The association between tumor mutational burden and prognosis is dependent on treatment context

MOGAN for LUAD Subtype Classification by Integrating Three Omics Data Types

Background

Lung adenocarcinoma (LUAD) is a highly heterogeneous cancer type with a poor prognosis. Accurate subtype identification can help guide its treatment. The traditional subtype identification methods using a single-omics approach make it difficult to comprehensively characterize the molecular features of LUAD. Identification of subtypes through multi-omics association strategies can effectively supplement the shortcomings of single-omics information.

Methods

In this study, we used the Generative Adversarial Network (GAN) to mine transcriptomic, proteomic, and epigenomic information and generate an integrated data set. The newly integrated data were then used to identify LUAD immune subtypes. In the improved GAN (MOGAN) method, we not only integrated multiple omics datasets but also included the interactions between proteins and genes and between methylation and genes. Thus, we achieved effective complementarity of multi-omics information.

Results

Two subtypes, MOGANTPM_S1 and MOGANTPM_S2, were identified using immune cell infiltration analysis and the integrated multi-omics data. MOGANTPM_S1 patients displayed higher immune cell infiltration, better prognosis, and sensitivity to immune checkpoint inhibitors (ICIs), while MOGANTPM_S2 had lower immune cell infiltration, poorer prognosis, and were insensitive to ICIs. Therefore, immunotherapy was more suitable for MOGANTPM_S1 patients in clinical practice. In addition, this study developed a LUAD subtype diagnostic model using the transcriptomic and proteomic features of five genes, which can be used to guide clinical subtype diagnosis.

Conclusions

In summary, the MOGAN method was applied to integrate three omics data types and successfully identify two LUAD immune subtypes with significant survival differences. This classification method may be useful for LUAD treatment decisions.

Establishment and verification of a prognostic signature associated with fatty acid metabolism in endometrial cancer

Endometrial carcinoma (EC) is one of the leading causes of mortality in women. Metabolic disorders, such as abnormal fatty acid metabolism (FAM), are considered to be indicators of tumorigenesis. However, to the best of our knowledge, the relationship between EC and FAM remains unclear. The process of FAM is associated with the function of immune cells, thus samples from The Cancer Genome Atlas were grouped according to immune infiltration levels. Subsequently, prognostic gene signatures were constructed based on selected FAM‑associated genes. The signature effect was validated, and enrichment analyses were conducted based on sample classification. Nomograms were used to predict survival, merging clinical data and the gene signature. Samples were divided into high‑ and low‑risk groups based on the gene signature. The survival status, clinical characteristics, enrichment analysis and immune infiltration were significantly different between high‑ and low‑risk groups. According to the nomogram, low microsatellite instability‑high as well as a high tumor mutation burden can be observed in the low‑nomo‑score group. Immune checkpoint inhibitor‑associated genes were differentially expressed between groups and 35 sensitive compounds were identified. Comprehensive bioinformatics analysis in EC revealed potential roles of FAM in tumorigenesis, the tumor microenvironment and prognosis, suggesting that FAM‑associated signatures are promising biomarkers for EC. These findings may improve the understanding of FAM in EC and pave the way for a more accurate assessment of prognosis and immunotherapy outcomes.

Clinical metric of tumor mutational burden depicts colorectal cancer patients at the extremes

PURPOSE

Rare cases of colorectal cancer patients with exceptionally good or poor prognosis often remain overlooked, limiting insights into prognostic factors and underlying mechanisms.

METHODS

This study developed an analytical framework to investigate cancer patients at the extremes using tumor mutational burden (TMB). By analyzing data from 1277 colorectal cancer patients who did not receive immunotherapy, this analysis assessed how patient survival varies with a broad range of TMB levels.

RESULTS

Among patients with TMB ≤ 10 mutations per megabase (mut/Mb), increasing TMB was associated with worse survival outcomes. In contrast, patients with TMB > 10 mut/Mb showed  increasingly improved survival. Notably, a small subgroup (3.83%) with TMB > 60 mut/Mb had significantly better survival outcomes.

CONCLUSIONS

These findings highlight TMB's dual role in colorectal cancer progression. This study suggests that atypical patients can coexist within the same "disease continuum" with typical patients, under the universal context unified by a shared cancer hallmark. TMB provides a useful biomarker for identifying these extremes, offering a clinical metric to better predict patient outcomes and personalize treatment strategies.

The phenogenomic landscapes of pleural mesothelioma tumor microenvironment predict clinical outcomes

BACKGROUND

Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy with limited therapeutic options. To improve patients management and treatment, more precise stratification strategies are needed. This study aimed to characterize the phenogenomic landscapes of MPM and to understand their influence on patients clinical outcomes.

METHODS

We conducted a phenogenomic analysis on 22 MPM patients using two high throughput approaches: imaging mass cytometry (IMC) with whole exome sequencing (WES). Resulting profiles were addressed for their clinical relevance to predict patients prognosis.

RESULTS

IMC revealed a highly heterogeneous tumor microenvironment (TME) with distinct tumor cell subpopulations. Notably, we identified a novel sarcomatoid-like cellular cluster associated with poor prognosis. The TME was also infiltrated with immune cells including macrophages and CD4+ T lymphocytes, that were more abundant in patients with favorable clinical outcomes. WES identified a complex genomic landscape with limited prognostic value for individual genetic alterations. However, tumor mutational burden (TMB) emerged as a potential predictive biomarker, inversely correlating with immune cell infiltration, particularly macrophages and CD4+ T lymphocytes.

CONCLUSIONS

Our findings underscore the intricate interplay between the tumor genome, TME composition, and clinical outcomes in MPM. These data support the potential of integrating genomic and TME profiling to develop more precise patient stratification strategies and potentially optimize therapeutic approaches, including immunotherapy.

A novel golgi related genes based correlation prognostic index can better predict the prognosis of glioma and responses to immunotherapy

BACKGROUND

The Golgi apparatus (GA) serves as the center of protein and lipid synthesis and modification within cells, playing a crucial role in regulating diverse cellular processes as a signaling hub. Dysregulation of GA function can give rise to a range of pathological conditions, including tumors. Notably, mutations in Golgi-associated genes (GARGs) are frequently observed in various tumors, and these mutations have been implicated in promoting tumor metastasis. However, the precise relationship between GARGs and glioma, a type of brain tumor, remains poorly understood. Therefore, the objective of this investigation was to assess the prognostic significance of GARGs in glioma and evaluate their impact on the immune microenvironment.

METHODS

The expression of GARGs was obtained from the TCGA and CGGA databases, encompassing a total of 1564 glioma samples (598 from TCGA and 966 from CGGA). Subsequently, a risk prediction model was constructed using LASSO regression and Cox analysis, and its efficacy was assessed. Additionally, qRT-PCR was employed to validate the expression of GARGs in relation to glioma prognosis. Furthermore, the association between GARGs and immunity, mutation, and drug resistance was investigated.

RESULTS

A selection of GARGs (SPRY1, CHST6, B4GALNT1, CTSL, ADCY3, GNL1, KIF20A, CHP1, RPS6, CLEC18C) were selected through differential expression analysis and Cox analysis, which were subsequently incorporated into the risk model. This model demonstrated favorable predictive efficiency, as evidenced by the area under the curve (AUC) values of 0.877, 0.943, and 0.900 for 1, 3, and 5-year predictions, respectively. Furthermore, the risk model exhibited a significant association with the tumor immune microenvironment and mutation status, as well as a diminished sensitivity to chemotherapy drugs. qRT-PCR analysis confirmed the up-regulation or down-regulation of the aforementioned genes in glioma.

CONCLUSION

The utilization of GARGs in our constructed model exhibits a high level of accuracy in prognosticating glioma and offers promising avenues for the development of therapeutic interventions targeting glioma.

Prognostic value and immune landscapes of disulfidptosis‑related lncRNAs in bladder cancer

Disulfidptosis, which was recently identified, has shown promise as a potential cancer treatment. Nonetheless, the precise role of long non-coding RNAs (lncRNAs) in this phenomenon is currently unclear. To elucidate their significance in bladder cancer (BLCA), a signature of disulfidptosis-related lncRNAs (DRlncRNAs) was developed and their potential prognostic significance was explored. BLCA sample data were sourced from The Cancer Genome Atlas. A predictive signature comprising DRlncRNAs was formulated and subsequently validated. The combination of this signature with clinical characteristics facilitated the development of a nomogram with practical clinical utility. Additionally, enrichment analysis was conducted, the tumor microenvironment (TME) was assessed, the tumor mutational burden (TMB) was analyzed, and drug sensitivity was explored. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to quantify lncRNA expression. The results revealed an eight-gene signature based on DRlncRNAs was established, and the predictive accuracy of the nomogram that incorporated the risk score [area under the curve (AUC)=0.733] outperformed the nomogram without it (AUC=0.703). High-risk groups were associated with pathways such as WNT signaling, focal adhesion and cell cycle pathways. The TME study revealed that high-risk patients had increased immune infiltration, whereas the TMB and tumor immune dysfunction and exclusion scores in low-risk patients indicated a potentially robust immune response. Drug sensitivity analysis identified appropriate antitumor drugs for each group. RT-qPCR experiments validated significant differences in DRlncRNAs expression between normal and BLCA cell lines. In conclusion, the prognostic risk signature, which includes the eight identified DRlncRNAs, demonstrates promise for predicting prognosis of patients with BLCA and guiding the selection of suitable immunotherapy and chemotherapy strategies.

