Signaling pathways involved in colorectal cancer: pathogenesis and targeted therapy.

Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Its complexity is influenced by various signal transduction networks that govern cellular proliferation, survival, differentiation, and apoptosis. The pathogenesis of CRC is a testament to the dysregulation of these signaling cascades, which culminates in the malignant transformation of colonic epithelium. This review aims to dissect the foundational signaling mechanisms implicated in CRC, to elucidate the generalized principles underpinning neoplastic evolution and progression. We discuss the molecular hallmarks of CRC, including the genomic, epigenomic and microbial features of CRC to highlight the role of signal transduction in the orchestration of the tumorigenic process. Concurrently, we review the advent of targeted and immune therapies in CRC, assessing their impact on the current clinical landscape. The development of these therapies has been informed by a deepening understanding of oncogenic signaling, leading to the identification of key nodes within these networks that can be exploited pharmacologically. Furthermore, we explore the potential of integrating AI to enhance the precision of therapeutic targeting and patient stratification, emphasizing their role in personalized medicine. In summary, our review captures the dynamic interplay between aberrant signaling in CRC pathogenesis and the concerted efforts to counteract these changes through targeted therapeutic strategies, ultimately aiming to pave the way for improved prognosis and personalized treatment modalities in colorectal cancer.

Spatial immunogenomic patterns associated with lymph node metastasis in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) with lymph node (LN) metastasis is linked to poor prognosis, yet the underlying mechanisms remain largely undefined. This study aimed to elucidate the immunogenomic landscape associated with LN metastasis in LUAD. METHODS: We employed broad-panel next-generation sequencing (NGS) on a cohort of 257 surgically treated LUAD patients to delineate the molecular landscape of primary tumors and identify actionable driver-gene alterations. Additionally, we used multiplex immunohistochemistry (mIHC) on a propensity score-matched cohort, which enabled us to profile the immune microenvironment of primary tumors in detail while preserving cellular metaclusters, interactions, and neighborhood functional units. By integrating data from NGS and mIHC, we successfully identified spatial immunogenomic patterns and developed a predictive model for LN metastasis, which was subsequently validated independently. RESULTS: Our analysis revealed distinct immunogenomic alteration patterns associated with LN metastasis stages. Specifically, we observed increased mutation frequencies in genes such as PIK3CG and ATM in LN metastatic primary tumors. Moreover, LN positive primary tumors exhibited a higher presence of macrophage and regulatory T cell metaclusters, along with their enriched neighborhood units (p < 0.05), compared to LN negative tumors. Furthermore, we developed a novel predictive model for LN metastasis likelihood, designed to inform non-surgical treatment strategies, optimize personalized therapy plans, and potentially improve outcomes for patients who are ineligible for surgery. CONCLUSIONS: This study offers a comprehensive analysis of the genetic and immune profiles in LUAD primary tumors with LN metastasis, identifying key immunogenomic patterns linked to metastatic progression. The predictive model derived from these insights marks a substantial advancement in personalized treatment, underscoring its potential to improve patient management.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Multi-omics analysis reveals the chemoresistance mechanism of proliferating tissue-resident macrophages in PDAC via metabolic adaptation.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer that typically demonstrates resistance to chemotherapy. Tumor-associated macrophages (TAMs) are essential in tumor microenvironment (TME) regulation, including promoting chemoresistance. However, the specific TAM subset and mechanisms behind this promotion remain unclear. We employ multi-omics strategies, including single-cell RNA sequencing (scRNA-seq), transcriptomics, multicolor immunohistochemistry (mIHC), flow cytometry, and metabolomics, to analyze chemotherapy-treated samples from both humans and mice. We identify four major TAM subsets within PDAC, among which proliferating resident macrophages (proliferating rMφs) are strongly associated with poor clinical outcomes. These macrophages are able to survive chemotherapy by producing more deoxycytidine (dC) and fewer dC kinases (dCKs) to decrease the absorption of gemcitabine. Moreover, proliferating rMφs promote fibrosis and immunosuppression in PDAC. Eliminating them in the transgenic mouse model alleviates fibrosis and immunosuppression, thereby re-sensitizing PDAC to chemotherapy. Consequently, targeting proliferating rMφs may become a potential treatment strategy for PDAC to enhance chemotherapy.

Selective oxidative protection leads to tissue topological changes orchestrated by macrophage during ulcerative colitis.

Ulcerative colitis is a chronic inflammatory bowel disorder with cellular heterogeneity. To understand the composition and spatial changes of the ulcerative colitis ecosystem, here we use imaging mass cytometry and single-cell RNA sequencing to depict the single-cell landscape of the human colon ecosystem. We find tissue topological changes featured with macrophage disappearance reaction in the ulcerative colitis region, occurring only for tissue-resident macrophages. Reactive oxygen species levels are higher in the ulcerative colitis region, but reactive oxygen species scavenging enzyme SOD2 is barely detected in resident macrophages, resulting in distinct reactive oxygen species vulnerability for inflammatory macrophages and resident macrophages. Inflammatory macrophages replace resident macrophages and cause a spatial shift of TNF production during ulcerative colitis via a cytokine production network formed with T and B cells. Our study suggests components of a mechanism for the observed macrophage disappearance reaction of resident macrophages, providing mechanistic hints for macrophage disappearance reaction in other inflammation or infection situations.

