Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma

Phosphoribosyl transferase domain containing 1: A prognostic biomarker in testicular germ cell tumors

Due to the heterogeneity and complex classification of testicular germ cell tumors (TGCTs), prognostic evaluation and therapeutic targets remain unclear. Therefore, identifying a novel biomarker to comprehensively assess TGCT prognosis and immunotherapy response is crucial. We collected data from 457 TGCT patient samples and 12 normal testicular samples across six cohorts. Differential expression analysis combined with univariate Cox regression identified prognostic markers for TGCT. Multivariate Cox regression and survival analysis further evaluated the prognostic value of phosphoribosyl transferase domain containing 1 (PRTFDC1). Immunohistochemistry on tissue microarrays validated PRTFDC1's predictive value in clinical samples. We then investigated the relationship between PRTFDC1 and somatic mutations, copy number variations, immune cell infiltration, and immunotherapy response. Through these analyses, we identified PRTFDC1 as an independent risk factor indicating poor prognosis in TGCT. Immunohistochemistry demonstrated high PRTFDC1 expression in TGCT tissues. Gene set enrichment analysis revealed that PRTFDC1 suppresses immune-related pathways. Immune infiltration showed that high PRTFDC1 expression is associated with low CD8+ T cell infiltration. Immunotherapy response analysis indicated that low PRTFDC1 expression predicts better immunotherapy response and favorable prognosis. In conclusion, this study elucidates the biological and clinical significance of PRTFDC1, suggesting it as an effective and reliable biomarker for predicting TGCT prognosis and immunotherapy response.

A comprehensive investigation of associations between cell death pathways and molecular and clinical features in pan-cancer

BACKGROUND

Regulated cell death (RCD) pathways play significant roles in tumorigenesis. However, systematic investigation into correlations between RCD and various molecular and clinical features, particularly anti-tumor immunity and immunotherapy response in pan-cancer remains lacking.

METHODS

Using the single-sample gene set enrichment analysis, we quantified the activities of six RCD pathways (apoptosis, autophagy, ferroptosis, cuproptosis, necroptosis, and pyroptosis) in each cancer specimen. Then, we explored associations of these six RCD pathways with tumor immunity, genomic instability, tumor phenotypes and clinical features, and responses to immunotherapy and targeted therapies in pan-cancer by statistical analyses.

RESULTS

Our results showed that the RCD (except autophagy) activities were oncogenic signatures, as evidenced by their hyperactivation in late stage or metastatic cancer patients, positive correlations with tumor proliferation, stemness, genomic instability and intratumor heterogeneity, and correlation with worse survival outcomes in cancer. In contrast, autophagy was a tumor suppressive signature as its associations with molecular and clinical features in cancer shows an opposite pattern compared to the other RCD pathways. Furthermore, heightened RCD (except cuproptosis) activities were correlated with increased sensitivity to immune checkpoint inhibitors. Additionally, elevated activities of pyroptosis, autophagy, cuproptosis and necroptosis were associated with increased drug sensitivity in a broad spectrum of anti-tumor targeted therapies, while the elevated activity of ferroptosis was correlated with decreased sensitivity to numerous targeted therapies.

CONCLUSION

RCD (except autophagy) activities correlate with unfavorable cancer prognosis, while the autophagy activity correlate with favorable clinical outcomes. RCD (except cuproptosis) activities are positive biomarkers for anti-tumor immunity and immunotherapy response.

Advances in TIL therapy: Expanding the horizons beyond melanoma

Tumor-infiltrating lymphocyte (TIL) therapy represents a breakthrough in solid tumor treatment, addressing unmet needs for patients with limited options. While its efficacy is established in advanced melanoma, TIL therapy shows early promise in non-small cell lung cancer, breast cancer, gynecological cancers, and head and neck cancers. However, challenges such as reduced T cell infiltration, lower tumor mutational burden (TMB), immunosuppressive tumor microenvironments (TME), and toxicity associated with the TIL therapy regimen hinder its broader application in these patient groups, compared with melanoma. To address these challenges, new approaches focus on the selection of tumor-reactive TIL, optimization of TIL expansion, combination of immune checkpoint inhibitors with TIL therapy to counteract immunosuppressive microenvironments, and genetic modification of TIL to enhance persistence and functionality. Larger clinical trials are essential to validate these innovations and standardize protocols. With continued advancements, TIL therapy has the potential to redefine the treatment landscape for advanced solid cancers.

APOL6 as a potential biomarker of immuno-correlation and therapeutic prediction in cancer immunotherapy

The emergence of immune checkpoint inhibitors (ICIs) has significantly revolutionized the approach to treating advanced cancers. Despite their remarkable efficacy, not all patients exhibit favorable responses to ICI therapy. Hence, more biomarkers for therapeutic prediction need to be discovered. In this study, we utilized public cohorts to investigate the predictive significance and immunological associations of apolipoprotein L6 (APOL6) in cancers. The expression of APOL6 was found to be enhanced in tumors of patients who exhibited strong immunotherapeutic responses across various types of cancer. Furthermore, APOL6 showed immune correlations in pan-cancer and was confirmed by the tissue microarray cohort and in vitro experiments. Overall, this study highlights that APOL6 serves as a beneficial biomarker for immune checkpoint inhibitors in patients with cancer. Additional research involving larger numbers of patients and the underlying mechanism is necessary to determine its effectiveness as a biomarker for predicting the benefits of ICIs.

Impaired T cell and neoantigen retention in time-serial analysis of metastatic non-small cell lung cancer in patients unresponsive to TIL cell therapy

Cell therapy with tumor-infiltrating lymphocytes (TILs) has yielded durable responses for multiple cancer types, but the causes of therapeutic resistance remain largely unknown. Here multidimensional analysis was performed on time-serial tumor and blood in a lung cancer TIL therapy trial. Using T cell receptor sequencing on both functionally expanded T cells and neoantigen-loaded tetramer-sorted T cells, we identified tumor antigen-specific T cell receptors. We then mapped clones into individual transcriptomes and found that tumor-reactive clonotypes expressed a dysfunctional program and lacked stem-like features among patients who lacked clinical benefit. Tracking tumor-reactive clonotypes over time, decay of antigen-reactive peripheral T cell clonotypes was associated with the emergence of progressive disease. Further, subclonal neoantigens previously targeted by infused T cells were subsequently absent within tumors at progression, suggesting potential adaptive resistance. Our findings suggest that targeting clonal antigens and circumventing dysfunctional states may be important for conferring clinical responses to TIL therapy.

Single-cell analysis and machine learning-based integration develop an immune-responsive signature of antigen-presenting cancer-associated fibroblasts in lung adenocarcinoma

Background

Cancer-associated fibroblasts (CAFs) are pivotal regulators of the tumor immune microenvironment, shaping immune responses and influencing therapeutic outcomes. While previous studies have predominantly focused on CAF subpopulations that impair responses to immune checkpoint inhibitors (ICIs), CAF subsets associated with favorable ICIs responses in lung adenocarcinoma (LUAD) remain underexplored. In this study, we integrated bulk RNA and single-cell RNA sequencing data from LUAD samples to identify CAF subpopulations relevant to ICIs efficacy.

Methods

Using a machine learning-driven approach, we developed a robust immune response signature based on this antigen-presenting CAFs (apCAFs) subset to predict ICIs responses.

Results

We uncovered a novel subset of apCAFs exhibiting macrophage-like features, characterized by the expression of major histocompatibility complex (MHC) class II, CD74, and costimulatory molecules (CD80, CD86, CD83, and CD40). This subset, distinct from classic apCAFs described in other cancer types, is strongly associated with favorable ICIs responses across multiple datasets. Notably, these macrophage-like apCAFs are present in LUAD samples prior to treatment, although their abundance varies among individuals. Patients classified as high-risk using signature calculated by a machine learning-driven approach exhibited lower overall survival rates and diminished immune cell infiltration following ICIs therapy.

Conclusions

Collectively, our findings establish a critical link between macrophage-like apCAFs and ICIs efficacy, offering a clinically applicable signature for patient stratification and guiding therapeutic strategies targeting the tumor microenvironment.

