Multiomic profiling of checkpoint inhibitor-treated melanoma: Identifying predictors of response and resistance, and markers of biological discordance

The PD-L1 Promoter Methylation Predicts Gene And Protein Expression Levels in Urothelial Carcinoma

We aimed to clarify the role of PD-L1 promoter methylation in bladder cancer by analyzing PD-L1 methylation and mRNA expression in FFPE samples, along with PD-L1 mRNA and protein levels in urine samples from bladder urothelial carcinoma patients. We analyzed PD-L1 promoter methylation in 43 bladder urothelial carcinoma tissue samples and 41 non-malignant bladder tissues using methylation-sensitive high-resolution melting analysis to assess two CpG islands (cg15837913, cg19724470). PD-L1 mRNA expression in tissues and urine samples, along with PD-L1 protein levels in urine, were evaluated. The bladder urothelial carcinoma group showed significantly higher methylation rates for cg19724470 and cg15837913 compared to controls (P = 0.016, P = 0.049 respectively). In the patient group, tissue PD-L1 mRNA expression was 15.22 times higher and urinary PD-L1 mRNA expression 6.56 times higher in the cg19724470 unmethylated group compared to the methylated group. Urinary PD-L1 protein concentration was twice as high in the cg19724470 unmethylated group compared to the methylated group. In the patients, tissue PD-L1 mRNA expression was 4.58 times higher and urinary PD-L1 mRNA expression 2.58 times higher in the cg15837913 unmethylated group compared to the methylated group. Moreover, the urinary PD-L1 protein concentration was 1.7 times higher in the cg15837913 unmethylated group than in the methylated group (P = 0.036). A positive correlation was observed between tissue PD-L1 mRNA and both urine PD-L1 mRNA and protein levels and between urine PD-L1 mRNA and protein levels. This study suggests that PD-L1 methylation may be a key epigenetic regulator influencing PD-L1 expression and disease pathogenesis in bladder urothelial carcinoma.

Pre-treatment DNA methylome and transcriptome profiles correlate with melanoma response to anti-PD1 immunotherapy

Successful immune checkpoint inhibitor (ICI) therapy occurs in only a fraction of melanoma patients, and yet all patients are susceptible to potentially serious ICI-related side-effects. No current biomarkers robustly predict ICI treatment response in melanoma patients. In this study we sought to identify methylome and transcriptome markers which have the potential to predict immunotherapy response in melanoma patients ahead of treatment with anti-PD1 ICI monotherapy. Using Infinium MethylationEPIC microarrays, we analysed DNA methylation profiles of >850,000 CpG sites in pre-treatment melanoma tissues from patients administered anti-PD-1 monotherapy as first-line treatment. In addition, we analysed transcriptomes using RNA-seq. DNA methylation and gene expression data were then statistically compared to patient response to anti-PD1 therapy. We identified 2579 DNA hypomethylation and hypermethylation alterations correlating with melanoma response to anti-PD1 therapy. An integrative analysis of DNA methylomes and transcriptomes identified a subset of 35 loci, 13 of which were significantly differentially methylated in both initial discovery and external validation datasets. Functional enrichment analysis of hypomethylated sites (p-value <0.05) in non-responders was associated with "Formation of the cornified envelope", "Regulation of epithelial cell proliferation", and "Purine-containing compound metabolic process". We have identified novel integrated DNA methylation and gene expression markers, which correlate with anti-PD1 treatment response in melanoma patients. These findings suggest a relationship between tumour-associated genomic DNA methylation, gene expression patterns, and anti-PD1 ICI immunotherapy response in melanoma patients.

MRI-based machine learning reveals proteasome subunit PSMB8-mediated malignant glioma phenotypes through activating TGFBR1/2-SMAD2/3 axis

Gliomas are the most prevalent and aggressive neoplasms of the central nervous system, representing a major challenge for effective treatment and patient prognosis. This study identifies the proteasome subunit beta type-8 (PSMB8/LMP7) as a promising prognostic biomarker for glioma. Using a multiparametric radiomic model derived from preoperative magnetic resonance imaging (MRI), we accurately predicted PSMB8 expression levels. Notably, radiomic prediction of poor prognosis was highly consistent with elevated PSMB8 expression. Our findings demonstrate that PSMB8 depletion not only suppressed glioma cell proliferation and migration but also induced apoptosis via activation of the transforming growth factor beta (TGF-β) signaling pathway. This was supported by downregulation of key receptors (TGFBR1 and TGFBR2). Furthermore, interference with PSMB8 expression impaired phosphorylation and nuclear translocation of SMAD2/3, critical mediators of TGF-β signaling. Consequently, these molecular alterations resulted in reduced tumor progression and enhanced sensitivity to temozolomide (TMZ), a standard chemotherapeutic agent. Overall, our findings highlight PSMB8's pivotal role in glioma pathophysiology and its potential as a prognostic marker. This study also demonstrates the clinical utility of MRI radiomics for preoperative risk stratification and pre-diagnosis. Targeted inhibition of PSMB8 may represent a therapeutic strategy to overcome TMZ resistance and improve glioma patient outcomes.

Multifaceted investigations of PSMB8 provides insights into prognostic prediction and immunological target in thyroid carcinoma

The Proteasome 20S subunit beta 8 (PSMB8) is an integral element of the immunoproteasome complex, playing a pivotal role in antigen processing. Despite its significance, the contributory role of PSMB8 in oncogenesis, particularly in thyroid carcinoma (THCA), has not been well-characterized. To address this gap in knowledge, our study endeavored to delineate the potential associations between PSMB8 and THCA. Transcriptomic profiles and clinical data of patients with THCA were retrieved from The Cancer Genome Atlas (TCGA) database to facilitate comprehensive analysis. Complementary resources from additional online databases were utilized to augment the study. Logistic regression analysis was employed to elucidate the relationship between PSMB8 and various clinicopathological parameters. Uni/multivariate Cox regression analyses were conducted to ascertain the independent prognostic factors for THCA patient outcomes. Quantitative polymerase chain reaction (qPCR) and western blot assays were employed to verify the expression level of PSMB8 in vitro. Our study demonstrated that PSMB8 was significantly upregulated in THCA, with its overexpression correlating with lymph node metastasis, extrathyroidal extension, and favorable prognosis. Immunohistochemistry substantiated a higher PSMB8 protein presence in THCA tissue compared to the normal, supporting its potential role as a moderately accurate diagnostic biomarker. Logistic regression analysis identified PSMB8 as a significant indicator of the N1 stage, classical histological subtype, and extrathyroidal extension. Age, T stage, and PSMB8 were further determined as independent prognostic factors for THCA. Functional investigations linked PSMB8 to immune processes, evidenced by its association with increased immune cell infiltration and higher stromal/immune scores, as well as a positive co-expression with several immune checkpoints. A constructed predicted competing endogenous RNA (ceRNA) network implicated PSMB8 in complex post-transcriptional regulation. Finally, in vitro assays confirmed the upregulation of PSMB8, underscoring its relevance in THCA and as a target for future research. Our work has preliminarily appraised PSMB8 as a biomarker with certain prognostic and diagnostic significance, and as a potential target for immunotherapy in THCA.

