Models Of Drug Response Research Articles

Abstract 515: Integrative analysis of LncRNA-drug interactions in cancer identified EPIC1 as an oncogenic lncRNA that regulates breast cancer tumorigenesis and iBET resistance through regulating MYC transcriptional activity

Abstract Emerging evidence suggests that lncRNAs can serve as promising biomarkers and therapeutic targets in cancer. Here, by integrating multiple dimensional pharmacogenomic data of 11,950 lncRNA in 5,605 tumors and 1,005 cancer cell lines, we build lncRNA drug response models for 265 anti-cancer compounds across 27 cancer types. Our study reveals the cancer cell lines can realistically recapitulate patient tumors in IncRNA genomic and epigenetic alterations. Using Elastic Net (EN) regression model combined with bootstrap cross validation, our analysis identified 162,327 unique lncRNA-drug interactions. The robustness of lncRNA based EN-models is validated by independent cancer cell line drug response data. By applying lncRNA EN-models of 49 FDA approved drugs to TCGA pan-cancer patient samples across 21 caner types, we have shown that the cancer cell line based EN-models could readily predict chemotherapy responses in patients with breast, stomach, thyroid, and colon rectal cancer. Further lncRNA-protein coding gene co-expression analysis reveals that drug-predictive lncRNAs regulated drug-metabolism and drug-target pathways, which provide underlying mechanism for lncRNAs' regulation of drug response. This analysis has identified a novel intergenic lncRNA, EPIC1, regulates breast cancer tumorigenesis and iBET (Bromodomain Inhibitors) resistance through directly interacting with MYC and regulating MYC transcriptional activity. Knockdown of EPIC1 in breast cancer cells leads to inhibition of colony formation, cell cycle arrest, and suppression of tumor growth in vitro and in vivo. Overexpression of EPIC1 increased MYC target expression, breast tumorigenesis in vitro and in vivo, and promote iBET resistance in breast cancer. Mechanistically, EPIC1 directly interacts with MYC through EPIC1's 129-283 nt region. EPIC1 knockdown reduces the occupancy of MYC protein to the promoters of its target genes (e.g., p21, CCNA2, CDC20, and CDC45) without influencing MYC expression. The oncogenic effect of EPIC1 can be abolished by MYC knockdown. To our best knowledge, this is the first study to link noncoding genotypes with drug response phenotypes in both cancer cell lines and patient tumors. Citation Format: Yue Wang, Zehua Wang, Bo Yang, Min Zhang, Da Yang. Integrative analysis of LncRNA-drug interactions in cancer identified EPIC1 as an oncogenic lncRNA that regulates breast cancer tumorigenesis and iBET resistance through regulating MYC transcriptional activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 515.

Physiological random processes in precision cancer therapy.

Many different physiological processes affect the growth of malignant lesions and their response to therapy. Each of these processes is spatially and genetically heterogeneous; dynamically evolving in time; controlled by many other physiological processes, and intrinsically random and unpredictable. The objective of this paper is to show that all of these properties of cancer physiology can be treated in a unified, mathematically rigorous way via the theory of random processes. We treat each physiological process as a random function of position and time within a tumor, defining the joint statistics of such functions via the infinite-dimensional characteristic functional. The theory is illustrated by analyzing several models of drug delivery and response of a tumor to therapy. To apply the methodology to precision cancer therapy, we use maximum-likelihood estimation with Emission Computed Tomography (ECT) data to estimate unknown patient-specific physiological parameters, ultimately demonstrating how to predict the probability of tumor control for an individual patient undergoing a proposed therapeutic regimen.

Deep Learning for Drug Discovery and Cancer Research: Automated Analysis of Vascularization Images.

