A new approach for prediction of tumor sensitivity to targeted drugs based on functional data

Abstract

BackgroundThe 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.ResultsWe 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.ConclusionsThe 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.

Combination therapy design for maximizing sensitivity and minimizing toxicity

“…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.…”

“…The synthetic models are simulated using a proliferation network structure based on probabilistic target inhibition maps [5,7]. Each cellular pathway, i, representing either a …”

“…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.…”

“…Consider a solid tumor tissue where the biopsy sample can be divided into separate samples to explore the heterogeneity. We can pass each biopsy sample through a drug screen to create a probabilistic target inhibition map (PTIM) [5] model. Let MT 1 , MT 2 , · · · , MT k denote the k models corresponding to the k spatial tumor biopsies.…”

In-Silico Methodologies for Cancer Multidrug Optimization

“…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].…”