DNA methylation in melanoma immunotherapy: mechanisms and therapeutic opportunities

Abnormal DNA methylation is a hallmark of cancer and a nearly universal feature of melanoma. DNA methylation plays well-appreciated melanoma cell-intrinsic roles, including silencing tumor-suppressor genes, regulating genomic stability, deregulating expression of oncogenes to potentiate proliferative signaling and tumor migration. With the recent success of immunological therapies for melanoma, important roles for DNA methylation are also emerging at the interface between melanoma and immune cells with the potential to regulate the anti-tumor immune response. These newly recognized roles for DNA methylation in controlling melanoma cell immunogenicity, expression of MHC and immune checkpoint molecules as well as T cell phenotypes in the tumor microenvironment raise the possibility of using DNA methylation to develop improved therapies and methylation-based biomarkers. In addition to reviewing the "immune dimension" of DNA methylation, we summarize recent developments with potential clinical applications in melanoma, such as targeted DNA methylation editing, single-cell methylation approaches, and measurement of circulating methylated DNA. An improved understanding of the immune roles of DNA methylation presents an exciting opportunity for continued improvement of care and outcomes for patients with melanoma.

Soluble CD27 differentially predicts resistance to anti-PD1 alone but not with anti-CTLA-4 in melanoma

Metastatic melanoma can be treated with anti-PD-1 monotherapy or in combination with anti-CTLA-4 or anti-Lag3. However, combination therapy is associated with a high risk of toxicity. Recently, we reported that high plasma soluble CD27 (sCD27) levels reflect the intratumoral interaction of CD70-CD27 and dysfunctional T cells in the tumor microenvironment of renal cell carcinoma. In this study, we first characterized the intratumoral expression of CD70 and CD27 in melanoma tumors and their interaction in vivo. We then reported a significant association between baseline sCD27 and anti-PD-1 resistance as assessed by progression-free survival, overall survival, or 12-month complete response in two prospective cohorts of melanoma patients. Multivariate analysis confirmed that sCD27 was independently associated with clinical outcomes. Notably, sCD27 did not predict clinical response to combination therapy in either cohort. This differential predictive value of sCD27 for the two therapeutic options was later confirmed by propensity score analysis. Our results suggest that high plasma sCD27 levels predict poorer efficacy of anti-PD1 monotherapy in metastatic melanoma, justifying therapeutic escalation with a combination of anti-PD1 and anti-CTLA-4.

Dual role of interferon-gamma in the response of melanoma patients to immunotherapy with immune checkpoint inhibitors

Interferon-gamma (IFN-γ) is a cytokine produced mainly by immune cells and can affect cancer cells by modulating the activity of multiple signaling pathways, including the canonical Janus-activated kinase/signal transducer and activator of transcription (JAK/STAT) cascade. In melanoma, IFN-γ can exert both anticancer effects associated with cell-cycle arrest and cell death induction and protumorigenic activity related to immune evasion leading to melanoma progression. Notably, IFN-γ plays a crucial role in the response of melanoma patients to immunotherapy with immune checkpoint inhibitors (ICIs), which are currently used in the clinic. As these agents target programmed death-1 (PD-1) and its ligand (PD-L1), cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) and lymphocyte-activation gene 3 (LAG-3), they are designed to restore the antimelanoma immune response. In this respect, IFN-γ produced by cells in the tumor microenvironment in response to ICIs has a beneficial influence on both immune and melanoma cells by increasing antigen presentation, recruiting additional T-cells to the tumor site, and inducing direct antiproliferative effects and apoptosis in melanoma cells. Therefore, IFN-γ itself and IFN-γ-related gene signatures during the response to ICIs can constitute biomarkers or predictors of the clinical outcome of melanoma patients treated with ICIs. However, owing to its multifaceted roles, IFN-γ can also contribute to developing mechanisms associated with the acquisition of resistance to ICIs. These mechanisms can be associated with either decreased IFN-γ levels in the tumor microenvironment or diminished responsiveness to IFN-γ due to changes in the melanoma phenotypes associated with affected activity of other signaling pathways or genetic alterations e.g., in JAK, which restricts the ability of melanoma cells to respond to IFN-γ. In this respect, the influence of IFN-γ on melanoma-specific regulators of the dynamic plasticity of the cell phenotype, including microphthalmia-associated transcription factor (MITF) and nerve growth factor receptor (NGFR)/CD271 can affect the clinical efficacy of ICIs. This review comprehensively discusses the role of IFN-γ in the response of melanoma patients to ICIs with respect to its positive influence and role in IFN-γ-related mechanisms of resistance to ICIs as well as the potential use of predictive markers on the basis of IFN-γ levels and signatures of IFN-γ-dependent genes.

Programmed Cell Death Ligand as a Biomarker for Response to Immunotherapy: Contribution of Mass Spectrometry-Based Analysis

Immune checkpoint inhibition is a major component in today's cancer immunotherapy. In recent years, the FDA has approved a number of immune checkpoint inhibitors (ICIs) for the treatment of melanoma, non-small-cell lung, breast and gastrointestinal cancers. These inhibitors, which target cytotoxic T-lymphocyte antigen-4, programmed cell death (PD-1), and programmed cell death ligand (PD-L1) checkpoints have assumed a leading role in immunotherapy. The same inhibitors exert significant antitumor effects by overcoming tumor cell immune evasion and reversing T-cell exhaustion. The initial impact of this therapy in cancer treatment was justly described as revolutionary, however, clinical as well as research data which followed demonstrated that these innovative drugs are costly, are associated with potentially severe adverse effects, and only benefit a small subset of patients. These limitations encouraged enhanced research and clinical efforts to identify predictive biomarkers to stratify patients who are most likely to benefit from this form of therapy. The discovery and characterization of this class of biomarkers is pivotal in guiding individualized treatment against various forms of cancer. Currently, there are three FDA-approved predictive biomarkers, however, none of which on its own can deliver a reliable and precise response to immune therapy. Present literature identifies the absence of precise predictive biomarkers and poor understanding of the mechanisms behind tumor resistance as the main obstacles facing ICIs immunotherapy. In the present text, we discuss the dual role of PD-L1 as a biomarker for response to immunotherapy and as an immune checkpoint. The contribution of mass spectrometry-based analysis, particularly the impact of protein post-translational modifications on the performance of this protein is underlined.

BTLA promoter hypomethylation correlates with enhanced immune cell infiltration, favorable prognosis, and immunotherapy response in melanoma

BACKGROUND

Immune checkpoint blockade (ICB)-based immunotherapy has significantly improved survival in advanced melanoma. However, many patients exhibit resistance to these therapies. This study examines the impact of BTLA promoter methylation on its expression, immune cell infiltration, and clinical outcomes, evaluating its potential as a prognostic and predictive biomarker for immunotherapy response.

METHODS

We analyzed methylation and gene expression data from public datasets (The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO)) and an in-house cohort of melanoma patients treated with ICB therapy at the First Affiliated Hospital of Zhengzhou University. We developed a quantitative methylation-specific PCR (qMSP) assay to measure methylation levels of the cg24157392 and cg03995631 CpG sites, and a targeted bisulfite sequencing assay was used to validate the accuracy of qMSP. We measured BTLA protein expression using multiplex immunofluorescence and immunohistochemical staining methods. Pearson correlation, survival analysis, and immune cell infiltration estimation were conducted to explore the associations between BTLA promoter methylation, mRNA and protein expression, clinical outcomes, and immune characteristics.

RESULTS

Hypomethylation at CpG sites cg24157392 and cg03995631 in the BTLA promoter were significantly associated with higher BTLA mRNA and protein expression. In the TCGA dataset, low methylation at these sites predicted longer overall survival and was validated in an independent cohort of 50 stage III/IV melanoma patients, with an area under the curve of 0.94 for predicting 5-year survival. Furthermore, BTLA promoter hypomethylation correlated with higher infiltration of immune cells, such as CD8+T cells, CD4+T cells, B cells, and macrophages. Additionally, low methylation at cg24157392 and cg03995631, as quantified by the qMSP assay, was significantly associated with better progression-free survival in patients treated with immune checkpoint inhibitors. These findings were further validated using GEO datasets.

