The optimal chemotherapy backbone and specific population of triple-negative breast cancer (TNBC) patients that benefit from neoadjuvant immunotherapy are not well established. This prospective, single-arm, phase II TREND trial assessed the efficacy and safety of tislelizumab plus nab-paclitaxel and epirubicin/cyclophosphamide-based chemotherapy as a neoadjuvant treatment for TNBC (ChiCTR2000035262). The primary endpoint was pathological complete response (pCR), with the secondary endpoints including safety assessment and objective response rate (ORR). ScRNA-seq, bulk RNA-seq, TCR-seq, cyTOF and WES were performed on pre-treatment and post-treatment samples. Among 53 total enrolled patients, 44 completed the combined neoadjuvant therapy, and 30 of 44 patients (68.18%) achieved pCR. Additionally, 14 out of 44 patients had a complete response (31.82%), with an ORR of 93.18%. The most commonly observed treatment-related adverse events (TRAEs) were alopecia, nausea and liver injury with 6 cases classified as grade 3 or higher adverse events. Immune response-related pathways, including TNF signaling pathway, T cell receptor signaling pathway, were enriched in pCR group. Pre-treatment model was identified and construct to predict response to immunotherapy. CDKN1A+ CD8 T lymphocytes were enriched in pCR group after neoadjuvant immunotherapy. Dynamic change of immune-related pathways at an early stage during the neoadjuvant immunotherapy may be associated with the treatment efficacy. In conclusion, neoadjuvant treatment of tislelizumab with nab-paclitaxel and anthracycline-based chemotherapy showed promising clinical activity and was well-tolerated among TNBC patients, without high incidence of TRAEs. These findings provide evidence supporting neoadjuvant tislelizumab with chemotherapy as an effective rational approach for treating TNBC.

To investigate the link between Latent Autoimmune Diabetes in Adults (LADA) and breast cancer (BC) subtypes using Mendelian randomization (MR) and identify potential immunological mediators. We used MR with genetic variants as instruments, leveraging genome-wide association studies data from Europeans to assess the relationship between LADA and BC subtypes. The study evaluated the mediating role of immune cells and employed methods like inverse variance weighted, MR Egger, and MR-PRESSO to ensure robust results. LADA showed a negative association with overall BC, human epidermal growth factor receptor 2-positive BC, and estrogen receptor-positive BC. The FinnGen Biobank replication and meta-analysis confirmed these inverse relationships. CD11c on CD62L+ myeloid dendritic cells mediated the relationship between LADA and BC risk. Our study indicated a negative association between LADA and BC development, especially for human epidermal growth factor receptor 2-positive and estrogen receptor-positive subtypes, with CD11c on CD62L+ myeloid dendritic cells as a key mediator. These findings suggest potential mechanisms for LADA's influence on BC risk and highlight opportunities for targeted prevention and treatment.

Head and neck squamous cell carcinoma (HNSCC) shows variable response to anti-programmed cell death protein 1 (PD-1) therapy, which can be partially explained by a combined positive score (CPS) of tumor and immune cell expression of programmed death-ligand 1 (PD-L1) within the local tumor microenvironment (TME). To better define TME immune determinants associated with treatment efficacy, we conduct a study of n = 48 HNSCC tumors from patients prior to pembrolizumab therapy. Our investigation combines a rapid bioorthogonal multiplex staining method with computational analysis of whole-slide imaging to capture the single-cell spatial heterogeneity and complexity of the TME. Analyzing 6,316 fields of view (FOVs), we provide comprehensive PD-L1 phenotyping and cell proximity assays across the entirety of tissue sections. While none of the PD-L1 metrics adequately predict response, we find that the spatial organization of CCR7+ dendritic cells (DCs) in niches better predicts overall patient survival than CPS alone. This study highlights the importance of understanding the spatial context of immune networks for immunotherapy.

Tumour-derived microparticles (TMPs), extracellular vesicles traditionally obtained upon ultraviolet (UV) radiation of tumour cells, hold promise in tumour immunotherapies and vaccines and have demonstrated potential as drug delivery systems for tumour treatment. However, concerns remain regarding the limited efficacy and safety of UV-derived TMPs. Here we introduce a microwave (MW)-assisted method for preparing TMPs, termed MW-TMPs. Brief exposure of tumour cells to short-wavelength MW radiation promotes the release of TMPs showing superior in vivo antitumour activity and safety compared with UV-TMPs. MW-TMPs induce immunogenic cell death and reprogramme suppressive tumour immune microenvironments in different lung tumour models, enabling dual targeting of tumour cells by natural killer and T cells. We show that they can efficiently deliver methotrexate to tumours, synergistically boosting the efficacy of PD-L1 blockade. This MW-TMP development strategy is simpler, more efficient and safer than traditional UV-TMP methods.

The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN+ iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN+ iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN+ iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN+ iCAFs and CD8+ Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN+ iCAFs and CD8+ exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8+ T cells, downregulation of GSN on CAFs drives CD8+ T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.

