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About the CTD² Network

Network Overview

The Center for Cancer Genomics supports the Cancer Target Discovery and Development (CTD²) initiative, a collaborative Network of research teams, or Centers. The primary goal of the Network is to bridge the knowledge gap between large-scale genomic datasets and the underlying etiology of cancer development, progression, and metastasis.

The current phase of the CTD² Network aims to employ the lessons learned from the previous phase (described in the manuscript Towards patient-based cancer therapeutics), build on the fundamental findings generated from genomic initiatives, and exploit this knowledge in a clinically relevant context. To achieve this goal, each Center uses a distinct array of advanced computational and functional systems biology approaches. CTD² Centers benefit from having complementary and specialized expertise within the Network.

Robust cross-Network collaborations enable the Network Centers to use a combination of state-of-the-art, high-throughput informatic and experimental approaches to address the following main objectives:

  • improve the understanding of how mutations in cancer genes affect downstream functions within protein and cellular pathways e.g., gain-of-function, loss-of-function, or neomorphic function
  • find new molecular targets with the goal to overcome innate or acquired resistance to treatment regimens e.g., clinical resistance related to inter- and intratumor heterogeneity
  • develop efficient strategies to identify and validate multiple targets and optimal combinations of perturbagens with the potential to eliminate all cancer cells, despite their clonal heterogeneity e.g., identifying combination of chemical or biological (such as immunotherapeutic) approaches

Project descriptions, datasets, and methodologies generated by the Centers are shared through the CTD² Data Portal. Analyzed data, experimental observations, and other positive results from the Centers’ efforts are compiled in the CTD² Dashboard. Algorithms and reagents developed through this initiative are listed on the Analytical Tools and Supported Reagents pages. CTD² is a “community resource project,” and all information in the Data Portal and Dashboard are openly available to the scientific community and can be accessed without restrictions.

Each Center uses a distinct array of advanced computational and functional systems biology approaches, collaborates organically, and shares data and resources with the cancer research community. Herewith, CTD² advances the understanding of cancer etiology, mechanism, and treatment and potentially accelerates development of clinically useful markers, targets, and therapeutics for precision oncology.

CTD² Research

The CTD² Network seeks to understand cancer complexity in terms of intra- and intertumor heterogeneities and their impact on innate or acquired resistance to chemotherapies and immunotherapies.

To achieve this goal, each Center uses a distinct array of advanced computational and systems biology methods, functional genomics and immunological approaches, small molecule and genetic screens.

These methods allow reconstruction of cell-context specific gene networks that underlie each cancer subtype. CTD² Centers gain power from having both complementary and reinforcing expertise.

Highlighted below are a few of the methodologies used by CTD² members:

  • Bioinformatics: Computational analysis of cancer molecular characterization data allows researchers to make predictions and hypotheses about biologically relevant phenotypes that can be tested experimentally.
  • Chemical genetics: Small molecule screens performed in the context of molecular phenotype of cancer models help probe biological pathways, discover possible therapeutic targets, and identify optimal combinations to overcome the resistance to therapy. Small molecules perturb cellular pathways in real time, providing experimental data that cannot be gathered by traditional genetic analysis.
  • Genome-wide gain-of-function: Gain-of-function technologies, including cDNA expression libraries and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) activation, assist in identifying oncogenes or genes whose overexpression either initiates or suppresses cancerous transformation. These studies also help in understanding the consequences of tumor heterogeneity, sensitivity, and resistance to drugs.
  • Genome-wide loss-of-function: Loss-of-function experimental approaches, including RNA interference and CRISPR/cas9 or CRISPR interference, reveal genes essential for tumor survival and synthetic lethality (where simultaneous perturbation of two genes leads to cell death).
  • Protein–protein interactions: Mutant allele-mediated oncogenic protein–protein interactions can help identify the function of genomic mutations and map critical cellular pathways to help inform therapeutic strategies.
  • Proteomics: Changes in protein levels, posttranslational modifications, and structure enable researchers to understand the modified proteins’ role in cellular processes and in tumor development.
  • In vivo gain- and loss-of-function models: Expansion of cell culture findings into animal models is important for determining which genes and genetic alterations are relevant in an organism.
  • Next-generation cell culture models: Biologically relevant cancer models like organoids, conditionally reprogrammed cells, and patient-derived xenograft models are used for high-throughput functional studies to identify targets, modulators, biomarkers, and determine drug resistance.
  • Immunological approaches: Techniques like immunophenotyping can identify genes associated with immune cell entry, persistence, and effector functions within tumors. Researchers also use computational analyses to identify potentially immunogenic peptides (neoantigens) that arise from cancer-specific changes in some genes. Gene expression network analyses aid in identifying multiple targets and combination of perturbagens with the potential to eliminate all cancer cells regardless of their clonal heterogeneity.

Data generated by CTD² Network members can be found on the CTD² Data Portal and the CTD² Dashboard.

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