TCGA Cohort Research Articles

Prognostic impact of caspase-8 mutation in oral cavity squamous cell carcinoma.

Identifying the drive genes and inhibiting their significant signals were persistently the main concepts in cancer treatment. However, for oral cavity squamous cell carcinoma (OCSCC), the most influential genes for overall survival (OS) remain unclear. A total of 120 OCSCC patients with corresponding pathologic specimens were collected in Taiwan. Whole-exome sequencing was done and the prognostic impact of each gene was analyzed. TCGA database was used to validate. The incidences of caspase-8 mutation were 22.1% and 10.9% in the Taiwan and TCGA cohort, respectively. In the Taiwan cohort, caspase-8 mutation was the most significant independent for OS with an adjusted hazard ratio (HR) ([95% CI]: 3.83 [1.84-7.99]). It was validated by the TCGA database (HR [95% CI]: 1.51 [1.00-2.29]). The 5-year OSs of the patients with or without caspase-8 mutation were 38.1% vs. 75.3% (p < 0.001) (HR [95% CI]: 3.264 [1.645-6.438]) in the Taiwan cohort, and 26.1% vs. 49.0% (p = 0.048) (1.513 [1.001-2.288]) in the TCGA cohorts, respectively. Caspase-8 mutation was also individually associated with poor prognosis for TNM stage I/II/III/IV, respectively. CASP8 R127* and R494*, defined as pathogenic mutations in ClinVar, were presented in both cohorts. Caspase-8 mutation was the most significant genetic alteration impacting prognosis.

Radiomics predicts the prognosis of patients with clear cell renal cell carcinoma by reflecting the tumor heterogeneity and microenvironment

PurposeWe aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment.MethodsThe clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What’s more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results.ResultsAt multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868–0.931), 0.854(0.819–0.899) and 0.831(0.813–0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05).ConclusionThe combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.

PPARγ and C/EBPα enable adipocyte differentiation upon inhibition of histone methyltransferase PRC2 in malignant tumors

Loss of terminal differentiation is a hallmark of cancer and offers a potential mechanism for differentiation therapy. Polycomb Repressive Complex 2 (PRC2) serves as the methyltransferase for K27 of histone H3 that is crucial in development. While PRC2 inhibitors show promise in treating various cancers, the underlying mechanisms remain incompletely understood. Here, we demonstrated that the inhibition or depletion of PRC2 enhanced adipocyte differentiation in malignant rhabdoid tumors (MRTs) and mesenchymal stem cells (MSCs), through upregulation of PPARG and CEBPA. Mechanistically, PRC2 directly represses their transcription through H3K27 methylation, as both genes exhibit a bivalent state in MSCs. Knockout of PPARG compromised C/EBPα expression and impeded the PRC2 inhibitor-induced differentiation into adipocytes. Furthermore, the combination of the PPARγ agonist rosiglitazone and the PRC2 inhibitor MAK683 exhibited a higher inhibition on Ki67 positivity in tumor xenograft compared to MAK683 alone. High CEBPA, PLIN1 and FABP4 levels positively correlated with favorable prognosis in sarcoma patients in TCGA cohort. Together, these findings unveil an epigenetic regulatory mechanism for PPARG and highlight the essential role of PPARγ and C/EBPα in the adipocyte differentiation of MRTs and sarcomas with a potential clinical implication.

Molecular Classification of HNSCC Based on Inflammatory Response-Related Genes - Integrated Single-Cell and Bulk RNA-Seq Analysis.

Tumor cells, inflammatory cells, and chemical factors collaboratively orchestrate a sophisticated signaling network, culminating in the formation of the inflammatory tumor microenvironment (TME). The present study sought to explore the nature of the inflammatory response in HNSCC and to decipher its influence on immunotherapeutic. A thorough analysis was performed utilizing the TCGA cohort along with two GEO cohorts. Unsupervised clustering of 200 inflammatory response-related genes (IRGs) was applied using the k-means algorithm to explore the heterogeneity of HNSCC. Additionally, a prognostic signature based on IRGs genes was constructed using Lasso regression. Meanwhiles, the expression of IRGs were identified in tumors and paracancerous tissues at the single-cell level. The crosstalk between IRGs was explored using CellChat and the patterns of incoming and outgoing signals were identified. Finally, qPCR was used to verify the expression of hub genes. There were significant differences in immune-cell function and immune-cell infiltration among three inflammatory response clusters. Additionally, we also constructed a prognostic model which could predicted the responses of common chemotherapeutic drugs and immunotherapy. Furthermore, qPCR and sc-RNA seq corroborated that the expression profiles of the prognostic genes were largely in alignment with the findings from the bioinformatics analysis. Ultimately, the molecular docking demonstrated favorable binding affinities between the pivotal gene-SCC7 and four chemotherapeutic drugs. This research has uniquely shed light on the intricate connection between the inflammatory response profiles and the immune infiltration patterns in HNSCC.

