithium Stannate for the Photodegradation of 2,4-Dichlorophenol: Enhanced Hole Oxidative Capability and Concentrations.

A systematic evaluation of enhancing photocatalysis via aliovalent cation doping is conducted. Cation In3+, being p-type-doped, was chosen to substitute the Sn site (Sn4+) in Li2SnO3, and the photodegradation of 2,4-dichlorophenol was applied as a model reaction. Specifically, Li2Sn0.90In0.10O3 exhibited superior catalytic performance; the photodegradation efficiency reached about 100% within only 12 min. This efficiency is far greater than that of pure Li2SnO3 under identical conditions. Density functional theory calculations reveal that introducing In3+ increased the electron mobility, yet decreased the hole mobility, leading to photogenerated carrier separation. However, photoluminescence and time-resolved photoluminescence suggest that In3+ induced nonradiative coupling in the matrix, reducing the photogenerated carrier separation ratio compared with that of Li2SnO3. The optical band gap of Li2Sn0.90In0.10O3 was almost unchanged compared with that of Li2SnO3 via ultraviolet-visible absorption. The increased photocatalytic efficiency was ascribed to the lower valence band position and enhanced hole concentrations by valence band X-ray photoelectron spectroscopy and electrochemical measurements. Finally, a 2,4-dichlorophenol degradation pathway, an intermediate toxicity assessment, and a photocatalytic mechanism were proposed. This work offers insights into designing and optimizing semiconductor photocatalysts with high performance.

Unpaired low-dose computed tomography image denoising using a progressive cyclical convolutional neural network.

BACKGROUND: Reducing the radiation dose from computed tomography (CT) can significantly reduce the radiation risk to patients. However, low-dose CT (LDCT) suffers from severe and complex noise interference that affects subsequent diagnosis and analysis. Recently, deep learning-based methods have shown superior performance in LDCT image-denoising tasks. However, most methods require many normal-dose and low-dose CT image pairs, which are difficult to obtain in clinical applications. Unsupervised methods, on the other hand, are more general. PURPOSE: Deep learning methods based on GAN networks have been widely used for unsupervised LDCT denoising, but the additional memory requirements of the model also hinder its further clinical application. To this end, we propose a simpler multi-stage denoising framework trained using unpaired data, the progressive cyclical convolutional neural network (PCCNN), which can remove the noise from CT images in latent space. METHODS: Our proposed PCCNN introduces a noise transfer model that transfers noise from LDCT to normal-dose CT (NDCT), denoised CT images generated from unpaired CT images, and noisy CT images. The denoising framework also contains a progressive module that effectively removes noise through multi-stage wavelet transforms without sacrificing high-frequency components such as edges and details. RESULTS: Compared with seven LDCT denoising algorithms, we perform a quantitative and qualitative evaluation of the experimental results and perform ablation experiments on each network module and loss function. On the AAPM dataset, compared with the contrasted unsupervised methods, our denoising framework has excellent denoising performance increasing the peak signal-to-noise ratio (PSNR) from 29.622 to 30.671, and the structural similarity index (SSIM) was increased from 0.8544 to 0.9199. The PCCNN denoising results were relatively optimal and statistically significant. In the qualitative result comparison, PCCNN without introducing additional blurring and artifacts, the resulting image has higher resolution and complete detail preservation, and the overall structural texture of the image is closer to NDCT. In visual assessments, PCCNN achieves a relatively balanced result in noise suppression, contrast retention, and lesion discrimination. CONCLUSIONS: Extensive experimental validation shows that our scheme achieves reconstruction results comparable to supervised learning methods and has performed well in image quality and medical diagnostic acceptability.

Research Note: Xylooligosaccharide directly attenuates Salmonella Typhimurium colonization and its induction of impairments in intestinal barrier and growth performance of broilers.

Xylooligosaccharide (XOS) is known as a prebiotic, however, it is unknown whether XOS can directly protect against bacterial infection. This study aimed to investigate the direct inhibitory effects of XOS on Salmonella Typhimurium colonization and the inductive impairments in gut health and growth performance in broilers. We first probed the inhibitory effects of XOS on S. Typhimurium adhesion and its induction of intestinal epithelial cell (IPEC-J2) injuries. Afterward, 168 one-day-old yellow-feathered broilers were randomly divided into 3 groups (7 replicates/group): negative control (NC, received a basal diet), positive control (PC, received a basal diet with S. Typhimurium challenge) and XOS group (PC birds + 1,500 mg/kg XOS). All birds except those in NC were orally challenged with S. Typhimurium from 8 to 10 d of age. Parameters were analyzed on d 11. The results showed that XOS inhibited S. Typhimurium adhesion and the inductive injuries of IPEC-J2 cells by lowering (P < 0.05) certain adhesion-related genes expression of this bacterium. It also alleviated S. Typhimurium-induced increase (P < 0.05) in the expression of certain inflammatory cytokines and tight junction (TJ) proteins of IPEC-J2 cells. Supplementing XOS to S. Typhimurium-challenged broilers attenuated the elevations (P < 0.05) in S. Typhimurium colonization of ileal mucosa and its translocation to the liver and spleen, as well as increased (P < 0.05) certain TJ proteins expression of ileum. Besides, XOS addition normalized S. Typhimurium-induced impairments (P < 0.05) in ileal morphology, final body weight and average daily gain in broilers. Collectively, supplemental XOS directly suppressed intestinal colonization of S. Typhimurium by diminishing its adhesiveness and subsequently mitigated destructions in intestinal barriers, thus contributing to weaken growth retardation in challenged broilers. Our findings provide a new insight into the mechanisms of XOS limiting Salmonella infection in chickens.