Likely drug candidates which are identified in traditional pre-clinical drug screens often fail in patient trials, increasing the societal burden of drug discovery. A major contributing factor to this phenomenon is the failure of traditional in vitro models of drug response to accurately mimic many of the more complex properties of human biology. We have recently introduced a new microphysiological system for growing vascularized, perfused microtissues that more accurately models human physiology and is suitable for large drug screens. In this work, we develop a machine learning model that can quickly and accurately flag compounds which effectively disrupt vascular networks from images taken before and after drug application in vitro. The system is based on a convolutional neural network and achieves near perfect accuracy while committing potentially no expensive false negatives.

Physiologically Based Pharmacokinetic and Pharmacodynamic Analysis Enabled by Microfluidically Linked Organs-on-Chips.

Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches are beginning to be integrated into drug development and approval processes because they enable key pharmacokinetic (PK) parameters to be predicted from in vitro data. However, these approaches are hampered by many limitations, including an inability to incorporate organ-specific differentials in drug clearance, distribution, and absorption that result from differences in cell uptake, transport, and metabolism. Moreover, such approaches are generally unable to provide insight into pharmacodynamic (PD) parameters. Recent development of microfluidic Organ-on-a-Chip (Organ Chip) cell culture devices that recapitulate tissue-tissue interfaces, vascular perfusion, and organ-level functionality offer the ability to overcome these limitations when multiple Organ Chips are linked via their endothelium-lined vascular channels. Here, we discuss successes and challenges in the use of existing culture models and vascularized Organ Chips for PBPK and PD modeling of human drug responses, as well as in vitro to in vivo extrapolation (IVIVE) of these results, and how these approaches might advance drug development and regulatory review processes in the future.

Genomic Determinants of Protein Abundance Variation in Colorectal Cancer Cells

SummaryAssessing the impact of genomic alterations on protein networks is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labeling to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the functional consequences of somatic genomic variants. The robust quantification of over 9,000 proteins and 11,000 phosphopeptides on average enabled the de novo construction of a functional protein correlation network, which ultimately exposed the collateral effects of mutations on protein complexes. CRISPR-cas9 deletion of key chromatin modifiers confirmed that the consequences of genomic alterations can propagate through protein interactions in a transcript-independent manner. Lastly, we leveraged the quantified proteome to perform unsupervised classification of the cell lines and to build predictive models of drug response in colorectal cancer. Overall, we provide a deep integrative view of the functional network and the molecular structure underlying the heterogeneity of colorectal cancer cells.

Abstract 1836: Patient-derived xenograft (PDX) models expressing HER2 reflect clinical responses to targeted HER2 inhibition

Abstract Background While HER2-directed agents are most often used for treating breast cancer, there is increasing evidence that these therapies may be of value in other solid tumors. Sequencing efforts and immunohistochemistry (IHC) have identified mutations, amplifications, and overexpression of HER2 in ovarian, HNSCC, NSCLC, and GI cancers. PDX models could permit evaluation of HER2 response/resistance mechanisms to optimize therapeutic strategies. In this pilot study, we evaluated the response of PDX models to HER2-targeted therapies and correlated responses to clinical outcomes. Materials and Methods PDX models were developed from a variety of patient solid tumors, evaluated by IHC for HER2 expression and next-generation sequencing for genomic alterations in HER2 (mutations, amplifications/deletions, and expression levels). Models were screened against single agent HER2-directed therapies including trastuzumab (n=15), trastuzumab emtansine (n=23), and lapatinib (n=10). Tumor regression (TR) values and RECIST criteria were determined and correlated with known literature-based response rates (RR) as well as individual patient outcomes. Results 32 PDX models from 30 patients were interrogated (primarily breast and colorectal). Twenty (63%) models have been sequenced to date; 13 (65%) harbor amplification at ERBB2 gene locus. Further, 56% (18/32) have been evaluated by IHC for HER2 to date: 50% have 2+ HER2 staining, 17% 3+ staining, and 33% 1+/- staining. Based on PDX tumor growth, stable disease/regression was observed in 10% of models screened against lapatinib (CR/PR=0%), 50% screened against trastuzumab (CR/PR=8%), and 67% tested against trastuzumab emtansine (CR/PR=14%). Only models with +2/+3 HER2 staining showed regression with HER2-targeted treatment, with nearly 70% of +1/- HER2 models showing progressive disease. Finally, there were 4 correlations to patient clinical outcomes available, with 3/4 (75%) of the PDX model responses mimicking those of the patient to the same treatment. Conclusion and Future Directions Extensive sequencing of human cancers has demonstrated HER2 amplification or mutation in numerous solid tumors, suggesting HER2-directed therapy could be applied more broadly in the clinic. Consistent with clinical findings, HER2 therapy responses depended upon the strength of HER2 expression (based on IHC). Nevertheless, response rates in PDX models varied depending on which HER2-targeted agent was deployed, highlighting the potential existence of differential mechanisms of de novo resistance/sensitivity. Comprehensive sequencing and drug testing of these PDX models is planned and could allow a deeper understanding of such mechanisms. In this context, application of PDX models for translational modeling of HER2 drug responses, particularly in the context of co-clinical trials, will continue to evolve. Citation Format: Daniel Ciznadija, Amir Sonnenblick, Jennifer Jaskowiak, Angela Davies, David Sidransky. Patient-derived xenograft (PDX) models expressing HER2 reflect clinical responses to targeted HER2 inhibition [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1836. doi:10.1158/1538-7445.AM2017-1836

