DNA methylation profiling deciphers three EMT subtypes with distinct prognoses and therapeutic vulnerabilities in breast cancer
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SUBMITTER: Sun S
PROVIDER: S-EPMC11310866 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundDNA damage repair (DDR) is a critical process that maintains genomic integrity and plays essential roles at both the cellular and organismic levels. Here, we aimed to characterize the DDR profiling of esophageal squamous cell carcinoma (ESCC), investigate the prognostic value of DDR-related features, and explore their potential for guiding personalized treatment strategies.MethodsWe analyzed bulk and single-cell transcriptomics data from 377 ESCC cases from our institution and other publicly available cohorts to identify major DDR subtypes. The heterogeneity in cellular and functional properties, tumor microenvironment (TME) characteristics, and prognostic significance of these DDR subtypes were investigated using immunogenomic analysis and in vitro experiments. Additionally, we experimentally validated a combinatorial immunotherapy strategy using syngeneic mouse models of ESCC.FindingsDDR alteration profiling enabled us to identify two distinct DDR subtypes, DDRactive and DDRsilent, which exhibited independent prognostic values in locoregional ESCC but not in metastatic ESCC. The DDRsilent subtype was characterized by an inflamed but immune-suppressed microenvironment with relatively high immune cell infiltration, abnormal immune checkpoint expression, T-cell exhaustion, and enrichment of cancer-related pathways. Moreover, DDR subtyping indicates that BRCA1 and HFM1 are robust and independent prognostic factors in locoregional ESCC. Finally, we proposed and verified that the concomitant triggering of GITR or blockade of BTLA with PD-1 blockade or cisplatin chemotherapy represents effective combination strategies for high-risk locoregional ESCC tumors.InterpretationOur discovery of DDR-based molecular subtypes will enhance our understanding of tumor heterogeneity and have significant clinical implications for the therapeutic and management strategies of locoregional ESCC.FundingThis study was supported by the National Key R&D Program of China (2021YFC2501000, 2022YFC3401003), National Natural Science Foundation of China (82172882), the Beijing Natural Science Foundation (7212085), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-018, 2021-I2M-1-067), the Fundamental Research Funds for the Central Universities (3332021091), and the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences (2019PT310027).
| S-EPMC10518355 | biostudies-literature
Project description:BackgroundTo identify radiomic subtypes of clear cell renal cell carcinoma (ccRCC) patients with distinct clinical significance and molecular characteristics reflective of the heterogeneity of ccRCC.MethodsQuantitative radiomic features of ccRCC were extracted from preoperative CT images of 160 ccRCC patients. Unsupervised consensus cluster analysis was performed to identify robust radiomic subtypes based on these features. The Kaplan-Meier method and chi-square test were used to assess the different clinicopathological characteristics and gene mutations among the radiomic subtypes. Subtype-specific marker genes were identified, and gene set enrichment analyses were performed to reveal the specific molecular characteristics of each subtype. Moreover, a gene expression-based classifier of radiomic subtypes was developed using the random forest algorithm and tested in another independent cohort (n = 101).ResultsRadiomic profiling revealed three ccRCC subtypes with distinct clinicopathological features and prognoses. VHL, MUC16, FBN2, and FLG were found to have different mutation frequencies in these radiomic subtypes. In addition, transcriptome analysis revealed that the dysregulation of cell cycle-related pathways may be responsible for the distinct clinical significance of the obtained subtypes. The prognostic value of the radiomic subtypes was further validated in another independent cohort (log-rank P = 0.015).ConclusionIn the present multi-scale radiogenomic analysis of ccRCC, radiomics played a central role. Radiomic subtypes could help discern genomic alterations and non-invasively stratify ccRCC patients.
| S-EPMC8065300 | biostudies-literature
Project description:Triple-negative breast cancer (TNBC) is characterized by an aggressive clinical presentation and a paucity of clinically actionable genomic alterations. Here, we utilized the Cancer Genome Atlas (TCGA) to explore the proteogenomic landscape of TNBC subtypes to see whether genomic alterations can be inferred from proteomic data. We found only 4% of the protein level changes are explained by mutations, while 21% of the protein and 35% of the transcriptomics changes were determined by copy number alterations (CNAs). We found tighter coupling between proteome and genome in some genes that are predicted to be the targets of drug inhibitors, including CDKs, PI3K, tyrosine kinase (TKI), and mTOR. The validation of our proteogenomic workflow using mass spectrometry Clinical Proteomic Tumor Analysis Consortium (MS-CPTAC) data also demonstrated the highest correlation between protein-RNA-CNA. The integrated proteogenomic approach helps to prioritize potentially actionable targets and may enable the acceleration of personalized cancer treatment.
