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Integrated analysis of tumor samples sheds light on tumor heterogeneity.

ABSTRACT: The heterogeneity of tumor samples is a major challenge in the analysis of high-throughput profiling of tumor biopsies and cell lines. The measured aggregate signals of multigenerational progenies often represent an average of several tumor subclones with varying genomic aberrations and different gene expression levels. The goal of the present study was to integrate copy number analyses from SNP-arrays and karyotyping, gene expression profiling, and pathway analyses to detect heterogeneity, identify driver mutations, and explore possible mechanisms of tumor evolution. We showed the heterogeneity of the studied samples, characterized the global copy number alteration profiles, and identified genes whose copy number status and expression levels were aberrant. In particular, we identified a recurrent association between two BRAF(V600E) and BRAF(V600K) mutations and changes in DKK1 gene expression levels, which might indicate an association between the BRAF and WNT pathways. These findings show that the integrated approaches used in the present study can robustly address the challenging issue of tumor heterogeneity in high-throughput profiling.

SUBMITTER: Parisi F

PROVIDER: S-EPMC3447199 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Integrated analysis of tumor samples sheds light on tumor heterogeneity.

Parisi Fabio F Micsinai Mariann M Strino Francesco F Ariyan Stephan S Narayan Deepak D Bacchiocchi Antonella A Cheng Elaine E Xu Fang F Li Peining P Kluger Harriet H Halaban Ruth R Kluger Yuval Y

The Yale journal of biology and medicine 20120925 3

The heterogeneity of tumor samples is a major challenge in the analysis of high-throughput profiling of tumor biopsies and cell lines. The measured aggregate signals of multigenerational progenies often represent an average of several tumor subclones with varying genomic aberrations and different gene expression levels. The goal of the present study was to integrate copy number analyses from SNP-arrays and karyotyping, gene expression profiling, and pathway analyses to detect heterogeneity, iden ...[more]

PMID: 23012583

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Project description:BackgroundGlioma, characterized by its cellular and molecular heterogeneity, presents formidable challenges in treatment strategy and prognostic assessment. The tumor microenvironment (TME) profoundly influences tumor behavior and treatment response, with tumor-associated neutrophils (TANs) playing a complex but understudied role. This study aimed to investigate the heterogeneity and role of TANs in glioma and to develop a prognostic model.MethodsAnalysis of scRNA-seq data identified cellular subpopulations and differentially expressed neutrophil-related genes (DE-NRGs). Bulk RNA-seq was obtained from four independent datasets. Molecular subtypes of glioma samples were determined by consensus clustering. WGCNA was conducted to elucidate the association between gene modules and subtypes. We developed a risk score model. Expression of selected genes was confirmed using immunohistochemistry (IHC). In vitro experiments were also performed for functional verification, including CCK8, EdU, Transwell, and TUNEL assays.ResultsA total of 108 DE-NRGs for TANs were identified based on scRNA-seq data. Two molecular subtypes were characterized, showing significant differences in prognosis and clinical features. Immune-related analyses demonstrated varied immunological characteristics between subtypes. The risk score model was constructed with 7 genes, including AEBP1, CAVIN1, DCTD, DEPP1, DUSP6, FKBP9, and UGCG. It showed significant prognostic value and was validated across three external datasets. The mutation landscape highlighted higher IDH mutation prevalence in low-risk groups. Drug sensitivity analysis indicated TMZ resistance in high-risk groups. In vitro experiments showed that UGCG could promote glioma cell proliferation, migration, and invasion, while decreasing apoptosis.ConclusionThis study explored the heterogeneity of TANs and developed a prognostic model, providing insights for prognostic prediction and guiding personalized treatment strategies in glioma. Declaration of Generative AI in Scientific Writing: The authors declare nonuse of generative AI and AI-assisted technologies in the writing process.

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Integrated analysis of tumor samples sheds light on tumor heterogeneity.

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Integrated analysis of tumor samples sheds light on tumor heterogeneity.

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