MUC16 Mutation Research Articles

Developing a prognostic signature: identifying differentially expressed genes in cardia and non-cardia gastric cancer for immunity and therapeutic sensitivity analysis.

Gastric cancer (GC) can be anatomically categorized into two subtypes; that is, cardia gastric cancer (CGC) and non-cardia gastric cancer (NCGC), which have distinct molecular mechanisms and prognoses. At present, the majority of pharmacological interventions for GC adhere to non-specific treatment regimens. The stratification of GC based on molecular disparities between CGC and NCGC has important clinical guidance value and could help in the development of precision therapies tailored to individual patient needs. Nevertheless, research in this specialized field remains notably limited. This study aims to investigate the molecular differences between CGC and NCGC and to leverage these differences to develop a prognostic risk scoring model (PRSM). We used patient data from The Cancer Genome Atlas (TCGA) and performed a differentially expressed gene (DEG) analysis between CGC and NCGC. A PRSM was developed from the prognosis-associated DEGs identified through Cox regression analyses and was well validated using Gene Expression Omnibus (GEO) data. A total of 339 DEGs were identified between CGC and NCGC, and four prognosis-associated genes were used to construct the PRSM. Using the risk coefficients and expression levels of signature genes, a median risk score (RS) was calculated to classify patients into high- and low-risk groups. The high-risk group had a significantly worse prognosis than the low-risk group. An in-depth analysis revealed that TP53 mutations were more prevalent in the high-risk group, and MUC16 mutations were more prevalent in the low-risk group. A gene set enrichment analysis (GSEA) and the CIBERSORT algorithm were used to assess the differences in the significantly enriched pathways and immune microenvironment in the high- and low-risk groups, respectively. The inhibitory concentration (IC50) values of the chemotherapy drugs for GC also varied between the two groups. This study elucidated the unique molecular characteristics of GC subtypes based on the anatomical site and provided a preliminary contribution for the development of precision medicine for GC.

Immunogenic cell death-related genes predict prognosis and response to immunotherapy in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is a malignancy with limited therapeutic options. Immunogenic cell death (ICD) has the potential to enhance the efficacy of cancer therapy by triggering immune responses. We aimed to explore the potential of ICD-based classification in predicting prognosis and response to immunotherapy for LUSC. RNA-seq information and clinical data of LUSC patients were obtained from The Cancer Genome Atlas (TCGA) dataset. ICD-related gene expressions in LUSC samples were analyzed by consensus clustering. Subsequently, differentially expressed genes (DEGs) between different ICD-related subsets were analyzed. Tumor mutation burden, immune cell infiltration, and survival analyses were conducted between different ICD subsets. Finally, an ICD-related risk signature was constructed and evaluated in LUSC patients, and the immunotherapy responses based on the gene expressions were also forecasted. ICD-high and ICD-low groups were defined, and 1466 DEGs were identified between the two subtypes. These DEGs were mainly enriched in collagen-containing extracellular matrix, cytokine-cytokine receptor interaction, the PI3K-Akt signaling pathway, and neuroactive ligand-receptor interaction. Furthermore, the ICD-low group exhibited a favorable prognosis, enhanced TTN and MUC16 mutation frequencies, increased infiltrating immune cells, and downregulated immune checkpoint expressions. Furthermore, we demonstrated that an ICD-related model (based on CD4, NLRP3, NT5E, and TLR4 genes) could forecast the prognosis of LUSC, and ICD risk scores were lower in the responder group. In summary, the predicted values of ICD-related genes (CD4, NLRP3, NT5E, and TLR4) for the prognosis and response to immunotherapy in LUSC were verified in the study, which benefits immunotherapy-based interventions for LUSC patients.

Predicting recurrent glioblastoma clinical outcome to immune checkpoint inhibition and low-dose bevacizumab with tumor in situ fluid circulating tumor DNA analysis