Radiotherapy is a pillar of breast cancer treatment; however, it remains unclear how radiotherapy modulates the tumor microenvironment. We investigated this question in a cohort of 20 patients with estrogen-receptor positive (ER+) breast tumors who received neoadjuvant radiotherapy. Tumor biopsies were collected before and 7 days postradiation. Single-cell DNA sequencing (scDNA-seq) and scRNA-seq were conducted on 8 and 11 patients, respectively, at these two time points. The scRNA data showed increased infiltration of naive-like CD4 T cells and an early, activated CD8 T cell population following radiotherapy. Radiotherapy also eliminated existing cytotoxic T cells and resulted in myeloid cell increases. In tumor cells, the scDNA-seq data showed a high genomic selection of subclones in half of the patients with high ER expression, while the remaining number had low genomic selection and an interferon response. Collectively, these data provide insight into the impact of radiotherapy in ER+ breast cancer patients.

Despite the crucial role of T cell clones in anti-tumor activity, their characterization and association with clinical outcomes following immune checkpoint inhibitors are lacking. Here, we analyzed paired single-cell RNA sequencing/T cell receptor sequencing of 767,606 T cells across 460 samples spanning 6 cancer types. We found a robust signature of response based on expanded CD8+ clones that differentiates responders from non-responders. Analysis of persistent clones showed transcriptional changes that are differentially induced by therapy in the different response groups, suggesting an improved reinvigoration capacity in responding patients. Moreover, a gene trajectory analysis revealed changes in the pseudo-temporal state of de novo clones that are associated with clinical outcomes. Lastly, we found that clones shared between tumor and blood are more abundant in non-responders and execute distinct transcriptional programs. Overall, our results highlight differences in clonal transcriptional states that are linked to patient response, offering valuable insights into the mechanisms driving effective anti-tumor immunity.

Treatment of early breast cancer is currently experiencing a rapid evolution because of important insight into tumor subtypes and continuous development and improvement of novel therapeutics. Historically considered non-immunogenic, breast cancer has seen a paradigm shift with increased understanding of immune microenvironment, which have revealed extensive heterogeneity in tumor-associated inflammation. Notably, the more aggressive breast cancer subtypes, including triple-negative and HER2-positive, have exhibited favorable responses to combined chemo-immunotherapy protocols. Neoadjuvant therapy has emerged as the standard of care for these tumors, with pathological complete response used as a surrogate endpoint for long-term clinical outcomes and coincidently expediting new drug approval. The neoadjuvant setting affords a unique opportunity for in vivo treatment response evaluation and effects on the tumor microenvironment. In this review, the predictive and prognostic value of the tumor immune microenvironment before, during, and after treatment across various therapeutic regimens, tailored to distinct breast cancer subtypes, is carefully examined.

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by limited targeted therapies and high recurrence rates. While immune checkpoint inhibitors (ICIs) have shown promise, their efficacy as monotherapy is limited. Clinically, ICIs demonstrate significant benefit primarily when combined with chemotherapy, particularly in the neoadjuvant setting for early-stage TNBC, which yields superior outcomes compared to adjuvant therapy. This review elucidates the tumor immunological principles underlying these observations. We discussed how the suppressive tumor microenvironment (TME), progressive T cell exhaustion, and associated epigenetic scarring constrain ICI monotherapy effectiveness. Crucially, we highlight the immunological advantages of the neoadjuvant approach: the presence of the primary tumor provides abundant antigens, and intact tumor-draining lymph nodes (TDLNs) act as critical sites for ICI-mediated priming and expansion of naïve and precursor exhausted T cells. This robust activation within TDLNs enhances systemic anti-tumor immunity and expands the T cell repertoire, a process less effectively achieved in the adjuvant setting after tumor resection. These mechanisms provide a strong rationale for the improved pathological complete response (pCR) rates and event-free survival observed with neoadjuvant chemoimmunotherapy, as demonstrated in trials like KEYNOTE-522. We further explore the implications for adjuvant therapy decisions based on treatment response, the challenges of ICI resistance, the need for predictive biomarkers, management of immune-related adverse events (irAEs), and future therapeutic directions. Understanding the dynamic interplay between chemotherapy, ICIs, T cells, and the TME, particularly the role of TDLNs in the neoadjuvant context, is essential for optimizing immunotherapy strategies and improving outcomes for patients with TNBC.

Postpartum mammary gland remodeling after a pregnancy/lactation cycle is characterized by mechanisms of cell death and inflammation. Here, we show that SEMA7A promotes PD-L1 expression in immune cells of the mammary tissue during involution. These same phenotypes are mimicked in the microenvironment of SEMA7A-expressing tumors, which partially respond to αPD-1/αPD-L1 treatments in vivo. However, cells that remain after treatment are enriched for SEMA7A expression. Therefore, we tested a monoclonal antibody that directly targets SEMA7A-expressing tumors, in part, by reducing SEMA7A-mediated upregulation of PD-L1. In vivo, the SEMA7A monoclonal antibody reduces tumor growth and/or promotes complete regression of mouse mammary tumors, reduces some immunosuppressive phenotypes in the tumor microenvironment, and restores cytotoxic T cells, suggesting that SEMA7A may be a candidate for immune-based therapy for breast cancer patients.

