An Integrated Approach to Anti-Cancer Drug Sensitivity Prediction

Berlow, Noah; Haider, Saad; Wan, Qian; Geltzeiler, Mathew; Davis, Lara E.; Keller, Charles; Pal, Ranadip

doi:10.1109/tcbb.2014.2321138

Cited by 26 publications

(40 citation statements)

References 38 publications

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“…Our cell proliferation models are based on probabilistic target inhibition map (PTIM) framework [4][5][6][7] that consists of a series of blocks where each block contains a set of targets connected in parallel. To find the ideal target inhibition profile, we proposed a lexicographic search method to effectively search through all possible solutions.…”

Section: Discussionmentioning

confidence: 99%

“…Based on these two biological constraints and limited drug perturbation experiments, we can arrive at an inferred PTIM model that can provide an estimate of sensitivity for all possible target inhibitions. The details of the model are available at [4][5][6] along with biological validation at [17]. Note that a PTIM can also be approximately represented as a tumor proliferation circuit as shown in Fig.…”

Section: Model Typementioning

confidence: 99%

“…The genetic characterization based methodologies have severe limitations when the cancer type shows numerous aberrations among the samples and consequently predicting sensitivity based on similar steady state genetic characterizations provide limited accuracy. We have recently considered the modeling of tumor sensitivity using functional drug response data [4,5], along *Correspondence: ranadip.pal@ttu.edu 1 Department of Electrical and Computer Engineering, Texas Tech University, 1012 Boston Ave, 79409 Lubbock, TX, USA Full list of author information is available at the end of the article with a functional and genetic characterization based integrated modeling [6]. Models were designed based on the in vitro tumor response to a set of drugs with known targets.…”

Section: Introductionmentioning

confidence: 99%

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Combination therapy design for maximizing sensitivity and minimizing toxicity

Matlock

Berlow

Keller

et al. 2016

Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

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Background: Design of personalized targeted therapies involve modeling of patient sensitivity to various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor cells to targeted drugs without modeling the effect of the drugs on normal cells. In this article, we consider the individual modeling of drug responses to tumor and normal cells and utilize them to design targeted combination therapies that maximize sensitivity over tumor cells and minimize toxicity over normal cells. Results:The problem is formulated as maximizing sensitivity over tumor cell models while maintaining sensitivity below a threshold over normal cell models. We utilize the constrained structure of tumor proliferation models to design an accelerated lexicographic search algorithm for generating the optimal solution. For comparison purposes, we also designed two suboptimal search algorithms based on evolutionary algorithms and hill-climbing based techniques. Results over synthetic models and models generated from Genomics of Drug Sensitivity in Cancer database shows the ability of the proposed algorithms to arrive at optimal or close to optimal solutions in significantly lower number of steps as compared to exhaustive search. We also present the theoretical analysis of the expected number of comparisons required for the proposed Lexicographic search that compare favorably with the observed number of computations. Conclusions:The proposed algorithms provide a framework for design of combination therapy that tackles tumor heterogeneity while satisfying toxicity constraints.

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Section: Discussionmentioning

confidence: 99%

Section: Model Typementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combination therapy design for maximizing sensitivity and minimizing toxicity

Matlock

Berlow

Keller

et al. 2016

Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Self Cite

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“…Their goal is to maximize the efficacy on heterogeneous tumor cells while minimizing the toxicity over normal cells. They created a probabilistic target inhibition map (PTIM) [43][44][45][46] that model the tumor proliferation. They generated a set of PTIM models for different breast-cancer and B-cell lymphoma cancer cell lines from the GDSC database [47].…”

Section: Search Algorithmsmentioning

confidence: 99%

In-Silico Methodologies for Cancer Multidrug Optimization

Hasan¹,

Eldin

Khedr

et al. 2018

IJCT

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Drug combinations is considered as an effective strategy designed to control complex diseases like cancer. Combinations of drugs can effectively decrease side effects and enhance adaptive resistance. Therefore, increasing the likelihood of defeating complex diseases in a synergistic way. This is due to overcoming factors such as off-target activities, network robustness, bypass mechanisms, cross-talk across compensatory escape pathways and the mutational heterogeneity which results in alterations within multiple molecular pathways. The plurality of effective drug combinations used in clinic were found out through experience. The molecular mechanisms underlying these drug combinations are often not clear. It is not easy to suggest new drug combinations. Computational approaches are proposed to reduce the search space for defining the most promising combinations and prioritizing their experimental evaluation. In this paper, we review methods, techniques and hypotheses developed for in silico methodologies for drug combination discovery in cancer and discuss the limitations and challenges of these methods.

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“…Rather than using the entire cell response curve as the measure of sensitivity, a specific feature of the curve such as AUC (Area Under the dose [31,32]. Each feature of the dose response curve can produce its own set of concerns.…”

Section: Data Characterizationsmentioning

confidence: 99%

Algorithms for Drug Sensitivity Prediction

et al. 2016

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Precision medicine entails the design of therapies that are matched for each individual patient. Thus, predictive modeling of drug responses for specific patients constitutes a significant challenge for personalized therapy. In this article, we consider a review of approaches that have been proposed to tackle the drug sensitivity prediction problem especially with respect to personalized cancer therapy. We first discuss modeling approaches that are based on genomic characterizations alone and further the discussion by including modeling techniques that integrate both genomic and functional information. A comparative analysis of the prediction performance of four representative algorithms, elastic net, random forest, kernelized Bayesian multi-task learning and deep learning, reflecting the broad classes of regularized linear, ensemble, kernelized and neural network-based models, respectively, has been included in the paper. The review also considers the challenges that need to be addressed for successful implementation of the algorithms in clinical practice.

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An Integrated Approach to Anti-Cancer Drug Sensitivity Prediction

Cited by 26 publications

References 38 publications

Combination therapy design for maximizing sensitivity and minimizing toxicity

Combination therapy design for maximizing sensitivity and minimizing toxicity

In-Silico Methodologies for Cancer Multidrug Optimization

Algorithms for Drug Sensitivity Prediction

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