ObjectiveMost recurrent glioblastoma (rGBM) patients do not benefit from immune checkpoint inhibition, emphasizing the necessity for response biomarkers. This study evaluates whether tumor in situ fluid (TISF) circulating tumor DNA (ctDNA) could serve as a biomarker for response to low-dose bevacizumab (Bev) plus anti-PD-1 therapy in rGBM patients, aiming to enhance systemic responses to immunotherapy.MethodsIn this phase II trial, 32 GBM patients with first recurrence after standard therapy were enrolled and then received tislelizumab plus low-dose Bev each cycle. TISF samples were analyzed for ctDNA using a 551-gene panel before each treatment.ResultsThe median progression-free survival (mPFS) and overall survival (mOS) were 8.2 months (95% CI, 5.2–11.1) and 14.3 months (95% CI, 6.5–22.1), respectively. The 12-month OS was 43.8%, and the objective response rate was 56.3%. Patients with more than 20% reduction in the mutant allele fraction and tumor mutational burden after treatment were significantly associated with better prognosis compared to baseline TISF-ctDNA. Among detectable gene mutations, patients with MUC16 mutation, EGFR mutation & amplification, SRSF2 amplification, and H3F3B amplification were significantly associated with worse prognosis.ConclusionsLow-dose Bev plus anti-PD-1 therapy significantly improves OS in rGBM patients, offering guiding significance for future individualized treatment strategies. TISF-ctDNA can monitor rGBM patients' response to combination therapy and guide treatment.Clinical trial registrationThis trial is registered with ClinicalTrials.gov, NCT05540275.

Identification and validation of calcium extrusion-related genes prognostic signature in colon adenocarcinoma.

Disruptions in calcium homeostasis are associated with a wide range of diseases, and play a pivotal role in the development of cancer. However, the construction of prognostic models using calcium extrusion-related genes in colon adenocarcinoma (COAD) has not been well studied. We aimed to identify whether calcium extrusion-related genes serve as a potential prognostic biomarker in the COAD progression. We constructed a prognostic model based on the expression of calcium extrusion-related genes (SLC8A1, SLC8A2, SLC8A3, SLC8B1, SLC24A2, SLC24A3 and SLC24A4) in COAD. Subsequently, we evaluated the associations between the risk score calculated by calcium extrusion-related genes and mutation signature, immune cell infiltration, and immune checkpoint molecules. Then we calculated the immune score, stromal score, tumor purity and estimate score using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm. The response to immunotherapy was assessed using tumor immune dysfunction and exclusion (TIDE). Finally, colorectal cancer cells migration, growth and colony formation assays were performed in RKO cells with the overexpression or knockdown SLC8A3, SLC24A2, SLC24A3, or SLC24A4. We found that patients with high risk score of calcium extrusion-related genes tend to have a poorer prognosis than those in the low-risk group. Additionally, patients in high-risk group had higher rates of KRAS mutations and lower MUC16 mutations, implying a strong correlation between KRAS and MUC16 mutations and calcium homeostasis in COAD. Moreover, the high-risk group showed a higher infiltration of regulatory T cells (Tregs) in the tumor microenvironment. Finally, our study identified two previously unreported model genes (SLC8A3 and SLC24A4) that contribute to the growth and migration of colorectal cancer RKO cells. Altogether, we developed a prognostic risk model for predicting the prognosis of COAD patients based on the expression profiles of calcium extrusion-related genes, Furthermore, we validated two previously unreported tumor suppressor genes (SLC8A3 and SLC24A4) involved in colorectal cancer progression.

Genomic profiling informs therapies and prognosis for patients with hepatocellular carcinoma in clinical practice

Hepatocellular carcinoma (HCC) genomic research has discovered actionable genetic changes that might guide treatment decisions and clinical trials. Nonetheless, due to a lack of large-scale multicenter clinical validation, these putative targets have not been converted into patient survival advantages. So, it's crucial to ascertain whether genetic analysis is clinically feasible, useful, and whether it can be advantageous for patients. We sequenced tumour tissue and blood samples (as normal controls) from 111 Chinese HCC patients at Qingdao University Hospital using the 508-gene panel and the 688-gene panel, respectively. Approximately 95% of patients had gene variations related to targeted treatment, with 50% having clinically actionable mutations that offered significant information for targeted therapy. Immune cell infiltration was enhanced in individuals with TP53 mutations but decreased in patients with CTNNB1 and KMT2D mutations. More notably, we discovered that SPEN, EPPK1, and BRCA2 mutations were related to decreased median overall survival, although MUC16 mutations were not. Furthermore, we found mutant MUC16 as an independent protective factor for the prognosis of HCC patients after curative hepatectomy. In conclusion, this study connects genetic abnormalities to clinical practice and potentially identifies individuals with poor prognoses who may benefit from targeted treatment or immunotherapy.

Based on scRNA-seq and bulk RNA-seq to establish tumor immune microenvironment-associated signature of skin melanoma and predict immunotherapy response.