Abstract 4834: 3D modeling of immune cell interactions in breast cancer and prediction of immunotherapy response

Abstract While breast cancer has an overall 5-year survival rate of 89%, the rate for patients with stage 4 metastatic disease is only 26%. Immunotherapies have the potential to improve the prognosis for these patients while also providing better treatment options for all breast cancer patients since they have fewer side effects enabling longer treatment times and the use of combination therapies and reduced chances of developing resistance. Currently these treatments are tested in standard 2D cell cultures that are inaccurate in mimicking in vivo drug response or animal models where the immune system differs from humans in numerous ways including T-cell subsets, cytokine receptors, and costimulatory molecule expression. We have developed 3D models of human breast cancer that span the subtypes, ER+, HER2+, and triple negative, incorporate numerous stromal cell types, fibroblasts and adipocytes, and include different immune cells, macrophages and T-cells under either static or perfusion culture systems. These models have been used to examine how tumor cells influence macrophage differentiation using undifferentiated peripheral blood mononuclear cells (PBMCs), how M1 and M2 macrophages influence tumor cell survival and proliferation, how the combination of these cell types influence cytokine secretion, and how the microenvironment affects macrophage invasion. We have also used these complex models to examine response of tumor cells and T-cells to checkpoint inhibitors through standard viability assays and flow cytometry. These models have several potential uses which include the ability to quickly answer whether a particular immunotherapy agent is effective for that particular patient-specific manner and to screen potential novel immunotherapeutic candidates and/or combinations prior to clinical use. Citation Format: Qi Guo, Stephen Shuford, Brian McKinley, Mary Rippon, Wendy Cornett, Mark O'Rourke, David Schammel, Jeff Edenfield, David L. Kaplan, Hal E. Crosswell, Teresa Desrochers. 3D modeling of immune cell interactions in breast cancer and prediction of immunotherapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4834. doi:10.1158/1538-7445.AM2017-4834

Mesenchymal stem cells support growth and organization of host-liver colorectal-tumor organoids and possibly resistance to chemotherapy

