Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

Most Tissue-Resident Macrophages Except Microglia Are Derived from Fetal Hematopoietic Stem Cells.

Macrophages are one of the most diverse cell populations in terms of their anatomical location and functional specialization during both homeostasis and disease. Although it has been shown in different fate mapping models that some macrophages present in adult tissues are already established during fetal development, their exact origins are still under debate. In the current study, we developed a fate mapping strain, based on the Kit locus, which allowed us to readdress "the origins" question. Different types of macrophages from various adult tissues were traced to their fetal or adult sources by inducing labeling in precursors at several time points either during fetal development or in adult mice. We show that all adult macrophages, resident or infiltrating, are progenies of classical hematopoietic stem cells (HSC) with the exception of microglia and, partially epidermal Langerhans cells, which are yolk sac (YS)-derived.

VG161 activates systemic antitumor immunity in pancreatic cancer models as a novel oncolytic herpesvirus expressing multiple immunomodulatory transgenes.

The VG161 represents the first recombinant oncolytic herpes simplex virus type 1 carrying multiple synergistic antitumor immuno-modulating factors. Here, we report its antitumor mechanisms and thus provide firm theoretical foundation for the upcoming clinical application in pancreatic cancer. Generally, the VG161-mediated antitumor outcomes were analyzed by a collaboration of techniques, namely the single-cell sequencing, airflow-assisted desorption electrospray ionization-mass spectrometry imaging (AFADSI-MSI) and nanostring techniques. In vitro, the efficacy of VG161 together with immune checkpoint inhibitors (ICIs) has been successfully shown to grant a long-term antitumor effect by altering tumor immunity and remodeling tumor microenvironment (TME) metabolisms. Cellular functional pathways and cell subtypes detected from patient samples before and after the treatment had undergone distinctive changes including upregulated CD8+ T and natural killer cells. More importantly, significant antitumor signals have emerged since the administration of VG161 injection. In conclusion, VG161 can systematically activate acquired and innate immunity in pancreatic models, as well as improve the tumor immune microenvironment, indicative of strong antitumor potential. The more robusting antitumor outcome for VG161 monotherapy or in combination with other therapies on pancreatic cancer is worth of being explored in further clinical trials.

Topological analysis of hepatocellular carcinoma tumour microenvironment based on imaging mass cytometry reveals cellular neighbourhood regulated reversely by macrophages with different ontogeny.

OBJECTIVE: Hepatocellular carcinoma (HCC) tumour microenvironment (TME) is highly complex with diverse cellular components organising into various functional units, cellular neighbourhoods (CNs). And we wanted to define CN of HCC while preserving the TME architecture, based on which, potential targets for novel immunotherapy could be identified. DESIGN: A highly multiplexed imaging mass cytometry (IMC) panel was designed to simultaneously quantify 36 biomarkers of tissues from 134 patients with HCC and 7 healthy donors to generate 562 highly multiplexed histology images at single-cell resolution. Different function units were defined by topological analysis of TME. CN relevant to the patients' prognosis was identified as specific target for HCC therapy. Transgenic mouse models were used to validate the novel immunotherapy target for HCC. RESULTS: Three major types of intratumour areas with distinct distribution patterns of tumorous, stromal and immune cells were identified. 22 cellular metaclusters and 16 CN were defined. CN composed of various types of cells formed regional function units and the regional immunity was regulated reversely by resident Kupffer cells and infiltrating macrophages with protumour and antitumour function, respectively. Depletion of Kupffer cells in mouse liver largely enhances the T cell response, reduces liver tumour growth and sensitises the tumour response to antiprogrammed cell death protein-1 treatment. CONCLUSION: Our findings reveal for the first time the various topological function units of HCC TME, which also presents the largest depository of pathological landscape for HCC. This work highlights the potential of Kupffer cell-specific targeting rather than overall myeloid cell blocking as a novel immunotherapy for HCC treatment.

Ultrasmall Ruthenium Nanoparticles with Boosted Antioxidant Activity Upregulate Regulatory T Cells for Highly Efficient Liver Injury Therapy.

