New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges.

Despite a standardized diagnostic examination, cancer of unknown primary (CUP) is a rare metastatic malignancy with an unidentified tissue of origin (TOO). Patients diagnosed with CUP are typically treated with empiric chemotherapy, although their prognosis is worse than those with metastatic cancer of a known origin. TOO identification of CUP has been employed in precision medicine, and subsequent site-specific therapy is clinically helpful. For example, molecular profiling, including genomic profiling, gene expression profiling, epigenetics and proteins, has facilitated TOO identification. Moreover, machine learning has improved identification accuracy, and non-invasive methods, such as liquid biopsy and image omics, are gaining momentum. However, the heterogeneity in prediction accuracy, sample requirements and technical fundamentals among the various techniques is noteworthy. Accordingly, we systematically reviewed the development and limitations of novel TOO identification methods, compared their pros and cons and assessed their potential clinical usefulness. Our study may help patients shift from empirical to customized care and improve their prognoses.

Construction of TME and Identification of crosstalk between malignant cells and macrophages by SPP1 in hepatocellular carcinoma.

Liver cancer accounts for 6% of all malignancies causing death worldwide, and hepatocellular carcinoma (HCC) is the most common histological type. HCC is a heterogeneous cancer, but how the tumour microenvironment (TME) of HCC contributes to the progression of HCC remains unclear. In this study, we investigated the immune microenvironment by multiomics analysis. The tumour immune infiltration characteristics of HCC were determined at the genomic, epigenetic, bulk transcriptome and single-cell levels by data from The Cancer Genome Atlas portal and the Gene Expression Omnibus (GEO). An epigenetic immune-related scoring system (EIRS) was developed to stratify patients with poor prognosis. SPP1, one gene in the EIRS system, was identified as an immune-related predictor of poor survival in HCC patients. Through receptor-ligand pair analysis in single-cell RNA-seq, SPP1 was indicated to mediate the crosstalk between HCC cells and macrophages via SPP1-CD44 and SPP1-PTGER4 association. In vitro experiments further validate SPP1 can trigger the polarization of macrophages to M2-phenotype tumour-associated macrophages (TAMs).

Depiction of the genomic and genetic landscape identifies CCL5 as a protective factor in colorectal neuroendocrine carcinoma.

BACKGROUND: Colorectal neuroendocrine carcinomas (CRNECs) are highly aggressive tumours with poor prognosis and low incidence. To date, the genomic landscape and molecular pathway alterations have not been elucidated. METHODS: Tissue sections and clinical information of CRNEC (n = 35) and CR neuroendocrine tumours (CRNETs) (n = 25) were collected as an in-house cohort (2010-2020). Comprehensive genomic and expression panels (AmoyDx® Master Panel) were applied to identify the genomic and genetic alterations of CRNEC. Through the depiction of the genomic landscape and transcriptome profile, we compared the difference between CRNEC and CRNET. Reverse transcription-polymerase chain reaction and immunofluorescence staining were performed to confirm the genetic alterations. RESULTS: High tumour mutation load was observed in CRNEC compared with CRNET. CRNECs showed a "cold" immune landscape and increased endothelial cell activity compared with NETs. Importantly, PAX5 was aberrantly expressed in CRNEC and predicted a poor prognosis of CRNECs. CCL5, a factor that is considered an immunosuppressive factor in several tumour types, was strongly expressed in CRNEC patients with long-term survival and correlated with high CD8+ T cell infiltration. CONCLUSION: Through the depiction of the genomic landscape and transcriptome profile, we demonstrated alterations in molecular pathways and potential targets for immunotherapy in CRNEC.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels tumour heterogeneity plus M2-like tumour-associated macrophage infiltration and aggressiveness in TNBC.

