Addressing Genetic Tumor Heterogeneity through Computationally Predictive Combination Therapy
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.
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- "These limitations can be addressed using the systems biology framework incorporating qualitative and quantitative models, since these models consider the topology of the network and the quantitative measure of molecular activities (such as protein activity, mRNA † expression and metabolite concentration). Thus in silico systems biology is regarded as a promising avenue to discover a combination of targets and modulators to produce synergistic effects or avoid antagonist effects.  "
ABSTRACT: Ligand- and structure-based drug design approaches complement phenotypic and target screens, respectively, and are the two major frameworks for guiding early-stage drug discovery efforts. Since the beginning of this century, the advent of the genomic era has presented researchers with a myriad of high throughput biological data (parts lists and their interaction networks) to address efficacy and toxicity, augmenting the traditional ligand- and structure-based approaches. This data rich era has also presented us with challenges related to integrating and analyzing these multi-platform and multi-dimensional datasets and translating them into viable hypotheses. Hence in the present paper, we review these existing approaches to drug discovery research and argue the case for a new systems biology based approach. We present the basic principles and the foundational arguments/underlying assumptions of the systems biology based approaches to drug design. Systems biology data types (key entities, their attributes and their relationships with each other, and data models/representations), software and tools used for both retrospective- and prospective-analysis, and the hypotheses that can be inferred are also discussed. In addition, we summarize some of the existing resources for a systems biology based drug discovery paradigm (open TG-GATEs, DrugMatrix, CMap and LINCs) in terms of their strengths and limitations.Current topics in medicinal chemistry 08/2015; · 3.45 Impact Factor
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- "Thereby, there is a need to design adaptive drug combinations to achieve tumor response, reduce chances of relapse, and prolong patient survival. In line with this, bioinformatics and systems biology approaches interrogating the huge amount of patient datasets produced until now may provide models and signatures that are more comprehensive and predictive than the mutation status alone [21, 141, 142]. Rational combination therapies guided by real-time monitoring of tumor evolution along treatment, coupled with integrated omics approaches, will ultimately inform trial design to improve patients care in the coming years. "
ABSTRACT: Only approximately 10 % of genetically unselected patients with chemorefractory metastatic colorectal cancer experience tumor regression when treated with the anti-epidermal growth factor receptor (EGFR) antibodies cetuximab or panitumumab (“primary” or “de novo” resistance). Moreover, nearly all patients whose tumors initially respond inevitably become refractory (“secondary” or “acquired” resistance). An ever-increasing number of predictors of both primary and acquired resistance to anti-EGFR antibodies have been described, and it is now evident that most of the underlying mechanisms significantly overlap. By trying to extrapolate a unifying perspective out of many idiosyncratic details, here, we discuss the molecular underpinnings of therapeutic resistance, summarize research efforts aimed to improve patient selection, and present alternative therapeutic strategies that are now under development to increase response and combat relapse.Journal of Molecular Medicine 07/2014; 92(7). DOI:10.1007/s00109-014-1161-2 · 4.74 Impact Factor
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- "However, human cancers frequently demonstrate a great variation in DNA content, and cancer cells are known to be heterogeneous with regard to their antigenic properties15. The genetic instability of tumor cells enables them to mutate and develop mechanisms of resisting anticancer therapies, which is more likely to occur when a single tumorigenic pathway is targeted than when multiple pathways are targeted simultaneously. "
ABSTRACT: Most women with ovarian cancer are diagnosed at an advanced stage and there are few therapeutic options. Recently, monoclonal antibody therapies have had limited success, thus more effective antibodies are needed to improve long-term survival. In this report, we prepared polyclonal rabbit anti-ovarian cancer antibody (Poly Ab) by immunizing rabbits with the human ovarian cancer cell line SKOV3. The Poly Ab bound to SKOV3 and inhibited the cancer cells proliferation. Western blot analysis was conducted, which indicated that Poly Ab inhibited cancer cells through apoptosis involving the caspase signaling pathway including caspase-3 and caspase-9. Finally, compared with the control antibody, administration of Poly Ab reached 64% and 72% tumor inhibition in the subcutaneous and intraperitoneal xenograft mouse model, respectively. Our findings suggest that Poly Ab is an effective agent for apoptosis induction and may be useful as a safe anticancer agent for ovarian cancer therapy.Scientific Reports 05/2014; 4:4984. DOI:10.1038/srep04984 · 5.58 Impact Factor