Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers

PD-1/PD-L1 inhibitors related adverse events: A bibliometric analysis from 2014 to 2024

Programmed cell death-1 (PD-1) inhibitors and programmed cell death ligand 1 (PD-L1) inhibitors are considered effective alternatives for the primary treatment of recurrent metastatic cancers. However, they can induce various adverse events affecting multiple organ systems, potentially diminishing patients' quality of life, and even leading to treatment interruptions. Adverse events related to PD-1/PD-L1 inhibitors differ from those associated with CTLA-4 inhibitors and are more commonly observed in the treatment of solid tumors. This study aimed to address the knowledge gap regarding adverse events related to PD-1/PD-L1 inhibitors. A visual bibliometric network was constructed using VOSviewer, CiteSpace, R software, and the Web of Science Core Collection (WoSCC) to quantitatively analyze this research field. Future research directions were also explored. The USA ranked first in publication count and total citations. Over time, publication types transitioned from case reports to clinical trials. Research on for nivolumab was the most prevalent. The spectrum of cancers treated by PD-1/PD-L1 inhibitors expanded beyond melanoma and lung cancer to include renal cell carcinoma, esophageal cancer, and others. Common adverse events included pneumonitis, myasthenia gravis, and vitiligo. There was a significant increase in multi-phase clinical trials and studies related to biomarkers. This study offers valuable insights for potential collaborators and institutions, highlighting trends in the study of adverse events related to PD-1/PD-L1 inhibitors. The management of these adverse events has become more refined and standardized. Biomarker research and multi-phase clinical trials are likely to be key areas of focus in future studies.

The search for a TNBC vaccine: the guardian vaccine

Nearly 20 million people are diagnosed with cancer each year with breast cancer being the most common among women. Triple negative breast cancer (TNBC), defined by its no/low expression of ER and PR and lack of amplification of HER2, makes up 15-20% of all breast cancer cases. While patients overall have a higher response to chemotherapy, this subgroup is associated with the lowest survival rate indicating significant clinical and molecular heterogeneity demanding alternate treatment options. Therefore, new therapies have been explored, with a large focus on utilizing the immune system. A whole host of immunotherapies have been studied including immune checkpoint inhibitors, now standard of care for eligible patients, and possibly the most exciting and promising is that of a TNBC vaccine. While currently there are no approved TNBC vaccines, this review highlights many promising studies and points to an antigen, p53, which we believe is highly relevant for TNBC.

Molecular and immunological features associated with long-term benefits in metastatic NSCLC patients undergoing immune checkpoint blockade

INTRODUCTION

Immunotherapy is firmly established as a treatment regimen in various solid tumors, driven by its exceptional benefits in a selected group of patients. Despite widespread adoption of immune checkpoint blockade (ICB) across diverse solid tumors, the quest for a clinically informative biomarker for long-term benefit remains unmet.

METHODS

A total of 49 patients with metastatic NSCLC treated with ICB were included. Long-term (LTR) and short-term responders (STR) were defined as those with a response to ICB lasting more than 24 months or less than 6 months, respectively. Longitudinal blood specimens were collected before ICB treatment initiation and early-on treatment. Plasma ctDNA next-generation sequencing panel (NGS) and serum proteomics were performed. GeoMx DSP on baseline tumor tissue was performed in a subset of patients.

RESULTS

Our analysis revealed specific characteristics of LTR compared with STR, namely higher PD-L1 in tumor cells (p = 0.005) and higher incidence of irAEs (p = 0.001). Genomic features associated with lack of benefit from ICB included co-occurring mutations in KRAS/STK11 and TP53/KMT2D (p < 0.05). At a baseline, LTR patients exhibited higher serum levels of proteins related with apoptosis (CASP8, PRKRA), chemotaxis, immune proteasome, processing of MHC class I (S100A4, PSMD9, RNF41) and immune homeostasis (HAVCR1, ARG1) (p < 0.05). Protein spatial profiling of tumor samples showed higher levels of proteins linked with the presence of immune cells (CD45), T cells (CD8), antigen presentation (HLA-DR) and immune regulation proteins (PD-L1, IDO1) within the tumor and tumor stroma component (p < 0.05) in LTR patients. Serum longitudinal analysis identified a set of proteins that presented distinct dynamics in LTR compared to STR, making them interesting candidates to evaluate as early predictors of treatment efficacy.

CONCLUSIONS

Our multimodal analysis of patients with metastatic NSCLC treated with ICB identified clinicopathological and immunological features associated with long-term benefits. The presence of preexisting antitumor immunity emerged as a strong predictor of long-term benefits, providing insights for potential biomarkers and therapeutic strategies for enhancing ICB outcomes in metastatic NSCLC.

Neurogranin facilitates maintaining the immunosuppressive state of hepatocellular carcinoma by promoting TGF-β1 secretion

Immunotherapy has revolutionized cancer treatment, but its effectiveness is limited due to the complexity of the tumor immune microenvironment. Identifying reliable biomarkers that can predict immunotherapy response is essential for enhancing treatment strategies. This study evaluated the potential of Neurogranin (NRGN) as a biomarker for prognosis and immunotherapy response across multiple cancers. Through pan-cancer bioinformatics analyses, coupled with in vitro and in vivo experiments, we explored NRGN's differential expression across various cancer types and its role in the immune microenvironment. Our approach involved database mining, immune genomic feature correlation analyses, and functional validation through NRGN knockdown and overexpression studies. The results revealed differential NRGN expression across cancers, particularly hepatocellular carcinoma (HCC), where elevated levels correlated with immune evasion, poor prognosis, and upregulation of checkpoint genes like TGFB1. NRGN modulated T cell activity and macrophage polarization by regulating the TGF-β pathway through interaction with TCF4 and promoting its nuclear localization, driving tumor progression. Targeting TGF-β with anti-TGF-β and anti-PD-1 antibodies additively inhibited HCC in an Nrgn-dependent manner in mice. These findings indicate that NRGN may serve as a promising immunotherapeutic target, as its overexpression predicts poor prognosis and immune evasion, thereby offering insights for improving immunotherapy and developing new treatments.

Designing Clinical Trials for Patients With Rare Cancers: Connecting the Zebras

The field of rare cancer research is rapidly transforming, marked by significant progress in clinical trials and treatment strategies. Rare cancers, as defined by the National Cancer Institute, occur in fewer than 150 cases per million people each year, yet they collectively represent a significant portion of all cancer diagnoses. Because of their infrequency, these cancers pose distinct challenges for clinical trials, including limited patient populations, geographical dispersion, and a general lack of awareness of treatment options. Economic limitations further complicate drug development, making initiatives such as the Orphan Drug Act essential for incentivizing research. The advent of next-generation sequencing (NGS) and precision medicine has been instrumental in identifying actionable genetic alterations in parallel with an explosion in the development of genomically targeted therapies, immunotherapies, and antibody drug conjugates. Advances in clinical NGS, precision medicine, and tumor-agnostic therapies have become central to the progress in rare cancer research. The development and approval of tumor-agnostic drugs, such as BRAF, NTRK, and RET inhibitors, and immunotherapy for mismatch repair deficient/microsatellite instability-high status cancers highlight the potential of personalized treatments across diverse cancer types and across the age spectrum. Collaborative trials from cooperative groups including SWOG DART, ASCO TAPUR, NCI-MATCH, pediatric COG-match, DRUP, IMPRESS, and innovative registrational basket and platform trials (eg, VE-Basket, ROAR, LIBRETTO-001, ARROW), along with patient advocacy group-run trials like TRACK, are enhancing access to clinical trials. In addition, artificial intelligence has the potential to improve the trial matching process. An integrated approach, combining these innovations in collaboration with multiple stakeholders, is crucial for advancing rare cancer research, offering hope for better patient outcomes and quality of life.

Systemic immune-inflammation index as a predictor of survival in non-small cell lung cancer patients undergoing immune checkpoint inhibition: A systematic review and meta-analysis

BACKGROUND

This meta-analysis aims to evaluate the association between pretreatment systemic immune-inflammation index (SII) levels and progression-free survival (PFS) and overall survival (OS) in NSCLC patients receiving immune checkpoint inhibitors (ICIs).

METHODS

A systematic search was conducted across PubMed, Embase, and Web of Science. Hazard ratios (HRs) with 95 % confidence intervals (CIs) for PFS and OS were extracted or calculated. Random-effects models were employed to pool the results and subgroup analyses were performed based on study characteristics, treatment regimens, and analytical methods.

RESULTS

Two prospective and 11 retrospective studies involving 2342 NSCLC patients treated with ICIs were included. A high pretreatment SII was significantly associated with poor PFS (HR: 2.05, 95 % CI: 1.59-2.64, p < 0.001; I2 = 42 %) and poor OS (HR: 1.54, 95 % CI: 1.29-1.82, p < 0.001; I2 = 22 %). Subgroup analyses according to the country of the study, lines of treatment, cancer stage, methods for determining the cutoffs of SII, and the analytic models showed consistent results (p for subgroup difference all > 0.05). Interestingly, the subgroup analyses indicated a stronger association in patients receiving ICIs alone versus those receiving concurrent chemotherapy (p for subgroup difference = 0.04).

CONCLUSIONS

High pretreatment SII is associated with worse PFS and OS in NSCLC patients treated with ICIs, particularly for the patients receiving ICIs alone without concurrent chemotherapy.

Deep learning radiomics analysis for prediction of survival in patients with unresectable gastric cancer receiving immunotherapy

Objective

Immunotherapy has become an option for the first-line therapy of advanced gastric cancer (GC), with improved survival. Our study aimed to investigate unresectable GC from an imaging perspective combined with clinicopathological variables to identify patients who were most likely to benefit from immunotherapy.

