CESAM

CESAM identifies gene dysregulation caused by somatic copy-number alterations by integrating copy-number profiles, gene expression data, and topologically associating domain annotations to infer cis-regulatory element rearrangements in cancer genomes.

Key Features:

• Multi-Omics Data Integration: Integrates somatic copy-number alteration (SCNA) data, gene expression profiles, and topologically associating domain (TAD) annotations to analyze regulatory effects of genomic alterations.
• Cis-Regulatory Element Rearrangement Detection: Infers structural rearrangements of cis-regulatory elements (CREs), including enhancer hijacking events associated with gene activation.
• Pan-Cancer Genomic Analysis: Analyzes large cancer genome datasets to identify genes affected by CRE rearrangements across multiple cancer types.
• Candidate Oncogene Identification: Detects genes such as IRS4, SMARCA1, and TERT that may be dysregulated through CRE rearrangements.
• Cancer-Type-Specific Analysis: Identifies regulatory alterations specific to individual cancer types, including enhancer hijacking events involving IGF2 in colorectal cancer.

Scientific Applications:

• Cancer Genomics Research: Investigates the regulatory impact of somatic copy-number alterations on gene expression in cancer genomes.
• Oncogene Discovery: Identifies candidate oncogenes activated through cis-regulatory element rearrangements.
• Regulatory Genome Analysis: Studies interactions between genome structural alterations, chromatin architecture, and gene regulation.

Methodology:

CESAM integrates somatic copy-number alterations, gene expression profiles, and topologically associating domain annotations to infer rearrangements of cis-regulatory elements that alter gene regulation in cancer genomes.