Prediction of checkpoint inhibitor immunotherapy efficacy for cancer using routine blood tests and clinical data

Predicting whether a patient with cancer will benefit from immune checkpoint inhibitors (ICIs) without resorting to advanced genomic or immunologic assays is an important clinical need. To address this, we developed and evaluated SCORPIO, a machine learning system that utilizes routine blood tests (complete blood count and comprehensive metabolic profile) alongside clinical characteristics from 9,745 ICI-treated patients across 21 cancer types. SCORPIO was trained on data from 1,628 patients across 17 cancer types from Memorial Sloan Kettering Cancer Center. In two internal test sets comprising 2,511 patients across 19 cancer types, SCORPIO achieved median time-dependent area under the receiver operating characteristic curve (AUC(t)) values of 0.763 and 0.759 for predicting overall survival at 6, 12, 18, 24 and 30 months, outperforming tumor mutational burden (TMB), which showed median AUC(t) values of 0.503 and 0.543. Additionally, SCORPIO demonstrated superior predictive performance for predicting clinical benefit (tumor response or prolonged stability), with AUC values of 0.714 and 0.641, compared to TMB (AUC = 0.546 and 0.573). External validation was performed using 10 global phase 3 trials (4,447 patients across 6 cancer types) and a real-world cohort from the Mount Sinai Health System (1,159 patients across 18 cancer types). In these external cohorts, SCORPIO maintained robust performance in predicting ICI outcomes, surpassing programmed death-ligand 1 immunostaining. These findings underscore SCORPIO's reliability and adaptability, highlighting its potential to predict patient outcomes with ICI therapy across diverse cancer types and healthcare settings.

Construction and validation of immunogenic cell death-related molecular clusters, signature, and immune landscape in pancreatic cancer

Pancreatic cancer (PC) is a malignancy of the gastrointestinal tract that is characterized by a poor prognosis. This study investigates the roles of immunogenic cell death (ICD) genes in the prognosis and progression of PC. Expression data for PC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, while ICD genes were sourced from published literature. We explored the expression patterns and identified two distinct clusters based on ICD genes. Kaplan-Meier analysis, differential expression analysis, tumor mutational burden analysis, and immune cell infiltration analysis were performed on these clusters. An ICD gene-based risk model was developed, categorizing samples from the TCGA and GEO datasets into low- and high-risk groups. Additionally, we investigated the expression levels of the genes included in the risk model within the TCGA cohort and our own samples. Finally, a loss-of-function assay was conducted to assess the role of MYD88 in PC. Two clusters of PC samples were identified, patients in the ICD-low cluster exhibited a higher degree of immune cell enrichment. The survival time of patients in the low-risk group was longer than that of those in the high-risk group. The genes included in the risk model (CASP1, MYD88, and PIK3CA) showed upregulated expression levels in tumor samples. Furthermore, the predictive accuracy of our risk model was validated using our own samples. Genetic inhibition of MYD88 led to significantly decreased proliferation and migration of PC cells in the loss-of-function assay. There were disparities in survival time and tumor immune microenvironment (TIME) between two ICD gene clusters. Additionally, we developed an ICD-related risk model that was validated as an independent prognostic indicator for patients with PC.

Long-term follow-up of combination therapy with pembrolizumab and anlotinib in thoracic SMARCA4-deficient undifferentiated tumor: a case report and molecular features

Thoracic SMARCA4-deficient undifferentiated tumors (SMARCA4-UTs), recently recognized as a rare malignancy described in the 5th edition of the World Health Organization Classification of Tumors, are characterized by an inactivating mutation in SMARCA4, most commonly found in the mediastinum of male smokers. Despite the aggressive nature and poor prognosis associated with these tumors, which have a median survival time of approximately 4-7 months, no standardized treatment guidelines are currently established. There are currently no reported cases of extended progression-free survival (PFS) in SMARCA4-UT patients treated with surgery and immunotherapy. Here, we report the clinical features and genomic information of a SMARCA4-UT case in which the patient responded significantly to a combination therapy involving surgery, immunotherapy, and amlotinib. A 56-year-old male non-smoker presented with a mass in the superior lobe of left lung and left hilar adenopathy. A left upper lobectomy and lymphadenectomy were performed, and postoperative pathology confirmed that the tumor was Thoracic SMARCA4-UT. The patient subsequently received chemotherapy with pemetrexed and carboplatin. Five months post-operation, the disease progressed with left adrenal metastasis and mediastinal adenopathy. An adrenalectomy was performed, followed by whole exon sequencing (WES). SMARCA4, SMARCA2 and SMARCA1 gene mutations were detected in this case. Given a tumor proportion score (TPS) of 60% for programmed death-ligand 1(22C3)immunoexpression and high TMB(361.32 muts/Mb), a combination of Pembrolizumab plus anlotinib was initiated as a second-line approach. After 46 cycles, the patient demonstrated no disease progression with a PR lasting 31 months and long progression-free survival(PFS) of 43 months. The lung tumor was initially detected in September 2020, and the patient remained alive at the latest follow-up in November 2024. This case offers a long-term follow-up of the effectiveness and safety of combining pembrolizumab and anlotinib in advanced SMARCA4-UT, and substantiates the role of long-term immunotherapy in preventing radiographic/clinical recurrence following surgery. This case illustrates new potential efficacy of immunotherapy in combination with surgery as a treatment approach of SMARCA4-UT.

A multi-task deep learning model based on comprehensive feature integration and self-attention mechanism for predicting response to anti-PD1/PD-L1

BACKGROUND

Immune checkpoint inhibitor (ICI) has been widely used in the treatment of advanced cancers, but predicting their efficacy remains challenging. Traditional biomarkers are numerous but exhibit heterogeneity within populations. For comprehensively utilizing the ICI-related biomarkers, we aim to conduct multidimensional feature selection and deep learning model construction.

METHODS

We used statistical and machine learning methods to map features of different levels to next-generation sequencing gene expression. We integrated genes from different sources into the feature input of a deep learning model, by means of self-attention mechanism.

RESULTS

We performed feature selection at the single-cell sequencing level, PD-L1 (CD274) analysis level, tumor mutational burden (TMB)/mismatch repair (MMR) level, and somatic copy number alteration (SCNA) level, obtaining 96 feature genes. Based on the pan-cancer dataset, we trained a multi-task deep learning model. We tested the model in the bladder urothelial carcinoma testing set 1 (AUC = 0.62, n = 298), bladder urothelial carcinoma testing set 2 (AUC = 0.66, n = 89), non-small cell lung cancer testing set (AUC = 0.85, n = 27), and skin cutaneous melanoma testing set (AUC = 0.71, n = 27).

CONCLUSION

Our study demonstrates the potential of the deep learning model for integrating multidimensional features in predicting the outcome of ICI. Our study also provides a potential methodological case for medical scenarios requiring the integration of multiple levels of features.

Non-B DNA-informed mutation burden as a marker of treatment response and outcome in cancer

BACKGROUND

Genomic instability is crucial in tumorigenesis, with Tumour Mutation Burden (TMB) being a biomarker to indicate therapeutic effectiveness, particularly in immunotherapy. However, TMB is not always a reliable predictor and displays heterogeneity. Non-B DNA, susceptible to mutations, play a significant role in cancer development, indicating their potential merit when combined with mutation for enhanced markers in cancer.

METHODS

We assessed mutations and non-B DNA interplay as biomarkers. Our methodology quantifies tumour mutations and their co-localization with non-B DNA, using survival and drug sensitivity assessments for clinical relevance.

RESULTS

We introduce two novel markers, 'nbTMB' (non-B-informed tumour mutation burden) and 'mlTNB' (mutation-localised tumour non-B burden). In case studies: (1) nbTMB informs on survival heterogeneity among TMB-high patients undergoing immunotherapy whereas TMB is unable to further differentiate; (2) nbTMB informs on altered cisplatin sensitivity among ovarian cancer cell lines whereas TMB is unable to differentiate; and (3) mlTNB informs on survival heterogeneity among early-stage pancreatic cancer progressors in whom other markers of genomic instability fail to differentiate.

CONCLUSIONS

These novel markers offer a nuanced approach to enhance our understanding of treatment responses and outcomes in cancer, underscoring the need for a comprehensive exploration of the interplay between non-B and B-DNA features.

Cell Cycle-Related LncRNA-Based Prognostic Model for Hepatocellular Carcinoma: Integrating Immune Microenvironment and Treatment Response

OBJECTIVE

Hepatocellular carcinoma (HCC) presents substantial genetic and phenotypic diversity, making it challenging to predict patient outcomes. There is a clear need for novel biomarkers to better identify high-risk individuals. Long non-coding RNAs (lncRNAs) are known to play key roles in cell cycle regulation and genomic stability, and their dysregulation has been closely linked to HCC progression. Developing a prognostic model based on cell cycle-related lncRNAs could open up new possibilities for immunotherapy in HCC patients.

METHODS

Transcriptomic data and clinical samples were obtained from the TCGA-HCC dataset. Cell cycle-related gene sets were sourced from existing studies, and coexpression analysis identified relevant lncRNAs (correlation coefficient >0.4, P<0.001). Univariate analysis identified prognostic lncRNAs, which were then used in a LASSO regression model to create a risk score. This model was validated via cross-validation. HCC samples were classified on the basis of their risk scores. Correlations between the risk score and tumor mutational burden (TMB), tumor immune infiltration, immune checkpoint gene expression, and immunotherapy response were evaluated via R packages and various methods (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCP-COUNTER, XCELL, and EPIC).