Method

Patients with unresectable GC who were consecutively treated with immunotherapy at two different medical centers of Chinese PLA General Hospital were included and divided into the training and validation cohorts, respectively. A deep learning neural network, using a multimodal ensemble approach based on CT imaging data before immunotherapy, was trained in the training cohort to predict survival, and an internal validation cohort was constructed to select the optimal ensemble model. Data from another cohort were used for external validation. The area under the receiver operating characteristic curve was analyzed to evaluate performance in predicting survival. Detailed clinicopathological data and peripheral blood prior to immunotherapy were collected for each patient. Univariate and multivariable logistic regression analysis of imaging models and clinicopathological variables was also applied to identify the independent predictors of survival. A nomogram based on multivariable logistic regression was constructed.

Result

A total of 79 GC patients in the training cohort and 97 patients in the external validation cohort were enrolled in this study. A multi-model ensemble approach was applied to train a model to predict the 1-year survival of GC patients. Compared to individual models, the ensemble model showed improvement in performance metrics in both the internal and external validation cohorts. There was a significant difference in overall survival (OS) among patients with different imaging models based on the optimum cutoff score of 0.5 (HR = 0.20, 95 % CI: 0.10-0.37, P < 0.001). Multivariate Cox regression analysis revealed that the imaging models, PD-L1 expression, and lung immune prognostic index were independent prognostic factors for OS. We combined these variables and built a nomogram. The calibration curves showed that the C-index of the nomogram was 0.85 and 0.78 in the training and validation cohorts.

Conclusion

The deep learning model in combination with several clinical factors showed predictive value for survival in patients with unresectable GC receiving immunotherapy.

Monoclonal antibody immune therapy response instrument for stratification and cost-effective personalized approaches in 3PM-guided pan cancer management

Background

Immune checkpoint inhibitors (ICIs), such as anti-PD-1, anti-PD-L1, and anti-CTLA-4 therapies, have revolutionized cancer treatment by harnessing the body's immune system to eliminate cancer cells. Despite their considerable promise, the efficacy of ICIs significantly differs based on tumor types and specific patient conditions, highlighting the necessity for personalized approaches in the framework of predictive preventive personalized medicine (PPPM; 3PM).

Main body

This review proposes a stratification instrument within the 3PM framework to enhance the therapeutic efficacy of ICIs across Pan-cancer. Predictive approaches need to be utilized to enhance the effectiveness of ICIs. For example, biomarkers such as particular genetic alterations and metabolic pathways provide key information on patient treatment responses. To predict treatment outcomes, uncover resistance mechanisms, and tailor medications, we examine biomarkers including PDL-1 and CTLA4. Focusing on cancers like melanoma, bladder, and renal cell carcinoma, we highlight advances in combination therapies and cellular approaches to overcome resistance. We conducted an analysis of clinical trials and public datasets (TCGA, GEO) to evaluate ICI responses across number of cancer types. Survival analysis employed Kaplan-Meier curves and Cox regression. Pan-cancer analysis shows response rates ranging from 19.8% in bladder cancer to > 39% in melanoma when combination therapy is used, emphasizing the potential of 3PM to improve outcomes. By exploring resistance mechanisms and emerging therapeutic innovations, we propose a cost-effective model for better patient stratification and care. Validation of this model requires standardized biomarkers and prospective trials, promising a shift toward precision oncology.

Conclusion

Within the 3PM framework, this review addresses the urgent need for cost-effective stratification tools and adaptive combinatorial strategies to optimize outcomes.

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

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

Comprehensive analysis of FAM83B in pan-cancer and preliminary exploration in esophageal squamous cell carcinoma

FAM83B is a novel oncogene that mediates transformation. Despite emerging evidence supporting an association between FAM83B and cancer, a holistic view of FAM83B's correlation with pan-cancer is limited and its carcinogenic and radioresistant roles in esophageal squamous cell carcinoma (ESCC) remain to be explored. Using data from the TCGA project, GTEx database, and other online resources, we comprehensively examined FAM83B expression, genetic mutation, copy number variations (CNV), methylation, prognosis, function, immune-associated analyses, and drug sensitivity in pan-cancer. In addition, the biological function of FAM83B in ESCC was verified by CCK-8, colony formation assays, and flow cytometry. We discovered aberrant expression of FAM83B affected prognosis in various malignant tumors. Abnormal FAM83B mRNA expression was associated with CNV and methylation. Significant correlations were also observed between FAM83B expression and immune cell infiltration, immune checkpoints, tumor mutational burden (TMB), and microsatellite instability (MSI) in malignancies. In vitro experiments indicated that FAM83B mRNA and protein were upregulated in ESCC, and knockdown of FAM83B significantly inhibited the proliferation while promoting apoptosis and radiosensitivity of ESCC. These results suggest the multiple functional roles of FAM83B in pan-cancer and provide an attractive diagnostic and therapeutic biomarker for certain cancer types, especially ESCC.

Concurrent SMARCA4-deficient and poorly differentiated adenocarcinomas in separate lung lobes: a case report and literature review

BACKGROUND

SMARCA4 and SMARCA2, mutually exclusive catalytic ATPase subunits of human mammalian Switch/Sucrose-Nonfermentable chromatin remodeling enzymes, function as tumor suppressor genes. SMARCA4-deficient adenocarcinoma (SMARCA4-dADC) is a relatively rare subtype of TTF1/P40-negative non-small cell lung cancer. The concurrent presentation of SMARCA4-dADC and poorly differentiated adenocarcinoma with SMARCA2 (also known as BRM) loss in separate lobes of the same patient is even less common. This report describes such a case involving the simultaneous occurrence of these two tumor types in distinct locations within the lungs.

CASE PRESENTATION

A 68-year-old male presented with a three-week history of vague pain in the right side of the chest, with no obvious trigger. Imaging revealed solid masses in the upper and lower lobes of the right lung with bilateral enlarged cervical lymph nodes. So, both of these masses underwent wedge resection. Histopathological examination confirmed that the lower lobe tumor was SMARCA4-dADC, while the upper lobe tumor was diagnosed as poorly differentiated adenocarcinoma. Although histologically similar, both exhibiting predominantly solid sheets and complex glandular structures, the two tumors displayed distinct immunohistochemical and molecular profiles. The lower lobe mass showed complete loss of BRG1 protein expression and partial loss of BRM. Immunohistochemical analysis revealed negative expression of TTF1, Napsin A, SALL4, CD34, and SOX2, and positive expression of CK7, pan-Cytokeratin (CK-pan), and HepPar-1. Molecular analysis identified mutations in SMARCA4, KRAS, and STK11. Conversely, the upper lobe mass retained BRG1 expression but showed complete loss of BRM protein expression, and negative expression of SALL4, CD34, and HepPar-1, positive expression of CK7, CK-pan, TTF1, Napsin A, and SOX2. A KRAS mutation was also detected in this tumor.

CONCLUSION

The simultaneous occurrence of SMARCA4-dADC and conventional adenocarcinoma in different locations within the same patient is exceedingly rare. However, the distinct immunophenotypic and molecular characteristics of SMARCA4-dADC differentiate it as a unique entity from conventional adenocarcinoma. We recommend including SMARCA4 in the marker panel used to evaluate TTF1-negative adenocarcinomas of potential or uncertain pulmonary origin. This report underscores the diagnostic challenge of concurrent SMARCA4-dADC and poorly differentiated adenocarcinoma, proposing a standardized immunohistochemical workflow to guide therapeutic decisions.

Immunotherapy in breast cancer: current landscape and emerging trends

Breast cancer remains one of the most prevalent malignancies worldwide, underscoring an urgent need for innovative therapeutic strategies. Immunotherapy has emerged as a transformative frontier in this context. In triple-negative breast cancer (TNBC), the combination of immunotherapy based on PD-1/PD-L1 immune checkpoint inhibitors (ICIs) with chemotherapy has proven efficacious in both early and advanced clinical trials. These encouraging results have led to the approval of ICIs for TNBC, opening up new therapeutic avenues for challenging-to-treat patient populations. Furthermore, a multitude of ongoing trials are actively investigating the efficacy of immunotherapy-based combinations, including ICIs in conjunction with chemotherapy, targeted therapy and radiation therapy, as well as other novel strategies such as bispecific antibodies, CAR-T cells and cancer vaccines across all breast cancer subtypes, including HR-positive/HER2-negative and HER2-positive disease. This review provides a comprehensive overview of current immunotherapeutic approaches in breast cancer, highlighting pivotal findings from recent clinical trials and the potential impact of these advancements on patient outcomes.

Integrative Analysis of Immune- and Metabolism-Related Genes Identifies Robust Prognostic Signature and PYCR1 as a Carcinogenic Regulator in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is distinguished by metabolic irregularities and unique immunological profiles. Nevertheless, the comprehensive examination of immune and metabolic attributes within the tumor microenvironment of ccRCC remains inadequately elucidated. In this study, we identified two distinct molecular subtypes (C1 and C2) of ccRCC using the non-negative matrix factorization (NMF) algorithm. Utilizing univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we developed a prognostic signature comprising eight immune- and metabolism-related genes (IMRGs) associated with the tumor microenvironment. The validation of this signature was performed using both testing and entire datasets. A nomogram was developed using IMRGs prognostic signature and various clinical parameters, including age and TNM stage. We also performed the in vitro experiments to validate the carcinogenic role of PYCR1 in ccRCC cells. Subtype C1 exhibited a more favorable prognosis and higher levels of immune cell infiltration compared to subtype C2. The AUCs of the nomogram at 1-, 3-, and 5-year intervals (AUC = 0.874, 0.820, and 0.794) were slightly higher than those of the IMRGs signature alone (AUC = 0.773, 0.755, and 0.764). The association between risk score and immune checkpoint expressions, immunophenoscore (IPS), and microsatellite instability (MSI) collectively predicted treatment efficacy accurately. Additionally, in vitro experiments confirmed the involvement of PYCR1 in promoting the aggressive behaviors of ccRCC cells, as evidenced by reduced proliferation, invasion, and enhanced apoptosis upon PYCR1 knockdown. In conclusion, the IMRGs signature shows promise in predicting prognostic risk, assessing the effectiveness of immunotherapy, and tailoring treatment for ccRCC patients.