Polyamine-Depleting Hydrogen-Bond Organic Frameworks Unleash Dendritic Cell and T Cell Vigor for Targeted CRISPR/Cas-Assisted Cancer Immunotherapy

Polyamines have tantalized cancer researchers as a potential means to rein in the rampant growth of cancer cells. However, clinical trials in recent decades have disappointed in delivering notable progress. Herein, a microfluidic-assisted synthetic hydrogen-bond organic framework (HOF) as a polyamine-depleting nanoplatforms designed to unleash the vigor of both dendritic cells (DCs) and T cells for precision cancer immunotherapy is reported. Upon internalization by tumor cells, the loaded plasma amine oxidase (PAO) in HOF efficiently depletes polyamines, remolding the tumor microenvironment and alleviating T-cell immunosuppression. This process also generates acrolein and H2O2, triggering CRISPR-assisted neoantigen generation. Specifically, Acrolein induces carbonyl stress, increasing mutational burdens. Simultaneously, HOF leverages the energy from the bis[2,4,5-trichloro-6-(pentyloxycarbonyl)phenyl] oxalate (CPPO)-H2O2 reaction for CRET-triggered singlet oxygen production, leading to thioether bond cleavage and release CRISPR-Cas9. Once released, CRISPR-Cas9 knocks out the DNA mismatch repair (MMR)-related MLH1 gene, further elevating mutational burdens and generating neoantigens, ideal targets for DCs. This dual-action strategy not only corrects T-cell immunosuppression but also enhances DC efficacy, presenting a powerful approach for tumor immunotherapy.

Developing and validating a prognostic disulfidptosis-related signature for glioblastoma: predicting radioresistance and synergestic effect with immunotherapy

BACKGROUND

Programmed cell death (PCD) modulated radioresistance is one of the predominant causes of treatment failure in glioblastoma (GBM). Disulfidptosis, a newly discovered form of PCD, plays a crucial role in GBM progression. However, the association among disulfidptosis, radiosensitivity and radiotherapy (RT) in GBM remain unclear.

METHODS

We systematically analyzed disulfidptosis-related genes in 1075 GBM patients and constructed a disulfidptosis-related gene signature (DRS). Correlations among the DRS, patient prognosis and immune microenvironment were fully explored. The effects of DRS and EFEMP2 on radiotherapy efficacy were investigated via single cell sequencing analysis and validated via in vitro and in vivo experiments.

RESULTS

The DRS was identified as a robust and independent prognostic biomarker for GBM by multivariate Cox regression analysis, receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) in multiple cohorts. High DRS is characterized by radioresistance, and EFEMP2 was proven to be the key gene involved in this process by single cell sequencing analysis, CCK-8 assay and a clonogenic survival assay. In high-DRS patients, the cancer-immunity cycle is attenuated because the antitumor cytotoxicity of CD8+ T cells is inhibited by immune checkpoints. Preclinically, the overexpression of EFEMP2 induced radioresistance and enhancing the efficacy of programmed cell death ligand-1 (PD-L1) blockade in GL261-bearing mice. The combination of irradiation and anti-PD-L1 therapy had a synergistic effect on GBM murine models in which EFEMP2 was overexpressed.

CONCLUSION

Our study bioinformatically and experimentally reveals the molecular landscape of disulfidptosis in GBM, develops a predictive signature for predicting prognosis as well as radioresistance, and provides a synergistic treatment that combines radiotherapy with immunotherapy for radioresistant GBM patients with high DRS or EFEMP2 expression.

Progesterone receptor-dependent downregulation of MHC class I promotes tumor immune evasion and growth in breast cancer

BACKGROUND

Breast cancer (BC) continues to be a major health concern with 250,000 new cases diagnosed annually in the USA, 75% of which are hormone receptor positive (HR+), expressing estrogen receptor alpha (ER) and/or the progesterone receptor (PR). Although ER-targeted therapies are available, 30% of patients will develop resistance, underscoring the need for new non-ER/estrogen-based treatments. Notably, HR+BCs exhibit poor lymphocyte infiltration and contain an immunosuppressive microenvironment, which contributes to the limited efficacy of immunotherapies in HR+BC. In this study, we demonstrate that PR/progesterone signaling reduces major histocompatibility complex (MHC) Class I expression, facilitating immune evasion and escape from immune-based clearance of PR+tumors.

METHODS

To determine the effect of PR/progesterone on MHC Class I expression, we treated human and mouse mammary tumor cell lines with progesterone and/or interferon (IFN) and measured expression of genes involved in antigen processing and presentation (APP), as well as surface MHC Class I expression. We used the OT-I/SIINFEKL model antigen system to measure the impact of progesterone on immune cell-mediated killing of modified tumor cells. We also analyzed two large BC clinical cohorts to determine how PR expression correlates with APP gene expression and MHC Class I expression in ER-positive tumors.

RESULTS

In vitro, we show that PR/progesterone signaling reduces APP gene expression and MHC class I expression in human and breast mammary tumor cell lines. PR-mediated attenuation of APP/MHC Class I expression is more pronounced in the presence of IFN. In immune cell killing assays, PR-expressing mammary tumor cells treated with progesterone are protected from immune-mediated cytotoxicity. We demonstrate that PR expression in vivo prevents immune-mediated rejection of xenoantigen-modified mammary tumor cell lines through mechanisms involving MHC Class I expression and CD8 T cells. Data analysis of two large BC cohorts reveals lower APP gene expression and MHC Class I expression in ER/PR-positive tumors compared with ER-positive/PR-negative tumors. These findings show that HR+BCs, specifically PR+tumors, downregulate APP/MHC class I machinery through PR/progesterone signaling. Use of pharmacological PR/progesterone inhibitors may reverse these effects in patients with BC, thereby improving immunosurveillance and response to immunotherapies.

Tumor microenvironment: recent advances in understanding and its role in modulating cancer therapies

Tumor microenvironment (TME) denotes the non-cancerous cells and components presented in the tumor, including molecules produced and released by them. Interactions between cancer cells, immune cells, stromal cells, and the extracellular matrix within the TME create a dynamic ecosystem that can either promote or hinder tumor growth and spread. The TME plays a pivotal role in either promoting or inhibiting tumor growth and dissemination, making it a critical factor to consider in the development of effective cancer therapies. Understanding the intricate interplay within the TME is crucial for devising effective cancer therapies. Combination therapies involving inhibitors of immune checkpoint blockade (ICB), and/or chemotherapy now offer new approaches for cancer therapy. However, it remains uncertain how to best utilize these strategies in the context of the complex tumor microenvironment. Oncogene-driven changes in tumor cell metabolism can impact the TME to limit immune responses and present barriers to cancer therapy. Cellular and acellular components in tumor microenvironment can reprogram tumor initiation, growth, invasion, metastasis, and response to therapies. Components in the TME can reprogram tumor behavior and influence responses to treatments, facilitating immune evasion, nutrient deprivation, and therapeutic resistance. Moreover, the TME can influence angiogenesis, promoting the formation of blood vessels that sustain tumor growth. Notably, the TME facilitates immune evasion, establishes a nutrient-deprived milieu, and induces therapeutic resistance, hindering treatment efficacy. A paradigm shift from a cancer-centric model to a TME-centric one has revolutionized cancer research and treatment. However, effectively targeting specific cells or pathways within the TME remains a challenge, as the complexity of the TME poses hurdles in designing precise and effective therapies. This review highlights challenges in targeting the tumor microenvironment to achieve therapeutic efficacy; explore new approaches and technologies to better decipher the tumor microenvironment; and discuss strategies to intervene in the tumor microenvironment and maximize therapeutic benefits.

