Molecular and genetic properties of tumors associated with local immune cytolytic activity

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.

Identification of a novel immunogenic death-associated model for predicting the immune microenvironment in lung adenocarcinoma from single-cell and Bulk transcriptomes

Background: Studies on immunogenic death (ICD) in lung adenocarcinoma are limited, and this study aimed to determine the function of ICD in LUAD and to construct a novel ICD-based prognostic model to improve immune efficacy in lung adenocarcinoma patients. Methods: The data for lung adenocarcinoma were obtained from the Cancer Genome Atlas (TCGA) database and the National Center for Biotechnology Information (GEO). The single-cell data were obtained from Bischoff P et al. To identify subpopulations, we performed descending clustering using TSNE. We collected sets of genes related to immunogenic death from the literature and identified ICD-related genes through gene set analysis of variance (GSVA) and weighted gene correlation network analysis (WGCNA). Lung adenocarcinoma patients were classified into two types using consistency clustering. The difference between the two types was analyzed to obtain differential genes. An immunogenic death model (ICDRS) was established using LASSO-Cox analysis and compared with lung adenocarcinoma models of other individuals. External validation was performed in the GSE31210 and GSE50081 cohorts. The efficacy of immunotherapy was assessed using the TIDE algorithm and the IMvigor210, GSE78220, and TCIA cohorts. Furthermore, differences in mutational profiles and immune microenvironment between different risk groups were investigated. Subsequently, ROC diagnostic curves and KM survival curves were used to screen ICDRS key regulatory genes. Finally, RT-qPCR was used to verify the differential expression of these genes. Results: Eight ICD genes were found to be highly predictive of LUAD prognosis and significantly correlated with it. Multivariate analysis showed that patients in the low-risk group had a higher overall survival rate than those in the high-risk group, indicating that the model was an independent predictor of LUAD. Additionally, ICDRS demonstrated better predictive ability compared to 11 previously published models. Furthermore, significant differences in biological function and immune cell infiltration were observed in the tumor microenvironment between the high-risk and low-risk groups. It is noteworthy that immunotherapy was also significant in both groups. These findings suggest that the model has good predictive efficacy. Conclusions: The ICD model demonstrated good predictive performance, revealing the tumor microenvironment and providing a new method for evaluating the efficacy of pre-immunization. This offers a new strategy for future treatment of lung adenocarcinoma.

Prognostic value of four immune-related genes in lower-grade gliomas: a biomarker discovery study

Introduction

The tumor microenvironment and IRGs are highly correlated with tumor occurrence, progression, and prognosis. However, their roles in grade II and III gliomas, termed LGGs in this study, remain to be fully elucidated. Our research aims to develop immune-related features for risk stratification and prognosis prediction in LGG.

Methods

Using the ssGSEA method, we assessed the immune characteristics of the LGG population. We conducted differential analysis using LGG samples from the TCGA database and normal samples from GTEx, identifying 412 differentially expressed immune-related genes (DEIRGs). Subsequently, we utilized univariate Cox, LASSO, and multivariate Cox regression analyses to establish both a gene predictive model and a nomogram predictive model.

Results

Here, we found that the ESTIMATE score, immune score and stromal score of high-immunity, high-grade and isocitrate dehydrogenase (IDH) wild-type glioma were higher than those of the corresponding group, and the tumor purity was lower. Higher ESTIMATE scores, stromal scores and immune scores indicated a poor prognosis in patients with LGG. Our four-gene prognostic model demonstrated superior accuracy compared to other molecular features. Validation using the CGGA as a testing set and the combined TCGA and CGGA cohort confirmed its robust prognostic value. Additionally, a nomogram integrating the prognostic model and clinical variables showed enhanced predictive capability.

Discussion

Our study highlights the prognostic significance of the identified four DEIRGs (KLRC3, MR1, PDIA2, and RFXAP) in LGG patients. The predictive model and nomogram developed herein offer valuable tools for personalized treatment strategies in LGG. Future research should focus on further validating these findings and exploring the functional roles of these DEIRGs within the LGG tumor microenvironment.

Unveiling major histocompatibility complex-mediated pan-cancer immune features by integrated single-cell and bulk RNA sequencing

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy, yet persistent challenges such as low response rate and significant heterogeneity necessitate attention. The pivotal role of the major histocompatibility complex (MHC) in ICI efficacy, its intricate impacts and potentials as a prognostic marker, warrants comprehensive exploration. This study integrates single-cell RNA sequencing (scRNA-seq), bulk RNA-seq, and spatial transcriptomic analyses to unveil pan-cancer immune characteristics governed by the MHC transcriptional feature (MHC.sig). Developed through scRNA-seq analysis of 663,760 cells across diverse cohorts and validated in 30 solid cancer types, the MHC.sig demonstrates a robust correlation between immune-related genes and infiltrating immune cells, highlighting its potential as a universal pan-cancer marker for anti-tumor immunity. Screening the MHC.sig for therapeutic targets using CRISPR data identifies potential genes for immune therapy synergy and validates its predictive efficacy for ICIs responsiveness across diverse datasets and cancer types. Finally, analysis of cellular communication patterns reveals interactions between C1QC+macrophages and malignant cells, providing insights into potential therapeutic agents and their sensitivity characteristics. This comprehensive analysis positions the MHC.sig as a promising marker for predicting immune therapy outcomes and guiding combinatorial therapeutic strategies.

Case report: Immune response characterization of a pseudoprogression in a PD-L1-negative, TMB-low, KEAP1/STK11 co-mutated metastatic NSCLC

A patient with a PD-L1-negative, TMB-low, KEAP1/STK11 co-mutated metastatic non-small cell lung cancer (NSCLC) experienced a multisite radiological progression at 3 months after initiation of chemoimmunotherapy as first-line treatment for metastatic disease. After the radiological progression, while she was not undergoing treatment, the patient had spontaneous lesions shrinkage and further achieved a prolonged complete response. Genomic and transcriptomic data collected at baseline and at the time of pseudoprogression allowed us to biologically characterize this rare response pattern. We observed the presence of a tumor-specific T-cell response against tumor-specific neoantigens (TNAs). Endogenous retroviruses (ERVs) expression following chemoimmunotherapy was also observed, concurrent with biological features of an anti-viral-like innate immune response with type I IFN signaling and production of CXCR3-associated chemokines. This is the first biological characterization of a NSCLC pseudoprogression under chemoimmunotherapy followed by a prolonged complete response in a PD-L1-negative, TMB-low, KEAP1/STK11 co-mutated NSCLC. These clinical and biological data underline that even patients with multiple factors of resistance to immune checkpoint inhibitors could trigger a tumor-specific immune response to tumor neoantigen, leading to complete eradication of the tumor and probably a vaccinal immune response.

Single-Cell Analysis Provides New Insights into the Roles of Tertiary Lymphoid Structures and Immune Cell Infiltration in Kidney Injury and Chronic Kidney Disease

Chronic kidney disease (CKD) is a global health concern with high morbidity and mortality. Acute kidney injury (AKI) is a pivotal risk factor for the progression of CKD, and the rate of AKI-to-CKD progression increases with aging. Intrarenal inflammation is a fundamental mechanism underlying AKI-to-CKD progression. Tertiary lymphoid structures (TLSs), ectopic lymphoid aggregates formed in nonlymphoid organs, develop in aged injured kidneys, but not in young kidneys, with prolonged inflammation and maladaptive repair, which potentially exacerbates AKI-to-CKD progression in aged individuals. Dysregulated immune responses are involved in the pathogenesis of various kidney diseases, such as IgA nephropathy, lupus nephritis, and diabetic kidney diseases, thereby deteriorating kidney function. TLSs also develop in several kidney diseases, including transplanted kidneys and renal cell carcinoma. However, the precise immunologic mechanisms driving AKI-to-CKD progression and development of these kidney diseases remain unclear, which hinders the development of novel therapeutic approaches. This review aims to describe recent findings from single-cell analysis of cellular heterogeneity and complex interactions among immune and renal parenchymal cells, which potentially contribute to the pathogenesis of AKI-to-CKD progression and other kidney diseases, highlighting the mechanisms of formation and pathogenic roles of TLSs in aged injured kidneys.

Immune-related gene signature improves prognosis prediction in patients with breast cancer and associates it with tumor immunity and inflammatory response

BACKGROUND

The prognostic potential of immune-related genes, particularly immune checkpoint inhibitors (ICIs) and long non-coding RNAs (lncRNAs), is gaining attention for evaluating the prognosis of breast cancer patients.

METHODS

We analyzed 23 datasets to identify 15 ICI-related mRNAs and 5 immune-related lncRNAs, creating a robust immune score (IS). This score was used to classify patients into high and low IS groups and assess their survival outcomes.

