scholarly journals Methodological Challenges in Translational Drug Response Modeling in Cancer

2019 ◽  
Author(s):  
Lisa-Katrin Schätzle ◽  
Ali Hadizadeh Esfahani ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
R Package ◽  
Systematic Investigation ◽  
Computational Aspect ◽  
Ongoing Research ◽  
Model Components ◽  
Response Modeling ◽  
Translational Models

AbstractTranslational models directly relating drug response-specific processes observed in vitro to their in vivo role in cancer patients constitute a crucial part of the development of personalized medication. Unfortunately, ongoing research is often confined by the irreproducibility of the results in other contexts. While the inconsistency of pharmacological data has received great attention recently, the computational aspect of this crisis still deserves closer examination. Notably, studies often focus only on isolated model characteristics instead of examining the overall workflow and the interplay of individual model components. Here, we present a systematic investigation of translational models using the R-package FORESEE. Our findings confirm that with the current exploitation of the available data and the prevailing trend of optimizing methods to only one specific use case, modeling solutions will continue to suffer from non-transferability. Instead, the conduct of developing translational approaches urgently needs to change to retrieve clinical relevance in the future.

scholarly journals Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 885
Author(s):  
Robert F. Gruener ◽  
Alexander Ling ◽  
Ya-Fang Chang ◽  
Gladys Morrison ◽  
Paul Geeleher ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Discovery ◽  
Cell Line ◽  
Drug Response ◽  
Drug Efficacy ◽  
Standard Of Care ◽  
Breast Cancer Patients ◽  
Response Modeling

(1) Background: Drug imputation methods often aim to translate in vitro drug response to in vivo drug efficacy predictions. While commonly used in retrospective analyses, our aim is to investigate the use of drug prediction methods for the generation of novel drug discovery hypotheses. Triple-negative breast cancer (TNBC) is a severe clinical challenge in need of new therapies. (2) Methods: We used an established machine learning approach to build models of drug response based on cell line transcriptome data, which we then applied to patient tumor data to obtain predicted sensitivity scores for hundreds of drugs in over 1000 breast cancer patients. We then examined the relationships between predicted drug response and patient clinical features. (3) Results: Our analysis recapitulated several suspected vulnerabilities in TNBC and identified a number of compounds-of-interest. AZD-1775, a Wee1 inhibitor, was predicted to have preferential activity in TNBC (p < 2.2 × 10−16) and its efficacy was highly associated with TP53 mutations (p = 1.2 × 10−46). We validated these findings using independent cell line screening data and pathway analysis. Additionally, co-administration of AZD-1775 with standard-of-care paclitaxel was able to inhibit tumor growth (p < 0.05) and increase survival (p < 0.01) in a xenograft mouse model of TNBC. (4) Conclusions: Overall, this study provides a framework to turn any cancer transcriptomic dataset into a dataset for drug discovery. Using this framework, one can quickly generate meaningful drug discovery hypotheses for a cancer population of interest.

scholarly journals FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines

2019 ◽  
Vol 35 (19) ◽  
pp. 3846-3848 ◽  
Author(s):  
Lisa-Katrin Turnhoff ◽  
Ali Hadizadeh Esfahani ◽  
Maryam Montazeri ◽  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Training Algorithms ◽  
Anti Cancer ◽  
Model Training ◽  
Response Modeling

Abstract Summary Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient datasets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art pre-processing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. Availability and implementation FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE and https://doi.org/10.17605/OSF.IO/RF6QK, and provides vignettes for documentation and application both online and in the Supplementary Files 2 and 3. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CD44 Targeted Nanomaterials for Treatment of Triple-Negative Breast Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 898
Author(s):  
Ghazal Nabil ◽  
Rami Alzhrani ◽  
Hashem Alsaab ◽  
Mohammed Atef ◽  
Samaresh Sau ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
In Vivo Studies ◽  
High Dose ◽  
Luminal A ◽  
Combination Strategy ◽  
Ongoing Research