Interleukin-16 enhances anti-tumor immune responses by establishing a Th1 cell-macrophage crosstalk through reprogramming glutamine metabolism in mice

Overcoming immunosuppression in the tumor microenvironment (TME) is crucial for developing novel cancer immunotherapies. Here, we report that IL-16 administration enhances the polarization of T helper 1 (Th1) cells by inhibiting glutamine catabolism through the downregulation of glutaminase in CD4+ T cells and increases the production of Th1 effector cytokine IFN-γ, thus improving anti-tumor immune responses. Moreover, we find that establishing an IL-16-dependent, Th1-dominant TME relies on mast cell-produced histamine and results in the increased expression of the CXCR3 ligands in tumor-associated macrophages (TAM), thereby improving the therapeutic effectiveness of immune checkpoint blockade (ICB). Cancer patients exhibit impaired production of IL-16, which correlates with poorer prognosis. Additionally, low IL-16 production is associated with unresponsiveness to immunotherapy in cancer patients. Collectively, our findings provided new insights into the biological function of IL-16, emphasizing its potential clinical significance as a therapeutic approach to augment anti-tumor immunity and sensitize ICB-based cancer immunotherapy.

Preclinical data and design of a phase I clinical trial of neoantigen-reactive TILs for advanced epithelial or ICB-resistant solid cancers

Background

Adoptive cell therapy (ACT) of ex vivo expanded tumor-infiltrating lymphocytes (TILs) can mediate objective tumor regression in 28%-49% of metastatic melanoma patients. However, the efficacy of TIL therapy in most epithelial cancers remains limited. We present the design of a phase I clinical study that aims to assess the safety and efficacy of NEXTGEN-TIL, a TIL product selected based on ex vivo neoantigen recognition, in patients with advanced epithelial tumors and immune checkpoint blockade (ICB)-resistant solid tumors.

Materials and methods

Pre-rapid expansion protocol (REP) TIL cultures expanded in high-dose interleukin 2 (HD-IL-2) from patients with metastatic solid tumors were screened for recognition of autologous tumor cell lines (TCLs) and/or neoantigens. Six good manufacturing practice (GMP)-grade validations of pre-REP TIL expansion were carried out and TIL cultures from these six intermediate products were selected to carry out the clinical-scale GMP validation of the REP.

Results

TILs expanded in 82% of patient-derived tumor biopsies across different cancer types and these frequently contained tumor- and neoantigen-reactive T cells. During GMP validations, a variable number of TIL cultures expanded, constituting the intermediate products (pre-REP). Three finished products were manufactured using a REP which reached cell doses ranging from 4.3e9 to 1.1e11 and met the established specifications. The NEXTGEN-TIL clinical trial entails a first expansion of TILs from tumor fragments in HD-IL-2 followed by TIL screening for neoantigen recognition and REP of selected neoantigen-reactive TIL cultures. Treatment involves a classical non-myeloablative lymphodepleting chemotherapy followed by NEXTGEN-TIL product administration together with HD-IL-2.

Conclusions

NEXTGEN-TIL exploits ex vivo expanded neoantigen-reactive TIL to potentially improve efficacy in patients with epithelial and ICB-resistant tumors, with a safety profile like traditional TILs.

Novel post-translational modification learning signature reveals B4GALT2 as an immune exclusion regulator in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) presents significant challenges in prognosis and treatment efficacy evaluation. While post-translational modifications are known to influence tumor progression, their prognostic value in LUAD remains largely unexplored.

METHODS

We developed a post-translational modification learning signature (PTMLS) using machine learning techniques, analyzing data from 1231 LUAD patients across seven global cohorts. The signature's efficacy in predicting immunotherapy response was evaluated using 12 immunotherapy cohorts spanning multiple cancer types (n=1201). An in-house LUAD tissue cohort (n=171) was used to validate beta-1,4-galactosyltransferase 2's (B4GALT2's) prognostic significance. The role of B4GALT2 in immune exclusion was investigated through in vivo and in vitro experiments.

RESULTS

The established PTMLS exhibited exceptional predictive capabilities in LUAD patient outcomes, surpassing the efficacy of 98 existing LUAD prognostic indicators. The system's predictive value was validated across diverse malignancy categories for immunotherapeutic response assessment. From a biological perspective, significant correlations were observed between PTMLS and immunological parameters, whereby elevated PTMLS levels were characterized by attenuated immune responses and immunologically cold neoplastic features. Within the PTMLS framework, B4GALT2 was identified as a crucial molecular component (r=0.82, p<0.05), and its heightened expression was linked to unfavorable clinical outcomes in LUAD cases, particularly in specimens exhibiting CD8-depleted phenotypes. The spatial distribution patterns between B4GALT2 and immune cell populations, specifically CD8+ T lymphocytes and CD20+ B lymphocytes, were elucidated through multiplexed immunofluorescence analysis. Laboratory investigations subsequently established B4GALT2's regulatory influence on LUAD cellular expansion in both laboratory cultures and animal models. Significantly, suppression of B4GALT2 was found to enhance CD8+ T lymphocyte populations and their functional status, thereby potentiating anti-programmed cell death protein 1 immunotherapeutic efficacy in animal studies. This phenomenon was characterized by reduced CD62L+CD8 T lymphocyte levels alongside elevated GZMB+/CD44+/CD69+CD8 T cell populations.

CONCLUSION

The developed PTMLS system represents an effective instrument for individualized prognostic evaluation and immunotherapy stratification in both LUAD and diverse cancer populations. The identification of B4GALT2 as a previously unrecognized oncogenic factor involved in immune exclusion presents a novel therapeutic avenue for LUAD treatment and immunotherapy optimization.

Multi-omics and single-cell analysis reveals machine learning-based pyrimidine metabolism-related signature in the prognosis of patients with lung adenocarcinoma

Background: Pyrimidine metabolism is a hallmark of tumor metabolic reprogramming, while its significance in the prognostic and therapeutic implications of patients with lung adenocarcinoma (LUAD) still remains unclear. Methods: In this study, an integrated framework of various machine learning and deep learning algorithms was used to develop the pyrimidine metabolism-related signature (PMRS). Its efficacy in genomic stability, chemotherapy and immunotherapy resistance was evaluated through comprehensive multi-omics analysis. The single-cell landscape of patients between PMRS subgroups was also elucidated. Subsequently, the biological functions of LYPD3, the most important coefficient factor in the PMRS model, were experimentally validated in LUAD cell lines. Results: The PMRS model with "random survival forest" algorithm exhibited the best performance and was utilized for further analysis. It displayed excellent accuracy and stability in various model evaluation assays. Compared to the PMRS-high subgroup, patients with lower PMRS scores had better survival outcomes, more stable genomic characteristics and higher sensitivity to immunotherapy. Single-cell analysis indicated that as PMRS increased, epithelial cells gradually exhibited malignant phenotypes with enhanced pyrimidine metabolism, while PMRS-high patients showed an inhibitory status of tumor immune microenvironment. Further experiments indicated that LYPD3 promoted the malignant progression in LUAD cell lines. Conclusion: Our study constructed the PMRS model, highlighting its potential value in the treatment and prognosis of LUAD patients and providing new insights into the individualized precision treatment for LUAD patients.

Molecular subtyping combined with multiomics analysis to study correlation between TACE refractoriness and tumor stemness in hepatocellular carcinoma

BACKGROUND

Transarterial chemoembolization (TACE) refractoriness is a significant challenge in treating intermediate to advanced-stage hepatocellular carcinoma (HCC). A few studies suggest that liver cancer stem cells (LCSCs) may be associated with TACE refractoriness. This study aims to explore the potential correlation between TACE refractoriness and HCC stemness, highlighting its clinical significance.

METHODS

This research encompassed the analysis of diverse HCC datasets, including RNA-sequencing, microarray, single-cell RNA-sequencing, and clinical cohorts. We identified common genes between TACE refractoriness and tumor stemness (TSGs). Unsupervised clustering was employed to classify HCC patients into different clusters based on TSGs (TRS clusters). The study explored the differences in clinical prognosis, biological characteristics, genomic variations, immune landscapes, and treatment responses among the TRS clusters.

RESULTS

Patients with TACE-refractoriness demonstrated significantly higher tumor stemness. Our study identified 33 TSGs and established two TRS clusters, including C1 and C2. C1 was associated with TACE refractoriness, elevated tumor stemness, and poorer prognosis. Genomic alterations were found to be significantly different between the TRS clusters. The C1 exhibited signs of immunosuppression and lower activity of immune effector cells, while the C2 had a more robust immune response and higher level of immune cell presence. Single-cell RNA-seq revealed distinct cell type characteristics in each subtypes, with the C1 showing a higher proportion of stem cells and malignant cells.