In this study, we developed an integrated single cell transcriptomic (scRNAseq) atlas of human breast cancer (BC), the largest resource of its kind, totaling > 600,000 cells across 138 patients. Rigorous integration and annotation of publicly available scRNAseq data enabled a highly resolved characterization of epithelial, immune, and stromal heterogeneity within the tumor microenvironment (TME). Within the immune compartment we were able to characterize heterogeneity of CD4, CD8 T cells and macrophage subpopulations. Within the stromal compartment, subpopulations of endothelial cells (ECs) and cancer associated fibroblasts (CAFs) were resolved. Within the cancer epithelial compartment, we characterized the functional heterogeneity of cells across the axes of stemness, epithelial-mesenchymal plasticity, and canonical cancer pathways. Across all subpopulations observed in the TME, we performed a multi-resolution survival analysis to identify epithelial cell states and immune cell types which conferred a survival advantage in both The Cancer Genome Atlas (TCGA) and METABRIC. We also identified robust associations between TME composition and clinical phenotypes such as tumor subtype and grade that were not discernible when the analysis was limited to individual datasets, highlighting the need for atlas-based analyses. This atlas represents a valuable resource for further high-resolution analyses of TME heterogeneity within BC.

Breast cancer (BC) remains one of the most common malignancies among women worldwide, with persistently poor prognosis despite advancements in diagnostics and therapies. Long non-coding RNAs (lncRNAs) and coagulation-related genes (CRGs) are increasingly recognized for their roles in prognosis and immune modulation. Using transcriptomic data from 1,045 BC patients in TCGA, we identified CRG-associated lncRNAs via coexpression analysis (Pearson |R|> 0.4, p < 0.001) and constructed a prognostic model through univariate Cox analysis, LASSO regression with tenfold cross-validation (λ = 0.05), and multivariate Cox analysis. The model stratified patients into high- and low-risk groups with distinct overall survival (HR = 3.21, p < 0.001) and demonstrated robust predictive accuracy (AUC = 0.795 at 1 year). Functional enrichment revealed immune-related pathways (e.g., cytokine signaling, PD-L1 regulation), and high-risk patients exhibited elevated tumor mutational burden (TMB) and PD-L1 expression, suggesting enhanced immunotherapy responsiveness. Drug sensitivity analysis identified 5 targeted agents (e.g., BIBW2992) with differential efficacy between risk groups. This CRG-lncRNA signature provides a novel tool for prognosis prediction and personalized immunotherapy in BC, illuminating crosstalk between coagulation and immune pathways.

Penile cancer is a rare but aggressive malignancy, characterized by early lymphatic spread which is the most critical prognostic factor. Treatment options for patients with locally advanced and metastatic disease are limited, primarily relying on cisplatin-based chemotherapy, which is characterized by high toxicity and early resistance. In recent years, there has been a growing interest on translational research exploring the tumor microenvironment, enabling the identification of novel potential therapeutic targets. Emerging preclinical evidence supports the use of immune checkpoint inhibitors, antibody-drug conjugates and novel exploratory therapies targeting myeloid-derived suppressor cells and tumor associated macrophages, as well as their combinations. However, robust phase III trials investigating such therapies are currently lacking. A deeper understanding of the penile cancer immune landscape and the role of specific mutations in carcinogenesis, might lead to the development of novel combination strategies to overcome cisplatin resistance and disease progression, and to a better selection of patients for inclusion in future clinical trials.

Avian Leukosis Virus (ALV) can induce tumorigenesis and immune suppression by acting on lymphocytes in the bone marrow. In this study, single-cell RNA sequencing (scRNA-seq) was used to analyze chicken bone marrow lymphocytes under Avian Leukosis Virus subtype J (ALV-J) infection. Using subgroup-specific marker genes and cell state analysis, we identified 18 distinct cell clusters, including 8 T cell clusters, 2 B cell clusters, 5 tumor-like cell clusters, and 3 unidentified clusters. Gene expression analysis revealed that in the 10 T/B lymphocyte clusters, the differentially expressed genes in double-positive T cells, B1-like B cells, and cytotoxic T cells were highly enriched in pathways related to viral infection and immune response. These three cell populations exhibited high proportions and significant changes after infection, suggesting a strong immune response to ALV-J infection. Additionally, during ALV-J infection, the proportion of regulatory T cells and CTLA4 T cells increased, while immune suppressive factors TGFB1 and IL16 were highly expressed across the cell populations, indicating an immune-suppressive state in bone marrow lymphocytes. Moreover, ALV-J infected all cell populations; however, within the same cluster, only a fraction of the cells expressed ALV-J viral genes. Notably, in all cells expressing ALV-J viral genes, the "Rho family GTPase signaling pathway" associated with antiviral responses was activated. The Rho family, which is a key regulator of cytoskeletal reorganization and cell polarity, also plays a critical role in tumor cell proliferation and metastasis. Further analysis using Ingenuity Pathway Analysis (IPA) software predicted key upstream regulators of immune response, such as MYC and MCYN. In conclusion, this study identifies key genes and signaling pathways involved in immune responses of different lymphocyte subpopulations triggered by ALV-J infection in bone marrow. These findings contribute to a better understanding of the immune mechanisms in ALV-J-infected bone marrow lymphocytes and provide insights for discovering breeding loci for ALV-J resistance.