CONCLUSIONS

BTLA promoter hypomethylation serves as a significant biomarker for favorable prognosis and enhanced response to ICB therapy in melanoma. The developed qMSP assays for cg24157392 and cg03995631 accurately quantified methylation levels and demonstrated their potential for clinical application in patient stratification and personalized immunotherapy.

Pan-cancer characterization of m6A-mediated regulation of T cell exhaustion dynamics and clinical relevancies in human cancers

T cell exhaustion (TEX) is a major barrier to effective immunotherapy. The role of N6-methyladenosine (m6A) modification in regulating immune cell function has been recognized, but its impact on TEX dynamics across cancer types and clinical outcomes remains unclear. Here, we conducted a pan-cancer analysis integrating multi-omics data from cell lines, single-cell RNA sequencing, and pan-cancer and immunotherapy datasets to explore the dynamic interplay between m6A modification and TEX. We found that m6A modification influences key TEX-associated genes at both the cellular and single-cell levels, with distinct expression patterns across the exhaustion spectrum. Based on m6A-TEX interactions, three pan-cancer subtypes were identified, each with unique molecular profiles, immune phenotypes, and survival outcomes. The TexLm6AL subtype, characterized by low m6A activity and low TEX, correlated with high immune infiltration, increased cytolytic activity, and favorable survival, whereas the TexLm6AH and TexHm6AH subtypes with higher m6A activity were associated with poorer survival. Multivariate analysis confirmed the prognostic value of this classification independent of traditional clinical factors. Moreover, m6A-TEX crosstalk influenced responses to immune checkpoint blockade therapies. Our findings provide novel insights into the role of m6A in TEX regulation and underscore the potential of m6A regulators as biomarkers and therapeutic targets for advancing cancer immunotherapy.

Opportunities for Discovery Using Neoadjuvant Immune Checkpoint Blockade in Melanoma

Treatment of resectable advanced-stage melanoma with neoadjuvant immunotherapy is rapidly becoming the new standard of care due to significant improvements in event-free survival (EFS) compared to surgery first followed by immunotherapy. The level of responsiveness seen in patients receiving immune checkpoint inhibitors (ICIs) must be mechanistically understood not only for the standardization of treatment but also to advance the novel concept of personalized cancer immunotherapy. This review aims to elucidate markers of the tumor microenvironment (TME) and blood that can predict treatment outcome. Interestingly, the canonical proteins involved in the molecular interactions that immunotherapies aim to disrupt have not been consistent indicators of treatment response, which amplifies the necessity for further research on the predictive model. Other major discussions surrounding neoadjuvant therapy involve the higher-level investigation of ICI efficacy due to the ability to examine a post-treatment tumor molecularly and pathologically, which this review will also cover. As neoadjuvant ICI becomes the standard of care in advanced melanoma treatment, further research aiming to identify more predictive biomarkers of treatment response to advance medical decision-making and patient care should continue to be sought after.

Predicting Survival Rates in Brain Metastases Patients from Non-Small Cell Lung Cancer Using Radiomic Signatures Associated with Tumor Immune Heterogeneity

Non-small cell lung cancer (NSCLC) frequently metastasizes to the brain, significantly worsened prognoses. This study aimed to develop an interpretable model for predicting survival in NSCLC patients with brain metastases (BM) integrating radiomic features and RNA sequencing data. 292 samples are collected and analyzed utilizing T1/T2 MRIs. Bidirectional stepwise logistic regression is employed to identify significant variables, facilitating the construction of a prognostic model, which is benchmarked against four machine learning algorithms. BM tissue samples are processed for RNA extraction and sequencing. The optimal model achieved an AUC of 0.96 and a C-index of 0.89 in the train set and an AUC of 0.84 with a C-index of 0.78 in the test set, indicating strong predictive performance and generalizability. Patients from Xiangya Hospital are stratified into high-risk (n = 11) and low-risk (n = 30) groups. RNA sequencing revealed an enrichment of immune-related pathways, particularly the interferon (IFN) pathway in the low-risk group. Immune cell infiltration analysis identified a significant presence of CD8+-T cells, IFNγ-6/-18 in the low-risk group, suggesting an immunologically favorable tumor microenvironment. These findings highlight the potential of combining radiomic and RNA sequencing data for improved survival predictions and personalized treatment strategies in BM patients from NSCLC.

Eravacycline improves the efficacy of anti-PD1 immunotherapy via AP1/CCL5 mediated M1 macrophage polarization in melanoma

Screening approved library is a promising and safe strategy to overcome the limitation of low response rate and drug resistance in immunotherapy. Accumulating evidence showed that the application of antibiotics has been considered to reduce the effectiveness of anti-PD1 immunotherapy in tumor treatment, however, in this study, an antibiotic drug (Eravacycline, ERV) was identified to improve the efficacy of anti-PD1 immunotherapy in melanoma through screening approved library. Administration of ERV significantly attenuated melanoma cells growth as well as directly or indirectly benefited M1 macrophage polarization. Meanwhile, ERV treatment significantly induced cellular autophagy via damage of mitochondria, leading to up-regulation of ROS production, subsequently, raised CCL5 secretion through elevation AP1 binding to CCL5 promoter via p38 or JNK1/2 activation. Knockdown of Ccl5 expression attenuated ERV triggered M1 macrophage polarization in melanoma cells. Clinical analysis revealed a positive association between high expression of CCL5 and improved prognosis as well as a favorable anti-PD1 therapy in melanoma patients. As expected, application of ERV improved the efficacy of anti-PD1. Overall, our results approved that ERV enhances the efficacy of anti-PD1 immunotherapy in melanoma by promoting the polarization of M1 macrophages, which provided novel therapeutic strategy for improving the effectiveness of melanoma anti-PD1 immunotherapy.

Risk factors for immunoresistance in advanced non-small cell lung cancer and the advantages of targeted therapy in improving prognosis

OBJECTIVES

The advent of immunotherapy has transformed the therapeutic landscape for advanced non-small cell lung cancer (NSCLC); nonetheless, the emergence of resistance to immunotherapy poses a considerable obstacle. Our research sought to identify factors contributing to immunotherapy resistance and to assess the effectiveness of subsequent treatments in patients with advanced NSCLC who have been exposed to immune checkpoint inhibitors (ICIs).

METHODS

This retrospective study analyzed data from 232 individuals with advanced NSCLC who were treated with ICIs during January 2020 to December 2023. Based on their response to ICIs, these patients were classified into two groups: immunoresistance group (IM group) and non-immunoresistance group (NIM group). Data collected included demographics, clinical parameters, cytokine profiles, tumor mutational burden (TMB), PD-L1 expression, overall survival (OS), progression-free survival (PFS), and adverse events. The association between risk factors and immunoresistance were assessed, and second-line treatment outcomes were evaluated.