CONCLUSION

Our findings establish a connection between TACE refractoriness and tumor stemness in HCC, proposing a novel subtype classification to guide personalized treatment. Insights gained may facilitate overcoming TACE refractoriness and the development of innovative therapies.

Comprehensive Analyses of PANoptosome with Potential Implications in Cancer Prognosis and Immunotherapy

Cell death resistance significantly contributes to poor therapeutic outcomes in various cancers. PANoptosis, a unique inflammatory programmed cell death (PCD) pathway activated by specific triggers and regulated by the PANoptosome, possesses key features of apoptosis, pyroptosis, and necroptosis, but these cannot be accounted for by any of the three PCD pathways alone. While existing studies on PANoptosis have predominantly centered on infectious and inflammatory diseases, its role in cancer malignancy has been understudied. In this comprehensive investigation, we conducted pan-cancer analyses of PANoptosome component genes across 33 cancer types. We characterized the genetic, epigenetic, and transcriptomic landscapes, and introduced a PANoptosome-related potential index (PANo-RPI) for evaluating the intrinsic PANoptosome assembly potential in cancers. Our findings unveil PANo-RPI as a prognostic factor in numerous cancers, including KIRC, LGG, and PAAD. Crucially, we established a significant correlation between PANo-RPI and tumor immune responses, as well as the infiltration of diverse lymphoid and myeloid cell subsets across nearly all cancer types. Moreover, a high PANo-RPI was consistently associated with improved immunotherapy response and efficacy, as evidenced by re-analysis of multiple immunotherapy cohorts. In conclusion, our study suggests that targeting PANoptosome components and modulating PANoptosis may hold tremendous therapeutic potential in the context of cancer.

Tertiary Lymphoid Structures are Linked to Enhanced Antitumor Immunity and Better Prognosis in Muscle-Invasive Bladder Cancer

The prognosis for muscle-invasive bladder cancer (MIBC) remains poor, and reliable prognostic markers have yet to be identified. Tertiary lymphoid structures (TLS) have been associated with favorable outcomes in certain cancers. However, the relationship between TLS and MIBC remains unclear. A multi-omics approach is utilized, leveraging single-cell RNA sequencing, spatial transcriptomics, bulk RNA sequencing, and immunohistochemistry, to investigate the roles of B cells and TLS in MIBC. These findings indicate that elevated levels of B cells and TLS correlate with improved prognoses in patients with MIBC, aligning with the robust antitumor immune responses observed in the TLS region. From a mechanistic perspective, CXCL13 serves as a critical cytokine for TLS formation in MIBC, primarily secreted by clonally expanded CXCL13+ T cells. This cytokine interacts with the CXCR5 receptor on NR4A2+ B cells, promoting TLS development. Plasma cells arising within the TLS microenvironment predominantly produce the IGHG antibody, potentially enhancing the phagocytic capabilities of C1QC+ macrophages. From an application standpoint, a TLS-specific gene signature is developed that effectively predicts outcomes in MIBC and other cancers. This study highlights the prognostic potential of TLS in MIBC and reveals immune mechanisms, offering insights for personalized treatment strategies.

Improving Outcomes in Hepatocellular Carcinoma through Integration of Machine Learning: Development of a Tumor-Associated Macrophage Signature

INTRODUCTION

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally. Macrophages, as essential components of the immune system, play crucial roles in immune regulation, inflammation modulation, and antitumor activity. However, it remains unclear whether tumor-associated macrophages can serve as prognostic markers for HCC.

METHODS

First, we identified tumor-associated macrophages based on single-cell data from GSE140228. Then, using a machine learning approach with a combination of 101 module genes, we constructed an optimal prognostic model. Subsequently, we compared our constructed model with other published prognostic models for HCC. Finally, we utilized the generated model score to predict the response to chemotherapy and immune therapy.

RESULTS

First, we identified clusters of tumor-associated macrophages using single-cell data. Subsequently, we calculated the tumor-associated macrophage score based on module genes from the previous step. Compared to traditional clinical indicators, tumor-associated macrophage signature (TAMS) exhibits significant advantages. The TAMS C-index not only predicts overall survival, but also recurrence-free survival in HCC patients. Additionally, there was a higher prevalence of TP53 mutations in HCC patients with high TAMS. Furthermore, patients with low TAMS showed greater sensitivity to immunotherapy compared to those with high TAMS. Notably, the number and intensity of interactions between TAM and other T lymphocytes were significantly higher than those involving other cell populations. Interestingly, the high TAMS group exhibited significantly elevated levels of immune checkpoint markers and M2 macrophage markers.

CONCLUSION

TAMS can serve as a novel and potent tool, offering improved treatment options and prognostic assessment for patients with HCC.

Art of TIL immunotherapy: SITC's perspective on demystifying a complex treatment

In a first for solid cancers, cellular immunotherapy has entered standard of care in the treatment of patients with metastatic melanoma. The infusion of autologous tumor-infiltrating T lymphocytes (TIL) is capable of mediating durable tumor regression and is now Food and Drug Administration-approved for patients with disease refractory to immune checkpoint inhibitors. Since the advent of chimeric antigen receptor (CAR) T cells for patients with hematological malignancies, a growing network of centers capable of delivering effector T cell products to patients has developed. Administration of TIL can be layered onto that institutional framework, but there are many complex and unique aspects to TIL immunotherapy. The highly multidisciplinary clinical expertise and coordination required to successfully and safely deliver TIL to patients began within the National Cancer Institute Surgery Branch and have been subsequently adopted worldwide. The general steps, most of which require hospital inpatient resources, include a surgical procedure to harvest sufficient tumor for TIL manufacturing, admission for non-myeloablative lymphodepleting chemotherapy followed by TIL, and intravenous interleukin-2 (IL-2, aldesleukin). Here, we provide the principles, practice, and required resources underlying the efficient and safe delivery of TIL immunotherapy derived from the clinical expertise of high-volume centers around the world. This article enhances published clinical practice guidelines by providing underlying clinical rationale and data-driven examples to demystify TIL immunotherapy in order to facilitate uptake and improve patient access to this promising treatment modality in clinical and research settings.

OASL promotes immune evasion in pancreatic ductal adenocarcinoma by enhancing autolysosome-mediated degradation of MHC-I

Rationale: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with a global prevalence and poor prognosis, largely due to immune escape mechanisms. However, the potential reasons for the decreased infiltration of cytotoxic T lymphocytes (CTLs) in PDAC remain inadequately understood. In this study, we aimed to elucidate the molecular mechanisms contributing to the low-CTLs infiltration in patients with PDAC. Methods: Bioinformatic analyses were used to identify key factors associated with low-CTLs infiltration in PDAC and the role of oligoadenylate synthetase-like (OASL) was mainly focused in our study. Immunohistochemistry (IHC) was used to assess the relationship between the expression of OASL and the prognosis of patients. Western blotting, Flow cytometry, Co-immunoprecipitation and Immunofluorescence were applied to elucidate the molecular mechanism by which OASL mediates immune escape in PDAC. The orthotopic PDAC models were constructed to evaluate the effects of OASL-knockdown on CD8+ T cells infiltration and tumor growth in vivo. Results: OASL was found to be significantly upregulated in PDAC and negatively correlated with the major histocompatibility complex class I (MHC-I) expression, which is associated with worse patient prognosis. Notably, OASL-knockdown leads to a significant increase in CD8+ T cell infiltration and slows tumor growth in vivo. Mechanistic studies revealed that OASL -knockdown restored the total and surface MHC-I level through impairing neighbor of BRCA1 gene 1 (NBR1)-mediated autophagy-lysosomal degradation of MHC-I. Conclusions: Targeting OASL enhances the immune response in PDAC, providing a novel therapeutic strategy to improve outcomes in PDAC patients.

Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a highly aggressive tumor with one of the highest morbidity and mortality rates in the world. Nucleotide metabolic processes are critical for cancer development, progression, and alteration of the tumor microenvironment. However, the effect of nucleotide metabolism on LUAD remains to be thoroughly investigated.

Methods

Transcriptomic and clinical data of LUAD were downloaded and organized from TCGA and GEO databases. Genes related to nucleotide metabolism were downloaded from the Msigdb database. Genes associated with LUAD prognosis were identified using univariate COX analysis, and a prognostic risk model was constructed using the machine learning combination of Lasso + Stepcox. The model's predictive validity was evaluated using KM survival and timeROC curves. Based on the prognostic model, LUAD patients were classified into different nucleotide metabolism subtypes, and the differences between patients of different subtypes were explored in terms of genomic mutations, functional enrichment, tumor immune characteristics, and immunotherapy responses. Finally, the key gene SNRPA was screened, and a series of in vitro experiments were performed on LUAD cell lines to explore the role of SNRPA in LUAD.

Result

LUAD patients could be accurately categorized into subtypes based on the nucleotide metabolism-related prognostic risk score (NMBRS). There were significant differences in prognosis between patients of different subtypes, and the NMBRS showed high accuracy in predicting the prognosis of LUAD patients. In addition, patients of different subtypes showed significant differences in genomic mutation and functional enrichment and exhibited different anti-tumor immune profiles. Importantly, NMBRS can be used to predict the responsiveness of LUAD patients to immunotherapy. The results of in vitro cellular experiments indicate that SNRPA plays an important role in the development and progression of lung adenocarcinoma.

Conclusion

This study comprehensively reveals the prognostic value and clinical application of nucleotide metabolism in LUAD. A prognostic signature constructed based on genes related to nucleotide metabolism accurately predicted the prognosis of LUAD patients, and this signature can be used as a guide for LUAD immunotherapy.

Distinct immune signatures for predicting the immunotherapy efficacy of esophageal squamous cell carcinoma or adenocarcinoma

Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC) are distinct histological subtypes of esophageal cancer. The tumor microenvironment of each subtype significantly influences the efficacy of immunotherapy. However, the characteristics of the tumor microenvironments of both subtypes, as well as their specific impacts on immunotherapy outcomes, still require further elucidation. Through the integration of gene expression profiles from ESCC and EAC obtained from The Cancer Genome Atlas database, alongside tumor tissues derived from Chinese patients, we identified TNFSF10, CXCL10, IL17RB, and CSF2 as pivotal immune molecules with significant prognostic implications. Elevated expression levels of TNFSF10 correlated with adverse outcomes in individuals diagnosed with ESCC. In contrast to patients from other geographical regions, CXCL10, IL17RB, and CSF2 exhibited distinct prognostic implications in Chinese patients with esophageal cancer. The Cox risk scores derived from the molecules TNFSF10 and CXCL10 for ESCC and IL17RB and CSF2 for EAC were used to assess their predictive capacity for immunotherapy efficacy. The results indicate that patients with lower Cox risk scores demonstrated an enhanced response to immunotherapeutic interventions. This study revealed significant disparities in the expression and functionality of immune-related molecules between ESCC and EAC and highlighted the potential of Cox risk scores derived from immune-related molecules to predict the efficacy of immunotherapy in patients. The findings underscore the clinical relevance of these biomarkers and emphasize the necessity for developing ethnic-specific biomarkers to guide personalized immunotherapy strategies between ESCC and EAC.

Engineered Cellular Therapies for the Treatment of Thoracic Cancers

Thoracic malignancies (lung cancers and malignant pleural mesothelioma) are prevalent worldwide and are associated with high morbidity and mortality. Effective treatments are needed for patients with advanced disease. Cell therapies are a promising approach to the treatment of advanced cancers that make use of immune effector cells that have the ability to mediate antitumor immune responses. In this review, we discuss the prospect of chimeric antigen receptor-T (CAR-T) cells, natural killer (NK) cells, T cell receptor-engineered (TCR-T) cells, and tumor-infiltrating lymphocytes (TILs) as treatments for thoracic malignancies. CAR-T cells and TILs have proven successful in several hematologic cancers and advanced melanoma, respectively, but outside of melanoma, results have thus far been unsuccessful in most other solid tumors. NK cells and TCR-T cells are additional cell therapy platforms with their own unique advantages and challenges. Obstacles that must be overcome to develop effective cell therapy for these malignancies include selecting an appropriate target antigen, combating immunosuppressive cells and signaling molecules present in the tumor microenvironment, persistence, and delivering a sufficient quantity of antitumor immune cells to the tumor. Induced pluripotent stem cells (iPSCs) offer great promise as a source for both NK and T cell-based therapies due to their unlimited expansion potential. Here, we review clinical trial data, as well as recent basic scientific advances that offer insight into how we may overcome these obstacles, and provide an overview of ongoing trials testing novel strategies to overcome these obstacles.

The challenges and opportunities of applying tumour mutational burden analysis to precision cancer medicine

The discovery and development of immune checkpoint inhibitors (ICIs) has revolutionised the management of human cancers. However, only a subset of patients responds to ICI therapy, even though immune evasion is a hallmark of cancer. Initially, treatment was administered to patients on the basis of expression levels of one of the targets of ICI therapy, programmed cell death ligand 1. In clinical trials, the high response rate of melanoma and non-small cell lung cancer patients to ICI therapy supported the basic premise of cancer immunotherapy, that tumour-specific mutated proteins trigger an immune response. Tumour mutational burden subsequently emerged as a potential biomarker for response to ICI therapy. This review summarises the evidence supporting the scientific rationale for TMB as a biomarker for ICI therapy and focuses on some of the major challenges associated with incorporation of TMB into routine clinical practice.

Differential Infiltration of Key Immune T-Cell Populations Across Malignancies Varying by Immunogenic Potential and the Likelihood of Response to Immunotherapy

Background: Solid tumors vary by the immunogenic potential of the tumor microenvironment (TME) and the likelihood of response to immunotherapy. The emerging literature has identified key immune cell populations that significantly impact immune activation or suppression within the TME. This study investigated candidate T-cell populations and their differential infiltration within different tumor types as estimated from mRNA co-expression levels of the corresponding cellular markers. Methods: We analyzed the mRNA co-expression levels of cellular biomarkers that define stem-like tumor-infiltrating lymphocytes (TILs), tissue-resident memory T-cells (TRM), early dysfunctional T-cells, late dysfunctional T-cells, activated-potentially anti-tumor (APA) T-cells and Butyrophilin 3A (BTN3A) isoforms, utilizing clinical and transcriptomic data from 1892 patients diagnosed with melanoma, bladder, ovarian, or pancreatic carcinomas. Real-world data were collected under the Total Cancer Care Protocol and the Avatar® project (NCT03977402) across 18 cancer centers. Furthermore, we compared the survival outcomes following immune checkpoint inhibitors (ICIs) based on immune cell gene expression. Results: In melanoma and bladder cancer, the estimated infiltration of APA T-cells differed significantly (p = 4.67 × 10-12 and p = 5.80 × 10-12, respectively) compared to ovarian and pancreatic cancers. Ovarian cancer had lower TRM T-cell infiltration than melanoma, bladder, and pancreatic (p = 2.23 × 10-8, 3.86 × 10-28, and 7.85 × 10-9, respectively). Similar trends were noted with stem-like, early, and late dysfunctional T-cells. Melanoma and ovarian expressed BTN3A isoforms more than other malignancies. Higher densities of stem-like TILs; TRM, early and late dysfunctional T-cells; APA T-cells; and BTN3A isoforms were associated with increased survival in melanoma (p = 0.0075, 0.00059, 0.013, 0.005, 0.0016, and 0.041, respectively). The TRM gene signature was a moderate predictor of survival in the melanoma cohort (AUROC = 0.65), with similar findings in testing independent public datasets of ICI-treated patients with melanoma (AUROC 0.61-0.64). Conclusions: Key cellular elements related to immune activation are more heavily infiltrated within ICI-responsive versus non-responsive malignancies, supporting a central role in anti-tumor immunity. In melanoma patients treated with ICIs, higher densities of stem-like TILs, TRM T-cells, early dysfunctional T-cells, late dysfunctional T-cells, APA T-cells, and BTN3A isoforms were associated with improved survival.