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Tumor heterogeneity and interaction with tumor microenvironment play a crucial role in neoadjuvant chemotherapy (NAC) resistance in breast cancer (BRCA). Unraveling this dynamic interaction may help uncover novel therapeutic targets. Here, dynamic changes in tumor states and cellular composition are systemically characterized using 175,825 single-cell transcriptomics from naïve and post-treatment biopsies of BRCA patients receiving NAC. CDK16+ tumors are identified featured with luminal progenitor cell (LPC)-like tumor cells enriched in the triple-negative subtype of BRCA, associated with chemo-resistance. Integrating single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and six independent public gene expression profiles that underwent chemotherapy revealed that POSTN+ cancer-associated fibroblasts (CAFs) are closely localized and interacted with CDK16+ LPC-like tumor cells to promote chemo-resistance. In vivo, CDK16 knockdown in tumor cells combined with chemotherapy significantly enhanced therapeutic efficacy. This in-house scRNA-seq from a mouse model validated that CDK16 knockdown reduced the LPC-like tumor cell signature, and the interaction of tumor featured with LPC-like tumor cells and POSTN+ CAFs. Together, the systematically integrated analyses uncovered an interaction network of CDK16+ tumor and POSTN+ CAFs that contributed to NAC- resistance, providing a new strategy for targeting CDK16 to enhance chemotherapy efficacy.

Breast cancer stands as one of the most prevalent malignancies affecting women. Alterations in molecular pathways in cancer cells represent key regulatory disruptions that drive malignancy, influencing cancer cell survival, proliferation, and potentially modulating therapeutic responsiveness. Therefore, decoding the intricate molecular mechanisms and identifying novel therapeutic targets through systematic computational approaches are essential steps toward advancing effective breast cancer treatments. In this study, we developed an integrative computational framework that combines single-cell RNA sequencing (scRNA-seq) and multi-omics analyses to delineate the functional characteristics of malignant cell subsets in breast cancer patients. Our analyses revealed a significant correlation between cholesterol biosynthesis and HER2 expression in malignant breast cancer cells, supported by proteomics data, gene expression profiles, drug treatment scores, and cell-surface HER2 intensity measurements. Given previous evidence linking cholesterol biosynthesis to HER2 membrane dynamics, we proposed a combinatorial strategy targeting both pathways. Experimental validation through clonogenic and viability assays demonstrated that simultaneous inhibition of cholesterol biosynthesis (via statins) and HER2 (via Neratinib) synergistically reduced malignant breast cancer cells, even in HER2-negative contexts. Through systematic analysis of scRNA-seq and multi-omics data, our study computationally identified and experimentally validated cholesterol biosynthesis and HER2 as novel combinatorial therapeutic targets in breast cancer. This data-driven approach highlights the potential of leveraging multiple molecular profiling techniques to uncover previously unexplored treatment strategies.

The tumour microenvironment of rare ovarian germ cell tumours (OGCT) and sex-cord stromal tumours (SCST) remains unexplored. To better understand their immune and stromal landscape, we constructed a blueprint using single-cell RNA sequencing (scRNA-seq).

We performed scRNA-seq of 66, 919 cells from twelve fresh tumour samples: seven adult granulosa cell tumour (aGSCT), one juvenile GSCT (jGSCT), one Sertoli-Leydig (SL) tumour, two immature teratoma (IT) and one dysgerminoma (DG). We characterised immune cell subtypes and fibroblasts based on their specific marker genes. Validation included combined positive score (CPS) of 46 OGCTs and 66 SCSTs, and bulk RNA sequencing (n = 32).

Cell clustering and annotation revealed a immune-activated microenvironment in DG, driven by PD-1- exhausted T cells, reflected in high CPS (≥10) and upregulated immune pathways. IT samples displayed no immunoreactive profile, consistent with a negative CPS. aGSCTs exhibited a fibroblast-enriched, immune-desert phenotype, with low T cell infiltration and increased immunosuppressive LYVE1 and CX3CR1+ macrophages, corresponding to negative CPS.

We constructed a detailed blueprint of the OGCT and SCSTs microenvironment of, elucidating potential modulators that shape their immune landscape. The immune-suppressive environment in aGSCTs likely limits immunotherapy response, as immunosuppressive macrophages inhibit T cell expansion along with EMT activation and fibroblast predominance.

Understanding the effect of gut microbiota function on immune checkpoint inhibitor (ICI) responses is urgently needed. Here, we integrated 821 fecal metagenomes from 12 datasets to identify differentially abundant genes and construct random forest models to predict ICI response. Gene markers demonstrated excellent predictive performance, with an average area under the curve (AUC) of 0.810. Pathway analyses revealed that quorum sensing (QS), ABC transporters, flagellar assembly, and amino acid biosynthesis pathways were enriched between responders (R) and non-responders (NRs) across 12 datasets. Furthermore, luxS, manA, fliC, and trpB exhibited consistent changes between R and NR across 12 datasets. Follow-up microbiota transplant experiments showed that inter-species signaling by different QS autoinducer-2 (AI-2) molecules (synthesized by luxS) can act on overall community function to promote the colonization of Akkermansia muciniphila, which is associated with superior ICI responses. Together, our data highlight the role of gut microbiota function in modulating the microbiome and antitumor immunity.