RESULTS

Four cell cycle-related lncRNAs (AC009549.1, AC090018.2, PKD1P6-NPIPP1, and TMCC1-AS1) were significantly upregulated in HCC. These lncRNAs were used to create a risk score (risk score=0.492×AC009549.1+1.390×AC090018.2+1.622×PKD1P6-NPIPP1+0.858×TMCC1-AS1). This risk score had superior predictive value compared to traditional clinical factors (AUC=0.738). A nomogram was developed to illustrate the 1-year, 3-year, and 5-year overall survival (OS) rates for individual HCC patients. Significant differences in TMB, immune response, immune cell infiltration, immune checkpoint gene expression, and drug responsiveness were observed between the high-risk and low-risk groups.

CONCLUSION

The risk score model we developed enhances the prognostication of HCC patients by identifying those at high risk for poor outcomes. This model could lead to new immunotherapy strategies for HCC patients.

Unveiling the role of TGF-β signaling pathway in breast cancer prognosis and immunotherapy

Introduction

The TGF-β signaling pathway (TSP) is pivotal in tumor progression. Nonetheless, the connection between genes associated with the TSP and the clinical outcomes of breast cancer, as well as their impact on the tumor microenvironment and immunotherapeutic responses, remains elusive.

Methods

Breast cancer transcriptomic and single-cell sequencing data were obtained from the The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We identified 54 genes associated with the TSP from the Molecular Signatures Database (MSigDB) and analyzed both data types to evaluate TSP activity. Using weighted gene co-expression network analysis (WGCNA), we identified modules linked to TSP activity. To assess patient risk, we used 101 machine learning algorithms to develop an optimal TGF-β pathway-related prognostic signature (TSPRS). We then examined immune activity and response to immune checkpoint inhibitors and chemotherapy in these groups. Finally, we validated ZMAT3 expression levels clinically and confirmed its relevance in breast cancer using CCK-8 and migration assays.

Results

At the single-cell level, TSP activity was most notable in endothelial cells, with higher activity in normal tissues compared to tumors. TSPRS was developed. This signature's accuracy was confirmed through internal and external validations. A nomogram incorporating the TSPRS was created to improve prediction accuracy. Further studies showed that breast cancer patients categorized as low-risk by the TSPRS had higher immune phenotype scores and more immune cell infiltration, leading to better prognosis and enhanced immunotherapy response. Additionally, a strong link was found between the TSPRS risk score and the effectiveness of anti-tumor agents. Silencing the ZMAT3 gene in the TSPRS significantly reduced the proliferation and invasiveness of breast cancer cells.

Discussion

Our study developed a TSPRS, which emerges as a potent predictive instrument for the prognosis of breast cancer, offering novel perspectives on the immunotherapeutic approach to the disease.

THOR: a TMB heterogeneity-adaptive optimization model predicts immunotherapy response using clonal genomic features in group-structured data

With the increasing number of indications for immune checkpoint inhibitors in early and advanced cancers, the prospect of a tumor-agnostic biomarker to prioritize patients is compelling. Tumor mutation burden (TMB) is a widely endorsed biomarker that quantifies nonsynonymous mutations within tumor DNA, essential for neoantigen production, which, in turn, correlates with the immune response and guides decision-making. However, the general clinical application of TMB-relying on simple mutational counts targeted at a single endpoint-does not adequately capture the complex clonal structure of tumors nor the multifaceted nature of prognostic indicators. This recognition has spurred the exploration of sophisticated high-dimensional regression techniques. Unfortunately, the limited cohort sizes in immunotherapy trials have hindered the full potential of these advanced methods. Our approach considers patient subgroups as related yet distinct entities, enabling precise tailoring and refinement to address subgroup-specific dynamics. Given the deficiencies and the constraints, we introduce a TMB heterogeneity-optimized regression (THOR). This innovative model enhances the predictive capabilities of TMB by integrating tumor clonality and a diverse spectrum of clinical endpoints, further augmented by fusion techniques across subgroups to facilitate robust data sharing and interpretation. Our simulations validate THOR's superiority in parameter estimation for statistical inference. Clinically, we assess the utility of THOR in a structured cohort of 238 cancer patients undergoing immunotherapy, supplemented by 2212 patients across 19 subgroups from public datasets. The forecast of the responses and comparison of survival hazards demonstrate that THOR significantly enhances patient stratification and prognostic predictions by incorporating complex immunogenetic biology and subgroup-specific dynamics.

Whole-exome profiles of inflammatory breast cancer and pathological response to neoadjuvant chemotherapy

BACKGROUND

Neoadjuvant chemotherapy (NACT) became a standard treatment strategy for patients with inflammatory breast cancer (IBC) because of high disease aggressiveness. However, given the heterogeneity of IBC, no molecular feature reliably predicts the response to chemotherapy. Whole-exome sequencing (WES) of clinical tumor samples provides an opportunity to identify genomic alterations associated with chemosensitivity.

METHODS

We retrospectively applied WES to 44 untreated IBC primary tumor samples and matched normal DNA. The pathological response to NACT, assessed on operative specimen, distinguished the patients with versus without pathological complete response (pCR versus no-pCR respectively). We compared the mutational profiles, spectra and signatures, pathway mutations, copy number alterations (CNAs), HRD, and heterogeneity scores between pCR versus no-pCR patients.

RESULTS

The TMB, HRD, and mutational spectra were not different between the complete (N = 13) versus non-complete (N = 31) responders. The two most frequently mutated genes were TP53 and PIK3CA. They were more frequently mutated in the complete responders, but the difference was not significant. Only two genes, NLRP3 and SLC9B1, were significantly more frequently mutated in the complete responders (23% vs. 0%). By contrast, several biological pathways involved in protein translation, PI3K pathway, and signal transduction showed significantly higher mutation frequency in the patients with pCR. We observed a higher abundance of COSMIC signature 7 (due to ultraviolet light exposure) in tumors from complete responders. The comparison of CNAs of the 3808 genes included in the GISTIC regions between both patients' groups identified 234 genes as differentially altered. The CIN signatures were not differentially represented between the complete versus non-complete responders. Based on the H-index, the patients with heterogeneous tumors displayed a lower pCR rate (11%) than those with less heterogeneous tumors (35%).

CONCLUSIONS

This is the first study aiming at identifying correlations between the WES data of IBC samples and the achievement of pCR to NACT. Our results, obtained in this 44-sample series, suggest a few subtle genomic alterations associated with pathological response. Additional investigations are required in larger series.

Unveiling an anoikis-related risk model and the role of RAD9A in colon cancer

OBJECTIVE

Colorectal cancer (CRC), specifically colon adenocarcinoma, is the third most prevalent and the second most lethal form of cancer. Anoikis is found to be specialized form of programmed cell death (PCD), which plays a pivotal role in tumor progression. This study aimed to investigate the role of the anoikis related genes (ARGs) in colon cancer.

METHODS

Consensus unsupervised clustering, differential expression analysis, tumor mutational burden analysis, and analysis of immune cell infiltration were utilized in the study. For the analysis of RNA sequences and clinical data of COAD patients, data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were obtained. A prognostic scoring system for overall survival (OS) prediction was developed using Cox regression and LASSO regression analysis. Furthermore, loss-of-function assay was utilized to explore the role of RAD9A played in the progression of colon cancer.

RESULTS

The prognostic value of a risk score composed of NTRK2, EPHA2, RAD9A, CDC25C, and SNAI1 genes was significant. Furthermore, these findings suggested potential mechanisms that may influence prognosis, supporting the development of individualized treatment plans and management of patient outcomes. Further experiments confirmed that RAD9A could promote proliferation and metastasis of colon cancer cells. These effects may be achieved by affecting the phosphorylation of AKT.

CONCLUSION

Differences in survival time and the tumor immune microenvironment (TIME) were observed between two gene clusters associated with ARGs. In addition, a prognostic risk model was established and confirmed as an independent risk factor. Furthermore, our data indicated that RAD9A promoted tumorigenicityby activating AKT in colon cancer.

Exploring the role of mitophagy-related genes in breast cancer: subtype classification and prognosis prediction

Background: Breast cancer (BC) is the most common cancer among women globally and poses the leading health threat to women worldwide, with persistently high incidence rates. Mitophagy is a selective autophagy process that specifically targets mitochondria within the cell, maintaining cellular energy balance and metabolic health by identifying and degrading damaged mitochondria. Although there is an understanding of the relationship between mitophagy and cancer, the specific mechanisms remain unclear due to the complexity and diversity of mitophagy, suggesting that it could be an effective and more targeted therapeutic approach for BC. Methods: In this study, we meticulously examined the BC expression and clinical pathology data from The Cancer Genome Atlas (TCGA) to assess the expression profiles, copy number variations (CNV), and to investigate the correlation, function, and prognostic impact of 34 mitophagy-related genes (MRGs). Differentially expressed genes (DEGs) were identified based on group classification. Lasso and Cox regression were used to determine prognostic genes for constructing a nomogram. Single-cell analysis mapped the distribution of these genes in BC cells. Additionally, the association between gene-derived risk scores and factors such as immune infiltration, tumor mutational burden (TMB), cancer stem cell (CSC) index, and drug responses was studied. In vitro experiments were conducted to confirm the analyses. Results: We included 34 MRGs and subsequently generated a risk score for 7 genes, including RPLP2, PCDHGA2, PRKAA2, CLIC6, FLT3, CHI3L1, and IYD. It was found that the low-risk group had better overall survival (OS) in BC, higher immune scores, but lower tumor mutational burden (TMB) and cancer stem cell (CSC) index, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and staging were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and showed good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS of 0.895, 0.765, and 0.728, respectively. Conclusion: Our findings underscore the profound impact of prognostic genes on the immune response and prognostic outcomes in BC, indicating that they can provide new avenues for personalized BC treatment and potentially improve clinical outcomes.