Impact of clonal TP53 mutations with loss of heterozygosity on adjuvant chemotherapy and immunotherapy in gastric cancer

BackgroundThis study aimed to reveal the effect of TP53 status on clinical outcomes and underlying mechanism in gastric cancer (GC) patients.MethodsTP53 status was divided into three groups according to genome sequencing, namely clonal mutations with LOH (C-LOH), clonal diploid or subclonal mutations (CD-SC), and wild type (WT). The p53 protein activity was divided into over-expression (OE), Null and WT according to immunohistochemical staining. Four cohorts, including the TCGA, SMC, ZSHS and FUSCC cohort, were analyzed for association between TP53 mutation status and clinical outcomes and the underlying mechanism.ResultsIn TCGA cohort, TP53 CD-SC were associated with superior overall survival compared to TP53 C-LOH cases. GC patients could benefit from ACT only in TP53 CD-SC/ p53 OE and TP53/ p53 WT subgroups, and TP53 C-LOH subgroup demonstrated the worst response to pembrolizumab among three subgroups. Genomic and immunophenotypic deconvolution revealed that TP53 C-LOH, CD-SC and WT differed for genomic and immune-related features.ConclusionsTP53 C-LOH GCs with genomic instability and immune evasion phenotype have poor clinical outcomes in patients treated with ACT or immunotherapy.

Combining radiomics and molecular biomarkers: a novel economic tool to improve diagnostic ability in papillary thyroid cancer.

A preoperative diagnosis to distinguish malignant from benign thyroid nodules accurately and sensitively is urgently important. However, existing clinical methods cannot solve this problem satisfactorily. The aim of this study is to establish a simple, economic approach for preoperative diagnosis in eastern population. Our retrospective study included 86 patients with papillary thyroid cancer and 29 benign cases. The ITK-SNAP software was used to draw the outline of the area of interest (ROI), and Ultrosomics was used to extract radiomic features. Whole-transcriptome sequencing and bioinformatic analysis were used to identify candidate genes for thyroid nodule diagnosis. RT-qPCR was used to evaluate the expression levels of candidate genes. SVM diagnostic model was established based on the METLAB 2022 platform and LibSVM 3.2 language package. The radiomic model was first established. The accuracy is 73.0%, the sensitivity is 86.1%, the specificity is 17.6%, the PPV is 81.6%, and the NPV is 23.1%. Then, CLDN10, HMGA2, and LAMB3 were finally screened for model building. All three genes showed significant differential expressions between papillary thyroid cancer and normal tissue both in our cohort and TCGA cohort. The molecular model was established based on these genetic data and partial clinical information. The accuracy is 85.9%, the sensitivity is 86.1%, the specificity is 84.6%, the PPV is 96.9%, and the NPV is 52.4%. Considering that the above two models are not very effective, We integrated and optimized the two models to construct the final diagnostic model (C-thyroid model). In the training set, the accuracy is 96.7%, the sensitivity is 100%, the specificity is 93.8%, the PPV is 93.3%, and the NPV is 100%. In the validation set, the accuracy is 97.6%, the sensitivity remains 100%, the specificity is 84.6%, the PPV is 97.3%, and the NPV is 100%. A diagnostic panel is successfully established for eastern population through a simple, economic approach using only four genes and clinical data.

The signature genes of cuproptosis associates with tumor immune microenvironment and predicts prognosis in kidney renal clear cell carcinoma.

