Pan-cancer association of HLA gene expression with cancer prognosis and immunotherapy efficacy

TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology

Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning and incomplete omics inference. This model enhances multi-omics sample representation and empowers various downstream oncology tasks with incomplete multi-omics datasets. By employing interpretable learning, we characterize the contributions of distinct omics features to clinical outcomes. The TMO-Net model serves as a versatile framework for cross-modal multi-omics learning in oncology, paving the way for tumor omics-specific foundation models.

Pan-cancer analysis of CDKN2A alterations identifies a subset of gastric cancer with a cold tumor immune microenvironment

BACKGROUND

Although CDKN2A alteration has been explored as a favorable factor for tumorigenesis in pan-cancers, the association between CDKN2A point mutation (MUT) and intragenic deletion (DEL) and response to immune checkpoint inhibitors (ICIs) is still disputed. This study aims to determine the associations of CDKN2A MUT and DEL with overall survival (OS) and response to immune checkpoint inhibitors treatment (ICIs) among pan-cancers and the clinical features of CDKN2A-altered gastric cancer.

METHODS

This study included 45,000 tumor patients that underwent tumor sequencing across 33 cancer types from four cohorts, the MSK-MetTropism, MSK-IMPACT, OrigiMed2020 and TCGA cohorts. Clinical outcomes and genomic factors associated with response to ICIs, including tumor mutational burden, copy number alteration, neoantigen load, microsatellite instability, tumor immune microenvironment and immune-related gene signatures, were collected in pan-cancer. Clinicopathologic features and outcomes were assessed in gastric cancer. Patients were grouped based on the presence of CDKN2A wild type (WT), CDKN2A MUT, CDKN2A DEL and CDKN2A other alteration (ALT).

RESULTS

Our research showed that CDKN2A-MUT patients had shorter survival times than CDKN2A-WT patients in the MSK MetTropism and TCGA cohorts, but longer OS in the MSK-IMPACT cohort with ICIs treatment, particularly in patients having metastatic disease. Similar results were observed among pan-cancer patients with CDKN2A DEL and other ALT. Notably, CDKN2A ALT frequency was positively related to tumor-specific objective response rates to ICIs in MSK MetTropism and OrigiMed 2020. Additionally, individuals with esophageal carcinoma or stomach adenocarcinoma who had CDKN2A MUT had poorer OS than patients from the MSK-IMPACT group, but not those with adenocarcinoma. We also found reduced levels of activated NK cells, T cells CD8 and M2 macrophages in tumor tissue from CDKN2A-MUT or DEL pan-cancer patients compared to CDKN2A-WT patients in TCGA cohort. Gastric cancer scRNA-seq data also showed that CDKN2A-ALT cancer contained less CD8 T cells but more exhausted T cells than CDKN2A-WT cancer. A crucial finding of the pathway analysis was the inhibition of three immune-related pathways in the CDKN2A ALT gastric cancer patients, including the interferon alpha response, inflammatory response, and interferon gamma response.

CONCLUSIONS

This study illustrates the CDKN2A MUT and DEL were associated with a poor outcome across cancers. CDKN2A ALT, on the other hand, have the potential to be used as a biomarker for choosing patients for ICI treatment, notably in esophageal carcinoma and stomach adenocarcinoma.

Prognostic model incorporating immune checkpoint genes to predict the immunotherapy efficacy for lung adenocarcinoma: a cohort study integrating machine learning algorithms

This study aimed to develop and validate a nomogram based on immune checkpoint genes (ICGs) for predicting prognosis and immune checkpoint blockade (ICB) efficacy in lung adenocarcinoma (LUAD) patients. A total of 385 LUAD patients from the TCGA database and 269 LUAD patients in the combined dataset (GSE41272 + GSE50081) were divided into training and validation cohorts, respectively. Three different machine learning algorithms including random forest (RF), least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and support vector machine (SVM) were employed to select the predictive markers from 82 ICGs to construct the prognostic nomogram. The X-tile software was used to stratify patients into high- and low-risk subgroups based on the nomogram-derived risk scores. Differences in functional enrichment and immune infiltration between the two subgroups were assessed using gene set variation analysis (GSVA) and various algorithms. Additionally, three lung cancer cohorts receiving ICB therapy were utilized to evaluate the ability of the model to predict ICB efficacy in the real world. Five ICGs were identified as predictive markers across all three machine learning algorithms, leading to the construction of a nomogram with strong potential for prognosis prediction in both the training and validation cohorts (all AUC values close to 0.800). The patients were divided into high- (risk score ≥ 185.0) and low-risk subgroups (risk score < 185.0). Compared to the high-risk subgroup, the low-risk subgroup exhibited enrichment in immune activation pathways and increased infiltration of activated immune cells, such as CD8 + T cells and M1 macrophages (P < 0.05). Furthermore, the low-risk subgroup had a greater likelihood of benefiting from ICB therapy and longer progression-free survival (PFS) than did the high-risk subgroup (P < 0.05) in the two cohorts receiving ICB therapy. A nomogram based on ICGs was constructed and validated to aid in predicting prognosis and ICB treatment efficacy in LUAD patients.

Loss of heterozygosity impacts MHC expression on the immune microenvironment in CDK12-mutated prostate cancer

BACKGROUND

In prostate cancer (PCa), well-established biomarkers such as MSI status, TMB high, and PDL1 expression serve as reliable indicators for favorable responses to immunotherapy. Recent studies have suggested a potential association between CDK12 mutations and immunotherapy response; however, the precise mechanisms through which CDK12 mutation may influence immune response remain unclear. A plausible explanation for immune evasion in this subset of CDK12-mutated PCa may be reduced MHC expression.

RESULTS

Using genomic data of CDK12-mutated PCa from 48 primary and 10 metastatic public domain samples and a retrospective cohort of 53 low-intermediate risk primary PCa, we investigated how variation in the expression of the MHC genes affected associated downstream pathways. We classified the patients based on gene expression quartiles of MHC-related genes and categorized the tumors into "High" and "Low" expression levels. CDK12-mutated tumors with higher MHC-expressed pathways were associated with the immune system and elevated PD-L1, IDO1, and TIM3 expression. Consistent with an inflamed tumor microenvironment (TME) phenotype, digital cytometric analyses identified increased CD8 + T cells, B cells, γδ T cells, and M1 Macrophages in this group. In contrast, CDK12-mutated tumors with lower MHC expression exhibited features consistent with an immune cold TME phenotype and immunoediting. Significantly, low MHC expression was also associated with chromosome 6 loss of heterozygosity (LOH) affecting the entire HLA gene cluster. These LOH events were observed in both major clonal and minor subclonal populations of tumor cells. In our retrospective study of 53 primary PCa cases from this Institute, we found a 4% (2/53) prevalence of CDK12 mutations, with the confirmation of this defect in one tumor through Sanger sequencing. In keeping with our analysis of public domain data this tumor exhibited low MHC expression at the RNA level. More extensive studies will be required to determine whether reduced HLA expression is generally associated with primary tumors or is a specific feature of CDK12 mutated PCa.

CONCLUSIONS

These data show that analysis of CDK12 alteration, in the context of MHC expression levels, and LOH status may offer improved predictive value for outcomes in this potentially actionable genomic subgroup of PCa. In addition, these findings highlight the need to explore novel therapeutic strategies to enhance MHC expression in CDK12-defective PCa to improve immunotherapy responses.

Uncovering a novel DNA repair-related radiosensitivity model for evaluation of radiotherapy susceptibility in uterine corpus endometrial cancer

Background

Uterine corpus endometrial cancer (UCEC) exhibit heterogeneity in their DNA repair capacity, which can impact their response to radiotherapy. Our study aimed to identify potential DNA repair-related biomarkers for predicting radiation response in UCEC.

Methods

We conducted a thorough analysis of 497 UCEC samples obtained from TCGA database. Using LASSO-COX regression analysis, we constructed a radiosensitivity signature and subsequently divided patients into the radiosensitive (RS) and the radioresistant (RR) groups based on their radiosensitivity index. The GSVA and GSEA were performed to explore functional annotations. The CIBERSORT and ESTIMATE algorithms were utilized to investigate the immune infiltration status of the two groups. Additionally, we utilized the Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenotype Score (IPS), and pRRophetic algorithms to predict the effectiveness of different treatment modalities.

Results

We constructed a radiosensitivity index consists of four DNA repair-related genes. Patients in the RS group demonstrated significantly improved prognosis compared to patients in the RR group when treated with radiotherapy. We observed that the RS group exhibited a higher proportion of the POLE ultra-mutated subtype, while the RR group had a higher proportion of the copy number high subtype. GSVA enrichment analysis revealed that the RS group exhibited enrichment in DNA damage repair pathways. Notably, the RS group demonstrated a higher proportion of naïve B cells and follicular helper T cells, while regulatory T cells (Tregs) and memory B cells were more abundant in the RR group. Furthermore, patients in the RS-PD-L1-high subgroup exhibited enrichment in immune-related pathways and increased sensitivity to immunotherapy, which is likely to contribute to their improved prognosis. Additionally, we conducted in vitro experiments to validate the expression of radiosensitivity genes in non-radioresistant (AN3CA) and radioresistant (AN3CA/IR) endometrial cancer cells.

Conclusions

In conclusion, our research successfully constructed a radiosensitivity signature with robust predictive capacity. These findings shed light on the association between immune activation, PD-L1 expression, and the response to immunotherapy in the context of radiotherapy.

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

Background

Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition.

Method

To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy.

Results

We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity.

Conclusion

Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

Identification of an immune-related signature as a prognostic classifier for patients with early-stage head and neck squamous cell carcinoma

Background

Head and neck squamous cell carcinoma (HNSCC) is the most common type and accounts for 90% of all head and neck cancer cases. Despite advances in early diagnosis and treatment strategies-chemotherapy, surgical resection, and radiotherapy-5-year survival remains grim. For patients with early-stage HNSCC, accurately predicting clinical outcomes is challenging. Considering the pivotal role of the immune system in HNSCC, we developed a reliable immune-related gene signature (IRGS) and explored its predictive accuracy in patients with early-stage HNSCC.

