Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma

Machine learning predicting the effects microstructures of biomass hard carbon have on the electrochemical performance of SIBs anodes

This study involved the development of machine learning models to investigate how the microstructures of biomass-based hard carbon (HC) influence sodium storage mechanisms and performance. The goal was to pinpoint the key microstructural features of biomass-based HC that impact the initial coulombic efficiency (ICE) and reversible capacity (RC) of sodium-ion batteries (SIBs). To achieve this, a database was established to correlate structural parameters of HC with essential sodium storage performance metrics (referred to as the Hard Carbon-SIBs, HCSs database). The XGBoost model exhibited high accuracy and excellent generalization in predicting both RC and ICE, achieving coefficients of determination (R2) of 0.88 and 0.77, respectively. SHAP analysis indicated that variations in specific surface area (SSA) significantly affected both electrochemical properties, while PDP analysis identified the key input features influencing RC and ICE. The findings suggest that this methodology holds significant promise for advancing the development of electrochemical energy storage materials.

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.

ARHGDIB as a prognostic biomarker and modulator of the immunosuppressive microenvironment in glioma

BACKGROUND

Glioma, a prevalent malignant intracranial tumor, exhibits limited therapeutic efficacy due to its immunosuppressive microenvironment, leading to a poor prognosis for patients. ARHGDIB is implicated in the remodeling of the tumor microenvironment and plays a significant role in the pathogenesis of various tumors. However, its regulatory effect within the immune microenvironment of glioma remains unclear.

METHODS

The mRNA expression pattern of ARHGDIB was analyzed using public databases, and its expression was further validated in our collected cohort through quantitative PCR (qPCR) and immunohistochemistry (IHC). Kaplan-Meier survival analysis and LASSO-Cox regression were employed to ascertain the clinical significance of ARHGDIB in glioma. Subsequently, we systematically evaluated the association between ARHGDIB expression and immune characteristics within the glioma microenvironment, as well as its potential to predict treatment response in glioma. Additionally, in vitro experiments were conducted to elucidate the role of ARHGDIB in remodeling the glioma microenvironment and promoting tumor malignancy progression.

RESULTS

Combined with bioinformatics analysis of public databases and validation with qPCR and IHC on our cohort, our findings indicate that ARHGDIB is markedly overexpressed in glioma and correlates with poor patient prognosis, thereby serving as a potential biomarker for adverse outcomes in glioma. Functional enrichment and immune infiltration analyses reveal that ARHGDIB is implicated in the recruitment of immunosuppressive cells, such as M2 macrophages and neutrophils, contributing to the alteration of the glioma immunosuppressive microenvironment and hindering the immune response. Further investigations through single-cell sequencing, immunohistochemistry, immunofluorescence, and in vitro experiments demonstrate that ARHGDIB exhibits an expression pattern akin to CD163, with its overexpression inducing M2 macrophage polarization and facilitating glioma cell proliferation and migration.

CONCLUSIONS

ARHGDIB emerges as a novel marker for tumor-associated macrophages, playing a crucial role in shaping the immunosuppressive microenvironment and representing a promising prognostic biomarker for glioma.

Translation dysregulation in cancer as a source for targetable antigens

Aberrant peptides presented by major histocompatibility complex (MHC) molecules are targets for tumor eradication, as these peptides can be recognized as foreign by T cells. Protein synthesis in malignant cells is dysregulated, which may result in the generation and presentation of aberrant peptides that can be exploited for T cell-based therapies. To investigate the role of translational dysregulation in immunological tumor control, we disrupt translation fidelity by deleting tRNA wybutosine (yW)-synthesizing protein 2 (TYW2) in tumor cells and characterize the downstream impact on translation fidelity and immunogenicity using immunopeptidomics, genomics, and functional assays. These analyses reveal that TYW2 knockout (KO) cells generate immunogenic out-of-frame peptides. Furthermore, Tyw2 loss increases tumor immunogenicity and leads to anti-programmed cell death 1 (PD-1) checkpoint blockade sensitivity in vivo. Importantly, reduced TYW2 expression is associated with increased response to checkpoint blockade in patients. Together, we demonstrate that defects in translation fidelity drive tumor immunogenicity and may be leveraged for cancer immunotherapy.

A Golgi apparatus-related signature predicts the immune microenvironment and prognosis of gastric cancer

In recent years, numerous studies have provided evidence of the involvement of the Golgi apparatus (GA) in various stages of cancer development. Nonetheless, the specific impact of GA-related characteristics on gastric cancer (GC) progression remains ambiguous. We utilized LASSO and multivariate COX regression methods to develop a GA-associated risk score (GARS). The GARS is constructed from seven signature genes, which are highly expressed in tumors. In our research, we have found that GARS is an effective indicator for predicting the prognosis of GC, chemotherapy sensitivity, and immune therapy response. Patients in the low GARS group exhibit characteristics such as a good prognosis, increased sensitivity to immune therapy, 5-fluorouracil, and paclitaxel. Finally, our experimental results confirm that knocking down F2R significantly reduces the proliferation and migration abilities of GC cells. This study highlights the importance of GA characteristics in predicting the prognosis of GC and in developing personalized treatment strategies. The experimental findings on F2R offer valuable theoretical insights for the diagnosis and management of GC.

PathNetDRP: a novel biomarker discovery framework using pathway and protein-protein interaction networks for immune checkpoint inhibitor response prediction

BACKGROUND

Predicting immune checkpoint inhibitor (ICI) response remains a significant challenge in cancer immunotherapy. Many existing approaches rely on differential gene expression analysis or predefined immune signatures, which may fail to capture the complex regulatory mechanisms underlying immune response. Network-based models attempt to integrate biological interactions, but they often lack a quantitative framework to assess how individual genes contribute within pathways, limiting the specificity and interpretability of biomarkers. Given these limitations, we developed PathNetDRP, a framework that integrates biological pathways, protein-protein interaction networks, and machine learning to identify functionally relevant biomarkers for ICI response prediction.

RESULTS

We introduce PathNetDRP, a novel biomarker discovery approach that applies the PageRank algorithm to prioritize ICI-associated genes, maps them to relevant biological pathways, and calculates PathNetGene scores to quantify their contribution to immune response. Unlike conventional methods that focus solely on gene expression differences, PathNetDRP systematically incorporates biological context to improve biomarker selection. Validation across multiple independent cancer cohorts showed that PathNetDRP achieved strong predictive performance, with cross-validation the area under the receiver operating characteristic curves increasing from 0.780 to 0.940. Interestingly, PathNetDRP did not merely improve predictive accuracy; it also provided insights into key immune-related pathways, reinforcing its potential for identifying clinically relevant biomarkers.

CONCLUSION

The biomarkers identified by PathNetDRP demonstrated robust predictive performance across cross-validation and independent validation datasets, suggesting their potential utility in clinical applications. Furthermore, enrichment analysis highlighted key immune-related pathways, providing a deeper understanding of their role in ICI response regulation. While these findings underscore the promise of PathNetDRP, future work will explore the integration of additional predictive features, such as tumor mutational burden and microsatellite instability, to further refine its applicability.

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.

Comprehensive analysis of immune subtype characterization on identification of potential cells and drugs to predict response to immune checkpoint inhibitors for hepatocellular carcinoma

Immunosubtyping enables the segregation of immune responders from non-responders. However, numerous studies failed to focus on the integration of cellular heterogeneity and immunophenotyping in the prediction of hepatocellular carcinoma (HCC) patients' response to immune checkpoint inhibitors (ICIs). We categorized HCC patients into various immune subtypes based on feature scores linked to ICI response. Single-cell sequencing technology was to investigate the cellular heterogeneity of different immune subtypes and acquire significant ICI response-associated cells. Candidate drugs were identified using a blend of various drug databases and network approaches. HCC patients were divided into two distinct immune subtypes based on characterization scores of 151 immune-related gene sets. Patients in both subtypes showed varying overall survival, immunity levels, biological activities, and TP53 mutation rates. Subtype 1-related natural killer cells showed a positive correlation with immune-promoting scores but a negative correlation with immune-suppressing scores. Notably, docetaxel sensitivity in HCC patients rose as the levels of subtype 1-related natural killer cells increased. Our study demonstrated that immune subtypes have cellular heterogeneity in predicting response to ICIs. A combination of subtype 1-associated natural killer cells and docetaxel may offer new hope for ICI treatment in HCC.