RESULTS

Patients with high IS showed significantly poorer overall survival (OS) and progression-free survival (PFS) compared to those with low IS. Multivariate Cox regression analysis confirmed IS as an independent prognostic factor. Additionally, high IS was associated with higher mutation loads and neoantigen profiles, while low IS correlated with enhanced immune cell infiltration.

CONCLUSIONS

The immune score developed from ICI-related mRNAs and lncRNAs effectively predicts the prognosis of breast cancer patients and highlights the differential immune and inflammatory responses between patients with varying levels of immune score. This underscores the relevance of IS in guiding therapeutic decisions and tailoring patient management strategies in clinical settings.

RAD51 High-Expressed Hepatocellular Carcinomas are Associated With High Cell Proliferation

INTRODUCTION

RAD51 is a pivotal DNA repair gene managing double-stranded DNA break recognition and repair. RAD51 high expression was associated with adverse outcomes in other cancer types. This study aims to investigate the tumor microenvironment and immune landscape in the RAD51 high-expressed Hepatocellular Carcinoma (HCCs).

METHODS

A total of 467 patients from two large independent cohorts with clinical and transcriptomic data were obtained. The cohort was dichotomized based on the median RAD51 gene expression. xCell and Gene Set Enrichment Analysis (GSEA) were used.

RESULTS

RAD51 high-expressed HCCs were associated with worse recurrence-free, progression-free, disease-specific, and overall survival (all P < 0.05). While RAD51 high-expressed HCCs were associated with intratumoral heterogeneity, homologous recombination deficiency, and fraction altered scores, mutation or neoantigens were not increased in this group. xCell analysis demonstrated inconsistent immune cell infiltration between two cohorts. Cytolytic activity as well as GSEA with immune-related gene sets also demonstrated inconsistent results between two cohorts as well. On the other hand, RAD51 expression was significantly increased in higher-grade tumors, larger tumors, and higher clinical stages. RAD51 high-expressed HCCs were found to have elevated proliferation score. Furthermore, GSEA exhibited significant enrichment of all the cell proliferation-related gene sets in the Hallmark collection, including E2F targets, G2M checkpoint, Mitotic spindle, MYC targets, and MTORC1 signaling consistently in both cohorts (all false discovery rate < 0.25).

CONCLUSIONS

RAD51 high-expressed HCCs were associated with worse survival and with increased cell proliferation and were not necessarily associated with immune infiltration or inflammation.

Detecting HLA loss of heterozygosity within a standard diagnostic sequencing workflow for prognostic and therapeutic opportunities

To enable interrogation of tumor HLA LOH as a clinical diagnostic for precision oncology, we developed and validated an assay that detects HLA LOH within the context of an FDA-approved clinical diagnostic test, Tempus xT CDx. Validation was conducted via: (1) analytical evaluation of 17 archival patient samples and 42 cell line admixtures and (2) independent clinical evaluation of LOH prevalence in the HLA-A gene (HLA-A LOH) across 10,982 patients. To evaluate the prognostic relevance of HLA-A LOH we assessed 256 immunotherapy-treated non-small cell lung cancer (NSCLC) patients. To determine the feasibility of prospectively identifying and enrolling HLA-A LOH patients into a clinical trial, we established BASECAMP-1 (NCT04981119). We observed a positive predictive agreement of 97% and a negative predictive agreement of 100% in samples with ≥ 40% tumor purity. We observed HLA-A LOH in 16.1% of patients (1771/10,982), comparable to previous reports. HLA-A LOH was associated with longer survival among NSCLC adenocarcinoma patients (HR = 0.60, 95% CI [0.37, 0.96], p = 0.032) with a trend towards shorter survival among squamous cell patients (HR = 1.64, 95% CI [0.80, 3.41], p = 0.183). In 20 months, we prospectively screened 1720 subjects using the Tempus AWARE program, identifying 26 HLA-A*02 LOH patients at 8 sites, with 14 (54%) enrolled into BASECAMP-1. In conclusion, we developed and validated an investigational assay that detects tumor HLA LOH within an FDA-approved clinical diagnostic test, enabling HLA LOH utilization in diagnostic, prognostic, and therapeutic applications.

Identification of pyroptosis subtypes and prognosis model of hepatocellular carcinoma based on pyroptosis-related genes

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignant tumor with poor prognosis. Pyroptosis, a type of programmed cell death, regulates tumor cell development. However, the role of pyroptosis-related genes (PRGs) in HCC and their association with prognosis are unclear.

METHODS

We conducted bioinformatics analysis to identify PRGs in The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) patients. Consensus clustering classified patients into different subtypes. We used LASSO regression to established a pyroptosis subtype-related score (PSRS) related to prognosis. OncoPredict identified potential pharmaceuticals based on PSRS.

RESULTS

We found 20 HCC-related PRGs in 335 TCGA-LIHC patients. Consensus clustering classified patients into two subtypes. Subtype I had better overall survival and higher response to anti-PD1 treatment. The prognostic model involving 20 genes predicted poorer prognosis for high-PSRS group. The model was validated in two external cohorts. OncoPredict identified 65 potential pharmaceuticals based on PSRS.

CONCLUSION

Our investigation revealed a correlation between pyroptosis and HCC. We established PSRS as independent risk factors for predicting prognosis. The study paves the way for using PRGs as prognostic biomarkers and exploring personalized therapy for HCC.

Prognostic and therapeutic potential of imbalance between PD-1+CD8 and ICOS+Treg cells in advanced HBV-HCC

Over 50% of patients with hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) are diagnosed at an advanced stage, which is characterized by immune imbalance between CD8+ T cells and regulatory T (Treg) cells that accelerates disease progression. However, there is no imbalance indicator to predict clinical outcomes. Here, we show that the proportion of CD8+ T cells decreases and Treg cells increases in advanced HBV-HCC patients. During this stage, CD8+ T cells and Treg cells expressed the coinhibitory molecule PD-1 and the costimulatory molecule ICOS, respectively. Additionally, the ratio between PD-1+CD8 and ICOS+Tregs showed significant changes. Patients were further divided into high- and low-ratio groups: PD-1+CD8 and ICOS+Tregs high- (PD-1/ICOShi) and low-ratio (PD-1/ICOSlo) groups according to ratio median. Compared with PD-1/ICOSlo patients, the PD-1/ICOShi group had better clinical prognosis and weaker CD8+ T cells exhaustion, and the T cell-killing and proliferation functions were more conservative. Surprisingly, the small sample analysis found that PD-1/ICOShi patients exhibited a higher proportion of tissue-resident memory T (TRM) cells and had more stable killing capacity and lower apoptosis capacity than PD-1/ICOSlo advanced HBV-HCC patients treated with immune checkpoint inhibitors (ICIs). In conclusion, the ratio between PD-1+CD8 and ICOS+Tregs was associated with extreme immune imbalance and poor prognosis in advanced HBV-HCC. These findings provide significant clinical implications for the prognosis of advanced HBV-HCC and may serve as a theoretical basis for identifying new targets in immunotherapy.

Loss of STK11 Suppresses Lipid Metabolism and Attenuates KRAS-Induced Immunogenicity in Patients with Non-Small Cell Lung Cancer

As many as 30% of the patients with non-small cell lung cancer harbor oncogenic KRAS mutations, which leads to extensive remodeling of the tumor immune microenvironment. Although co-mutations in several genes have prognostic relevance in KRAS-mutated patients, their effect on tumor immunogenicity are poorly understood. In the present study, a total of 189 patients with non-small cell lung cancer underwent a standardized analysis including IHC, whole-exome DNA sequencing, and whole-transcriptome RNA sequencing. Patients with activating KRAS mutations demonstrated a significant increase in PDL1 expression and CD8+ T-cell infiltration. Both were increased in the presence of a co-occurring TP53 mutation and lost with STK11 co-mutation. Subsequent genomic analysis demonstrated that KRAS/TP53 co-mutated tumors had a significant decrease in the expression of glycolysis-associated genes and an increase in several genes involved in lipid metabolism, notably lipoprotein lipase, low-density lipoprotein receptor, and LDLRAD4. Conversely, in the immune-excluded KRAS/STK11 co-mutated group, we observed diminished lipid metabolism and no change in anaerobic glycolysis. Interestingly, in patients with low expression of lipoprotein lipase, low-density lipoprotein receptor, or LDLRAD4, KRAS mutations had no effect on tumor immunogenicity. However, in patients with robust expression of these genes, KRAS mutations were associated with increased immunogenicity and associated with improved overall survival. Our data further suggest that the loss of STK11 may function as a metabolic switch, suppressing lipid metabolism in favor of glycolysis, thereby negating KRAS-induced immunogenicity. Hence, this concept warrants continued exploration, both as a predictive biomarker and potential target for therapy in patients receiving ICI-based immunotherapy.