Methods

We examined immune gene expression profiles and clinical information from 230 early-stage HNSCC specimens, including 100 cases from The Cancer Genome Atlas (TCGA), 49 cases from the Gene Expression Omnibus (GEO; GSE65858), and 81 cases from an independent clinical cohort. The prognostic signature was constructed using Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) Cox algorithm. We also explored the IRGS-related biological pathways and immune landscape using bioinformatics analysis.

Results

A nine-immune-gene signature was generated to significantly stratify patients into high and low-risk groups. High risk patients exhibited shorter survival time [hazard ratio (HR) =13.795, 95% confidence interval (CI): 3.275-58.109, P<0.001]. The signature demonstrated robust prognostic ability in the training and validation sets and could independently predict overall survival (OS) and relapse-free survival (RFS). Subsequently, the receiver operating characteristic (ROC) curve and C-index confirmed the signature's predictive accuracy compared to clinical parameters. Additionally, cases classified as low risk showed more immune cell infiltration than high-risk cases.

Conclusions

Our novel IRGS is a reliable and robust classifier for accurate patient stratification and prognostic evaluation. Future studies will attempt to affirm the signature's clinical application to early-stage HNSCC.

Multiple Immunomodulatory Strategies Based on Targeted Regulation of Proprotein Convertase Subtilisin/Kexin Type 9 and Immune Homeostasis against Hepatocellular Carcinoma

Immunotherapy is the most promising systemic therapy for hepatocellular carcinoma. However, the outcome remains poor. Proprotein convertase subtilisin/kexin type 9 (PCSK9) plays a role in altering cell-surface protein levels, potentially undermining the efficacy of immunotherapy against tumors. This highlights its potential as a target for antitumor therapy. Herein, CaCO3-based nanoparticles coencapsulated with DOX, an immunogenic cell death (ICD) inducer, and evolocumab was developed to enhanced the efficacy of immunotherapy. The obtained DOX/evolocumab-loaded CaCO3 nanoparticle (named DECP) exhibits a good capacity of acid neutralization and causes ICD of cancer cells. In addition, DECP is able to evaluate the cell-surface level of MHC-I, a biomarker that correlates positively with patients' overall survival. Upon intravenous injection, DECP accumulates within the tumor site, leading to growth inhibition of hepa1-6 bearing subcutaneous tumors. Specifically, DECP treatment causes augmented ratios of matured dendritic cells, tumor-infiltrating CD8+ T cells and natural killing cells, while concurrently depleting Foxp3+ regulatory T cells. Peritumoral delivery of DECP enhances the immune response of distant tumors and exhibits antitumor effects when combined with intravenous αPD-L1 therapy in a bilateral tumor model. This study presents CaCO3-based nanoparticles with multiple immunomodulatory strategies against hepatocellular carcinoma by targeting PCSK9 inhibition and modulating immune homeostasis in the unfavorable TME.

Novel application of the ferroptosis-related genes risk model associated with disulfidptosis in hepatocellular carcinoma prognosis and immune infiltration

Hepatocellular carcinoma (HCC) stands as the prevailing manifestation of primary liver cancer and continues to pose a formidable challenge to human well-being and longevity, owing to its elevated incidence and mortality rates. Nevertheless, the quest for reliable predictive biomarkers for HCC remains ongoing. Recent research has demonstrated a close correlation between ferroptosis and disulfidptosis, two cellular processes, and cancer prognosis, suggesting their potential as predictive factors for HCC. In this study, we employed a combination of bioinformatics algorithms and machine learning techniques, leveraging RNA sequencing data, mutation profiles, and clinical data from HCC samples in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) databases, to develop a risk prognosis model based on genes associated with ferroptosis and disulfidptosis. We conducted an unsupervised clustering analysis, calculating a risk score (RS) to predict the prognosis of HCC using these genes. Clustering analysis revealed two distinct HCC clusters, each characterized by significantly different prognostic and immune features. The median RS stratified HCC samples in the TCGA, GEO, and ICGC cohorts into high-and low-risk groups. Importantly, RS emerged as an independent prognostic factor in all three cohorts, with the high-risk group demonstrating poorer prognosis and a more active immunosuppressive microenvironment. Additionally, the high-risk group exhibited higher expression levels of tumor mutation burden (TMB), immune checkpoints (ICs), and human leukocyte antigen (HLA), suggesting a heightened responsiveness to immunotherapy. A cancer stem cell infiltration analysis revealed a higher similarity between tumor cells and stem cells in the high-risk group. Furthermore, drug sensitivity analysis highlighted significant differences in response to antitumor drugs between the two risk groups. In summary, our risk prognostic model, constructed based on ferroptosis-related genes associated with disulfidptosis, effectively predicts HCC prognosis. These findings hold potential implications for patient stratification and clinical decision-making, offering valuable theoretical insights in this field.

Integrative HLA typing of tumor and adjacent normal tissue can reveal insights into the tumor immune response

BACKGROUND

The HLA complex is the most polymorphic region of the human genome, and its improved characterization can help us understand the genetics of human disease as well as the interplay between cancer and the immune system. The main function of HLA genes is to recognize "non-self" antigens and to present them on the cell surface to T cells, which instigate an immune response toward infected or transformed cells. While sequence variation in the antigen-binding groove of HLA may modulate the repertoire of immunogenic antigens presented to T cells, alterations in HLA expression can significantly influence the immune response to pathogens and cancer.

METHODS

RNA sequencing was used here to accurately genotype the HLA region and quantify and compare the level of allele-specific HLA expression in tumors and patient-matched adjacent normal tissue. The computational approach utilized in the study types classical and non-classical Class I and Class II HLA alleles from RNA-seq while simultaneously quantifying allele-specific or personalized HLA expression. The strategy also uses RNA-seq data to infer immune cell infiltration into tumors and the corresponding immune cell composition of matched normal tissue, to reveal potential insights related to T cell and NK cell interactions with tumor HLA alleles.

RESULTS

The genotyping method outperforms existing RNA-seq-based HLA typing tools for Class II HLA genotyping. Further, we demonstrate its potential for studying tumor-immune interactions by applying the method to tumor samples from two different subtypes of breast cancer and their matched normal breast tissue controls.

CONCLUSIONS

The integrative RNA-seq-based HLA typing approach described in the study, coupled with HLA expression analysis, neoantigen prediction and immune cell infiltration, may help increase our understanding of the interplay between a patient's tumor and immune system; and provide further insights into the immune mechanisms that determine a positive or negative outcome following treatment with immunotherapy such as checkpoint blockade.

Class II HLA-DRB4 is a predictive biomarker for survival following immunotherapy in metastatic non-small cell lung cancer

Immune checkpoint inhibitors (ICI) are important treatment options for metastatic non-small cell lung cancer (mNSCLC). However, not all patients benefit from ICIs and can experience immune-related adverse events (irAEs). Limited understanding exists for germline determinants of ICI efficacy and toxicity, but Human Leukocyte Antigen (HLA) genes have emerged as a potential predictive biomarker. We performed HLA typing on 85 patients with mNSCLC, on ICI therapy and analyzed the impact of HLA Class II genotype on progression free survival (PFS), overall survival (OS), and irAEs. Most patients received pembrolizumab (83.5%). HLA-DRB4 genotype was seen in 34/85 (40%) and its presence correlated with improved OS in both univariate (p = 0.022; 26.3 months vs 10.2 months) and multivariate analysis (p = 0.011, HR 0.49, 95% CI [0.29, 0.85]). PFS did not reach significance (univariate, p = 0.12, 8.2 months vs 5.1 months). Eleven patients developed endocrine irAEs. HLA-DRB4 was the predominant genotype among these patients (9/11, 81.8%). Cumulative incidence of endocrine irAEs was higher in patients with HLA-DRB4 (p = 0.0139). Our study is the first to suggest that patients with metastatic NSCLC patients on ICI therapy with HLA-DRB4 genotype experience improved survival outcomes. Patients with HLA-DRB4 had the longest median OS (26.3 months). Additionally, we found a correlation between HLA-DRB4 and the occurrence of endocrine irAEs.

Loss of Human Leukocyte Antigen and Immune Escape in Head and Neck Cancer

OBJECTIVES/HYPOTHESIS

Cancer cells evade recognition by the immune system to survive. Head and neck squamous cell carcinoma (HNSCC) is characterized by high levels of immune infiltration and mutation-associated neoantigens; therefore, immune evasion is likely to be an important mechanism in HNSCC tumorigenesis and progression. A commonly employed mechanism of immune evasion is downregulation of human leukocyte antigen (HLA) or loss of heterozygosity (LOH) in tumor cells. The objective of this study was to integrate multi-dimensional genomic and transcriptomic data from HNSCC tumors to better understand the clinical and immunologic implications of HLA LOH.

STUDY TYPE/DESIGN

Cross-sectional integrated clinical and genomic analysis.

METHODS

Whole-exome sequencing and RNA-sequencing data from 522 tumors profiled in The Cancer Genome Atlas HNSCC cohort were analyzed and integrated with secondary analyses including immune cell deconvolution data. Associations were analyzed with categorical hypothesis testing and multivariable logistic and Cox regression.

RESULTS

HLA LOH was a prevalent event that was identified in 53% of HNSCC tumors; in many cases, more than one class I HLA gene was targeted for LOH. HLA LOH was more common in advanced-stage tumors. Tumors with somatic HLA LOH had tumor microenvironments defined by decreased lymphocyte and T cell infiltration.

CONCLUSIONS

HLA LOH is one of the most prevalent genetic alterations in HNSCC, and is associated with a cold immune microenvironment, suggesting that HLA LOH is a means of immune evasion. It may have value as a predictive biomarker or potential as a cancer cell-specific therapeutic target.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:160-165, 2024.

