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Systematic multi-omics cell line profiling uncovers principles of Ewing sarcoma fusion oncogene-mediated gene regulation.

ABSTRACT: Ewing sarcoma (EwS) is characterized by EWSR1-ETS fusion transcription factors converting polymorphic GGAA microsatellites (mSats) into potent neo-enhancers. Although the paucity of additional mutations makes EwS a genuine model to study principles of cooperation between dominant fusion oncogenes and neo-enhancers, this is impeded by the limited number of well-characterized models. Here we present the Ewing Sarcoma Cell Line Atlas (ESCLA), comprising whole-genome, DNA methylation, transcriptome, proteome, and chromatin immunoprecipitation sequencing (ChIP-seq) data of 18 cell lines with inducible EWSR1-ETS knockdown. The ESCLA shows hundreds of EWSR1-ETS-targets, the nature of EWSR1-ETS-preferred GGAA mSats, and putative indirect modes of EWSR1-ETS-mediated gene regulation, converging in the duality of a specific but plastic EwS signature. We identify heterogeneously regulated EWSR1-ETS-targets as potential prognostic EwS biomarkers. Our freely available ESCLA (http://r2platform.com/escla/) is a rich resource for EwS research and highlights the power of comprehensive datasets to unravel principles of heterogeneous gene regulation by chimeric transcription factors.

SUBMITTER: Orth MF

PROVIDER: S-EPMC10333306 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Systematic multi-omics cell line profiling uncovers principles of Ewing sarcoma fusion oncogene-mediated gene regulation.

Orth Martin F MF Surdez Didier D Faehling Tobias T Ehlers Anna C AC Marchetto Aruna A Grossetête Sandrine S Volckmann Richard R Zwijnenburg Danny A DA Gerke Julia S JS Zaidi Sakina S Alonso Javier J Sastre Ana A Baulande Sylvain S Sill Martin M Cidre-Aranaz Florencia F Ohmura Shunya S Kirchner Thomas T Hauck Stefanie M SM Reischl Eva E Gymrek Melissa M Pfister Stefan M SM Strauch Konstantin K Koster Jan J Delattre Olivier O Grünewald Thomas G P TGP

Cell reports 20221201 10

Ewing sarcoma (EwS) is characterized by EWSR1-ETS fusion transcription factors converting polymorphic GGAA microsatellites (mSats) into potent neo-enhancers. Although the paucity of additional mutations makes EwS a genuine model to study principles of cooperation between dominant fusion oncogenes and neo-enhancers, this is impeded by the limited number of well-characterized models. Here we present the Ewing Sarcoma Cell Line Atlas (ESCLA), comprising whole-genome, DNA methylation, transcriptome, ...[more]

PMID: 36476851

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Project description:Adamantinoma-like Ewing sarcoma (ALES) has traditionally been considered a variant of Ewing sarcoma because it generally harbors EWSR1::FLI1 fusions despite showing diffuse positivity for keratins and p40. However, it has become increasingly recognized that different tumors can have identical translocations, including shared fusions between carcinomas and sarcomas, raising questions as to whether ALES might represent a separate entity. Using methylation profiling, we further explored the relationship between Ewing sarcoma and ALES. The archives of multiple institutions were searched for candidate cases of ALES. DNA methylation profiling was performed and results were compared to corresponding data from conventional Ewing sarcoma. Twelve cases of ALES (5 previously reported) were identified in 10 men and 2 women (aged 20-72 years; median age, 41.5 years). Cases included tumors arising in the parotid gland (3), sinonasal cavity (2), submandibular gland (2), thyroid gland (1), neck (1), gingiva (1), hypopharynx (1), and mandible (1). Histologic review consistently showed sheets and nests of basaloid cells within a fibromyxoid or hyalinized stroma. All tumors were positive for at least 1 keratin and CD99 expression, whereas all 10 cases tested were positive for p63 or p40; S100 protein expression was noted in 2 cases. Cases harbored either EWSR1::FLI1 fusions (n = 6), FUS::FLI1 fusions (n = 1), and/or EWSR1 rearrangements (n = 6). Methylation profiling was successful in 11/12 cases evaluated. Unsupervised clustering and dimensionality reduction (Uniform Manifold Approximation and Projection) of DNA methylation data revealed a distinct methylation cluster for all 11 cases, including the tumor with the FUS::FLI1 fusion, which clearly segregated them from the conventional Ewing sarcoma. Follow-up (n = 11, 1-154 months) revealed that 4 patients experienced recurrence and 6 developed metastatic disease. ALES demonstrates a distinct methylation signature from conventional Ewing sarcoma. This finding adds to the distinctive immunoprofile of ALES, suggesting that these 2 tumors should be considered distinct entities rather than histologic extremes of the same disease.

Project description:Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

Dataset Information

Systematic multi-omics cell line profiling uncovers principles of Ewing sarcoma fusion oncogene-mediated gene regulation.

Publications

Systematic multi-omics cell line profiling uncovers principles of Ewing sarcoma fusion oncogene-mediated gene regulation.

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