Immune Checkpoint Inhibitors and Immunosuppressive Tumor Microenvironment: Current Challenges and Strategies to Overcome Resistance

Immune checkpoint inhibitors (ICIs) are shown to improve cancer treatment effectiveness by boosting the immune system of the patient. Nevertheless, the unique and highly suppressive TME poses a significant challenge, causing heterogeneity of response or resistance in a considerable number of patients. This review focuses on the evasive attributes of the TME. Immune evasion mechanism in TME include immunosuppressive cells, cytokine and chemokine signaling, metabolic alterations and overexpression of immune checkpoint molecules such as PD-1, CTLA-4, LAG-3, TIM-3, TIGIT, BTLA and their interactions within the TME. In addition, this review focuses on the overcoming resistance by targeting immunosuppressive cells, normalizing tumor blood vessels, blocking two or three checkpoints simultaneously, combining vaccines, oncolytic viruses and metabolic inhibitors with ICIs or other therapies. This review also focuses on the necessity of finding predictive markers for the stratification of patients and to check response of ICIs treatment. It remains to be made certain by new research and intelligent innovations how these discoveries of the TME and its interplay facilitate ICI treatment and change the face of cancer treatment.

Construction and validation of a prognostic model for colorectal cancer based on migrasome-related long non-coding RNAs

Background

Colon adenocarcinoma (COAD) is a globally prevalent and deadly malignancy of the digestive system. Recently, migrasomes have gained significant attention as important regulators of tumor cell migration and metastasis. The current research developed a highly accurate prognostic model using migrasome-related long non-coding RNAs (lncRNAs) in COAD, providing new insights for prognostic assessment and immunotherapy of COAD patients.

Methods

RNA sequencing data from COAD patients were acquired from The Cancer Genome Atlas Program (TCGA) database to construct a prognostic lncRNA model based on known migrasome-related genes (MRGs). The model's predictive accuracy was then assessed using concordance index (C-index) analysis, nomograms, principal component analysis, and receiver operating characteristic curves. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify significant differences in biological functions and signaling pathways associated with differentially expressed genes in the high-risk subgroup. A comprehensive evaluation of the model incorporated clinical-pathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognostic genes in COAD patients were validated via quantitative reverse transcription polymerase chain reaction (RT-qPCR). Furthermore, the role of LCMT1-AS1 in colorectal cancer was examined through CCK-8 assays, colony formation assays, and Transwell experiments.

Results

Migrasome-related lncRNAs were identified as robust prognostic predictors for COAD. Multivariate analysis revealed that the risk score derived from these lncRNAs is an independent prognostic factor for COAD. Patients in the low-risk group exhibited significantly longer overall survival (OS) compared to those in the high-risk group. Accordingly, the nomogram prediction model we developed, which integrates clinical features and risk scores, demonstrated excellent prognostic performance. In vitro experiments further showed that LCMT1-AS1 promotes the proliferation and migration of COAD cells.

Predictive biomarkers in the era of immunotherapy for gastric cancer: current achievements and future perspectives

Gastric cancer (GC) is one of the primary contributors to cancer-related mortality on a global scale. It holds a position within the top five most prevalent malignancies both in terms of occurrence and fatality rates. Immunotherapy, as a breakthrough cancer treatment, brings new hope for GC patients. Various biomarkers, such as the expression of programmed death ligand-1 (PD-L1), the microsatellite instability (MSI) status, tumor mutational burden (TMB), and Epstein-Barr virus (EBV) infection, demonstrate potential to predict the effectiveness of immunotherapy in treating GC. Nevertheless, each biomarker has its own limitations, which leads to a significant portion of patients continue to be unresponsive to immunotherapy. With the understanding of the tumor immune microenvironment (TIME), genome sequencing technology, and recent advances in molecular biology, new molecular markers, such as POLE/POLD1mutations, circulating tumor DNA, intestinal flora, lymphocyte activation gene 3 (LAG-3), and lipid metabolism have emerged. This review aims to consolidate clinical evidence to offer a thorough comprehension of the existing and emerging biomarkers. We discuss the mechanisms, prospects of application, and limitations of each biomarker. We anticipate that this review will open avenues for fresh perspectives in the investigation of GC immunotherapy biomarkers and promote the precise choice of treatment modalities for gastric cancer patients, thereby advancing precision immuno-oncology endeavors.

The Use of Mutational Signatures to Decipher the Inter-Relationship of Reactive Oxygen Species with Homologous Recombination and Non-Homologous End-Joining Deficiencies as Well as Their Effects on APOBEC Mutagenesis in Breast Cancer

Background: Defective DNA repair systems result in the accumulation of mutations, loss of genomic integrity, and eventually cancer. Following initial malignant transformation due to specific DNA damage and defective DNA repair, cancer cells become reliant upon other DNA repair pathways for their survival. The co-occurrence of specific repair deficiencies brings catastrophic outcomes such as cell death for cancer cells and thus holds promise as a potential therapeutic strategy. Exploring the co-occurrence and mutual exclusivity of mutational signatures provides valuable knowledge regarding combinations of defective repair pathways that are cooperative and confer selective advantage to cancer cells and those that are detrimental and cannot be tolerated by them. Methods: Taking advantage of mutational signature profiling, we analyzed whole-genome sequences of 1014 breast cancers to reveal the underlying mutational processes and their interrelationships. Results: We found an inverse relationship between deficiencies of homologous recombination (HRd) and non-homologous end joining (NHEJd) with reactive oxygen species (ROS). Moreover, HRd and NHEJd co-occurred with APOBEC but were mutually exclusive with mismatch repair deficiency (MMRd) and ROS. Our analysis revealed that SBS8 and SBS39 signatures of currently unknown etiology correlate with NHEJd. ID1 and ID2 signatures co-occur with ROS and have mutual exclusivity with HRd, SBS8, SBS39 and NHEJd. The ID4 signature, with currently unknown etiology, has mutual exclusivity with HRd and NHEJd and co-occurred with ROS. On the other hand, the ID15 signature, with currently unknown etiology, co-occurred with SBS8, SBS39, HRd, NHEJd and DBS2, while having an inverse relationship with MMRd and ROS. Comparing the mutational signatures of HRd and non-HRd TNBC genomes reveals the unique presence of ROS signatures in non-HRd tumors and the lack of ROS signature in HRd tumors. Conclusion: Taken together, these analyses indicate the possible application of mutation signatures and their interactions in advancing patient stratification and suggest appropriate therapies targeting the make-up of individual tumors' mutational processes. Ultimately, this information provides the opportunity to discover promising synthetic lethal candidates targeting DNA repair systems.

Identify Modules Associated with Immunotherapy Response from Mouse Tumor Profiles for Stratifying Cancer Patients

Immune checkpoint inhibitors (ICIs) have demonstrated significant clinical benefits in cancer treatment, but only a minority of patients exhibit favorable response, highlighting the importance of determining patients who will benefit from immunotherapy. Currently, patient datasets regarding immunotherapy response are scarce, while ample experiments can be performed on syngeneic mouse tumor models to generate valuable data. Therefore, how to effectively utilize mouse data to identify predictors of immunotherapy response and subsequently transfer relevant knowledge to predict human response to ICIs is a question worth studying. In this study, we propose a novel methodology to address this issue. Firstly, we identify gene modules associated with immunotherapy response from mouse tumor profiles based on cancer gene panels. Subsequently, these identified modules are employed to build prediction models for immunotherapy response based on mouse data. Furthermore, we transfer these models to predict ICIs responses of human cancer patients. Experimental results demonstrate that the gene modules identified from mouse data are reliable predictors of immunotherapy response. The mouse-based models built on these modules could be transferred to humans, effectively predicting drug responses and survival outcomes for cancer patients. Compared to conventional cancer biomarkers and existing prediction models based on mouse data, our method exhibits superior performance. These findings provide a valuable reference for further in-depth research on immunotherapy response prediction model based on mouse tumor profiles, with the potential for transfer applications in human cancer therapy.

Generalizable AI predicts immunotherapy outcomes across cancers and treatments

Immune checkpoint inhibitors have become standard care across many cancers, but most patients do not respond. Predicting response remains challenging due to complex tumor-immune interactions and the poor generalizability of current biomarkers and models. Predictors such as tumor mutational burden, PD-L1 expression, and transcriptomic signatures often fail across cancer types, therapies, and clinical settings. There is a clear need for a robust, interpretable model that captures shared immune response principles and adapts to diverse clinical contexts. We present Compass, a foundation model for predicting immunotherapy response from pan-cancer transcriptomic data using a concept bottleneck architecture. Compass encodes tumor gene expression through 44 biologically grounded immune concepts representing immune cell states, tumor-microenvironment interactions, and signaling pathways. Trained on 10,184 tumors across 33 cancer types, Compass outperforms 22 baseline methods in 16 independent clinical cohorts spanning seven cancers and six immune checkpoint inhibitors, increasing precision by 8.5%, Matthews correlation coefficient by 12.3%, and area under the precision-recall curve by 15.7%, with minimal or no additional training. The model generalizes to unseen cancer types and treatments, supporting indication selection and patient stratification in early-phase clinical trials. Survival analysis shows that Compass-stratified responders have significantly longer overall survival (hazard ratio = 4.7, p < 0.0001). Personalized response maps link gene expression to immune concepts, revealing distinct mechanisms of response and resistance. For example, among immune-inflamed non-responders, Compass identifies distinct resistance programs involving TGF- β signaling, endothelial exclusion, CD4+ T cell dysfunction, and B cell deficiency. By combining mechanistic interpretability with transfer learning, Compass provides mechanistic insights into treatment response variability, supports clinical decision-making, and informs trial design.