Despite having yielded extensive breakthroughs in cancer research, traditional 2D cell cultures have limitations in studying cancer progression and metastasis and screening therapeutic candidates. 3D systems can allow cells to grow, migrate, and interact with each other and the surrounding matrix, resulting in more realistic constructs. Furthermore, interactions between host tissue and developing tumors influence the susceptibility of tumors to drug treatments. Host-liver colorectal-tumor spheroids composed of primary human hepatocytes, mesenchymal stem cells (MSC) and colon carcinoma HCT116 cells were created in simulated microgravity rotating wall vessel (RWV) bioreactors. The cells were seeded on hyaluronic acid-based microcarriers, loaded with liver-specific growth factors and ECM components. Only in the presence of MSC, large tumor foci rapidly formed inside the spheroids and increased in size steadily over time, while not greatly impacting albumin secretion from hepatocytes. The presence of MSC appeared to drive self-organization and formation of a stroma-like tissue surrounding the tumor foci and hepatocytes. Exposure to a commonly used chemotherapeutic 5-FU showed a dose-dependent cytotoxicity. However, if tumor organoids were allowed to mature in the RWV, they were less sensitive to the drug treatment. These data demonstrate the potential utility of liver tumor organoids for cancer progression and drug response modeling.

397 Predictive modeling of drug response and mechanism of action in alopecia areata clinical trials

397 Predictive modeling of drug response and mechanism of action in alopecia areata clinical trials

CImbinator: a web-based tool for drug synergy analysis in small- and large-scale datasets.

MotivationDrug synergies are sought to identify combinations of drugs particularly beneficial. User-friendly software solutions that can assist analysis of large-scale datasets are required.ResultsCImbinator is a web-service that can aid in batch-wise and in-depth analyzes of data from small-scale and large-scale drug combination screens. CImbinator offers to quantify drug combination effects, using both the commonly employed median effect equation, as well as advanced experimental mathematical models describing dose response relationships.Availability and ImplementationCImbinator is written in Ruby and R. It uses the R package drc for advanced drug response modeling. CImbinator is available at http://cimbinator.bioinfo.cnio.es, the source-code is open and available at https://github.com/Rbbt-Workflows/combination_index. A Docker image is also available at https://hub.docker.com/r/mikisvaz/rbbt-ci_mbinator/.Supplementary information Supplementary data are available at Bioinformatics online.

Combination therapy design for maximizing sensitivity and minimizing toxicity

BackgroundDesign 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.ResultsThe 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.ConclusionsThe proposed algorithms provide a framework for design of combination therapy that tackles tumor heterogeneity while satisfying toxicity constraints.

Abstract P6-07-33: Metrics of drug sensitivity based on growth rate inhibition correct for the confounding effects of variable division rates