A novel prognostic and therapeutic target biomarker based on complement-related gene signature in gastric cancer

Background

Gastric cancer (GC) is one of the most prevalent cancer types that reduce human life expectancy. The current tumor-node-metastasis (TNM) staging system is inadequate in identifying higher or lower risk of GC patients because of tumor heterogeneity. Research shows that complement plays a dual role in the tumor development and progression of GC.

Methods

We downloaded GC data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A complement-related risk signature was constructed through bioinformatics analysis. Subsequently, the predictive ability of this signature was validated with GSE84437 dataset, and a nomogram integrating risk score and common clinical factors was established. Besides, we evaluated the association of risk score with the immune and stromal cell infiltration in TCGA. Furthermore, immunotherapy response prediction and drug susceptibility analysis were conducted to access the ability of the risk signature in predicting the therapeutic effect.

Results

A complement-related gene (CRG) signature, based on six genes (SPLG, C9, ITIH1, ZFPM2, CD36, and SERPINE1), was established. In both the training and validation sets, the overall survival of GC patients in the high-risk group was lower than that of the low-risk group, and the nomogram to predict the 1-, 2-, and 3-year survival rates of GC patients was developed. In addition, CIBERSORT algorithm showed the high-risk patients had higher levels of immune cell infiltration than low-risk patients, and the ESTIMATE results implied that the high-risk group had more stromal component in tumor microenvironment. Besides, compared to the low-risk group, there were higher expressions of most immune checkpoint genes and HLA genes in the high-risk group, and the high-risk patients showed higher sensitivity to the chemotherapy and targeted drugs (axitinib, dasatinib, pazopanib, saracatinib, sunitinib and temsirolimus).

Conclusions

The novel CRG signature may act as a reliable, efficient tool for prognostic prediction and treatment guidance in future clinical practice.

Identification and validation of eight lysosomes-related genes signatures and correlation with immune cell infiltration in lung adenocarcinoma

Lung cancer is the leading cause of cancer-related death. Lysosomes are key degradative compartments that maintain protein homeostasis. In current study, we aimed to construct a lysosomes-related genes signature to predict the overall survival (OS) of patients with Lung Adenocarcinoma (LUAD). Differentially expressed lysosomes-related genes (DELYs) were analyzed using The Cancer Genome Atlas (TCGA-LUAD cohort) database. The prognostic risk signature was identified by Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox proportional hazards regression and multivariate Cox analysis. The predictive performance of the signature was assessed by Kaplan-Meier curves and Time-dependent receiver operating characteristic (ROC) curves. Gene set variant analysis (GSVA) was performed to explore the potential molecular biological function and signaling pathways. ESTIMATE and single sample gene set enrichment analysis (ssGSEA) were applied to estimate the difference of tumor microenvironment (TME) between the different risk subtypes. An eight prognostic genes (ACAP3, ATP8B3, BTK, CAV2, CDK5R1, GRIA1, PCSK9, and PLA2G3) signature was identified and divided patients into high-risk and low-risk groups. The prognostic signature was an independent prognostic factor for OS (HR > 1, p < 0.001). The molecular function analysis suggested that the signature was significantly correlated with cancer-associated pathways, including angiogenesis, epithelial mesenchymal transition, mTOR signaling, myc-targets. The low-risk patients had higher immune cell infiltration levels than high-risk group. We also evaluated the response to chemotherapeutic, targeted therapy and immunotherapy in high- and low-risk patients with LUAD. Furthermore, we validated the expression of the eight gene expression in LUAD tissues and cell lines by qRT-PCR. LYSscore signature provide a new modality for the accurate diagnosis and targeted treatment of LUAD and will help expand researchers' understanding of new prognostic models.

An effective disease diagnostic model related to pyroptosis in ischemic cardiomyopathy

Pyroptosis is involved in ischemic cardiomyopathy (ICM). The study aimed to investigate the pyroptosis-related genes and clarify their diagnostic value in ICM. The bioinformatics method identified the differential pyroptosis genes between the normal control and ICM samples from online datasets. Then, protein-protein interaction (PPI) and function analysis were carried out to explore the function of these genes. Following, subtype analysis was performed using ConsensusClusterPlus, functions, immune score, stromal score, immune cell proportion and human leukocyte antigen (HLA) genes between subtypes were investigated. Moreover, optimal pyroptosis genes were selected using the least absolute shrinkage and selection operator (LASSO) analysis to construct a diagnostic model and evaluate its effectiveness using receiver operator characteristic (ROC) analysis. Twenty-one differential expressed pyroptosis genes were identified, and these genes were related to immune and pyroptosis. Subtype analysis identified two obvious subtypes: sub-1 and sub-2. And LASSO identified 13 optimal genes used to construct the diagnostic model. The diagnostic model in ICM diagnosis with the area under ROC (AUC) was 0.965. Our results suggested that pyroptosis was tightly associated with ICM.

Comprehensive analysis of cuproptosis-associated LncRNAs predictive value and related CeRNA network in acute myeloid leukemia

Background

Acute myeloid leukemia (AML) is characterized by a high recurrence and mortality rate. Cuproptosis is involved in cell death regulation in in a variety of solid tumors. Long non-coding RNAs that regulate cuproptosis genes in the pathogenesis of acute leukemia have yet to be explored.

Methods

First, cuproptosis genes with distinct expression levels were discovered by contrasting AML with normal samples from the TCGA and GTEx cohorts. Pearson correlation and univariate Cox-regression analysis were performed to identify cuproptosis-associated lncRNAs with significant prognostic values. Then the least absolute shrinkage and selection operator (LASSO) Cox regression was utilized to establish a multi-gene signature to predict AML prognosis. Next, Kaplan-Meier estimator, receiver operating characteristic curve, and a nomogram were performed to evaluate the predictive capacity of the risk signature. Functional enrichment analyses were employed to assess their function. Moreover, qRT-PCR testing of lncRNA expression in AML samples was conducted. The competing endogenous RNA (ceRNA) network was constructed to find the target genes.

Results

A risk model based on the signature of three cuproptosis-associated lncRNAs was developed. The results showed that the model possessed excellent prognostic potential. The nomogram raised the accuracy in predicting AML survival. In addition, functional enrichment analyses demonstrated an enrichment of inflammatory and immune-related pathways. Moreover, correlations between the risk signature and clinicopathological variables, tumor mutational burden, RNA stemness score, immune profile, and drug sensitivity were observed. Furthermore, we discovered that TRAF3IP2-AS1 may function as a ceRNA to regulate cuproptosis and ferroptosis gene expression.

Conclusion

The risk signature established in this study could serve as a reliable biosignature for AML prognosis. And the findings presented here may facilitate research on cuproptosis in AML.

Prediction of Tumor Microenvironment Characteristics and Treatment Response in Lung Squamous Cell Carcinoma by Pseudogene OR7E47P-related Immune Genes

OBJECTIVE

Pseudogenes are initially regarded as nonfunctional genomic sequences, but some pseudogenes regulate tumor initiation and progression by interacting with other genes to modulate their transcriptional activities. Olfactory receptor family 7 subfamily E member 47 pseudogene (OR7E47P) is expressed broadly in lung tissues and has been identified as a positive regulator in the tumor microenvironment (TME) of lung adenocarcinoma (LUAD). This study aimed to elucidate the correlation between OR7E47P and tumor immunity in lung squamous cell carcinoma (LUSC).

METHODS

Clinical and molecular information from The Cancer Genome Atlas (TCGA) LUSC cohort was used to identify OR7E47P-related immune genes (ORIGs) by weighted gene correlation network analysis (WGCNA). Based on the ORIGs, 2 OR7E47P clusters were identified using non-negative matrix factorization (NMF) clustering, and the stability of the clustering was tested by an extreme gradient boosting classifier (XGBoost). LASSO-Cox and stepwise regressions were applied to further select prognostic ORIGs and to construct a predictive model (ORPScore) for immunotherapy. The Botling cohorts and 8 immunotherapy cohorts (the Samstein, Braun, Jung, Gide, IMvigor210, Lauss, Van Allen, and Cho cohorts) were included as independent validation cohorts.

RESULTS

OR7E47P expression was positively correlated with immune cell infiltration and enrichment of immune-related pathways in LUSC. A total of 57 ORIGs were identified to classify the patients into 2 OR7E47P clusters (Cluster 1 and Cluster 2) with distinct immune, mutation, and stromal programs. Compared to Cluster 1, Cluster 2 had more infiltration by immune and stromal cells, lower mutation rates of driver genes, and higher expression of immune-related proteins. The clustering performed well in the internal and 5 external validation cohorts. Based on the 7 ORIGs (HOPX, STX2, WFS, DUSP22, SLFN13, GGCT, and CCSER2), the ORPScore was constructed to predict the prognosis and the treatment response. In addition, the ORPScore was a better prognostic factor and correlated positively with the immunotherapeutic response in cancer patients. The area under the curve values ranged from 0.584 to 0.805 in the 6 independent immunotherapy cohorts.

CONCLUSION

Our study suggests a significant correlation between OR7E47P and TME modulation in LUSC. ORIGs can be applied to molecularly stratify patients, and the ORPScore may serve as a biomarker for clinical decision-making regarding individualized prognostication and immunotherapy.