RESULTS

Key risk factors for immunoresistance included lower TMB, higher levels of interleukin-10 (IL-10), and PD-L1 expression ≥ 50%. TMB was inversely correlated with immunoresistance (rho = -0.838, P < 0.001). In multivariate analysis, IL-10 remained a significant risk factor (OR = 33.654, P = 0.021), whereas TMB was protective (OR = 0.786, P < 0.001). Second-line targeted therapy significantly improved OS (8.72 ± 2.02 months) and PFS (5.37 ± 2.15 months) compared to chemotherapy (OS: 7.93 ± 2.13 months; PFS: 4.86 ± 1.68 months) (P < 0.05). The targeted therapy group experienced distinct side effects, notably increased hypertension and hand-foot syndrome, while chemotherapy group had higher rates of fatigue (P < 0.05).

CONCLUSION

Immunoresistance in advanced NSCLC is influenced by IL-10, TMB, and PD-L1 expression. Targeted therapies offer superior outcomes than chemotherapy, though side effect management remains crucial.

Personalized Immunity: Neoantigen-Based Vaccines Revolutionizing Hepatocellular Carcinoma Treatment

Hepatocellular carcinoma (HCC), the most prevalent form of primary liver cancer, presents significant therapeutic challenges due to its molecular complexity, late-stage diagnosis, and inherent resistance to conventional treatments. The intermediate to low mutational burden in HCC and its ability to evade the immune system through multiple mechanisms complicate the development of effective therapies. Recent advancements in immunotherapy, particularly neoantigen-based vaccines, offer a promising, personalized approach to HCC treatment. Neoantigens are tumor-specific peptides derived from somatic mutations in tumor cells. Unlike normal cellular antigens, neoantigens are foreign to the immune system, making them highly specific targets for immunotherapy. Neoantigens arise from genetic alterations such as point mutations, insertions, deletions, and gene fusions, which are expressed as neoepitopes that are not present in healthy tissues, thus evading the immune tolerance mechanisms that typically protect normal cells. Preclinical and early-phase clinical studies of neoantigen-based vaccines have shown promising results, demonstrating the ability of these vaccines to elicit robust T cell responses against HCC. The aim of the current review is to provide an in-depth exploration of the therapeutic potential of neoantigen-based vaccines in HCC, focusing on neoantigen identification, vaccine platforms, and their integration with immune checkpoint inhibitors to enhance immunogenicity. It also evaluates preclinical and clinical data on efficacy and safety while addressing challenges in clinical translation. By taking advantage of the unique antigenic profile of each patient's tumor, neoantigen-based vaccines represent a promising approach in the treatment of HCC, offering the potential for improved patient outcomes, long-term remission, and a shift towards personalized, precision medicine in liver cancer therapy.

Coupling of response biomarkers between tumor and peripheral blood in patients undergoing chemoimmunotherapy

Platinum-based chemotherapy in combination with anti-PD-L1 antibodies has shown promising results in mesothelioma. However, the immunological mechanisms underlying its efficacy are not well understood and there are no predictive biomarkers to guide treatment decisions. Here, we combine time course RNA sequencing (RNA-seq) of peripheral blood mononuclear cells with pre-treatment tumor transcriptome data from the single-arm, phase 2 DREAM trial (N = 54). Single-cell RNA-seq and T cell receptor sequencing (TCR-seq) reveal that CD8+ T effector memory (TEM) cells with stem-like properties are more abundant in peripheral blood of responders and that this population expands upon treatment. These peripheral blood changes are linked to the transcriptional state of the tumor microenvironment. Combining information from both compartments, rather than individually, is most predictive of response. Our study highlights complex interactions between the tumor and immune cells in peripheral blood during objective tumor responses to chemoimmunotherapy. This trial is registered with the Australian New Zealand Clinical Trials Registry, number ACTRN12616001170415.

Identification of prognostic tumor microenvironment in patients with advanced hepatocellular carcinoma treated with hepatic arterial infusion chemotherapy combined with lenvatinib and PD-1 inhibitors

BACKGROUND

In advanced hepatocellular carcinoma (HCC), the triple combination therapy of hepatic arterial infusion chemotherapy (HAIC) with lenvatinib and programmed cell death protein-1 (PD-1) inhibitors has shown promise as a front-line treatment. This study aimed to explore the tumor microenvironment (TME) characteristics of the population benefiting most from this treatment.

METHODS

The study included 44 patients, with 38 ultimately receiving the HAIC + FOLFOX + lenvatinib + PD-1 inhibitor treatment. Tumor response was evaluated using modified RECIST criteria, classifying patients as responders (complete or partial response) or non-responders (stable or progressive disease). Overall survival (OS), progression-free survival (PFS), and adverse events (AEs) were assessed. Additionally, genetic sequencing and RNA analysis were conducted on biopsy samples to identify TME differences between the two groups.

RESULTS

Among the 38 patients, 22 responded favorably, showing significantly longer median OS (not-reached vs. 8.6 months) and median PFS (15.3 months vs. 2.0 months) compared to non-responders. Common AEs included AST elevation, stomachache, nausea, and hypertension, with limited severe AEs. Genetic analysis revealed no significant differences in DNA features between the groups. However, RNA analysis indicated that responders had a more robust immune status, better drug sensitivity, and increased immune cell infiltration. Notably, higher levels of tumor-infiltrating T lymphocytes were linked to better responses, longer PFS, and OS.

CONCLUSION

The differences in the initial TME of patients, especially in tumor-infiltrating T lymphocytes, may be potential biomarkers for predicting response and prognosis. This finding provides clues to search for biomarkers for this triple combination therapy in advanced HCC.

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

Preclinical in vitro models of HNSCC and their role in drug discovery - an emphasis on the cancer microenvironment and microbiota

INTRODUCTION

Head and neck squamous cell carcinoma (HNSCC) is the seventh most common cancer worldwide. Treatment options and patient outcomes have not improved significantly over the past decades, increasing the need for better preclinical models. Holistic approaches that include an intact and functional immune compartment along with the patient's individual tumor microbiome will help improve the predictive value of novel drug efficacy.

AREAS COVERED

In this review, we describe the challenges of modeling the complex and heterogeneous tumor landscape in HNSCC and the importance of sophisticated patient-specific 3D in vitro models to pave the way for clinical trials with novel immunomodulatory drugs. We also discuss the impact of the tumor microbiome and the potential implications for prospective drug screening and validation trials.

EXPERT OPINION

The repertoire of well-characterized preclinical 3D in vitro models continues to grow. With the increasing attention to the complex cellular, immunological, molecular, and spatio-temporal characteristics of tumors, well-designed proof-of-concept studies to test novel drug efficacy are on the verge of providing valuable, practice-changing insights for clinical trials. Bringing together expertise and improving collaboration between clinicians, academics, and regulatory agencies will facilitate the translation of preclinical findings into clinically meaningful outcomes.

Tumor Methylation Burden (TMeB) in Non-Small Cell Lung Cancer: A New Way of Thinking About Epigenetics

Lung cancer represents a substantial proportion of cancer-associated mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for most of these cases [...].

Potential predictive biomarkers in antitumor immunotherapy: navigating the future of antitumor treatment and immune checkpoint inhibitor efficacy

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment modality, offering promising outcomes for various malignancies. However, the efficacy of ICIs varies among patients, highlighting the essential need of accurate predictive biomarkers. This review synthesizes the current understanding of biomarkers for ICI therapy, and discusses the clinical utility and limitations of these biomarkers in predicting treatment outcomes. It discusses three US Food and Drug Administration (FDA)-approved biomarkers, programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), and microsatellite instability (MSI), and explores other potential biomarkers, including tumor immune microenvironment (TIME)-related signatures, human leukocyte antigen (HLA) diversity, non-invasive biomarkers such as circulating tumor DNA (ctDNA), and combination biomarker strategies. The review also addresses multivariable predictive models integrating multiple features of patients, tumors, and TIME, which could be a promising approach to enhance predictive accuracy. The existing challenges are also pointed out, such as the tumor heterogeneity, the inconstant nature of TIME, nonuniformed thresholds and standardization approaches. The review concludes by emphasizing the importance of biomarker research in realizing the potential of personalized immunotherapy, with the goal of improving patient selection, treatment strategies, and overall outcomes in cancer treatment.