Integrating anoikis and ErbB signaling insights with machine learning and single-cell analysis for predicting prognosis and immune-targeted therapy outcomes in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) poses a significant global health challenge due to its poor prognosis and limited therapeutic modalities. Anoikis and ErbB signaling pathways are pivotal in cancer cell proliferation and metastasis, but their relevance in HCC remains insufficiently explored.

Methods

This study evaluates the prognostic significance of anoikis and ErbB signaling pathways in HCC by utilizing data from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), three additional independent validation cohorts, and an in-house cohort. Advanced bioinformatics analyses and 167 machine learning models based on leave-one-out cross-validation (LOOCV) were used to predict HCC prognosis and assess outcomes of immune-targeted therapies. Additionally, key biological processes of the anoikis and ErbB signaling pathways in HCC were further investigated.

Results

The single sample Gene Set Enrichment Analysis revealed a strong correlation between upregulated ErbB signaling in high anoikis-expressing tumors and poor clinical outcomes. The development of the Anoikis-ErbB Related Signature (AERS) using the LASSO + RSF model demonstrated robust predictive capabilities, as validated across multiple patient cohorts, and proved effective in predicting responses to immune-targeted therapies. Further investigation highlighted activated NOTCH signaling pathways and decreased macrophage infiltration was associated with resistance to sorafenib and immune checkpoint inhibitors, as evidenced by bulk and single-cell RNA sequencing (scRNA-seq).

Conclusion

AERS provides a novel tool for clinical prognosis and paves the way for immune-targeted therapeutic approaches, underscoring the potential of integrated molecular profiling in enhancing treatment strategies for HCC.

A disulfidptosis-related lncRNA signature for analyzing tumor microenvironment and clinical prognosis in hepatocellular carcinoma

Introduction

Disulfidptosis is a recently identified form of non-apoptotic programmed cell death which distinguishes itself from classical cell death pathways. However, the prognostic implications of disulfidptosis-related long non-coding RNAs (DRLs) and their underlying mechanisms in hepatocellular carcinoma (HCC) remain largely unexplored.

Methods

In this study, we leveraged RNA-sequencing data and clinical information of HCC patients from the TCGA database. Through expression correlation and prognostic correlation analyses, we identified a set of top-performing long non-coding RNAs. Subsequently, a 5-DRLs predictive signature was established by conducting a Lasso regression analysis.

Results

This signature effectively stratified patients into high- and low-risk groups, revealing notable differences in survival outcomes. Further validation through univariate and multivariate Cox regression analyses confirmed that the risk score derived from our signature independently predicted the prognosis of HCC patients. Moreover, we observed significant disparities in immune cell infiltration and tumor mutation burden (TMB) between the two risk groups, shedding light on the potential connection between immune-related mechanisms and disulfidptosis. Notably, the signature also exhibited predictive value in the context of chemotherapeutic drug sensitivity and immunotherapy efficacy for HCC patients. Finally, we performed experimental validation at both cellular and patient levels and successfully induced a disulfidptosis phenotype in HCC cells.

Discussion

In general, this multifaceted approach provides a comprehensive overview of DRLs profiles in HCC, culminating in the establishment of a novel risk signature that holds promise for predicting prognosis and therapy outcomes of HCC patients.

Immune control in acute myeloid leukemia

Acute myeloid leukemia (AML) is a genetically heterogeneous disease, in that a multitude of oncogenic drivers and chromosomal abnormalities have been identified and associated with the leukemic transformation of myeloid blasts. However, little is known as to how individual mutations influence the interaction between the immune system and AML cells and the efficacy of the immune system in AML disease control. In this review, we will discuss how AML cells potentially activate the immune system and what evidence there is to support the role of the immune system in controlling this disease. We will specifically examine the importance of antigen presentation in fostering an effective anti-AML immune response, explore the disruption of immune responses during AML disease progression, and discuss the emerging role of the oncoprotein MYC in driving immune suppression in AML.

Integrated profile of tumor stage and mutational burden predicts disparate clinical responses to immune checkpoint inhibitors: A risk-benefit study

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, having demonstrated efficacy and leading to regulatory approvals of ICIs in cancers characterized by high tumor mutation burden (TMB). However, there remains a gap in determining their applicability and risk-benefit profile, across the broad spectrum of patients whose tumors harbor varying TMB levels across distinct tumor stages. By interrogating a large contemporary cohort comprised of 10,233 patients with a diagnosis of cancer across all tumor stages and TMB levels, this study revealed significantly improved overall survival (OS) following ICI therapy (P < .0001) in patients with a combination of ≥10 mut/Mb and stage IV disease. In contrast, ICI therapy is associated with markedly worse OS in patients with low TMB levels <10 mut/Mb and stages I, II, and III cancer. These findings highlight the critical interplay between TMB, tumor stage, and ICI treatment outcomes, underscoring the importance of integrating clinical and genetic characteristics in weighing the risk-benefit balance of ICI therapy. Although maximizing therapeutic benefits is crucial, it is equally important to identify and manage potential risks that may not be immediately apparent. This may require enrolling patients with less-severe or early-stage disease to enable long-term follow-up with effective clinical surveillance. By comprehensively evaluating the added benefit of improved treatment efficacy and the potential risk of adverse treatment outcome, a risk-benefit profile can optimize immunotherapy regimens, with profound implications for clinical decision-making and regulatory approvals of ICI.

Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer

BACKGROUND

Personalized medicine remains an unmet need in ovarian cancer due to its heterogeneous nature and complex immune microenvironments, which has gained increasing attention in the era of immunotherapy. A key obstacle is the lack of reliable biomarkers to identify patients who would benefit significantly from the therapy. While conventional clinicopathological factors have exhibited limited efficacy as prognostic indicators in ovarian cancer, multi-omics profiling presents a promising avenue for comprehending the interplay between the tumor and immune components. Here we aimed to leverage the individual proteomic and transcriptomic profiles of ovarian cancer patients to develop an effective protein-based signature capable of prognostication and distinguishing responses to immunotherapy.

METHODS

The workflow was demonstrated based on the Reverse Phase Protein Array (RPPA) and RNA-sequencing profiles of ovarian cancer patients from The Cancer Genome Atlas (TCGA). The algorithm began by clustering patients using immune-related gene sets, which allowed us to identify immune-related proteins of interest. Next, a multi-stage process involving LASSO and Cox regression was employed to distill a prognostic signature encompassing five immune-related proteins. Based on the signature, we subsequently calculated the risk score for each patient and evaluated its prognostic performance by comparing this model with conventional clinicopathological characteristics.

RESULTS

We developed and validated a protein-based prognostic signature in a cohort of 377 ovarian cancer patients. The risk signature outperformed conventional clinicopathological factors, such as age, grade, stage, microsatellite instability (MSI), and homologous recombination deficiency (HRD) status, in terms of prognoses. Patients in the high-risk group had significantly unfavorable overall survival (p < 0.001). Moreover, our signature effectively stratified patients into subgroups with distinct immune landscapes. The high-risk group exhibited higher levels of CD8 T-cell infiltration and a potentially greater proportion of immunotherapy responders. The co-activation of the TGF-β pathway and cancer-associated fibroblasts could impair the ability of cytotoxic T cells to eliminate cancer cells, leading to poor outcomes in the high-risk group.

CONCLUSIONS

The protein-based signature not only aids in evaluating the prognosis but also provides valuable insights into the tumor immune microenvironments in ovarian cancer. Together our findings highlight the importance of a thorough understanding of the immunosuppressive tumor microenvironment in ovarian cancer to guide the development of more effective immunotherapies.

Extrachromosomal circular DNAs in prostate adenocarcinoma: global characterizations and a novel prediction model

Background

The role of focal amplifications and extrachromosomal circular DNA (eccDNA) is still uncertain in prostate adenocarcinoma (PRAD). Here, we first mapped the global characterizations of eccDNA and then investigate the characterization of eccDNA-amplified key differentially expressed encoded genes (eKDEGs) in the progression, immune response and immunotherapy of PRAD.

Methods

Circular_seq was used in conjunction with the TCGA-PRAD transcriptome dataset to sequence, annotate, and filter for eccDNA-amplified differentially expressed coding genes (eDEGs) in PRAD and para-cancerous normal prostate tissues. Afterwards, risk models were created and eKDEGs linked to the PRAD prognosis were identified using Cox and Lasso regression analysis. The immune microenvironment of the risk model was quantified using a variety of immunological algorithms, which also identified its characteristics with regard to immunotherapy, immune response, and immune infiltration.

Results

In this research, there was no significant difference in the size, type, and chromosomal distribution of eccDNA in PRAD and para-cancerous normal prostate tissues. However, 4,290 differentially expressed eccDNAs were identified and 1,981 coding genes were amplified. Following that, 499 eDEGs were tested in conjunction with the transcriptome dataset from TCGA-PRAD. By using Cox and Lasso regression techniques, ZNF330 and PITPNM3 were identified as eKDEGs of PRAD, and a new PRAD risk model was conducted based on this. Survival analysis showed that the high-risk group of this model was associated with poor prognosis and validated in external data. Immune infiltration analysis showed that the model risks affected immune cell infiltration in PRAD, not only mediating changes in immune cell function, but also correlating with immunophenotyping. Furthermore, the high-risk group was negatively associated with anti-CTLA-4/anti-PD-1 response and mutational burden. In addition, Tumor Immune Dysfunction and Exclusion analyses showed that high-risk group was more prone to immune escape. Drug sensitivity analyses identified 10 drugs, which were instructive for PRAD treatment.