Triple-negative breast cancer (TNBC) is characterized by a more aggressive clinical course with extensive inter- and intra-tumour heterogeneity. Combination of single-cell and bulk tissue transcriptome profiling allows the characterization of tumour heterogeneity and identifies the association of the immune landscape with clinical outcomes. We identified inter- and intra-tumour heterogeneity at a single-cell resolution. Tumour cells shared a high correlation amongst stemness, angiogenesis, and EMT in TNBC. A subset of cells with concurrent high EMT, stemness and angiogenesis was identified at the single-cell level. Amongst tumour-infiltrating immune cells, M2-like tumour-associated macrophages (TAMs) made up the majority of macrophages and displayed immunosuppressive characteristics. CIBERSORT was applied to estimate the abundance of M2-like TAM in bulk tissue transcriptome file from The Cancer Genome Atlas (TCGA). M2-like TAMs were associated with unfavourable prognosis in TNBC patients. A TAM-related gene signature serves as a promising marker for predicting prognosis and response to immunotherapy. Two commonly used machine learning methods, random forest and SVM, were applied to find the genes that were mostly associated with M2-like TAM densities in the gene signature. A neural network-based deep learning framework based on the TAM-related gene signature exhibits high accuracy in predicting the immunotherapy response.

A novel genomic classification system of gastric cancer via integrating multidimensional genomic characteristics.

BACKGROUND: Gastric cancer (GC) is one of the leading causes of cancer deaths with high heterogeneity. There is currently a paucity of clinically applicable molecular classification system to guide precise medicine. METHODS: A total of 70 Chinese patients with GC were included in this study and whole-exome sequencing was performed. Unsupervised clustering was undertaken to identify genomic subgroups, based on mutational signature, copy number variation, neoantigen, clonality, and essential genomic alterations. Subgroups were characterized by clinicopathological factors, molecular features, and prognosis. RESULTS: We identified 32 significantly mutated genes (SMGs), including TP53, ARID1A, PIK3CA, CDH1, and RHOA. Of these, PREX2, PIEZO1, and FSIP2 have not been previously reported in GC. Using a novel genome-based classification method that integrated multidimensional genomic features, we categorized GC into four subtypes with distinct clinical phenotypes and prognosis. Subtype 1, which was predominantly Lauren intestinal type, harbored recurrent TP53 mutation and ERBB2 amplification, high tumor mutation burden (TMB)/tumor neoantigen burden (TNB), and intratumoral heterogeneity, with a liver metastasis tendency. Subtype 2 tended to occur at an elder age, accompanying with frequent TP53 and SYNE1 mutations, high TMB/TNB, and was associated with poor prognosis. Subtype 3 and subtype 4 included patients with mainly diffuse/mixed type tumors, high frequency of peritoneal metastasis, and genomical stability, whereas subtype 4 was associated with a favorable prognosis. CONCLUSIONS: By integrating multidimensional genomic characteristics, we proposed a novel genomic classification system of GC associated with clinical phenotypes and provided a new insight to facilitate genome-guided risk stratification and disease management.

In vitro cellular and proteome assays identify Wnt pathway and CDKN2A-regulated senescence affected in mesenchymal stem cells from mice after a chronic LD gamma irradiation in utero.