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

Background

Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis.

Methods

We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm.

Findings

The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line.

Conclusion

In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

Development of a novel centrosome-related risk signature to predict prognosis and treatment response in lung adenocarcinoma

BACKGROUND

Abnormalities of centrosomes, the major microtubular organizing centers of animal cells and regulators of cell cycle progression, usually accelerate tumor progression, but their prognostic value in lung adenocarcinoma (LUAD) remains insufficiently explored.

METHODS

We collected centrosome genes from the literature and identified LUAD-specific centrosome-related genes (CRGs) using the single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA). Univariate Cox was performed to screen prognostic CRGs. Consistent clustering was performed to classify LUAD patients into two subgroups, and centrosome-related risk score signatures were constructed by Lasso and multivariate Cox regression to predict overall survival (OS). We further explored the correlation between CRS and patient prognosis, clinical manifestations, mutation status, tumor microenvironment, and response to different treatments.

RESULTS

We constructed centrosome-associated prognostic features and verified that CRS could effectively predict 1-, 3-, and 5-year survival in LUAD patients. In addition, patients in the high-risk group exhibited elevated tumor mutational loads and reduced levels of immune infiltration, particularly of T and B cells. Patients in the high-risk group were resistant to immunotherapy and sensitive to 5-fluoropyrimidine and gefitinib. The key gene spermine synthase (SRM) is highly expressed at the mRNA and protein levels in LUAD.

DISCUSSION

Our work develops a novel centrosome-related prognostic signature that accurately predicts OS in LUAD and can assist in clinical diagnosis and treatment.

DNA-PK inhibition enhances neoantigen diversity and increases T cell responses to immunoresistant tumors

Effective antitumor T cell activity relies on the expression and MHC presentation of tumor neoantigens. Tumor cells can evade T cell detection by silencing the transcription of antigens or by altering MHC machinery, resulting in inadequate neoantigen-specific T cell activation. We identified the DNA-protein kinase inhibitor (DNA-PKi) NU7441 as a promising immunomodulator that reduced immunosuppressive proteins, while increasing MHC-I expression in a panel of human melanoma cell lines. In tumor-bearing mice, combination therapy using NU7441 and the immune adjuvants stimulator of IFN genes (STING) ligand and the CD40 agonist NU-SL40 substantially increased and diversified the neoantigen landscape, antigen-presenting machinery, and, consequently, substantially increased both the number and repertoire of neoantigen-reactive, tumor-infiltrating lymphocytes (TILs). DNA-PK inhibition or KO promoted transcription and protein expression of various neoantigens in human and mouse melanomas and induced sensitivity to immune checkpoint blockade (ICB) in resistant tumors. In patients, protein kinase, DNA-activated catalytic subunit (PRKDC) transcript levels were inversely correlated with MHC-I expression and CD8+ TILs but positively correlated with increased neoantigen loads and improved responses to ICB. These studies suggest that inhibition of DNA-PK activity can restore tumor immunogenicity by increasing neoantigen expression and presentation and broadening the neoantigen-reactive T cell population.

Comprehensive Characterization of the Integrin Family Across 32 Cancer Types

Integrin genes are widely involved in tumorigenesis. Yet, a comprehensive characterization of integrin family members and their interactome at the pan-cancer level is lacking. Here, we systematically analyzed integrin family in approximately 10,000 tumors across 32 cancer types. Globally, integrins represent a frequently altered and misexpressed pathway, with alteration and dysregulation overall being protumorigenic. Expression dysregulation, better than mutational landscape, of integrin family successfully identifies a subgroup of aggressive tumors with a high level of proliferation and stemness. The results reveal that several molecular mechanisms collectively regulate integrin expression in a context-dependent manner. For potential clinical usage, we constructed a weighted scoring system, integrinScore, to measure integrin signaling patterns in individual tumors. Remarkably, integrinScore was consistently correlated with predefined molecular subtypes in multiple cancers, with integrinScore-high tumors being more aggressive. Importantly, integrinScore was cancer-dependent and closely associated with proliferation, stemness, tumor microenvironment, metastasis, and immune signatures. IntegrinScore also predicted patients' response to immunotherapy. By mining drug databases, we unraveled an array of compounds that may modulate integrin signaling. Finally, we built a user-friendly database, Pan-cancer Integrin Explorer (PIExplorer; http://computationalbiology.cn/PIExplorer), to facilitate researchers to explore integrin-related knowledge. Collectively, we provide a comprehensive characterization of integrins across cancers and offer gene-specific and cancer-specific rationales for developing integrin-targeted therapy.

Tumor-reactive T cell clonotype dynamics underlying clinical response to TIL therapy in melanoma

Adoptive cell therapy (ACT) using in vitro expanded tumor-infiltrating lymphocytes (TILs) has inconsistent clinical responses. To better understand determinants of therapeutic success, we tracked TIL clonotypes from baseline tumors to ACT products and post-ACT blood and tumor samples in melanoma patients using single-cell RNA and T cell receptor (TCR) sequencing. Patients with clinical responses had baseline tumors enriched in tumor-reactive TILs, and these were more effectively mobilized upon in vitro expansion, yielding products enriched in tumor-specific CD8+ cells that preferentially infiltrated tumors post-ACT. Conversely, lack of clinical responses was associated with tumors devoid of tumor-reactive resident clonotypes and with cell products mostly composed of blood-borne clonotypes that persisted in blood but not in tumors post-ACT. Upon expansion, tumor-specific TILs lost tumor-associated transcriptional signatures, including exhaustion, and responders exhibited an intermediate exhausted effector state after TIL engraftment in the tumor, suggesting functional reinvigoration. Our findings provide insight into the nature and dynamics of tumor-specific clonotypes associated with clinical response to TIL-ACT, with implications for treatment optimization.

Efficacy of TIL therapy in advanced cutaneous melanoma in the current immuno-oncology era: updated systematic review and meta-analysis

BACKGROUND

Adoptive cell therapy with tumor-infiltrating lymphocytes (TIL-ACT) has consistently shown efficacy in advanced melanoma. New results in the field provide now the opportunity to assess overall survival (OS) after TIL-ACT and to examine the effect of prior anti-programmed cell death protein 1/programmed death-ligand 1 [anti-PD-(L)1] therapy on its efficacy.

METHODS

A comprehensive search was conducted in PubMed up to 29 February 2024. Ιn this meta-analysis we focused on studies including high-dose interleukin 2, doubling the patient numbers from our previous meta-analysis conducted up to December 2018 and using OS as the primary endpoint. Objective response rate (ORR), complete response rate (CRR), and duration of response were secondary endpoints. Findings are synthesized using tables, Kaplan-Meier plots, and forest plots. Pooled estimates for ORR and CRR were derived from fixed or random effects models.

RESULTS

A total of 13 high-dose interleukin 2 studies were included in this updated meta-analysis, with OS information available for 617 patients. No difference was found in median OS between studies with prior anti-PD-(L)1 treatment {n = 238; 17.5 months [95% confidence interval (CI) 13.8-20.5 months]} and without [n = 379; 16.3 months (95% CI 14.2-20.6 months)] (log-rank P = 0.53). ORR was estimated to be 34% (95% CI 16%-52%) and 44% (95% CI 37%-51%), for the studies with and without prior anti-PD-(L)1, respectively. The pooled estimate for CRR was 10% for both groups. No statistically significant difference was observed between the two groups, either for ORR (P = 0.15) or CRR (P = 0.45).

CONCLUSIONS

Prior anti-PD-(L)1 treatment has no effect on the clinical response or survival benefit from TIL-ACT in advanced cutaneous melanoma. The benefit of TIL therapy in the second-line setting is also present after anti-PD-(L)1 treatment. Our data reinforce the evidence that TIL-ACT should be considered as a treatment of choice in second line for metastatic melanoma patients failing anti-PD-(L)1 therapy.