Integrated multi-omics reveal lactate metabolism-related gene signatures and PYGL in predicting HNSCC prognosis and immunotherapy efficacy

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) treatment faces significant clinical challenges. Lactate metabolism plays a crucial role in the initiation of many cancers and the tumor microenvironment (TME). However, the prognostic significance of lactate metabolism-related genes (LMRGs) and the role of TME in HNSCC require further elucidation.

METHODS

We built a prognostic multigene signature with LMRGs and systematically correlated the risk signature with immunological characteristics and immunotherapy efficacy. Next, a series of single-cell sequencing analyses were used to characterize lactate metabolism in TME. Finally, single-cell sequencing analysis, immunofluorescence analyses, and a series of in vitro experiments were used to explore the role of PYGL in HNSCC. Potential drugs targeting PYGL were screened using AutoDock 4.2.

RESULTS

A prognostic multigene signature based on LMRGs was developed, which effectively stratified patients into high- and low-risk groups, with significant differences in overall survival (OS) and progression-free survival (PFS). Patients in the low-risk group exhibited reduced lactate metabolism, higher CD8 + T cell infiltration, and improved response to immunotherapy. Single-cell sequencing revealed that tumor cells had the most active lactate metabolism compared to other cells in the TME. PYGL, identified as the most critical prognostic gene, was highly expressed in tumor-associated macrophages and played a role in inhibiting M1 macrophage polarization. Knockdown of PYGL led to reduced lactate levels, and its expression was inversely correlated with CD8 + T cell infiltration. Furthermore, PYGL was involved in copper-dependent cell death, highlighting its potential as a therapeutic target. Drug screening identified elesclomol, which showed promising results in PYGL-knockdown cells.

CONCLUSIONS

The study established a robust LMRGs-based prognostic model that not only predicts patient survival but also correlates with the immune microenvironment in HNSCC. PYGL emerged as a key biomarker with significant implications for both prognosis and therapeutic intervention. Its role in regulating lactate metabolism and immune suppression suggests that targeting PYGL could enhance the efficacy of immunotherapies. This research provides a foundation for future clinical strategies aimed at improving outcomes in HNSCC by modulating the tumor's metabolic and immune landscapes.

Overcoming immunotherapy resistance in hepatocellular carcinoma by targeting myeloid IL-8/CXCR2 signaling

Durable responses to immune checkpoint blockade (ICB) in hepatocellular carcinoma (HCC) are limited to a minority of patients, yet reliable biomarkers are still lacking. Inflammatory cytokines such as interleukin-8 (IL-8) are associated with HCC progression, and IL-8 is known as the chemoattractant for immunosuppressive myeloid cells. Therefore, we aim to elucidate the ICB resistance mechanisms mediated by the activation of the IL-8/CXCR2 pathway. Single-cell RNA sequencing (scRNA-seq) was performed in advanced HCC patients with baseline and on-treatment biopsy after pembrolizumab in a phase 2 clinical trial cohort. Our data revealed that IL-8 and its receptor, CXCR2, mainly derived from immunosuppressive myeloid-derived suppressor cells (MDSCs). In particular, the high circulating IL-8 level was strongly associated with poor ICB response. This myeloid IL-8/CXCR2 pathway was further elucidated in our ICB-resistant orthotopic mouse model using AZD5069, a clinically available CXCR2 antagonist. Suppression of the IL-8/CXCR2 pathway significantly abrogated MDSC trafficking and immunosuppressive activity, which sensitized the anti-PD-L1 blockade to reduce tumor growth and prolong survival. The association between myeloid IL-8 and ICB therapeutic outcomes also extended to multiple cancer types. Collectively, our study not only suggests a potential non-invasive biomarker for patient stratification and monitoring of ICB response but it also provides a proof of concept for combinational immunotherapy to benefit patients who are non-responsive to ICB monotherapy.

TCGEx: a powerful visual interface for exploring and analyzing cancer gene expression data

Analyzing gene expression data from the Cancer Genome Atlas (TCGA) and similar repositories often requires advanced coding skills, creating a barrier for many researchers. To address this challenge, we developed The Cancer Genome Explorer (TCGEx), a user-friendly, web-based platform for conducting sophisticated analyses such as survival modeling, gene set enrichment analysis, unsupervised clustering, and linear regression-based machine learning. TCGEx provides access to preprocessed TCGA data and immune checkpoint inhibition studies while allowing integration of user-uploaded data sets. Using TCGEx, we explore molecular subsets of human melanoma and identify microRNAs associated with intratumoral immunity. These findings are validated with independent clinical trial data on immune checkpoint inhibitors for melanoma and other cancers. In addition, we identify cytokine genes that can be used to predict treatment responses to various immune checkpoint inhibitors prior to treatment. Built on the R/Shiny framework, TCGEx offers customizable features to adapt analyses for diverse research contexts and generate publication-ready visualizations. TCGEx is freely available at https://tcgex.iyte.edu.tr , providing an accessible tool to extract insights from cancer transcriptomics data.

Unravelling Cancer Immunity: Coagulation.Sig and BIRC2 as Predictive Immunotherapeutic Architects

Immune checkpoint inhibitors (ICIs) represent a groundbreaking advancement in cancer therapy, substantially improving patient survival rates. Our comprehensive research reveals a significant positive correlation between coagulation scores and immune-related gene expression across 30 diverse cancer types. Notably, tumours exhibiting high coagulation scores demonstrated enhanced infiltration of cytotoxic immune cells, including CD8+ T cells, natural killer (NK) cells, and macrophages. Leveraging the TCGA pan-cancer database, we developed the Coagulation.Sig model, a sophisticated predictive framework utilising a coagulation-related genes (CRGs) to forecast immunotherapy outcomes. Through rigorous analysis of ten ICI-treated cohorts, we identified and validated seven critical CRGs: BIRC2, HMGB1, STAT2, IFNAR1, BID, SPATA2, IL33 and IFNG, which form the foundation of our predictive model. Functional analyses revealed that low-risk tumours characterised by higher immune cell populations, particularly CD8+ T cells, demonstrated superior ICI responses. These tumours also exhibited increased mutation rates, elevated neoantigen loads, and greater TCR/BCR diversity. Conversely, high-risk tumours displayed pronounced intratumor heterogeneity (ITH) and elevated NRF2 pathway activity, mechanisms strongly associated with immune evasion. Experimental validation highlighted BIRC2 as a promising therapeutic target. Targeted BIRC2 knockdown, when combined with anti-PD-1 therapy, significantly suppressed tumour growth, enhanced CD8+ T cell infiltration, and amplified IFN-γ and TNF-α secretion in tumour models. Our findings position the Coagulation.Sig model as a novel, comprehensive approach to personalised cancer treatment, with BIRC2 emerging as both a predictive biomarker and a potential therapeutic intervention point.

A Single-Cell Atlas of RNA Alternative Splicing in the Glioma-Immune Ecosystem

Single-cell analysis has refined our understanding of cellular heterogeneity in glioma, yet RNA alternative splicing (AS)-a critical layer of transcriptome regulation-remains underexplored at single-cell resolution. Here, we present a pan-glioma single-cell AS analysis in both tumor and immune cells through integrating seven SMART-seq2 datasets of human gliomas. Our analysis reveals lineage-specific AS across glioma cellular states, with the most divergent AS landscapes between mesenchymal- and neuronal-like glioma cells, exemplified by AS in TCF12 and PTBP2. Comparison between core and peripheral glioma cells highlights AS-redox co-regulation of cytoskeleton organization. Further analysis of glioma-infiltrating immune cells reveals potential isoform-level regulation of protein glycosylation in regulatory T cells and a link between MS4A7 AS in macrophages and clinical response to anti-PD-1 therapy. This study emphasizes the role of AS in glioma cellular heterogeneity, highlighting the importance of an isoform-centric approach to better understand the complex biological processes driving tumorigenesis.

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.