Liver metastasis is the most common site of metastasis in colorectal cancer, and the prognosis of colorectal cancer patients with liver metastasis is extremely poor. Revealing the key genes of CLM and implementing targeted interventions is of great significance for colorectal cancer patients. By using the weighted gene co-expression network analysis (WGCNA) algorithm, key gene modules related to metastasis in colorectal cancer were identified. Subsequently, immune-regulating and prognostic-influencing key gene sets were identified from these modules to construct a prognostic model related to colorectal cancer metastasis. Genetic background differences underlying this model were analyzed using colorectal cancer methylation and mutation data, followed by Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) analysis of the relevant biological processes associated with the model. The value of predicting tumor drug response through the model was assessed using drug half maximal inhibitory concentration (IC50) data from colorectal cancer cell lines. Subsequently, utilizing single-cell sequencing data about liver metastasis, the colorectal cancer immune microenvironment reflected in the predictive model was analyzed, and a key gene set of the model was identified. Lastly, experimental validation was conducted to investigate the regulatory effects of photodynamic therapy (PDT) on the key genes of the model, and the cytotoxic effect of PDT on colorectal cancer was confirmed. An immune-related gene prognostic model regulating CLM was constructed, consisting of HSPA1A, ULBP2, RBP7, OXT, SLC11A1, INHBB, and ICOS. This model can predict the clinical response of colorectal cancer patients to Oxaliplatin, Cisplatin, Irinotecan, and 5-Fluorouracil. Single-cell sequencing results demonstrate that the model is associated with an immunosuppressive microenvironment in CLM. The higher the model's riskscore, the weaker the MHC-I, MHC-II, and various tumor immune signaling pathway networks in the colorectal cancer microenvironment. Causal analysis reveals that SLC11A1, ICOS, and HSPA1A play key roles in this model. PDT can kill colorectal cancer cells, inhibit colorectal cancer cell metastasis, significantly influence the expression of genes such as SLC11A1, ICOS, and HSPA1A in these processes, and suppress the infiltration of macrophages in the colorectal microenvironment, inhibiting the immune escape process of PD-1/PD-L1. A prognostic model based on immunity regulated by PDT has been established for assessing the prognosis of CLM patients, as well as clinical responses to chemotherapy drugs and immunotherapy.

Immune checkpoint blockade (ICB) therapy has transformed cancer treatment, yet many patients fail to respond. Employing single-cell multiomics, we unveil T cell dynamics influencing ICB response across 480 pan-cancer and 27 normal tissue samples. We identify four immunotherapy response-associated T cells (IRATs) linked to responsiveness or resistance and analyze their pseudotemporal patterns, regulatory mechanisms, and T cell receptor clonal expansion profiles specific to each response. Notably, transforming growth factor β1 (TGF-β1)+ CD4+ and Temra CD8+ T cells negatively correlate with therapy response, in stark contrast to the positive response associated with CXCL13+ CD4+ and CD8+ T cells. Validation with a cohort of 23 colorectal cancer (CRC) samples confirms the significant impact of TGF-β1+ CD4+ and CXCL13+ CD4+ and CD8+ T cells on ICB efficacy. Our study highlights the effectiveness of single-cell multiomics in pinpointing immune markers predictive of immunotherapy outcomes, providing an important resource for crafting targeted immunotherapies for successful ICB treatment across cancers.

We leverage a clinical trial (NCT04080804) that compared neoadjuvant anti-PD-1, anti-PD-1+CTLA-4, and anti-PD-1+LAG-3 therapies in head and neck squamous cell carcinoma patients. Combination therapies promote higher pathologic response rates versus monotherapy, and major pathologic response is associated with better survival. To address whether successful immune checkpoint inhibitor (ICI) regimens act through similar or distinct pathways, we robustly and longitudinally characterize transcriptional and proteomic dynamics of CD8+ tumor-infiltrating lymphocytes (TILs) in a clonal manner. Anti-PD-1+LAG-3 reprograms CD8+ TIL with type-I interferon response and exhaustion gene programs into effector memory and resident memory (TEM/TRM). In contrast, anti-PD-1+CTLA-4 activates and expands pre-existing TEM/TRM CD8+ TIL, but does not rejuvenate exhausted phenotypes into T effector cells. Anti-PD-1+LAG-3, but not anti-PD-1+CTLA-4, induces widespread TCR sharing among the different transcriptional states, as well as increased TCR diversity in responding patients. Our data suggest doublet regimen-specific transcriptional and clonal dynamics of tumor-reactive CD8+ T cells.

Artificial intelligence (AI) is increasingly being utilized in cancer research as a computational strategy for analyzing multiomics datasets. Advances in single-cell and spatial profiling technologies have contributed significantly to our understanding of tumor biology, and AI methodologies are now being applied to accelerate translational efforts, including target discovery, biomarker identification, patient stratification, and therapeutic response prediction. Despite these advancements, the integration of AI into clinical workflows remains limited, presenting both challenges and opportunities. This review discusses AI applications in multiomics analysis and translational oncology, emphasizing their role in advancing biological discoveries and informing clinical decision-making. Key areas of focus include cellular heterogeneity, tumor microenvironment interactions, and AI-aided diagnostics. Challenges such as reproducibility, interpretability of AI models, and clinical integration are explored, with attention to strategies for addressing these hurdles. Together, these developments underscore the potential of AI and multiomics to enhance precision oncology and contribute to advancements in cancer care.