Pancancer analysis of the correlations of HS6ST2 with prognosis, tumor immunity, and drug resistance

HS6ST2 has ability to encodes a member of the heparan sulfate (HS) sulfotransferase gene family, which catalyze the transfer of sulfate to HS and a crucial regulator of cell growth, differentiation, adhesion, and migration. Although mounting evidence supports a vital role for HS6ST2 in tumorigenesis of some cancers, no pan-cancer analysis of HS6ST2 has been reported. Therefore, we aimed to explore the prognostic value of HS6ST2 in 33 cancer types and investigate its potential immune function. Based on data from The Cancer Genome Atlas, Cancer Cell Lines Encyclopedia, Genotype Tissue Expression, and GSCA, we used a range of bioinformatics approaches to explore the potential carcinogenic role of HS6ST2, analysis of HS6ST2 and prognosis, DNA methylation, RNA methylation, microsatellite instability (MSI), tumor mutation burden (TMB), and immune cell infiltration in different tumors. The results show that HS6ST2 was highly expressed in most cancers but lower in Breast invasive carcinoma, Kidney Chromophobe, Kidney renal clear cell carcinoma, Kidney renal papillary cell carcinoma, and Uterine Corpus Endometrial Carcinoma. Moreover, HS6ST2 is positively or negatively associated with prognosis in different cancers. HS6ST2 expression was not only associated with MSI in 5 cancer types and associated with TMB in 10 cancer types, and it's significantly correlated with DNA methylation in 13 types of cancer, but it's correlated with RNA methylation related genes in most cancer. HS6ST2 expression was correlated with immune cell infiltration, immune-related genes, tumor immune microenvironment, and drug resistance in various cancers. Eventually, HS6ST2 was validated in human lung adenocarcinoma tissues. Our study reveals that HS6ST2 can function as a prognostic marker in various malignant tumors because of its role in tumorigenesis and tumor immunity.

Construction of prognostic risk markers for cervical cancer combined with anoikis-related genes and their clinical significance

CONTEXT

Several studies have demonstrated that anoikis affects the development, metastasis and prognosis of cancer.

AIMS

This study aimed to identify anoikis-related marker genes in cervical cancer (CC).

METHODS

Least absolute shrinkage and selection operator (LASSO) combined with Cox regression analysis was used to construct a prognostic model and analyse the independent prognostic ability of riskscore. Receiver operating characteristic curve (ROC) and survival curves were used to evaluate and verify the performance and accuracy of the model. The nomogram of CC prognostic model was drawn using riskscore combined with clinical information. We analysed the relationship between prognostic riskscore and immune infiltration level and analysed immunophenoscore. Finally, qRT-PCR assay was used to verify the feature genes.

KEY RESULTS

By Cox analysis, we found that the prognostic risk model could effectively predict the risk of CC in patients independently of other clinical factors. Both the levels of immune infiltration and the immunophenoscore were significantly lower in high-risk CC patients than those in low-risk patients, revealing that high-risk patients were likely to have bad response to immunotherapy. The qRT-PCR results of the feature genes were consistent with the results of gene expression in the database.

CONCLUSIONS

The prognostic model constructed, based on anoikis-related genes in CC, could predict the prognosis of CC patients.

IMPLICATIONS

The model described here can provide effective support for assessing prognostic risk and devising personalised protocols during clinical treatment.

Prognostic survival biomarkers of tumor-fused dendritic cell vaccine therapy in patients with newly diagnosed glioblastoma

Dendritic cell (DC)-based immunotherapy has been applied to glioblastoma (GBM); however, biomarkers informing response remain poorly understood. We conducted a phase I/IIa clinical trial investigating tumor-fused DC (TFDC) immunotherapy following temozolomide-based chemoradiotherapy in patients with newly diagnosed GBM and determined prognostic factors in patients receiving TFDC immunotherapy. Twenty-eight adult patients with GBM isocitrate dehydrogenase (IDH) wild-type (IDH-WT) were enrolled; 127 TFDC vaccine injections (4.5 ± 2.6 times/patient) were administered. Patients with GBM IDH-WT had a respectable 5-year survival rate (24%), verifying the clinical activity of TFDC immunotherapy, particularly against O6-methylguanine-DNA methyltransferase (MGMT) unmethylated GBM (5-year survival rate: 33%). To identify novel factors influencing overall survival (OS) in GBM IDH-WT treated with TFDC immunotherapy, clinical parameters were assessed and comprehensive molecular profiling involving transcriptome and exome analyses was performed. MGMT promoter methylation status, extent of tumor resection, and vaccine parameters (administration frequency, DC and tumor cell numbers, and fusion ratio) were not associated with survival following TFDC immunotherapy. Old age and pre- and post-operative Karnofsky performance status were significantly correlated with OS. Low HLA-A expression and lack of CCDC88A, KRT4, TACC2, and TONSL mutations in tumor cells were correlated with better prognosis. We validated the activity of TFDC immunotherapy against GBM IDH-WT, including chemoresistant, MGMT promoter unmethylated cases. The identification of molecular biomarkers predictive of TFDC immunotherapy efficacy in GBM IDH-WT will facilitate the design of and patient stratification in a phase-3 trial to maximize treatment benefits.

Update in Immunotherapy for Advanced Non-Small Cell Lung Cancer: Optimizing Treatment Sequencing and Identifying the Best Choices

The introduction of immunotherapy has brought about a paradigm shift in the management of advanced non-small cell lung cancer (NSCLC). It has not only significantly improved the prognosis of patients but has also become a cornerstone of treatment, particularly in those without oncogenic driver mutations. Immune checkpoint inhibitors (ICIs) play a crucial role in the treatment of lung cancer and can be classified into two main groups: Anti-cytotoxic T lymphocyte antigen-4 (Anti-CTLA-4) and anti-T-cell receptor programmed cell death-1 or its ligand (Anti-PD-1 and Anti-PD-L1). Certainly, the landscape of approved first line immunotherapeutic approaches has expanded to encompass monotherapy, immunotherapy-exclusive protocols, and combinations with chemotherapy. The complexity of decision-making in this realm arises due to the absence of direct prospective comparisons. However, a thorough analysis of the long-term efficacy and safety data derived from pivotal clinical trials can offer valuable insights into optimizing treatment for different patient subsets. Moreover, ongoing research is investigating emerging biomarkers and innovative therapeutic strategies that could potentially refine the current treatment approach even further. In this comprehensive review, our aim is to highlight the latest advances in immunotherapy for advanced NSCLC, including the mechanisms of action, efficacy, safety profiles, and clinical significance of ICI.

Antigen presentation in cancer - mechanisms and clinical implications for immunotherapy

Over the past decade, the emergence of effective immunotherapies has revolutionized the clinical management of many types of cancers. However, long-term durable tumour control is only achieved in a fraction of patients who receive these therapies. Understanding the mechanisms underlying clinical response and resistance to treatment is therefore essential to expanding the level of clinical benefit obtained from immunotherapies. In this Review, we describe the molecular mechanisms of antigen processing and presentation in tumours and their clinical consequences. We examine how various aspects of the antigen-presentation machinery (APM) shape tumour immunity. In particular, we discuss genomic variants in HLA alleles and other APM components, highlighting their influence on the immunopeptidomes of both malignant cells and immune cells. Understanding the APM, how it is regulated and how it changes in tumour cells is crucial for determining which patients will respond to immunotherapy and why some patients develop resistance. We focus on recently discovered molecular and genomic alterations that drive the clinical outcomes of patients receiving immune-checkpoint inhibitors. An improved understanding of how these variables mediate tumour-immune interactions is expected to guide the more precise administration of immunotherapies and reveal potentially promising directions for the development of new immunotherapeutic approaches.

Epigenetic drugs as new emerging therapeutics: What is the scale's orientation of application and challenges?

Epigenetics is the study of heritable changes in gene expression or function without altering the DNA sequence. Important factors are part of epigenetic events, such as methylation, DNA histone rearrangements, nucleosome transposition, and non-coding RNAs. Dysregulated epigenetic mechanics are associated with various cancers' initiation, development, and metastasis. It is known that the occurrence and development of cancer can be controlled by regulating unexpected epigenetic events. Epi-drugs are used singly or in combination with chemotherapy and enhance antitumor activity, reduce drug resistance, and stimulate the host immune response. Despite these benefits, epigenetic therapy as a single therapy or in combination with other drugs leads to adverse effects. This review article introduces and compares the advantages, disadvantages, and side effects of using these drugs for the first time since their introduction. Also, this article describes the mechanism of action of various epigenetic drugs. Recommendations for future use of epigenetic drugs as cancer therapeutics are suggested as an overall conclusion.

Durable response of lung carcinoma patients to EGFR tyrosine kinase inhibitors is determined by germline polymorphisms in some immune-related genes

BACKGROUND

Non-small cell lung cancer is a very poor prognosis disease. Molecular analyses have highlighted several genetic alterations which may be targeted by specific therapies. In clinical practice, progression-free survival on EGFR TKI treatment is between 12 and 14 months. However, some patients progress rapidly in less than 6 months, while others remain free of progression for 16 months or even longer during EGFR TKI treatment.

METHODS

We sequenced tumor exomes from 135 lung cancer patients (79 with EGFR-wildtype (WT), 56 with EGFR-mutant tumors) enrolled in the ALCAPONE trial (genomic analysis of lung cancers by next generation sequencing for personalized treatment).

RESULTS

Some germline polymorphisms were enriched in the EGFR-mutant subset compared to EGFR-WT tumors or to a reference population. However, the most interesting observation was the negative impact of some germline SNPs in immunity-related genes on survival on EGFR TKI treatment. Indeed, the presence of one of three particular SNPs in the HLA-DRB5 gene was associated with a decreased PFS on EGFR TKI. Moreover, some SNPs in the KIR3DL1 and KIR3DL2 genes were linked to a decrease in both progression-free and overall survival of patients with EGFR-mutant tumors.

CONCLUSION

Our data suggest that SNPs in genes expressed by immune cells may influence the response to targeted treatments, such as EGFR TKIs. This indicates that the impact of these cells may not be limited to modulating the response to immunotherapies. Further studies are needed to determine the exact mechanisms underlying this influence and to identify the associated predictive and prognostic markers that would allow to refine treatments and so improve lung cancer patient outcomes.

TRIAL REGISTRATION

NCT02281214: NGS Genome Analysis in Personalization of Lung Cancer Treatment (ALCAPONE).