Skin cutaneous melanoma (SKCM), a form of skin cancer, ranks among the most formidable and lethal malignancies. Exploring tumor microenvironment (TME)-based prognostic indicators would help improve the efficacy of immunotherapy for SKCM patients. This study analyzed SKCM scRNA-seq data to cluster non-malignant cells that could be used to explore the TME into nine immune/stromal cell types, including B cells, CD4 T cells, CD8 T cells, dendritic cells, endothelial cells, Fibroblasts, macrophages, neurons, and natural killer (NK) cells. Using data from The Cancer Genome Atlas (TCGA), we employed SKCM expression profiling to identify differentially expressed immune-associated genes (DEIAGs), which were then incorporated into weighted gene co-expression network analysis (WGCNA) to investigate TME-associated hub genes. Discover candidate small molecule drugs based on pivotal genes. Tumor immune microenvironment-associated genes (TIMAGs) for constructing TIMAS were identified and validated. Finally, the characteristics of TIAMS subgroups and the ability of TIMAS to predict immunotherapy outcomes were analyzed. We identified five TIMAGs (CD86, CD80, SEMA4D, C1QA, and IRF1) and used them to construct TIMAS. In addition, five potential SKCM drugs were identified. The results showed that TIMAS-low patients were associated with immune-related signaling pathways, high MUC16 mutation frequency, high T cell infiltration, and M1 macrophages, and were more favorable for immunotherapy. Collectively, TIMAS constructed by comprehensive analysis of scRNA-seq and bulk RNA-seq data is a promising marker for predicting ICI treatment outcomes and improving individualized therapy for SKCM patients.

Molecular and immunological characteristics of postoperative relapse in lymph node-positive esophageal squamous cell cancer.

The molecular and immunological characteristics of primary tumors and positive lymph nodes in esophageal squamous cell carcinoma (ESCC) are unknown and the relationship with recurrence is unclear, which this study attempted to explore. A total of 30 ESCC patients with lymph node positive (IIB-IVA) were enrolled. Among them, primary tumor and lymph node specimens were collected from each patient, and subjected to 551-tumor-targeted DNA sequencing and 289-immuno-oncology RNA panel sequencing to identify the different molecular basis and immunological features, respectively. The primary tumors exhibited a higher mutation burden than lymph nodes (p < 0.001). One-year recurrent ESCC exhibited a higher Mucin16 (MUC16) mutation rate (p = 0.038), as well as univariate and multivariate analysis revealed that MUC16 mutation is independent genetic factor associated with reduced relapse-free survival (univariate, HR: 5.39, 95% CI: 1.67-17.4, p = 0.005; multivariate, HR: 7.36, 95% CI: 1.79-30.23, p = 0.006). Transcriptomic results showed non-relapse group had higher cytolytic activity (CYT) score (p = 0.025), and was enriched in the IFN-α pathway (p = 0.036), while those in the relapsed group were enriched in the TNF-α/NF-κB (p = 0.001) and PI3K/Akt pathway (p = 0.014). The difference in molecular characteristics between primary lesions and lymph nodes may be the cause of the inconsistent clinical outcomes. Mutations of MUC16 and poor immune infiltration are associated with rapid relapse of nodes-positive ESCC.

Identifying Diffuse Glioma Subtypes Based on Pathway Enrichment Evaluation.

Gliomas are highly heterogeneous in molecular, histology, and microenvironment. However, a classification of gliomas by integrating different tumor microenvironment (TME) components remains unexplored.Based on the enrichment scores of 17 pathways involved in immune, stromal, DNA repair, and nervous system signatures in diffuse gliomas, we performed consensus clustering to uncover novel subtypes of gliomas. Consistently in three glioma datasets (TCGA-glioma, CGGA325, and CGGA301), we identified three subtypes: Stromal-enriched (Str-G), Nerve-enriched (Ner-G), and mixed (Mix-G). Ner-G was charactered by low immune infiltration levels, stromal contents, tumor mutation burden, copy number alterations, DNA repair activity, cell proliferation, epithelial-mesenchymal transformation, stemness, intratumor heterogeneity, androgen receptorexpression and EGFR, PTEN, NF1 and MUC16 mutation rates, while high enrichment of neurons and nervous system pathways, and high tumor purity, estrogen receptor expression, IDH1 and CIC mutation rates, temozolomide response rate and overall and disease-free survival rates. In contrast, Str-G displayed contrastive characteristics to Ner-G. Our analysis indicates that the heterogeneity between glioma cells and neurons is lower than that between glioma cells and immune and stromal cells. Furthermore, the abundance of neurons is positively associated with clinical outcomes in gliomas, while the enrichment of immune and stromal cells has a negative association with them. Our classification method provides new insights into the tumor biology of gliomas, as well as clinical implications for the precise management of this disease.