| S-EPMC10854633 | biostudies-literature
Project description:Cancer-associated fibroblasts (CAFs) are highly heterogeneous. With the lack of a comprehensive understanding of CAFs' functional distinctions, it remains unclear how cancer treatments could be personalized based on CAFs in a patient's tumor. We have established a living biobank of CAFs derived from biopsies of patients' non-small lung cancer (NSCLC) that encompasses a broad molecular spectrum of CAFs in clinical NSCLC. By functionally interrogating CAF heterogeneity using the same therapeutics received by patients, we identify three functional subtypes: (1) robustly protective of cancers and highly expressing HGF and FGF7; (2) moderately protective of cancers and highly expressing FGF7; and (3) those providing minimal protection. These functional differences among CAFs are governed by their intrinsic TGF-β signaling, which suppresses HGF and FGF7 expression. This CAF functional classification correlates with patients' clinical response to targeted therapies and also associates with the tumor immune microenvironment, therefore providing an avenue to guide personalized treatment.
| S-EPMC8578451 | biostudies-literature
Project description:Objectives: In the present study, we aimed to determine the candidate genes that may function as biomarkers to further distinguish patients with isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GBM), which are heterogeneous with respect to clinical outcomes. Materials and Methods: We selected 41 candidate genes associated with overall survival (OS) using univariate Cox regression from IDH-wildtype GBM patients based on RNA sequencing (RNAseq) expression data from the Chinese Glioma Genome Atlas (CGGA, n = 105) and The Cancer Genome Atlas (TCGA, n = 139) cohorts. Next, a seven-gene-based risk signature was formulated according to Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm in the CGGA RNAseq database as a training set, while another 525 IDH-wildtype GBM patient TCGA datasets, consisting of RNA sequencing and microarray data, were used for validation. Patient survival in the low- and high-risk groups was calculated using Kaplan-Meier survival curve analysis and the log-rank test. Uni-and multivariate Cox regression analysis was used to assess the prognosis value. Gene oncology (GO) and gene set enrichment analysis (GSEA) were performed for the functional analysis of the seven-gene-based risk signature. Results: We developed a seven-gene-based signature, which allocated each patient to a risk group (low or high). Patients in the high-risk group had dramatically shorter overall survival than their low-risk counterparts in three independent cohorts. Univariate and multivariate analysis showed that the seven-gene signature remained an independent prognostic factor. Moreover, the seven-gene risk signature exhibited a striking prognostic validity, with AUC of 78.4 and 73.9%, which was higher than for traditional "age" (53.7%, 62.4%) and "GBM sub-type" (57.7%, 52.9%) in the CGGA- and TCGA-RNAseq databases, respectively. Subsequent bioinformatics analysis predicted that the seven-gene signature was involved in the inflammatory response, immune response, cell adhesion, and apoptotic process. Conclusions: Our findings indicate that the seven-gene signature could be a potential prognostic biomarker. This study refined the current classification system of IDH-wildtype GBM and may provide a novel perspective for the research and individual therapy of IDH-wildtype GBM.
| S-EPMC6929203 | biostudies-literature
Project description:Malignant peripheral nerve sheath tumor (MPNST) is a highly aggressive sarcoma, and a lethal neurofibromatosis type 1-related malignancy, with little progress made on treatment strategies. Here, we apply a multiplatform integrated molecular analysis on 108 tumors spanning the spectrum of peripheral nerve sheath tumors to identify candidate drivers of MPNST that can serve as therapeutic targets. Unsupervised analyses of methylome and transcriptome profiles identify two distinct subgroups of MPNSTs with unique targetable oncogenic programs. We establish two subgroups of MPNSTs: SHH pathway activation in MPNST-G1 and WNT/ß-catenin/CCND1 pathway activation in MPNST-G2. Single nuclei RNA sequencing characterizes the complex cellular architecture and demonstrate that malignant cells from MPNST-G1 and MPNST-G2 have neural crest-like and Schwann cell precursor-like cell characteristics, respectively. Further, in pre-clinical models of MPNST we confirm that inhibiting SHH pathway in MPNST-G1 prevent growth and malignant progression, providing the rational for investigating these treatments in clinical trials.