Understanding the cellular and molecular heterogeneity in colorectal cancer through the use of single-cell RNA sequencing

The very prevalent nature, genetic variability, and intricate tumor microenvironment (TUME) of colorectal cancer (COREC) are its defining features. In order to better understand the molecular and cellular make-up of COREC, this work used single-cell RNA sequencing (SRNAS) to isolate and characterize important cell types as well as their interactions within the TUME. Our analysis of 51,204 cells yielded six distinct types: epithelial, fibroblast, endothelial, T&NK, B, and myeloid. C3 B cells were shown to be the most active in immunological regulation, according to chemokine signaling study, which was one of seven clusters of B cells that were thoroughly subtyped. The examination of copy number variation (CONUV) revealed a great deal of genetic variability, especially in epithelial cells. We traced the activity of three key transcription factor clusters (M1, M2, and M3) across all B cell subtypes using transcription factor analysis. We created a predictive model that correctly sorts patients according to survival results by using marker genes from C3 B cells. In addition, the relationship between genetic changes and the immune system was better understood by tumor mutational burden (TUMUB) and immune infiltration studies. Our research sheds light on the genetic complexity and cellular variety of COREC, which in turn opens up new possibilities for targeted treatments and individualized approaches to patient care.

Pan-cancer analysis of GJB5 as a novel prognostic and immunological biomarker

Gap junction protein B5 (GJB5, also known as Connexin 31.1) has recently been reported to be downregulated in several cancer types, where it functions primarily as a tumor suppressor in cancers such as melanoma and non-small cell lung cancer (NSCLC). However, there no reports describing its prognostic and immunological roles in pan-cancer. This study evaluated the association of GJB5 in various cancer types by a comprehensive pan-cancer analysis. The differential GJB5 expression in tumor and adjacent tissues acquired from The Cancer Genome Atlas (TCGA) databases was compared. Furthermore, univariate Cox regression and Kaplan-Meier survival analyses were performed to assess the influence of GJB5 on the disease-specific survival (DSS), disease-free interval (DFI), clinical stage, progression-free interval (PFI), and overall survival (OS) in various cancers. Moreover, the levels of GJB5 and its activity in the tumor microenvironment were assessed via the Tumor Immune Single-cell Hub (TISCH). In addition, the biological importance of GJB5 levels in various cancers was further assessed via Gene Set Enrichment Analysis. Tumor-Immune System Interactions Database (TISIDB) and Tumor Immune Estimation Resource Database 2.0 (TIMER2.0) tools indicated that GJB5 affected the tumor's immune infiltration potential. This research also evaluated the association of GJB5 with immune features: immune modulatory genes, tumor mutational burden (TMB), and microsatellite instability (MSI). The data indicated that enhanced GJB5 level was linked to worse DFI, OS, PFI, and DSS in some cancers. Additionally, GJB5 level was positively related to immune modulatory genes, TMB, immune cell infiltration, immunological checkpoints, and MSI in malignancies. Furthermore, our study demonstrated that GJB5 was upregulated in colorectal cancer tissues compared to normal tissues. We also assessed GJB5 expression across various pancreatic cell lines. Notably, GJB5 was highly expressed in pancreatic cancer cells relative to normal pancreatic epithelial cells. Additionally, GJB5 knockdown in pancreatic cancer cells resulted in a significant reduction in cell proliferation. In summary, the findings indicated the potential of GJB5 as a prospective prognostic indicator and immunological biomarker.

Crosstalk of lactate metabolism-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in colon adenocarcinoma

Colon adenocarcinoma (COAD) is a common malignant tumor of digestive tract and lactate metabolism has been linked to tumor development and progression. In this study, we sought to investigate the influence of lactate metabolism-related genes (LRGs) prognosis. We also aimed to identify distinct LRG-related clusters and develop a risk signature for assessing patient prognosis, immunological characteristics, and response to therapy. We analyzed data from The Cancer Genome Atlas (TCGA) to reveal the expression and mutational features of LRGs in COAD patients. In the integrated TCGA and GSE39582 cohort, consensus clustering analysis was employed to classify patients into two distinct LRG-related clusters. Using differentially expressed genes (DRGs) from these two clusters, we established a LRG-related gene cluster and prognostic signature which was used to classify patients into high-risk and low-risk groups. An validation cohort was used to validate the predictive ability of risk signature and expression of 6 candidate LRGs was confirmed through quantitative real-time PCR (qRT-PCR). Nomograms were created to visually represent the clinical value of LRG-related signature. Furthermore, we extensively examined differences in immune cell infiltration, tumor mutational load (TMB), microsatellite instability (MSI) and drug sensitivity between two risk groups. Analysis of the integrated TCGA and GSE39582 cohorts revealed two distinct LRG-related clusters and gene clusters with significant differences in overall survival (OS) and tumor microenvironment. We developed a LRG-related signature comprising 6 candidate LRGs that reliably predicted OS and qRT-PCR validation confirmed the expression of LRGs. Based on the median risk score, patients were divided into low-risk and high-risk groups, with low-risk group showing better survival. Furthermore, patients in high-risk group were more sensitive to chemotherapy and associated with higher TMB, higher proportion of MSI-H. Our study provides a valuable method for guiding clinical management and personalized treatment of COAD patients, which offers new insights into individualized treatment strategies, ultimately improving patient outcomes.

Prognostic molecular subtype reveals the heterogeneity of tumor immune microenvironment in gastric cancer

Gastric cancer (GC) remains a leading cause of cancer-related deaths and exhibits considerable heterogeneity among patients. Thus, accurate classifications are essential for predicting prognosis and developing personalized therapeutic strategies. To address this, we retrospectively analyzed multi-omics data from 359 GC samples, incorporating transcriptomic RNA (mRNA), DNA methylation, mutation data, and clinical parameters. Using ten clustering algorithms, we integrated these datasets to classify GC into molecular subtypes. The robustness of our clustering approach was externally validated using an independent cohort generated from different sequencing technologies, and we characterized the heterogeneity of each subtype. Our analysis identified three distinct molecular subtypes of GC, designated CS1, CS2, and CS3. These subtypes exhibited significant differences in survival outcomes, activation of cancer-related pathways, immune microenvironment composition, genomic alterations, and responses to immunotherapy and chemotherapy. Notably, Cathepsin V (CTSV) was significantly downregulated in the immunologically active and highly responsive CS3 subtype, while it was upregulated in the immunologically exhausted CS2 subtype. These findings suggest that CTSV could serve as both a prognostic marker and a molecular classifier. Furthermore, this study provides the first evidence of CTSV's high expression in GC and its potential role in tumor progression. The novel clustering approach, based on ten clustering algorithms and comprehensive analysis of multi-omics data in gastric cancer, can guide prognosis, characterize different clinical and biological features, and elucidate the tumor immune microenvironment, providing insights into the intratumor heterogeneity of GC and potential novel therapeutic strategies. Additionally, CTSV emerges as a prognostic marker linked to tumor immunity and disease progression, which lays the foundation for improved stratification strategies and the development of targeted therapeutic approaches in GC.

Combinational therapeutic strategies to overcome resistance to immune checkpoint inhibitors

Over the past few years, immune checkpoint inhibitors resulted in magnificent and durable successes in treating cancer; however, only a minority of patients respond favorably to the treatment due to a broad-spectrum of tumor-intrinsic and tumor-extrinsic factors. With the recent insights gained into the mechanisms of resistance, combination treatment strategies to overcome the resistance and enhance the therapeutic potential of immune checkpoint inhibitors are emerging and showing promising results in both pre-clinical and clinical settings. This has been derived through multiple interconnected mechanisms such as enhancing tumor immunogenicity, improving neoantigen processing and presentation in addition to augmenting T cell infiltration and cytotoxic potentials. In the clinical settings, several avenues of combination treatments involving immune checkpoint inhibitors were associated with considerable improvement in the therapeutic outcome in terms of patient's survival and tumor growth control. This, in turn, increased the spectrum of cancer patients benefiting from the unprecedented and durable effects of immune checkpoint inhibitors leading to their adoption as a first-line treatment for certain cancers. Moreover, the significance of precision medicine in cancer immunotherapy and the unmet demand to develop more personalized predictive biomarkers and treatment strategies are also highlighted in this review.

A Novel Prognostic Signature Integrating Immune and Glycolytic Pathways for Enhanced Prognosis and Immunotherapy Prediction in Hepatocellular Carcinoma

Background

This study aimed to establish an immune-glycolysis-related prognostic signature (IGRPS) to predict hepatocellular carcinoma (HCC) outcomes. Additionally, it explored the role of this signature in the tumor immune microenvironment (TIME), glycolytic pathways, and immunotherapy.