Conclusion

ZNF330 and PITPNM are the eKDEGs for PRAD, which can be used as potential new prognostic markers. The two-factor combined risk model can effectively assess the survival and prognosis of PRAD patients, but also can predict the different responses of immunotherapy to PRAD patients, which may provide new ideas for PRAD immunotherapy.

Overall survival prediction of gastric cancer using the gene signature of CT-detected extramural venous invasion combined with M2 macrophages infiltration

BACKGROUND

CT-detected Extramural venous invasion (EMVI) is known as an independent risk factor for distant metastasis in patients with advanced gastric cancer (GC). However, the molecular basis is not clear. In colorectal cancer, M2 macrophages plays a vital role in determining EMVI. This study aimed to investigate the relationship between CT-detected EMVI and the M2 macrophages as well as prognosis predictionusing a radiogenomic approach.

METHOD

We utilized EMVI-related genes (from mRNA sequencing of 13 GC samples correlated with EMVI score by spearman analysis, P < 0.01) to overlap the co-expression genes of WGCNA module and M2 macrophages related genes (from mRNA data of 371 GC patients in TCGA database), generating a total of 136 genes. An EMVI-M2-prognosis-related hub gene signature was constructed by COX and least absolute shrinkage and selection operator (LASSO) analysis from a training cohort TCGA database (n = 371) and validated it in a validation cohort from GEO database (n = 357). High- and low-risk groups were divided by hub gene (EGFLAM and GNG11) signature-derived risk scores. We assessed its predictive ability through Kaplan-Meier (K-M) curve and COX analysis. Furthermore, we utilized ESTIMATE to detect tumor mutation burden (TMB) and evaluate sensitivity to immune checkpoint inhibitors (ICIs). Expression of hub genes was tested using western blotting and immunohistochemistry (IHC) analysis.

RESULTS

The overall survival (OS) was significantly reduced in the high-risk group (Training/Validation: AUC = 0.701/0.620; P < 0.001/0.003). Furthermore, the risk score was identified as an independent predictor of OS in multivariate COX regression analyses (Training/Validation: HR = 1.909/1.928; 95% CI: 1.225-2.974/1.308-2.844). The low-risk group exhibited significantly higher TMB levels (P = 1.6e- 07) and greater sensitivity to ICIs. Significant higher expression of hub-genes was identified on multiple GC cell lines and original samples. Hub-genes knockdown in gastric cancer cell lines inhibited their proliferation, metastatic and invasive capacity to varying degrees. In vivo experiments indicate that EGFLAM, as one of the hub genes, its high expression can serve as a biomarker for low response to immunotherapy.

CONCLUSION

Our study demonstrated EMVI-M2 gene signature could effectively predict the prognosis of GC tissue, reflecting the relationship between EMVI and M2 macrophages.

Microsatellite Instability, Tumor Mutational Burden, and Response to Immune Checkpoint Blockade in Patients with Prostate Cancer

PURPOSE

Patients with microsatellite instability-high/mismatch repair-deficient (MSI-H/dMMR) and high tumor mutational burden (TMB-H) prostate cancers are candidates for pembrolizumab. We define the genomic features, clinical course, and response to immune checkpoint blockade (ICB) in patients with MSI-H/dMMR and TMB-H prostate cancers without MSI [TMB-H/microsatellite stable (MSS)].

EXPERIMENTAL DESIGN

We sequenced 3,244 tumors from 2,257 patients with prostate cancer. MSI-H/dMMR prostate cancer was defined as an MSIsensor score ≥10 or MSIsensor score ≥3 and <10 with a deleterious MMR alteration. TMB-H was defined as ≥10 mutations/megabase. PSA50 and RECIST responses were assigned. Overall survival and radiographic progression-free survival (rPFS) were compared using log-rank test.

RESULTS

Sixty-three (2.8%) men had MSI-H/dMMR, and 33 (1.5%) had TMB-H/MSS prostate cancers. Patients with MSI-H/dMMR and TMB-H/MSS tumors more commonly presented with grade group 5 and metastatic disease at diagnosis. MSI-H/dMMR tumors had higher TMB, indel, and neoantigen burden compared with TMB-H/MSS. Twenty-seven patients with MSI-H/dMMR and 8 patients with TMB-H/MSS tumors received ICB, none of whom harbored polymerase epsilon (polE) catalytic subunit mutations. About 45% of patients with MSI-H/dMMR had a RECIST response, and 65% had a PSA50 response. No patient with TMB-H/MSS had a RECIST response, and 50% had a PSA50 response. rPFS tended to be longer in patients with MSI-H/dMMR than in patients with TMB-H/MSS who received immunotherapy. Pronounced differences in genomics, TMB, or MSIsensor score were not detected between MSI-H/dMMR responders and nonresponders.

CONCLUSIONS

MSI-H/dMMR prostate cancers have greater TMB, indel, and neoantigen burden than TMB-H/MSS prostate cancers, and these differences may contribute to profound and durable responses to ICB.

Pan-cancer analysis suggests that LY6H is a potential biomarker of diagnosis, immunoinfiltration, and prognosis

LY6H, a member of the lymphocyte antigen-6(LY6) gene family, is located on human chromosomes 6, 8, 11 and 19. This superfamily is characterized by the presence of LU domains. It has demonstrated its emerging significance in various cancers including adenocarcinoma, bladder cancer, ovarian cancer and skin cancer. However, comprehensive pan-cancer analyses have not been conducted to investigate its role in diagnosis, prognosis and immunological prediction. By conducting comprehensive analysis of patient data obtained from publicly available databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), University of Alabama at Birmingham (UALCAN), The Comparative Toxicological Genomics Database (CTD), cBioportal, cancerSEA, and UCSC, we systematically investigated the differential expression of LY6H in 33 different types of human tumors. Additionally, we thoroughly analyzed the diagnostic, prognostic, and immunoinfiltration value of LY6H. Simultaneously, we examined the correlation between LY6H and tumor stemness, methylation patterns, drug sensitivity, gene alterations as well as single cell functions. Furthermore, protein-protein interaction networks and gene-gene interaction networks for LY6H were constructed. Moreover, we also explored the network relationship between LY6H and chemical compounds or genes. The results revealed that LY6H exhibited high expression levels in most cancers which were further validated through Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Immunohistochemistry (IHC) analysis using Hepatocellular carcinoma (HCC) samples. Moreover, LY6H displayed early diagnostic potential in 12 tumors while also showing positive or negative correlations with prognosis across different tumor types. Additionally, it was found that LY6H played a pivotal role in regulating immune-infiltrating cells across multiple cancers whereas the correlation between LY6H expression and immune-related genes varied depending on their specific types. Furthermore, the expression of LY6H was significantly associated with DNA methylation patterns in 21 cancers. Therefore, LY6H could serve as an adjunctive biomarker for early tumor detection as well as a prognostic indicator for diverse malignancies.

Randomized, open-label, phase 2 study of nivolumab plus ipilimumab or nivolumab monotherapy in patients with advanced or metastatic solid tumors of high tumor mutational burden

BACKGROUND

Checkpoint inhibitor therapy has demonstrated overall survival benefit in multiple tumor types. Tumor mutational burden (TMB) is a predictive biomarker for response to immunotherapies. This study evaluated the efficacy of nivolumab+ipilimumab in multiple tumor types based on TMB status evaluated using either tumor tissue (tTMB) or circulating tumor DNA in the blood (bTMB).

PATIENTS AND METHODS

Patients with metastatic or unresectable solid tumors with high (≥10 mutations per megabase) tTMB (tTMB-H) and/or bTMB (bTMB-H) who were refractory to standard therapies were randomized 2:1 to receive nivolumab+ipilimumab or nivolumab monotherapy in an open-label, phase 2 study (CheckMate 848; NCT03668119). tTMB and bTMB were determined by the Foundation Medicine FoundationOne® CDx test and bTMB Clinical Trial Assay, respectively. The dual primary endpoints were objective response rate (ORR) in patients with tTMB-H and/or bTMB-H tumors treated with nivolumab+ipilimumab.

RESULTS

In total, 201 patients refractory to standard therapies were randomized: 135 had tTMB-H and 125 had bTMB-H; 82 patients had dual tTMB-H/bTMB-H. In patients with tTMB-H, ORR was 38.6% (95% CI 28.4% to 49.6%) with nivolumab+ipilimumab and 29.8% (95% CI 17.3% to 44.9%) with nivolumab monotherapy. In patients with bTMB-H, ORR was 22.5% (95% CI 13.9% to 33.2%) with nivolumab+ipilimumab and 15.6% (95% CI 6.5% to 29.5%) with nivolumab monotherapy. Early and durable responses to treatment with nivolumab+ipilimumab were seen in patients with tTMB-H or bTMB-H. The safety profile of nivolumab+ipilimumab was manageable, with no new safety signals.

CONCLUSIONS

Patients with metastatic or unresectable solid tumors with TMB-H, as determined by tissue biopsy or by blood sample when tissue biopsy is unavailable, who have no other treatment options, may benefit from nivolumab+ipilimumab.

TRIAL REGISTRATION NUMBER

NCT03668119.

Investigation of transcriptional and immunological disparities among patient groups with varied prognostic risk factors in cholangiocarcinoma

BACKGROUND

This study explores molecular features associated with better prognosis in cholangiocarcinoma (CCA).