Somatic mutations that affect early genetic progression and immune microenvironment in gastric carcinoma

Gastric carcinoma (GC) is a high heterogeneity and malignant tumor with a poor prognosis. The current implementation of immunotherapy in GC is limited due to the insufficient exploration of immune-related mutations and speculated early mutation events. Therefore, we performed whole-exome sequencing on 40 patients with GC to explore their genetic characteristics, shedding light on the order of genetic events, somatic mutations impacting the immune microenvironment, and potential biomarkers for immunotherapy. Regarding genetic events, TP53 disruptions were identified as frequent and early events in GC progression, often occurring alongside other gene mutations. The mutations occurring in GANS, SMAD4, and POLE were early independent events. Patients harboring CSMD3, FAT4, FLG, KMT2C, LRP1B, MUC5B, MUC16, PLEC, RNF43, SYNE1, TP53, TTN, XIRP2, and ZFHX4 mutations tended to have decreased B cells, T cells, macrophage, neutrophil, and dendritic cells infiltration, except for the ARID1A gene mutations. We also found patients with microsatellite instability-high tumors had higher homologous recombination deficiency (HRD) scores. HRD showed a positive correlation with tumor mutational burden, which might serve as indirect evidence supporting the potential of HRD as a biomarker for GC. These findings highlighted GC's high heterogeneity and complexity and provided valuable insights into the somatic mutations that affect early genetic progression and immune microenvironment.

Characterizing and forecasting neoantigens-resulting from MUC mutations in COAD

BackgroundThe treatment for colon adenocarcinoma (COAD) faces challenges in terms of immunotherapy effectiveness due to multiple factors. Because of the high tumor specificity and immunogenicity, neoantigen has been considered a pivotal target for cancer immunotherapy. Therefore, this study aims to identify and predict the potential tumor antigens of MUC somatic mutations (MUCmut) in COAD.MethodsThree databases of TCGA, TIMER2.0, and cBioPortal were used for a detailed evaluation of the association between MUCmut and multi-factors like tumor mutation burden (TMB), microsatellite instability (MSI), prognosis, and the tumor microenvironment within the context of total 2242 COAD patients. Next, TSNAdb and the differential agretopicity index (DAI) were utilized to predict high-confidence neopeptides for MUCmut based on 531 COAD patients’ genomic information. DAI was calculated by subtraction of its predicted HLA binding affinity of the MUCmut peptide from the corresponding wild-type peptide.ResultsThe top six mutation frequencies (14 to 2.9%) were from MUC16, MUC17, MUC5B, MUC2, MUC4 and MUC6. COAD patients with MUC16 and MUC4 mutations had longer DFS and PFS. However, patients with MUC13 and MUC20 mutations had shorter OS. Patients with the mutation of MUC16, MUC5B, MUC2, MUC4, and MUC6 exhibited higher TMB and MSI. Moreover, these mutations from the MUC family were associated with the infiltration of diverse lymphocyte cells and the expression of immune checkpoint genes. Through TSNAdb 1.0/NetMHCpan v2.8, 452 single nucleotide variants (SNVs) of MUCmut peptides were identified. Moreover, through TSNAdb2.0/NetMHCpan v4.0, 57 SNVs, 1 Q-frame shift (TS), and 157 short insertions/deletions (INDELs) of MUCmut were identified. Finally, 10 high-confidence neopeptides of MUCmut were predicted by DAI.ConclusionsTogether, our findings establish the immunogenicity and therapeutic potential of mutant MUC family-derived neoantigens. Through combining the tools of TSNAdb and DAI, a group of novel MUCmut neoantigens were identified as potential targets for immunotherapy.

Abstract 7612: Genetic predictors of anti-PD(L)-1 therapy response in hepatocellular carcinoma