Identification of methionine metabolism related prognostic model and tumor suppressive functions of BHMT in hepatocellular carcinoma

Given the resistance to conventional treatments and limitations of immune checkpoint blockade therapy in hepatocellular carcinoma (HCC), it is imperative to explore novel prognostic models and biomarkers. The dependence of cancer cell on exogenous methionine, known as Hoffman effect, is a hallmark of HCC, with numerous studies reporting a strong correlation between methionine metabolism and tumor development. Betaine-homocysteine S-methyltransferase (BHMT), a critical component of methionine metabolism pathway, has polymorphisms linking to poor prognosis in multiple cancers. Nevertheless, there is little literature regarding the relationship between methionine metabolism and incidence, mortality of HCC, as well as the function of BHMT in HCC progression. In this study, by analyzing multiple datasets, we constructed a methionine metabolism-related prognostic model and thoroughly investigated the influence of BHMT on the prognosis of HCC. Bioinformatics analysis revealed a marked decrease in BHMT expression in HCC, which was linked to adverse clinical outcomes. CIBERSORT results suggest that BHMT promotes infiltration of M1 macrophages. Our results suggest its potential as an ideal prognostic biomarker for anti PD-L1 immunotherapy. In summary, this study innovatively provides first methionine metabolism-related prognostic model and unveils the tumor suppressive function of BHMT in HCC, providing potential mechanism by which BHMT exert its function.

Sea cucumber polysaccharides overcome immunotherapy resistance in tumor-bearing mice via modulation of the gut microbiome

Cancer immunotherapy has been successful in patients with different types of cancers, but its efficacy in treating certain types of colorectal cancer (CRC) is limited. The aim of this study was to explore whether sea cucumber polysaccharides (SCP) could impact resistance to anti-programmed cell death-1 (anti-PD1) immunotherapy of CRC and the role of microbiota in mediating their effects. Mice inoculated with immunotherapy resistant CT-26 CRC cells were pretreated with SCP, followed by treatment with/without the anti-PD1 antibody. SCP alone exhibited no inhibitory effect on tumor growth, but they drastically enhanced the efficacy of anti-PD1 treatment, which alone showed minimal effect on tumor development. Compared to anti-PD1 only treatment, a combination of SCP and anti-PD1 increased CD8+ T cells, especially IFN-γ+ cytotoxic CD8+ T cells, and decreased regulatory CD4+ T cells. SCP modulated gut microbiota and increased the relative abundance of bacteria including Bifidobacterium and Faecalibaculum. A fecal microbiota transplantation experiment showed that the sensitizing effect of SCP was at least partly mediated by microbiota. Furthermore, oral supplementation of Bifidobacterium pseudolongum or Faecalibaculum rodentium recapitulated the beneficial effect of SCP in potentiating anti-PD1 efficacy. Altogether, these findings demonstrated that SCP could be potentially developed as a dietary adjuvant to increase the efficacy of immunotherapy in CRC.

Ancestral Differences in Anticancer Treatment Efficacy and Their Underlying Genomic and Molecular Alterations

SIGNIFICANCE

Our study charts a global landscape of ancestry-associated differences in therapeutic efficacy, highlighting the importance of considering ancestry in anticancer therapies.

Tumor and blood B-cell abundance outperforms established immune checkpoint blockade response prediction signatures in head and neck cancer

BACKGROUND

Immunotherapy has improved the outcomes for some patients with head and neck squamous-cell carcinoma (HNSCC). However, the low and variable response rates observed highlight the need for robust response biomarkers to select patients for treatment.

PATIENTS AND METHODS

We assembled and analyzed a large HNSCC dataset, encompassing 11 clinical cohorts including 1232 patient samples, spanning a variety of disease subtypes and immune checkpoint blockade (ICB) treatment types, tissue sources, data modalities, and timing of measurements. We conducted a comprehensive evaluation of the predictive power of various cell types, traditional biomarkers, and emerging predictors in both blood and tumor tissues of HNSCC patients.

RESULTS

Tumor B-cell infiltration emerged as a strong and robust predictor of both patient survival and ICB response. It outperformed all other established biomarkers of response to ICB, including the tertiary lymphoid structure signature and numerous T-cell-based signatures. B-cell infiltration was associated with a 'hot' antitumor microenvironment that promotes tumor eradication. Furthermore, B-cell levels in peripheral blood mononuclear cells (PBMCs) correlated strongly with tumor B-cell levels and demonstrated high predictive value for ICB response, with high odds ratios (≥7.8) in two independent clinical cohorts.

CONCLUSION

B-cell abundance, whether assessed in PBMCs or tumor tissues, is one of the strongest predictors of ICB response in HNSCC. For translation to patient care, measuring B-cell abundance in PBMCs via cytometry offers a practical and accessible tool for clinical decision making.

Mathematically mapping the network of cells in the tumor microenvironment

Cell-cell interaction (CCI) networks are key to understanding disease progression and treatment response. However, existing methods for inferring these networks often aggregate data across patients or focus on cell-type level interactions, providing a generalized overview but overlooking patient heterogeneity and local network structures. To address this, we introduce "random cell-cell interaction generator" (RaCInG), a model based on random graphs to derive personalized networks leveraging prior knowledge on ligand-receptor interactions and bulk RNA sequencing data. We applied RaCInG to 8,683 cancer patients to extract 643 network features related to the tumor microenvironment and unveiled associations with immune response and subtypes, enabling prediction and explanation of immunotherapy responses. RaCInG demonstrated robustness and showed consistencies with state-of-the-art methods. Our findings highlight RaCInG's potential to elucidate patient-specific network dynamics, offering insights into cancer biology and treatment responses. RaCInG is poised to advance our understanding of complex CCI s in cancer and other biomedical domains.

DNA methylation status classifies pleural mesothelioma cells according to their immune profile: implication for precision epigenetic therapy

BACKGROUND

Co-targeting of immune checkpoint inhibitors (ICI) CTLA-4 and PD-1 has recently become the new first-line standard of care therapy of pleural mesothelioma (PM) patients, with a significant improvement of overall survival (OS) over conventional chemotherapy. The analysis by tumor histotype demonstrated greater efficacy of ICI therapy compared to standard chemotherapy in non-epithelioid (non-E) vs. epithelioid (E) PM, although some E PM patients also benefit from ICI treatment. This evidence suggests that molecular tumor features, beyond histotype, could be relevant to improve the efficacy of ICI therapy in PM. Among these, tumor DNA methylation emerges as a promising factor to explore, due to its potential role in driving the immune phenotype of cancer cells. Therefore, we utilized a panel of cultured PM cells of different histotype to provide preclinical evidence supporting the role of the tumor methylation landscape, along with its pharmacologic modulation, to prospectively improve the efficacy of ICI therapy of PM patients.

METHODS

The methylome profile (EPIC array) of distinct E (n = 5) and non-E (n = 9) PM cell lines was analyzed, followed by integrated analysis with their associated transcriptomic profile (Clariom S array), before and after in vitro treatment with the DNA hypomethylating agent (DHA) guadecitabine. The most variable methylated probes were selected to calculate the methylation score (CIMP index) for each cell line at baseline. Genes that were differentially expressed (DE) and differentially methylated (DM) were then selected for gene ontology analysis.

RESULTS

The CIMP index stratified PM cell lines into two distinct classes, CIMP (hyper-methylated; n = 7) and LOW (hypo-methylated; n = 7), regardless of their E or non-E histotype. Integrated methylome and transcriptome analyses revealed that CIMP PM cells exhibited a substantial number of hyper-methylated, silenced genes, which negatively impacted their immune phenotype compared to LOW PM cells. Treatment with DHA reverted the methylation-driven immune-compromised profile of CIMP PM cells and enhanced the constitutive immune-favorable profile of LOW PM cells.

CONCLUSION

The study highlighted the relevance of DNA methylation in shaping the constitutive immune classification of PM cells, independent of their histological subtypes. The identified role of DHA in shifting the phenotype of PM cells towards an immune-favorable state highlights its potential for evaluation in phase I/II clinical trials investigating the efficacy of epigenetic-based ICI combinations to reverse cancer immune resistance mechanisms.