Prediction of Prognosis, Immunotherapy and Chemotherapy with an Immune-Related Risk Score Model for Endometrial Cancer

Endometrial cancer (EC) is the most common gynecologic cancer. The overall survival remains unsatisfying due to the lack of effective treatment screening approaches. Immunotherapy as a promising therapy has been applied for EC treatment, but still fails in many cases. Therefore, there is a strong need to optimize the screening approach for clinical treatment. In this study, we employed co-expression network (GCN) analysis to mine immune-related GCN modules and key genes and further constructed an immune-related risk score model (IRSM). The IRSM was proved effective as an independent predictor of poor prognosis. The roles of IRSM-related genes in EC were confirmed by IHC. The molecular basis, tumor immune microenvironment and clinical characteristics of the IRSM were revealed. Moreover, the IRSM effectiveness was associated with immunotherapy and chemotherapy. Patients in the low-risk group were more sensitive to immunotherapy and chemotherapy than those in the high-risk group. Interestingly, the patients responding to immunotherapy were also more sensitive to chemotherapy. Overall, we developed an IRSM which could be used to predict the prognosis, immunotherapy response and chemotherapy sensitivity of EC patients. Our analysis not only improves the treatment of EC but also offers targets for personalized therapeutic interventions.

The current advances of lncRNAs in breast cancer immunobiology research

Breast cancer is the most frequently diagnosed malignancy and the leading cause of cancer-related death in women worldwide. Breast cancer development and progression are mainly associated with tumor-intrinsic alterations in diverse genes and signaling pathways and with tumor-extrinsic dysregulations linked to the tumor immune microenvironment. Significantly, abnormal expression of lncRNAs affects the tumor immune microenvironment characteristics and modulates the behavior of different cancer types, including breast cancer. In this review, we provide the current advances about the role of lncRNAs as tumor-intrinsic and tumor-extrinsic modulators of the antitumoral immune response and the immune microenvironment in breast cancer, as well as lncRNAs which are potential biomarkers of tumor immune microenvironment and clinicopathological characteristics in patients, suggesting that lncRNAs are potential targets for immunotherapy in breast cancer.

A novel integrated approach to predicting cancer immunotherapy efficacy

Immunotherapies have revolutionized cancer treatment modalities; however, predicting clinical response accurately and reliably remains challenging. Neoantigen load is considered as a fundamental genetic determinant of therapeutic response. However, only a few predicted neoantigens are highly immunogenic, with little focus on intratumor heterogeneity (ITH) in the neoantigen landscape and its link with different features in the tumor microenvironment. To address this issue, we comprehensively characterized neoantigens arising from nonsynonymous mutations and gene fusions in lung cancer and melanoma. We developed a composite NEO2IS to characterize interplays between cancer and CD8+ T-cell populations. NEO2IS improved prediction accuracy of patient responses to immune-checkpoint blockades (ICBs). We found that TCR repertoire diversity was consistent with the neoantigen heterogeneity under evolutionary selections. Our defined neoantigen ITH score (NEOITHS) reflected infiltration degree of CD8+ T lymphocytes with different differentiation states and manifested the impact of negative selection pressure on CD8+ T-cell lineage heterogeneity or tumor ecosystem plasticity. We classified tumors into distinct immune subtypes and examined how neoantigen-T cells interactions affected disease progression and treatment response. Overall, our integrated framework helps profile neoantigen patterns that elicit T-cell immunoreactivity, enhance the understanding of evolving tumor-immune interplays and improve prediction of ICBs efficacy.

Combining WGCNA and machine learning to construct basement membrane-related gene index helps to predict the prognosis and tumor microenvironment of HCC patients and verifies the carcinogenesis of key gene CTSA

Hepatocellular carcinoma (HCC) is a malignant tumor with high recurrence and metastasis rates and poor prognosis. Basement membrane is a ubiquitous extracellular matrix and is a key physical factor in cancer metastasis. Therefore, basement membrane-related genes may be new targets for the diagnosis and treatment of HCC. We systematically analyzed the expression pattern and prognostic value of basement membrane-related genes in HCC using the TCGA-HCC dataset, and constructed a new BMRGI based on WGCNA and machine learning. We used the HCC single-cell RNA-sequencing data in GSE146115 to describe the single-cell map of HCC, analyzed the interaction between different cell types, and explored the expression of model genes in different cell types. BMRGI can accurately predict the prognosis of HCC patients and was validated in the ICGC cohort. In addition, we also explored the underlying molecular mechanisms and tumor immune infiltration in different BMRGI subgroups, and confirmed the differences in response to immunotherapy in different BMRGI subgroups based on the TIDE algorithm. Then, we assessed the sensitivity of HCC patients to common drugs. In conclusion, our study provides a theoretical basis for the selection of immunotherapy and sensitive drugs in HCC patients. Finally, we also considered CTSA as the most critical basement membrane-related gene affecting HCC progression. In vitro experiments showed that the proliferation, migration and invasion abilities of HCC cells were significantly impaired when CTSA was knocked down.

Tackling the current dilemma of immunotherapy in extensive-stage small cell lung cancer: A promising strategy of combining with radiotherapy

Progress in the treatment of small cell lung cancer (SCLC) has been modest over the past decades until the advent of immune checkpoint inhibitors, which have redefined the standard first-line treatment for extensive-stage SCLC (ES-SCLC). However, despite the positive results of several clinical trials, the limited survival benefit achieved suggests that the priming and sustaining of immunotherapeutic efficacy are poor and further investigation is urgently needed. In this review, we aim to summarize the potential mechanisms underlying the limited efficacy of immunotherapy and intrinsic resistance in ES-SCLC, including impaired antigen presentation and limited T cell infiltration. Moreover, to tackle the current dilemma, given the synergistic effects of radiotherapy on immunotherapy, especially the unique advantages of low-dose radiotherapy (LDRT), such as less immunosuppression and lower radiation toxicity, we propose radiotherapy as a booster to enhance the immunotherapeutic efficacy by overcoming the poor priming effect. Recent clinical trials, including ours, have also focused on adding radiotherapy, including LDRT, to first-line treatment of ES-SCLC. Additionally, we also suggest combination strategies to sustain the immunostimulatory effect of radiotherapy, as well as the cancer-immunity cycle, and further improve survival outcomes.

Identification of potential resistance mechanisms and therapeutic targets for the relapse of BCMA CAR-T therapy in relapsed/refractory multiple myeloma through single-cell sequencing

BACKGROUND

BCMA CAR-T is highly effective for relapsed/refractory multiple myeloma(R/R-MM) and significantly improves the survival of patients. However, the short remission time and high relapse rate of MM patients treated with BCMA CAR-T remain bottlenecks that limit long-term survival. The immune microenvironment of the bone marrow (BM) in R/R-MM may be responsible for this. The present study aims to present an in-depth analysis of resistant mechanisms and to explore potential novel therapeutic targets for relapse of BCMA CAR-T treatment via single-cell RNA sequencing (scRNA-seq) of BM plasma cells and immune cells.

METHODS

This study used 10X Genomic scRNA-seq to identify cell populations in R/R-MM CD45⁺ BM cells before BCMA CAR-T treatment and relapse after BCMA CAR-T treatment. Cell Ranger pipeline and CellChat were used to perform detailed analysis.

RESULTS

We compared the heterogeneity of CD45⁺ BM cells before BCMA CAR-T treatment and relapse after BCMA CAR-T treatment. We found that the proportion of monocytes/macrophages increased, while the percentage of T cells decreased at relapse after BCMA CAR-T treatment. We then reclustered and analyzed the alterations in plasma cells, T cells, NK cells, DCs, neutrophils, and monocytes/macrophages in the BM microenvironment before BCMA CAR-T treatment and relapse after BCMA CAR-T treatment. We show here that the percentage of BCMA positive plasma cells increased at relapse after BCMA CAR-T cell therapy. Other targets such as CD38, CD24, SLAMF7, CD138, and GPRC5D were also found to be expressed in plasma cells of the R/R-MM patient at relapse after BCMA CAR-T cell therapy. Furthermore, exhausted T cells, TIGIT⁺NK cells, interferon-responsive DCs, and interferon-responsive neutrophils, increased in the R/R-MM patient at relapse after BCMA CAR-T cell treatment. Significantly, the proportion of IL1β^hi Mφ, S100A9^hi Mφ, interferon-responsive Mφ, CD16^hi Mφ, MARCO ^hi Mφ, and S100A11^hi Mφ significantly increased in the R/R-MM patient at relapse after BCMA CAR-T cell therapy. Cell-cell communication analysis indicated that monocytes/macrophages, especially the MIF and APRIL signaling pathway are key players in R/R-MM patient at relapse after BCMA CAR-T cell therapy.

CONCLUSION

Taken together, our data extend the understanding of intrinsic and extrinsic relapse of BCMA CAR-T treatment in R/R-MM patient and the potential mechanisms involved in the alterations of antigens and the induced immunosuppressive microenvironment, which may provide a basis for the optimization of BCMA CAR-T strategies. Further studies should be performed to confirm these findings.

Genomic profiling of idiopathic peri-hilar cholangiocarcinoma reveals new targets and mutational pathways

Peri-hilar cholangiocarcinoma (pCCA) is chemorefractory and limited genomic analyses have been undertaken in Western idiopathic disease. We undertook comprehensive genomic analyses of a U.K. idiopathic pCCA cohort to characterize its mutational profile and identify new targets. Whole exome and targeted DNA sequencing was performed on forty-two resected pCCA tumors and normal bile ducts, with Gene Set Enrichment Analysis (GSEA) using one-tailed testing to generate false discovery rates (FDR). 60% of patients harbored one cancer-associated mutation, with two mutations in 20%. High frequency somatic mutations in genes not typically associated with cholangiocarcinoma included mTOR, ABL1 and NOTCH1. We identified non-synonymous mutation (p.Glu38del) in MAP3K9 in ten tumors, associated with increased peri-vascular invasion (Fisher's exact, p < 0.018). Mutation-enriched pathways were primarily immunological, including innate Dectin-2 (FDR 0.001) and adaptive T-cell receptor pathways including PD-1 (FDR 0.007), CD4 phosphorylation (FDR 0.009) and ZAP70 translocation (FDR 0.009), with overlapping HLA genes. We observed cancer-associated mutations in over half of our patients. Many of these mutations are not typically associated with cholangiocarcinoma yet may increase eligibility for contemporary targeted trials. We also identified a targetable MAP3K9 mutation, in addition to oncogenic and immunological pathways hitherto not described in any cholangiocarcinoma subtype.

