Systems Biology of Tumor Progression and Drug Resistance
Principal Investigator: Andrea Califano, Ph.D.
At Columbia University, CTD2 funding supports efforts to study the systems biology of tumor progression and drug resistance. Researchers led by principal investigator Andrea Califano have developed a pipeline called Cancer Target High-Throughput Optimized Discovery and Evaluation (caTHODE). This pipeline uses both computational and experimental methods to efficiently discover and validate master regulators within the genomic networks that give rise to specific cancer subtypes. Master regulators are key nodes within networks of interacting genes and proteins that act as bottlenecks through which many different cellular signals must pass to initiate downstream activity. For this reason, researchers believe that master regulators may constitute points of vulnerability within a tumor. By computationally predicting and then experimentally validating the roles of master regulators in tumor progression and resistance to chemotherapy, this work is helping to generate a genome-wide list of prioritized targets for further investigation.
The caTHODE pipeline, which is intended to be scalable and effective for any tumor phenotype, utilizes a combination of methods developed at Columbia University. These include:
Computational algorithms developed in the Califano laboratory that dissect and interrogate networks of transcriptional, post-transcriptional, and post-translational regulatory interactions.
Genome-wide RNAi screens developed in the laboratory of José Silva (Icahn School of Medicine at Mount Sinai) to validate these master regulators.
High-throughput chemical screening assays developed in the lab of Brent Stockwell to identify and validate small-molecule inhibitors of targets associated with phenotypes for tumor progression and drug resistance.
To date, the Columbia Center’s contributions to CTD2 include the discovery and validation of therapeutic targets, chemical modulators, and biomarkers in three distinct tumor subtypes: glioblastoma multiforme; glucocorticoid resistant T cell acute lymphoblastic leukemia; and an aggressive subtype of diffuse large B cell lymphoma that originates from the progression of follicular lymphoma. In addition, Columbia researchers developed collaborations with other CTD2 Centers focusing on additional cancer subtypes. These studies enabled the following findings:
Glioblastoma multiforme: In the mesenchymal phenotype of glioblastoma, the Columbia Center identified four modulators that harbor mutations. Mesenchymal glioblastoma is associated with the worst clinical outcomes for patients with brain cancers. In collaboration with Stuart Schreiber (Broad Institute), they also identified several novel inhibitors of signal transducer and activator of transcription 3 (STAT3), a key transcription factor within the regulatory network that promotes the mesenchymal phenotype.
T cell acute lymphoblastic leukemia: Researchers at Columbia University identified several candidate master regulators of glucocorticoid (GC) resistance and validated three genes. When these genes were silenced, GC-induced apoptosis increased and GC transcriptional activity was activated. Biochemical and functional assays revealed a mechanism of glucocorticoid resistance, and high-throughput screening uncovered an experimental compound that restores GC sensitivity. In a follow-up study, they inferred and validated additional transcription factors that are master regulators of GC resistance.
Diffuse large B cell lymphoma (DLBCL): NF-κB pathway activation is a hallmark of the most aggressive form of DLBCL, the activated B cell DLBCL (ABC-DLBCL) subtype. The Columbia team identified transcription factors and signaling molecules that are critical to ABC-DLBCL and identified master regulators that contribute to follicular lymphoma transformation.
Ovarian serous cystadenocarcinoma: To identify molecular mechanisms of ovarian cancer pathogenesis, Columbia Center researchers reconstructed the transcriptional, post-transcriptional, and post-translational networks of ovarian serous cystadenocarcinoma. They identified master regulators that indicate poor prognosis, drive tumorigenesis, and promote resistance to cisplatin chemotherapy. Factors within regulatory networks that modulate the activity of the master regulators were also revealed.
Non-small cell lung cancer: To dissect the genome-wide signal transduction network that is regulated by tyrosine kinases, the Califano Lab applied a modified version of the Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) algorithm (pARACNe) to a published dataset of phospho-proteomic profiles of non-small cell lung tumors. They also used a modified version of the Master Regulator Inference Algorithm (MARINa) algorithm to compare gene expression patterns and identify master regulators in 50 cell lines.