Unraveling immune heterogeneity across pan-cancer and deep insights in lung adenocarcinoma based on alternative splicing

Alternative splicing (AS) participates in tumor development and tumor microenvironment formation. However, the landscape of immune-infiltrating AS events in pan-cancer and mechanisms of AS in lung adenocarcinoma (LUAD) have not been comprehensively characterized. We systematically profiled the immune-infiltrating AS event landscape of pan-cancer using data from The Cancer Genome Atlas, analyzing both commonalities and specific characteristics among different cancer types. We found that AS events tend to occur specifically in one cancer type rather than in multiple cancer types. AS events were used to classify 512 LUAD samples into 2 subtypes by unsupervised clustering: the aberrant splicing subtype and the immune-infiltrating subtype. The 2 subtypes showed significant differences in clinicopathology, prognosis, transcriptomics, genomics, and immune microenvironment. We constructed a classification signature comprising 10 genes involved in 14 AS events using logistic regression. The robustness of the signature was validated in 3 independent datasets using survival analysis. To explore AS mechanisms in LUAD, we constructed subtype-specific coexpression networks using Pearson correlation analysis. AS event of AKT3 regulated by splicing factor ENOX1 was associated with poor prognosis in LUAD. Overall, we outline AS events associated with immune infiltration in pan-cancer, and this study provides insights into AS mechanisms in LUAD patient classification.

A model-based factorization method for scRNA data unveils bifurcating transcriptional modules underlying cell fate determination

Manifold-learning is particularly useful to resolve the complex cellular state space from single-cell RNA sequences. While current manifold-learning methods provide insights into cell fate by inferring graph-based trajectory at cell level, challenges remain to retrieve interpretable biology underlying the diverse cellular states. Here, we described MGPfactXMBD, a model-based manifold-learning framework and capable to factorize complex development trajectories into independent bifurcation processes of gene sets, and thus enables trajectory inference based on relevant features. MGPfactXMBD offers a more nuanced understanding of the biological processes underlying cellular trajectories with potential determinants. When bench-tested across 239 datasets, MGPfactXMBD showed advantages in major quantity-control metrics, such as branch division accuracy and trajectory topology, outperforming most established methods. In real datasets, MGPfactXMBD recovered the critical pathways and cell types in microglia development with experimentally valid regulons and markers. Furthermore, MGPfactXMBD discovered evolutionary trajectories of tumor-associated CD8+ T cells and yielded new subtypes of CD8+ T cells with gene expression signatures significantly predictive of the responses to immune checkpoint inhibitor in independent cohorts. In summary, MGPfactXMBD offers a manifold-learning framework in scRNA-seq data which enables feature selection for specific biological processes and contributing to advance our understanding of biological determination of cell fate.

HCNetlas: A reference database of human cell type-specific gene networks to aid disease genetic analyses

Cell type-specific actions of disease genes add a significant layer of complexity to the genetic architecture underlying diseases, obscuring our understanding of disease mechanisms. Single-cell omics have revealed the functional roles of genes at the cellular level, identifying cell types critical for disease progression. Often, a gene impact on disease through its altered network within specific cell types, rather than mere changes in expression levels. To explore the cell type-specific roles of disease genes, we developed HCNetlas (human cell network atlas), a resource cataloging cell type-specific gene networks (CGNs) for various healthy tissue cells. We also devised 3 network analysis methods to investigate cell type-specific functions of disease genes. These methods involve comparing HCNetlas CGNs with those derived from disease-affected tissue samples. These methods find that systemic lupus erythematosus genes predominantly function in myeloid cells, and Alzheimer's disease genes mainly play roles in inhibitory and excitatory neurons. Additionally, they suggest that many lung cancer-related genes may exert their roles in immune cells. These findings suggest that HCNetlas has the potential to link disease-associated genes to cell types of action, facilitating development of cell type-resolved diagnostics and therapeutic strategies for complex human diseases.

A Distinguished Roadmap of Fibroblast Senescence in Predicting Immunotherapy Response and Prognosis Across Human Cancers

The resistance of tumors to immune checkpoint inhibitors (ICI) may be intricately linked to cellular senescence, although definitive clinical validation remains elusive. In this study, comprehensive pan-cancer scRNA-seq analyses identify fibroblasts as exhibiting the most pronounced levels of cellular senescence among tumor-associated cell populations. To elucidate this phenomenon, a fibroblast senescence-associated transcriptomic signature (FSS), which correlated strongly with protumorigenic signaling pathways and immune dysregulation that fosters tumor progression, is developed. Leveraging the FSS, the machine learning (ML) framework demonstrates exceptional accuracy in predicting ICI response and survival outcomes, achieving superior area under curve (AUC) values across validation, testing, and in-house cohorts. Strikingly, FSS consistently outperforms established signatures in predictive robustness across diverse cancer subtypes. From an integrative analysis of 17 CRISPR/Cas9 libraries, CDC6 emerges as a pivotal biomarker for pan-cancer ICI response and prognostic stratification. Mechanistically, experimental evidence reveals that CDC6 in tumor cells orchestrates fibroblast senescence via TGF-β1 secretion and oxidative stress, subsequently reprogramming the tumor microenvironment and modulating ICI response. These findings underscore the translational potential of targeting fibroblast senescence as a novel therapeutic strategy to mitigate immune resistance and enhance antitumor efficacy.

A physics informed neural network approach to quantify antigen presentation activities at single cell level using omics data

Antigen processing and presentation via major histocompatibility complex (MHC) molecules are central to immune surveillance. Yet, quantifying the dynamic activity of MHC class I and II antigen presentation remains a critical challenge, particularly in diseases like cancer, infection and autoimmunity where these pathways are often disrupted. Current methods fall short in providing precise, sample-specific insights into antigen presentation, limiting our understanding of immune evasion and therapeutic responses. Here, we present PSAA (PINN-empowered Systems Biology Analysis of Antigen Presentation Activity), which is designed to estimate sample-wise MHC class I and class II antigen presentation activity using bulk, single-cell, and spatially resolved transcriptomics or proteomics data. By reconstructing MHC pathways and employing pathway flux estimation, PSAA offers a detailed, stepwise quantification of MHC pathway activity, enabling predictions of gene-specific impacts and their downstream effects on immune interactions. Benchmarked across diverse omics datasets and experimental validations, PSAA demonstrates a robust prediction accuracy and utility across various disease contexts. In conclusion, PSAA and its downstream functions provide a comprehensive framework for analyzing the dynamics of MHC antigen presentation using omics data. By linking antigen presentation to immune cell activity and clinical outcomes, PSAA both elucidates key mechanisms driving disease progression and uncovers potential therapeutic targets.

Global trends and emerging insights in BRAF and MEK inhibitor resistance in melanoma: a bibliometric analysis

Objective

This study aims to perform a comprehensive bibliometric analysis of global research on BRAF and MEK inhibitor resistance in melanoma, identifying key research trends, influential contributors, and emerging themes from 2003 to 2024.

Methods

A systematic search was conducted in the Web of Science Core Collection (WoSCC) database to retrieve publications related to BRAF and MEK inhibitor resistance from 1 January 2003, to 1 September 2024. Bibliometric analyses, including publication trends, citation networks, and keyword co-occurrence patterns, were performed using VOSviewer and CiteSpace. Collaborative networks, co-cited references, and keyword burst analyses were mapped to uncover shifts in research focus and global cooperation.

Results

A total of 3,503 documents, including 2,781 research articles and 722 review papers, were analyzed, highlighting significant growth in this field. The United States, China, and Italy led in publication volume and citation impact, with Harvard University and the University of California System among the top contributing institutions. Research output showed three phases of growth, peaking in 2020. Keyword and co-citation analyses revealed a transition from early focus on BRAF mutations and MAPK pathway activation to recent emphasis on immunotherapy, combination therapies, and non-apoptotic cell death mechanisms like ferroptosis and pyroptosis. These trends reflect the evolving priorities and innovative approaches shaping the field of resistance to BRAF and MEK inhibitors in melanoma.

Conclusion

Research on BRAF and MEK inhibitor resistance has evolved significantly. This analysis provides a strategic framework for future investigations, guiding the development of innovative, multi-modal approaches to improve treatment outcomes for melanoma patients.