Nanozymes exhibiting antioxidant activity are beneficial for the treatment of oxidative stress-associated diseases. Ruthenium nanoparticles (RuNPs) with multiple enzyme-like activities have attracted growing attention, but the relatively low antioxidant enzyme-like activities hinder their practical biomedical applications. Here, a size regulation strategy is presented to significantly boost the antioxidant enzyme-like activities of RuNPs. It is found that as the size of RuNPs decreases to ≈2.0 nm (sRuNP), the surface-oxidized Ru atoms become dominant, thus possessing an unprecedentedly boosted antioxidant activity as compared to medium-sized (≈3.9 nm) or large-sized counterparts (≈5.9 nm) that are mainly composed of surface metallic Ru atoms. Notably, based on their antioxidant enzyme-like activities and ultrasmall size, sRuNP can not only sustainably ameliorate oxidative stress but also upregulate regulatory T cells in late-stage acetaminophen (APAP)-induced liver injury (ALI). Consequently, sRuNPs perform highly efficient therapeutic efficiency on ALI mice even when treated at 6 h after APAP intoxication. This strategy is insightful for tuning the catalytic performances of nanozymes for their extensive biomedical applications.

Nanoparticle-Stabilized Oxygen Microcapsules Prepared by Interfacial Polymerization for Enhanced Oxygen Delivery.

Most tumors have more severe hypoxia levels than normal tissue; tumor hypoxia is thus a useful target for cancer treatment. Here, we develop an effective oxygen delivery vehicle of polydopamine-nanoparticle-stabilized oxygen microcapsules by interfacial polymerization. The oxygen microcapsules have excellent biocompatibility. Oxygen could easily diffuse out from the microcapsules, thus increasing and maintaining the microenvironment at an oxygen-rich state. In vitro cell cultures confirm that oxygen microcapsules could effectively improve the hypoxia microenvironment, showing the lowest fluorescent intensity of hypoxia-green-labeled cells. When injected subcutaneously in vivo, oxygen microcapsules could also improve the tumor's hypoxia microenvironment, thus suppressing the growth of tumor. Synergetic therapy using oxygen microcapsules and gemcitabine drugs is an effective way for tumor treatment, showing the best performance in suppressing the tumor's growth.

Long-Lived Innate IL-17-Producing γ/δ T Cells Modulate Antimicrobial Epithelial Host Defense in the Colon.

Intestinal IL-17-producing cells, including Th17, γ/δ T, and innate lymphoid cells, are differentially distributed along the gastrointestinal tract. In this study, we show that the gut IL-17-producing γ/δ T (γ/δ T17) cells develop before birth and persist in the tissue as long-lived cells with minimal turnover. Most colon γ/δ T17 cells express, together with Vγ4 and CCR6, the scavenger receptor 2 and are mainly restricted to innate lymphoid follicles in the colon. Colon γ/δ T cells in mice that lack conventional dendritic cells 2 produced increased amounts of IL-17 with concomitant heightened epithelial antimicrobial response, such as the C-type lectins Reg3γ and Reg3ß. In the absence of γ/δ T cells or after IL-17 neutralization, this epithelial response was dramatically reduced, underlining the protective role of this unique subpopulation of innate γ/δ T17 cells in the colonic mucosa.

Harnessing Nanotechnology: Emerging Strategies for Multiple Myeloma Therapy.

Advances in nanotechnology have provided novel avenues for the diagnosis and treatment of multiple myeloma (MM), a hematological malignancy characterized by the clonal proliferation of plasma cells in the bone marrow. This review elucidates the potential of nanotechnology to revolutionize myeloma therapy, focusing on nanoparticle-based drug delivery systems, nanoscale imaging techniques, and nano-immunotherapy. Nanoparticle-based drug delivery systems offer enhanced drug targeting, reduced systemic toxicity, and improved therapeutic efficacy. We discuss the latest developments in nanocarriers, such as liposomes, polymeric nanoparticles, and inorganic nanoparticles, used for the delivery of chemotherapeutic agents, siRNA, and miRNA in MM treatment. We delve into nanoscale imaging techniques which provide spatial multi-omic data, offering a holistic view of the tumor microenvironment. This spatial resolution can help decipher the complex interplay between cancer cells and their surrounding environment, facilitating the development of highly targeted therapies. Lastly, we explore the burgeoning field of nano-immunotherapy, which employs nanoparticles to modulate the immune system for myeloma treatment. Specifically, we consider how nanoparticles can be used to deliver tumor antigens to antigen-presenting cells, thus enhancing the body's immune response against myeloma cells. In conclusion, nanotechnology holds great promise for improving the prognosis and quality of life of MM patients. However, several challenges remain, including the need for further preclinical and clinical trials to assess the safety and efficacy of these emerging strategies. Future research should also focus on developing personalized nanomedicine approaches, which could tailor treatments to individual patients based on their genetic and molecular profiles.

Multi-Instance Multi-Task Learning for Joint Clinical Outcome and Genomic Profile Predictions From the Histopathological Images.