Gliomas, including both glioblastoma multiforme (GBM) and lower-grade glioma (LGG), present a substantial challenge in neuro-oncology because of genetic heterogeneity and unsatisfactory prognosis. This study aimed to conduct a comprehensive multi-omics analysis of gliomas using various bioinformatics approaches to identify potential therapeutic targets and prognostic markers. A comprehensive analysis was conducted on 1327 sequencing data samples alongside their relevant clinical information sourced from The Cancer Genome Atlas (TCGA) pertaining to glioblastoma (GBM), low-grade glioma (LGG), the Chinese Glioma Genome Atlas (CCGA) and University of California Santa Cruz Xena (UCSC Xena) datasets. These tools were employed for gene expression profiling, survival analysis, and cell communication mapping. Spatial transcriptomics revealed the localization of mesenchymal (MES)-like malignant tumors, and drug sensitivity analysis was performed to evaluate responses to quinpirole and meropenem. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) framework was utilized to gauge the responsiveness to immunotherapy. The MES-like malignant and monocyte/macrophage (mono/macro) cell subsets showed high hallmark scores, playing key roles in the tumor microenvironment. MES-like malignant marker gene scores correlated with overall survival across datasets, whereas mono/macro marker gene scores were significant in the TCGA-LGG and CCGA datasets. Key interactions between these cell types were found, especially with CD14-ITGB2, LGALS1-CD69, and APOE-TREM2. The mono/macro cell subset demonstrated better immune therapy responsiveness, as indicated by lower TIDE scores. Spatial transcriptomics revealed that MES-like malignant tumors are predominantly localized in four distinct regions, with the marker genes CHI3L1 and ADM confirming these locations. Drug sensitivity analysis revealed differential responses of the MES-like malignant cell subset to quinpirole and meropenem. Our results offer fresh perspectives on the differential roles of MES-like malignant and monocyte/macrophage cell subsets in tumor progression and immune modulation, providing novel insights into glioma biology.

Breast cancer is the most prevalent malignancy in women and exhibits significant heterogeneity. The tumor microenvironment (TME) plays a critical role in tumorigenesis, progression, and response to therapy. However, its impact on the prognosis and immunotherapy responses is incompletely understood. Using public databases, we conducted a comprehensive investigation of transcriptome and single-cell sequencing data. After performing immune infiltration analysis, we conducted consensus clustering, weighted gene co-expression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (Lasso) regression to identify independent prognostic genes in breast cancer. Subsequently, we developed a prognostic model for patients with breast cancer. Tumor Immune Dysfunction and Exclusion (TIDE) values were used to assess patient's responsiveness to breast cancer. Based on single-cell RNA-sequencing data, we identified various cell types through cluster analysis and investigated the expression of prognostic model genes in each cell type. The drug sensitivity of targeted therapeutic agents for breast cancer treatment was analyzed in different cell types. We identified 12 independent prognostic genes associated with breast cancer and used these genes to construct a prognostic model. The prognostic model accurately discriminated between patients classified as high- and low-risk, providing precise prognostic predictions for individual patients. Additionally, our model exhibited a robust capacity to predict the immunotherapeutic response in breast cancer patients. Our investigation revealed a notable association between the proportion of endothelial cells (ECs) and patient prognosis in breast cancer. A prognostic model for breast cancer was formulated that showed close associations between prognosis and response to immunotherapy. For patients predicted by our model to not respond effectively to immunotherapeutic agents, it may be considered to combine immunotherapeutic agents with targeted therapeutic agents identified through our drug sensitivity analysis, which could potentially enhance treatment efficacy.

Advanced esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity and distinct patterns of immune cell infiltration. Regulatory T cells (Tregs), in particular, play a critical role in shaping an immunosuppressive tumor microenvironment (TME), which is associated with poor clinical outcomes.

We developed a prognostic model by integrating GEO-derived bulk RNA sequencing data and single-cell transcriptome. Model predictions were confirmed through RT-qPCR, Western blot, and immunohistochemistry on clinical specimens, while in vitro assays (CCK8, transwell invasion, scratch, colony formation, and immunofluorescence) validated the function of SAP18 in cell proliferation, invasion, and ECM remodeling.

Expression patterns of the 5 Tregs-associated genes in clinical specimens aligned with model predictions, underscoring the model's robustness. The high-risk subgroup was associated with upregulated extracellular matrix (ECM) remodeling, an abundance of immune-suppressive cells, higher TP53 mutation rate, and limited benefit from immunotherapy. In contrast, the low-risk subgroup exhibited anti-tumor immunity. Cell-cell communication analysis also implicated the collagen pathway in Tregs-mediated immune evasion in ESCC. Functional assays indicated that SAP18 in the prognostic model significantly promotes proliferation, invasion, and ECM reconstruction, further highlighting its potential as a therapeutic target.

Our findings elucidate the role of Tregs in the TME, underscoring significant potential of SAP18, which is essential for assessing patient prognosis and may facilitate the development of personalized therapies for ESCC.

Immunotherapies have had unprecedented success in the treatment of multiple cancer types, albeit with variable response rates. Unraveling the complex network of immune cells within the tumor microenvironment (TME) may provide additional insights to enhance antitumor immunity and improve clinical response. Many studies have shown that NK cells or innate lymphoid cells (ILC) have regulatory capacity. Here, we identified CD103 as a marker that was found on CD56+ cells that were associated with a poor proliferative capacity of tumor-infiltrating lymphocytes in culture. We further demonstrated that CD103+CD56+ ILCs isolated directly from tumors represented a distinct ILC population that expressed unique surface markers (such as CD49a and CD101), transcription factor networks, and transcriptomic profiles compared with CD103-CD56+ NK cells. Using single-cell multiomic and spatial approaches, we found that these CD103+CD56+ ILCs were associated with CD8+ T cells with reduced expression of granzyme B. Thus, this study identifies a population of CD103+CD56+ ILCs with potentially inhibitory functions that are associated with a TME that includes CD8+ T cells with poor antitumor activity. Further studies focusing on these cells may provide additional insights into the biology of an inhibitory TME.