Immune-related glycosylation genes based classification predicts prognosis and therapy options of osteosarcoma

Osteosarcoma is the most common primary bone malignancy, with a very poor prognosis. Aberrant glycosylation is close involvement in osteosarcoma. Accordingly, this study aimed at investigating the role of glycosylation genes in the prognosis and therapy options of osteosarcoma. The microenvironment of osteosarcoma was assessed using estimate algorithm. A total of 20 immune-related glycosylation genes (IRGGs) was identified using Pearson correlation analysis. Accordingly, osteosarcoma patients were divided into C1 and C2 type using consensus clustering. Multiple algorithms (Xcell, MCP-counter, ssGSEA, epic, quantiseq), cancer immune cycle analysis, and GSVA were applied to estimate the immune, molecule and metabolism characteristics of osteosarcoma, indicating that C1 type was featured with high immune infiltration, high glycosylation, enriched MEK signaling, and good prognosis, while C2 type was characterized by more metastasis, enriched immunotherapy-positive gene signatures, high tumor mutation burden, and poor prognosis. Results from TIDE algorithm and immunotherapy datasets suggested the C2 type's preference of immune checkpoint inhibitors (ICIs), while data of GDSC, CMap analysis and cell experiments indicated that C1 type was sensitivity to MEK inhibitor PD0325901. In addition, univariate Cox and Lasso analysis was combined to establish an IRGGs' risk score containing 6 genes (B3GNT8, FUT7, GAL3ST4, GALNT14, HS3ST2, and MFNG). The data of DCA and ROC indicated its well prediction of prognosis in osteosarcoma. Finally, cellular location analysis showed that the 6 genes not only distributed in tumor cells but also in immune cells. In summary, the classification and risk score based on IRGGs effectively predicted the prognosis and therapy options of osteosarcoma. Further studies on IRGGs may contribute to the understanding of cancer immunity in osteosarcoma.

TBX3 shapes an immunosuppressive microenvironment and induces immunotherapy resistance

Background: Identifying biomarkers that predict immunotherapy efficacy and discovering new targets for combination therapies are critical elements for improving the prognosis of bladder cancer (BLCA) patients. Methods: Firstly, we explored the expression patterns of TBX3 in normal and pan-cancer tissues and the correlation between TBX3 and the immune microenvironment using data from multiple public databases. Then, we combined various techniques, including bulk RNA sequencing, single-cell RNA sequencing, high-throughput cytokine arrays, functional experiments, ProcartaPlex multiplex immunoassays and TissueFAXS panoramic tissue quantification assays, to demonstrate that TBX3 shapes an immunosuppressive tumor microenvironment (TME) in BLCA. Results: We identified TBX3 as a key factor associated with the immunosuppressive microenvironment in BLCA through a systematic multi-omics analysis. We found that TBX3 is primarily expressed in malignant cells, where TBX3high tumor cells increase the secretion of TGFβ1, which promotes the infiltration of cancer-associated fibroblasts (CAFs), thereby forming an immunosuppressive microenvironment. We further demonstrated that TBX3 enhances TGFβ1 expression by binding to the TGFβ1 promoter, and blocking TGFβ1 counteracts the immunosuppressive effects of TBX3. Moreover, TBX3 reduced the cancer-killing efficiency of CD8+ T cells by decreasing the proportion of GZMB+ CD8+ T cells, and knocking down TBX3 combined with anti-PD-1 treatment increased CD8+ T cell infiltration and reduced CAFs in vivo. We also validated the inverse relationship between TBX3+ malignant cells and CD8+ T cells and the positive relationship with CAFs in tissue microarrays. Lastly, we found that TBX3 predicted immunotherapy efficacy in our real-world immunotherapy cohort and multiple public cohorts. Conclusion: In summary, TBX3 promotes BLCA progression and immunotherapy resistance by inducing an immunosuppressive microenvironment, and targeting TBX3 could enhance the efficacy of immunotherapy for BLCA.

Identification of SETD4 as an Onco-Immunological Biomarker Encompassing the Tumor Microenvironment, Prognoses, and Therapeutic Responses in Various Human Cancers

BACKGROUND

SET domain-containing protein 4 (SETD4) is a histone methyltransferase that has been shown to modulate cell proliferation, differentiation, and inflammatory responses by regulating histone H4 trimethylation (H4K20me3). Previous reports have demonstrated its function in the quiescence of cancer stem cells as well as drug resistance in several cancers. A limited number of systematic studies have examined SETD4's role in the tumor microenvironment, pathogenesis, prognosis, and therapeutic response.

METHODS

Utilizing The Cancer Genome Atlas database, and other publicly accessible platforms, we comprehensively analyzed SETD4 gene expression, methylation patterns, and prognostic significance. Furthermore, we investigated its association with cancer-related pathways, the immune microenvironment, immunotherapy markers, and drug resistance signatures of chemotherapy. Additionally, qRT-PCR was performed to validate SETD4 expression in clinical specimens.

RESULTS

The expression of SETD4 was abnormal across a variety of cancer types and the expression of SETD4 in colorectal cancer tissues was verified in clinical specimens. The upregulation of SETD4 may be a prognostic risk factor predicting poor overall survival and progression-free survival. The analysis revealed that the mRNA level of SETD4 was modulated by promoter methylation, and patients with lower methylation levels showed shorter survival times. Pathway analysis showed that SETD4 influenced several key cell cycle pathways, including the G2M checkpoint, and mitotic spindle pathways. In addition, SETD4 negatively affects immune cell infiltration in most cancers, including B cells, CD8 T cells, and macrophages. The correlation between SETD4 and cancer stemness as well as homologous recombination deficiency varied across tumor types, suggesting that SETD4 may play a multifaceted role in tumor resistance. Notably, we identified several potential agents targeting SETD4.

CONCLUSIONS

This study demonstrates that SETD4 is an immune-oncogenic molecule in multiple cancers, with the potential to be a diagnosis, prognosis, and targeted therapy marker.

Comprehensive Pan-cancer Analysis of CMPK2 as Biomarker and Prognostic Indicator for Immunotherapy

BACKGROUND

UMP-CMP kinase 2 (CMPK2) is involved in mitochondrial DNA synthesis, which can be oxidized and released into the cytoplasm in innate immunity. It initiates the assembly of NLRP3 inflammasomes and mediates various pathological processes such as human immunodeficiency virus infection and systemic lupus erythematosus. However, the role of CMPK2 in tumor progression and tumor immunity remains unclear.

METHODS

We identified CMPK2 expression patterns in the Genotype Tissue-Expression (GTEx), The Cancer Genome Atlas (TCGA), and the Cancer Cell Line Encyclopedia (CCLE) databases. Validation was performed using immunohistochemical staining data from the Human Protein Atlas (HPA) database and qPCR experiments. Receiver operating characteristic curve analysis and Kaplan-Meier survival analysis were conducted to assess the clinical relevance of CMPK2 expression. The Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) algorithm and the Tumor IMmune Estimation Resource (TIMER) database were used to evaluate the correlation between CMPK2 and immune infiltration in tumors. The Tumor Immune Syngeneic Mouse (TISMO) database and other public datasets were utilized to assess the impact of CMPK2 on immune therapy response. MEXPRESS and MethSurv databases were employed to investigate the effects of methylation on CMPK2 expression.

RESULTS

CMPK2 expression was elevated in 23 cancers and decreased in two cancers. Furthermore, CMPK2 expression had a high diagnostic value for 16 cancers. Elevated CMPK2 expression was associated with lower overall survival (OS), disease-specific survival (DSS), and progression- free interval (PFI) in four cancers. Immune microenvironment-related analysis revealed strong associations between CMPK2 expression and immune cell infiltration, as well as immune checkpoint expression across various tumors. Notably, in four mouse immunotherapy cohorts, CMPK2 expression in treated mouse tumors was higher post-treatment. In five clinical immunotherapy cohorts, patients with high CMPK2 expression show better responses to immunotherapy. Moreover, the methylation level of CMPK2 gene was closely correlated to its expression and tumor prognosis. Among these cancers, the clinical and immunological indications of skin cutaneous melanoma (SKCM) are particularly closely related to CMPK2 expression.

CONCLUSION

Our analysis preliminarily describes the complex function of CMPK2 in cancer progression and immune microenvironment, highlighting its potential as a diagnostic and therapeutic target for immunotherapy.