Ligand-based pharmacophore modelling, structure optimisation, and biological evaluation for the identification of 2-heteroarylthio-N-arylacetamides as novel HSP90 C-terminal inhibitors.

Targeting Heat shock protein 90 (HSP90) C-terminus is an important strategy to develop HSP90 inhibitors without inducing heat shock response. The development of C-terminal inhibitors, however, is hampered by a lack of understanding regarding the interaction between the HSP90 C-terminus and the present inhibitors. We collected seven classical and structurally diverse HSP90 C-terminal inhibitors and constructed a ligand-based pharmacophore model. The subsequent virtual screening and structural optimisation led to the identification of 2-heteroarylthio-N-arylacetamides as novel HSP90 C-terminal inhibitors. 9 and 27 exhibited strong antitumour activity in vitro by inhibiting proliferation and inducing apoptosis in multiple cancer cell lines. These compounds disrupted the interaction between HSP90 C-terminus and peptidylprolyl isomerase D, exerting a stronger inhibitory effect than novobiocin. 27 significantly induced the degradation of HSP90 clients without triggering heat shock response. In an in vivo study using 4T1 mice breast cancer models, 9 showed a potent antitumour effect without obvious toxicity.

Hypoxia and inflammatory factor preconditioning enhances the immunosuppressive properties of human umbilical cord mesenchymal stem cells.

BACKGROUND: Mesenchymal stem cells (MSCs) have great potential for the treatment of various immune diseases due to their unique immunomodulatory properties. However, MSCs exposed to the harsh inflammatory environment of damaged tissue after intravenous transplantation cannot exert their biological effects, and therefore, their therapeutic efficacy is reduced. In this challenging context, an in vitro preconditioning method is necessary for the development of MSC-based therapies with increased immunomodulatory capacity and transplantation efficacy. AIM: To determine whether hypoxia and inflammatory factor preconditioning increases the immunosuppressive properties of MSCs without affecting their biological characteristics. METHODS: Umbilical cord MSCs (UC-MSCs) were pretreated with hypoxia (2% O2) exposure and inflammatory factors (interleukin-1ß, tumor necrosis factor-α, interferon-γ) for 24 h. Flow cytometry, polymerase chain reaction, enzyme-linked immunosorbent assay and other experimental methods were used to evaluate the biological characteristics of pretreated UC-MSCs and to determine whether pretreatment affected the immunosuppressive ability of UC-MSCs in coculture with immune cells. RESULTS: Pretreatment with hypoxia and inflammatory factors caused UC-MSCs to be elongated but did not affect their viability, proliferation or size. In addition, pretreatment significantly decreased the expression of coagulation-related tissue factors but did not affect the expression of other surface markers. Similarly, mitochondrial function and integrity were retained. Although pretreatment promoted UC-MSC apoptosis and senescence, it increased the expression of genes and proteins related to immune regulation. Pretreatment increased peripheral blood mononuclear cell and natural killer (NK) cell proliferation rates and inhibited NK cell-induced toxicity to varying degrees. CONCLUSION: In summary, hypoxia and inflammatory factor preconditioning led to higher immunosuppressive effects of MSCs without damaging their biological characteristics.

Nomogram incorporating log odds of positive lymph nodes improves prognostic prediction for ovarian serous carcinoma: a real-world retrospective cohort study.

OBJECTIVES: Ovarian serous carcinoma (OSC) is a major cause of gynaecological cancer death, yet there is a lack of reliable prognostic models. To address this, we developed and validated a nomogram based on conventional clinical characteristics and log odds of positive lymph nodes (LODDS) to predict the prognosis of OSC patients. SETTING: A Real-World Retrospective Cohort Study from the Surveillance, Epidemiology and End Results programme. PARTICIPANTS: We obtained data on 4192 patients diagnosed with OSC between 2010 and 2015. Eligibility criteria included specific diagnostic codes, OSC being the primary malignant tumour and age at diagnosis over 18 years. Exclusion criteria were missing information on various factors and unknown cause of death or survival time. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome were overall survival (OS) and ovarian cancer-specific survival (OCSS). RESULTS: For OS and OCSS outcomes, we selected 7 and 5 variables, respectively, to establish the nomogram. In the training and validation cohorts, the C index for OS or OCSS was 0.716 or 0.718 and 0.731 or 0.733, respectively, with a 3-year time-dependent area under the curve (AUC) of 0.745 or 0.751 and a 5-year time-dependent AUC of 0.742 or 0.751. Calibration curves demonstrated excellent consistency between predicted and observed outcomes. The Net Reclassification Index, integrated discrimination improvement and decision curve analysis curves indicated that our nomogram performed better than the International Federation of Gynaecology and Obstetrics (FIGO) staging system in predicting OS and OCSS for OSC patients in both the training and validation cohorts. CONCLUSION: Our nomogram, which includes LODDS, offers higher accuracy and reliability than the FIGO staging system and can predict overall and OCSS in OSC patients.

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.

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.