Advances in Vaccines for Melanoma

Personalized neoantigen vaccines have achieved major advancements in recent years, with studies in melanoma leading progress in the field. Early clinical trials have demonstrated their feasibility, safety, immunogenicity, and potential efficacy. Advances in sequencing technologies and neoantigen prediction algorithms have substantively improved the identification and prioritization of neoantigens. Innovative delivery platforms now support the rapid and flexible production of vaccines. Several ongoing efforts in the field are aimed at improving the integration of large datasets, refining the training of prediction models, and ensuring the functional validation of vaccine immunogenicity.

Single-cell profiling uncovers proliferative cells as key determinants of survival outcomes in lower-grade glioma patients

Lower-grade gliomas (LGGs), despite their generally indolent clinical course, are characterized by invasive growth patterns and genetic heterogeneity, which can lead to malignant transformation, underscoring the need for improved prognostic markers and therapeutic strategies. This study utilized single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq to identify a novel cell type, referred to as "Prol," characterized by increased proliferation and linked to a poor prognosis in patients with LGG, particularly under the context of immunotherapy interventions. A signature, termed the Prol signature, was constructed based on marker genes specific to the Prol cell type, utilizing an artificial intelligence (AI) network that integrates traditional regression, machine learning, and deep learning algorithms. This signature demonstrated enhanced predictive accuracy for LGG prognosis compared to existing models and showed pan-cancer prognostic potential. The mRNA expression of the key gene PTTG1 from the Prol signature was further validated through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Our findings not only provide novel insights into the molecular and cellular mechanisms of LGG but also offer a promising avenue for the development of targeted biomarkers and therapeutic interventions.

Advancements and Challenges in Personalized Therapy for BRAF-Mutant Melanoma: A Comprehensive Review

Over the past several decades, advancements in the treatment of BRAF-mutant melanoma have led to the development of BRAF inhibitors, BRAF/MEK inhibitor combinations, anti-PD-1 therapy, and anti-CTLA4 therapy. Although these therapies have shown substantial efficacy in clinical trials, their sustained effectiveness is often challenged by the tumor microenvironment, which is a highly heterogeneous and complex milieu of immunosuppressive cells that affect tumor progression. The era of personalized medicine holds substantial promise for the tailoring of treatments to individual genetic profiles. However, tumor heterogeneity and immune evasion mechanisms contribute to the resistance to immunotherapy. Despite these challenges, tumor-infiltrating lymphocyte (TIL) therapy, as exemplified by lifileucel, has demonstrated notable efficacy against BRAF V600-mutant melanoma. Additionally, early response biomarkers, such as COX-2 and MMP2, along with FDG-PET imaging, offer the potential to improve personalized immunotherapy by predicting patient responses and determining the optimal treatment duration. Future efforts should focus on reducing the T-cell harvesting periods and costs associated with TIL therapy to enhance efficiency and accessibility.

Immunological hide-and-seek: epigenetically reprogrammed cancer cells and the dynamics of CD8+ T cells

Cancer remains a global health burden, shaped by both genetic mutations and epigenetic dysregulation. Epigenetic alteration plays a pivotal role in tumorigenesis, immune response modulation, and the emergence of treatment resistance. This review emphasizes the intricate interplay between epigenetically reprogrammed cancer cells and the tumor microenvironment (TME), a relationship central to the immunoediting concept, which encompasses elimination, equilibrium, and escape phases. This review highlights the significance of CD8+ T cells as potent anticancer agents and discusses the mechanisms by which tumor cells evade immune surveillance and evolve resistance to immunotherapy. Such evasion entails the regulation of inhibitory molecules, antigen presentation machinery, and cytokine milieu. Furthermore, this review explores the complex dynamics culminating in CD8+ T cell dysfunction within the TME. In summary, this work offers insights into the indispensable role of epigenetic mechanisms in bolstering cancer cell survival amidst immunological challenges within the TME.

Pan-cancer single-cell dissection reveals phenotypically distinct B cell subtypes

Characterizing the compositional and phenotypic characteristics of tumor-infiltrating B cells (TIBs) is important for advancing our understanding of their role in cancer development. Here, we establish a comprehensive resource of human B cells by integrating single-cell RNA sequencing data of B cells from 649 patients across 19 major cancer types. We demonstrate substantial heterogeneity in their total abundance and subtype composition and observe immunoglobulin G (IgG)-skewness of antibody-secreting cell isotypes. Moreover, we identify stress-response memory B cells and tumor-associated atypical B cells (TAABs), two tumor-enriched subpopulations with prognostic potential, shared in a pan-cancer manner. In particular, TAABs, characterized by a high clonal expansion level and proliferative capacity as well as by close interactions with activated CD4 T cells in tumors, are predictive of immunotherapy response. Our integrative resource depicts distinct clinically relevant TIB subsets, laying a foundation for further exploration of functional commonality and diversity of B cells in cancer.

Tumor-associated macrophage clusters linked to immunotherapy in a pan-cancer census

Transcriptional heterogeneity of tumor-associated macrophages (TAMs) has been investigated in individual cancers, but the extent to which these states transcend tumor types and represent a general feature of cancer remains unclear. We performed pan-cancer single-cell RNA sequencing analysis across nine cancer types and identified distinct monocyte/TAM composition patterns. Using spatial analysis from clinical study tissues, we assessed TAM functions in shaping the tumor microenvironment (TME) and influencing immunotherapy. Two specific TAM clusters (pro-inflammatory and pro-tumor) and four TME subtypes showed distinct immunological features, genomic profiles, immunotherapy responses, and cancer prognosis. Pro-inflammatory TAMs resided in immune-enriched niches with exhausted CD8+ T cells, while pro-tumor TAMs were restricted to niches associated with a T-cell-excluded phenotype and hypoxia. We developed a machine learning model to predict immune checkpoint blockade response by integrating TAMs and clinical data. Our study comprehensively characterizes the common features of TAMs and highlights their interaction with the TME.

The immunosuppressive landscape in tumor microenvironment

Recent advances in cancer immunotherapy, especially immune checkpoint inhibitors (ICIs), have revolutionized the clinical outcome of many cancer patients. Despite the fact that impressive progress has been made in recent decades, the response rate remains unsatisfactory, and many patients do not benefit from ICIs. Herein, we summarized advanced studies and the latest insights on immune inhibitory factors in the tumor microenvironment. Our in-depth discussion and updated landscape of tumor immunosuppressive microenvironment may provide new strategies for reversing tumor immune evasion, enhancing the efficacy of ICIs therapy, and ultimately achieving a better clinical outcome.

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.

Integrative and comprehensive pan-cancer analysis of ubiquitin specific peptidase 11 (USP11) as a prognostic and immunological biomarker

The significance of USP11 as a critical regulator in cancer has garnered substantial attention, primarily due to its catalytic activity as a deubiquitinating enzyme. Nonetheless, a thorough evaluation of USP11 across various cancer types in pan-cancer studies remains absent. Our analysis integrates data from a variety of sources, including five immunotherapy cohorts, thirty-three cohorts from The Cancer Genome Atlas (TCGA), and sixteen cohorts from the Gene Expression Omnibus (GEO), two of which involve single-cell transcriptomic data. Our findings indicate that aberrant USP11 expression is predictive of survival outcomes across various cancer types. The highest frequency of genomic alterations was observed in uterine corpus endometrial carcinoma (UCEC), with single-cell transcriptome analysis revealing significantly higher USP11 expression in plasmacytoid dendritic cells and mast cells. Notably, USP11 expression was associated with the infiltration levels of CD8+ T cells and natural killer (NK) activated cells. Additionally, in the skin cutaneous melanoma (SKCM) phs000452 cohort, patients with higher USP11 mRNA levels during immunotherapy experienced a significantly shorter median progression-free survival. USP11 emerges as a promising molecular biomarker with significant potential for predicting patient prognosis and immunoreactivity across various cancer types.