Navigating the immune landscape with plasma cells: A pan-cancer signature for precision immunotherapy

Immunotherapy has revolutionized cancer treatment; however, predicting patient response remains a significant challenge. Our study identified a novel plasma cell signature, Plasma cell.Sig, through a pan-cancer single-cell RNA sequencing analysis, which predicts patient outcomes to immunotherapy with remarkable accuracy. The signature was developed using rigorous machine learning algorithms and validated across multiple cohorts, demonstrating superior predictive power with an area under the curve (AUC) exceeding 0.7. Notably, the low-risk group, as classified by Plasma cell.Sig, exhibited enriched immune cell infiltration and heightened tumor immunogenicity, indicating an enhanced responsiveness to immunotherapy. Conversely, the high-risk group showed reduced immune activity and potential mechanisms of immune evasion. These findings not only enhance understanding of the intrinsic and extrinsic immune landscapes within the tumor microenvironment but also pave the way for more precise, biomarker-guided immunotherapy approaches in oncology.

Protocol for using scCURE to construct an immunotherapy outcome prediction model

It is challenging to predict the immunotherapy outcome from the baseline status of patients with cancer. Here, we introduce a protocol for constructing an immunotherapy prediction model utilizing single-cell RNA sequencing (scRNA-seq)-based changed and unchanged cell recognition during immunotherapy (scCURE). Initially, we describe the steps for using scCURE to discriminate unchanged cells with similar cellular and molecular functions from whole scRNA-seq data. Subsequently, we demonstrate ways to construct immunotherapy outcome prediction models using either scRNA-seq or bulk RNA sequencing (RNA-seq) data, predicated on the unchanged cells identified by scCURE. For complete details on the use and execution of this protocol, please refer to Zou et al.1.

Single-cell RNA sequencing-derived signatures define response patterns to atezolizumab + bevacizumab in advanced hepatocellular carcinoma

BACKGROUND & AIMS

The combination of atezolizumab and bevacizumab (atezo+bev) is the current standard of care for advanced hepatocellular carcinoma (HCC), providing a median overall survival (OS) of 19.2 months. Here, we aim to uncover the underlying cellular processes driving clinical benefit vs. resistance to atezo+bev.

METHODS

We harnessed the power of single-cell RNA sequencing in advanced HCC to derive gene expression signatures recapitulating 21 cell phenotypes. These signatures were applied to 422 RNA-sequencing samples of patients with advanced HCC treated with atezo+bev (n = 317) vs. atezolizumab (n = 47) or sorafenib (n = 58) as comparators.

RESULTS

We unveiled two distinct patterns of response to atezo+bev. First, an immune-mediated response characterised by the combined presence of CD8+ T effector cells and pro-inflammatory CXCL10+ macrophages, representing an immune-rich microenvironment. Second, a non-immune, angiogenesis-related response distinguishable by a reduced expression of the VEGF co-receptor neuropilin-1 (NRP1), a biomarker that specifically predicts improved OS upon atezo+bev vs. sorafenib (p = 0.039). Primary resistance was associated with an enrichment of immunosuppressive myeloid populations, namely CD14+ monocytes and TREM2+ macrophages, and Notch pathway activation. Based on these mechanistic insights we define "Immune-competent" and "Angiogenesis-driven" molecular subgroups, each associated with a significantly longer OS with atezo+bev vs. sorafenib (p of interaction = 0.027), and a "Resistant" subset.

CONCLUSION

Our study unveils two distinct molecular subsets of clinical benefit to atezolizumab plus bevacizumab in advanced HCC ("Immune-competent" and "Angiogenesis-driven") as well as the main traits of primary resistance to this therapy, thus providing a molecular framework to stratify patients based on clinical outcome and guiding potential strategies to overcome resistance.

IMPACT AND IMPLICATIONS

Atezolizumab + bevacizumab (atezo+bev) is standard of care in advanced hepatocellular carcinoma (HCC), yet molecular determinants of clinical benefit to the combination remain unclear. This study harnesses the power of single-cell RNA sequencing, deriving gene expression signatures representing 21 cell subtypes in the advanced HCC microenvironment. By applying these signatures to RNA-sequencing samples, we reveal two distinct response patterns to atezo+bev and define molecular subgroups of patients ("Immune-competent" and "Angiogenesis-driven" vs. "Resistant") with differential clinical outcomes upon treatment with atezo+bev, pointing towards the role of immunosuppressive myeloid cell types and Notch pathway activation in primary resistance to atezo+bev. These results may help refine treatment strategies and improve outcomes for patients with advanced HCC, while also guiding future research aimed at overcoming resistance mechanisms.

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.

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

The widespread use of high-throughput sequencing technologies has revolutionized the understanding of biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome and clinical response. However, an open-source R package covering state-of-the-art machine-learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct a machine learning-based integration model with elegant performance (Mime). Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting the outcomes of patients compared with other published models. Furthermore, the PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from the PIEZO1-associated signatures demonstrated high potential as a glioma target. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields. The Mime package is available on GitHub (https://github.com/l-magnificence/Mime).

The pharmacogenomic and immune landscape of snoRNAs in human cancers

Small nucleolar RNAs (snoRNAs) are a class of non-coding RNAs primarily known for their role in the chemical modification of other RNAs. Recent studies suggested that snoRNAs may play a broader role in anti-cancer treatments such as targeted therapies and immunotherapies. Despite these insights, the comprehensive landscape of snoRNA associations with drug response and immunotherapy outcomes remains unexplored. In this study, we identified 79,448 and 75,185 associations between snoRNAs and drug response using data from VAEN and CancerRxTissue, respectively. Additionally, we discovered 29,199 associations between snoRNAs and immune checkpoint genes and 47,194 associations between snoRNAs and immune cell infiltrations. Sixteen snoRNAs were significantly correlated with immunotherapy objective response rate (ORR), and 92 snoRNAs showed significantly differential expression between cancers with high and low ORR. Furthermore, we identified 17 snoRNAs with significantly differential expression between cancer types with high and low immune-related adverse event (irAE) reporting odds ratio (ROR). Several snoRNAs, such as SNORD92, and SNORD83B, may represent promising biomarkers or therapeutic targets that needs further investigation. To facilitate further research, we developed a user-friendly portal, Pharmacogenomic and Immune Landscape of SnoRNA (PISNO, https://hanlaboratory.com/PISNO/), enabling researchers to visualize, browse, and download multi-dimensional data. This study highlights the potential of snoRNAs as biomarkers or therapeutic targets, paving the way for more effective and personalized anti-cancer treatments.

A novel computational model ITHCS for enhanced prognostic risk stratification in ESCC by correcting for intratumor heterogeneity

Intratumor heterogeneity significantly challenges the accuracy of existing prognostic models for esophageal squamous cell carcinoma (ESCC) by introducing biases related to the varied genetic and molecular landscapes within tumors. Traditional models, relying on single-sample, single-region bulk RNA sequencing, fall short of capturing the complexity of intratumor heterogeneity. To fill this gap, we developed a computational model for intratumor heterogeneity corrected signature (ITHCS) by employing both multiregion bulk and single-cell RNA sequencing to pinpoint genes that exhibit consistent expression patterns across different tumor regions but vary significantly among patients. Utilizing these genes, we applied multiple machine-learning algorithms for sophisticated feature selection and model construction. The ITHCS model significantly outperforms existing prognostic indicators in accuracy and generalizability, markedly reducing sampling biases caused by intratumor heterogeneity. This improvement is especially notable in the prognostic assessment of early-stage ESCC patients, where the model exhibits exceptional predictive power. Additionally, we found that the risk score based on ITHCS may be associated with epithelial-mesenchymal transition characteristics, indicating that high-risk patients may exhibit a diminished efficacy to immunotherapy.

CapHLA: a comprehensive tool to predict peptide presentation and binding to HLA class I and class II

Human leukocyte antigen class I (HLA-I) and class II (HLA-II) proteins play an essential role in epitope binding and presentation to initiate an immune response. Accurate prediction of peptide-HLA (pHLA) binding and presentation is critical for developing effective immunotherapies. However, current tools can predict antigens exclusively for pHLA-I or pHLA-II, but not both; have constraints on peptide length; and commonly show unsatisfactory predictive accuracy. Here, we developed a convolution and attention-based model, CapHLA, trained with eluted ligand and binding affinity mass spectrometry data, to predict peptide presentation probability (PB) and binding affinities (BA) for HLA-I and HLA-II. In comparison with 11 other methods, CapHLA consistently showed improved performance in predicting pHLA BA and PB, particularly in HLA-II and non-classical peptide length datasets. Using CapHLA PB and BA predictions in combination with antigen expression level (EP) from transcriptomic data, we developed a neoantigen quality model for predicting immunotherapy response. In analyses of clinical response among 276 cancer patients given immunotherapy and overall survival in 7228 cancer patients, our neoantigen quality model outperformed other genetics-based models in predicting response to checkpoint inhibitors and patient prognosis. This study provides a versatile neoantigen screening tool, illustrating the prognostic value of neoantigen quality.