Reliable data on the effects of chronic prenatal exposure to low dose (LD) of ionizing radiation in humans are missing. There are concerns about adverse long-term effects that may persist throughout postnatal life of the offspring. Due to their slow cell cycle kinetics and life-long residence time in the organism, mesenchymal stem cells (MSCs) are more susceptible to low level genotoxic stress caused by extrinsic multiple LD events. The aim of this study was to investigate the effect of chronic, prenatal LD gamma irradiation to the biology of MSCs later in life. C3H mice were exposed in utero to chronic prenatal irradiation of 10 mGy/day over a period of 3 weeks. Two years later, MSCs were isolated from the bone marrow and analyzed in vitro for their radiosensitivity, for cellular senescence and for DNA double-strand break recognition after a second acute gamma-irradiation. In addition to these cellular assays, changes in protein expression were measured using HPLC-MS/MS and dysregulated molecular signaling pathways identified using bioinformatics. We observed radiation-induced proteomic changes in MSCs from the offspring of in utero irradiated mice (leading to ~ 9.4% of all detected proteins being either up- or downregulated) as compared to non-irradiated controls. The proteomic changes map to regulation pathways involved in the extracellular matrix, the response to oxidative stress, and the Wnt signaling pathway. In addition, chronic prenatal LD irradiation lead to an increased rate of in vitro radiation-induced senescence later in life and to an increased number of residual DNA double-strand breaks after 4 Gy irradiation, indicating a remarkable interaction of in vivo radiation in combination with a second acute dose of in vitro radiation. This study provides the first insight into a molecular mechanism of persistent MSC damage response by ionizing radiation exposure during prenatal time and will help to predict therapeutic safety and efficacy with respect to a clinical application of stem cells.

A novel epigenetic signature for overall survival prediction in patients with breast cancer.

BACKGROUND: Breast cancer is the most common malignancy in female patients worldwide. Because of its heterogeneity in terms of prognosis and therapeutic response, biomarkers with the potential to predict survival or assist in making treatment decisions in breast cancer patients are essential for an individualised therapy. Epigenetic alterations in the genome of the cancer cells, such as changes in DNA methylation pattern, could be a novel marker with an important role in the initiation and progression of breast cancer. METHOD: DNA methylation and RNA-seq datasets from The Cancer Genome Atlas (TCGA) were analysed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Applying gene ontology (GO) and single sample gene set enrichment analysis (ssGSEA) an epigenetic signature associated with the survival of breast cancer patients was constructed that yields the best discrimination between tumour and normal breast tissue. A predictive nomogram was built for the optimal strategy to distinguish between high- and low-risk cases. RESULTS: The combination of mRNA-expression and of DNA methylation datasets yielded a 13-gene epigenetic signature that identified subset of breast cancer patients with low overall survival. This high-risk group of tumor cases was marked by upregulation of known cancer-related pathways (e.g. mTOR signalling). Subgroup analysis indicated that this epigenetic signature could distinguish high and low-risk patients also in different molecular or histological tumour subtypes (by Her2-, EGFR- or ER expression or different tumour grades). Using Gene Expression Omnibus (GEO) the 13-gene signature was confirmed in four external breast cancer cohorts. CONCLUSION: An epigenetic signature was discovered that effectively stratifies breast cancer patients into low and high-risk groups. Since its efficiency appears independent of other known classifiers (such as staging, histology, metastasis status, receptor status), it has a high potential to further improve likely individualised therapy in breast cancer.

A novel 4-gene signature for overall survival prediction in lung adenocarcinoma patients with lymph node metastasis.

BACKGROUND: Lung adenocarcinoma (LUAD) patients experiencing lymph node metastasis (LNM) always exhibit poor clinical outcomes. A biomarker or gene signature that could predict survival in these patients would have a substantial clinical impact, allowing for earlier detection of mortality risk and for individualized therapy. METHODS: With the aim to identify a novel mRNA signature associated with overall survival, we analysed LUAD patients with LNM extracted from The Cancer Genome Atlas (TCGA). LASSO Cox regression was applied to build the prediction model. An external cohort was applied to validate the prediction model. RESULTS: We identified a 4-gene signature that could effectively stratify a high-risk subset of these patients, and time-dependent receiver operating characteristic (tROC) analysis indicated that the signature had a powerful predictive ability. Gene Set Enrichment Analysis (GSEA) showed that the high-risk subset was mainly associated with important cancer-related hallmarks. Moreover, a predictive nomogram was established based on the signature integrated with clinicopathological features. Lastly, the signature was validated by an external cohort from Gene Expression Omnibus (GEO). CONCLUSION: In summary, we developed a robust mRNA signature as an independent factor to effectively classify LUAD patients with LNM into low- and high-risk groups, which might provide a basis for personalized treatments for these patients.