Methods

We analyzed RNA-seq, single-cell sequencing, and immune- and glycolysis-related gene datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Using weighted gene co-expression network analysis (WGCNA), F-test, and Cox regression, we identified key survival-related immune and glycolytic genes (SRIGRGs) and developed an IGRPS through multivariate Cox regression. The IGRPS's predictive performance was validated in training and validation cohorts using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and a prognostic nomogram. Its correlation with TIME and its ability to predict immunotherapy outcomes were also assessed. In vitro experiments were conducted to analyze the expression and function of IGRPS genes in HCC.

Results

Thirteen SRIGRGs were identified for constructing the IGRPS. Patients with low-risk scores had significantly longer survival times. The area under the curve (AUC) for ROC curves was over 0.73 for training and 0.7 for validation cohorts, with C-indices of 0.721 and 0.79, respectively. IGRPS was confirmed as an independent prognostic indicator. Patients in the low-risk group showed better responses to combined anti-CTLA4 and anti-PD-1 therapies. In vitro experiments indicated that PRKAG1 and B3GAT3 were upregulated, enhancing glycolysis and promoting HCC cell proliferation and migration.

Conclusion

The IGRPS, based on immune- and glycolysis-related genes, effectively predicted prognosis and immunotherapy responses in HCC patients.

Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning

Over the past several decades, there has been a significant increase in the number of breast cancer patients. Among the four subtypes of breast cancer, Her2-positive breast cancer is one of the most aggressive breast cancers. In this study, we screened the differentially expressed genes from The Cancer Genome Atlas-Breast cancer database and analyzed the relationship between immune cell infiltration and differentially expressed genes using weighted gene co-expression network analysis. By constructing a module-trait relationships heatmap, the red module, which had the highest correlation value with M1 macrophages, was selected. Twenty hub genes were selected based on a protein-protein interaction network. Then, four overlapping M1 macrophage infiltration-related genes (M1 MIRGs), namely CCDC69, PPP1R16B, IL21R, and FOXP3, were obtained using five machine-learning algorithms. Subsequently, nomogram models were constructed to predict the incidence of Her2-positive breast cancer patients. The outer datasets and receiver operating characteristic curve analysis were used to validate the accuracy of the four M1 MIRGs and nomogram models. The average value of the area under the curve for the nomogram models was higher than 0.75 in both the training and testing sets. After that, survival analysis showed that higher expression of CCDC69, PPP1R16B, and IL21R were associated with overall survival of Her2-positive breast cancer patients. The expression of CCDC69 and PPP1R16B could lead to more benefits than the expression of IL21R and FOXP3 for immunotherapy. Lastly, we conducted immunohistochemistry staining to validate the aforementioned results. In conclusion, we found four M1 MIRGs that may be helpful for the diagnosis, prognosis, and immunotherapy of Her2-positive breast cancer.

Progress of PD-1/PD-L1 immune checkpoint inhibitors in the treatment of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a highly heterogeneous cancer with substantial recurrence potential. Currently, surgery and chemotherapy are the main treatments for this disease. However, chemotherapy is often limited by several factors, including low bioavailability, significant systemic toxicity, inadequate targeting, and multidrug resistance. Immune checkpoint inhibitors (ICIs), including those targeting programmed death protein-1 (PD-1) and its ligand (PD-L1), have been proven effective in the treatment of various tumours. In particular, in the treatment of TNBC with PD-1/PD-L1 inhibitors, both monotherapy and combination chemotherapy, as well as targeted drugs and other therapeutic strategies, have broad therapeutic prospects. In addition, these inhibitors can participate in the tumour immune microenvironment (TIME) through blocking PD-1/PD-L1 binding, which can improve immune efficacy. This article provides an overview of the use of PD-1/PD-L1 inhibitors in the treatment of TNBC and the progress of multiple therapeutic studies. To increase the survival of TNBC patients, relevant biomarkers for predicting the efficacy of PD-1/PD-L1 inhibitor therapy have been explored to identify new strategies for the treatment of TNBC.

Induction of macrophage efferocytosis in pancreatic cancer via PI3Kγ inhibition and radiotherapy promotes tumour control

BACKGROUND

The immune suppression mechanisms in pancreatic ductal adenocarcinoma (PDAC) remain unknown, but preclinical studies have implicated macrophage-mediated immune tolerance. Hence, pathways that regulate macrophage phenotype are of strategic interest, with reprogramming strategies focusing on inhibitors of phosphoinositide 3-kinase-gamma (PI3Kγ) due to restricted immune cell expression. Inhibition of PI3Kγ alone is ineffective in PDAC, despite increased infiltration of CD8+ T cells.

OBJECTIVE

We hypothesised that the immune stimulatory effects of radiation, and its ability to boost tumour antigen availability could synergise with PI3Kγ inhibition to augment antitumour immunity.

DESIGN

We used orthoptic and genetically engineered mouse models of pancreatic cancer (LSL-KrasG12D/+;Trp53R172H/+;Pdx1-Cre). Stereotactic radiotherapy was delivered using contrast CT imaging, and PI3Kγ inhibitors by oral administration. Changes in the tumour microenvironment were quantified by flow cytometry, multiplex immunohistochemistry and RNA sequencing. Tumour-educated macrophages were used to investigate efferocytosis, antigen presentation and CD8+ T cell activation. Single-cell RNA sequencing data and fresh tumour samples with autologous macrophages to validate our findings.

RESULTS

Tumour-associated macrophages that employ efferocytosis to eradicate apoptotic cells can be redirected to present tumour antigens, stimulate CD8+ T cell responses and increase local tumour control. Specifically, we demonstrate how PI3Kγ signalling restricts inflammatory macrophages and that inhibition supports MERTK-dependent efferocytosis. We further find that the combination of PI3Kγ inhibition with targeted radiotherapy stimulates inflammatory macrophages to invoke a pathogen-induced like efferocytosis that switches from immune tolerant to antigen presenting.

CONCLUSIONS

Our data supports a new immunotherapeutic approach and a translational rationale to improve survival in PDAC.

Identifying predictors of overall survival among patients with TMB-low metastatic cancer treated with immune checkpoint inhibitors

BACKGROUND

Immune checkpoint inhibitors (ICIs) have significantly advanced cancer therapy, yet their efficacy in tumors with low tumor mutational burden (TMB) remains suboptimal. In this study, we aimed to elucidate the impact of somatic mutations on overall survival (OS) in TMB-low patients treated with ICIs and to explore the potential for personalized treatment selection through machine learning.

METHODS

We conducted a comprehensive analysis of 1172 TMB-low (TMB < 10 mutations per megabase) patients with cancer receiving ICIs, examining the association between specific gene mutations and OS. Additionally, we developed a decision tree model (DTM) to predict OS based on clinical features and tumor mutational profiles.

RESULTS

Our findings reveal that mutations in DAXX, HLA-A, H3C2, IGF1R, CTNNB1, SMARCA4, KMT2D, and TP53 are significantly associated with poorer survival outcomes in the multivariate analysis. Remarkably, for renal cell carcinoma (RCC) patients, VHL mutations predicted improved OS following ICI even when adjusted for age, sex, and microsatellite instability (MSI) status in both multivariate analysis and the DTM model.

CONCLUSIONS

These results reinforce the prevailing notion that TMB alone does not predict ICI response, highlighting the critical role of individual gene mutations in TMB-low tumors under ICI therapy. Furthermore, our study demonstrates the promise of machine learning models in optimizing ICI treatment decisions, paving the way for more precise and effective therapeutic strategies in this patient population.

Lessons learned from a decade of immune checkpoint inhibition: The good, the bad, and the ugly

Discovering the brakes/checkpoints that cancer places on the immune system to prevent being eradicated led to the 2018 Nobel Prize and the development of multiple Food and Drug Administration-approved immune checkpoint inhibitors (ICIs). ICIs have transformed the treatment of numerous cancer types and, remarkably, some patients with end-stage metastatic disease can achieve durable, complete remissions - cures. Still, ICIs cause significant immune-related toxicities, and most tumors are resistant. Unusual progression patterns such as pseudo-progression and hyper-progression (accelerated progression) can occur. Biomarkers for ICI response/resistance include microsatellite instability, high tumor mutational burden, and PD-L1 immunohistochemistry positivity; but they are imperfect, perhaps because of immune system complexity. Herein, we explore the good, the bad, and the ugly of ICIs in cancer treatment.

Case Report: Lasting complete response to pembrolizumab in mismatch repair-deficient cardiac sarcoma: a genomic characterization

Sarcomas are traditionally considered "cold" tumors with poor response to immunotherapy. However, evidence accumulating over the last years shows that immune checkpoint inhibitors (ICIs) may have a role in selected sarcoma patients according to predictive markers. Here, we report the case of a woman diagnosed with a primary cardiac undifferentiated sarcoma. Following failure of standard first line chemotherapy, high-throughput sequencing (HTS) revealed a high tumor mutational burden (TMB), pathogenic mutations in FAT1 and NOTCH2 and a microsatellite instability (MSI)-associated signature. Immunohistochemistry confirmed mismatch repair-deficiency (MMRd) and abundant CD8+ tumor-infiltrating lymphocytes (TILs), in the absence of tertiary lymphoid structures. The patient was, therefore, treated with the ICI pembrolizumab, reaching a complete response that continues to persist at last follow-up, more than seven years from initial diagnosis and nearly six years from initiation of ICI treatment. This case illustrates the importance of performing HTS in rare sarcomas given the availability of efficient therapies, such as those for tumors displaying high TMB or MMRd/MSI. In agreement with other reports, it supports the contention that MMRd/MSI status and high numbers of TILs are valuable predictive markers of response to immunotherapy in sarcomas.

MRI-based radiomics model for predicting endometrial cancer with high tumor mutation burden

PURPOSE

To evaluate the performance of MRI-based radiomics in predicting endometrial cancer (EC) with a high tumor mutation burden (TMB-H).