Triple-Negative Breast Cancer (TNBC) represents a highly aggressive subtype of breast cancer with an unfavorable prognosis, characterized by minimal immune infiltration and pronounced immune suppression, resulting in a limited response to immunotherapy. In this study, a multifunctional Arsenene-Vanadene nanodot (AsV) drug delivery system is introduced, which responds to the tumor microenvironment by releasing arsenic and vanadium. Arsenic undergoes oxidation to generate highly toxic trivalent arsenic, which induces apoptosis in tumor cells while utilizing apoptotic cell debris to transiently activate the immune system. Additionally, arsenic binds to cysteine, indirectly facilitating ferroptosis. Concurrently, vanadium's redox cycling properties are harnessed to trigger a Fenton-like reaction, promoting lipid peroxidation. Furthermore, ferroptosis is enhanced through the depletion of glutathione and inactivation of glutathione peroxidase 4 (GPX4), leading to the release of damage-associated molecular patterns and thereby amplifying the anti-tumor immune response. This study represents the first instance of integrating arsenene's apoptosis-inducing properties with vanadium's ferroptosis-enhancing effects, providing a synergistic approach to improving the immunotherapeutic response and offering a potential strategy for enhancing TNBC prognosis. STATEMENT OF SIGNIFICANCE: Triple-negative breast cancer (TNBC) exhibits resistance to immunotherapy due to its highly immunosuppressive tumor microenvironment. In this study, tumour-responsive Arsenene-Vanadene nanodots (AsV) were developed to induce a synergistic effect by triggering apoptosis and ferroptosis through microenvironment-specific mechanisms. The arsenic component generates cytotoxic trivalent arsenic, promoting apoptosis while binding to cysteine, thereby reducing GSH synthesis. Simultaneously, vanadium initiates lipid peroxidation through Fenton-like reactions and disruption of the glutathione/GPX4 axis, further amplifying ferroptotic cell death. This dual-action system transforms tumor cell debris into immune-stimulating signals while circumventing conventional immunotherapy limitations. As the first strategy integrating arsenic-induced apoptosis with vanadium-enhanced ferroptosis, this approach provides a mechanistic framework to overcome TNBC immunosuppression through coordinated cell death pathways, demonstrating potential for precision nanomedicine applications.

Triple-negative breast cancer (TNBC), characterized by high heterogeneity and invasiveness. Currently, inducing immunogenic cell death (ICD) of tumor cells through approaches such as radiotherapy and chemotherapy is an effective strategy to enhance the response to anti-programmed death-ligand 1 antibody (aPD-L1) therapy in TNBC. However, radiotherapy and chemotherapy treatments often upregulate PD-L1 expression in tumor cells, thereby weakening the tumor cells' response to aPD-L1. Celastrol exhibits broad-spectrum and potent anti-tumor activity, efficiently inducing ICD without increasing PD-L1 levels in tumor cells.

This study aims to elucidate the tumor-targeting effects of celastrol-loaded liposomes and its synergistic efficacy and mechanism of action in combination with aPD-L1 against TNBC.

The Rg3 liposomes loaded with celastrol (Cel-Rg3-Lp) were prepared using the thin-film hydration method. BALB/c mice were utilized to establish an in situ breast cancer model. Mice were intravenously injected with Cel-Rg3-Lp at a dosage of celastrol 1 mg/kg once every two days for a total of 7 injections. Flow cytometry, western blot, and immunofluorescence techniques were employed to investigate the synergistic effects and mechanisms of Cel-Rg3-Lp combined with aPD-L1 in the treatment of TNBC.

The findings of this study demonstrate that after 7 administrations of Cel-Rg3-Lp (1 mg/kg celastrol, intravenously), significant anti-tumor effects are observed, including the recruitment of CD8+T cells and dendritic cells (DCs), while reducing the infiltration of immunosuppressive cells. The therapeutic efficacy was further enhanced when combined with aPD-L1. Additionally, Cel-Rg3-Lp markedly downregulated glucose-regulated protein 78 (GRP78) expression, thereby inducing ICD in tumor cells.

This study successfully constructed a multifunctional liposome and proposed a mechanism for inducing ICD through the GRP78-endoplasmic reticulum stress pathway. The liposome downregulates GRP78, triggering endoplasmic reticulum stress in tumor cells, inducing ICD, activating DCs, and enhancing antigen presentation to T cells. This improves the tumor immune microenvironment and provides a theoretical foundation for combining Cel-Rg3-Lp with aPD-L1 in the treatment of TNBC. This mechanism opens unique prospects for using celastrol in TNBC therapy and enhancing the effectiveness of immunotherapy.

Recent findings reveal that the precursors of exhausted CD8+ T (CD8+ Tpex) cells possess stem-like signatures in tumor immunity, which originate from tumor draining lymph node (TdLN)-derived tumor-specific memory (CD8+ TTSM) cells. Both of these T subsets can be collectively referred to as stem-like CD8+ T cells, which demonstrate robust self-renewal ability and can proliferate and differentiate into transitory effector-like exhausted T cells (Texint). There are reports that chemotherapeutic drugs can promote the antitumor immune responses of patients by increasing the number of CD8+ T cells; however, whether chemotherapeutic drugs increase these two stem-like CD8+ T cells remain further exploration.