A pan-cancer analysis of DDR1 in prognostic signature and tumor immunity, drug resistance

Disk-like domain receptor 1 (DDR1) is a crucial regulator of pro-inflammatory mediators and matrix-degrading enzymes. Although mounting evidence supports a vital role for DDR1 in the tumorigenesis of some cancers, no pan-cancer analysis of DDR1 has been reported. Therefore, we aimed to explore the prognostic value of DDR1 in 33 cancer types and investigate its potential immune function. We used a range of bioinformatics approaches to explore the potential carcinogenic role of DDR1 in multiple cancers. We found that DDR1 was expressed at high levels in most cancers. DDR1 expression was positively or negatively associated with prognosis in different cancers. DDR1 expression was significantly associated with DNA methylation in 8 cancers, while there was a correlation between DDR1 expression and RNA methylation-related genes and mismatch repair gene in most cancers. Furthermore, DDR1 expression was significantly associated with microsatellite instability in 6 cancers and tumor mutation burden in 11 cancers. In addition, DDR1 expression was also significantly correlated with immune cell infiltration, tumor microenvironment, immune-related genes, and drug resistance in various cancers. In conclusion, DDR1 can serve as a potential therapeutic target and prognostic marker for various malignancies due to its vital role in tumorigenesis and tumor immunity.

Integrated pancancer analysis reveals the oncogene characteristics and prognostic value of DIP2B in breast cancer

BACKGROUND

Disco-interaction protein 2 homologue B (DIP2B) plays an important role in DNA methylation. There have been many reports on DIP2B in various diseases, but neither the diagnostic value nor the prognostic value of DIP2B across cancer types has been deeply explored.

METHODS

The expression levels of DIP2B in 33 cancer types were analysed based on data sets from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. The relationships of DIP2B expression with immune cell infiltration and immune-related gene expression were studied via the CIBERSORT, ESTIMATE and TISIDB tools. Gene set variation analysis (GSVA) was performed to identify pathways related to DIP2B. DIP2B knockdown by siRNA was performed in breast cancer cell lines to investigate the effect on proliferation, apoptosis and migration. The relationships of DIP2B expression with clinicopathological features and prognosis were analysed based on immunohistochemistry.

RESULTS

DIP2B was highly expressed in 26 of 33 cancer types and was significantly associated with poor overall survival (OS) in breast invasive carcinoma (BRCA), mesothelioma and chromophobe renal cell carcinoma (each P < 0.05). DIP2B showed a negative correlation with the immune score, the infiltration levels of key immune killer cells (CD8 + T cells, activated NK cells and plasma cells), and the expression of major histocompatibility complex-related genes and chemokine-related genes in BRCA. Subtype analysis showed that DIP2B expression was associated with poor OS in Her-2 + BRCA patients (P < 0.05). DIP2B showed a negative correlation with immune killer cell infiltration and immune regulatory genes in BRCA subtypes. In BRCA, the GSVA results revealed that genes correlating positively with DIP2B were enriched in cancer-related pathways (PI3K-AKT) and cell-cycle-related pathways (MITOTIC_SPINDLE, G2M_CHECKPOINT and E2F_TARGETS), while genes correlating negatively with DIP2B were enriched in DNA_REPAIR. Knockdown of the DIP2B gene induced a reduction in proliferation and migration and an increase in apoptosis in breast cancer cell lines. DIP2B expression was associated with lymph node metastasis and poor histological grade in BRCA according to immunohistochemistry (each P < 0.05). DIP2B expression predicted reduced disease-free survival and OS in BRCA patients (each P < 0.05), especially those with the Her-2 + subtype (P = 0.023 and P = 0.069).

CONCLUSIONS

DIP2B may be a prognostic biomarker for BRCA, especially for the Her-2 + subtype. DIP2B is associated with a "cold" tumour immune microenvironment in BRCA and might serve as a future target for immunotherapy.

Immune gene patterns and characterization of the tumor immune microenvironment associated with cancer immunotherapy efficacy

Although immunotherapy has revolutionized cancer management, most patients do not derive benefits from it. Aiming to explore an appropriate strategy for immunotherapy efficacy prediction, we collected 6251 patients' transcriptome data from multicohort population and analyzed the data using a machine learning algorithm. In this study, we found that patients from three immune gene clusters had different overall survival when treated with immunotherapy (P < 0.001), and that these clusters had differential states of hypoxia scores and metabolism functions. The immune gene score showed good immunotherapy efficacy prediction (AUC was 0.737 at 20 months), which was well validated. The immune gene score, tumor mutation burden, and long non-coding RNA score were further combined to build a tumor immune microenvironment signature, which correlated more strongly with overall survival (AUC, 0.814 at 20 months) than when using a single variable. Thus, we recommend using the characterization of the tumor immune microenvironment associated with immunotherapy efficacy via a multi-omics analysis of cancer.

Tracking the evolution of esophageal squamous cell carcinoma under dynamic immune selection by multi-omics sequencing

Intratumoral heterogeneity (ITH) has been linked to decreased efficacy of clinical treatments. However, although genomic ITH has been characterized in genetic, transcriptomic and epigenetic alterations are hallmarks of esophageal squamous cell carcinoma (ESCC), the extent to which these are heterogeneous in ESCC has not been explored in a unified framework. Further, the extent to which tumor-infiltrated T lymphocytes are directed against cancer cells, but how the immune infiltration acts as a selective force to shape the clonal evolution of ESCC is unclear. In this study, we perform multi-omic sequencing on 186 samples from 36 primary ESCC patients. Through multi-omics analyses, it is discovered that genomic, epigenomic, and transcriptomic ITH are underpinned by ongoing chromosomal instability. Based on the RNA-seq data, we observe diverse levels of immune infiltrate across different tumor sites from the same tumor. We reveal genetic mechanisms of neoantigen evasion under distinct selection pressure from the diverse immune microenvironment. Overall, our work offers an avenue of dissecting the complex contribution of the multi-omics level to the ITH in ESCC and thereby enhances the development of clinical therapy.

Pervasiveness of HLA allele-specific expression loss across tumor types

BACKGROUND

Efficient presentation of mutant peptide fragments by the human leukocyte antigen class I (HLA-I) genes is necessary for immune-mediated killing of cancer cells. According to recent reports, patient HLA-I genotypes can impact the efficacy of cancer immunotherapy, and the somatic loss of HLA-I heterozygosity has been established as a factor in immune evasion. While global deregulated expression of HLA-I has also been reported in different tumor types, the role of HLA-I allele-specific expression loss - that is, the preferential RNA expression loss of specific HLA-I alleles - has not been fully characterized in cancer.

METHODS

Here, we use RNA and whole-exome sequencing data to quantify HLA-I allele-specific expression (ASE) in cancer using our novel method arcasHLA-quant.

RESULTS

We show that HLA-I ASE loss in at least one of the three HLA-I genes is a pervasive phenomenon across TCGA tumor types. In pancreatic adenocarcinoma, tumor-specific HLA-I ASE loss is associated with decreased overall survival specifically in the basal-like subtype, a finding that we validated in an independent cohort through laser-capture microdissection. Additionally, we show that HLA-I ASE loss is associated with poor immunotherapy outcomes in metastatic melanoma through retrospective analyses.

CONCLUSIONS

Together, our results highlight the prevalence of HLA-I ASE loss and provide initial evidence of its clinical significance in cancer prognosis and immunotherapy treatment.

HLA and tumour immunology: immune escape, immunotherapy and immune-related adverse events

PURPOSE

As molecules responsible for presenting antigens to T lymphocytes, leukocytes antigens (HLAs) play a vital role in cancer immunology. This review aims to provide current understanding of HLAs in tumour immunology.

METHODS

Perspectives on how HLA alterations may contribute to the immune escape of cancer cells and resistance to immunotherapy, and potential methods to overcome HLA defects were summarized. In addition, we discussed the potential association between HLA and immune-related adverse events (irAEs), which has not been reviewed elsewhere.

RESULTS

Downregulation, loss of heterogeneity and entire loss of HLAs are responsible for the immune escape of tumour cells. The strategies to overcome the HLA defects can be effective therapies of cancer. Compared with classical HLA-I, non-classical HLA-I molecules, such as HLA-E and HLA-G, appear to be more reliable predictors of prognosis, as they tend to play immunosuppressive roles in antitumor response. Relative diversified or high expression of classical HLA-I are potential predictors of favourable response of immunotherapy. Certain HLA types may be associated to enhanced affinity to self-antigen-mimicked tumour-antigens, thus may positively correlated to irAEs triggered by checkpoint inhibitors.

CONCLUSIONS

Further studies exploring the relationship between HLAs and cancer may not only lead to the development of novel therapies but also bring about better management of irAEs.

Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data

Biomedical multi-modality data (also named multi-omics data) refer to data that span different types and derive from multiple sources in clinical practices (e.g. gene sequences, proteomics and histopathological images), which can provide comprehensive perspectives for cancers and generally improve the performance of survival models. However, the performance improvement of multi-modality survival models may be hindered by two key issues as follows: (1) how to learn and fuse modality-sharable and modality-individual representations from multi-modality data; (2) how to explore the potential risk-aware characteristics in each risk subgroup, which is beneficial to risk stratification and prognosis evaluation. Additionally, learning-based survival models generally refer to numerous hyper-parameters, which requires time-consuming parameter setting and might result in a suboptimal solution. In this paper, we propose an adaptive risk-aware sharable and individual subspace learning method for cancer survival analysis. The proposed method jointly learns sharable and individual subspaces from multi-modality data, whereas two auxiliary terms (i.e. intra-modality complementarity and inter-modality incoherence) are developed to preserve the complementary and distinctive properties of each modality. Moreover, it equips with a grouping co-expression constraint for obtaining risk-aware representation and preserving local consistency. Furthermore, an adaptive-weighted strategy is employed to efficiently estimate crucial parameters during the training stage. Experimental results on three public datasets demonstrate the superiority of our proposed model.

