Browsing by Author "Davis, Lara E."
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Item A new approach for prediction of tumor sensitivity to targeted drugs based on functional data(2013) Berlow, Noah (TTU); Davis, Lara E.; Cantor, Emma L.; Séguin, Bernard; Keller, Charles; Pal, Ranadip (TTU)Background: The success of targeted anti-cancer drugs are frequently hindered by the lack of knowledge of the individual pathway of the patient and the extreme data requirements on the estimation of the personalized genetic network of the patient's tumor. The prediction of tumor sensitivity to targeted drugs remains a major challenge in the design of optimal therapeutic strategies. The current sensitivity prediction approaches are primarily based on genetic characterizations of the tumor sample. We propose a novel sensitivity prediction approach based on functional perturbation data that incorporates the drug protein interaction information and sensitivities to a training set of drugs with known targets.Results: We illustrate the high prediction accuracy of our framework on synthetic data generated from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and an experimental dataset of four canine osteosarcoma tumor cultures following application of 60 targeted small-molecule drugs. We achieve a low leave one out cross validation error of <10% for the canine osteosarcoma tumor cultures using a drug screen consisting of 60 targeted drugs.Conclusions: The proposed framework provides a unique input-output based methodology to model a cancer pathway and predict the effectiveness of targeted anti-cancer drugs. This framework can be developed as a viable approach for personalized cancer therapy. © 2013 Berlow et al.; licensee BioMed Central Ltd.Item Probabilistic modeling of personalized drug combinations from integrated chemical screen and molecular data in sarcoma(2019) Berlow, Noah E. (TTU); Rikhi, Rishi; Geltzeiler, Mathew; Abraham, Jinu; Svalina, Matthew N.; Davis, Lara E.; Wise, Erin; Mancini, Maria; Noujaim, Jonathan; Mansoor, Atiya; Quist, Michael J.; Matlock, Kevin L. (TTU); Goros, Martin W.; Hernandez, Brian S.; Doung, Yee C.; Thway, Khin; Tsukahara, Tomohide; Nishio, Jun; Huang, Elaine T.; Airhart, Susan; Bult, Carol J.; Gandour-Edwards, Regina; Maki, Robert G.; Jones, Robin L.; Michalek, Joel E.; Milovancev, Milan; Ghosh, Souparno (TTU); Pal, Ranadip (TTU); Keller, CharlesBackground: Cancer patients with advanced disease routinely exhaust available clinical regimens and lack actionable genomic medicine results, leaving a large patient population without effective treatments options when their disease inevitably progresses. To address the unmet clinical need for evidence-based therapy assignment when standard clinical approaches have failed, we have developed a probabilistic computational modeling approach which integrates molecular sequencing data with functional assay data to develop patient-specific combination cancer treatments. Methods: Tissue taken from a murine model of alveolar rhabdomyosarcoma was used to perform single agent drug screening and DNA/RNA sequencing experiments; results integrated via our computational modeling approach identified a synergistic personalized two-drug combination. Cells derived from the primary murine tumor were allografted into mouse models and used to validate the personalized two-drug combination. Computational modeling of single agent drug screening and RNA sequencing of multiple heterogenous sites from a single patient's epithelioid sarcoma identified a personalized two-drug combination effective across all tumor regions. The heterogeneity-consensus combination was validated in a xenograft model derived from the patient's primary tumor. Cell cultures derived from human and canine undifferentiated pleomorphic sarcoma were assayed by drug screen; computational modeling identified a resistance-abrogating two-drug combination common to both cell cultures. This combination was validated in vitro via a cell regrowth assay. Results: Our computational modeling approach addresses three major challenges in personalized cancer therapy: synergistic drug combination predictions (validated in vitro and in vivo in a genetically engineered murine cancer model), identification of unifying therapeutic targets to overcome intra-tumor heterogeneity (validated in vivo in a human cancer xenograft), and mitigation of cancer cell resistance and rewiring mechanisms (validated in vitro in a human and canine cancer model). Conclusions: These proof-of-concept studies support the use of an integrative functional approach to personalized combination therapy prediction for the population of high-risk cancer patients lacking viable clinical options and without actionable DNA sequencing-based therapy.