SIGNIFICANCE

In patients with lung cancer, we demonstrate that KRAS mutations increase tumor immunogenicity; however, KRAS/STK11 co-mutated patients display an immune-excluded phenotype. KRAS/STK11 co-mutated patients also demonstrated significant downregulation of several key lipid metabolism genes, many of which were associated with increased immunogenicity and improved overall survival in KRAS-mutated patients. Hence, alteration to lipid metabolism warrants further study as a potential biomarker and target for therapy in patients with KRAS-mutated lung cancer.

3D bioprinted tumor model: a prompt and convenient platform for overcoming immunotherapy resistance by recapitulating the tumor microenvironment

BACKGROUND

Cancer immunotherapy is receiving worldwide attention for its induction of an anti-tumor response. However, it has had limited efficacy in some patients who acquired resistance. The dynamic and sophisticated complexity of the tumor microenvironment (TME) is the leading contributor to this clinical dilemma. Through recapitulating the physiological features of the TME, 3D bioprinting is a promising research tool for cancer immunotherapy, which preserves in vivo malignant aggressiveness, heterogeneity, and the cell-cell/matrix interactions. It has been reported that application of 3D bioprinting holds potential to address the challenges of immunotherapy resistance and facilitate personalized medication.

CONCLUSIONS AND PERSPECTIVES

In this review, we briefly summarize the contributions of cellular and noncellular components of the TME in the development of immunotherapy resistance, and introduce recent advances in 3D bioprinted tumor models that served as platforms to study the interactions between tumor cells and the TME. By constructing multicellular 3D bioprinted tumor models, cellular and noncellular crosstalk is reproduced between tumor cells, immune cells, fibroblasts, adipocytes, and the extracellular matrix (ECM) within the TME. In the future, by quickly preparing 3D bioprinted tumor models with patient-derived components, information on tumor immunotherapy resistance can be obtained timely for clinical reference. The combined application with tumoroid or other 3D culture technologies will also help to better simulate the complexity and dynamics of tumor microenvironment in vitro. We aim to provide new perspectives for overcoming cancer immunotherapy resistance and inspire multidisciplinary research to improve the clinical application of 3D bioprinting technology.

Antitumor immunity and prognosis value elicited by FAT3 and LRP1B co-mutation in endometrial cancer

OBJECTIVE

FAT3 and LRP1B are two tumor suppressor genes with high mutation frequency in multiple cancer types, we sought to investigate the prognostic and immunological significance of these two genes in EC.

METHODS

Based on a cohort of 502 EC samples, we conducted a comprehensive analysis of its multidimensional data types including genomic, transcriptomic, and clinical information, the potential impact of FAT3 and LRP1B co-mutation on antitumor immune response and prognosis were systematically discussed.

RESULTS

We observed that FAT3 and LRP1B co-mutation was not only defined a dataset with prominently increased TMB, decreased tumor aneuploidy, and specially enriched in MSI-H subtype, but also manifested increased expression of immune-related markers, especially exclusive upregulation of PD-L1 levels and higher PD-L1+/CD8A+ proportion. Further analysis focused on lymphocyte infiltration and pathway enrichment explored the immune cell composition of the microenvironment and underlying molecular mechanisms affecting tumor development. Furthermore, EC patients with FAT3 and LRP1B co-mutation possessed significantly prolonged PFS and OS, and the co-mutation status was proved to be an independent prognostic factor. And a nomogram with high predictive performance was constructed by incorporating co-mutation with clinical features. More strikingly, the prognosis of MSI-H patients in EC with co-mutation was significantly improved, and their survival reached a level consistent with the POLE subtype.

CONCLUSIONS

In endometrial cancer, co-mutation of FAT3 and LRP1B not only leads to activation of the immune state, but also represents a subgroup with an improved prognosis, particularly in the MSI-H subtype.

Identification and validation of tryptophan-related gene signatures to predict prognosis and immunotherapy response in lung adenocarcinoma reveals a critical role for PTTG1

Introduction

Tryptophan metabolism is strongly associated with immunosuppression and may influence lung adenocarcinoma prognosis as well as tumor microenvironment alterations.

Methods

Sequencing datasets were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Two different clusters were identified by consensus clustering, and prognostic models were established based on differentially expressed genes (DEGs) in the two clusters. We investigated differences in mutational landscapes, enrichment pathways, immune cell infiltration, and immunotherapy between high- and low-risk scoring groups. Single-cell sequencing data from Bischoff et al. were used to identify and quantify tryptophan metabolism, and model genes were comprehensively analyzed. Finally, PTTG1 was analyzed at the pan-cancer level by the pan-TCGA cohort.

Results

Risk score was defined as an independent prognostic factor for lung adenocarcinoma and was effective in predicting immunotherapy response in patients with lung adenocarcinoma. PTTG1 is one of the key genes, and knockdown of PTTG1 in vitro decreases lung adenocarcinoma cell proliferation and migration and promotes apoptosis and down-regulation of tryptophan metabolism regulators in lung adenocarcinoma cells.

Discussion

Our study revealed the pattern and molecular features of tryptophan metabolism in lung adenocarcinoma patients, established a model of tryptophan metabolism-associated lung adenocarcinoma prognosis, and explored the roles of PTTG1 in lung adenocarcinoma progression, EMT process, and tryptophan metabolism.

Genomics and proteomics to determine novel molecular subtypes and predict the response to immunotherapy and the effect of bevacizumab in glioblastoma

Glioblastoma (GBM) is a highly aggressive, infiltrative malignancy that cannot be completely cured by current treatment modalities, and therefore requires more precise molecular subtype signatures to predict treatment response for personalized precision therapy. Expression subtypes of GBM samples from the Cancer Genome Atlas (TCGA) were identified using BayesNM and compared with existing molecular subtypes of GBM. Biological features of the subtypes were determined by single-sample gene set enrichment analysis. Genomic and proteomic data from GBM samples were combined and Genomic Identification of Significant Targets in Cancer analysis was used to screen genes with recurrent somatic copy-number alterations phenomenon. The immune environment among subtypes was compared by assessing the expression of immune molecules and the infiltration of immune cells. Molecular subtypes adapted to immunotherapy were identified based on Tumor Immune Dysfunction and Exclusion (TIDE) score. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was performed on the expression profiles of S2, S3 and S4 in TCGA-GBM and RPPA to determine the respective corresponding best predictive model. Four novel molecular subtypes were classified. Specifically, S1 exhibited a low proliferative profile; S2 exhibited the profile of high proliferation, IDH1 mutation, TP53 mutation and deletion; S3 was characterized by high immune scores, innate immunity and adaptive immune infiltration scores, with the lowest TIDE score and was most likely to benefit from immunotherapy; S4 was characterized by high proliferation, EGFR amplification, and high protein abundance, and was the most suitable subtype for bevacizumab. LASSO analysis constructed the best prediction model composed of 13 genes in S2 with an accuracy of 96.7%, and the prediction model consisting of 17 genes in S3 with an accuracy of 86.7%, and screened 14 genes as components of the best prediction model in S4 with an accuracy of 93%. To conclude, our study classified reproducible and robust molecular subtypes of GBM, and these findings might contribute to the identification of patients responding to immunotherapy, thereby improving GBM prognosis.

Dual blockade immunotherapy targeting PD-1/PD-L1 and CTLA-4 in lung cancer

Cancer immunotherapies, represented by immune checkpoint inhibitors (ICIs), have reshaped the treatment paradigm for both advanced non-small cell lung cancer and small cell lung cancer. Programmed death receptor-1/programmed death receptor ligand-1 (PD-1/PD-L1) and cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) are some of the most common and promising targets in ICIs. Compared to ICI monotherapy, which occasionally demonstrates treatment resistance and limited efficacy, the dual blockade immunotherapy targeting PD-1/PD-L1 and CTLA-4 operates at different stages of T cell activation with synergistically enhancing immune responses against cancer cells. This emerging dual therapy heralds a new direction for cancer immunotherapy, which, however, may increase the risk of drug-related adverse reactions while improving efficacy. Previous clinical trials have explored combination therapy strategy of anti-PD-1/PD-L1 and anti-CTLA-4 agents in lung cancer, yet its efficacy remains to be unclear with the inevitable incidence of immune-related adverse events. The recent advent of bispecific antibodies has made this sort of dual targeting more feasible, aiming to alleviate toxicity without compromising efficacy. Thus, this review highlights the role of dual blockade immunotherapy targeting PD-1/PD-L1 and CTLA-4 in treating lung cancer, and further elucidates its pre-clinical mechanisms and current advancements in clinical trials. Besides, we also provide novel insights into the potential combinations of dual blockade therapies with other strategies to optimize the future treatment mode for lung cancer.

TG6050, an oncolytic vaccinia virus encoding interleukin-12 and anti-CTLA-4 antibody, favors tumor regression via profound immune remodeling of the tumor microenvironment

BACKGROUND

TG6050 was designed as an improved oncolytic vector, combining the intrinsic properties of vaccinia virus to selectively replicate in tumors with the tumor-restricted expression of recombinant immune effectors to modify the tumor immune phenotype. These properties might be of particular interest for "cold" tumors, either poorly infiltrated or infiltrated with anergic T cells.