METHODS AND RESULTS

The transcriptomic and whole-exome sequencing data obtained from paired tissues of 70 were analyzed, grouping them based on progression-free survival (PFS), differentiation degree, and lymph node metastasis. Among the 70 patients, the TP53 gene mutation frequency was the highest (53%), while FLG gene mutation occurred exclusively in the long PFS group. In the comparison between long and short survival groups, the short PFS group exhibited higher monocyte infiltration levels (p = 0.0287) and upregulation of genes associated with cancer-related transcriptional misregulation, chemokine signaling, and cytokine-cytokine receptor interactions. Differences in immune cell infiltration and gene expression were significant across differentiation and lymph node metastasis groups. Particularly noteworthy was the marked increase in CD8 T cell and NK cell infiltration (p = 0.0291, 0.0459) in the lymph node metastasis group, significantly influences prognosis. Additionally, genes related to platinum resistance, Th17 cell differentiation, and Th1 and Th2 cell differentiation pathways were overexpressed in this group. In summary, higher monocyte infiltration levels in the short PFS group, along with elevated expression of genes associated with cancer-related pathways, suggest a poorer prognosis. The significant increase in CD8 T cell and NK cell infiltration reflects an enhanced anti-tumor immune response, underscoring the relevance of immune infiltration levels and gene expression in predicting outcomes for CCA patients.

CONCLUSIONS

In this study, we elucidated the pertinent molecular mechanisms and pathways that influence the prognosis of CCAs through comprehensive multi-omics analysis.

LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features

Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patient responses remains challenging. Here, we analyzed a large dataset of 2,881 ICB-treated and 841 non-ICB-treated patients across 18 solid tumor types, encompassing a wide range of clinical, pathologic and genomic features. We developed a clinical score called LORIS (logistic regression-based immunotherapy-response score) using a six-feature logistic regression model. LORIS outperforms previous signatures in predicting ICB response and identifying responsive patients even with low tumor mutational burden or programmed cell death 1 ligand 1 expression. LORIS consistently predicts patient objective response and short-term and long-term survival across most cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling precise patient stratification. As an accurate, interpretable method using a few readily measurable features, LORIS may help improve clinical decision-making in precision medicine to maximize patient benefit. LORIS is available as an online tool at https://loris.ccr.cancer.gov/ .

SPOP expression is associated with tumor-infiltrating lymphocytes in pancreatic cancer

BACKGROUND

Speckle Type POZ Protein (SPOP), despite its tumor type-dependent role in tumorigenesis, primarily as a tumor suppressor gene is associated with a variety of different cancers. However, its function in pancreatic cancer remains uncertain.

METHODS

SPOP expression and the association between its expression and patient prognosis and immune function were evaluated using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), The Tumor Immune Estimation Resource 2.0 (TIMER2.0) database, cBioportal, and various bioinformatic databases. Enrichment analysis of SPOP and the association between SPOP expression with clinical stage and grade were analyzed using the R software package. Then immunohistochemistry (IHC) was used to estimate the correlation between SPOP and tumor-infiltrating lymphocytes (TILs) in patients with pancreatic cancer.

RESULTS

As part of our study, we assessed that SPOP was anomalously expressed in kinds of cancers, associated with clinical stage and outcomes. Meanwhile, SPOP also played a crucial role in the tumor microenvironment (TME). The expression level of SPOP was significantly correlated to tumor-infiltrating immune cells (TICs) in pancreatic cancer.

CONCLUSIONS

Our study uncovered the potential corrections in SPOP with TICs, suggesting that SPOP may act as a biomarker for immunotherapy in pancreatic cancer.

Immune Checkpoint Inhibitors for Pediatric Cancers: Is It Still a Stalemate?

The knowledge surrounding the application of immune checkpoint inhibitors (ICIs) in the treatment of pediatric cancers is continuously expanding and evolving. These therapies work by enhancing the body's natural immune response against tumors, which may have been suppressed by certain pathways. The effectiveness of ICIs in treating adult cancers has been widely acknowledged. However, the results of early phase I/II clinical trials that exclusively targeted the use of ICIs for treating different pediatric cancers have been underwhelming. The response rates to ICIs have generally been modest, except for cases of pediatric classic Hodgkin lymphoma. There seems to be a notable disparity in the immunogenicity of childhood cancers compared to adult cancers, potentially accounting for this phenomenon. On average, childhood cancers tend to have significantly fewer neoantigens. In recent times, there has been a renewed sense of optimism regarding the potential benefits of ICI therapies for specific groups of children with cancer. In initial research, individuals diagnosed with pediatric hypermutated and SMARCB1-deficient cancers have shown remarkable positive outcomes when treated with ICI therapies. This is likely due to the underlying biological factors that promote the expression of neoantigens and inflammation within the tumor. Ongoing trials are diligently assessing the effectiveness of ICIs for pediatric cancer patients in these specific subsets. This review aimed to analyze the safety and effectiveness of ICIs in pediatric patients with different types of highly advanced malignancies.

Baseline biomarkers of efficacy and on-treatment immune-profile changes associated with bempegaldesleukin plus nivolumab

In PIVOT IO 001 (NCT03635983), the combination of the investigational interleukin-2 agonist bempegaldesleukin (BEMPEG) with nivolumab (NIVO) had no added clinical benefit over NIVO monotherapy in unresectable/metastatic melanoma. Pre-defined baseline and on-treatment changes in selected biomarkers were analyzed to explore the potential mechanisms underlying the clinical observations. In each treatment arm, higher baseline tumor mutational burden or immune infiltration/inflammation was associated with improved efficacy compared with lower levels. On-treatment peripheral biomarker changes showed that BEMPEG + NIVO increased all immune cell subset counts interrogated, including regulatory T cells. This was followed by attenuation of the increase in CD8 + T cells, conventional CD4 + T cells, and systemic interferon gamma levels at later treatment cycles in the combination arm. Changes in tumor biomarkers were comparable between arms. These biomarker results help provide a better understanding of the mechanism of action of BEMPEG + NIVO and may help contextualize the clinical observations from PIVOT IO 001.

Comprehensive analysis of PHF5A as a potential prognostic biomarker and therapeutic target across cancers and in hepatocellular carcinoma

OBJECTIVE

Cancer is a predominant cause of death globally. PHD-finger domain protein 5 A (PHF5A) has been reported to participate in various cancers; however, there has been no pan-cancer analysis of PHF5A. This study aims to present a novel prognostic biomarker and therapeutic target for cancer treatment.

METHODS

This study explored PHF5A expression and its impact on prognosis, tumor mutation burden (TMB), microsatellite instability (MSI), functional status and tumor immunity across cancers using various public databases, and validated PHF5A expression and its correlation with survival, immune evasion, angiogenesis, and treatment response in hepatocellular carcinoma (HCC) using bioinformatics tools, qRT-PCR and immunohistochemistry (IHC).

RESULTS

PHF5A was differentially expressed between tumor and corresponding normal tissues and was correlated with prognosis in diverse cancers. Its expression was also associated with TMB, MSI, functional status, tumor microenvironment, immune infiltration, immune checkpoint genes and tumor immune dysfunction and exclusion (TIDE) score in diverse malignancies. In HCC, PHF5A was confirmed to be upregulated by qRT-PCR and IHC, and elevated PHF5A expression may promote immune evasion and angiogenesis in HCC. Additionally, multiple canonical pathways were revealed to be involved in the biological activity of PHF5A in HCC. Moreover, immunotherapy and transcatheter arterial chemoembolization (TACE) worked better in the low PHF5A expression group, while sorafenib, chemotherapy and AKT inhibitor were more effective in the high expression group.

CONCLUSIONS

This study provides a comprehensive understanding of the biological function of PHF5A in the carcinogenesis and progression of various cancers. PHF5A could serve as a tumor biomarker related to prognosis across cancers, especially HCC, and shed new light on the development of novel therapeutic targets.

Identifying subtypes and developing prognostic models based on N6-methyladenosine and immune microenvironment related genes in breast cancer

Breast cancer (BC) is the most prevalent cancer in women globally. The tumor microenvironment (TME), comprising epithelial tumor cells and stromal elements, is vital for breast tumor development. N6-methyladenosine (m6A) modification plays a key role in RNA metabolism, influencing its various aspects such as stability and translation. There is a notable link between m6A methylation and immune cells in the TME, although this relationship is complex and not fully deciphered. In this research, BC expression and clinicopathological data from TCGA were scrutinized to assess expression profiles, mutations, and CNVs of 31 m6A genes and immune microenvironment-related genes, examining their correlations, functions, and prognostic impacts. Lasso and Cox regression identified prognostic genes for constructing a nomogram. Single-cell analyses mapped the distribution and patterns of these genes in BC cell development. We investigated associations between gene-derived risk scores and factors like immune infiltration, TME, checkpoints, TMB, CSC indices, and drug response. As a complement to computational analyses, in vitro experiments were conducted to confirm these expression patterns. We included 31 m6A regulatory genes and discovered a correlation between these genes and the extent of immune cell infiltration. Subsequently, a 7-gene risk score was generated, encompassing HSPA2, TAP1, ULBP2, CXCL1, RBP1, STC2, and FLT3. It was observed that the low-risk group exhibited better overall survival (OS) in BC, with higher immune scores but lower tumor mutational burden (TMB) and cancer stem cell (CSC) indices, as well as lower IC50 values for commonly used drugs. To enhance clinical applicability, age and stage were incorporated into the risk score, and a more comprehensive nomogram was constructed to predict OS. This nomogram was validated and demonstrated good predictive performance, with area under the curve (AUC) values for 1-year, 3-year, and 5-year OS being 0.848, 0.807, and 0.759, respectively. Our findings highlight the profound impact of prognostic-related genes on BC immune response and prognostic outcomes, suggesting that modulation of the m6A-immune pathway could offer new avenues for personalized BC treatment and potentially improve clinical outcomes.