Abstract Introduction: Immunotherapy targeting PD-1/PD-L1 shows promise in treating hepatocellular carcinoma (HCC), but standard predictors like PD-L1 expression are unreliable for gauging treatment response. RNA sequencing has identified a BMS 4-gene inflammatory signature that could more accurately forecast clinical outcomes. However, genomic alterations and tumor mutational burden (TMB) haven't consistently predicted anti-PD(L)-1 therapy response in HCC. Therefore, the aim of this research is to identify potential genetic alternation that could serve as predictive marker for anti-PD(L)-1 therapy responses in advanced HCC. Methods: Formalin-fixed paraffin-embedded (FFPE) tumor tissues from 38 HCC patients were analyzed using targeted next-generation sequencing with the ACTOncoTM panel before anti-PD(L)-1 therapy. This analysis identified somatic variants across 440 genes and calculated TMB. In silico tools like NetMHC and IEDB predicted neo-peptide-HLA class I binding affinity. Additionally, peripheral blood mononuclear cells provided germline data for paired TMB calculations, with tumor responses assessed by RECIST criteria. Results: Within a cohort of 167 patients with HCC undergoing ICI therapy, 35 (20.9%) responded to treatment, while 132 (79.1%) did not. Available formalin-fixed paraffin-embedded (FFPE) samples from this population included those from 14 responders and 30 non-responders. After conducting quality control measures, which assessed tumor purity, DNA quantity and integrity, as well as sequencing quality, 13 samples from responders and 25 from non-responders qualified for further analysis. Gene alteration analysis identified mutations in TP53 (47%), MUC16 (34%), CTNNB1 (32%), USH2A (16%), ARID1A (13%), NOTCH3 (13%), and ADAMTS16 (11%) of the cases. A TP53 mutation was linked to poor overall survival, whereas mutations in CTNNB1 and the RTK/RAS/RAF pathway did not correlate with clinical outcomes. The median TMB, predicted at 3.2 ± 4.3 mutations per megabase (muts/Mb), was nominally higher in patients who achieved disease control compared to non-responders (4.6 vs. 3.7 muts/Mb, p=0.53). Utilizing paired TMB analysis, those with disease control exhibited a higher TMB trend than non-responders (6.0 vs. 2.2 muts/Mb, p=0.06). Notably, the 13 cases with MUC16 mutations were associated with a significantly higher predicted TMB (10.0 vs. 3.3 muts/Mb, p=0.0015). Conclusions: This study suggests that certain genetic alterations, especially TP53 mutations, may have predictive value for the response to anti-PD(L)-1 therapy in HCC. Moreover, an elevated TMB, particularly when associated with MUC16 mutations, seems to be correlated with improved disease control. Citation Format: San-Chi Chen, Nai-Jung Chiang, MingHuang Chen, Muh-Hwa Yang. Genetic predictors of anti-PD(L)-1 therapy response in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7612.

Establishment of a Dynamic Ctdna Monitoring System to Predict the Prognosis of CAR-T Cell Therapy in R/R B-NHL Patients

Introduction: The objective of this study is to establish a prediction system using circulating tumor DNA (ctDNA) to determine the effectiveness of CAR-T cell therapy in patients with relapsed or refractory B-cell non-Hodgkin lymphoma (R/R B-NHL). Furthermore, the study aims to compare the mutation characteristics between different therapeutic groups to provide guidance for the prevention and follow-up treatment of patients who fail CAR-T cell therapy. Methods: In this part of the study, a total of 79 (192 serum samples) R/R B-NHL patients with CAR-T cell therapy were collected for ctDNA detection (187 lymphoma-related gene panel), including 62 samples before treatment (T0), 69 samples at 1 week (T1) and 62 samples at 4 weeks (T2) after reinfusion. Differences of categorical variables were tested using the chi-square test or Fisher's exact test, and differences in survival events between different groups were compared using Kaplan-Meier analysis and log-rank test. Results: The results of Kaplan-Meier analysis showed that patients with ctDNA mutation genes &amp;gt;10 before CAR-T cell therapy had poorer OS (1-year OS rate: 0% vs 74.9%, 2-year OS rate: 0% vs 68%, P&amp;lt;0.001) and PFS (1-year PFS rate: 0% vs 52.5%, 2-year PFS rate: 0% vs 36.6%, P=0.0056). Patients with MYD88, FAT1 and BTG2 mutation before CAR-T cell therapy had poorer OS, while patients with MUC16 mutation had better OS. The CR rate in patients with TP53 mutation before CAR-T cell treatment was significantly lower than that of patients without TP53 mutation (33.3% vs 68.1%, P=0.02). However, the TP53 mutation status before treatment did not have an impact on the long-term survival of patients. All patients with TP53 mutations at 4 weeks after CAR-T cell therapy failed to achieve CR, and OS was poorer (1-year OS rate: 37.5% vs 66.4%; 2-year OS rate: 12.5% vs 56.3%, P=0.0023). For CR patients, patients with BCR mutation at 4 weeks after treatment had poorer OS (2-year OS rate: 40.9% vs 76.1%, P=0.035). At one week after CAR-T cell therapy(Figure 1), patients without mutations of CDKN2A, CBLB, APC, SPEN, KMT2D, CARD11, FOXO1 and PDGFRB, were more likely to achieve CR (76.6% vs 28.6%, P&amp;lt;0.001), and had better OS (1-year OS rate: 81.5% vs 38.9%, 2-year OS rate: 62.2% vs 5%, P&amp;lt;0.001) and PFS (1-year PFS rate: 67.2% vs 0%, P&amp;lt;0.001). Conclusions: In this study, we evaluated the characteristics of gene mutation between different therapeutic groups, and a gene set was screened to predict the efficacy of CAR-T cell therapy in R/R B-NHL patients, helping clinicians accurately evaluate the efficacy and assisting in decision-making of treatment options.