With the remarkable success of digital histopathology and the deep learning technology, many whole-slide pathological images (WSIs) based deep learning models are designed to help pathologists diagnose human cancers. Recently, rather than predicting categorical variables as in cancer diagnosis, several deep learning studies are also proposed to estimate the continuous variables such as the patients' survival or their transcriptional profile. However, most of the existing studies focus on conducting these predicting tasks separately, which overlooks the useful intrinsic correlation among them that can boost the prediction performance of each individual task. In addition, it is sill challenge to design the WSI-based deep learning models, since a WSI is with huge size but annotated with coarse label. In this study, we propose a general multi-instance multi-task learning framework (HistMIMT) for multi-purpose prediction from WSIs. Specifically, we firstly propose a novel multi-instance learning module (TMICS) considering both common and specific task information across different tasks to generate bag representation for each individual task. Then, a soft-mask based fusion module with channel attention (SFCA) is developed to leverage useful information from the related tasks to help improve the prediction performance on target task. We evaluate our method on three cancer cohorts derived from the Cancer Genome Atlas (TCGA). For each cohort, our multi-purpose prediction tasks range from cancer diagnosis, survival prediction and estimating the transcriptional profile of gene TP53. The experimental results demonstrated that HistMIMT can yield better outcome on all clinical prediction tasks than its competitors.

Spatial proteomic profiling elucidates immune determinants of neoadjuvant chemo-immunotherapy in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) presents significant clinical and therapeutic challenges due to its aggressive nature and generally poor prognosis. We initiated a Phase II clinical trial (ChiCTR1900027160) to assess the efficacy of a pioneering neoadjuvant chemo-immunotherapy regimen comprising programmed death-1 (PD-1) blockade (Toripalimab), nanoparticle albumin-bound paclitaxel (nab-paclitaxel), and the oral fluoropyrimidine derivative S-1, in patients with locally advanced ESCC. This study uniquely integrates clinical outcomes with advanced spatial proteomic profiling using Imaging Mass Cytometry (IMC) to elucidate the dynamics within the tumor microenvironment (TME), focusing on the mechanistic interplay of resistance and response. Sixty patients participated, receiving the combination therapy prior to surgical resection. Our findings demonstrated a major pathological response (MPR) in 62% of patients and a pathological complete response (pCR) in 29%. The IMC analysis provided a detailed regional assessment, revealing that the spatial arrangement of immune cells, particularly CD8+ T cells and B cells within tertiary lymphoid structures (TLS), and S100A9+ inflammatory macrophages in fibrotic regions are predictive of therapeutic outcomes. Employing machine learning approaches, such as support vector machine (SVM) and random forest (RF) analysis, we identified critical spatial features linked to drug resistance and developed predictive models for drug response, achieving an area under the curve (AUC) of 97%. These insights underscore the vital role of integrating spatial proteomics into clinical trials to dissect TME dynamics thoroughly, paving the way for personalized and precise cancer treatment strategies in ESCC. This holistic approach not only enhances our understanding of the mechanistic basis behind drug resistance but also sets a robust foundation for optimizing therapeutic interventions in ESCC.

An artificial metabzyme for tumour-cell-specific metabolic therapy.

Metabolic dysregulation constitutes a pivotal feature of cancer progression. Enzymes with multiple metal active sites play a major role in this process. Here we report the first metabolic-enzyme-like FeMoO4 nanocatalyst, dubbed 'artificial metabzyme'. It showcases dual active centres, namely, Fe2+ and tetrahedral Mo4+, that mirror the characteristic architecture of the archetypal metabolic enzyme xanthine oxidoreductase. Employing spatially dynamic metabolomics in conjunction with the assessments of tumour-associated metabolites, we demonstrate that FeMoO4 metabzyme catalyses the metabolic conversion of tumour-abundant xanthine into uric acid. Subsequent metabolic adjustments orchestrate crosstalk with immune cells, suggesting a potential therapeutic pathway for cancer. Our study introduces an innovative paradigm in cancer therapy, where tumour cells are metabolically reprogrammed to autonomously modulate and directly interface with immune cells through the intervention of an artificial metabzyme, for tumour-cell-specific metabolic therapy.

Rational Identification of Novel Antibody-Drug Conjugate with High Bystander Killing Effect against Heterogeneous Tumors.