A novel molecular subtyping based on multi-omics analysis for prognosis predicting in colorectal melanoma: A 16-year prospective multicentric study.

Colorectal melanoma (CRM) is a rare malignant tumor with severe complications, and there is currently a lack of systematic research. We conducted a study that combined proteomics and mutation data of CRM from a cohort of three centers over a 16-years period (2005-2021). The patients were divided into a training set consisting of two centers and a testing set comprising the other center. Unsupervised clustering was conducted on the training set to form two molecular subtypes for clinical characterization and functional analysis. The testing set was used to validate the survival differences between the two subtypes. The comprehensive analysis identified two subtypes of CRM: immune exhausted C1 cluster and DNA repair C2 cluster. The former subtype exhibited characteristics of metabolic disturbance, immune suppression, and poor prognosis, along with APC mutations. A machine learning algorithm named Support Vector Machine (SVM) was applied to predict the classification of CRM patients based on protein expression in the external testing cohort. Two subtypes of primary CRM with clinical and proteomic characteristics provides a reference for subsequent diagnosis and treatments.

Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy.

The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore the impact of APOE+ macrophages on ICI therapy using single-cell RNA sequencing (scRNA-seq) and machine learning methods. The scRNA-seq and bulk RNA-seq data are Integrated to construct an M.Sig model for predicting ICI response based on the distinct molecular signatures of macrophage and machine learning algorithms. Comprehensive single-cell analysis as well as in vivo and in vitro experiments are applied to explore the potential mechanisms of the APOE+ macrophage in affecting ICI response. The M.Sig model shows clear advantages in predicting the efficacy and prognosis of ICI therapy in pan-cancer patients. The proportion of APOE+ macrophages is higher in ICI non-responders of triple-negative breast cancer compared with responders, and the interaction and longer distance between APOE+ macrophages and CD8+ exhausted T (Tex) cells affecting ICI response is confirmed by multiplex immunohistochemistry. In a 4T1 tumor-bearing mice model, the APOE inhibitor combined with ICI treatment shows the best efficacy. The M.Sig model using real-world immunotherapy data accurately predicts the ICI response of pan-cancer, which may be associated with the interaction between APOE+ macrophages and CD8+ Tex cells.

Nano-enhanced immunotherapy: Targeting the immunosuppressive tumor microenvironment.

The tumor microenvironment (TME), which is mostly composed of tumor cells, immune cells, signaling molecules, stromal tissue, and the vascular system, is an integrated system that is conducive to the formation of tumors. TME heterogeneity makes the response to immunotherapy different in different tumors, such as "immune-cold" and "immune-hot" tumors. Tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells are the major suppressive immune cells and their different phenotypes interact and influence cancer cells by secreting different signaling factors, thus playing a key role in the formation of the TME as well as in the initiation, growth, and metastasis of cancer cells. Nanotechnology development has facilitated overcoming the obstacles that limit the further development of conventional immunotherapy, such as toxic side effects and lack of targeting. In this review, we focus on the role of three major suppressive immune cells in the TME as well as in tumor development, clinical trials of different drugs targeting immune cells, and different attempts to combine drugs with nanomaterials. The aim is to reveal the relationship between immunotherapy, immunosuppressive TME and nanomedicine, thus laying the foundation for further development of immunotherapy.

A Pathologically Friendly Strategy for Determining the Organ-specific Spatial Tumor Microenvironment Topology in Lung Adenocarcinoma Through the Integration of snRandom-seq and Imaging Mass Cytometry.