Characterizing the Survival-Associated Interactions Between Tumor-Infiltrating Lymphocytes and Tumors From Pathological Images and Multi-Omics Data.

The tumor-infiltrating lymphocytes (TILs) and its correlation with tumors have shown significant values in the development of cancers. Many observations indicated that the combination of the whole-slide pathological images (WSIs) and genomic data can better characterize the immunological mechanisms of TILs. However, the existing image-genomic studies evaluated the TILs by the combination of pathological image and single-type of omics data (e.g., mRNA), which is difficulty in assessing the underlying molecular processes of TILs holistically. Additionally, it is still very challenging to characterize the intersections between TILs and tumor regions in WSIs and the high dimensional genomic data also brings difficulty for the integrative analysis with WSIs. Based on the above considerations, we proposed an end-to-end deep learning framework i.e., IMO-TILs that can integrate pathological image with multi-omics data (i.e., mRNA and miRNA) to analyze TILs and explore the survival-associated interactions between TILs and tumors. Specifically, we firstly apply the graph attention network to describe the spatial interactions between TILs and tumor regions in WSIs. As to genomic data, the Concrete AutoEncoder (i.e., CAE) is adopted to select survival-associated Eigengenes from the high-dimensional multi-omics data. Finally, the deep generalized canonical correlation analysis (DGCCA) accompanied with the attention layer is implemented to fuse the image and multi-omics data for prognosis prediction of human cancers. The experimental results on three cancer cohorts derived from the Cancer Genome Atlas (TCGA) indicated that our method can both achieve higher prognosis results and identify consistent imaging and multi-omics bio-markers correlated strongly with the prognosis of human cancers.

Induction of Tumor Ferroptosis-Dependent Immunity via an Injectable Attractive Pickering Emulsion Gel.

The combination of ferroptosis inducers and immune checkpoint blockade can enhance antitumor effects. However, the efficacy in tumors with low immunogenicity requires further investigation. In this work, a water-in-oil Pickering emulsion gel is developed to deliver (1S, 3R)-RSL-3 (RSL-3), a ferroptosis inducer dissolved in iodized oil, and programmed death-1 (PD-1) antibody, the most commonly used immune checkpoint inhibitor dissolved in water, with optimal characteristics (RSL-3 + PD-1@gel). Tumor lipase degrades the continuous oil phase, which results in the slow release of RSL-3 and PD-1 antibody and a notable antitumor effect against low-immunogenic hepatocellular carcinoma and pancreatic cancer. Intriguingly, the RSL-3 + PD-1@gel induces ferroptosis of tumor cells, resulting in antitumor immune response via accumulation of helper T lymphocyte cells and cytotoxic T cells. Additionally, the single-cell sequence profiling analysis during tumor treatment reveals the induction of ferroptosis in tumor cells together with strong antitumor immune response in ascites.

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.

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.

Preoperative sintilimab plus transarterial chemoembolization for hepatocellular carcinoma exceeding the Milan criteria: A phase II trial.

BACKGROUND AND AIMS: Many patients with HCC of Barcelona Clinic Liver Cancer (BCLC) stage A exceeding the Milan criteria, or of BCLC stage B, can undergo resection after successful preoperative therapy, but an optimal approach has not been identified. We investigated preoperative drug-eluting bead transarterial chemoembolization (DEB-TACE) plus sintilimab, in this setting. APPROACH AND RESULTS: In this prospective, phase II study (NCT04174781), adults with HCC of BCLC stage A exceeding the Milan criteria, or BCLC stage B, and ineligible for surgical resection, received sintilimab 200 mg and DEB-TACE. The primary endpoint was progression-free survival by modified RECIST. Secondary endpoints included objective response rate, pathologic response rate, and safety. At the data cutoff (July 2022), among 60 patients, the objective response rate was 62% (37/60) and 51 patients had undergone surgery. After a median follow-up of 26.0 months (range, 3.4-31.8), the median progression-free survival was 30.5 months (95% CI: 16.1-not reached). Among patients undergoing surgery, median progression-free survival was not reached and the 12-month progression-free survival rate was 76% (95% CI: 67-91). A pathologic complete response was achieved in 14% (7/51) of these patients. All patients experienced at least one adverse event, but these were generally manageable. Exploratory analyses showed an association between cytokeratin, V-domain Ig-containing Suppressor of T-cell Activation, CD68, CD169, and cluster 13 fibroblasts and recurrence after surgery. CONCLUSIONS: Sintilimab plus DEB-TACE before surgery showed good efficacy and safety in patients with HCC of BCLC stage A exceeding the Milan criteria or BCLC stage B.

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.