Tpex cell-associated subpopulations in human colorectal tumors were analyzed by using single-cell sequencing data. CT26 and B16 tumor models of wild type and Eomes conditional knockout mice were constructed, and the changes of TTSM, Tpex and Tex subsets in mice were dissected by flow cytometry after treatment with decitabine (DAC), doxorubicin (DOX) and 5-Fluorouracil (5-FU).

In this study, we demonstrated that DAC and 5-FU expanded CD8+ TTSM cells in TdLNs. At the same time, we validated that DAC and 5-FU substantially promoted the expansion of CD62L+CD8+ Tpex cells and subsequently increased effector function of CX3CR1+ CD8+ Texint cells. In addition, the conditional knockout of transcription factor Eomes in CD8+ T cells partially eliminated DAC-amplified CD62L+ CD8+ Tpex cells, but had no effect on such CD8+ T subset expanded by 5-FU.

The present study demonstrated that both DAC and 5-FU promoted the differentiation of stem-like CD8+ TTSM cells in TdLNs and significantly enhanced the differentiation and expansion of stem-like CD62L+ CD8+ Tpex and CX3CR1+ Texint cells in tumor microenvironment. The knockout of Eomes partially influenced the role of DAC in promoting the differentiation and expansion of stem-like CD8+ T cells.

Dendritic cells (DCs) are specialized antigen-presenting cells that are present at low abundance in the circulation and tissues; they serve as crucial immune sentinels by continually sampling their environment, migrating to secondary lymphoid organs and shaping adaptive immune responses through antigen presentation. Owing to their ability to orchestrate tolerogenic or immunogenic responses to a specific antigen, DCs have a pivotal role in antitumour immunity and the response to immune checkpoint blockade and other immunotherapeutic approaches. The multifaceted functions of DCs are acquired through a complex, multistage process called maturation. Although the role of inflammatory triggers in driving DC maturation was established decades ago, less is known about DC maturation in non-inflammatory contexts, such as during homeostasis and in cancer. The advent of single-cell technologies has enabled an unbiased, high-dimensional characterization of various DC states, including mature DCs. This approach has clarified the molecular programmes associated with DC maturation and also revealed how cancers exploit these pathways to subvert immune surveillance. In this Review, we discuss the mechanisms by which cancer disrupts DC maturation and highlight emerging therapeutic opportunities to modulate DC states. These insights could inform the development of DC-centric immunotherapies, expanding the arsenal of strategies to enhance antitumour immunity.

Batch effect correction (BEC) is fundamental to integrate multiple single-cell RNA sequencing datasets, and its success is critical to empower in-depth interrogation for biological insights. However, no simple metric is available to evaluate BEC performance with sensitivity to data overcorrection, which erases true biological variations and leads to false biological discoveries. Here, we propose RBET, a reference-informed statistical framework for evaluating the success of BEC. Using extensive simulations and six real data examples including scRNA-seq and scATAC-seq datasets with different numbers of batches, batch effect sizes and numbers of cell types, we demonstrate that RBET evaluates the performance of BEC methods more fairly with biologically meaningful insights from data, while other methods may lead to false results. Moreover, RBET is computationally efficient, sensitive to overcorrection and robust to large batch effect sizes. Thus, RBET provides a robust guideline on selecting case-specific BEC method, and the concept of RBET is extendable to other modalities.

The process of tumor development involves tumor cells eluding detection and suppression of immune responses, which can cause decreased tumor cell antigenicity, expression of immunosuppressive molecules, and immunosuppressive cell recruitment to the tumor microenvironment (TME). Immunologically and genomically integrated analysis (immunogenomic analysis) of patient specimens has revealed that oncogenic aberrant signaling is involved in both carcinogenesis and immune evasion. In noninflamed cancers such as epidermal growth factor receptor (EGFR)-mutated lung cancers, genetic abnormalities in cancer cells contribute to the formation of an immunosuppressive TME by recruiting immunosuppressive cells, which cannot be fully explained by the cancer immunoediting hypothesis. This review summarizes the latest findings regarding the links between cancer genetic abnormalities and immunosuppression causing clinical resistance to immunotherapy. We propose the concepts of immunogenomic cancer evolution, in which cancer cell genomic evolution shapes the immunosuppressive TME, and immunogenomic precision medicine, in which cancer immunotherapy can be combined with molecularly targeted reagents that modulate the immunosuppressive TME.

Gut microbiome influences tumorigenesis and tumor progression through regulating the tumor microenvironment (TME) and modifying blood metabolites. However, the mechanisms by which gut microbiome and blood metabolites regulate the TME in multiple myeloma (MM) remain unclear. By employing16S rRNA gene sequencing coupled with metagenomics and ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry, we find that Lachnospiraceae are high and phosphatidylcholine (PC) are low in MM patients. We further show that Lachnospiraceae inhibits PC production from MM cells and enhances cytotoxic CD8 T cell function. Mechanistically, PC promotes Sb9 mRNA maturation in MM cells by LIN28A/B via lysophosphatidic acid, thus enhances exosamal Sb9 production. Exosamal Sb9 then reduces GZMB expression by suppressing tumor protein p53 (TP53) UFMylation via the competitive binding of TP53 with the ubiquitin-fold modifier conjugating enzyme 1 in CD8 T cells. We thus show that Lachnospiraceae and PC may be potential therapeutic targets for MM treatment.