Immunogenetic Profiles and Associations of Breast, Cervical, Ovarian, and Uterine Cancers

It is increasingly recognized that the human immune response influences cancer risk, progression, and survival; consequently, there is growing interest in the role of human leukocyte antigen (HLA), genes that play a critical role in initiating the immune response, on cancer. Recent evidence documented clustering of cancers based on immunogenetic profiles such that breast and ovarian cancers clustered together as did uterine and cervical cancers. Here we extend that line of research to evaluate the HLA profile of those 4 cancers and their associations. Specifically, we evaluated the associations between the frequencies of 127 HLA alleles and the population prevalences of breast, ovarian, cervical, and uterine cancer in 14 countries in Continental Western Europe. Factor analysis and hierarchical clustering were used to evaluate groupings of cancers based on their immunogenetic profiles. The results documented highly similar immunogenetic profiles for breast and ovarian cancers that were characterized predominantly by protective HLA effects. In addition, highly similar immunogenetic profiles for cervical and uterine cancers were observed that were, conversely, characterized by susceptibility effects. In light of the role of HLA in host immune system protection against non-self antigens, these findings suggest that certain cancers may be associated with similar contributory factors such as viral oncoproteins or neoantigens.

Multi-omics characterization of a scoring system to quantify hypoxia patterns in patients with head and neck squamous cell carcinoma

BACKGROUND

The 5-year survival rate of patients with head and neck squamous cell carcinoma (HNSCC) remains  < 50%. Hypoxia patterns are a hallmark of HNSCC that are associated with its occurrence and progression. However, the precise role of hypoxia during HNSCC, such as the relationship between hypoxia, tumor immune landscape and cell communication orchestration remains largely unknown. The current study integrated data from bulk and single-cell RNA sequencing analyses to define the relationship between hypoxia and HNSCC.

METHODS

A scoring system named the hypoxia score (HS) was constructed based on hypoxia-related genes (HRGs) expression. The predictive value of HS response for patient outcomes and different treatments was evaluated. Single-cell datasets and cell communication were utilized to rule out cell populations which hypoxia targeted on.

RESULTS

The survival outcomes, immune/Estimate scores, responses to targeted inhibitors, and chemotherapeutic, and immunotherapy responses were distinct between a high HS group and a low HS group (all P < 0.05). Single-cell datasets showed different distributions of HS in immune cell populations (P < 0.05). Furthermore, HLA-DPA1/CD4 axis was identified as a unique interaction between CD4 + T Conv and pDC cells.

CONCLUSIONS

Altogether, the quantification for hypoxia patterns is a potential biomarker for prognosis, individualized chemotherapeutic and immunotherapy strategies. The portrait of cell communication characteristics over the HNSCC ecosystem enhances the understanding of hypoxia patterns in HNSCC.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and therapeutic response in lung squamous cell carcinoma

Cancer immunotherapy is an increasingly successful strategy for treating patients with advanced or conventionally drug-resistant cancers. T cells have been proved to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in lung squamous cell carcinoma (LUSC). In this study, we first performed a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data from the gene expression omnibus (GEO) database to identify 72 T-cell marker genes. Subsequently, we constructed a 5-gene prognostic signature in the training cohort based on the T-cell marker genes from the cancer genome atlas (TCGA) database, which was further validated in the testing cohort and GEO cohort. The areas under the receiver operating characteristic curve at 1-, 3-, and 5-years were 0.614, 0.713 and 0.702 in the training cohort, 0.669, 0.603 and 0.645 in the testing cohort, 0.661, 0.628 and 0.590 in the GEO cohort, respectively. Furthermore, we created a highly reliable nomogram to facilitate clinical application. Gene set enrichment analysis showed that immune-related pathways were mainly enriched in the high-risk group. Tumor immune microenvironment indicated that high-risk group exhibited higher immune score, stromal score, and immune cell infiltration levels. Moreover, genes of the immune checkpoints and human leukocyte antigen family were all overexpressed in high-risk group. Drug sensitivity revealed that low-risk group was sensitive to 8 chemotherapeutic drugs and high-risk group to 4 chemotherapeutic drugs. In short, our study reveals a novel prognostic signature based on T-cell marker genes, which provides a new target and theoretical support for LUSC patients.

Ferredoxin 1 is a cuproptosis-key gene responsible for tumor immunity and drug sensitivity: A pan-cancer analysis

Ferredoxin 1 (FDX1) functions by transferring electrons from NADPH to mitochondrial cytochrome P450 via the ferredoxin reductase and is the key regulator in copper-dependent cell death. Although mounting evidence supports a vital role for FDX1 in tumorigenesis of some cancers, no pan-cancer analysis of FDX1 has been reported. Therefore, we aimed to explore the prognostic value of FDX1 in pan-cancer and investigate its potential immune function. Based on data from The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, Genotype Tissue-Expression, Human Protein Atlas, and Gene Set Cancer Analysis, we used a range of bioinformatics approaches to explore the potential carcinogenic role of FDX1, including analyzing the relationship between FDX1 expression and prognosis, DNA methylation, RNA methylation-related genes, mismatch repair (MMR) gene, microsatellite instability (MSI), tumor mutation burden (TMB), tumor microenvironment (TME), immune-related genes, and drug sensitivity in different tumors. The results show that FDX1 was lowly expressed in most cancers but higher in glioblastoma multiforme, stomach adenocarcinoma, and uterine corpus endometrial carcinoma. Moreover, FDX1 expression was positively or negatively associated with prognosis in different cancers. FDX1 expression was significantly associated with DNA methylation in 6 cancers, while there was a correlation between FDX1 expression and RNA methylation-related genes and MMR gene in most cancers. Furthermore, FDX1 expression was significantly associated with MSI in 8 cancers and TMB in 10 cancers. In addition, FDX1 expression was also significantly correlated with immune cell infiltration, immune-related genes, TME, and drug resistance in various cancers. An experiment in vitro showed FDX1 is downregulated by elesclomol, resulting in inhibiting cell viability of bladder cancer, clear cell renal cell carcinoma, and prostate cancer cells. Our study reveals that FDX1 can serve as a potential therapeutic target and prognostic marker for various malignancies due to its vital role in tumorigenesis and tumor immunity.

Signature of seven cuproptosis-related lncRNAs as a novel biomarker to predict prognosis and therapeutic response in cervical cancer

Background: Given the high incidence and high mortality of cervical cancer (CC) among women in developing countries, identifying reliable biomarkers for the prediction of prognosis and therapeutic response is crucial. We constructed a prognostic signature of cuproptosis-related long non-coding RNAs (lncRNAs) as a reference for individualized clinical treatment.

Methods: A total of seven cuproptosis-related lncRNAs closely related to the prognosis of patients with CC were identified and used to construct a prognostic signature via least absolute shrinkage and selection operator regression analysis in the training set. The predictive performance of the signature was evaluated by Kaplan–Meier (K-M) analysis, receiver operating characteristic (ROC) analysis, and univariate and multivariate Cox analyses. Functional enrichment analysis and single-sample gene set enrichment analysis were conducted to explore the potential mechanisms of the prognostic signature, and a lncRNA–microRNA–mRNA network was created to investigate the underlying regulatory relationships between lncRNAs and cuproptosis in CC. The associations between the prognostic signature and response to immunotherapy and targeted therapy were also assessed. Finally, the prognostic value of the signature was validated using the CC tissues with clinical information in my own center.

Results: A prognostic signature was developed based on seven cuproptosis-related lncRNAs, including five protective factors (AL441992.1, LINC01305, AL354833.2, CNNM3-DT, and SCAT2) and two risk factors (AL354733.3 and AC009902.2). The ROC curves confirmed the superior predictive performance of the signature compared with conventional clinicopathological characteristics in CC. The ion transport-related molecular function and various immune-related biological processes differed significantly between the two risk groups according to functional enrichment analysis. Furthermore, we discovered that individuals in the high-risk group were more likely to respond to immunotherapy and targeted therapies including trametinib and cetuximab than those in the low-risk group. Finally, CC tissues with clinical data from my own center further verify the robustness of the seven-lncRNA risk signature.

Conclusion: We generated a cuproptosis-related lncRNA risk signature that could be used to predict prognosis of CC patients. Moreover, the signature could be used to predict response to immunotherapy and chemotherapy and thus could assist clinicians in making personalized treatment plans for CC patients.

Significance of a Tumor Mutation Burden Gene Signature with Prognosis and Immune Feature of Gastric Cancer Patients

Gastric cancer (GC) is a common digestive tumor which ranks the fourth most common malignancy worldwide. Immunotherapy is a promising treatment for GC, especially for advanced gastric cancer (AGC). However, in clinical practice, not all patients are sensitive to immunotherapy. Recent studies showed that tumor mutation burden (TMB) is closely correlated with the response of immunotherapy. The current study identified a TMB-related genes' signature to predict the prognosis and immune feature of GC patients. Firstly, we acquired the TMB data and expression data from The Cancer Genome Atlas (TCGA) and the National Center for Biotechnology Information (NCBI) GEO databases. Then, we extracted TMB-related genes from the expression data of TCGA and two GEO cohorts. By using univariate Cox analysis, we identified that the 429 genes were correlated to GC patients' overall survival. Subsequently, an immune prognostic signature was constructed by using the least absolute shrinkage and selection operator analysis (LASSO) and multivariate Cox regression analysis. The signature could be utilized to predict the prognosis of GC patients. In addition, the signature showed a closed correlation with immune feature of GC patients. In conclusion, our risk signature could offer hints for the prognosis of GC patients and might provide insights to formulate new immunotherapy strategies for GC patients.

