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Transcription factor activity variation across cancer lineages

International collaborative efforts employing high-throughput sequencing methods have led to the characterization of genomic alterations arising in multiple cancers and tumor cell lines. These data have highlighted the complexity of this mutational landscape and, in particular, they have revealed a strong cancer lineage dependence with respect to both, the mutational patterns and their phenotypic outcomes: the mechanisms driving tumors are dependent of the molecular particularities of the cell they are derived from.

Quantification of the transcription factor activity. In each tumor, the gene expression levels will be modeled as a function of the activity of the transcription factors that regulate them; the activities will then be learned from the models. . To establish the list of the TF targets, we will use chromatin immunoprecipitation coupled with deep-seqencing (ChIP-seq) data.  The TF activities will be biologically relevant descriptors of the tumors allowing both a reduction of the data complexity and a contextualization with respect to their molecular specificities.

We are deconvoluting the complexity of the tumor lineage expression program by modeling transcription factor activity in order to identify genomic alterations relevant for the tumorigenesis of specific cancer lineages. This approach exploits existing datasets and data generated within the MILES consortium, in which numerous tumors have been characterized at the genomic and transcriptomic level.
This computational biology sub-project is integrating data of multiple cancer types. We will discriminate tumors based on their molecular status, which may allow improved diagnostics. We will integrate cellular specificities to contextualize the genomic alterations. Moreover, analyzing the impact of genetic alterations on transcription factor activity as a function of tumor type will provide important mechanistic insights for the improved classification and stratification of tumors. The project is structured around four principal aims:

  • Aim 1. Quantitatively model the activity of transcription factors in a large set of tumors of different lineage
  • Aim 2. Stratify tumors according to transcription factor activities.
  • Aim 3. Investigate the molecular mechanisms underlying transcription factor activity by building a transcription factoractivity regulatory network.
  • Aim 4. Identify genomic alterations specific of tumor lineage or of subgroup of tumors presenting similar transcription factor activity profiles.

The identified lineage specific tumor drivers will be ideal drug targets as their specificity can potentially lead to reduced side effects in therapeutic strategies.

Transcription factor, cancer

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