Sintilimab plus decitabine for higher-risk treatment-naïve myelodysplastic syndromes: efficacy, safety, and biomarker analysis of a phase II, single-arm trial

BACKGROUND

Immunotherapy combined with azacitidine was feasible in higher-risk myelodysplastic syndromes (MDSs) with limited sample size of treatment-naïve patients, while the optimization of treatment strategies, including the optimal immune checkpoint inhibitor and hypomethylating agent and possible benefiting population, remained undefined. This study first evaluates the efficacy and safety of sintilimab, a PD-1 blockade, plus decitabine in treatment-naïve higher-risk MDS patients and investigates biomarkers for predicting treatment response.

METHODS

In this phase II, single-arm trial (ChiCTR2100044393), treatment-naïve higher-risk MDS patients with an International Prognostic Scoring System-Revised score >3.5 received sintilimab (200 mg, days 1 and 22) and decitabine (20 mg/m2, day 1-5) over 6-week cycles. The primary endpoint was the overall response rate (ORR), including complete remission (CR), partial remission (PR) or marrow CR.

RESULTS

A total of 54 eligible patients were enrolled and treated, with 25 (46.3%) having very high-risk MDS. Among 53 evaluable patients, the ORR was 77.4% (n=41), including 26.4% CR (n=14). The overall clinical improvement rate (CR, PR, marrow CR or hematological improvement) reached 81.1%. With a median follow-up of 20.0 months, the median event-free survival was 23 months with 12 progressing to acute myeloid leukemia. Median overall survival was not reached. Treatment was generally well tolerated, with hematologic toxicities being the most common adverse events. Biomarker analysis highlighted a negative correlation between T cell exhaustion markers, particularly TIM-3 and PD-1, with ORR.

CONCLUSIONS

The combination of sintilimab and decitabine shows promise efficacy for higher-risk MDS, with a favorable safety profile. The potential predictive value of T cell exhaustion biomarkers might help screen the possible benefiting population.

TRIAL REGISTRATION NUMBER

ChiCTR210044393.

PRF1 as a prognostic gene for lymphatic metastasis in skin melanoma

BACKGROUND

Melanoma is a highly aggressive tumor, predominantly found in the skin, recognized as skin cutaneous melanoma (SKCM). Lymph node metastasis is commonly used as the route of metastasis in SKCM, necessitating the discovery of prognostic genes associated with this process for improved prognosis.

METHODS

The prognostic significance of lymph node metastasis in SKCM was assessed through Kaplan-Meier analysis in SEER and TCGA-SKCM datasets. Prognostic genes were identified and a prognostic risk model was constructed Enrichment analysis and immune cell infiltration analysis were also carried out.Moreover, a validation in vitro and in vivo were conducted by CCK8,flow cytometry, transwell and animal study.

RESULTS

The Kaplan-Meier survival curve revealed that patients with lymph node metastasis had a worse prognosis compared to those without. FCGR3B and PRF1 were screened by TCGA analysis.Additionally, significant differences in nine immune cell types were observed between the two risk groups. Notably, a strong positive association with CD8 T cells and a negative relationship with M2 macrophages were exhibited by PRF1. The validation of our nomogram were conducted in vitro and in vivo, and the results showed the correlations between CD8+ T cell and PRF1.

CONCLUSION

In summary, two prognostic genes (FCGR3B and PRF1) were identified, and a prognostic risk model was developed for SKCM. These findings provide a novel approach for the diagnosis and treatment of SKCM.

MFAP3L predicts tumor microenvironment status, molecular subtypes and clinical benefit in patients with bladder cancer

Bladder cancer (BLCA), ranking as the tenth most prevalent malignancy globally, imposes a substantial public health and socio-economic challenge. Despite ongoing efforts by urologists to identify novel molecular subtypes and treatment paradigms, the intrinsic heterogeneity of BLCA continues to obstruct the efficacy of current diagnostic and therapeutic evaluations, leaving a gap in the comprehensive management of BLCA. This necessitates an in-depth investigation into the BLCA tumor microenvironment (TME) to identify pivotal molecules like MFAP3L. Our research concentrated on MFAP3L, commencing with a pan-cancer analysis of its immune profile. We discovered that MFAP3L exhibits a significant negative correlation with numerous immune components and markers in BLCA, a trend not observed in other cancer types. Within the TCGA-BLCA cohort, patients were classified into High-MFAP3L and Low-MFAP3L groups according to their MFAP3L transcript levels. Our exploration into the BLCA TME delved into immune infiltration, molecular subtype patterns, and treatment preferences within these MFAP3L groups. High MFAP3L expression was linked to favorable prognoses, luminal subtypes, and low immune infiltration, inversely associated with various immune molecules and characteristics. Additionally, high MFAP3L expressors exhibited diminished immune checkpoint levels, suggesting enhanced immunotherapy tolerance and sensitivity to oncogenic pathway targeting. Conversely, low MFAP3L expression correlated with poor outcomes, basal subtypes, increased immune infiltration, and heightened gene mutation rates, alongside sensitivity to radiotherapy, EGFR-targeted treatments, and immunotherapy. Hence, MFAP3L emerges as a critical yet underexplored gene in BLCA, offering insights into immune status within the TME and aiding in molecular subtyping and therapeutic decision-making.

Integrative multi-omics analysis reveals the role of tumor-associated endothelial cells and their signature in prognosis of intrahepatic cholangiocarcinoma

This study aims to investigate the interplay between tumor-associated endothelial cells (TECs) and immune cells within the tumor microenvironment (TME) and its impact on tumor prognosis. We conducted single-cell RNA sequencing (scRNA-seq) of tumor, normal, and lymph node tissues obtained from intrahepatic cholangiocarcinoma (ICC) patients to reveal the role of TECs in tumor angiogenesis and their significant heterogeneity. Meanwhile, we identified genes highly expressed in TECs and constructed TEC signatures (TEC.Sig). Next, we calculated TEC scores of samples based on TEC.Sig. Patients with higher TEC scores exhibited a higher frequency of KRAS mutations, which was associated with increased infiltration of neutrophils and immature dendritic cells (iDCs), and decreased numbers of natural killer (NK), CD4 + T, and CD8 + T effector memory (Tem) cells, indicating an inflammation-dominated immunosuppressive phenotype. In contrast, BAP1 mutations and CXCL12 overexpression showed a contrasting trend. Spatial transcriptomics analysis and histological experiments further confirmed that TECs interacted with various tumor-killing immune cells through the CXCL12/CXCR4 axis. Multiple tumor immunotherapy datasets confirmed that the TEC.Sig could predict patient responses to immunotherapy. The TEC score is a promising and reliable biomarker for predicting genetic mutations and prognosis in ICC patients. Enhancing the regulation of the CXCL12/CXCR4 signaling pathway may represent a potential novel therapeutic target for ICC treatment.

A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade

Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance frequently develops. To explore ICB resistance mechanisms, we develop Immunotherapy Resistance cell-cell Interaction Scanner (IRIS), a machine learning model aimed at identifying cell-type-specific tumor microenvironment ligand-receptor interactions relevant to ICB resistance. Applying IRIS to deconvolved transcriptomics data of the five largest melanoma ICB cohorts, we identify specific downregulated interactions, termed resistance downregulated interactions (RDI), as tumors develop resistance. These RDIs often involve chemokine signaling and offer a stronger predictive signal for ICB response compared to upregulated interactions or the state-of-the-art published transcriptomics biomarkers. Validation across multiple independent melanoma patient cohorts and modalities confirms that RDI activity is associated with CD8 + T cell infiltration and highly manifested in hot/brisk tumors. This study presents a strongly predictive ICB response biomarker, highlighting the key role of downregulating chemotaxis-associated ligand-receptor interactions in inhibiting lymphocyte infiltration in resistant tumors.