Cuproptosis is a new form of cell death, which has great potential to be developed in tumors treatment. Our study aimed to explore the predictive value of cuproptosis-related genes (CRGs) in various cancers, with a focus on kidney renal clear cell carcinoma (KIRC). A total of 9502 pan-cancer patients from TCGA cohort were enrolled. The relationships between CRGs and overall survival (OS) or disease-free survival (DFS) were analyzed. Gene Set Variation Analysis (GSVA) enrichment analysis was performed to explore the expression differences of CRGs. Multivariate Cox regression analysis was used to evaluate the association between GSVA scores and patient survival. KEGG and GO analyses were employed to identify the biological functions and pathways. The expression and prognostic characteristics of FDX1 were examined to evaluate the correlation between FDX1 and KIRC. Cell experiments were conducted to verify whether FDX1 was involved in cuproptosis of Caki-1 cells induced by Elesclomol. Positive cuproptosis signature genes(pos.cu.sig) exhibited the correlation with prognosis in KIRC, and all of these genes showed differential expression between KIRC and normal tissues. The GSVA score of pos.cu.sig was associated with excellent survival (HR=0.61, P<0.05), which can also serve as an independent prognostic factor for KIRC. There was a close correlation between pos.cu.sig and the tumor immune microenvironment in KIRC by KEGG and GO analysis. FDX1 expression was correlated with KIRC grade and positively associated with prognosis in KIRC patients. Compared with the control group, cell proliferation and migration were significantly inhibited, FDX1 expression was up-regulated, and Fe-S cluster protein content was decreased of Caki-1 cells after Elesclomol treatment. This study provides compelling evidence that cuproptosis is closely linked to the prognosis of KIRC. FDX1 holds promise as a viable biomarker and therapeutic target for assessing the effectiveness of tumor immunotherapy in KIRC.

Prognostic value of four immune-related genes in lower-grade gliomas: a biomarker discovery study.

The tumor microenvironment and IRGs are highly correlated with tumor occurrence, progression, and prognosis. However, their roles in grade II and III gliomas, termed LGGs in this study, remain to be fully elucidated. Our research aims to develop immune-related features for risk stratification and prognosis prediction in LGG. Using the ssGSEA method, we assessed the immune characteristics of the LGG population. We conducted differential analysis using LGG samples from the TCGA database and normal samples from GTEx, identifying 412 differentially expressed immune-related genes (DEIRGs). Subsequently, we utilized univariate Cox, LASSO, and multivariate Cox regression analyses to establish both a gene predictive model and a nomogram predictive model. Here, we found that the ESTIMATE score, immune score and stromal score of high-immunity, high-grade and isocitrate dehydrogenase (IDH) wild-type glioma were higher than those of the corresponding group, and the tumor purity was lower. Higher ESTIMATE scores, stromal scores and immune scores indicated a poor prognosis in patients with LGG. Our four-gene prognostic model demonstrated superior accuracy compared to other molecular features. Validation using the CGGA as a testing set and the combined TCGA and CGGA cohort confirmed its robust prognostic value. Additionally, a nomogram integrating the prognostic model and clinical variables showed enhanced predictive capability. Our study highlights the prognostic significance of the identified four DEIRGs (KLRC3, MR1, PDIA2, and RFXAP) in LGG patients. The predictive model and nomogram developed herein offer valuable tools for personalized treatment strategies in LGG. Future research should focus on further validating these findings and exploring the functional roles of these DEIRGs within the LGG tumor microenvironment.

Development of prognostic model incorporating a ferroptosis/cuproptosis-related signature and mutational landscape analysis in muscle-invasive bladder cancer