Research on molecular characteristics of ADME-related genes in kidney renal clear cell carcinoma

Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are named ADME genes. However, the comprehensive role of ADME genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Using the clinical and gene expression data of KIRC patients downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and the Gene Expression Omnibus (GEO) databases, we cluster patients into two patterns, and the population with a relatively poor prognosis demonstrated higher level of immunosuppressive cell infiltration and higher proportion of glycolytic subtypes. Then, 17 ADME genes combination identified through the least absolute shrinkage and selection operator algorithm (LASSO, 1000 times) was utilized to calculate the ADME score. The ADME score was found to be an independent predictor of prognosis in KIRC and to be tightly associated with the infiltration level of immune cells, metabolic properties, tumor-related signaling pathways, genetic variation, and responses to chemotherapeutics. Our work revealed the characteristics of ADME in KIRC. Assessing the ADME profiles of individual patients can deepen our comprehension of tumor microenvironment (TME) features in KIRC and can aid in developing more personalized and effective therapeutic strategies.

Immunopeptidomics in the cancer immunotherapy era

Cancer is the primary cause of death worldwide, and conventional treatments are painful, complicated, and have negative effects on healthy cells. However, cancer immunotherapy has emerged as a promising alternative. Principle of cancer immunotherapy is the re-activation of T-cell to combat the tumor that presents the peptide antigen on major histocompatibility complex (MHC). Those peptide antigens are identified with the set of omics technology, proteomics, genomics, and bioinformatics, which referred to immunopeptidomics. Indeed, immunopeptidomics can identify the neoantigens that are very useful for cancer immunotherapies. This review explored the use of immunopeptidomics for various immunotherapies, i.e., peptide-based vaccines, immune checkpoint inhibitors, oncolytic viruses, and chimeric antigen receptor T-cell. We also discussed how the diversity of neoantigens allows for the discovery of novel antigenic peptides while post-translationally modified peptides diversify the overall peptides binding to MHC or so-called MHC ligandome. The development of immunopeptidomics is keeping up-to-date and very active, particularly for clinical application. Immunopeptidomics is expected to be fast, accurate and reliable for the application for cancer immunotherapies.

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.

ADAR-Mediated A>I(G) RNA Editing in the Genotoxic Drug Response of Breast Cancer

Epitranscriptomics is a field that delves into post-transcriptional changes. Among these modifications, the conversion of adenosine to inosine, traduced as guanosine (A>I(G)), is one of the known RNA-editing mechanisms, catalyzed by ADARs. This type of RNA editing is the most common type of editing in mammals and contributes to biological diversity. Disruption in the A>I(G) RNA-editing balance has been linked to diseases, including several types of cancer. Drug resistance in patients with cancer represents a significant public health concern, contributing to increased mortality rates resulting from therapy non-responsiveness and disease progression, representing the greatest challenge for researchers in this field. The A>I(G) RNA editing is involved in several mechanisms over the immunotherapy and genotoxic drug response and drug resistance. This review investigates the relationship between ADAR1 and specific A>I(G) RNA-edited sites, focusing particularly on breast cancer, and the impact of these sites on DNA damage repair and the immune response over anti-cancer therapy. We address the underlying mechanisms, bioinformatics, and in vitro strategies for the identification and validation of A>I(G) RNA-edited sites. We gathered databases related to A>I(G) RNA editing and cancer and discussed the potential clinical and research implications of understanding A>I(G) RNA-editing patterns. Understanding the intricate role of ADAR1-mediated A>I(G) RNA editing in breast cancer holds significant promise for the development of personalized treatment approaches tailored to individual patients' A>I(G) RNA-editing profiles.

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.

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

Introduction

While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy.

Methods

Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.

Results

Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort.

Discussion

Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

LRP1 induces anti-PD-1 resistance by modulating the DLL4-NOTCH2-CCL2 axis and redirecting M2-like macrophage polarisation in bladder cancer

The tumour microenvironment (TME) drives bladder cancer (BLCA) progression. Targeting the TME has emerged as a promising strategy for BLCA treatment in recent years. Furthermore, checkpoint blockade therapies are only beneficial for a minority of patients with BLCA, and drug resistance is a barrier to achieving significant clinical effects of anti-programmed cell death protein-1 (PD-1)/programmed death protein ligand-1 (PD-L1) therapy. In this study, higher low-density lipoprotein receptor-related protein 1 (LRP1) levels were related to a poorer prognosis for patients with various cancers, including those with higher grades and later stages of BLCA. Enrichment analysis demonstrated that LRP1 plays a role in the epithelial-mesenchymal transition (EMT), NOTCH signalling pathway, and ubiquitination. LRP1 knockdown in BLCA cells delayed BLCA progression both in vivo and in vitro. Furthermore, LRP1 knockdown suppressed EMT, reduced DLL4-NOTCH2 signalling activity, and downregulated M2-like macrophage polarisation. Patients with BLCA and higher LRP1 levels responded weakly to anti-PD-1 therapy in the IMvigor210 cohort. Moreover, LRP1 knockdown enhanced the therapeutic effects of anti-PD-1 in mice. Taken together, our findings suggest that LRP1 is a potential target for improving the efficacy of anti-PD-1/PD-L1 therapy by preventing EMT and M2-like macrophage polarisation by blocking the DLL4-NOTCH2 axis.

Deciphering lung adenocarcinoma evolution: Integrative single-cell genomics identifies the prognostic lung progression associated signature

We employed single-cell analysis techniques, specifically the inferCNV method, to dissect the complex progression of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) through minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC). This approach enabled the identification of Cluster 6, which was significantly associated with LUAD progression. Our comprehensive analysis included intercellular interaction, transcription factor regulatory networks, trajectory analysis, and gene set variation analysis (GSVA), leading to the development of the lung progression associated signature (LPAS). Interestingly, we discovered that the LPAS not only accurately predicts the prognosis of LUAD patients but also forecasts genomic alterations, distinguishes between 'cold' and 'hot' tumours, and identifies potential candidates suitable for immunotherapy. PSMB1, identified within Cluster 6, was experimentally shown to significantly enhance cancer cell invasion and migration, highlighting the clinical relevance of LPAS in predicting LUAD progression and providing a potential target for therapeutic intervention. Our findings suggest that LPAS offers a novel biomarker for LUAD patient stratification, with significant implications for improving prognostic accuracy and guiding treatment decisions.

Prediction of prognosis and immunotherapy efficacy based on metabolic landscape in lung adenocarcinoma by bulk, single-cell RNA sequencing and Mendelian randomization analyses

Immunotherapy has been a remarkable clinical advancement in cancer treatment, but only a few patients benefit from it. Metabolic reprogramming is tightly associated with immunotherapy efficacy and clinical outcomes. However, comprehensively analyzing their relationship is still lacking in lung adenocarcinoma (LUAD). Herein, we evaluated 84 metabolic pathways in TCGA-LUAD by ssGSEA. A matrix of metabolic pathway pairs was generated and a metabolic pathway-pair score (MPPS) model was established by univariable, LASSO, multivariable Cox regression analyses. The differences of metabolic reprogramming, tumor microenvironment (TME), tumor mutation burden and drug sensitivity in different MPPS groups were further explored. WGCNA and 117 machine learning algorithms were performed to identify MPPS-related genes. Single-cell RNA sequencing and in vitro experiments were used to explore the role of C1QTNF6 on TME. The results showed MPPS model accurately predicted prognosis and immunotherapy efficacy of LUAD patients regardless of sequencing platforms. High-MPPS group had worse prognosis, immunotherapy efficacy and lower immune cells infiltration, immune-related genes expression and cancer-immunity cycle scores than low-MPPS group. Seven MPPS-related genes were identified, of which C1QTNF6 was mainly expressed in fibroblasts. High C1QTNF6 expression in fibroblasts was associated with more infiltration of M2 macrophage, Treg cells and less infiltration of NK cells, memory CD8+ T cells. In vitro experiments validated silencing C1QTNF6 in fibroblasts could inhibit M2 macrophage polarization and migration. The study depicted the metabolic landscape of LUAD and constructed a MPPS model to accurately predict prognosis and immunotherapy efficacy. C1QTNF6 was a promising target to regulate M2 macrophage polarization and migration.

Predicting immune checkpoint therapy response in three independent metastatic melanoma cohorts

While Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy. Evaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders. Six proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort. Our study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

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