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.

Immunogenic cell death signatures from on-treatment tumor specimens predict immune checkpoint therapy response in metastatic melanoma

Melanoma is a highly malignant form of skin cancer that typically originates from abnormal melanocytes. Despite significant advances in treating metastatic melanoma with immune checkpoint blockade (ICB) therapy, a substantial number of patients do not respond to this treatment and face risks of recurrence and metastasis. This study collected data from multiple datasets, including cohorts from Riaz et al., Gide et al., MGH, and Abril-Rodriguez et al., focusing on on-treatment samples during ICB therapy. We used the single-sample gene set enrichment analysis (ssGSEA) method to calculate immunogenic cell death scores (ICDS) and employed an elastic network algorithm to construct a model predicting ICB efficacy. By analyzing 18 ICD gene signatures, we identified 9 key ICD gene signatures that effectively predict ICB treatment response for on-treatment metastatic melanoma specimens. Results showed that patients with high ICD scores had significantly higher response rates to ICB therapy compared to those with low ICD scores. ROC analysis demonstrated that the AUC values for both the training and validation sets were around 0.8, indicating good predictive performance. Additionally, survival analysis revealed that patients with high ICD scores had longer progression-free survival (PFS). This study used an elastic network algorithm to identify 9 ICD gene signatures related to the immune response in metastatic melanoma. These gene features can not only predict the efficacy of ICB therapy but also provide references for clinical decision-making. The results indicate that ICD plays an important role in metastatic melanoma immunotherapy and that expressing ICD signatures can more accurately predict ICB treatment response and prognosis for on-treatment metastatic melanoma specimens, thus providing a basis for personalized treatment.

A Predictive Network-Based Immune Checkpoint Blockade Immunotherapeutic Signature Optimizing Patient Selection and Treatment Strategies

Immune checkpoint blockade (ICB) therapy has brought significant advancements to the field of oncology. However, the diverse responses among patients highlight the need for more accurate predictive tools. In this study, insights are drawn from tumor-immunology pathways, and a novel network-based ICB immunotherapeutic signature, termed ICBnetIS, is constructed. The signature is derived from advanced biological network-based computational strategies involving co-expression networks and molecular interactions networks. The efficacy of ICBnetIS is established through its association with enhanced patient survival and a robust immune response characterized by diverse immune cell infiltration and active anti-tumor immune pathways. The validation process positions ICBnetIS as an effective tool in predicting responses to ICB therapy, analyzing ICB data from a broad collection of over 700 samples from multiple cancer types of more than 15 datasets. It achieves an aggregated prediction AUC of 0.784, which outperforms the other nine renowned immunotherapeutic signatures, indicating the superior predictive capability of ICBnetIS. To sum up, the findings suggest ICBnetIS as a potent tool in predicting ICB therapy responses, offering significant implications for patient selection and treatment optimization in oncology. The study highlights the role of ICBnetIS in advancing personalized treatment strategies, potentially transforming the clinical landscape of ICB therapy.

Prediction of immunotherapy response using mutations to cancer protein assemblies

While immune checkpoint inhibitors have revolutionized cancer therapy, many patients exhibit poor outcomes. Here, we show immunotherapy responses in bladder and non-small cell lung cancers are effectively predicted by factoring tumor mutation burden (TMB) into burdens on specific protein assemblies. This approach identifies 13 protein assemblies for which the assembly-level mutation burden (AMB) predicts treatment outcomes, which can be combined to powerfully separate responders from nonresponders in multiple cohorts (e.g., 76% versus 37% bladder cancer 1-year survival). These results are corroborated by (i) engineered disruptions in the predictive assemblies, which modulate immunotherapy response in mice, and (ii) histochemistry showing that predicted responders have elevated inflammation. The 13 assemblies have diverse roles in DNA damage checkpoints, oxidative stress, or Janus kinase/signal transducers and activators of transcription signaling and include unexpected genes (e.g., PIK3CG and FOXP1) for which mutation affects treatment response. This study provides a roadmap for using tumor cell biology to factor mutational effects on immune response.

Integrative analysis of pan-cancer single-cell data reveals a tumor ecosystem subtype predicting immunotherapy response

Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to improved responses to immunotherapy. Then, a novel immunotherapy-responsive ecotype signature (IRE.Sig) was established and validated through the analysis of pan-cancer data. Utilizing IRE.Sig, machine learning models successfully predicted ICI responses in both validation and testing cohorts, achieving area under the curve (AUC) values of 0.72 and 0.71, respectively. Furthermore, using 5 CRISPR screening cohorts, we identified several potential drugs that may augment the efficacy of ICI. We also elucidated the candidate cellular biomarkers of response to the combined treatment of pembrolizumab plus eribulin in breast cancer. This signature has the potential to serve as a valuable tool for patients in selecting appropriate immunotherapy treatments.

A Comprehensive Proteogenomic and Spatial Analysis of Innate and Acquired Resistance of Metastatic Melanoma to Immune Checkpoint Blockade Therapies

While a subset of patients with metastatic melanoma achieves durable responses to immune checkpoint blockade (ICB) therapies, the majority ultimately exhibit either innate or acquired resistance to these treatments. However, the molecular mechanisms underlying resistance to ICB therapies remain elusive and are warranted to elucidate. Here, we comprehensively investigated the tumor and tumor immune microenvironment (TIME) of paired pre- and post-treatment tumor specimens from metastatic melanoma patients who were primary or secondary resistance to anti-CTLA-4 and/or anti-PD-1/PD-L1 therapies. Differentially expressed gene (DEG) analysis and single-sample gene set enrichment analysis (ssGSEA) with transcriptomic data identified cell cycle and c-MYC signaling as pathway-based resistance signatures. And weighted gene co-expression network analysis (WGCNA) revealed the activation of a cross-resistance meta-program involving key signaling pathways related to tumor progression in ICB resistant melanoma. Moreover, spatially-resolved, image-based immune monitoring analysis by using NanoString's digital spatial profiling (DSP) and Cyclic Immunofluorescence (CyCIF) showed infiltration of suppressive immune cells in the tumor microenvironment of melanoma with resistance to ICB therapies. Our study reveals the molecular mechanisms underlying resistance to ICB therapies in patients with metastatic melanoma by conducting such integrated analyses of multi-dimensional data, and provides rationale for salvage therapies that will potentially overcome resistance to ICB therapies.

Statement of translational relevance

This study paves the way for the creation of innovative therapeutic strategies, aimed at subverting resistance to immune checkpoint blockade (ICB) therapies in metastatic melanoma patients. By unraveling the specific molecular mechanisms underlying resistance, scientists can design effective alternative treatments that target pathways such as pathways associated with cell cycle dysregulation and c-MYC signaling. Furthermore, through the application of advanced immune monitoring techniques such as NanoString Digital Spatial Profiling (DSP) and Cyclic Immunofluorescence (CyCIF), this study has significantly enriched our understanding of the tumor microenvironment. This enhanced characterization facilitates the discovery of potential biomarkers that may forecast a patient's response to ICB treatment. Ultimately, these advancements could potentially refine patient outcomes and foster the development of more personalized cancer treatments in the future.

Dissecting transcriptome signals of anti-PD-1 response in lung adenocarcinoma

Immune checkpoint blockades are actively adopted in diverse cancer types including metastatic melanoma and lung cancer. Despite of durable response in 20-30% of patients, we still lack molecular markers that could predict the patient responses reliably before treatment. Here we present a composite model for predicting anti-PD-1 response based on tumor mutation burden (TMB) and transcriptome sequencing data of 85 lung adenocarcinoma (LUAD) patients who received anti-PD-(L)1 treatment. We found that TMB was a good predictor (AUC = 0.81) for PD-L1 negative patients (n = 20). For PD-L1 positive patients (n = 65), we built an ensemble model of 100 XGBoost learning machines where gene expression, gene set activities and cell type composition were used as input features. The transcriptome-based models showed excellent accuracy (AUC > 0.9) and highlighted the contribution of T cell activities. Importantly, nonresponder patients with high prediction score turned out to have high CTLA4 expression, which suggested that neoadjuvant CTLA4 combination therapy might be effective for these patients. Our data and analysis results provide valuable insights into developing biomarkers and strategies for treating LUAD patients using immune checkpoint inhibitors.