Research

The CTD² Network seeks to understand cancer complexity in terms of intra- and inter-tumor heterogeneities and their impact on innate or acquired resistance to chemo- and immunotherapies. To achieve this goal, each Center utilizes 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. The 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 and/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 iinterference 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, post-translational 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 (CRC), and patient-derived xenograft models are used for high-throughput functional studies to identify targets, modulators, biomarkers, and determine drug resistance etc.
• 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.