BackgroundMuscle-invasive bladder cancer (MIBC) is a prevalent and aggressive malignancy. Ferroptosis and cuproptosis are recently discovered forms of programmed cell death (PCD) that have attracted much attention. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes remain unclear.MethodsData from the TCGA-BLCA project (as the training set), cBioPortal database, and GEO datasets (GSE13507 and GSE32894, as the test sets) were utilized to identify hub ferroptosis/cuproptosis-related genes (FRGs and CRGs) and develop a prognostic signature. Differential expression analysis (DEA) was conducted, followed by univariate and multivariate Cox’s regression analyses and multiple machine learning (ML) techniques to select genetic features. The performance of the ferroptosis/cuproptosis-related signature was evaluated using Kaplan–Meier (K–M) survival analysis and receiver-operating characteristics (ROC) curves. Mutational and tumour immune microenvironment landscapes were also explored. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) experiments confirmed the expression patterns of the hub genes, and functional assays assessed the effects of SCD knockdown on cell viability, proliferation, and migration.ResultsDEA revealed dysregulated FRGs and CRGs in the TCGA MIBC cohort. SCD, DDR2, and MT1A were identified as hub genes. A prognostic signature based on the sum of the weighted expression of these genes demonstrated strong predictive efficacy in the training and test sets. Nomogram incorporating this signature accurately predicted 1-, 3-, and 5-year survival probabilities in the TCGA cohort and GSE13507 dataset. Copy number variation (CNV) and tumour immune microenvironment analysis revealed that high risk score level groups were associated with immunosuppression and lower tumour purity. The associations of risk scores with immunotherapy and chemical drugs were also explored, indicating their potential for guiding treatment for MIBC patients. The dysregulated expression patterns of three hub genes were validated by RT-qPCR experiments.ConclusionsTargeting hub FRGs and CRGs could be a promising therapeutic approach for MIBC. Our prognostic model offers a new framework for MIBC subtyping and can inform personalized therapeutic strategies.

MDK promotes M2 macrophage polarization to remodel the tumour microenvironment in clear cell renal cell carcinoma

The efficacy of immunotherapy for clear cell renal cell carcinoma (ccRCC), especially advanced ccRCC, is limited, presenting a clinical challenge. This limitation is closely tied to the immune regulation network. Understanding the heterogeneity of the tumour microenvironment (TME) is crucial for developing advanced ccRCC therapies. Using publicly available ccRCC data (scRNA-seq, bulk RNA-seq, and somatic mutation data), a multiomics study was performed to explore TME heterogeneity. Three distinct ccRCC immune subtypes were identified through combined scRNA-seq and bulk RNA-seq analysis. A prognostic model based on unique cell signalling molecules in immunosuppressive tumour subtype was validated in the TCGA and CheckMate cohorts. MDK emerged as a critical regulatory gene in the immunosuppressive subtype, predicting a poor ccRCC prognosis and a poor immunotherapy response. MDK promotes M2 macrophage polarization via the MDK-LRP1 interaction, and the inhibition of MDK suppressed M2 polarization. This study revealed the heterogeneity of the ccRCC TME and a reliable prognostic model, shedding light on the vital role of MDK in the immunosuppressive TME and paving the way for optimized ccRCC immunotherapy.

25 Enrichment of tertiary lymphoid structures provides novel insight into mediators of anti-tumor immune activity in sarcomatoid renal cell carcinoma