Analysis of the feasibility and prognostic value of circulating tumor DNA monitoring in detecting gene mutations in patients with diffuse large B-cell lymphoma receiving chimeric antigen receptor T-cell therapy

Objective: To explore the prognostic value of circulating tumor DNA (ctDNA) testing in patients with refractory/relapsed diffuse large B-cell lymphoma (R/R DLBCL) undergoing chimeric antigen receptor T-cell (CAR-T) therapy, and to guide the prevention and subsequent treatment of CAR-T-cell therapy failure. Methods: In this study, 48 patients with R/R DLBCL who received CAR-T-cell therapy at the First Affiliated Hospital of Zhejiang University School of Medicine between December 2017 and March 2022 were included. Furthermore, ctDNA testing of 187 lymphoma-related gene sets was performed on peripheral blood samples obtained before treatment. The patients were divided into complete remission and noncomplete remission groups. The chi-square test and t-test were used to compare group differences, and the Log-rank test was used to compare the differences in survival. Results: Among the patients who did not achieve complete remission after CAR-T-cell therapy for R/R DLBCL, the top ten genes with the highest mutation frequencies were TP53 (41%), TTN (36%), BCR (27%), KMT2D (27%), IGLL5 (23%), KMT2C (23%), MYD88 (23%), BTG2 (18%), MUC16 (18%), and SGK1 (18%). Kaplan-Meier survival analysis revealed that patients with ctDNA mutation genes >10 had poorer overall survival (OS) rate (1-year OS rate: 0 vs 73.8%, P<0.001) and progression-free survival (PFS) rate (1-year PFS rate: 0 vs 51.8%, P=0.011) compared with patients with ctDNA mutation genes ≤10. Moreover, patients with MUC16 mutation positivity before treatment had better OS (2-year OS rate: 56.8% vs 26.7%, P=0.046), whereas patients with BTG2 mutation positivity had poorer OS (1-year OS rate: 0 vs 72.5%, P=0.005) . Conclusion: ctDNA detection can serve as a tool for evaluating the efficacy of CAR-T-cell therapy in patients with R/R DLBCL. The pretreatment gene mutation burden, mutations in MUC16 and BTG2 have potential prognostic value.

Characterization of tumor-associated reactive astrocytes in gliomas by single-cell and bulk tumor sequencing.

Astrocytes constitute approximately 30% of cells in gliomas and play important roles in synapse construction and survival. Recently, JAK/STAT pathway activation associated with a new type of astrocyte was reported. However, the implications of these tumor-associated reactive astrocytes (TARAs) in glioma are not known. We comprehensively assessed TARAs in gliomas, both in single cells and at the bulk tumor level, by analyzing five independent datasets. First, we analyzed two single-cell RNA sequencing datasets of 35,563 cells from 23 patients to estimate the infiltration level of TARAs in gliomas. Second, we collected clinical information and genomic and transcriptomic data of 1,379 diffuse astrocytoma and glioblastoma samples from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas datasets to evaluate the genomic, transcriptomic and clinical characteristics of TARA infiltration. Third, we downloaded expression profiles of recurrent glioblastoma samples from patients receiving PD-1 inhibitors to analyze the predictive value of TARAs for immune checkpoint inhibition. Single-cell RNA sequencing data showed TARAs were abundant in the glioma micro-environment (15.7% in the CGGA dataset and 9.1% in the Gene Expression Omnibus GSE141383 dataset, respectively). Bulk tumor sequencing data showed that the extent of TARA infiltration was highly associated with major clinical and molecular features of astrocytic gliomas. Patients with more TARA infiltration were more likely to have MUC16, FLG, and PICK3A mutations, chromosome 9p21.3, 10q23.3, and 13q14.2 deletions and 7p11.2 amplification. Gene Ontology analysis revealed that the high level of astrocyte infiltration was characterized by immune and oncogenic pathways, such as the inflammatory response, positive regulation of the JAK-STAT cascade, positive regulation of NIK/NF-kappa B signaling and the tumor necrosis factor biosynthetic process. Patients with greater TARA infiltration showed inferior prognosis. Meanwhile, the extent of reactive astrocyte infiltration exhibited a predictive value for recurrent glioblastoma patients undergoing anti-PD-1 immune therapy. TARA infiltration might promote glioma tumor progression and can be used as a diagnostic, predictive and prognostic marker in gliomas. Prevention of TARA infiltration might be a new therapeutic strategy for glioma.