Exploring resistance to immune checkpoint inhibitors and targeted therapies in melanoma

Melanoma is the most aggressive form of skin cancer, characterized by a poor prognosis, and its incidence has risen rapidly over the past 30 years. Recent therapies, notably immunotherapy and targeted therapy, have significantly improved the outcome of patients with metastatic melanoma. Previously dismal five-year survival rates of below 5% have shifted to over 50% of patients surviving the five-year mark, marking a significant shift in the landscape of melanoma treatment and survival. Unfortunately, about 50% of patients either do not respond to therapy or experience early or late relapses following an initial response. The underlying mechanisms for primary and secondary resistance to targeted therapies or immunotherapy and relapse patterns remain not fully identified. However, several molecular pathways and genetic factors have been associated with melanoma resistance to these treatments. Understanding these mechanisms paves the way for creating novel treatments that can address resistance and ultimately enhance patient outcomes in melanoma. This review explores the mechanisms behind immunotherapy and targeted therapy resistance in melanoma patients. Additionally, it describes the treatment strategies to overcome resistance, which have improved patients' outcomes in clinical trials and practice.

Companion diagnostics and predictive biomarkers for PD-1/PD-L1 immune checkpoint inhibitors therapy in malignant melanoma

Programmed cell death receptor 1 (PD-1), when bound to the ligand programmed death-ligand 1 (PD-L1), can suppress cellular immunity and play a critical role in the initiation and development of cancer. Immune drugs targeting these two sites have been developed for different cancers, including malignant melanoma. The accompanying diagnostic method has been approved by the FDA to guide patient medication. However, the method of immunohistochemical staining, which varies widely due to the antibody and staining cut-off values, has certain limitations in application and does not benefit all patients. Increasing researches begin to focus on new biomarkers to improve objective response rates and survival in cancer patients. In this article, we enumerated three major groups, including tumour microenvironment, peripheral circulation, and gene mutation, which covered the current main research directions. In the future, we hope those biomarkers may be used to guide the treatment of patients with malignant melanoma.

Size matters: integrating tumour volume and immune activation signatures predicts immunotherapy response

Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, providing significant benefit to patients across various tumour types, including melanoma. However, around 40% of melanoma patients do not benefit from ICI treatment, and accurately predicting ICI response remains challenging. We now describe a novel and simple approach that integrates immune-associated transcriptome signatures and tumour volume burden to better predict ICI response in melanoma patients. RNA sequencing was performed on pre-treatment (PRE) tumour specimens derived from 32 patients with advanced melanoma treated with combination PD1 and CTLA4 inhibitors. Of these 32 patients, 11 also had early during treatment (EDT, 5-15 days after treatment start) tumour samples. Tumour volume was assessed at PRE for all 32 patients, and at first computed tomography (CT) imaging for the 11 patients with EDT samples. Analysis of the Hallmark IFNγ gene set revealed no association with ICI response at PRE (AUC ROC curve = 0.6404, p = 0.24, 63% sensitivity, 71% specificity). When IFNg activity was evaluated with tumour volume (ratio of gene set expression to tumour volume) using logistic regression to predict ICI response, we observed high discriminative power in separating ICI responders from non-responders (AUC = 0.7760, p = 0.02, 88% sensitivity, 67% specificity); this approach was reproduced with other immune-associated transcriptomic gene sets. These findings were further replicated in an independent cohort of 23 melanoma patients treated with PD1 inhibitor. Hence, integrating tumour volume with immune-associated transcriptomic signatures improves the prediction of ICI response, and suggest that higher levels of immune activation relative to tumour burden are required for durable ICI response.

Tumour mutational burden: clinical utility, challenges and emerging improvements

Tumour mutational burden (TMB), defined as the total number of somatic non-synonymous mutations present within the cancer genome, varies across and within cancer types. A first wave of retrospective and prospective research identified TMB as a predictive biomarker of response to immune-checkpoint inhibitors and culminated in the disease-agnostic approval of pembrolizumab for patients with TMB-high tumours based on data from the Keynote-158 trial. Although the applicability of outcomes from this trial to all cancer types and the optimal thresholds for TMB are yet to be ascertained, research into TMB is advancing along three principal avenues: enhancement of TMB assessments through rigorous quality control measures within the laboratory process, including the mitigation of confounding factors such as limited panel scope and low tumour purity; refinement of the traditional TMB framework through the incorporation of innovative concepts such as clonal, persistent or HLA-corrected TMB, tumour neoantigen load and mutational signatures; and integration of TMB with established and emerging biomarkers such as PD-L1 expression, microsatellite instability, immune gene expression profiles and the tumour immune contexture. Given its pivotal functions in both the pathogenesis of cancer and the ability of the immune system to recognize tumours, a profound comprehension of the foundational principles and the continued evolution of TMB are of paramount relevance for the field of oncology.

Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma

PURPOSE

We aim to improve the prediction of response or resistance to immunotherapies in patients with melanoma. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment.

EXPERIMENTAL DESIGN

We collected gene expression data spatially from three cellular compartments defined by CD68+ macrophages, CD45+ leukocytes, and S100B+ tumor cells in 55 immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification.

RESULTS

We achieved robust performance of compartment-specific signatures in predicting the outcome of immune checkpoint inhibitors in the discovery cohort. Of the three signatures, the S100B signature showed the best performance in the validation cohort (N = 45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the eight-gene S100B signature, five genes (PSMB8, TAX1BP3, NOTCH3, LCP2, and NQO1) with positive coefficients predict the response, and three genes (KMT2C, OVCA2, and MGRN1) with negative coefficients predict the resistance to treatment.

CONCLUSIONS

We conclude that the spatially defined compartment signatures utilize tumor and tumor microenvironment-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.

Radiomic signatures associated with tumor immune heterogeneity predict survival in locally recurrent nasopharyngeal carcinoma

BACKGROUND

The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma is limited because of their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in locally recurrent nasopharyngeal carcinoma.

METHODS

This multicenter, retrospective study included 921 patients with locally recurrent nasopharyngeal carcinoma. A machine learning signature and nomogram based on pretreatment magnetic resonance imaging features were developed for predicting overall survival in a training cohort and validated in 2 independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA-sequencing analysis.

RESULTS

The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving concordance indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables statistically improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with statistically distinct overall survival rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-sequencing analysis revealed differences in interferon response and lymphocyte infiltration between risk groups.

CONCLUSIONS

A magnetic resonance imaging-based radiomic signature predicted overall survival more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for locally recurrent nasopharyngeal carcinoma patients.

The Tumor Microbiome as a Predictor of Outcomes in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibitors

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and its potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICI). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA sequencing (RNA-seq) was conducted on the formalin-fixed, paraffin-embedded and fresh frozen tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival >24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The age of the 71 patients with metastatic melanoma ranged from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy-responsive versus nonresponsive tumors. Responders showed significant enrichment of bacteriophages in the phylum Uroviricota, and nonresponders showed enrichment of several bacteria, including Campylobacter jejuni. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs.

SIGNIFICANCE

We analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.