Bystander-killing payloads can significantly overcome the tumor heterogeneity issue and enhance the clinical potential of antibody-drug conjugates (ADC), but the rational design and identification of effective bystander warheads constrain the broader implementation of this strategy. Here, graph attention networks (GAT) are constructed for a rational bystander killing scoring model and ADC construction workflow for the first time. To generate efficient bystander-killing payloads, this model is utilized for score-directed exatecan derivatives design. Among them, Ed9, the most potent payload with satisfactory permeability and bioactivity, is further used to construct ADC. Through linker optimization and conjugation, novel ADCs are constructed that perform excellent anti-tumor efficacy and bystander-killing effect in vivo and in vitro. The optimal conjugate T-VEd9 exhibited therapeutic efficacy superior to DS-8201 against heterogeneous tumors. These results demonstrate that the effective scoring approach can pave the way for the discovery of novel ADC with promising bystander payloads to combat tumor heterogeneity.

CircARCN1 aggravates atherosclerosis by regulating HuR-mediated USP31 mRNA in macrophages.

AIMS: Circular RNAs (circRNAs) are considered important regulators of biological processes, but their impact on atherosclerosis development, a key factor in coronary artery disease (CAD), has not been fully elucidated. We aimed to investigate their potential use in patients with CAD and the pathogenesis of atherosclerosis. METHODS AND RESULTS: Patients with stable angina (SA) or acute coronary syndrome (ACS) and controls were selected for transcriptomic screening and quantification of circRNAs in blood cells. We stained carotid plaque samples for circRNAs and performed gain- and loss-of-function studies in vitro. Western blots, protein interaction analysis, and molecular approaches were used to perform the mechanistic study. ApoE-/- mouse models were employed in functional studies with adeno-associated virus-mediated genetic intervention. We demonstrated elevated circARCN1 expression in peripheral blood mononuclear cells from patients with SA or ACS, especially in those with ACS. Furthermore, higher circARCN1 levels were associated with a higher risk of developing SA and ACS. We also observed elevated expression of circARCN1 in carotid artery plaques. Further analysis indicated that circARCN1 was mainly expressed in monocytes and macrophages, which was also confirmed in atherosclerotic plaques. Our in vitro studies provided evidence that circARCN1 affected the interaction between HuR and ubiquitin-specific peptidase 31 (USP31) mRNA, resulting in attenuated USP31-mediated NF-κB activation. Interestingly, macrophage accumulation and inflammation in atherosclerotic plaques were markedly decreased when circARCN1 was knocked down with adeno-associated virus in macrophages of ApoE-/- mice, while circARCN1 overexpression in the model exacerbated atherosclerotic lesions. CONCLUSIONS: Our findings provide solid evidence macrophagic-expressed circARCN1 plays a role in atherosclerosis development by regulating HuR-mediated USP31 mRNA stability and NF-κB activation, suggesting that circARCN1 may serve as a factor for atherosclerotic lesion formation.

A multiomics analysis-assisted deep learning model identifies a macrophage-oriented module as a potential therapeutic target in colorectal cancer.

Colorectal cancer (CRC) is a common malignancy involving multiple cellular components. The CRC tumor microenvironment (TME) has been characterized well at single-cell resolution. However, a spatial interaction map of the CRC TME is still elusive. Here, we integrate multiomics analyses and establish a spatial interaction map to improve the prognosis, prediction, and therapeutic development for CRC. We construct a CRC immune module (CCIM) that comprises FOLR2+ macrophages, exhausted CD8+ T cells, tolerant CD8+ T cells, exhausted CD4+ T cells, and regulatory T cells. Multiplex immunohistochemistry is performed to depict the CCIM. Based on this, we utilize advanced deep learning technology to establish a spatial interaction map and predict chemotherapy response. CCIM-Net is constructed, which demonstrates good predictive performance for chemotherapy response in both the training and testing cohorts. Lastly, targeting FOLR2+ macrophage therapeutics is used to disrupt the immunosuppressive CCIM and enhance the chemotherapy response in vivo.

Case Report: Partial response to single-agent pembrolizumab in a chemotherapy-resistant metastatic pancreatic cancer patient with a high tumor mutation burden.

Single-agent immune checkpoint blockade has shown no clinical benefits in pancreatic cancer. Recently, the programmed cell death protein 1 (PD-1) antibody pembrolizumab has been recommended as a treatment option for high tumor mutational burden (TMB) solid tumors based on the data from a basket trial. However, no pancreatic cancer patients were enrolled in that trial. Whether pancreatic cancer patients with high TMB respond to PD-1 blockade as well remains unclear. Here, we report a case with a partial response to single-agent immunotherapy with pembrolizumab in pancreatic cancer with high TMB after the failure of several lines of chemotherapy. This result indicates that single-agent immunotherapy may be effective in pancreatic cancer patients with high TMB. In addition, in order to understand the basic immune state of our patients, we also analyzed the changes in immune cells in peripheral blood with cytometry by time-of-flight mass spectrometry (CyTOF) before and after pembrolizumab treatment.