METHODS

A total of 122 patients with pathologically confirmed EC (40 TMB-H, 82 non-TMB-H) were included in this retrospective study. Patients were randomly divided into training and testing cohorts in a ratio of 7:3. Radiomics features were extracted from sagittal T2-weighted images and contrast-enhanced T1-weighted images. Then, the logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms were used to construct radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the diagnostic performance of each model, and decision curve analysis was used to determine their clinical application value.

RESULTS

Four radiomics features were selected to build the radiomics models. The three models had similar performance, achieving 0.771 (LR), 0.892 (RF), and 0.738 (SVM) in the training cohort, and 0.787 (LR), 0.798 (RF), and 0.777 (SVM) in the testing cohort. The decision curve demonstrated the good clinical application value of the LR model.

CONCLUSIONS

The MRI-based radiomics models demonstrated moderate predictive ability for TMB-H EC and thus may be a tool for preoperative, noninvasive prediction of TMB-H EC.

Neoantigen-specific tumor-infiltrating lymphocytes in gastrointestinal cancers: a phase 2 trial

Adoptive transfer of unselected autologous tumor-infiltrating lymphocytes (TILs) has mediated meaningful clinical responses in patients with metastatic melanoma but not in cancers of gastrointestinal epithelial origin. In an evolving single-arm phase 2 trial design, TILs were derived from and administered to 91 patients with treatment-refractory mismatch repair proficient metastatic gastrointestinal cancers in a schema with lymphodepleting chemotherapy and high-dose interleukin-2 (three cohorts of an ongoing trial). The primary endpoint of this study was the objective response rate as measured using Response Evaluation Criteria in Solid Tumors 1.0; safety was a descriptive secondary endpoint. In the pilot phase, no clinical responses were observed in 18 patients to bulk, unselected TILs; however, when TILs were screened and selected for neoantigen recognition (SEL-TIL), three responses were seen in 39 patients (7.7% (95% confidence interval (CI): 2.7-20.3)). Based on the high levels of programmed cell death protein 1 in the infused TILs, pembrolizumab was added to the regimen (SEL-TIL + P), and eight objective responses were seen in 34 patients (23.5% (95% CI: 12.4-40.0)). All patients experienced transient severe hematologic toxicities from chemotherapy. Seven (10%) patients required critical care support. Exploratory analyses for laboratory and clinical correlates of response were performed for the SEL-TIL and SEL-TIL + P treatment arms. Response was associated with recognition of an increased number of targeted neoantigens and an increased number of administered CD4+ neoantigen-reactive TILs. The current strategy (SEL-TIL + P) exceeded the parameters of the trial design for patients with colorectal cancer, and an expansion phase is accruing. These results could potentially provide a cell-based treatment in a population not traditionally expected to respond to immunotherapy. ClinicalTrials.gov identifier: NCT01174121 .

DYNC2H1 mutation as a potential predictive biomarker for immune checkpoint inhibitor efficacy in NSCLC and melanoma

Dynein cytoplasmic 2 heavy chain 1 (DYNC2H1) is reported to play a potential role in cancer immunotherapy. However, the association between DYNC2H1 mutation and the clinical benefit of immunotherapy in non-small cell lung cancer (NSCLC) and melanoma remains to be elucidated. We collected data from three public immune checkpoint inhibitor (ICI)-treated NSCLC cohorts (n = 137 in total) and seven ICI-treated melanoma cohorts (n = 418 in total) to explore the potential of DYNC2H1 mutation as a predictive biomarker. The clinical outcomes, including the objective response rate (ORR) and progression-free survival (PFS), of patients with DYNC2H1 mutations are significantly better than those of patients with wild-type DYNC2H1. Multivariate Cox regression analysis confirmed that DYNC2H1 mutation was an independent predictive factor for ICI efficacy in NSCLC and melanoma. In addition, DYNC2H1 mutation exhibited no prognostic value for NSCLC or melanoma. Tumour mutational burden (TMB) and tumour neoantigen burden (TNB) were significantly higher in patients with DYNC2H1 mutation than in those with wild-type DYNC2H1 in both NSCLC and melanoma cohort. The analysis of immune-related genes and immune cell enrichment revealed an association between DYNC2H1 mutation and increased immune infiltration, revealing a potential mechanism underlying the predictive role of DYNC2H1 mutation in immunotherapy efficacy. In conclusion, DYNC2H1 mutation serves as a predictive biomarker of ICI efficacy in NSCLC and melanoma.

High tumor mutation burden mitigates the negative impact of chemotherapy history on immune checkpoint blockade therapy

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small-cell lung cancer (NSCLC) accounting for the majority of cases. Immune checkpoint inhibitor (ICI) therapy, particularly with PD-1 inhibitors like nivolumab, has become a critical treatment option for advanced NSCLC. ICI therapy has revolutionized treatment, but prior chemotherapy may diminish ICI treatment efficacy. Tumor mutation burden (TMB) has emerged as a crucial predictor of ICI response, yet its interaction with chemotherapy history in ICI therapy is not fully understood. In this study, I investigate the impact of chemotherapy history on ICI treatment outcomes, focusing on TMB as a potential mitigating factor. Analyzing data from 512 patients with advanced NSCLC treated with PD-1/PD-L1 or CTLA-4 inhibitors, this sudy found that prior chemotherapy significantly reduced objective response rates (ORR) to ICI therapy, particularly in patients with low TMB (<15 mut/Mb). However, in patients with high TMB (≥15 mut/Mb), the negative impact of chemotherapy history on ICI treatment efficacy is minimal, suggesting that high TMB mitigates chemotherapy-induced resistance to ICI therapy. Furthermore, while chemotherapy history is associated with worse overall survival (OS) and progression-free survival (PFS) following ICI therapy in low-TMB patients, no such association is observed in high-TMB patients. These findings highlight the importance of TMB as a predictive biomarker, emphasizing the need for optimal treatment sequencing and personalized therapeutic strategies to overcome chemotherapy-induced immune resistance and maximize ICI treatment efficacy. These results suggest that ICI therapy may be more beneficial as a first-line treatment, particularly for patients with low TMB.

Efficacy of combined immunotherapy and targeted therapy in overcoming barriers to postoperative recurrence in squamous subtype anaplastic thyroid carcinoma with abscess: a case report and literature review

Background

Molecularly targeted therapies and immunotherapy are increasingly being employed in the treatment of aggressive, recurrent thyroid cancer. Evidence from several studies indicates that a significant proportion of tumor patients derive limited benefit from immunotherapy as a monotherapy, with vascular abnormalities in solid tumors contributing to immune evasion. Numerous studies, both domestic and international, have assessed the efficacy of combining immune checkpoint inhibitors with antiangiogenic agents across various tumor types. These studies suggest that such combination therapies are effective in controlling disease progression and extending survival, among other outcomes. Nevertheless, further research is warranted to substantiate these findings and optimize treatment protocols.

Methods

This study aims to describe a patient diagnosed with anaplastic thyroid carcinoma (ATC) combined with primary squamous cell carcinoma of the thyroid (PSCCT) and concurrent thyroid abscess. The patient experienced local recurrence and metastasis following surgical intervention, radiotherapy, and chemotherapy, and was found to be PD-1 negative. Disease progression was effectively controlled through combination therapy with anlotinib and tislelizumab. Additionally, a comprehensive review of the relevant literature was conducted.

Results

The patient exhibited disease recurrence 8 months postoperatively, notwithstanding the administration of adjuvant radiotherapy and chemotherapy. The local recurrent mass demonstrated minimal reduction following 4 cycles of targeted therapy with anlotinib. However, subsequent treatment with a combination of anlotinib and tislelizumab resulted in a substantial reduction of the neck mass and enlarged cervical lymph nodes after 12 cycles. The patient tolerated the combination therapy well, experiencing no significant adverse effects aside from pronounced fatigue. Thus, the combination therapy with anlotinib and tislelizumab proved effective in controlling the disease.

Conclusion

The management of postoperative recurrence of ATC-PSCCT presents significant challenges, as recurrent tumors typically demonstrate increased aggressiveness and resistance to pharmacological interventions, necessitating multimodal therapeutic approaches. Tislelizumab, an immune checkpoint inhibitor, may facilitate immune-mediated tumor clearance through the activation of various immune cells, including natural killer cells and macrophages. Despite the patient's PD-1 negativity, the combination of anlotinib and tislelizumab may exert synergistic effects through distinct mechanisms, thereby potentially enhancing therapeutic efficacy. The integration of a multi-targeted tyrosine kinase inhibitor within this combination therapy regimen warrants further investigation.

Pan-cancer analysis and experimental verification of cytochrome B561 as a prognostic and therapeutic biomarker in breast cancer

OBJECTIVE

This study investigates Cytochrome B561 (CYB561) expression in Pan-Cancer, its relationship with immune invasion, and its prognostic value in Breast Cancer (BRCA) patients.

METHODS

Data from The Cancer Genome Atlas (TCGA) were analyzed. CYB561 expression in normal and tumor tissues was examined, with correlations to immune invasion, mutation, and immune checkpoints. Wilcoxon rank-sum test assessed expression differences. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. Logistic regression, Kaplan-Meier, and Cox regression analyses evaluated clinicopathological features and survival outcomes. A Cox multivariate analysis-based Nomogram predicted CYB561's prognostic impact. CYB561 knockout in breast cancer cells assessed functional effects. Single-cell RNA sequencing identified prognostic biomarkers.