| S-EPMC10172395 | biostudies-literature
Project description:BackgroundOvarian cancer (OC) is a highly heterogeneous and malignant gynecological cancer, thereby leading to poor clinical outcomes. The study aims to identify and characterize clinically relevant subtypes in OC and develop a diagnostic model that can precisely stratify OC patients, providing more diagnostic clues for OC patients to access focused therapeutic and preventative strategies.MethodsGene expression datasets of OC were retrieved from TCGA and GEO databases. To evaluate immune cell infiltration, the ESTIMATE algorithm was applied. A univariate Cox analysis and the two-sided log-rank test were used to screen OC risk factors. We adopted the ConsensusClusterPlus algorithm to determine OC subtypes. Enrichment analysis based on KEGG and GO was performed to determine enriched pathways of signature genes for each subtype. The machine learning algorithm, support vector machine (SVM) was used to select the feature gene and develop a diagnostic model. A ROC curve was depicted to evaluate the model performance.ResultsA total of 1,273 survival-related genes (SRGs) were firstly determined and used to clarify OC samples into different subtypes based on their different molecular pattern. SRGs were successfully stratified in OC patients into three robust subtypes, designated S-I (Immunoreactive and DNA Damage repair), S-II (Mixed), and S-III (Proliferative and Invasive). S-I had more favorable OS and DFS, whereas S-III had the worst prognosis and was enriched with OC patients at advanced stages. Meanwhile, comprehensive functional analysis highlighted differences in biological pathways: genes associated with immune function and DNA damage repair including CXCL9, CXCL10, CXCL11, APEX, APEX2, and RBX1 were enriched in S-I; S-II combined multiple gene signatures including genes associated with metabolism and transcription; and the gene signature of S-III was extensively involved in pathways reflecting malignancies, including many core kinases and transcription factors involved in cancer such as CDK6, ERBB2, JAK1, DAPK1, FOXO1, and RXRA. The SVM model showed superior diagnostic performance with AUC values of 0.922 and 0.901, respectively. Furthermore, a new dataset of the independent cohort could be automatically analyzed by this innovative pipeline and yield similar results.ConclusionThis study exploited an innovative approach to construct previously unexplored robust subtypes significantly related to different clinical and molecular features for OC and a diagnostic model using SVM to aid in clinical diagnosis and treatment. This investigation also illustrated the importance of targeting innate immune suppression together with DNA damage in OC, offering novel insights for further experimental exploration and clinical trial.
| S-EPMC10740166 | biostudies-literature
Project description:BackgroundLower-grade gliomas (LGGs) show highly metabolic heterogeneity and adaptability. To develop effective therapeutic strategies targeting metabolic processes, it is necessary to identify metabolic differences and define metabolic subtypes. Here, we aimed to develop a classification system based on metabolic gene expression profile in LGGs.MethodsThe metabolic gene profile of 402 diffuse LGGs from the Cancer Genome Atlas (TCGA) was used for consensus clustering to determine robust clusters of patients, and the reproducibility of the classification system was evaluated in three Chinese Glioma Genome Atlas (CGGA) cohorts. Then, the metadata set for clinical characteristics, immune infiltration, metabolic signatures and somatic alterations was integrated to characterise the features of each subtype.ResultsWe successfully identified and validated three highly distinct metabolic subtypes in LGGs. M2 subtype with upregulated carbohydrate, nucleotide and vitamin metabolism correlated with worse prognosis, whereas M1 subtype with upregulated lipid metabolism and immune infiltration showed better outcome. M3 subtype was associated with low metabolic activities and displayed good prognosis. Three metabolic subtypes correlated with diverse somatic alterations. Finally, we developed and validated a metabolic signature with better performance of prognosis prediction.ConclusionsOur study provides a new classification based on metabolic gene profile and highlights the metabolic heterogeneity within LGGs.
| S-EPMC8292364 | biostudies-literature
Project description:Despite molecular and clinical heterogeneity, small cell lung cancer (SCLC) is treated as a single entity with predictably poor results. Using tumor expression data and non-negative matrix factorization, we identify four SCLC subtypes defined largely by differential expression of transcription factors ASCL1, NEUROD1, and POU2F3 or low expression of all three transcription factor signatures accompanied by an Inflamed gene signature (SCLC-A, N, P, and I, respectively). SCLC-I experiences the greatest benefit from the addition of immunotherapy to chemotherapy, while the other subtypes each have distinct vulnerabilities, including to inhibitors of PARP, Aurora kinases, or BCL-2. Cisplatin treatment of SCLC-A patient-derived xenografts induces intratumoral shifts toward SCLC-I, supporting subtype switching as a mechanism of acquired platinum resistance. We propose that matching baseline tumor subtype to therapy, as well as manipulating subtype switching on therapy, may enhance depth and duration of response for SCLC patients.
| S-EPMC8143037 | biostudies-literature
Project description:Meningiomas are the most common primary intracranial tumors. There are no effective medical therapies for meningioma patients, and new treatments have been encumbered by limited understanding of meningioma biology. Here, we use DNA methylation profiling on 565 meningiomas integrated with genetic, transcriptomic, biochemical, proteomic and single-cell approaches to show meningiomas are composed of three DNA methylation groups with distinct clinical outcomes, biological drivers and therapeutic vulnerabilities. Merlin-intact meningiomas (34%) have the best outcomes and are distinguished by NF2/Merlin regulation of susceptibility to cytotoxic therapy. Immune-enriched meningiomas (38%) have intermediate outcomes and are distinguished by immune infiltration, HLA expression and lymphatic vessels. Hypermitotic meningiomas (28%) have the worst outcomes and are distinguished by convergent genetic and epigenetic mechanisms driving the cell cycle and resistance to cytotoxic therapy. To translate these findings into clinical practice, we show cytostatic cell cycle inhibitors attenuate meningioma growth in cell culture, organoids, xenografts and patients.
| S-EPMC9374001 | biostudies-literature