Abstract Background Renal cell carcinoma (RCC) comprises various histological subtypes, with clear cell RCC (ccRCC) being the most prevalent histotype. RCC with sarcomatoid features (sRCC) is a unique kidney cancer subtype associated with aggressive biological features and poor clinical outcomes that can arise from multiple RCC histologies, most commonly ccRCC. While clinically aggressive, sRCC paradoxically has also demonstrated preferential responsiveness to immune checkpoint blockade (ICB) therapies in subgroup analyses of multiple phase III trials. However, the mediators of this immune sensitivity are largely unknown. We therefore applied transcriptomic techniques to identify orchestrators of immune activity within the sRCC tumor microenvironment (TME). Methods Nephrectomy specimens from patients with sRCC and ccRCC were procured for single cell RNA sequencing (scRNAseq). Clustering and dimensionality reduction were performed, and cell populations were annotated based on expression of canonical lineage markers. Immune populations (CD8+ T cells, CD4+ T cells, B/plasma cells, myeloid cells) were computationally extracted and differential gene expression between sRCC- and ccRCC-derived cells within each subpopulation was performed. Gene expression programs enriched in sRCC samples by scRNAseq were validated on publicly available bulk gene expression data comparing sRCC to ccRCC. Spatial transcriptomics were performed on sRCC tumor sections using the 10X Visium platform. Results Across 18 RCC specimens (10 sRCC; 8 ccRCC), 73,123 cells were analyzed by scRNAseq. Within the CD8+ T cell compartment, CXCL13 was the most significantly enriched nuclear-encoded gene in sRCC samples (log 2-fold change=1.29; q&amp;lt;0.001). CXCL13 was also significantly enriched in CD4+ T cells from sRCC (q&amp;lt;0.001), suggesting enhanced presence of follicular T cells within the sRCC TME. As follicular T cells function in support of B cells, we next interrogated the B lymphocyte population. sRCC samples were enriched for mature B cell and plasma cells, with a five-fold increase in the relative abundance of plasma cells compared to ccRCC samples. Immune deconvolution of patient-derived RNA sequencing from the IMmotion 151 trial revealed a significant increase in the predicted proportion of B lymphocytes (including plasma cells) within the sRCC TME relative to ccRCC (p=0.034). Further, over 20 B lymphocyte activation and maturation pathways were consistently enriched (q&amp;lt;0.25) in sRCC across clinical datasets (Javelin101, CheckMate, and TCGA KIPAN), including Signaling by the B Cell Receptor, Positive Regulation of B Cell Activation, and Immunoglobulin Production Involved in Immunoglobulin Mediated Immune Response, amongst others. B lymphocytes mediate anti-tumor immunity through antibody dependent cellular cytotoxicity (ADCC), and thus the phagocytic effectors of ADCC were interrogated. Myeloid populations differentially expressed FCγR3A in sRCC vs ccRCC (q&amp;lt;0.001), which was recapitulated in bulk gene expression populations (q=0.002, 0.12, and 1.08x10-4 in Javelin101, CheckMate, and TCGA cohorts, respectively). Given the enrichment of follicular T cell and differentiated B lymphocyte programs, we explored the presence of tertiary lymphoid structures (TLS) in sRCC. Two distinct TLS signatures – the 12 Chemokine Score and the TLS Imprint Signature – were significantly enriched in sRCC vs ccRCC across RCC patient datasets (Figure 1). Furthermore, spatial transcriptomics were applied to H&amp;E slides of sRCC to successfully identify the presence of TLS by expression of TLS-associated genes adjacent to sarcomatoid regions. Conclusions TLS, which have previously been associated with response to ICB in RCC (Meylan et al. Immunity. 2022), are transcriptomically enriched for in sRCC, paralleling an observed increase in CXCL13-expressing T cells and differentiated B lymphocytes. Together, TLS and their constituents offer a previously unexplored mediator of immunosurveillance in the sRCC TME that may underlie the paradoxical responsiveness to ICB seen within this population clinically.

The E3 ubiquitin ligase Herc1 modulates the response to nucleoside analogs in acute myeloid leukemia

AbstractFor several decades, induction therapy with nucleoside analogs, in particular cytarabine (Ara-C) and, to a lesser extent, fludarabine, has been the standard of care for patients diagnosed with acute myeloid leukemia (AML). However, the antitumor efficacy of nucleoside analogs is often limited by intrinsic and acquired drug resistance, thereby leading to poor therapeutic response and suboptimal clinical outcomes. In this study, we used genome-wide CRISPR-based pharmacogenomic screening to map the genetic factors that modulate the response to nucleoside analogs in AML and identified the E3 ubiquitin ligase, Herc1, as a key modulator of Ara-C response in mouse AML models driven by the KMT2A/MLLT3 fusion or by the constitutive coexpression of Hoxa9 and Meis1, both in vitro and in vivo. Loss of HERC1 enhanced nucleoside analog–induced cell death in both murine and human AML cell lines by compromising cell cycle progression. In-depth proteomic analysis and subsequent validation identified deoxycytidine kinase as a novel target of Herc1 in both mouse AML models. We observed that HERC1 is overexpressed in AML when compared with other cancer types and that higher HERC1 expression was associated with shorter overall survival in patients with AML in the The Cancer Gene Atlas program (TCGA) and BEAT-AML cohorts. Collectively, this study highlights the importance of HERC1 in the response of AML cells to nucleoside analogs, thereby establishing this E3 ubiquitin ligase as a novel predictive biomarker and potential therapeutic target for the treatment of AML.

Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma.

Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC). A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation. A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance. In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms.