METHODS

 TG6050, an oncolytic vaccinia virus encodes single-chain human interleukin-12 (hIL-12) and full-length anti-cytotoxic T-lymphocyte-associated antigen-4 (@CTLA-4) monoclonal antibody. The relevant properties of TG6050 (replication, cytopathy, transgenes expression and functionality) were extensively characterized in vitro. The biodistribution and pharmacokinetics of the viral vector, @CTLA-4 and IL-12, as well as antitumoral activities (alone or combined with immune checkpoint inhibitors) were investigated in several "hot" (highly infiltrated) and "cold" (poorly infiltrated) syngeneic murine tumor models. The mechanism of action was deciphered by monitoring both systemic and intratumoral immune responses, and by tumor transcriptome analysis. The safety of TG6050 after repeated intravenous administrations was evaluated in cynomolgus monkeys, with a focus on the level of circulating IL-12.

RESULTS

Multiplication and propagation of TG6050 in tumor cells in vitro and in vivo were associated with local expression of functional IL-12 and @CTLA-4. This dual mechanism translated into a strong antitumoral activity in both "cold" and "hot" tumor models (B16F10, LLC1 or EMT6, CT26, respectively) that was further amplified when combined with anti-programmed cell death protein-1. Analysis of changes in the tumor microenvironment (TME) after treatment with TG6050 showed increases in interferon-gamma, of CD8+T cells, and of M1/M2 macrophages ratio, as well as a drastic decrease of regulatory T cells. These local modifications were observed alongside bolstering a systemic and specific antitumor adaptive immune response. In toxicology studies, TG6050 did not display any observable adverse effects in cynomolgus monkeys.

CONCLUSIONS

TG6050 effectively delivers functional IL-12 and @CTLA-4 into the tumor, resulting in strong antitumor activity. The shift towards an inflamed TME correlated with a boost in systemic antitumor T cells. The solid preclinical data and favorable benefit/risk ratio paved the way for the clinical evaluation of TG6050 in metastatic non-small cell lung cancer (NCT05788926 trial in progress).

IRF8 deficiency-induced myeloid-derived suppressor cell promote immune evasion in lung adenocarcinoma

BACKGROUND

Patients with lung adenocarcinoma (LUAD) have a low response rate to immune checkpoint blockade. It is highly important to explore the tumor immune escape mechanism of LUAD patients and expand the population of patients who may benefit from immunotherapy.

METHODS

Based on 954 bulk RNA-seq data of LUAD patients and 15 single-cell RNA-seq data, the relationships between tumor immune dysfunction and exclusion (TIDE) scores and survival prognosis in each patient were calculated and evaluated, and the immune escape mechanism affecting the independent prognosis of LUAD patients was identified. Functional enrichment analysis explored the antitumour immune response and biological behavior of tumor cells among different LUAD groups. Single-cell annotation and pseudotemporal analysis were used to explore the target molecules and immune escape mechanisms of LUAD.

RESULTS

Myeloid-derived suppressor cells (MDSCs) and IRF8 were identified as risk and protective factors for the independent prognosis of LUAD patients, respectively. In the tumor microenvironment of patients with high infiltration of MDSCs, the antitumor immune response is significantly suppressed, while tumor cell division, proliferation, and distant metastasis are significantly enhanced. Single-cell RNA-seq analysis revealed that IRF8 is an important regulator of MDSC differentiation in LUAD myeloid cells. In addition, IRF8 may regulate the differentiation of MDSCs through the IL6-JAK-STAT3 signalling pathway.

CONCLUSIONS

IRF8 deficiency impairs the normal development of LUAD myeloid cells and induces their differentiation into MDSCs, thereby accelerating the immune escape of LUAD cells. IRF8-targeted activation to inhibit the formation of MDSCs may be a new target for immunotherapy in LUAD.

Exploring the prognostic implications of cuproptosis-associated alterations in clear cell renal cell carcinoma via in vitro experiments

This study investigated the impact of novel copper ionophores on the prognosis of clear cell renal cell carcinoma (ccRCC) and the tumor microenvironment (TME). The differential expression of 10 cuproptosis and 40 TME-pathway-related genes were measured in 531 tumor samples and 71 adjacent kidney samples in The Cancer Genome Atlas database. A risk score model was constructed with LASSO cox to predict the prognosis of ccRCC patients. Forest plot and function enrichment were used to study the biological function of the key genes in depth. The study found that the risk score model accurately predicted the prognosis of ccRCC patients. Patients with high scores had higher immune responses with a higher proportion of anti-tumor lymphocytes and a lower proportion of immunosuppressive M2-like macrophages. However, the high-score group also exhibited a higher proportion of T follicular helper cells and regulatory T cells. These results suggest that cuproptosis-based therapy may be worth further investigation for the treatment of ccRCC and TME. Subsequently, by using RNAi, we established the stable depletion models of FDX1 and PDHB in ccRCC cell lines 786-O and ACHN. Through CCK8, colony formation, and Transwell assays, we observed that the knockdown of FDX1 and PDHB could significantly reduce the capabilities of proliferation and migration in ccRCC cells. In conclusion, this study illuminates the potential effectiveness of copper ionophores in the treatment of ccRCC, with higher risk scores correlating with better TME immune responses. It sets the stage for future cuproptosis-based therapy research in ccRCC and other cancers, focusing on copper's role in TME.

CGPA: multi-context insights from the cancer gene prognosis atlas

Cancer transcriptomic data are used extensively to interrogate the prognostic value of targeted genes, yet basic scientists and clinicians have predominantly relied on univariable survival analysis for this purpose. This method often fails to capture the full prognostic potential and contextual relevance of the genes under study, inadvertently omitting a group of genes we term univariable missed-opportunity prognostic (UMOP) genes. Recognizing the complexity of revealing multifaceted prognostic implications, especially when extending the analysis to include various covariates and thresholds, we present the Cancer Gene Prognosis Atlas (CGPA). This platform greatly enhances gene-centric biomarker research across cancer types by offering an interactive and user-friendly interface for highly customized, in-depth prognostic analysis. CGPA notably supports data-driven exploration of gene pairs and gene-hallmark relationships, elucidating key composite biological mechanisms like synthetic lethality and immunosuppression. It further expands its capabilities to assess multi-gene panels using both public and user-provided data, facilitating a seamless mechanism-to-machine analysis. Additionally, CGPA features a designated portal for discovering prognostic gene modules using curated cancer immunotherapy data. Ultimately, CGPA's comprehensive, accessible tools allow cancer researchers, including those without statistical expertise, to precisely investigate the prognostic landscape of genes, customizing the model to fit specific research hypotheses and enhancing biomarker discovery and validation through a synergy of mechanistic and data-driven strategies.

Efficacy comparison of immune combination therapies in subgroups for advanced hepatocellular carcinoma patients: Systematic review and network meta-analysis

BACKGROUND

There is a lack of precision in the immunotherapy strategy tailored for patients exhibiting diverse clinical characteristics. This study aims to employ a rigorous network meta-analysis (NMA) approach to systematically evaluate the effectiveness of immune-combination therapies among patients with advanced hepatocellular carcinoma, taking into account their varying clinico-characteristics.

METHODS

Studies were retrieved from PubMed, Embase, Cochrane Library, and Web of Science databases. The included first-line phase III studies were categorized into three types: immunotherapy combined with anti-angiogenetic agents, immunotherapy combined with tyrosine kinase inhibitors, and dual immunotherapy, with sorafenib serving as the control group. The primary endpoint used to assess efficacy was overall survival (OS), facilitating a comparative analysis among the three treatment modalities. Furthermore, subgroup analyses were conducted to evaluate the varying effectiveness for patients with diverse clinico-characteristics. Secondary outcome measures included progression-free survival, objective response rate, and toxicity assessment.

RESULTS

A total of 6 studies were included in the NMA, encompassing a cohort of 3840 patients. The results revealed that immunotherapy combined with anti-angiogenetic agents exhibited a significantly enhanced therapeutic effect in terms of improving OS compared to sorafenib (HR = 0.61, 95% CrI, 0.42-0.90). Furthermore, based on various clinicopathological features, this combination therapy demonstrated superior OS responses in specific patient subgroups: BCLC C (HR = 0.63, 95% CrI, 0.42-0.93), ECOG 1 (HR = 0.57, 95% CrI, 0.36-0.91), with extrahepatic spread (EHS) (HR = 0.59, 95% CrI, 0.37-0.92), alpha fetoprotein (AFP)<400ng/ml (HR = 0.56, 95% CrI, 0.33-0.94) and viral hepatitis positivity (HR = 0.56, 95% CrI, 0.39-0.77) (especially HBV (HR = 0.58, 95% CrI, 0.40-0.85)). Importantly, the advantage of this combination therapy was even more pronounced in patients with viral hepatitis positivity. Also, the adverse events associated with immunotherapy combined with antiangiogenic drugs were moderate.

