Article

Addressing Genetic Tumor Heterogeneity through Computationally Predictive Combination Therapy

1Computational and Systems Biology Program, Massachusetts Institute of Technology.
Cancer Discovery (Impact Factor: 15.93). 12/2013; 4(2). DOI: 10.1158/2159-8290.CD-13-0465
Source: PubMed

ABSTRACT Recent tumor sequencing data suggests an urgent need to develop a methodology to directly address intra-tumor heterogeneity in the design of anti-cancer treatment regimens. We use RNA interference to model heterogeneous tumors, and demonstrate successful validation of computational predictions for how optimized drug combinations can yield superior effects on these tumors both in vitro and in vivo. Importantly, we discover here that for many such tumors knowledge of the predominant subpopulation is insufficient for determining the best drug combination. Surprisingly, in some cases the optimal drug combination does not include drugs that would treat any particular subpopulation most effectively, challenging straightforward intuition. We confirm examples of such a case with survival studies in a murine pre-clinical lymphoma model. Altogether, our approach provides new insights concerning design principles for combination therapy in the context of intratumoral diversity, data that should inform the development of drug regimens superior for complex tumors.

3 Followers
 · 
129 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The future of cancer treatment lies in personalized strategies designed to specifically target tumorigenic cell populations present in an individual. Although recent advances in directed therapies have greatly improved patient outcomes in some cancers, intuitive drug design is proving more difficult than expected owing largely to the complexity of human cancers. Intratumoral heterogeneity, the presence of multiple genotypically and/or phenotypically distinct cell subpopulations within a single tumor, is a likely cause of drug resistance. Advances in systems biology are helping to unravel the mysteries of cancer progression. In this issue of Cancer Discovery, Zhao and colleagues define a path for functional validation of computational modeling in the context of heterogeneous tumor populations and their potential for drug response and resistance. Cancer Discov; 4(2); 146-8. ©2014 AACR.
    Cancer Discovery 02/2014; 4(2):146-8. DOI:10.1158/2159-8290.CD-13-1042
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Invadopodia are dynamic protrusions in motile tumor cells whose function is to degrade extracellular matrix so that cells can enter into new environments. Invadopodia are specifically identified by microscopy as proteolytic invasive protrusions containing TKS5 and cortactin. The increasing complexity in models for the study of invadopodia, including engineered 3D environments, explants, or animal models in vivo, entails a higher level of microenvironment complexity as well as cancer cell heterogeneity. Such experimental setups are rich in information and offer the possibility of contextualizing invadopodia and other motility-related structures. That is, they hold the promise of revealing more realistic microenvironmental conditions under which the invadopodium assembles and functions or in which tumor cells switch to a different cellular phenotype (focal adhesion, lamellipodia, proliferation, and apoptosis). For such an effort, we need a systemic approach to microscopy, which will integrate information from multiple modalities. While the individual technologies needed to achieve this are mostly available, data integration and standardization is not a trivial process. In a systems microscopy approach, microscopy is used to extract information on cell phenotypes and the microenvironment while -omics technologies assess profiles of cancer cell and microenvironment genetic, transcription, translation, and protein makeups. Data are classified and linked via in silico modeling (including statistical and mathematical models and bioinformatics). Computational considerations create predictions to be validated experimentally by perturbing the system through use of genetic manipulations and molecular biology. With such a holistic approach, a deeper understanding of function of invadopodia in vivo will be reached, opening the potential for personalized diagnostics and therapies.
    Cell adhesion & migration 03/2014; 8(3). DOI:10.4161/cam.28349
  • [Show abstract] [Hide abstract]
    ABSTRACT: Tumors are inherently resilient and often develop resistance against cancer therapies, leading to poor patient outcomes. With the sophisticated analytical and computational tools now available, much has been revealed about how molecularly targeted drugs affect the biochemical networks of cancer cells, enabling the directed design of treatment regimens that can better thwart resistance. In this issue of Science Signaling, Morton et al. demonstrate a nanoparticle system capable of sequentially delivering two drugs: The first inhibits an oncogenic pathway to sensitize the cells to DNA damage-induced apoptosis; the second is a genotoxic drug that takes advantage of the vulnerable state of the cancer cells to kill them with enhanced efficiency.
    Science Signaling 05/2014; 7(325):pe13. DOI:10.1126/scisignal.2005386