Protocol for isolating single cells from human pancreatic cancer tissues and analyzing major immune cell populations using flow cytometry.

Here, we present a protocol for collecting, dissociating, isolating, staining, and analyzing immune cells from pancreatic cancer tissues for flow cytometry. The isolated cells can also be used for single-cell RNA sequencing and other related procedures. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2023).1.

FAM3L: Feature-Aware Multi-Modal Metric Learning for Integrative Survival Analysis of Human Cancers.

Survival analysis is to estimate the survival time for an individual or a group of patients, which is a valid solution for cancer treatments. Recent studies suggested that the integrative analysis of histopathological images and genomic data can better predict the survival of cancer patients than simply using single bio-marker, for different bio-markers may provide complementary information. However, for the given multi-modal data that may contain irrelevant or redundant features, it is still challenge to design a distance metric that can simultaneously discover significant features and measure the difference of survival time among different patients. To solve this issue, we propose a Feature-Aware Multi-modal Metric Learning method (FAM3L), which not only learns the metric for distance constraints on patients' survival time, but also identifies important images and genomic features for survival analysis. Specifically, for each modality of data, we firstly design one feature-aware metric that can be decoupled into a traditional distance metric and a diagonal weight for important feature identification. Then, in order to explore the complex correlation across multiple modality data, we apply Hilbert-Schmidt Independence Criterion (HSIC) to jointly learn multiple metrics. Finally, based on the learned distance metrics, we apply the Cox proportional hazards model for prognosis prediction. We evaluate the performance of our proposed FAM3L method on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), the experimental results demonstrate that our method can not only achieve superior performance for cancer prognosis, but also identify meaningful image and genomic features correlating strongly with cancer survival.

Runx3-overexpression cooperates with ex vivo AKT inhibition to generate receptor-engineered T cells with better persistence, tumor-residency, and antitumor ability.

BACKGROUND: Solid tumors pose unique roadblocks to treatment with chimeric antigen receptor (CAR) T cells, including limited T-cell persistence, inefficient tumor infiltration, and an immunosuppressive tumor microenvironment. To date, attempts to overcome these roadblocks have been unsatisfactory. Herein, we reported a strategy of combining Runx3 (encoding RUNX family transcription factor 3)-overexpression with ex vivo protein kinase B (AKT) inhibition to generate CAR-T cells with both central memory and tissue-resident memory characteristics to overcome these roadblocks. METHODS: We generated second-generation murine CAR-T cells expressing a CAR against human carbonic anhydrase 9 together with Runx3-overexpression and expanded them in the presence of AKTi-1/2, a selective and reversible inhibitor of AKT1/AKT2. We explored the influence of AKT inhibition (AKTi), Runx3-overexpression, and their combination on CAR-T cell phenotypes using flow cytometry, transcriptome profiling, and mass cytometry. The persistence, tumor-infiltration, and antitumor efficacy of CAR-T cells were evaluated in subcutaneous pancreatic ductal adenocarcinoma (PDAC) tumor models. RESULTS: AKTi generated a CD62L+central memory-like CAR-T cell population with enhanced persistence, but promotable cytotoxic potential. Runx3-overexpression cooperated with AKTi to generate CAR-T cells with both central memory and tissue-resident memory characteristics. Runx3-overexpression enhanced the potential of CD4+CAR T cells and cooperated with AKTi to inhibit the terminal differentiation of CD8+CAR T cells induced by tonic signaling. While AKTi promoted CAR-T cell central memory phenotype with prominently enhanced expansion ability, Runx3-overexpression promoted the CAR-T cell tissue-resident memory phenotype and further enhanced persistence, effector function, and tumor-residency. These novel AKTi-generated Runx3-overexpressing CAR-T cells exhibited robust antitumor activity and responded well to programmed cell death 1 blockade in subcutaneous PDAC tumor models. CONCLUSIONS: Runx3-overexpression cooperated with ex vivo AKTi to generate CAR-T cells with both tissue-resident and central memory characteristics, which equipped CAR-T cells with better persistence, cytotoxic potential, and tumor-residency ability to overcome roadblocks in the treatment of solid tumors.