Combined experimental and computational analysis of DNA damage signaling reveals context-dependent roles for Erk in apoptosis and G1/S arrest after genotoxic stress
ABSTRACT Following DNA damage, cells display complex multi-pathway signaling dynamics that connect cell-cycle arrest and DNA repair in G1, S, or G2/M phase with phenotypic fate decisions made between survival, cell-cycle re-entry and proliferation, permanent cell-cycle arrest, or cell death. How these phenotypic fate decisions are determined remains poorly understood, but must derive from integrating genotoxic stress signals together with inputs from the local microenvironment. To investigate this in a systematic manner, we undertook a quantitative time-resolved cell signaling and phenotypic response study in U2OS cells receiving doxorubicin-induced DNA damage in the presence or absence of TNFα co-treatment; we measured key nodes in a broad set of DNA damage signal transduction pathways along with apoptotic death and cell-cycle regulatory responses. Two relational modeling approaches were then used to identify network-level relationships between signals and cell phenotypic events: a partial least squares regression approach and a complementary new technique which we term 'time-interval stepwise regression.' Taken together, the results from these analysis methods revealed complex, cytokine-modulated inter-relationships among multiple signaling pathways following DNA damage, and identified an unexpected context-dependent role for Erk in both G1/S arrest and apoptotic cell death following treatment with this commonly used clinical chemotherapeutic drug.
Full-textDOI: · Available from: Leona D Samson, Aug 12, 2015
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- "Where toxicity had previously constrained the selection of combination therapies, researchers may now instead prioritize combinations based on specificity to controlling a particular phenotype. Understanding macromolecular pathways led to conclusions about synergies between doxorubicin and TNF-alpha therapies . The authors showed that administering TNF-alpha as an adjuvant to doxorubicin treatment increased apoptotic cell death in the presence of low-levels of DNA damage by using an integrated network approach. "
ABSTRACT: RNA-interference (RNAi) studies hold great promise for functional investigation of the significance of genetic variations and mutations, as well as potential synthetic lethalities, for understanding and treatment of cancer, yet technical and conceptual issues currently diminish the potential power of this approach. While numerous research groups are usefully employing this kind of functional genomic methodology to identify molecular mediators of disease severity, response, and resistance to treatment, findings are generally confounded by "off-target" effects. These effects arise from a variety of issues beyond non-specific reagent behavior, such as biological cross-talk and feedback processes so thus can occur even with specific perturbation. Interpreting RNAi results in a network framework instead of merely as individual "hits" or "targets" leverages contributions from all hit/target contributions to pathways via their relationships with other network nodes. This interpretation can ameliorate dependence on individual reagent performance and increase confidence in biological validation. Here we provide background on RNAi studies in cancer applications, review key challenges with functional genomics, and motivate the use of network models grounded in pathway analyses.Seminars in Cancer Biology 06/2013; 23(4). DOI:10.1016/j.semcancer.2013.06.004 · 9.33 Impact Factor
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ABSTRACT: Recent tumor genome sequencing confirmed that one tumor often consists of multiple cell subpopulations (clones) which bear different, but related, genetic profiles such as mutation and copy number variation profiles. Thus far, one tumor has been viewed as a whole entity in cancer functional studies. With the advances of genome sequencing and computational analysis, we are able to quantify and computationally dissect clones from tumors, and then conduct clone-based analysis. Emerging technologies such as single-cell genome sequencing and RNA-Seq could profile tumor clones. Thus, we should reconsider how to conduct cancer systems biology studies in the genome sequencing era. We will outline new directions for conducting cancer systems biology by considering that genome sequencing technology can be used for dissecting, quantifying and genetically characterizing clones from tumors. Topics discussed in Part 1 of this review include computationally quantifying of tumor subpopulations; clone-based network modeling, cancer hallmark-based networks and their high-order rewiring principles and the principles of cell survival networks of fast-growing clones. More information: http://www.cancer-systemsbiology.org/.Seminars in Cancer Biology 06/2013; 23(4). DOI:10.1016/j.semcancer.2013.06.002 · 9.33 Impact Factor
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- "However, for such detailed models the parameterization remains a major challenge. More coarse-grain modeling approaches, such as logical models or non-mechanistic statistical models require less data for parameterization (Kreeger et al, 2009; Morris et al, 2011; Saez-Rodriguez et al, 2011, 2009; Tentner et al, 2012). These approaches allow qualitative predictions, but typically fail to deal with feedback "
ABSTRACT: The epidermal growth factor receptor (EGFR) signaling network is activated in most solid tumors, and small-molecule drugs targeting this network are increasingly available. However, often only specific combinations of inhibitors are effective. Therefore, the prediction of potent combinatorial treatments is a major challenge in targeted cancer therapy. In this study, we demonstrate how a model-based evaluation of signaling data can assist in finding the most suitable treatment combination. We generated a perturbation data set by monitoring the response of RAS/PI3K signaling to combined stimulations and inhibitions in a panel of colorectal cancer cell lines, which we analyzed using mathematical models. We detected that a negative feedback involving EGFR mediates strong cross talk from ERK to AKT. Consequently, when inhibiting MAPK, AKT activity is increased in an EGFR-dependent manner. Using the model, we predict that in contrast to single inhibition, combined inactivation of MEK and EGFR could inactivate both endpoints of RAS, ERK and AKT. We further could demonstrate that this combination blocked cell growth in BRAF- as well as KRAS-mutated tumor cells, which we confirmed using a xenograft model.Molecular Systems Biology 06/2013; 9:673. DOI:10.1038/msb.2013.29 · 14.10 Impact Factor