CONCLUSIONS

Immunotherapy combined with anti-angiogenetic agents could represent the most effective first-line intervention for achieving improved OS, particularly in patients with viral hepatitis positivity.

Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.

DNA methylation profiling deciphers three EMT subtypes with distinct prognoses and therapeutic vulnerabilities in breast cancer

Background: Epithelial-mesenchymal transition (EMT), deemed a pivotal hallmark of tumours, is intricately regulated by DNA methylation and encompasses multiple states along tumour progression. The potential mechanisms that drive the intrinsic heterogeneity of breast cancer (BC) via EMT transformation have not been identified, presenting a significant obstacle in clinical diagnosis and treatment. Methods: A total of 7,602 patients have been included in this study. We leveraged integrated multiomics data (epigenomic, genomic, and transcriptomic data) to delineate the comprehensive landscape of EMT in BC. Subsequently, a subtyping classifier was developed through a machine learning framework proposed by us. Results: We classified the BC samples into three methylation-driven EMT subtypes with distinct features, namely, C1 (the mammary duct development subtype with TP53 activation), C2 (the immune infiltration subtype with high TP53 mutation), and C3 (the ERBB2 amplification subtype with an unfavorable prognosis). Specifically, patients with the C1 subtype might respond to endocrine therapy or the p53-MDM2 antagonist nutlin-3. Patients with the C2 subtype might benefit from combined therapeutic regimens involving radiotherapy, PARP inhibitors, and immune checkpoint blockade therapy. Patients with the C3 subtype might benefit from anti-HER2 agents such as lapatinib. Notably, to increase the clinical applicability of the EMT subtypes, we devised a 96-gene panel-based classifier via a machine learning framework. Conclusions: Our study identified three methylation-driven EMT subtypes with distinct prognoses and biological traits to capture heterogeneity in BC and provided a rationale for the use of this classification as a powerful tool for developing new strategies for clinical trials.

RNA vaccines for cancer: Principles to practice

Vaccines are the most impactful medicines to improve health. Though potent against pathogens, vaccines for cancer remain an unfulfilled promise. However, recent advances in RNA technology coupled with scientific and clinical breakthroughs have spurred rapid discovery and potent delivery of tumor antigens at speed and scale, transforming cancer vaccines into a tantalizing prospect. Yet, despite being at a pivotal juncture, with several randomized clinical trials maturing in upcoming years, several critical questions remain: which antigens, tumors, platforms, and hosts can trigger potent immunity with clinical impact? Here, we address these questions with a principled framework of cancer vaccination from antigen detection to delivery. With this framework, we outline features of emergent RNA technology that enable rapid, robust, real-time vaccination with somatic mutation-derived neoantigens-an emerging "ideal" antigen class-and highlight latent features that have sparked the belief that RNA could realize the enduring vision for vaccines against cancer.

Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma

The expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation. Integrative multi-omics analysis revealed four subtypes with differing prognoses. These glioma subtypes exhibited distinct immune-related characteristics as well as clinical and genetic features. By construction of a miRNA regulatory network, we identified candidate miRNAs associated with immune evasion and response to immunotherapy. Finally, eight prognosis related miRNAs were validated to promote cell migration, invasion and proliferation through in vitro experiments. Our study reveals the crosstalk among DNA methylation, copy number variation and miRNA expression for immune regulation in glioma, and could have important implications for patient stratification and development of biomarkers for immunotherapy approaches.

Immune Phenotype-Genotype Associations in Primary Clear Cell Renal Cell Carcinoma and Matched Metastatic Tissue

Adjuvant immunotherapy has been recently recommended for patients with metastatic clear cell renal cell carcinoma (ccRCC), but there are no tissue biomarkers to predict treatment response in ccRCC. Potential predictive biomarkers are mainly assessed in primary tumor tissue, whereas metastases (METs) remain understudied. To explore potential differences between genomic alterations and immune phenotypes in primary tumors and their matched METs, we analyzed primary tumors (PTs) of 47 ccRCC patients and their matched distant METs by comprehensive targeted parallel sequencing, whole-genome copy number variation analysis, determination of microsatellite instability, and tumor mutational burden. We quantified the spatial distribution of tumor-infiltrating CD8+ T cells and coexpression of the T-cell-exhaustion marker thymocyte selection-associated high mobility group box (TOX) by digital immunoprofiling and quantified tertiary lymphoid structures. Most METs were pathologically "cold." Inflamed, pathologically "hot" PTs were associated with decreased disease-free survival, worst for patients with high levels of CD8+TOX+ T cells. Interestingly, inflamed METs showed a relative increase in exhausted CD8+TOX+ T cells and increased accumulative size of tertiary lymphoid structures compared with PTs. Integrative analysis of molecular and immune phenotypes revealed BAP1 and CDKN2A/B deficiency to be associated with an inflamed immune phenotype. Our results highlight the distinct spatial distribution and differentiation of CD8+ T cells at metastatic sites, and the association of an inflamed microenvironment with specific genomic alterations.

RNA modification gene WDR4 facilitates tumor progression and immunotherapy resistance in breast cancer

INTRODUCTION

Growing interest toward RNA modification in cancer has inspired the exploration of gene sets related to multiple RNA modifications. However, a comprehensive elucidation of the clinical value of various RNA modifications in breast cancer is still lacking.

OBJECTIVES

This study aimed to provide a strategy based on RNA modification-related genes for predicting therapy response and survival outcomes in breast cancer patients.

METHODS

Genes related to thirteen RNA modification patterns were integrated for establishing a nine-gene-containing signature-RMscore. Alterations of tumor immune microenvironment and therapy response featured by different RMscore levels were assessed by bulk transcriptome, single-cell transcriptome and genomics analyses. The biological function of key RMscore-related molecules was investigated by cellular experiments in vitro and in vivo, using flow cytometry, immunohistochemistry and immunofluorescence staining.

RESULTS

This study has raised an effective therapy strategy for breast cancer patients after a well-rounded investigation of RNA modification-related genes. With a great performance of predicting patient prognosis, high levels of the RMscore proposed in this study represented suppressive immune microenvironment and therapy resistance, including adjuvant chemotherapy and PD-L1 blockade treatment. As the key contributor of the RMscore, inhibition of WDR4 impaired breast cancer progression significantly in vitro and in vivo, as well as participated in regulating cell cycle and mTORC1 signaling pathway via m7G modification.

CONCLUSION

Briefly, this study has developed promising and effective tactics to achieve the prediction of survival probabilities and treatment response in breast cancer patients.

Weighted centroid trees: a general approach to summarize phylogenies in single-labeled tumor mutation tree inference

MOTIVATION

Tumor trees, which depict the evolutionary process of cancer, provide a backbone for discovering recurring evolutionary processes in cancer. While they are not the primary information extracted from genomic data, they are valuable for this purpose. One such extraction method involves summarizing multiple trees into a single representative tree, such as consensus trees or supertrees.

RESULTS

We define the "weighted centroid tree problem" to find the centroid tree of a set of single-labeled rooted trees through the following steps: (i) mapping the given trees into the Euclidean space, (ii) computing the weighted centroid matrix of the mapped trees, and (iii) finding the nearest mapped tree (NMTP) to the centroid matrix. We show that this setup encompasses previously studied parent-child and ancestor-descendent metrics as well as the GraPhyC and TuELiP consensus tree algorithms. Moreover, we show that, while the NMTP problem is polynomial-time solvable for the adjacency embedding, it is NP-hard for ancestry and distance mappings. We introduce integer linear programs for NMTP in different setups where we also provide a new algorithm for the case of ancestry embedding called 2-AncL2, that uses a novel weighting scheme for ancestry signals. Our experimental results show that 2-AncL2 has a superior performance compared to available consensus tree algorithms. We also illustrate our setup's application on providing representative trees for a large real breast cancer dataset, deducing that the cluster centroid trees summarize reliable evolutionary information about the original dataset.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vasei/WAncILP.

The tumor microenvironment of benign and malignant salivary gland tumors

BACKGROUND

Treatment of salivary gland tumors (SGTs) remains challenging. Little is known about the immune landscape of SGTs. We aimed to characterize the tumor microenvironment in benign and malignant SGTs.

METHODS

Eleven benign and nine malignant tumors were collected from patients undergoing curative intent surgery. Specimens were analyzed using mass cytometry by time-of-flight. Immune cell populations were manually gated, and T cells were clustered using the FlowSOM algorithm. Population frequencies were compared between high-grade and low-grade malignancies, corrected for multiple hypothesis testing.

RESULTS

There were trends towards increased CD4+ and CD8+ T cells among malignant tumors. High-grade malignancies exhibited trends towards higher frequencies of CD8+ PD-1+ CD39+ CD103+ exhausted T cells, CD4+ FoxP3+ TCF-1+ CD127- Tregs, and CD69+ CD25- CD4+ T cells compared to low-grade malignancies.