Tissue-specific thresholds of mutation burden associated with anti-PD-1/L1 therapy benefit and prognosis in microsatellite-stable cancers

Immune checkpoint inhibitors (ICIs) targeting programmed cell death protein 1 or its ligand (PD-1/L1) have expanded the treatment landscape against cancers but are effective in only a subset of patients. Tumor mutation burden (TMB) is postulated to be a generic determinant of ICI-dependent tumor rejection. Here we describe the association between TMB and survival outcomes among microsatellite-stable cancers in a real-world clinicogenomic cohort consisting of 70,698 patients distributed across 27 histologies. TMB was associated with survival benefit or detriment depending on tissue and treatment context, with eight cancer types demonstrating a specific association between TMB and improved outcomes upon treatment with anti-PD-1/L1 therapies. Survival benefits were noted over a broad range of TMB cutoffs across cancer types, and a dose-dependent relationship between TMB and outcomes was observed in a subset of cancers. These results have implications for the use of cancer-agnostic and universal TMB cutoffs to guide the use of anti-PD-1/L1 therapies, and they underline the importance of tissue context in the development of ICI biomarkers.

Characterization of Non-Monotonic Relationships between Tumor Mutational Burden and Clinical Outcomes

Potential clinical biomarkers are often assessed with Cox regressions or their ability to differentiate two groups of patients based on a single cutoff. However, both of these approaches assume a monotonic relationship between the potential biomarker and survival. Tumor mutational burden (TMB) is currently being studied as a predictive biomarker for immunotherapy, and a single cutoff is often used to divide patients. In this study, we introduce a two-cutoff approach that allows splitting of patients when a non-monotonic relationship is present and explore the use of neural networks to model more complex relationships of TMB to outcome data. Using real-world data, we find that while in most cases the true relationship between TMB and survival appears monotonic, that is not always the case and researchers should be made aware of this possibility.

SIGNIFICANCE

When a non-monotonic relationship to survival is present it is not possible to divide patients by a single value of a predictor. Neural networks allow for complex transformations and can be used to correctly split patients when a non-monotonic relationship is present.

Construction and validation of a prognostic and therapeutic cuproptosis- and immune-related gene signature in hepatocellular carcinoma

Background

Abnormal accumulation of copper could induce cell death and tumor growth, and affect tumor immune escape by regulating programmed cell death ligand 1 (PD-L1) expression. This study aims to establish and verify a risk signature based on cuproptosis- and immune-related genes (CIRGs) for hepatocellular carcinoma (HCC) management.

Methods

HCC RNA-seq and clinical data were obtained from open databases. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were utilized to screen CIRGs and develop a risk signature. The signature's value for clinical applications, functional enrichment, tumor mutation burden (TMB), and immune profile analyses were investigated systematically.

Results

A risk signature was developed utilizing seven CIRGs, and it performed well in predicting the prognosis of HCC patients in both the training and external validation cohorts. The model's risk score was discovered to be related to important clinical features. Top 15 mutated genes in HCC were significantly different among different risk groups. High-risk patients showed higher TMB, and high TMB was closely identified with a poorer prognosis. Immune profile analyses showed that immune infiltration level was higher in low-risk patients than high-risk patients, and the level of immune checkpoint genes expression varied significantly between patients in two different risk groups. Low-risk patients responded well to immunotherapy treatment, whereas high-risk patients were more sensitive to sorafenib, doxorubicin, gemcitabine and AKT (also known as protein kinase B) inhibitors.

Conclusions

The established risk signature based on CIRGs can not only well predict the prognosis of HCC patients but is also promising in evaluating TMB and treatment response to immunotherapy, targeted therapy and chemotherapy, which has the potential to assist in the clinical management of HCC.

Strength of selection in lung tumors correlates with clinical features better than tumor mutation burden

Single nucleotide substitutions are the most common type of somatic mutations in cancer genome. The goal of this study was to use publicly available somatic mutation data to quantify negative and positive selection in individual lung tumors and test how strength of directional and absolute selection is associated with clinical features. The analysis found a significant variation in strength of selection (both negative and positive) among tumors, with median selection tending to be negative even though tumors with strong positive selection also exist. Strength of selection estimated as the density of missense mutations relative to the density of silent mutations showed only a weak correlation with tumor mutation burden. In the "all histology together" analysis we found that absolute strength of selection was strongly correlated with all clinically relevant features analyzed. In histology-stratified analysis selection was strongest in small cell lung cancer. Selection in adenocarcinoma was somewhat higher compared to squamous cell carcinoma. The study suggests that somatic mutation- based quantifying of directional and absolute selection in individual tumors can be a useful biomarker of tumor aggressiveness.

Integrative genomic and transcriptomic profiling of pulmonary sarcomatoid carcinoma identifies molecular subtypes associated with distinct immune features and clinical outcomes

Background

Pulmonary sarcomatoid carcinoma (PSC) is a rare and aggressive subtype of non-small cell lung cancer (NSCLC), characterized by the presence of epithelial and sarcoma-like components. The molecular and immune landscape of PSC has not been well defined.

Methods

Multiomics profiling of 21 pairs of PSCs with matched normal lung tissues was performed through targeted high-depth DNA panel, whole-exome, and RNA sequencing. We describe molecular and immune features that define subgroups of PSC with disparate genomic and immunogenic features as well as distinct clinical outcomes.

Results

In total, 27 canonical cancer gene mutations were identified, with TP53 the most frequently mutated gene, followed by KRAS. Interestingly, most TP53 and KRAS mutations were earlier genomic events mapped to the trunks of the tumors, suggesting branching evolution in most PSC tumors. We identified two distinct molecular subtypes of PSC, driven primarily by immune infiltration and signaling. The Immune High (IM-H) subtype was associated with superior survival, highlighting the impact of immune infiltration on the biological and clinical features of localized PSCs.

Conclusions

We provided detailed insight into the mutational landscape of PSC and identified two molecular subtypes associated with prognosis. IM-H tumors were associated with favorable recurrence-free survival and overall survival, highlighting the importance of tumor immune infiltration in the biological and clinical features of PSCs.

Identification of a disulfidptosis-related prognostic signature for prediction of the effect of treatment in patients with endometrial carcinoma

Background

Disulfide, an essential compounds family, has diverse biological activity and can affect the dynamic balance between physiological and pathological states. A recently published study found that aberrant accumulation of disulfide had a lethal effect on cells. This mechanism of cell death, named disulfidptosis, differs from other known cell death mechanisms, including cuproptosis, apoptosis, necroptosis, and pyroptosis. The relationship between disulfidptosis and development of cancer, in particular endometrial carcinoma, remains unclear.

Methods

To address this knowledge gap, we performed a preliminary analysis of samples from The Cancer Genome Atlas database. The samples were divided equally into a training group and a test group. A total of 2308 differentially expressed genes were extracted, and 11 were used to construct a prognostic model.

Results

Based on the risk score calculated using the prognostic model, the samples were divided into a high-risk group and a low-risk group. Survival time, tumor mutation burden, and microsatellite instability scores differed significantly between the two groups. Furthermore, a between-group difference in treatment effect was predicted. Comparison with other models in the literature indicated that this prognostic model had better predictive anility.

Conclusion

The results of this study provide a general framework for understanding the relationship between disulfidptosis and endometrial cancer that could be used for clinical evaluation and selection of appropriate personalized treatment strategies.

A novel angiogenesis-associated risk score predicts prognosis and characterizes the tumor microenvironment in colon cancer

Background

Angiogenesis of the tumor microenvironment (TME) can promote the proliferation and metastases of colon cancer (CC). However, there is a lack of bioinformatics analysis to comprehensively clarify the molecular characteristics, immune interaction characteristics and predictive values of angiogenesis characteristics in CC patients. This study aimed to perform a comprehensive elucidation of the correlation between angiogenesis and CC for the purpose of improving the clinical management of CC.

Methods

Angiogenesis-associated genes (AAGs) were evaluated in the population of CC patients from the Cancer Genome Atlas database and Gene Expression Omnibus dataset. The expression, prognostic role, and immune cell infiltration of AAGs were assessed first. And then we established the AAGs score to further explore the prognosis and treatment response of angiogenesis characteristics in individual patient.

Results

Totally, we identified two different molecular subtypes of angiogenesis, and there was a significant difference in the background of genome, expression profiles, prognosis, and characteristics of TME between two subtypes. And the AAGs score was independently associated with over survival in CC patients, the prognostic value was significant and confirmed in the entire cohort. And we also constructed a nomogram based on the risk score and clinical parameters to maximize the predictive ability of the risk score. Additionally, the AAGs score was significantly correlated with the tumor mutation burden score, cancer stem cell score and drug sensitivity.

Conclusions

Our study elucidated the role of angiogenesis characteristics in CC and the AAGs score could help clinicians plan for individual management with chemotherapy agents and promote the development of immunotherapy in CC. Prospective studies need to be conducted to further confirm our findings.

Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models

Objective

To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated.

Methods

A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model.

Results

The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model.