Abstract Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics suffer from a fundamental flaw when applied to growing cells: they are highly sensitive to the number of divisions that take place over the course of a response assay. Division rate varies with cell line, experimental conditions, and genetic alterations. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity while obscuring important biological insights and interfering with biomarker discovery. In this work, we derive alternative drug response metrics that are insensitive to number of divisions occurring during the assay. These are based on estimating growth rate inhibition (GR) in the presence of a drug using endpoint or time-course assays. The latter provides a direct measure of phenomena such as adaptive drug resistance. Using a simple model of drug response, we first show how GR50 and GRmax are superior to IC50 and Emax for assessing the effects of drugs in dividing cells. By expressing an oncogene in a transformed cell line, we illustrate how conventional metrics can lead to artefactual connections between mutations and drug sensitivity. We further validate the superiority of GR50 over IC50 values by reanalyzing a recently published large dataset of drug sensitivity and showing cases where difference in division rates is the only reason why IC50 values correlate with tissue type or genetic alterations. Using GR50 values prevents these artificial correlations and restores known connections between drug resistance and genomic markers. Finally, we show how GRmax values, which reflect efficacy, quantify differences in the phenotypic response and thus can be used to identify new biomarkers of sensitivity. Adopting GR metrics requires only modest changes in experimental protocols. GR values and metrics can be evaluated using scripts are available on github (www.github.com/sorgerlab/gr50_tools) or using an interactive website: www.grcalculator.org. We expect GR metrics to improve the use of drugs to identify response biomarkers, study mechanisms of cell signaling and growth, and identify drugs effective on specific patient-derived tumor cells.Drug sensitivity and resistance are conventionally quantified by IC50 or Emax values, but these metrics suffer from a fundamental flaw when applied to growing cells: they are highly sensitive to the number of divisions that take place over the course of a response assay. Division rate varies with cell line, experimental conditions, and genetic alterations. The dependency of IC50 and Emax on division rate creates artefactual correlations between genotype and drug sensitivity while obscuring important biological insights and interfering with biomarker discovery. In this work, we derive alternative drug response metrics that are insensitive to number of divisions occurring during the assay. These are based on estimating growth rate inhibition (GR) in the presence of a drug using endpoint or time-course assays. The latter provides a direct measure of phenomena such as adaptive drug resistance. Using a simple model of drug response, we first show how GR50 and GRmax are superior to IC50 and Emax for assessing the effects of drugs in dividing cells. By expressing an oncogene in a transformed cell line, we illustrate how conventional metrics can lead to artefactual connections between mutations and drug sensitivity. We further validate the superiority of GR50 over IC50 values by reanalyzing a recently published large dataset of drug sensitivity and showing cases where difference in division rates is the only reason why IC50 values correlate with tissue type or genetic alterations. Using GR50 values prevents these artificial correlations and restores known connections between drug resistance and genomic markers. Finally, we show how GRmax values, which reflect efficacy, quantify differences in the phenotypic response and thus can be used to identify new biomarkers of sensitivity. Adopting GR metrics requires only modest changes in experimental protocols. GR values and metrics can be evaluated using scripts are available on github (www.github.com/sorgerlab/gr50_tools) or using an interactive website: www.grcalculator.org. We expect GR metrics to improve the use of drugs to identify response biomarkers, study mechanisms of cell signaling and growth, and identify drugs effective on specific patient-derived tumor cells. Citation Format: Hafner M, Niepel M, Sorger PK. Metrics of drug sensitivity based on growth rate inhibition correct for the confounding effects of variable division rates [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P6-07-33.

Temozolomide in the Era of Precision Medicine.

In the January 1, 2017, issue of Cancer Research, Nagel and colleagues demonstrate the value of assays that determine the DNA repair capacity of cancers in predicting response to temozolomide. Using a fluorescence-based multiplex flow cytometric host cell reactivation assay that provides simultaneous readout of DNA repair capacity across multiple pathways, they show that the multivariate drug response models derived from cell line data were applicable to patient-derived xenograft models of glioblastoma. In this commentary, we first outline the mechanism of activity and current clinical application of temozolomide, which, until now, has been largely limited to glioblastoma. Given the challenges of clinical application of functional assays, we argue that functional readouts be approximated by genomic signatures. In this context, a combination of MGMT activity and mismatch repair (MMR) status of the tumor are important parameters that determine sensitivity to temozolomide. More reliable methods are needed to determine MGMT activity as DNA methylation, the current standard, does not accurately reflect the expression of MGMT. Also, genomics for MMR are warranted. Furthermore, based on patterns of MGMT expression across different solid tumors, we make a case for revisiting temozolomide use in a broader spectrum of cancers based on our current understanding of its molecular basis of activity. Cancer Res; 77(4); 823-6. ©2017 AACR.

Cheminformatics Modeling of Adverse Drug Responses by Clinically Relevant Mutants of Human Androgen Receptor.