Development of mutated β-catenin gene signature to identify CTNNB1 mutations from whole and spatial transcriptomic data in patients with HCC

Background & AimsPatients with β-catenin (encoded by CTNNB1)-mutated hepatocellular carcinoma (HCC) demonstrate heterogenous responses to first-line immune checkpoint inhibitors (ICIs). Precision-medicine based treatments for this subclass are currently in clinical development. Here, we report derivation of Mutated β-catenin Gene Signature (MBGS) to predict CTNNB1-mutational status in patients for future personalized medicine. MethodsCo-expression of mutant-Nrf2 and hMet ± mutant-β-catenin in murine livers in mice led to HCC development. Using bulk RNA-seq and intersectional transcriptomic analysis of various β-catenin-mutated and non-mutated HCC models, MBGS was derived. Integrated RNA/whole-exome-sequencing and spatial transcriptomic data from multiple HCC patient cohorts was assessed to address ability of MBGS to detect CTNNB1 mutation, tumor immune microenvironment and/or predict therapeutic responses. ResultsBulk RNA-seq comparing HCCs in mutant β-catenin-Nrf2, β-catenin-Met and β-catenin-Nrf2-Met to Nrf2-Met HCC model yielded 95 common upregulated genes. In TCGA-LIHC, differential gene expression analysis with FDR=0.05 and log2FC>1.5 on the 95 common genes comparing CTNNB1-mutated vs wild-type patients narrowed the gene panel to 13-genes MBGS. MBGS predicted CTNNB1-mutations in TCGA (n=374) and French (n=398) patient cohorts with ROC AUC of 0.90 and 0.94, respectively. Additionally, increased MBGS expression score was associated with lack of significant overall survival or progression-free survival effects in the atezolizumab-bevacizumab arm versus sorafenib arm in IMbrave150 cohort. MBGS performed comparable or superior to other CTNNB1-mutant classifiers. MBGS overlapped with Hoshida S3, Boyault G5/G6, and Chiang CTNNB1 subclass tumors in TCGA and in HCC spatial transcriptomic datasets visually depicting these tumors to be situated in an immune excluded tumor microenvironment. ConclusionsMBGS will aid in patient stratification to guide precision medicine therapeutics for CTNNB1-mutated HCC subclass as a companion diagnostic, as anti-β-catenin therapies become available.

Precision treatment paradigm: Genomic features and therapeutic implications in mesenchymal‐epithelial transition‐amplified gastric cancer

AbstractBackgroundDespite advances in treatments for gastric cancer (GC), new targets are needed to enhance treatment, especially when mesenchymal‐epithelial transition (MET) amplification is involved. Therefore, we investigated the genomic features of MET‐amplified GC and efficacy of anti‐MET therapy.MethodsGenomic features were investigated in 2284 patients (cohort 1) and 438 patients in the Cancer Genome Atlas (TCGA cohort). RNA data were obtained from TCGA for expression analysis. Clinical responses of 71 patients (cohort 2) were investigated. Survival rates were compared by Kaplan–Meier and log‐rank tests.ResultsIn cohort 1, 95 (4.2%) exhibited MET amplifications. In TCGA cohort, 15 (3.4%) exhibited MET amplifications. The five top mutated genes were TP53, CDH1, ARID1A, KMT2D and PIK3CA in cohort 1 and TTN, TP53, MUC16, LRP1B and SYNE1 in TCGA cohort. TP53, PIK3CA, KRAS and GNAS showed significant single nucleotide variants (SNVs) in cohort 1, and TP53 SNVs were significant in TCGA cohort. Fifteen and 18 genes showed significant copy number variations (CNVs) between the MET‐ and non‐MET‐amplified groups in cohort 1 and TCGA cohort, respectively. The PI3K pathway was significantly activated in Chinese patients with MET‐amplified GC (p = 0.002). TP53 expression is negatively associated with MET amplification. Real‐world data obtained with cohort 2 revealed that anti‐MET treatment significantly affected overall survival (OS) (p = 0.002), whereas CNVs did not affect progression‐free or OS even when treatment lines were considered.ConclusionsMET‐amplified GC has a distinctive mutation landscape. Our findings provide valuable information for clinicians who treat patients with MET‐amplified GC.

Knockdown of Anoikis-Associated Gene OCIAD2 Reduces Proliferation and Migration of Glioblastoma Cell Lines.