CONCLUSION

SGTs exhibit significant immunologic diversity. High-grade malignancies tended to have greater infiltration of exhausted CD8+ T cells and Tregs, which may guide future studies for immunotherapy strategies.

Characteristics of the immune environment in prostate cancer as an adjunct to immunotherapy

Background and Aims

The tumor microenvironment (TME) exerts an important role in carcinogenesis and progression. Several investigations have suggested that immune cell infiltration (ICI) is of high prognostic importance for tumor progression and patient survival in many tumors, particularly prostate cancer. The pattern of immune infiltration of PCa, on the other hand, has not been thoroughly understood.

Methods

The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets on PCa were obtained, and several datasets were merged into one data set using the "ComBat" algorithm. The ICI profiles of PCa patients were then to be uncovered by two computer techniques. The unsupervised clustering method was utilized to identify three ICI patterns in tumor samples, and Principal Component Analysis (PCA) was conducted to estimate the ICI score.

Results

Three different clusters of three ICIs were identified in 1341 PCa samples, which also correlated with different clinical features/characteristics and biological pathways. Patients with PCa are classified into high and low subtypes based on the ICI scores extracted from immune-associated signature genes. High ICI score subtypes are associated with a worse prognosis, which may intrigue the activation of cancer-related and immune-related pathways such as pathways involving Toll-like receptors, T-cell receptors, JAK-STAT, and natural killer cells. The ICI score was linked to tumor mutation load and immune/cancer-relevant signaling pathways, which explain prostate cancer's poor prognosis.

Conclusion

The findings of this study not only advanced our knowledge of the mechanism of immune response in the prostate tumor microenvironment but also provided a novel biomarker, that is, the ICI score, for disease prognosis and guiding precision immunotherapy.

A Deep Learning-Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma

Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in patients with epithelial ovarian cancer. However, TIL evaluation has not been used in routine clinical practice because of reproducibility issues. The current study developed two convolutional neural network models to detect TILs and to determine their spatial location in whole slide images, and established a spatial assessment pipeline to objectively quantify intraepithelial and stromal TILs in patients with high-grade serous ovarian carcinoma. The predictions of the established models showed a significant positive correlation with the number of CD8+ T cells and immune gene expressions. Patients with a higher density of intraepithelial TILs had a significantly prolonged overall survival and progression-free survival in multiple cohorts. On the basis of the density of intraepithelial and stromal TILs, patients were classified into three immunophenotypes: immune inflamed, excluded, and desert. The immune-desert subgroup showed the worst prognosis. Gene expression analysis showed that the immune-desert subgroup had lower immune cytolytic activity and T-cell-inflamed gene-expression profile scores, whereas the immune-excluded subgroup had higher expression of interferon-γ and programmed death 1 receptor signaling pathway. The established evaluation method provided detailed and comprehensive quantification of intraepithelial and stromal TILs throughout hematoxylin and eosin-stained slides. It has potential for clinical application for personalized treatment of patients with ovarian cancer.

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approaches, especially in the context of immunotherapy. In this study, we constructed an artificial intelligence (AI)-driven stemness-related gene signature (SRS) that deciphered LUAD prognosis and immunotherapy response. CytoTRACE analysis of single-cell RNA sequencing data identified genes associated with stemness in LUAD epithelial cells. An AI network integrating traditional regression, machine learning, and deep learning algorithms constructed the SRS based on genes associated with stemness. Subsequently, we conducted a comprehensive exploration of the connection between SRS and both intrinsic and extrinsic immune environments using multi-omics data. Experimental validation through siRNA knockdown in LUAD cell lines, followed by assessments of proliferation, migration, and invasion, confirmed the functional role of CKS1B, a top SRS gene. The SRS demonstrated high precision in predicting LUAD prognosis and likelihood of benefiting from immunotherapy. High-risk groups classified by the SRS exhibited decreased immunogenicity and reduced immune cell infiltration, indicating challenges for immunotherapy. Conversely, in vitro experiments revealed CKS1B knockdown significantly impaired aggressive cancer phenotypes like proliferation, migration, and invasion of LUAD cells, highlighting its pivotal role. These results underscore a close association between stemness and tumour immunity, offering predictive insights into the immune landscape and immunotherapy responses in LUAD. The newly established SRS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.

Unveiling a cuproptosis-related risk model and the role of FARSB in hepatocellular carcinoma

Background

Cuproptosis, a type of regulated cell death that was recently identified, has been linked to the development of a variety of diseases, among them being cancers. Nevertheless, the prognostic significance and therapeutic implications of the cuproptosis potential index in hepatocellular carcinoma (HCC) remain uncertain.

Methods

Single-sample gene set enrichment analysis (ssGSEA) and Weighted Gene Co-expression Network Analysis (WGCNA) methodology was conducted to ascertain the identification of modular genes that are closely linked to cuproptosis. In addition, the gene signature indicative of prognosis was formulated by employing univariate Cox regression analysis in conjunction with a random forest algorithm. The efficacy of this gene signature in predicting outcomes was confirmed through validation in both The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Furthermore, a study was undertaken to evaluate the association between the risk score and various clinical-pathological characteristics, explore the biological processes linked to the gene signature, and analyze tumor mutational burden and somatic mutations. Lastly, potential drugs targeting the identified gene signature were identified through screening.

Results

The results of our comprehensive analysis across multiple cancer types demonstrated a positive correlation between an elevated cuproptosis potential index (CPI) and an accelerated rate of tumor progression. Furthermore, employing the WGCNA technique, we successfully identified 640 genes associated with cuproptosis. Among these genes, we meticulously screened and validated a seven-gene signature (TCOF1, NOP58, TMEM69, FARSB, DHX37, SLC16A3, and CBX2) that exhibited substantial prognostic significance. Using the median risk score, the division of HCC patients into cohorts with high- and low-risk highlighted significant disparities in survival results, wherein the group with higher risk exhibited a less favorable overall survival. The risk score exhibited commendable predictive efficacy. Moreover, the in vitro knockdown of FARSB significantly hindered cell viability, induced G1 phase arrest, increased apoptosis, and impaired migration in HepG2 and Huh7 cells.

Conclusion

Our research has successfully identified a strong seven-gene signature linked to cuproptosis, which could be utilized for prognostic evaluation and risk stratification in patients with HCC. Furthermore, the discovered gene signature, coupled with the functional analysis of FARSB, presents promising prospects as potential targets for therapeutic interventions in HCC.

SPP1 promotes tumor progression in esophageal carcinoma by activating focal adhesion pathway

Background

Recurrence and metastasis are the major obstacles affecting the therapeutic efficacy and clinical outcomes for patients with esophageal carcinoma (ESCA). Secreted phosphoprotein 1 (SPP1) is considered as a hub gene in ESCA and is negatively associated with disease-free survival (DFS) in ESCA. However, the exact roles and underlying mechanisms remain elusive. This study aims to examine the roles of SPP1 on ESCA, and elucidate the potential mechanisms.

Methods

Bioinformatics were used to analyze the expression of SPP1 in ESCA tissues, and its relations with clinicopathological characteristics and clinical prognosis in patients with ESCA based on The Cancer Genome Atlas (TCGA) dataset. Loss-of-function was conducted to examine the roles of SPP1 on malignant behaviors of ESCA cells by cell counting kit-8 (CCK8), plate clone, wound healing, and transwell assays. Gene set enrichment analysis (GSEA) was conducted to screen the pathways associated with SPP1 in ESCA. Then, the enriched pathway and the underlying mechanism were elucidated by western blotting, cell adhesion, and cell spreading assays. Lastly, Y15 [a specific inhibitor of focal adhesion kinase (FAK)] was used to examine its potential to inhibit tumor growth in ESCA cells.

Results

SPP1 was upregulated in ESCA tissues compared to the adjacent nontumorous tissues, which was closely associated with clinical stage, lymph node metastasis, histological subtype, and p53 mutation. A high expression of SPP1 indicated a poor clinical prognosis in patients with ESCA. The knockdown of SPP1 inhibited cell proliferative, migratory, and invasive capacities in ESCA cells. GSEA indicated that the focal adhesion pathway was closely related with SPP1 in ESCA. Further studies confirmed that the knockdown of SPP1 suppressed cell adhesion ability and reduced the expression of p-FAK and p-Erk in ESCA cells. In addition, Y15 inhibited FAK autophosphorylation and dramatically inhibited cell proliferation, migration, and invasion in ESCA cells.

Conclusions

SPP1 promotes tumor progression in ESCA by activating FAK/Erk pathway, and FAK is a potential therapeutic target to overcome tumor recurrence and metastasis of ESCA.