Promising prognostic value of Transglutaminase type 2 and its correlation with tumor-infiltrating immune cells in skin cutaneous melanoma

Tissue Transglutaminases (TGs) are crosslinking enzymes with pleiotropic functions that have been linked to the development and progression of numerous cancers, with a recent focus on their ability to remodel the tumor microenvironment. Although several pieces of evidence demonstrated their importance in the regulation of the major signaling pathways that control oncogenesis, the correlation between TGs with clinical and pathological features remains controversial and to be further explored. Moreover, an assessment of the TGs alterations together with a functional analysis associated with clinical features and prognostic values are still lacking and would help to understand these intricacies, particularly in human cancers. In the present study, we processed data from numerous public datasets to investigate TGs distribution and prognostic signature in cancer patients. Here, we found that skin cutaneous melanoma (SKCM) shows the highest abundance of TGs mutations among the other human cancers. Interestingly, among all the TGs, TG2 is the only member whose expression is associated with a better overall survival in SKCM, although its expression increases with the worsening of the tumor phenotype. Our analysis revealed a strong positive association between TG2 expression and anti-tumoral immune response, which would explain the relationship between high mRNA levels and better overall survival. Our data suggest that TG2 may be presented as a new promising immune biomarker of prognosis in SKCM, which may contribute to identifying patients who would benefit the most from adjuvant immunotherapy.

Construction of an HLA Classifier for Early Diagnosis, Prognosis, and Recognition of Immunosuppression in Sepsis by Multiple Transcriptome Datasets

Background: Sepsis is a clinical syndrome, due to a dysregulated inflammatory response to infection. Accumulating evidence shows that human leukocyte antigen (HLA) genes play a key role in the immune responses to sepsis. Nevertheless, the effects of HLA genes in sepsis have still not been comprehensively understood.

Methods: A systematical search was performed in the Gene Expression Omnibus (GEO) and ArrayExpress databases from inception to 10 September 2021. Random forest (RF) and modified Lasso penalized regression were conducted to identify hub genes in multi-transcriptome data, thus we constructed a prediction model, namely the HLA classifier. ArrayExpress databases, as external validation, were utilized to evaluate its diagnostic, prognostic, and predictive performance. Immune cell infiltration score was calculated via CIBERSORTx tools and single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis (GSVA) and ssGSEA were conducted to determine the pathways that are significantly enriched in different subgroups. Next, we systematically correlated the HLA classifier with immunological characteristics from multiple perspectives, such as immune-related cell infiltration, pivotal molecular pathways, and cytokine expression. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to validate the expression level of HLA genes in clinical samples.

Results: A total of nine datasets comprising 1,251 patients were included. Based on RF and modified Lasso penalized regression in multi-transcriptome datasets, five HLA genes (B2M, HLA-DQA1, HLA-DPA1, TAP1, and TAP2) were identified as hub genes, which were used to construct an HLA classifier. In the discovery cohort, the HLA classifier exhibited superior diagnostic value (AUC = 0.997) and performed better in predicting mortality (AUC = 0.716) than clinical characteristics or endotypes. Encouragingly, similar results were observed in the ArrayExpress databases. In the E-MTAB-7581 dataset, the use of hydrocortisone in the HLA high-risk subgroup (OR: 2.84, 95% CI 1.07–7.57, p = 0.037) was associated with increased risk of mortality, but not in the HLA low-risk subgroup. Additionally, immune infiltration analysis by CIBERSORTx and ssGSEA revealed that B cells, activated dendritic cells, NK cells, T helper cells, and infiltrating lymphocytes (ILs) were significantly richer in HLA low-risk phenotypes, while Tregs and myeloid-derived suppressor cells (MDSCs) were more abundant in HLA high-risk phenotypes. The HLA classifier was significantly negatively correlated with B cells, activated dendritic cells, NK cells, T helper cells, and ILs, yet was significantly positively correlated with Tregs and MDSCs. Subsequently, molecular pathways analysis uncovered that cytokine-cytokine receptor (CCR) interaction, human leukocyte antigen (HLA), and antigen-presenting cell (APC) co-stimulation were significantly enriched in HLA low-risk endotypes, which was significantly negatively correlated with the HLA classifier in multi-transcriptome data. Finally, the expression levels of several cytokines (IL-10, IFNG, TNF) were significantly different between the HLA subgroups, and the ratio of IL-10/TNF was significantly positively correlated with HLA score in multi-transcriptome data. Results of qRT-PCR validated the higher expression level of B2M as well as lower expression level of HLA-DQA1, HLA-DPA1, TAP1, and TAP2 in sepsis samples compared to control sample.

Conclusion: Based on five HLA genes, a diagnostic and prognostic model, namely the HLA classifier, was established, which is closely correlated with responses to hydrocortisone and immunosuppression status and might facilitate personalized counseling for specific therapy.

Pan-cancer analysis identifies CD300 molecules as potential immune regulators and promising therapeutic targets in acute myeloid leukemia

BACKGROUND

CD300s are a group of proteins playing vital roles in immune responses. However, much is yet to be elucidated regarding the expression patterns and clinical significances of CD300s in cancers.

METHODS

In this study, we comprehensively investigated CD300s in a pan-cancer manner using multi-omic data from The Cancer Genome Atlas. We also studied the relationship between CD300s and the immune landscape of AML.

RESULTS

We found that CD300A-CD300LF were generally overexpressed in tumors (especially AML), whereas CD300LG was more often downregulated. In AML, transactivation of CD300A was not mediated by genetic alterations but by histone modification. Survival analyses revealed that high CD300A-CD300LF expression predicted poor outcome in AML patients; the prognostic value of CD300A was validated in seven independent datasets and a meta dataset including 1115 AML patients. Furthermore, we demonstrated that CD300A expression could add prognostic value in refining existing risk models in AML. Importantly, CD300A-CD300LF expression was closely associated with T-cell dysfunction score and could predict response to AML immunotherapy. Also, CD300A was found to be positively associated with HLA genes and critical immune checkpoints in AML, such as VISTA, CD86, CD200R1, Tim-3, and the LILRB family genes.

CONCLUSIONS

Our study demonstrated CD300s as potential prognostic biomarker and an ideal immunotherapy target in AML, which warrants future functional and clinical studies.

Immunogenetic clustering of 30 cancers

Human leukocyte antigen (HLA) genes have been implicated in cancer risk and shared heritability of different types of cancer. In this immunogenetic epidemiological study we first computed a Cancer-HLA profile for 30 cancer types characterized by the correlation between the prevalence of each cancer and the population frequency of 127 HLA alleles, and then used multidimensional scaling to evaluate the possible clustering of those Cancer-HLA associations. The results indicated the presence of three clusters, broadly reflecting digestive-skin-cervical cancers, reproductive and endocrine systems cancers, and brain and androgen-associated cancers. The clustering of cancer types documented here is discussed in terms of mechanisms underlying shared Cancer-HLA associations.

Predictive Markers for Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer

Immune checkpoint inhibitors (ICIs) have dramatically improved the outcomes of non-small cell lung cancer patients and have increased the possibility of long-term survival. However, few patients benefit from ICIs, and no predictive biomarkers other than tumor programmed cell death ligand 1 (PD-L1) expression have been established. Hence, the identification of biomarkers is an urgent issue. This review outlines the current understanding of predictive markers for the efficacy of ICIs, including PD-L1, tumor mutation burden, DNA mismatch repair deficiency, microsatellite instability, CD8⁺ tumor-infiltrating lymphocytes, human leukocyte antigen class I, tumor/specific genotype, and blood biomarkers such as peripheral T-cell phenotype, neutrophil-to-lymphocyte ratio, interferon-gamma, and interleukin-8. A tremendous number of biomarkers are in development, but individual biomarkers are insufficient. Tissue biomarkers have issues in reproducibility and accuracy because of intratumoral heterogeneity and biopsy invasiveness. Furthermore, blood biomarkers have difficulty in reflecting the tumor microenvironment and therefore tend to be less predictive for the efficacy of ICIs than tissue samples. In addition to individual biomarkers, the development of composite markers, including novel technologies such as machine learning and high-throughput analysis, may make it easier to comprehensively analyze multiple biomarkers.

CD93 Correlates With Immune Infiltration and Impacts Patient Immunotherapy Efficacy: A Pan-Cancer Analysis

Background: The clinical implementation of immune-checkpoint inhibitors (ICIs) targeting CTLA4, PD-1, and PD-L1 has revolutionized the treatment of cancer. However, the majority of patients do not derive clinical benefit. Further development is needed to optimize the approach of ICI therapy. Immunotherapy combined with other forms of treatment is a rising strategy for boosting antitumor responses. CD93 was found to sensitize tumors to immune-checkpoint blocker therapy after the blockade of its pathway. However, its role in immune and ICB therapy across pan-cancer has remained unexplored.

Methods: In this study, we provide a comprehensive investigation of CD93 expression in a pan-cancer manner involving 33 cancer types. We evaluated the association of CD93 expression with prognosis, mismatch repair, tumor mutation burden, and microsatellite instability, immune checkpoints, tumor microenvironment, and immune using multiple online datasets, including The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, Genotype Tissue-Expression, cBioPortal, Tumor Immune Estimation Resource database, and Tumor Immune Single-cell Hub.

Results: CD93 expression varied strongly among cancer types, and increased CD93 gene expression was associated with poor prognosis as well as higher immune factors in most cancer types. Additionally, the level of CD93 was significantly correlated with MMR, TMB, MSI, immune checkpoints, TME, and immune cell infiltration. Noticeably, our results mediated a strong positive contact between CD93 and CAFs, endothelial cells, myeloid dendritic cells, hematopoietic stem cells, mononuclear/macrophage subsets, and neutrophils while a negative correlation with Th1, MDSC, NK, and T-cell follicular helper in almost all cancers. Function analysis on CD93 revealed a link between itself and promoting cancers, inflammation, and angiogenesis.

Conclusion: CD93 can function as a prognostic marker in various malignant tumors and is integral in TME and immune infiltration. Inhibition of the CD93 pathway may be a novel and promising strategy for immunotherapy in human cancer. Further explorations of the mechanisms of CD93 in the immune system may help improve cancer therapy methods.