Glioblastoma (GBM) severely disrupts the quality of life of patients. Anoikis represents a significant mechanism in cancer invasion and metastasis. Our study focused on the prognostic relationship between the anoikis-associated gene and GBM and its effect on GBM cell progression. We downloaded 656 and 979 GBM sample data from TCGA and CGGA cohort datasets, respectively. Fifteen anoikis-associated genes were obtained from the GeneCards database and were subsequently clustered to identify differential genes associated with them. After LAASO screening, the expression values of the 5 differential genes were the sum of LASSO regression coefficients. Survival analysis and ROC curve analysis of anoikis scores were performed using the TCGA training and CGGA validation sets. The prognostic factors were analyzed using Cox regression analysis in GBM. Moreover, CCK-8, colony formation, wound healing, and transwell assay were used to evaluate GBM cell proliferation and migration. Significant differences were observed in the 5-year survival of GBM patients between the two subgroups. Then, our analysis demonstrated that high OCIAD2, FTLP3, IGFBP2, and H19 levels were associated with lower 5-year GBM survival rates, whereas high SFRP2 levels were associated with higher survival rates. Univariate Cox analysis indicated that GBM risk was linked to both anoikis score and grade, while multivariate Cox analysis indicated that GBM risk was associated with both anoikis score and age. Additionally, OCIAD2 was highly expressed in U251MG and T98G cells. Moreover, OCIAD2 silencing inhibited GBM cell proliferation and migration. This study demonstrated the potential of the anoikis-associated gene OCIAD2 as a prognostic biomarker for GBM. Furthermore, we validated in vitro that OCIAD2 promoted GBM cell progression.

A susceptibility gene signature for ERBB2-driven mammary tumour development and metastasis in collaborative cross mice

Deeper insights into ERBB2-driven cancers are essential to develop new treatment approaches for ERBB2+ breast cancers (BCs). We employed the Collaborative Cross (CC) mouse model to unearth genetic factors underpinning Erbb2-driven mammary tumour development and metastasis. 732 F1 hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains were monitored for mammary tumour phenotypes. GWAS pinpointed SNPs that influence various tumour phenotypes. Multivariate analyses and models were used to construct the polygenic score and to develop a mouse tumour susceptibility gene signature (mTSGS), where the corresponding human ortholog was identified and designated as hTSGS. The importance and clinical value of hTSGS in human BC was evaluated using public datasets, encompassing TCGA, METABRIC, GSE96058, and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validated invivo using genetically diverse MMTV-Erbb2 mice. Distinct variances in tumour onset, multiplicity, and metastatic patterns were observed in F1-hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified specific SNPs significantly associated with tumour onset, multiplicity, lung metastasis, and liver metastasis. Multivariate analyses flagged SNPs in 20 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b, Rer1, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a, Oxr1, Plxna2, and Tbc1d31) independently tied to various tumour characteristics, designated as a mTSGS. hTSGS scores (hTSGSS) based on their transcriptional level showed prognostic values, superseding clinical factors and PAM50 subtype across multiple human BC cohorts, and predicted pathological complete response independent of and superior to MammaPrint score in I-SPY2 study. The power of mTSGS score for predicting chemotherapy response was further validated in an invivo mouse MMTV-Erbb2 model, showing that, like findings in human patients, mouse tumours with low mTSGS scores were most likely to respond to treatment. Our investigation has unveiled many new genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that hTSGS holds promise as a biomarker for refining treatment strategies for patients with BC. The U.S. Department of Defense (DoD) Breast Cancer Research Program (BCRP) (BC190820), United States; MCIN/AEI/10.13039/501100011039 (PID2020-118527RB-I00, PDC2021-121735-I00), the "European Union Next Generation EU/PRTR," the Regional Government of Castile and León (CSI144P20), European Union.

Genome-Wide Methylation Patterns in Primary Uveal Melanoma: Development of MethylSig-UM, an Epigenomic Prognostic Signature to Improve Patient Stratification.