Conclusion

PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Strategies to Improve Drug Delivery Across the Blood-Brain Barrier for Glioblastoma

PURPOSE OF REVIEW

Glioblastoma remains resistant to most conventional treatments. Despite scientific advances in the past three decades, there has been a dearth of effective new treatments. New approaches to drug delivery and clinical trial design are needed.

RECENT FINDINGS

We discuss how the blood-brain barrier and tumor microenvironment pose challenges for development of effective therapies for glioblastoma. Next, we discuss treatments in development that aim to overcome these barriers, including novel drug designs such as nanoparticles and antibody-drug conjugates, novel methods of drug delivery, including convection-enhanced and intra-arterial delivery, and novel methods to enhance drug penetration, such as blood-brain barrier disruption by focused ultrasound and laser interstitial thermal therapy. Lastly, we address future opportunities, positing combination therapy as the best strategy for effective treatment, neoadjuvant and window-of-opportunity approaches to simultaneously enhance therapeutic effectiveness with interrogation of on-treatment biologic endpoints, and adaptive platform and basket trials as imperative for future trial design. New approaches to GBM treatment should account for the blood-brain barrier and immunosuppression by improving drug delivery, combining treatments, and integrating novel clinical trial designs.

Prognostic biomarkers associated with immune checkpoint inhibitors in hepatocellular carcinoma

The treatment of hepatocellular carcinoma (HCC), particularly advanced HCC, has been a serious challenge. Immune checkpoint inhibitors (ICIs) are landmark drugs in the field of cancer therapy in recent years, which have changed the landscape of cancer treatment. In the field of HCC treatment, this class of drugs has shown good therapeutic prospects. For example, atezolizumab in combination with bevacizumab has been approved as first-line treatment for advanced HCC due to significant efficacy. However, sensitivity to ICI therapy varies widely among HCC patients. Therefore, there is an urgent need to search for determinants of resistance/sensitivity to ICIs and to screen biomarkers that can predict the efficacy of ICIs. This manuscript reviews the research progress of prognostic biomarkers associated with ICIs in HCC in order to provide a scientific basis for the development of clinically individualised precision medication regimens.

Current State of Melanoma Therapy and Next Steps: Battling Therapeutic Resistance

Melanoma is the most aggressive and deadly form of skin cancer due to its high propensity to metastasize to distant organs. Significant progress has been made in the last few decades in melanoma therapeutics, most notably in targeted therapy and immunotherapy. These approaches have greatly improved treatment response outcomes; however, they remain limited in their abilities to hinder disease progression due, in part, to the onset of acquired resistance. In parallel, intrinsic resistance to therapy remains an issue to be resolved. In this review, we summarize currently available therapeutic options for melanoma treatment and focus on possible mechanisms that drive therapeutic resistance. A better understanding of therapy resistance will provide improved rational strategies to overcome these obstacles.

Tumor mutational burden for the prediction of PD-(L)1 blockade efficacy in cancer: challenges and opportunities

Tumor mutational burden (TMB) is a biomarker that measures the number of somatic mutations in a tumor's genome. TMB has emerged as a predictor of response to immune checkpoint inhibitors (ICIs) in various cancer types, and several studies have shown that patients with high TMB have better outcomes when treated with programmed death-ligand 1-based therapies. Recently, the Food and Drug Administration has approved TMB as a companion diagnostic for the use of pembrolizumab in solid tumors. However, despite its potential, the use of TMB as a biomarker for immunotherapy efficacy is limited by several factors. Here we review the limitations of TMB in predicting immunotherapy outcomes in patients with cancer and discuss potential strategies to optimize its use in the clinic.

Development of a breast cancer prognostic model based on vesicle-mediated transport-related genes to predict immune landscape and clinical drug therapy

BACKGROUND

Vesicle-mediated transport, vital for substance exchange and intercellular communication, is linked to tumor initiation and progression. This work was designed to study the role of vesicle-mediated transport-related genes (VMTRGs) in breast cancer (BC)prognosis.

METHODS

Univariate Cox analysis was utilized to screen prognosis-related VMTRGs. BC samples underwent unsupervised clustering based on VMTRGs to analyze survival, clinical factors, and immune cell abundance across different subtypes. We constructed a risk model using univariate Cox and LASSO regression analysis, with validation conducted using GEO datasets. Subsequently, we performed tumor mutational burden analysis, and immune landscape analysis on both groups. Ultimately, we conducted immunophenoscore (IPS) scoring to forecast immunotherapy and performed drug sensitivity analysis.

RESULTS

We identified 102 VMTRGs associated with BC prognosis. Using these 102 VMTRGs, BC patients were classified into 3 subtypes, with Cluster3 patients showing significantly better survival rates. We constructed a prognostic model for BC based on 12 VMTRGs that effectively predicted patient survival. Riskscore was an independent prognostic factor for BC patients. According to median risk score, high-risk group (HRG) had higher TMB values. The immune landscape of the HRG exhibited characteristics of cold tumor, with higher immune checkpoint expression levels and lower IPS scores, whereas Gemcitabine, Nilotinib, and Oxaliplatin were more suitable for treating low-risk group.

CONCLUSION

We classified BC subtypes and built a prognostic model based on VMTRGs. The genes in the prognostic model may serve as potential targets for BC therapy.

Development of fatty acid metabolism score based on gene signature for predicting prognosis and immunotherapy response in colon cancer

PURPOSE

Metabolic reprogramming is a novel hallmark and therapeutic target of cancer. Our study aimed to establish fatty acid metabolism-associated scores based on gene signature and investigated its effects on immunotherapy in colon cancer.

METHODS

Gene expression and clinical information were collected from Gene Expression Omnibus (GEO) database to identify a gene signature by non-negative matrix factorization (NMF) clustering and Cox regression analysis. Subsequently, we constructed the fatty acid metabolism score (FA-score) model by principal component analysis (PCA) and explored its relativity of prognosis and the response to immunotherapy in colon cancer. Finally, the Cancer Genome Atlas (TCGA) database was introduced and in vitro study was performed for verification.

RESULTS

The FA-score-high group had a higher level of fatty acid metabolism and was associated with worse patient overall survival. Significantly, FA-score correlated closely with the biomarkers of immunotherapy, and the FA-score-high group had a poorer therapeutic efficacy of immune checkpoint blockade. In vitro experiments demonstrated that ACSL5 may be a critical metabolic regulatory target.

CONCLUSIONS

Our study provided a comprehensive analysis of the heterogeneity of fatty acid metabolism in colon cancer. We highlighted the potential clinical utility of fatty acid metabolism-related genes to be biomarkers of colon cancer prognosis and targets to improve the effect of immunotherapy.

Potential crosstalk between SPP1 + TAMs and CD8 + exhausted T cells promotes an immunosuppressive environment in gastric metastatic cancer

BACKGROUND

Immunotherapy brings new hope to patients with advanced gastric cancer. However, liver metastases can reduce the efficacy of immunotherapy in patients. Tumor-associated macrophages (TAMs) may be the cause of this reduction in efficacy. SPP1 + TAMs are considered to have immunosuppressive properties. We aimed to investigate the involvement of SPP1 + TAMs in the metastasis of gastric cancer.

METHODS

The single-cell transcriptome was combined with batched BULK datasets for analysis. Animal models were used to verify the analysis results.

RESULTS

We reveal the interaction of SPP1 + TAMs with CD8 + exhausted T cells in metastatic cancer. Among these interactions, GDF15-TGFBR2 may play a key immunosuppressive role. We constructed an LR score to quantify interactions based on ligands and receptors. The LR score is highly correlated with various immune features and clinical molecular subtypes. The LR score may also guide the prediction of the efficacy of immunotherapy and prognosis.

CONCLUSIONS

The crosstalk between SPP1 + TAMs and CD8 + exhausted T cells plays a key immunosuppressive role in the gastric metastatic cancer microenvironment.

A senescence-related lncRNA signature predicts prognosis and reflects immune landscape in HNSCC

OBJECTIVE

Long noncoding RNAs (lncRNAs) regulate cancer cell senescence in many cancers. However, their specific involvement in head and neck squamous cell carcinoma (HNSCC) remains unclear. We are looking for an ingenious prognostic signature that utilizes senescence-related lncRNAs (SRlncRNAs) to predict prognosis and provide insights into the immune landscape in HNSCC.

MATERIALS AND METHODS

HNSCC clinical and Cellular senescence genes information were collected from The Cancer Genome Atlas and Human Aging Genomic Resources. Then we performed Cox and Lasso regression to locate SRlncRNAs related to the prognosis of HNSCC and built a predictive signature. Further, prognosis assessment, potential mechanisms, and immune status were assessed by Kaplan-Meier analysis, Gene Set Enrichment Analysis (GSEA), and CIBERSORT, respectively.

RESULTS

A prognosis prediction model based on sixteen SRlncRNAs was identified and internally validated. Then, patients with high-risk scores suffered an unfavorable overall survival (All p < 0.05). The risk score, age, and stage were independent prognostic parameters (all p < 0.001). Our model has good predictive ability (The AUC (area under the curves) 1-year = 0.707, AUC3-year = 0.748 and AUC5-year = 0.779). Subsequently, GESA revealed SRlncRNAs regulated immune responses. Patients in the high-risk group had higher tumor mutation burden and Tumor Immune Dysfunction and Exclusion but lower levels of 37 immune checkpoint genes, immune scores, and immune cells like CD8 + T cells, follicular helper T cells, and regulatory T cells.

CONCLUSIONS

A prognostic model based on SRlncRNAs is the potential target for improving immunotherapy outcomes for HNSCC.