Advances and prospects of biomarkers for immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) activate anti-cancer immunity by blocking T cell checkpoint molecules such as programmed death 1 (PD-1) and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). Although ICIs induce some durable responses in various cancer patients, they also have disadvantages, including low response rates, the potential for severe side effects, and high treatment costs. Therefore, selection of patients who can benefit from ICI treatment is critical, and identification of biomarkers is essential to improve the efficiency of ICIs. In this review, we provide updated information on established predictive biomarkers (tumor programmed death-ligand 1 [PD-L1] expression, DNA mismatch repair deficiency, microsatellite instability high, and tumor mutational burden) and potential biomarkers currently under investigation such as tumor-infiltrated and peripheral lymphocytes, gut microbiome, and signaling pathways related to DNA damage and antigen presentation. In particular, this review aims to summarize the current knowledge of biomarkers, discuss issues, and further explore future biomarkers.

Protein O-fucosyltransferase 1 promotes PD-L1 stability to drive immune evasion and directs liver cancer to immunotherapy

BACKGROUND AND AIMS

The immunosuppressive tumor microenvironment (TME) plays an essential role in cancer progression and immunotherapy response. Despite the considerable advancements in cancer immunotherapy, the limited response to immune checkpoint blockade (ICB) therapies in patients with hepatocellular carcinoma (HCC) remains a major challenge for its clinical implications. Here, we investigated the molecular basis of the protein O-fucosyltransferase 1 (POFUT1) that drives HCC immune evasion and explored a potential therapeutic strategy for enhancing ICB efficacy.

METHODS

De novo MYC/Trp53-/- liver tumor and the xenograft tumor models were used to evaluate the function of POFUT1 in immune evasion. Biochemical assays were performed to elucidate the underlying mechanism of POFUT1-mediated immune evasion.

RESULTS

We identified POFUT1 as a crucial promoter of immune evasion in liver cancer. Notably, POFUT1 promoted HCC progression and inhibited T-cell infiltration in the xenograft tumor and de novo MYC/Trp53-/- mouse liver tumor models. Mechanistically, we demonstrated that POFUT1 stabilized programmed death ligand 1 (PD-L1) protein by preventing tripartite motif containing 21-mediated PD-L1 ubiquitination and degradation independently of its protein-O-fucosyltransferase activity. In addition, we further demonstrated that PD-L1 was required for the tumor-promoting and immune evasion effects of POFUT1 in HCC. Importantly, inhibition of POFUT1 could synergize with anti-programmed death receptor 1 therapy by remodeling TME in the xenograft tumor mouse model. Clinically, POFUT1 high expression displayed a lower response rate and worse clinical outcome to ICB therapies.

CONCLUSIONS

Our findings demonstrate that POFUT1 functions as a novel regulator of tumor immune evasion and inhibition of POFUT1 may be a potential therapeutic strategy to enhance the efficacy of immune therapy in HCC.

DUX4 is a common driver of immune evasion and immunotherapy failure in metastatic cancers

Cancer immune evasion contributes to checkpoint immunotherapy failure in many patients with metastatic cancers. The embryonic transcription factor DUX4 was recently characterized as a suppressor of interferon-γ signaling and antigen presentation that is aberrantly expressed in a small subset of primary tumors. Here, we report that DUX4 expression is a common feature of metastatic tumors, with ~10-50% of advanced bladder, breast, kidney, prostate, and skin cancers expressing DUX4. DUX4 expression is significantly associated with immune cell exclusion and decreased objective response to PD-L1 blockade in a large cohort of urothelial carcinoma patients. DUX4 expression is a significant predictor of survival even after accounting for tumor mutational burden and other molecular and clinical features in this cohort, with DUX4 expression associated with a median reduction in survival of over 1 year. Our data motivate future attempts to develop DUX4 as a biomarker and therapeutic target for checkpoint immunotherapy resistance.

Deciphering and overcoming Anti-PD-1 resistance in Melanoma: A comprehensive review of Mechanisms, biomarker Developments, and therapeutic strategies

Worldwide, tens of thousands of people die from melanoma each year, making it the most frequently fatal form of cutaneous cancer. Immunotherapeutic advancements, particularly with anti-PD-1 medications, have significantly enhanced treatment outcomes over recent decades. With the broad application of anti-PD-1 therapies, insights into the mechanisms of resistance have evolved. Despite the development of combination treatments and early predictive biomarkers, a comprehensive synthesis of these advancements is absent in the current literature. This review underscores the prevailing knowledge of anti-PD-1 resistance mechanisms and underscores the critical role of robust predictive biomarkers in stratifying patients for targeted combinations of anti-PD-1 and other conventional or innovative therapeutic approaches. Additionally, we offer insights that may shape future melanoma treatment strategies.

A lactate-SREBP2 signaling axis drives tolerogenic dendritic cell maturation and promotes cancer progression

Conventional dendritic cells (DCs) are essential mediators of antitumor immunity. As a result, cancers have developed poorly understood mechanisms to render DCs dysfunctional within the tumor microenvironment (TME). After identification of CD63 as a specific surface marker, we demonstrate that mature regulatory DCs (mregDCs) migrate to tumor-draining lymph node tissues and suppress DC antigen cross-presentation in trans while promoting T helper 2 and regulatory T cell differentiation. Transcriptional and metabolic studies showed that mregDC functionality is dependent on the mevalonate biosynthetic pathway and its master transcription factor, SREBP2. We found that melanoma-derived lactate activates SREBP2 in tumor DCs and drives conventional DC transformation into mregDCs via homeostatic or tolerogenic maturation. DC-specific genetic silencing and pharmacologic inhibition of SREBP2 promoted antitumor CD8+ T cell activation and suppressed melanoma progression. CD63+ mregDCs were found to reside within the lymph nodes of several preclinical tumor models and in the sentinel lymph nodes of patients with melanoma. Collectively, this work suggests that a tumor lactate-stimulated SREBP2-dependent program promotes CD63+ mregDC development and function while serving as a promising therapeutic target for overcoming immune tolerance in the TME.

Creating a multifaceted prognostic model for cutaneous melanoma: the convergence of single-cell and bulk sequencing with machine learning

Background

Cutaneous melanoma is a highly heterogeneous cancer, and understanding the role of inflammation-related genes in its progression is crucial.

Methods

The cohorts used include the TCGA cohort from TCGA database, and GSE115978, GSE19234, GSE22153 cohort, and GSE65904 cohort from GEO database. Weighted Gene Coexpression Network Analysis (WGCNA) identified key inflammatory modules. Machine learning techniques were employed to construct prognostic models, which were validated across multiple cohorts, including the TCGA cohort, GSE19234, GSE22153, and GSE65904. Immune cell infiltration, tumor mutation load, and immunotherapy response were assessed. The hub gene STAT1 was validated through cellular experiments.

Results

Single-cell analysis revealed heterogeneity in inflammation-related genes, with NK cells, T cells, and macrophages showing elevated inflammation-related scores. WGCNA identified a module highly associated with inflammation. Machine learning yielded a CoxBoost + GBM prognostic model. The model effectively stratified patients into high-risk and low-risk groups in multiple cohorts. A nomogram and Receiver Operating Characteristic (ROC) curves confirmed the model's accuracy. Low-risk patients exhibited increased immune cell infiltration, higher Tumor Mutational Burden (TMB), and potentially better immunotherapy response. Cellular experiments validated the functional role of STAT1 in melanoma progression.