Heterogeneous organ-specific responses to immunotherapy exist in lung cancer. Dissecting tumor microenvironment (TME) can provide new insights into the mechanisms of divergent responses, the process of which remains poor, partly due to the challenges associated with single-cell profiling using formalin-fixed paraffin-embedded (FFPE) materials. In this study, single-cell nuclei RNA sequencing and imaging mass cytometry (IMC) are used to dissect organ-specific cellular and spatial TME based on FFPE samples from paired primary lung adenocarcinoma (LUAD) and metastases. Single-cell analyses of 84 294 cells from sequencing and 250 600 cells from IMC reveal divergent organ-specific immune niches. For sites of LUAD responding well to immunotherapy, including primary LUAD and adrenal gland metastases, a significant enrichment of B, plasma, and T cells is detected. Spatially resolved maps reveal cellular neighborhoods recapitulating functional units of the tumor ecosystem and the spatial proximity of B and CD4+ T cells at immunogenic sites. Various organ-specific densities of tertiary lymphoid structures are observed. Immunosuppressive sites, including brain and liver metastases, are deposited with collagen I, and T cells at these sites highly express TIM-3. This study originally deciphers the single-cell landscape of the organ-specific TME at both cellular and spatial levels for LUAD, indicating the necessity for organ-specific treatment approaches.

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.

Effects of KMT2D mutation and its exon 39 mutation on the immune microenvironment and drug sensitivity in colorectal adenocarcinoma.

Background: KMT2D mutation (KMT2DMT) was found to play an important role in cancer immunity and response to immune checkpoint inhibitors (ICIs). The present study aims to investigate the association between KMT2D exon 39 mutation (K-ex39MT) and molecular and clinical characteristics in colorectal adenocarcinoma (CRAD). Methods: We performed profiling of KMT2DMT and K-ex39MT via Kaplan-Meier analysis, cBioportal, Immune-related functional analysis and correlation analysis with TCGA and MSK cohorts to explore their effects on the prognosis, immune landscape, molecular characteristics and drug sensitivity in CRAD. Panel gene sequencing of 30 in-house CRAD tissues and multiple immunofluorescences (mIF) were also used. Results: In multi-cancer, patients with KMT2DMT have a worse overall survival (OS), and CRAD with K-ex39MT exhibited a greater degree of immune cellular infiltration. For CRAD, compared with KMT2D exon39 wild type (K-ex39WT), K-ex39MT patients had higher tumor mutational burden (TMB) and lower copy number alteration (CNA), and were accompanied by more immune cell infiltration including activated T cells, NK cells, Treg cells and exhausted T cells and enrichment of immune-related genes and pathways. In drug sensitivity prediction, K-ex39MT patients have a lower CTX-S score and IC50 of 5-Fluorouracil and irinotecan, and higher Tumor Immune Dysfunction and Rejection (TIDE) dysfunction score. Conclusions: CRAD patients with K-ex39MT have more abundant immune cell infiltration and enrichment of immune-related pathways and signatures. And they may be more sensitive to some chemotherapies but less to cetuximab.

Oxaliplatin plus irinotecan vs irinotecan as second-line treatment in pancreatic cancer patients: a randomized-controlled open-label Phase II study.

Background: Limited second-line therapeutic options are available for metastasis pancreatic cancer (mPC). We aimed to explore the efficacy and safety of oxaliplatin plus irinotecan (IROX) in mPC patients. Methods: This is an open-label, Phase 2, randomized study of mPC patients (aged 18-75 years) who failed when using gemcitabine plus S-1 as first-line therapy. Block randomization with a block size of four was used to randomly assign patients (1:1) between October 2015 and December 2017 to receive either IROX (oxaliplatin 85 mg/m2 and irinotecan 160 mg/m2) or irinotecan monotherapy (irinotecan 180 mg/m2) until disease progression, unacceptable adverse events, or consent withdrawal. The primary end point was overall survival, and the secondary end points were progression-free survival, overall response rate, and adverse event rate. Results: A total of 74 patients were enrolled in this study, including 44 males and 30 females, with an average age of 61 years. The median overall survival was 10.2 and 6.7 months (adjusted hazard ratio [HR], 0.7; 95% confidence interval [CI], 0.4-1.2; P = 0.20) and the median progression-free survival was 5.1 and 2.3 months (adjusted HR, 0.4; 95% CI, 0.2-0.6; P < 0.01) in the IROX group and irinotecan group, respectively. The overall response rates were 18.4% (7/38) in the IROX group and 5.5% (2/36) in the irinotecan group (P = 0.06). Grade 3-4 adverse events occurred in 34% (13/38) of patients in the IROX group and 19% (7/36) of patients in the irinotecan group (P = 0.15). Conclusions: IROX had no significant survival benefit over irinotecan monotherapy in our study. However, IROX reduced the risk of disease progression by 60%, with acceptable toxicity.