RESULTS

CYB561 was highly expressed in most tumors. BRCA showed the highest correlation with ESTIMATE scores and significant negative correlation with immune checkpoints. High CYB561 expression correlated with specific clinicopathological features and survival outcomes. The nomogram predicted BRCA prognosis. CYB561 knockout inhibited breast cancer cell proliferation. Seven predictive agents for CYB561 inhibition were identified.

CONCLUSIONS

CYB561 exhibits aberrant expression in tumors, particularly in BRCA, and serves as a predictive marker for immune-related therapies and a prognostic indicator in BRCA.

Immunotherapy for Advanced Conjunctival Squamous Cell Carcinoma: Treatment Failures

PURPOSE

To review the treatment response of advanced conjunctival squamous cell carcinoma (SCC) to systemic immune checkpoint inhibitor (ICI) therapy at a single institution.

METHODS

A retrospective review of patients treated at a single institution from 2015 to 2024 was conducted to identify those with advanced conjunctival SCC who had been treated with ICI therapy. Advanced disease included patients with orbital invasion of tumors, unresectable disease, or metastatic disease. Computed tomography imaging and tumor mutational burden data were evaluated for all patients.

RESULTS

Five patients with advanced conjunctival SCC were treated with ICIs. All patients had the American Joint Committee on Cancer stage cT3N0M0. All patients had best corrected visual acuity in the affected eye of 20/30 or better at presentation. All patients progressed while on ICIs, with 3 ultimately requiring exenteration at a median time of 6 months from initial diagnosis. One patient had progressive metastatic disease, and one had direct intracranial extension. All patients had low tumor mutational burden.

CONCLUSIONS

Unlike prior reports demonstrating complete treatment response while on ICI therapy in patients with advanced conjunctival SCC, the current study demonstrates that 5 of 5 patients had disease progression while on ICI therapy. All patients also had low tumor mutational burden. Tumor mutational burden may be important in predicting disease response to ICI in patients with conjunctival SCC; however, given the small number of patients with conjunctival SCC treated with ICI to date, more data are needed to understand the role of ICIs in conjunctival SCC.

PD-1 transcriptomic landscape across cancers and implications for immune checkpoint blockade outcome

Programmed cell death protein 1 (PD-1) is a critical immune checkpoint receptor and a target for cancer immune checkpoint inhibitors (ICI). We investigated PD-1 transcript expression across cancer types and its correlations to clinical outcomes. Using a reference population, PD-1 expression was calculated as percentiles in 489 of 514 patients (31 cancer types) with advanced/metastatic disease. PD-1 RNA expression varied across and within cancer types; pancreatic and liver/bile duct malignancies displayed the highest rates of high PD-1 (21.82% and 21.05%, respectively). Elevated CTLA-4, LAG-3, and TIGIT RNA expression were independently correlated with high PD-1. Although high PD-1 was not associated with outcome in immunotherapy-naïve patients (n = 272), in patients who received ICIs (n = 217), high PD-1 transcript expression was independently correlated with prolonged survival (hazard ratio 0.40; 95%CI, 0.18-0.92). This study identifies PD-1 as an important biomarker in predicting ICI outcomes, and advocates for comprehensive immunogenomic profiling in cancer management.

Different NGS identification methods of somatic mutation sites in solid tumors impact TMB results

BACKGROUND

Tumor mutation burden (TMB) is a predictive biomarker for assessing the response of various tumor types to immune checkpoint inhibitors (ICI). TMB is quantified based on somatic mutations identified by next-generation sequencing (NGS) using targeted panel data. This study aimed to investigate whether different NGS methods will affect the results of TMB detection in solid tumors.

MATERIALS AND METHODS

In this study, a hybrid capture NGS method was performed to identify Tumor-only (TO) tissue and tumor tissue and white blood cells Tumor Control (TC). The accuracy and specificity of the two employed methods were evaluated by the identification and analysis of standard reference data. Based on the quality control of FFPE samples, 24 pathological and imaging confirmed solid tumor samples were compared to assess the differences between the two methods in identifying and incorporating the mutation sites and the effect on TMB detection.

RESULT

The data identified 298 common genes in the detection range of TO and TC methods. The detection range of these genes primarily comprised exons and some introns. The coefficient of variation (CV%) between the detected variant and true mutation frequencies was < 10%, confirming their accuracy and specificity. Both methods detected increased mutations of TP53, CDKN2 A, KRAS, PTEN, EGFR, PIK3 CA, BRAF, BRCA2, FGFR2, and NRAS. The consistency rate of TMB was observed as 92% (22/24). The chi-square test indicated a significant difference in TMB results between TO and TC (χ2 = 16.667, p = 0.000, p < 0.001). Furthermore, Cohen's kappa analysis showed consistency in the TMB values detected by TO and TC methods, which were good and had high repeatability (kappa = 0.833, p = 0.000, p < 0.001). The Venn analysis revealed that the two methods identified and included different TMB sites, which in turn affected the TMB calculation results.

CONCLUSION

This study revealed that different algorithms and design panels for mutation filtering affect the TMB test results. When the TMB result is near the 10 mut/Mb threshold, different methods may yield different results. Moreover, a single test result can affect clinical treatment decisions. Therefore, it is recommended to use TO or TC combined with other tests for evaluating somatic mutations.

Overcoming cancer therapy resistance: From drug innovation to therapeutics

One of the major limitations of cancer therapy is the emergence of drug resistance. This review amis to provide a focused analysis of the multifactorial mechanisms underlying therapy resistance,with an emphasis on actionable insights for developing novel therapeutic strategies. It concisely outlines key factors contributing to therapy resistance, including drug delivery barriers, cancer stem cells (CSCs), epithelial-mesenchymal transition (EMT), cancer heterogeneity, tumor microenvironment (TME), genetic mutations, and alterlations in gene expression. Additionally, we explore how tumors evade targeted therapies through pathway-specific mechanisms that restore disrupted signaling pathways. The review critically evaluates innovative strategies designed to sensitize resistant tumor cells, such as targeted protein dedgradation, antibody-drug conjugates, structure-based drug design, allosteric drugs, multitarget drugs, nanomedicine and others We also highlight the importance of understanding the pharmacological actions of these agents and their integration into treatment regimens. By synthesizing current knowledge and identifying gaps in our understanding, this review aims to guide future research and improve patient outcomes in cancer therapy.

Society for Immunotherapy of Cancer (SITC) consensus statement on essential biomarkers for immunotherapy clinical protocols

Immunotherapy of cancer is now an essential pillar of treatment for patients with many individual tumor types. Novel immune targets and technical advances are driving a rapid exploration of new treatment strategies incorporating immune agents in cancer clinical practice. Immunotherapies perturb a complex system of interactions among genomically unstable tumor cells, diverse cells within the tumor microenvironment including the systemic adaptive and innate immune cells. The drive to develop increasingly effective immunotherapy regimens is tempered by the risk of immune-related adverse events. Evidence-based biomarkers that measure the potential for therapeutic response and/or toxicity are critical to guide optimal patient care and contextualize the results of immunotherapy clinical trials. Responding to the lack of guidance on biomarker testing in early-phase immunotherapy clinical trials, we propose a definition and listing of essential biomarkers recommended for inclusion in all such protocols. These recommendations are based on consensus provided by the Society for Immunotherapy of Cancer (SITC) Clinical Immuno-Oncology Network (SCION) faculty with input from the SITC Pathology and Biomarker Committees and the Journal for ImmunoTherapy of Cancer readership. A consensus-based selection of essential biomarkers was conducted using a Delphi survey of SCION faculty. Regular updates to these recommendations are planned. The inaugural list of essential biomarkers includes complete blood count with differential to generate a neutrophil-to-lymphocyte ratio or systemic immune-inflammation index, serum lactate dehydrogenase and albumin, programmed death-ligand 1 immunohistochemistry, microsatellite stability assessment, and tumor mutational burden. Inclusion of these biomarkers across early-phase immunotherapy clinical trials will capture variation among trials, provide deeper insight into the novel and established therapies, and support improved patient selection and stratification for later-phase clinical trials.

Revolutionary Cancer Therapy for Personalization and Improved Efficacy: Strategies to Overcome Resistance to Immune Checkpoint Inhibitor Therapy

Cancer remains a significant public health issue worldwide, standing as a primary contributor to global mortality, accounting for approximately 10 million fatalities in 2020 [...].

Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC

BACKGROUND

Immune checkpoint inhibitors (CPIs) have revolutionized cancer therapy for several tumor indications. However, a substantial fraction of patients treated with CPIs derive no benefit or have short-lived responses to CPI therapy. Identifying patients who are most likely to benefit from CPIs and deciphering resistance mechanisms is therefore essential for developing adjunct treatments that can abrogate tumor resistance.

PATIENTS AND METHODS

In this study, we used a machine learning approach that used the US-based nationwide de-identified Flatiron Health and Foundation Medicine non-small cell lung carcinoma (NSCLC) clinico-genomic database to identify genomic markers that predict clinical responses to CPI therapy. In total, we analyzed data from 4,433 patients with NSCLC.

RESULTS

Analysis of pretreatment genomic data from 1,511 patients with NSCLC identified. Of the 36 genomic signatures identified, 33 exhibited strong predictive capacity for CPI response (n=1150) compared with chemotherapy response (n=361), while three signatures were prognostic. These 36 genetic signatures had in common a core set of four genes (BRAF, BRIP1, FGF10, and FLT1). Interestingly, we observed that some (n=19) of the genes in the signatures (eg, TP53, EZH2, KEAP1 and FGFR2) had alternative mutations with contrasting clinical outcomes to CPI therapy. Finally, the genetic signatures revealed multiple biological pathways involved in CPI response, including MAPK, PDGF, IL-6 and EGFR signaling.

