Dataset Information

Integrative Analysis Identifies Multi-Omics Signatures That Drive Molecular Classification of Uveal Melanoma.

ABSTRACT: By iCluster analysis, we found that the integrative molecular classification of the UM was primarily driven by DNA copy number variation on chromosomes 3, 6 and 8, differential methylation and expression of genes involved in the immune system, cell morphogenesis, movement and migration, and differential mutation of genes including GNA11, BAP1, EIF1AX, SF3B1 and GNAQ. Integrative analysis revealed that pathways including IL6/JAK/STAT3 signaling, angiogenesis, allograft rejection, inflammatory response and interferon gamma response were hypomethylated and up-regulated in the M3 iSubtype, which was associated with a worse overall survival, compared to the D3 iSubtype. Using two independent gene expression datasets, we demonstrated that the subtype-driving genes had an excellent prognostic power in classifying UM into high- or low-risk groups for metastasis. Integrative analysis of UM multi-omics data provided a comprehensive view of UM biology for understanding the underlying mechanism leading to UM metastasis. The concordant molecular alterations at multi-omics levels revealed by our integrative analysis could be used for patient stratification towards personalized management and surveillance.

SUBMITTER: Mo Q

PROVIDER: S-EPMC8699355 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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

Integrative Analysis Identifies Multi-Omics Signatures That Drive Molecular Classification of Uveal Melanoma.

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