Identification of pancreatic adenocarcinoma immune subtype associated with tumor neoantigen from aberrant alternative splicing

Background

Pancreatic adenocarcinoma (PAAD) is referred to as an immunologically "cold" tumor that responds poorly to immunotherapy. A fundamental theory that explains the low immunogenicity of PAAD is the dramatically low tumor mutation burden (TMB) of PAAD tumors, which fails to induce sufficient immune response. Alternative splicing of pre-mRNA, which could alter the proteomic diversity of many cancers, has been reported to be involved in neoantigen production. Therefore, we aim to identify novel PAAD antigens and immune subtypes through systematic bioinformatics research.

Methods

Data for splicing analysis were downloaded from The Cancer Genome Atlas (TCGA) SpliceSeq database. Among the available algorithms, we chose CIBERSORT to evaluate the immune cell distribution among PAADs. The TCGA-PAAD expression matrix was used to construct a co-expression network. Single-cell analysis was performed based on the Seurat workflow.

Results

Integrated analysis of aberrantly upregulated genes, alternatively spliced genes, genes associated with nonsense-mediated RNA decay (NMD) factors, antigen presentation and overall survival (OS) in TCGA-PAAD revealed that PLEC is a promising neoantigen for PAAD-targeted therapy. We identified a C2 TCGA-PAAD subtype that had better prognosis and more CD8+ T-cell infiltration. We propose a novel immune subtyping system for PAAD to indicate patient prognosis and opportunities for immunotherapy, such as immune checkpoint (ICP) inhibitors.

Conclusions

In conclusion, the present study used a transcriptome-guided approach to screen neoantigen candidates based on alternative splicing, NMD factors, and antigen-presenting signatures for PAAD. A prognosis model with guidance of immunotherapy will aid in patient selection for appropriate treatment.

Pre-Existing Immunity Predicts Response to First-Line Immunotherapy in Non-Small Cell Lung Cancer Patients

T-cell-mediated anti-tumoral responses may have significant clinical relevance as a biomarker for response to immunotherapy. The value of peripheral blood pre-existing tumor antigen-specific T cells (PreI+) as a predictive immunotherapy biomarker in NSCLC patients was investigated, along with the frequency of various circulating immune cells. Fifty-two treatment-naïve, stage III/IV NSCLC patients, treated with front-line immune checkpoint inhibitors (ICI)-containing regimens were enrolled. PreI was calculated as the percentages of CD3+IFNγ+ cells after in vitro co-cultures of PBMCs with peptides against four different Tumor-Associated Antigens (TAA). Immunophenotyping of peripheral blood immune cells was performed using multicolor flow cytometry. PreI+ T cells were detected in 44% of patients. Median overall survival (OS) was significantly higher in PreI+ patients compared to PreI- patients (not reached vs. 321 days, respectively; p = 0.014). PreI+ patients had significantly higher numbers of possible exhausted CD3+CD8+PD-1+ cells and lower percentages of immunosuppressive Tregs compared to PreI- patients. Additionally, patients with PreI+ and low numbers of peripheral blood M-MDSCs had a significant survival advantage compared to the rest of the patients. Thus, combining pre-existing tumor antigen-specific immunity before initiation of ICI in NSCLC patients with selected immune-suppressive cells could identify patients who have a favorable clinical outcome when treated with ICI-containing regimens.

A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features

Background

Immune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer treatment, yet the absence of reliable biomarkers hampers precise patient response prediction.

Methods

We developed and validated a genomic mutation signature (GMS) employing a novel artificial intelligence network to forecast the prognosis of gastrointestinal cancer patients undergoing ICIs therapy. Subsequently, we explored the underlying immune landscapes across different subtypes using multiomics data. Finally, UMI-77 was pinpointed through the analysis of drug sensitization data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The sensitivity of UMI-77 to the AGS and MKN45 cell lines was evaluated using the cell counting kit-8 (CCK8) assay and the plate clone formation assay.

Results

Using the artificial intelligence network, we developed the GMS that independently predicts the prognosis of gastrointestinal cancer patients. The GMS demonstrated consistent performance across three public cohorts and exhibited high sensitivity and specificity for 6, 12, and 24-month overall survival (OS) in receiver operating characteristic (ROC) curve analysis. It outperformed conventional clinical and molecular features. Low-risk samples showed a higher presence of cytolytic immune cells and enhanced immunogenic potential compared to high-risk samples. Additionally, we identified the small molecule compound UMI-77. The half-maximal inhibitory concentration (IC50) of UMI-77 was inversely related to the GMS. Notably, the AGS cell line, classified as high-risk, displayed greater sensitivity to UMI-77, whereas the MKN45 cell line, classified as low-risk, showed less sensitivity.

Conclusion

The GMS developed here can reliably predict survival benefit for gastrointestinal cancer patients on ICIs therapy.

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Transcriptome Deconvolution Reveals Absence of Cancer Cell Expression Signature in Immune Checkpoint Blockade Response

Immune checkpoint therapy (ICB) has conferred significant and durable clinical benefit to some patients with cancer. However, most patients do not respond to ICB, and reliable biomarkers of ICB response are needed to improve patient stratification. Here, we performed a transcriptome-wide meta-analysis across 1,486 tumors from ICB-treated patients and tumors with expected ICB outcomes based on microsatellite status. Using a robust transcriptome deconvolution approach, we inferred cancer- and stroma-specific gene expression differences and identified cell-type specific features of ICB response across cancer types. Consistent with current knowledge, stromal expression of CXCL9, CXCL13, and IFNG were the top determinants of favorable ICB response. In addition, we identified a group of potential immune-suppressive genes, including FCER1A, associated with poor response to ICB. Strikingly, PD-L1 expression in stromal cells, but not cancer cells, is correlated with ICB response across cancer types. Furthermore, the unbiased transcriptome-wide analysis failed to identify cancer-cell intrinsic expression signatures of ICB response conserved across tumor types, suggesting that cancer cells lack tissue-agnostic transcriptomic features of ICB response.

SIGNIFICANCE

Our results challenge the prevailing dogma that cancer cells present tissue-agnostic molecular markers that modulate immune activity and ICB response, which has implications on the development of improved ICB diagnostics and treatments.

Immune evasion impacts the landscape of driver genes during cancer evolution

BACKGROUND

Carcinogenesis is driven by interactions between genetic mutations and the local tumor microenvironment. Recent research has identified hundreds of cancer driver genes; however, these studies often include a mixture of different molecular subtypes and ecological niches and ignore the impact of the immune system.

RESULTS

In this study, we compare the landscape of driver genes in tumors that escaped the immune system (escape +) versus those that did not (escape -). We analyze 9896 primary tumors from The Cancer Genome Atlas using the ratio of non-synonymous to synonymous mutations (dN/dS) and find 85 driver genes, including 27 and 16 novel genes, in escape - and escape + tumors, respectively. The dN/dS of driver genes in immune escaped tumors is significantly lower and closer to neutrality than in non-escaped tumors, suggesting selection buffering in driver genes fueled by immune escape. Additionally, we find that immune evasion leads to more mutated sites, a diverse array of mutational signatures and is linked to tumor prognosis.

CONCLUSIONS

Our findings highlight the need for improved patient stratification to identify new therapeutic targets for cancer treatment.

Decoupling the correlation between cytotoxic and exhausted T lymphocyte states enhances melanoma immunotherapy response prediction

Cytotoxic T lymphocyte (CTL) and terminal exhausted T lymphocyte (ETL) activities crucially influence immune checkpoint inhibitor (ICI) response. Despite this, the efficacy of ETL and CTL transcriptomic signatures for response prediction remains limited. Investigating this across the TCGA and publicly available single-cell cohorts, we find a strong positive correlation between ETL and CTL expression signatures in most cancers. We hence posited that their limited predictability arises due to their mutually canceling effects on ICI response. Thus, we developed DETACH, a computational method to identify a gene set whose expression pinpoints to a subset of melanoma patients where the CTL and ETL correlation is low. DETACH enhances CTL's prediction accuracy, outperforming existing signatures. DETACH signature genes activity also demonstrates a positive correlation with lymphocyte infiltration and the prevalence of reactive T cells in the tumor microenvironment (TME), advancing our understanding of the CTL cell state within the TME.

Dissecting the multifaceted roles of autophagy in cancer initiation, growth, and metastasis: from molecular mechanisms to therapeutic applications

Autophagy is a cytoplasmic defense mechanism that cells use to break and reprocess their intracellular components. This utilization of autophagy is regarded as a savior in nutrient-deficient and other stressful conditions. Hence, autophagy keeps contact with and responds to miscellaneous cellular tensions and diverse pathways of signal transductions, such as growth signaling and cellular death. Importantly, autophagy is regarded as an effective tumor suppressor because regular autophagic breakdown is essential for cellular maintenance and minimizing cellular damage. However, paradoxically, autophagy has also been observed to promote the events of malignancies. This review discussed the dual role of autophagy in cancer, emphasizing its influence on tumor survival and progression. Possessing such a dual contribution to the malignant establishment, the prevention of autophagy can potentially advocate for the advancement of malignant transformation. In contrast, for the context of the instituted tumor, the agents of preventing autophagy potently inhibit the advancement of the tumor. Key regulators, including calpain 1, mTORC1, and AMPK, modulate autophagy in response to nutritional conditions and stress. Oncogenic mutations like RAS and B-RAF underscore autophagy's pivotal role in cancer development. The review also delves into autophagy's context-dependent roles in tumorigenesis, metastasis, and the tumor microenvironment (TME). It also discusses the therapeutic effectiveness of autophagy for several cancers. The recent implication of autophagy in the control of both innate and antibody-mediated immune systems made it a center of attention to evaluating its role concerning tumor antigens and treatments of cancer.