Conclusion

Inflammation-related genes play a critical role in cutaneous melanoma progression. The developed prognostic model, nomogram, and validation experiments highlight the potential clinical relevance of these genes and provide a basis for further investigation into personalized treatment strategies for melanoma patients.

Molecular patterns of resistance to immune checkpoint blockade in melanoma

Immune checkpoint blockade (ICB) has improved outcome for patients with metastatic melanoma but not all benefit from treatment. Several immune- and tumor intrinsic features are associated with clinical response at baseline. However, we need to further understand the molecular changes occurring during development of ICB resistance. Here, we collect biopsies from a cohort of 44 patients with melanoma after progression on anti-CTLA4 or anti-PD1 monotherapy. Genetic alterations of antigen presentation and interferon gamma signaling pathways are observed in approximately 25% of ICB resistant cases. Anti-CTLA4 resistant lesions have a sustained immune response, including immune-regulatory features, as suggested by multiplex spatial and T cell receptor (TCR) clonality analyses. One anti-PD1 resistant lesion harbors a distinct immune cell niche, however, anti-PD1 resistant tumors are generally immune poor with non-expanded TCR clones. Such immune poor microenvironments are associated with melanoma cells having a de-differentiated phenotype lacking expression of MHC-I molecules. In addition, anti-PD1 resistant tumors have reduced fractions of PD1+ CD8+ T cells as compared to ICB naïve metastases. Collectively, these data show the complexity of ICB resistance and highlight differences between anti-CTLA4 and anti-PD1 resistance that may underlie differential clinical outcomes of therapy sequence and combination.

Stroma-infiltrating T cell spatiotypes define immunotherapy outcomes in adolescent and young adult patients with melanoma

The biological underpinnings of therapeutic resistance to immune checkpoint inhibitors (ICI) in adolescent and young adult (AYA) melanoma patients are incompletely understood. Here, we characterize the immunogenomic profile and spatial architecture of the tumor microenvironment (TME) in AYA (aged ≤ 30 years) and older adult (aged 31-84 years) patients with melanoma, to determine the AYA-specific features associated with ICI treatment outcomes. We identify two ICI-resistant spatiotypes in AYA patients with melanoma showing stroma-infiltrating lymphocytes (SILs) that are distinct from the adult TME. The SILhigh subtype was enriched in regulatory T cells in the peritumoral space and showed upregulated expression of immune checkpoint molecules, while the SILlow subtype showed a lack of immune activation. We establish a young immunosuppressive melanoma score that can predict ICI responsiveness in AYA patients and propose personalized therapeutic strategies for the ICI-resistant subgroups. These findings highlight the distinct immunogenomic profile of AYA patients, and individualized TME features in ICI-resistant AYA melanoma that require patient-specific treatment strategies.

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

BACKGROUND

Aberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes.

METHODS

We performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts.

RESULTS

We identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment.

CONCLUSIONS

Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma

PURPOSE

The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics.

METHODS

We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes.

RESULTS

Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM.

CONCLUSIONS

MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state.Communicated by Ramaswamy H. Sarma.

DNA Methylation Signatures Correlate with Response to Immune Checkpoint Inhibitors in Metastatic Melanoma

BACKGROUND

DNA methylation profiles have emerged as potential predictors of therapeutic response in various solid tumors.

OBJECTIVE

This study aimed to analyze the DNA methylation profiles of patients with stage IV metastatic melanoma undergoing first-line immune checkpoint inhibitor treatment and evaluate their correlation with a radiological response according to immune-related Response Evaluation Criteria in Solid Tumors (iRECIST).

METHODS

A total of 81 tissue samples from 71 patients with metastatic melanoma (27 female, 44 male) were included in this study. We utilized Illumina Methylation EPIC Beadchips to retrieve their genome-wide methylation profile by interrogating >850,000 CpG sites. Clustering based on the 500 most differentially methylated genes was conducted to identify distinct methylation patterns associated with immune checkpoint inhibitor response. Results were further aligned with an independent, previously published data set.

RESULTS

The median progression-free survival was 8.5 months (range: 0-104.1 months), and the median overall survival was 30.6 months (range: 0-104.1 months). Objective responses were observed in 29 patients (40.8%). DNA methylation profiling revealed specific signatures that correlated with radiological response to immune checkpoint inhibitors. Three distinct clusters were identified based on the methylation patterns of the 500 most differentially methylated genes. Cluster 1 (12/12) and cluster 2 (12/24) exhibited a higher proportion of responders, while cluster 3 (39/45) predominantly consisted of non-responders. In the validation data set, responders also showed more frequent hypomethylation although differences in the data sets limit the interpretation.

CONCLUSIONS

These findings suggest that DNA methylation profiling of tumor tissues might serve as a predictive biomarker for immune checkpoint inhibitor response in patients with metastatic melanoma. Further validation studies are warranted to confirm the efficiency of DNA methylation profiling as a predictive tool in the context of immunotherapy for metastatic melanoma.

A machine learning model identifies M3-like subtype in AML based on PML/RARα targets

The typical genomic feature of acute myeloid leukemia (AML) M3 subtype is the fusion event of PML/RARα, and ATRA/ATO-based combination therapy is current standard treatment regimen for M3 subtype. Here, a machine-learning model based on expressions of PML/RARα targets was developed to identify M3 patients by analyzing 1228 AML patients. Our model exhibited high accuracy. To enable more non-M3 AML patients to potentially benefit from ATRA/ATO therapy, M3-like patients were further identified. We found that M3-like patients had strong GMP features, including the expression patterns of M3 subtype marker genes, the proportion of myeloid progenitor cells, and deconvolution of AML constituent cell populations. M3-like patients exhibited distinct genomic features, low immune activity and better clinical survival. The initiative identification of patients similar to M3 subtype may help to identify more patients that would benefit from ATO/ATRA treatment and deepen our understanding of the molecular mechanism of AML pathogenesis.

Single-cell RNA sequencing in melanoma: what have we learned so far?

Over the past decade, there have been remarkable improvements in the treatment and survival rates of melanoma patients. Treatment resistance remains a persistent challenge, however, and is partly attributable to intratumoural heterogeneity. Melanoma cells can transition through a series of phenotypic and transcriptional cell states that vary in invasiveness and treatment responsiveness. The diverse stromal and immune contexture of the tumour microenvironment also contributes to intratumoural heterogeneity and disparities in treatment response in melanoma patients. Recent advances in single-cell sequencing technologies have enabled a more detailed understanding of melanoma heterogeneity and the underlying transcriptional programs that regulate melanoma cell diversity and behaviour. In this review, we examine the concept of intratumoural heterogeneity and the challenges it poses to achieving long-lasting treatment responses. We focus on the significance of next generation single-cell sequencing in advancing our understanding of melanoma diversity and the unique insights gained from single-cell studies.

Comprehensive genomic profiling on metastatic Melanoma: results from a network screening from 7 Italian Cancer Centres

BACKGROUND

The current therapeutic algorithm for Advanced Stage Melanoma comprises of alternating lines of Targeted and Immuno-therapy, mostly via Immune-Checkpoint blockade. While Comprehensive Genomic Profiling of solid tumours has been approved as a companion diagnostic, still no approved predictive biomarkers are available for Melanoma aside from BRAF mutations and the controversial Tumor Mutational Burden. This study presents the results of a Multi-Centre Observational Clinical Trial of Comprehensive Genomic Profiling on Target and Immuno-therapy treated advanced Melanoma.