Deep learning-based approach to predict multiple genetic mutations in colorectal and lung cancer tissues using hematoxylin and eosin-stained whole-slide images.

1549 Background: The presence of genetic mutations is a vital prognostic in many types of cancer. However, genomic testing is expensive and challenging to perform. In contrast, hematoxylin and eosin (H&amp;E) staining is relatively inexpensive and straightforward. Thus, in this study, we propose a method of predicting the presence of genetic mutations using H&amp;E-stained whole-slide images (WSIs). Methods: We divided each H&amp;E–stained WSI into small pieces or “patches.” We used a deep learning model to classify each patch based on the presence of tumor-containing regions. We then extracted image features from each tumor-containing patch using a deep learning-based feature extractor. We created image features for the entire WSI by concatenating the features of the patches. We then trained genetic mutation classification models using the WSI features as the input and the presence or absence of genetic mutations as the output. Finally, we evaluated the performance of these models using the area under the receiver operating characteristic curve (AUC). Results: First, we evaluated our methods using The Cancer Genome Atlas (TCGA) colorectal cancer dataset. We used H&amp;E–stained WSIs and data associated with Microsatellite Instability ( MSI) and BRAF gene mutations, which are directly relevant to therapeutic strategies, obtained from an independent clinical cohort of 566 patients with TCGA colon and rectum adenocarcinoma. We divided the data into training, validation, and test splits, comprising 367, 90, and 109 patients, respectively. We used the training and validation splits for model training and selection, and the test split for model evaluation. The AUC values of the classification models and associated 95% confidence intervals (CIs) were 0.721 (CI = 0.572–0.870) for MSI and 0.712 (CI = 0.547–0.877) for BRAF gene mutations. We also applied our approach to MUC16, KRAS, and ALK mutations using the TCGA lung cancer dataset. We divided 909 TCGA lung adenocarcinoma and lung squamous cell carcinoma patients into training, validation, and test splits, comprising 582, 146, and 181 patients, respectively. In contrast with those of the colorectal dataset, WSI image features were generated using all patches. The AUC values on the test splits were 0.897 (CI = 0.85–0.95) for MUC16, 0.845 (CI = 0.75–0.94) for KRAS, and 0.756 (CI = 0.57–0.94) for ALK mutations. Conclusions: We proposed an approach to predict the presence of genetic mutations using only H&amp;E–stained WSIs and evaluated its performance using colorectal and lung cancer datasets. Our model has the potential to predict the presence of certain genetic mutations with superior performance. These predictions can be used to improve the accuracy of prognostic prediction using WSIs alone.

Retracted: Prognostic Value of MUC16 Mutation and Its Correlation with Immunity in Hepatocellular Carcinoma Patients.

[This retracts the article DOI: 10.1155/2022/3478861.].

MUC16 mutation is associated with tumor grade, clinical features, and prognosis in glioma patients

MUC16 is a member of the attached mucin family that encodes cancer antigen 125 (CA-125), but the association of MUC16 status with grade and subtypes of glioma patients has not yet been established. Data for MUC16 mRNA expression in 37 different cancer types were considered, and genomic data from the Cancer Genome Atlas (TCGA) from 1051 low-grade glioma (LGG) patients and 833 glioblastoma (GBM) patients were analyzed. LGG and GBM has low expression of MUC16, but it is frequently mutated in GBM. Kaplan-Meier survival analysis, glioma subtypes, methylation, and isocitrate dehydrogenase (IDH1) status were all performed. We found that mutated-MUC16 in LGG patients is associated with better prognosis considering overall survival (OS), IDH1, methylation, 1p/19q, and 10q status. Conversely, MUC16 mutation were related with worse prognosis in GBM patients upon analyzing those same parameters. Therefore, MUC16 mutations may assist in glioma diagnosis and prognosis and should be further studied in this tumor type.

Molecular Classification of Genes Associated with Hypoxic Lipid Metabolism in Pancreatic Cancer.