The human androgen receptor (AR) is a ligand-activated transcription factor that plays a pivotal role in the development and progression of prostate cancer (PCa). Many forms of castration-resistant prostate cancer (CRPC) still rely on the AR for survival. Currently used antiandrogens face clinical limitations as drug resistance develops in patients over time since they all target the mutation-prone androgen binding site (ABS), where gain-of-function mutations eventually convert antagonists into agonists. With a significant number of reported distinct mutations located across the ABS, it is imperative to develop a prognostic platform which would equip clinicians with prior knowledge and actionable strategies if cases of previously unreported AR mutations are encountered. The goal of this study is to develop a theoretical approach that can predict such previously unreported AR mutants in response to current treatment options for PCa. The expected drug response by these mutants has been modeled using cheminformatics methodology. The corresponding QSAR pipeline has been created, which extracts key protein-ligand interactions and quantifies them by 4D molecular descriptors. The developed models reported with an accuracy reaching 90% and enable prediction of activation of AR mutants by its native ligand as well as assess whether known antiandrogens will act on them as agonists or antagonists. As a result, a previously uncharacterized mutant, T878G, has been predicted to be activated by the latest antiandrogen enzalutamide, and the corresponding experimental evaluation confirmed this prediction. Overall, the developed cheminformatics pipeline provides useful insights toward understanding the changing genomic landscape of advanced PCa.

Algorithms for Drug Sensitivity Prediction

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.

TANDEM: a two-stage approach to maximize interpretability of drug response models based on multiple molecular data types.

Clinical response to anti-cancer drugs varies between patients. A large portion of this variation can be explained by differences in molecular features, such as mutation status, copy number alterations, methylation and gene expression profiles. We show that the classic approach for combining these molecular features (Elastic Net regression on all molecular features simultaneously) results in models that are almost exclusively based on gene expression. The gene expression features selected by the classic approach are difficult to interpret as they often represent poorly studied combinations of genes, activated by aberrations in upstream signaling pathways. To utilize all data types in a more balanced way, we developed TANDEM, a two-stage approach in which the first stage explains response using upstream features (mutations, copy number, methylation and cancer type) and the second stage explains the remainder using downstream features (gene expression). Applying TANDEM to 934 cell lines profiled across 265 drugs (GDSC1000), we show that the resulting models are more interpretable, while retaining the same predictive performance as the classic approach. Using the more balanced contributions per data type as determined with TANDEM, we find that response to MAPK pathway inhibitors is largely predicted by mutation data, while predicting response to DNA damaging agents requires gene expression data, in particular SLFN11 expression. TANDEM is available as an R package on CRAN (for more information, see http://ccb.nki.nl/software/tandem). m.michaut@nki.nl or l.wessels@nki.nl Supplementary data are available at Bioinformatics online.

Abstract 2100: Imputation of drug sensitivity levels in The Cancer Genome Atlas using machine learning identifies novel predictors of chemotherapeutic response

Abstract The Cancer Genome Atlas (TCGA) represents a landmark undertaking that has collected genomic data from tumors of almost 15,000 cancer patients. Most tumors have been assayed using many high-throughput genomics techniques, such as whole-genome DNA copy number, gene expression, mutation status and DNA methylation. The project has also compiled a large set of matching clinical data; however, the utility of TCGA for pharmacogenomic discovery has been severely limited by the lack of cleanly measured drug response data. The absence of such data owes to the difficulty in obtaining this information from cancer patients, because individuals are typically treated with complex multi-drug regimes, which renders the precise measurement of a drug specific response phenotype intractable. However, precisely measured drug response data can be readily obtained in pre-clinical models, such as cell lines. Thus, in this study, we have implemented a machine learning based approach, where gene expression based predictive models of drug response are constructed on approximately 700 cancer cell lines; these models are then applied to nearly 15,000 TCGA tumor samples, for which gene expression data is also available, yielding a predicted drug sensitivity value in each TCGA sample. We used this approach to impute a drug sensitivity estimate for 138 drugs that were treated against the cell lines. Our approach allowed us to transform TCGA into a vast pharmacogenomics dataset, on an unprecedented scale (and thus power), which could be mined for novel associations relevant to chemotherapeutic response. As a proof-of-concept, we first investigated whether the existing small number of known clinically relevant associations could be recapitulated in these imputed data. As an example, we identified the predicted difference in sensitivity to Lapatinib, which interrupts ERBB2, as significantly greater in ERBB2 amplified tumors (P = 6 × 10-12), an association that is drug specific. In ERBB2 amplified tumors, large chunks of chromosome 17, containing many genes, are typically amplified; this recurring phenomenon often renders it impossible to identify the causative genes in genome wide copy number data using conventional approaches. Strikingly however, by interrogating the observed effect size of genes in this amplicon, ERBB2 could be identified as the precise causative drug target in our data, demonstrating the impressive increase in power obtained from this approach. Additionally, the second most significant association for Lapatinib is for EGFR, which is the known secondary target of this drug (P = 5 × 10-4). We also identified many novel associations; e.g. that a copy number amplification in ERLIN2, a gene shown to play a role in stabilizing microtubules, is associated with resistance to a class of chemotherapeutics that interact with the microtubules. This was subsequently validated with follow-up experiments. Citation Format: Paul Geeleher, R. Stephanie Huang, Fan Wang, Gladys Morrison. Imputation of drug sensitivity levels in The Cancer Genome Atlas using machine learning identifies novel predictors of chemotherapeutic response. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2100.