METHODS

82 samples, collected from 7 Italian Cancer Centres of FFPE-archived Metastatic Melanoma and matched blood were sequenced via a custom-made 184-gene amplicon-based NGS panel. Sequencing and bioinformatics analysis was performed at a central hub. Primary analysis was carried out via the Ion Reporter framework. Secondary analysis and Machine Learning modelling comprising of uni and multivariate, COX/Lasso combination, and Random Forest, was implemented via custom R/Python scripting.

RESULTS

The genomics landscape of the ACC-mela cohort is comparable at the somatic level for Single Nucleotide Variants and INDELs aside a few gene targets. All the clinically relevant targets such as BRAF and NRAS have a comparable distribution thus suggesting the value of larger scale sequencing in melanoma. No comparability is reached at the CNV level due to biotechnological biases and cohort numerosity. Tumour Mutational Burden is slightly higher in median for Complete Responders but fails to achieve statistical significance in Kaplan-Meier survival analysis via several thresholding strategies. Mutations on PDGFRB, NOTCH3 and RET were shown to have a positive effect on Immune-checkpoint treatment Overall and Disease-Free Survival, while variants in NOTCH4 were found to be detrimental for both endpoints.

CONCLUSIONS

The results presented in this study show the value and the challenge of a genomics-driven network trial. The data can be also a valuable resource as a validation cohort for Immunotherapy and Target therapy genomic biomarker research.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

DUX4 is a common driver of immune evasion and immunotherapy failure in metastatic cancers

Cancer immune evasion contributes to checkpoint immunotherapy failure in many patients with metastatic cancers. The embryonic transcription factor DUX4 was recently characterized as a suppressor of interferon-γ signaling and antigen presentation that is aberrantly expressed in a small subset of primary tumors. Here, we report that DUX4 expression is a common feature of metastatic tumors, with ~10-50% of advanced bladder, breast, kidney, prostate, and skin cancers expressing DUX4. DUX4 expression is significantly associated with immune cell exclusion and decreased objective response to PD-L1 blockade in a large cohort of urothelial carcinoma patients. DUX4 expression is a significant predictor of survival even after accounting for tumor mutational burden and other molecular and clinical features in this cohort, with DUX4 expression associated with a median reduction in survival of over one year. Our data motivate future attempts to develop DUX4 as a biomarker and therapeutic target for checkpoint immunotherapy resistance.

Long-Read Sequencing Reveals Alternative Splicing-Driven, Shared Immunogenic Neoepitopes Regardless of SF3B1 Status in Uveal Melanoma

Tumor-specific neoepitopes are promising targets in cancer immunotherapy. However, the identification of functional tumor-specific neoepitopes remains challenging. In addition to the most common source, single-nucleotide variants (SNV), alternative splicing (AS) represents another rich source of neoepitopes and can be utilized in cancers with low SNVs such as uveal melanoma (UM). UM, the most prevalent adult ocular malignancy, has poor clinical outcomes due to a lack of effective therapies. Recent studies have revealed the promise of harnessing tumor neoepitopes to treat UM. Previous studies have focused on neoepitope targets associated with mutations in splicing factor 3b subunit 1 (SF3B1), a key splicing factor; however, little is known about the neoepitopes that are commonly shared by patients independent of SF3B1 status. To identify the AS-derived neoepitopes regardless of SF3B1 status, we herein used a comprehensive nanopore long-read-sequencing approach to elucidate the landscape of AS and novel isoforms in UM. We also performed high-resolution mass spectrometry to further validate the presence of neoepitope candidates and analyzed their structures using the AlphaFold2 algorithm. We experimentally evaluated the antitumor effects of these neoepitopes and found they induced robust immune responses by stimulating interferon (IFN)γ production and activating T cell-based UM tumor killing. These results provide novel insights into UM-specific neoepitopes independent of SF3B1 and lay the foundation for developing therapies by targeting these actionable neoepitopes.

Tumor microenvironment-mediated immune profiles and efficacy of anti-PD-L1 antibody plus chemotherapy stratified by DLL3 expression in small-cell lung cancer

BACKGROUND

Delta-like ligand 3 (DLL3) is a therapeutic target in small-cell lung cancer (SCLC). However, how DLL3 expression status affects the tumor microenvironment (TME) and clinical outcomes in SCLC remains unclear.

METHODS

This retrospective study included patients with postoperative limited-stage (LS)-SCLC and extensive-stage (ES)-SCLC treated with platinum and etoposide (PE) plus anti-programmed cell death ligand 1 (PD-L1) antibody. We investigated the relationship of DLL3 expression with TME, mutation status, tumor neoantigens, and immunochemotherapy.

RESULTS

In the LS-SCLC cohort (n = 59), whole-exome sequencing revealed that DLL3High cases had significantly more neoantigens (P = 0.004) and a significantly higher rate of the signature SBS4 associated with smoking (P = 0.02) than DLL3Low cases. Transcriptome analysis in the LS-SCLC cohort revealed that DLL3High cases had significantly suppressed immune-related pathways and dendritic cell (DC) function. SCLC with DLL3High had significantly lower proportions of T cells, macrophages, and DCs than those with DLL3Low. In the ES-SCLC cohort (n = 30), the progression-free survival associated with PE plus anti-PD-L1 antibody was significantly worse in DLL3High cases than in DLL3Low cases (4.7 vs. 7.4 months, P = 0.01).

CONCLUSIONS

Although SCLC with DLL3High had a higher neoantigen load, these tumors were resistant to immunochemotherapy due to suppressed tumor immunity by inhibiting antigen-presenting functions.

The Warburg Effect Explained: Integration of Enhanced Glycolysis with Heterogeneous Mitochondria to Promote Cancer Cell Proliferation

The Warburg effect is the long-standing riddle of cancer biology. How does aerobic glycolysis, inefficient in producing ATP, confer a growth advantage to cancer cells? A new evaluation of a large set of literature findings covering the Warburg effect and its yeast counterpart, the Crabtree effect, led to an innovative working hypothesis presented here. It holds that enhanced glycolysis partially inactivates oxidative phosphorylation to induce functional rewiring of a set of TCA cycle enzymes to generate new non-canonical metabolic pathways that sustain faster growth rates. The hypothesis has been structured by constructing two metabolic maps, one for cancer metabolism and the other for the yeast Crabtree effect. New lines of investigation, suggested by these maps, are discussed as instrumental in leading toward a better understanding of cancer biology in order to allow the development of more efficient metabolism-targeted anticancer drugs.

A human mucosal melanoma organoid platform for modeling tumor heterogeneity and exploring immunotherapy combination options

Mucosal melanoma (MM), an aggressive rare subtype of melanoma, is distinct from cutaneous melanoma and has poor prognoses. We addressed the lack of cell models for MM by establishing 30 organoids of human oral MM (OMM), which retained major histopathological and functional features of parental tumors. Organoid groups derived from chronologically or intratumorally distinct lesions within the same individual displayed heterogeneous genetics, expression profiles, and drug responses, indicating rapid tumor evolution and poor clinical response. Furthermore, transcriptome analysis revealed receptor tyrosine kinases (RTKs) signaling, particularly NGFR, a nerve growth factor receptor, was significantly up-regulated in OMMs and organoids from patients resistant to anti-programmed cell death protein 1 (anti-PD-1) therapy. Combining anti-PD-1 with anlotinib (a phase 2 multitarget RTK inhibitor for OMM) or NGFR knockdown enhanced the effective activity of immune cells in organoid-immune cell coculture systems. Together, our study suggested that OMM organoids serve as faithful models for exploring tumor evolution and immunotherapy combination strategies.