Despite studies highlighting the prognostic utility of DNA methylation in primary uveal melanoma (pUM), it has not been translated into a clinically useful tool. We sought to define a methylation signature to identify newly diagnosed individuals at high risk for developing metastasis. Methylation profiling was performed on 41 patients with pUM with stage T2-T4 and at least three years of follow-up using the Illumina Infinium HumanMethylation450K BeadChip (N = 24) and the EPIC BeadChip (N = 17). Findings were validated in the TCGA cohort with known metastatic outcome (N = 69). Differentially methylated probes were identified in patients who developed metastasis. Unsupervised consensus clustering revealed three epigenomic subtypes associated with metastasis. To identify a prognostic signature, recursive feature elimination and random forest models were utilized within repeated cross-validation iterations. The 250 most commonly selected probes comprised the final signature, named MethylSig-UM. MethylSig-UM could distinguish individuals with pUM at diagnosis who develop future metastasis with an area under the curve of ~81% in the independent validation cohort, and remained significant in Cox proportional hazard models when combined with clinical features and established genomic biomarkers. Altered expression of immune-modulating genes were detected in MethylSig-UM positive tumors, providing clues for pUM resistance to immunotherapy. The MethylSig-UM model is available to enable additional validation in larger cohort sizes including T1 tumors.

An interpretable survival model for diffuse large B-cell lymphoma patients using a biologically informed visible neural network

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) and is characterized by high heterogeneity. Assessment of its prognosis and genetic subtyping hold significant clinical implications. However, existing DLBCL prognostic models are mainly based on transcriptomic profiles, while genetic variation detection is more commonly used in clinical practice. In addition, current clustering-based subtyping methods mostly focus on genes with high mutation frequencies, providing insufficient explanations for the heterogeneity of DLBCL. Here, we proposed VNNSurv (https://bio-web1.nscc-gz.cn/app/VNNSurv), a survival model for DLBCL patients based on a biologically informed visible neural network (VNN). VNNSurv achieved an average C-index of 0.72 on the cross-validation set (HMRN cohort, n = 928), outperforming the baseline methods. The remarkable interpretability of VNNSurv facilitated the identification of the most impactful genes and the underlying pathways through which they act on patient outcomes. When only the 30 highest-impact genes were used as genetic input, the overall performance of VNNSurv improved, and a C-index of 0.70 was achieved on the external TCGA cohort (n = 48). Leveraging these high-impact genes, including 16 genes with low (<5 %) alteration frequencies, we devised a genetic-based prognostic index (GPI) for risk stratification and a subtype identification method. We stratified the patient group according to the International Prognostic Index (IPI) into three risk grades with significant prognostic differences. Furthermore, the defined subtypes exhibited greater prognostic consistency than clustering-based methods. Broadly, VNNSurv is a valuable DLBCL survival model. Its high interpretability has significant value for precision medicine, and its framework is scalable to other diseases.

Identification of tumor-specific T cell signature predicting cancer immunotherapy response in bladder cancer by multi-omics analysis and experimental verification

BackgroundNumerous gene signatures predicting the prognosis of bladder cancer have been identified. However, a tumor-specific T cell signature related to immunotherapy response in bladder cancer remains under investigation.MethodsSingle-cell RNA and TCR sequencing from the Gene expression omnibus (GEO) database were used to identify tumor-specific T cell-related genes in bladder cancer. Subsequently, we constructed a tumor-specific T cell signature (TstcSig) and validated its clinical relevance for predicting immunotherapy response in multiple immunotherapy cohorts. Further analyses explored the immune characteristics of TstcSig in bladder cancer patients from other cohorts in the TCGA and GEO databases. Western blot (WB), multicolor immunofluorescence (MIF), qRT-PCR and flow cytometry assays were performed to validate the results of bioinformatics analysis.ResultsThe established TstcSig, based on five tumor-specific T cell-related genes, could predict outcomes in a bladder cancer immunotherapy cohort. This was verified using two additional immunotherapy cohorts and showed better predictive performance compared to 109 published T cell signatures. TstcSig was strongly correlated with immune characteristics such as immune checkpoint gene expression, tumor mutation burden, and T cell infiltration, as validated by single-cell and spatial transcriptomics datasets. Notably, the positive correlation between TstcSig and T cell infiltration was confirmed in the TCGA cohort. Furthermore, pan-cancer analysis demonstrated the heterogeneity of the prognostic value of TstcSig. Tumor-specific T cells highly expressed CD27, IFNG, GZMB and CXCL13 and secreted more effector cytokines for tumor cell killing, as validated experimentally.ConclusionWe developed a five-gene signature (including VAMP5, TIGIT, LCK, CD27 and CACYBP) based on tumor-specific T cell-related genes to predict the immunotherapy response in bladder cancer patients.