Abnormal lipid metabolism often occurs under hypoxic microenvironment, which is an important energy supplement for cancer cell proliferation and metastasis. We aimed to explore the lipid metabolism characteristics and gene expression features of pancreatic ductal adenocarcinoma (PDAC) related to hypoxia and identify biomarkers for molecular classification based on hypoxic lipid metabolism that are evaluable for PDAC prognosis and therapy. The multiple datasets were analyzed integratively, including corresponding clinical information of samples. PDAC possesses a distinct metabolic profile and oxygen level compared with normal pancreatic tissues, according to the bioinformatics methods. In addition, a study on untargeted metabolomics using Ultra Performance Liquid Chromatography Tandem Mass Spectrometry(UPLC-MS) revealed lipid metabolites differences affected by oxygen. Analysis of PDAC gene expression profiling in The Cancer Genome Atlas (TCGA) revealed that the sphingolipid process correlates closely with HIF1α. According to the characters of HIF-1 and sphingolipid, samples can be clustered into three subgroups using non-negative matrix factorization clustering. In cluster2, patients had an increased survival time. Relatively high MUC16 mutation arises in cluster2 and may positively influence the cancer survival rates. This study explored the expression pattern of lipid metabolism under hypoxia microenvironment in PDAC. On the basis of metabolic signatures, we identified the prognosis subtypes linking lipid metabolism to hypoxia. The classifications may be conducive to developing personalized treatment programs targeting metabolic profiles.

Bioinformatics Analysis Based on TCGA: MUC16 Mutation Correlates with Clinical Outcome in Gastric Cancer.

The prognosis of gastric cancer (GC) is difficult to predict due to the disease's complex genetic and phenotypic characteristics. MUC16 has been reported to be involved in the progression of several tumors. In this study, we aimed to explore whether MUC16 mutation had any impact on the prognosis or treatments of GC patients. Additionally, this analysis uncovered possible critical pathways related with these systems. On the cBioPortal, we were able to locate the pertinent data of patients with MUC16 mutations. And then, GSEA analysis identified differences in mRNA levels between mutant and wild-type MUC16 patients in terms of biological function annotation and pathways. The KEGG and GO analyses were also performed using the differentially expressed genes (DEGs). There were 139 individuals with GC who had the MUC16 mutation, which accounts for 32 percent, and the remaining patients had the MUC16 wild type. Survival assays revealed that patients with the MUC16 mutation had longer overall survival and disease-free survival. GSEA analysis revealed that cell cycle, cysteine and methionine metabolism, Huntington's disease, one carbon pool by folate, pyrimidine metabolism, pyruvate metabolism, RNA degradation, spliceosome, and valine leucine and isoleucine degradation were distinctly enriched in patients with MUC16 mutation type. Moreover, we identified 323 DEGs. Among them, 162 genes were upregulated, and 161 genes were downregulated. GO and KEGG assays indicated DEGs as enriched in pancreatic secretion, neuroactive ligand-receptor interaction, protein digestion and absorption, fat digestion and absorption, and glycerolipid metabolism. Overall, our data revealed that the MUC16 mutation in GC may affect the development of patients by altering several genes and pathways, indicating the importance of MUC16 mutation in the treatments of GC on an individual basis.

Prognostic Value of MUC16 Mutation and Its Correlation with Immunity in Hepatocellular Carcinoma Patients.

Objective Identifying gene mutation signatures will enable a better understanding for the occurrence, development, and prognosis of hepatocellular carcinoma (HCC) and provide some potential biomarkers for clinical practice. This study investigated the mutated genes in HCC patients and assessed their relationship with tumor mutation burden (TMB) and prognosis. Methods The somatic mutation annotation format (MAF) document, mRNA expression matrix, and clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and the Cancer Genome Atlas (TCGA) database. The differences of TMB between the mutant type and the wild-type genes were detected using the Mann–Whitney U test. The link of gene mutations with prognosis was explored by the Kaplan–Meier analysis. The proportion of 22 immune cells' composition was measured using CIBERSORT algorithm. Results The two databases screened 16 common mutated genes, which included TP53, TTN, LRP1B, ZFHX4, MUC16, OBSCN, CSMD3, FLG, CSMD1, SYNE1, SPTA1, USH2A, KMT2C, PCLO, HMCN1, and FAT3. After a series of analysis, MUC16 mutation was found to be highly correlated with TMB and was regarded as an independent factor predicting HCC. Furthermore, gene set enrichment analysis (GSEA) indicated that the MUC16 mutation was significantly involved in HCC cell metabolism. Conclusions MUC16 mutation seems to be a valuable potential biomarker for HCC development and its overall survival.