Hierarchical transcriptional network governing heterogeneous T cell exhaustion and its implications for immune checkpoint blockade.

The fundamental principle of immune checkpoint blockade (ICB) is to protect tumor-infiltrating T cells from being exhausted. Despite the remarkable success achieved by ICB treatment, only a small group of patients benefit from it. Characterized by a hypofunctional state with the expression of multiple inhibitory receptors, exhausted T (Tex) cells are a major obstacle in improving ICB. T cell exhaustion is a progressive process which adapts to persistent antigen stimulation in chronic infections and cancers. In this review, we elucidate the heterogeneity of Tex cells and offer new insights into the hierarchical transcriptional regulation of T cell exhaustion. Factors and signaling pathways that induce and promote exhaustion are also summarized. Moreover, we review the epigenetic and metabolic alterations of Tex cells and discuss how PD-1 signaling affects the balance between T cell activation and exhaustion, aiming to provide more therapeutic targets for applications of combinational immunotherapies.

Development of a whole-slide-level segmentation-based dMMR/pMMR deep learning detector for colorectal cancer.

To investigate whole-slide-level prediction in the field of artificial intelligence identification of dMMR/pMMR from hematoxylin and eosin (H&E) in colorectal cancer (CRC), we established a segmentation-based dMMR/pMMR deep learning detector (SPEED). Our model was approximately 1,700 times faster than that of the classification-based model. For the internal validation cohort, our model yielded an overall AUC of 0.989. For the external validation cohort, the model exhibited a high performance, with an AUC of 0.865. The human‒machine strategy further improved the model performance for external validation by an AUC up to 0.988. Our whole-slide-level prediction model provided an approach for dMMR/pMMR detection from H&E whole slide images with excellent predictive performance and less computer processing time in patients with CRC.

Small-molecule nanoprodrug with high drug loading and EGFR, PI3K/AKT dual-inhibiting properties for bladder cancer treatment.

Bladder cancer (BCa) is one of the most common malignancies worldwide. Although multiple efforts have been made, the 5-year survival rate of patients with BCa remains unchanged in recent years. Overexpression of the epidermal growth factor receptor (EGFR) is found in ≈74% of BCa tissue specimens; however, current EGFR-based targeted therapies show little benefit for BCa patients, as the EGFR downstream pathways appear to be circumvented by other receptor tyrosine kinases (RTKs). In this study, two natural products are identified, namely triptolide (TPL) and hesperidin (HSP), that target and inhibit the EGFR and its downstream PI3K/AKT pathway in BCa. To synergistically combine triptolide and hesperidin, a succinic acid linker was employed to conjugate them and formed an amphiphilic TPL-HSP EGFR-targeting prodrug (THE), which further self-assembled to generate nanoparticles (THE NPs). These NPs allowed the EGFR-targeted delivery of the triptolide and hesperidin, and simultaneous inhibition of the EGFR and PI3K/AKT both in vitro and in vivo. This study provides a promising EGFR-targeted delivery approach with the dual inhibition of the EGFR and PI3K/AKT, while also exhibiting a high drug loading and low toxicity. Our formulation may be a suitable option to deliver natural products for BCa treatment by EGFR-targeted therapy.

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.