Integrative Bioinformatics Approaches for Cancer Target Discovery
 
Qualify the functional relevance of genes in cancer
  Zoom in recurrent gene fusions from RNAseq data
  Revealing amplified gene fusions from genomic data
  Identifying druggable targets amplified in cancer
  Discovering cancer-specific genes as molecular targets
 
About the lab


   
 

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Welcome to Cagenome.org


Integrative Computational Technologies To Reveal Viable Cancer Targets

The Cancer Genome Project initiatives such as The Cancer Genome Atlas (TCGA) have generated a daunting amount of genomic and next-gen sequencing data for tens of thousands of human tumors. This provided unprecedented opportunity to systematically analyze the cancer genome to develop novel therapeutics, and also calls for innovative computational technologies that can reveal viable cancer targets and driving genetic aberrations from these multidimensional datasets.

In the past a few years, we have developed multiple integrative computational approaches to discovery viable cancer targets from the genomic and next-generation sequencing datasets. Here we provide the detailed introduction and protocols for the bioinformatics analyses that have been published in our previous studies. These analyses have led to the discovery of recurrent ESR1-CCDC170 gene fusions in more aggressive Luminal B breast cancers (Nature Comm 2014), TLK2 and MAP3K3 amplifications in aggressive luminal breast cancer (Nature Comm 2016 in press, the Journal of Pathology 2014). NFE2 rearrangements in lung cancer (Nature biotech 2009), KRAS gene fusions in a rare subset of metastatic breast cancers (Cancer Discovery 2011), and multiple tumor specific antigen targets (Cancer Research 2012).

 

Unique Integrative Bioinformatics Approaches

Integrative computational technologies we have innovated to reveal pathological genetic alterations and viable targets in cancer.

 


Wang Laboratory @ University of Pittsburgh Cancer Institute.