Abstract 42: Target inhibitory networks and drug response modeling

Abstract Cancer is a complex disease caused by multiple factors, which hamper effective drug discovery. Drug combination or multi target agents provide an alternate way to effectively modify disease networks. Synergistic drug pairs have special potential for treatment since they allow a desired effect to be achieved with lower total dose of administered medicine and usually with fewer side effects. However, the major challenge has been the prediction of chemotherapeutic efficacy based on the biological profile of the tumor. Because of the lack of gene expression data treated with drug combinations, in this study, we present a computational approach to identify effective drug combinations by exploiting high throughput data. We constructed tissue specific network pharmacology based on large-scale screening data on drug treatment efficacies of 130 drugs under clinical and preclinical investigation and drug-target binding affinities. We evaluated CNS cancer subset and applied logic based network algorithm to predict effective drug combinations based on drug-target interactions and single drug sensitivity profiles. Cancer cell based target inhibition network analysis in two case studies using glioma cell lines, (U87 and U251) identified distinct cell line survival pathways (p < 0.001), including cell proliferation, adhesion and growth factor signaling. We estimated pairwise drug synergy scores for all the target genes and identified several synergistic pairs with potential clinical relevance. Target inhibition modeling allowed systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways. Citation Format: Uma Shankavaram, Kevin Camphausen. Target inhibitory networks and drug response modeling. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Integrating Clinical Genomics and Cancer Therapy; Jun 13-16, 2015; Salt Lake City, UT. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(1_Suppl):Abstract nr 42.

Bringing Model-Based Prediction to Oncology Clinical Practice: A Review of Pharmacometrics Principles and Applications.

Despite much investment and progress, oncology is still an area with significant unmet medical needs, with new therapies and more effective use of current therapies needed. The emergent field of pharmacometrics combines principles from pharmacology (pharmacokinetics [PK] and pharmacodynamics [PD]), statistics, and computational modeling to support drug development and optimize the use of already marketed drugs. Although it has gained a role within drug development, its use in clinical practice remains scarce. The aim of the present study was to review the principal pharmacometric concepts and provide some examples of its use in oncology. Integrated population PK/PD/disease progression models as part of the pharmacometrics platform provide a powerful tool to predict outcomes so that the right dose can be given to the right patient to maximize drug efficacy and reduce drug toxicity. Population models often can be developed with routinely collected medical record data; therefore, we encourage the application of such models in the clinical setting by generating close collaborations between physicians and pharmacometricians. The present review details how the emerging field of pharmacometrics can integrate medical record data with predictive pharmacological and statistical models of drug response to optimize and individualize therapies. In order to make this routine practice in the clinic, greater awareness of the potential benefits of the field is required among clinicians, together with closer collaboration between pharmacometricians and clinicians to ensure the requisite data are collected in a suitable format for pharmacometrics analysis.

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib.

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.