ARID1A suppresses R-loop-mediated STING-type I interferon pathway activation of anti-tumor immunity

Clinical trials have identified ARID1A mutations as enriched among patients who respond favorably to immune checkpoint blockade (ICB) in several solid tumor types independent of microsatellite instability. We show that ARID1A loss in murine models is sufficient to induce anti-tumor immune phenotypes observed in ARID1A mutant human cancers, including increased CD8+ T cell infiltration and cytolytic activity. ARID1A-deficient cancers upregulated an interferon (IFN) gene expression signature, the ARID1A-IFN signature, associated with increased R-loops and cytosolic single-stranded DNA (ssDNA). Overexpression of the R-loop resolving enzyme, RNASEH2B, or cytosolic DNase, TREX1, in ARID1A-deficient cells prevented cytosolic ssDNA accumulation and ARID1A-IFN gene upregulation. Further, the ARID1A-IFN signature and anti-tumor immunity were driven by STING-dependent type I IFN signaling, which was required for improved responsiveness of ARID1A mutant tumors to ICB treatment. These findings define a molecular mechanism underlying anti-tumor immunity in ARID1A mutant cancers.

Dissecting gastric cancer heterogeneity and exploring therapeutic strategies using bulk and single-cell transcriptomic analysis and experimental validation of tumor microenvironment and metabolic interplay

Gastric cancer, the fifth most prevalent cancer worldwide, is often diagnosed in advanced stages with limited treatment options. Examining the tumor microenvironment (TME) and its metabolic reprogramming can provide insights for better diagnosis and treatment. This study investigates the link between TME factors and metabolic activity in gastric cancer using bulk and single-cell RNA-sequencing data. We identified two molecular subtypes in gastric cancer by analyzing the distinct expression patterns of 81 prognostic genes related to the TME and metabolism, which exhibited significant protein-level interactions. The high-risk subtype had increased stromal content, fibroblast and M2 macrophage infiltration, elevated glycosaminoglycans/glycosphingolipids biosynthesis, and fat metabolism, along with advanced clinicopathological features. It also exhibited low mutation rates and microsatellite instability, associating it with the mesenchymal phenotype. In contrast, the low-risk group showed higher tumor content and upregulated protein and sugar metabolism. We identified a 15-gene prognostic signature representing these characteristics, including CPVL, KYNU, CD36, and GPX3, strongly correlated with M2 macrophages, validated through single-cell analysis and an internal cohort. Despite resistance to immunotherapy, the high-risk group showed sensitivity to molecular targeted agents directed at IGF-1R (BMS-754807) and the PI3K-mTOR pathways (AZD8186, AZD8055). We experimentally validated these promising drugs for their inhibitory effects on MKN45 and MKN28 gastric cells. This study unveils the intricate interplay between TME and metabolic pathways in gastric cancer, offering potential for enhanced diagnosis, patient stratification, and personalized treatment. Understanding molecular features in each subtype enriches our comprehension of gastric cancer heterogeneity and potential therapeutic targets.

TM-Score predicts immunotherapy efficacy and improves the performance of the machine learning prognostic model in gastric cancer

Immunotherapy is becoming increasingly important, but the overall response rate is relatively low in the treatment of gastric cancer (GC). The application of tumor mutational burden (TMB) in predicting immunotherapy efficacy in GC patients is limited and controversial, emphasizing the importance of optimizing TMB-based patient selection. By combining TMB and major histocompatibility complex (MHC) related hub genes, we established a novel TM-Score. This score showed superior performance for immunotherapeutic selection (AUC = 0.808) compared to TMB, MSI status, and EBV status. Additionally, it predicted the prognosis of GC patients. Subsequently, a machine learning model adjusted by the TM-Score further improved the accuracy of survival prediction (AUC > 0.8). Meanwhile, we found that GC patients with low TM-Score had a higher mutation frequency, higher expression of HLA genes and immune checkpoint genes, and higher infiltration of CD8+ T cells, CD4+ helper T cells, and M1 macrophages. This suggests that TM-Score is significantly associated with tumor immunogenicity and tumor immune environment. Notably, based on the RNA-seq and scRNA-seq, it was found that AKAP5, a key component gene of TM-Score, is involved in anti-tumor immunity by promoting the infiltration of CD4+ T cells, NK cells, and myeloid cells. Additionally, siAKAP5 significantly reduced MHC-II mRNA expression in the GC cell line. In addition, our immunohistochemistry assays confirmed a positive correlation between AKAP5 and human leukocyte antigen (HLA) expression. Furthermore, AKAP5 levels were higher in patients with longer survival and those who responded to immunotherapy in GC, indicating its potential value in predicting prognosis and immunotherapy outcomes. In conclusion, TM-Score, as an optimization of TMB, is a more precise biomarker for predicting the immunotherapy efficacy of the GC population. Additionally, AKAP5 shows promise as a therapeutic target for GC.

Expression of the membrane tetraspanin claudin 18 on cancer cells promotes T lymphocyte infiltration and antitumor immunity in pancreatic cancer

Tumors weakly infiltrated by T lymphocytes poorly respond to immunotherapy. We aimed to unveil malignancy-associated programs regulating T cell entrance, arrest, and activation in the tumor environment. Differential expression of cell adhesion and tissue architecture programs, particularly the presence of the membrane tetraspanin claudin (CLDN)18 as a signature gene, demarcated immune-infiltrated from immune-depleted mouse pancreatic tumors. In human pancreatic ductal adenocarcinoma (PDAC) and non-small cell lung cancer, CLDN18 expression positively correlated with more differentiated histology and favorable prognosis. CLDN18 on the cell surface promoted accrual of cytotoxic T lymphocytes (CTLs), facilitating direct CTL contacts with tumor cells by driving the mobilization of the adhesion protein ALCAM to the lipid rafts of the tumor cell membrane through actin. This process favored the formation of robust immunological synapses (ISs) between CTLs and CLDN18-positive cancer cells, resulting in increased T cell activation. Our data reveal an immune role for CLDN18 in orchestrating T cell infiltration and shaping the tumor immune contexture.

Integrative molecular and spatial analysis reveals evolutionary dynamics and tumor-immune interplay of in situ and invasive acral melanoma

In acral melanoma (AM), progression from in situ (AMis) to invasive AM (iAM) leads to significantly reduced survival. However, evolutionary dynamics during this process remain elusive. Here, we report integrative molecular and spatial characterization of 147 AMs using genomics, bulk and single-cell transcriptomics, and spatial transcriptomics and proteomics. Vertical invasion from AMis to iAM displays an early and monoclonal seeding pattern. The subsequent regional expansion of iAM exhibits two distinct patterns, clonal expansion and subclonal diversification. Notably, molecular subtyping reveals an aggressive iAM subset featured with subclonal diversification, increased epithelial-mesenchymal transition (EMT), and spatial enrichment of APOE+/CD163+ macrophages. In vitro and ex vivo experiments further demonstrate that APOE+CD163+ macrophages promote tumor EMT via IGF1-IGF1R interaction. Adnexal involvement can predict AMis with higher invasive potential whereas APOE and CD163 serve as prognostic biomarkers for iAM. Altogether, our results provide implications for the early detection and treatment of AM.

Further knowledge and developments in resistance mechanisms to immune checkpoint inhibitors

The past decade has witnessed a revolution in cancer treatment, shifting from conventional drugs (chemotherapies) towards targeted molecular therapies and immune-based therapies, in particular immune-checkpoint inhibitors (ICIs). These immunotherapies release the host's immune system against the tumor and have shown unprecedented durable remission for patients with cancers that were thought incurable, such as metastatic melanoma, metastatic renal cell carcinoma (RCC), microsatellite instability (MSI) high colorectal cancer and late stages of non-small cell lung cancer (NSCLC). However, about 80% of the patients fail to respond to these immunotherapies and are therefore left with other less effective and potentially toxic treatments. Identifying and understanding the mechanisms that enable cancerous cells to adapt to and eventually overcome therapy can help circumvent resistance and improve treatment. In this review, we describe the recent discoveries on the onco-immunological processes which govern the tumor microenvironment and their impact on the resistance to PD-1/PD-L1 checkpoint blockade.