CONCLUSIONS

In summary, we found several genomic markers and pathways that provide insight into biological mechanisms affecting response to CPI therapy. The analyses identified novel targets and biomarkers that have the potential to provide candidates for combination therapies or patient enrichment strategies, which could increase response rates to CPI therapy in patients with NSCLC.

Integrative analysis of senescence-related genes identifies robust prognostic clusters with distinct features in hepatocellular carcinoma

INTRODUCTION

Senescence refers to a state of permanent cell growth arrest and is regarded as a tumor suppressive mechanism, whereas accumulative evidence demonstrate that senescent cells play an adverse role during cancer progression. The scarcity of specific and reliable markers reflecting senescence level in cancer impede our understanding of this biological basis.

OBJECTIVES

Senescence-related genes (SRGs) were collected for integrative analysis to reveal the role of senescence in hepatocellular carcinoma (HCC).

METHODS

Consensus clustering was used to subtype HCC based on SRGs. Several computational methods, including single sample gene set enrichment analysis (ssGSEA), fuzzy c-means algorithm, were performed. Data of drug sensitivities were utilized to screen potential therapeutic agents for different senescence patients. Additionally, we developed a method called signature-related gene analysis (SRGA) for identification of markers relevant to phenotype of interest. Experimental strategies consisting quantitative real-time PCR (qRT-PCR), β-galactosidase assay, western blot, and tumor-T cell co-culture system were used to validate the findings in vitro.

RESULTS

We identified three robust prognostic clusters of HCC patients with distinct survival outcome, mutational landscape, and immune features. We further extracted signature genes of senescence clusters to construct the senescence scoring system and profile senescence level in HCC at bulk and single-cell resolution. Senescence-induced stemness reprogramming was confirmed both in silico and in vitro. HCC patients with high senescence were immune suppressed and sensitive to Tozasertib and other drugs. We suggested that MAFG, PLIN3, and 4 other genes were pertinent to HCC senescence, and MAFG potentially mediated immune suppression, senescence, and stemness.

CONCLUSION

Our findings provide insights into the role of SRGs in patients stratification and precision medicine.

The role of lnc‑MAPKAPK5‑AS1 in immune cell infiltration in hepatocellular carcinoma: Bioinformatics analysis and validation

The oncogenic and tumor suppressor roles of lnc-MAPKAPK5-AS1 in multiple cancers suggest its complexity in modulating cancer progression. The expression and promoter methylation level of lnc-MAPKAPK5-AS1 in hepatocellular carcinoma (HCC) was investigated through data mining from The Cancer Genome Atlas and Gene Expression Omnibus and its significance in prognosis and immunity was explored. lnc-MAPKAPK5-AS1 was co-expressed with its protein-coding gene MAPKAPK5 in HCC and exhibited upregulation in HCC tissues as a result of hypomethylation of its promoter region. High expression of lnc-MAPKAPK5-AS1 was associated with poor prognosis. Enrichment analysis revealed that lnc-MAPKAPK5-AS1 is involved in immune and metabolic-related pathways. Changes in the expression of lnc-MAPKAPK5-AS1 affected plasma cells, T cells CD4+ memory resting, NK cells, macrophages M0/M1, and mast cells resting in the tumor microenvironment. lnc-MAPKAPK5-AS1 was found to correlate with multiple immune checkpoints. Analysis of the Sangerbox database revealed positive relationships between expression of lnc-MAPKAPK5-AS1, tumor mutational burden and microsatellite instability, which suggested that immunotherapy may be effective in tumors with high expression of lnc-MAPKAPK5-AS1. The expression of lnc-MAPKAPK5-AS1 was verified to indicate sensitivity to 16 common targeted drugs. Immunohistochemistry confirmed the expression of MAPKAPK5 protein in HCC and its prognostic significance. Weighted gene co-expression network analysis was applied to identify hub genes related to both immunoreactive score and gene expression. These results revealed that lnc-MAPKAPK5-AS1 may be involved in the occurrence and development of HCC as an oncogene and may represent a potential therapeutic target through modulating the substance metabolism and immune response.

Current status and future perspectives of immunotherapy for abdominal liposarcoma: From basic research to clinical practice

Liposarcoma is a highly heterogeneous type of soft tissue sarcoma originating from adipose tissue, characterized by complex biological behavior and invasiveness. Traditional treatments have shown limited efficacy in high-grade and metastatic liposarcoma, with unsatisfactory patient outcomes. In recent years, the breakthroughs of immunotherapy in various solid tumors have sparked interest in its potential application to liposarcoma. This review systematically examines the progress in basic research and clinical practice of immunotherapy for liposarcoma, discussing the tumor immune microenvironment, mechanisms of immune evasion, the application of immune checkpoint inhibitors, combination therapy strategies, the challenges faced, as well as the future direction, with an aim to provide a theoretical basis for personalized treatment of liposarcoma, promote the development of novel immunotherapy strategies, and ultimately improve patient prognosis and quality of life.

A Comprehensive Analysis Revealing BUB1B as a Potential Prognostic and Immunological Biomarker in Lung Adenocarcinoma

BUB1B, a member of the spindle assembly checkpoint family known as BUB1 mitotic checkpoint serine/threonine kinase B, has been associated with the promotion of tumor progression. Nevertheless, its specific contributions to tumorigenesis remain largely unexplored. This study seeks to offer a systematic and comprehensive analysis of the role of BUB1B in the progression of various cancers, with a particular focus on lung adenocarcinoma, utilizing a range of databases. We investigated BUB1B's role in pan-cancer using TCGA data, analyzing it with platforms like HPA, TIMER, TISIDB, GEPIA, cBioPortal, GDC, LinkedOmics, and CancerSEA. Additionally, we assessed BUB1B's impact on lung adenocarcinoma proliferation and migration through CCK-8, wound healing, transwell assays and Western blot analysis. This study found that BUB1B was upregulated in most cancers and was significantly linked to patient prognosis. Its expression correlated with immune cell infiltration and genetic markers of immunomodulators across different cancers. BUB1B was involved in the acute inflammatory response and IgA production pathways but negatively correlated with inflammation in lung adenocarcinoma. Moreover, the siRNA-mediated knockdown of BUB1B resulted in the inhibition of proliferation and migration of lung cancer cells in vitro. This study underscores the potential of BUB1B as a biomarker and a promising therapeutic target for patients with lung adenocarcinoma.

Significance of LRFN4 in prognosis and tumor microenvironment of lung adenocarcinoma

Background

LRFN4 is expressed in various tumors and leukemia cell lines. This study aims to explore the effect of LRFN4 in lung adenocarcinoma (LUAD).

Methods

The data on LUAD patients were collected from the Cancer Genome Atlas and Gene Expression Omnibus database. The expression of LRFN4 in LUAD and LUAD cell lines was analyzed via differential gene analysis, qRT-PCR assay, and Western blotting assay. The correlation of LRFN4 expression with the onset of LUAD were calculated using Pearson correlation analysis. The transcription factors correlated with LRFN4 expression were screened by differential expression analysis and Pearson correlation analysis. Moreover, the effect of LRFN4 on the immune landscape of LUAD was analyzed using CIBERSORT algorithm. The GDSC and CTRP databases were used to analyze the drug sensitivity of hub gene.

Results

LRFN4 was highly expressed in LUAD patients and cells, and LRFN4 expression was correlated with metastasis, pathological stages, and age of LUAD patients. The transcription factors E2F1 and E2F3 could regulate LRFN4 expression by binding upstream of LRFN4. The 8 immune cell infiltration levels were differential between LRFN4 high and LRFN4 low patients. The ESTIMATEScore and ImmuneScore levels were decreased, the TumorPurity level was elevated, and 6 immune checkpoint expressions were increased in LRFN4high patients. Moreover, LRFN4high patients had inferior prognosis. The mutation rate of TP53, TTN, and MUC6 and the level of TMB were increased in LRFN4 high patients. The expressions of TCF3, E2F1, E2F3, and LRFN4 were correlated with the IC50 of multiple drugs.

Conclusion

LRFN4 may serve as a novel prognostic biomarker for LUAD, shows specific overexpression in LUAD and may be a potential therapeutic target for LUAD patients.

Toll-like receptor 3: a double-edged sword

The discovery of Toll-like receptors (TLRs) and their role in dendritic cells earned the Nobel Prize for 2011 because TLRs profoundly enhanced our understanding of the immune system. Specifically, TLR3 is located within the endosomal compartments of dendritic cells and plays a crucial role in the immune response by acting as a pattern recognition receptor that detects both exogenous (viral) and endogenous (mammalian) double-stranded RNA. However, TLR3 activation is a double-edged sword in various immune-mediated diseases. On one hand, it can enhance anti-viral defenses and promote pathogen clearance, contributing to host protection. On the other hand, excessive or dysregulated TLR3 signaling can lead to chronic inflammation and tissue damage, exacerbating conditions such as autoimmune diseases, chronic viral infections, and cancer. In cancer, TLR3 expression has been linked to both favorable and poor prognoses, though the underlying mechanisms remain unclear. Recent clinical and preclinical advances have explored the use of TLR3 agonists in cancer immunotherapy, attempting to capitalize on their potential to enhance anti-tumor responses. The dual role of TLR3 highlights its complexity as a therapeutic target, necessitating careful modulation to maximize its protective effects while minimizing potential pathological consequences. In this review, we explore the intricate roles of TLR3 in immune responses across different disease contexts, including cancer, infections, autoimmune disorders, and allergies, highlighting both its protective and detrimental effects in these disorders, as well as progress in developing TLR3 agonists as part of the immunotherapy landscape.