Identified as the second leading cause of cancer-related deaths among American women after lung cancer, breast cancer of all types has been the focus of numerous research studies. Even though triple-negative breast cancer (TNBC) represents 15–20% of the number of breast cancer cases worldwide, its existing therapeutic options are fairly limited. Due to the pivotal role of the presence/absence of specific receptors to luminal A, luminal B, HER-2+, and TNBC in the molecular classification of breast cancer, the lack of these receptors has accounted for the aforementioned limitation. Thereupon, in an attempt to participate in the ongoing research endeavors to overcome such a limitation, the conducted study adopts a combination strategy as a therapeutic paradigm for TNBC, which has proven notable results with respect to both: improving patient outcomes and survivability rates. The study hinges upon an investigation of a promising NPs platform for CD44 mediated theranostic that can be combined with JAK/STAT inhibitors for the treatment of TNBC. The ability of momelotinib (MMB), which is a JAK/STAT inhibitor, to sensitize the TNBC to apoptosis inducer (CFM-4.16) has been evaluated in MDA-MB-231 and MDA-MB-468. MMB + CFM-4.16 combination with a combination index (CI) ≤0.5, has been selected for in vitro and in vivo studies. MMB has been combined with CD44 directed polymeric nanoparticles (PNPs) loaded with CFM-4.16, namely CD44-T-PNPs, which selectively delivered the payload to CD44 overexpressing TNBC with a significant decrease in cell viability associated with a high dose reduction index (DRI). The mechanism underlying their synergism is based on the simultaneous downregulation of P-STAT3 and the up-regulation of CARP-1, which has induced ROS-dependent apoptosis leading to caspase 3/7 elevation, cell shrinkage, DNA damage, and suppressed migration. CD44-T-PNPs showed a remarkable cellular internalization, demonstrated by uptake of a Rhodamine B dye in vitro and S0456 (NIR dye) in vivo. S0456 was conjugated to PNPs to form CD44-T-PNPs/S0456 that simultaneously delivered CFM-4.16 and S0456 parenterally with selective tumor targeting, prolonged circulation, minimized off-target distribution.

scholarly journals T1000: A reduced toxicogenomics gene set for improved decision making

2019 ◽  
Author(s):  
Othman Soufan ◽  
Jessica Ewald ◽  
Charles Viau ◽  
Doug Crump ◽  
Markus Hecker ◽  
...  
Keyword(s):  
Dose Response ◽  
Gene Clusters ◽  
Gene Set ◽  
Chemical Hazards ◽  
Gene Sets ◽  
Toxicological Research ◽  
Multiple Species ◽  
Response Modeling

There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets.Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g.,in vitroand in vivo, dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available.

scholarly journals FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines

2018 ◽  
Author(s):  
Lisa-Katrin Turnhoff ◽  
Ali Hadizadeh Esfahani ◽  
Maryam Montazeri ◽  
Nina Kusch ◽  
Andreas Schuppert
Keyword(s):  
Drug Response ◽  
Drug Efficacy ◽  
Response Prediction ◽  
R Package ◽  
Supplementary Information ◽  
Supplementary File ◽  
Data Sets ◽  
Training Algorithms ◽  
Model Training

Translational models that utilize omics data generated in in vitro studies to predict the drug efficacy of anti-cancer compounds in patients are highly distinct, which complicates the benchmarking process for new computational approaches. In reaction to this, we introduce the uniFied translatiOnal dRug rESponsE prEdiction platform FORESEE, an open-source R-package. FORESEE not only provides a uniform data format for public cell line and patient data sets, but also establishes a standardized environment for drug response prediction pipelines, incorporating various state-of-the-art preprocessing methods, model training algorithms and validation techniques. The modular implementation of individual elements of the pipeline facilitates a straightforward development of combinatorial models, which can be used to re-evaluate and improve already existing pipelines as well as to develop new ones. Availability and Implementation: FORESEE is licensed under GNU General Public License v3.0 and available at https://github.com/JRC-COMBINE/FORESEE . Supplementary Information: Supplementary Files 1 and 2 provide detailed descriptions of the pipeline and the data preparation process, while Supplementary File 3 presents basic use cases of the package. Contact: [email protected]

scholarly journals A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research

2019 ◽  
Vol 9 (21) ◽  
pp. 4719 ◽  
Author(s):  
Shimwe Dominique Niyonambaza ◽  
Praveen Kumar ◽  
Paul Xing ◽  
Jessy Mathault ◽  
Paul De Koninck ◽  
...  
Keyword(s):  
Imaging Techniques ◽  
Ongoing Research ◽  
Engineering Research ◽  
Detection Techniques ◽  
In Vivo Detection ◽  
Low Concentrations ◽  
The Subject ◽  
The Brain

Neurotransmitters as electrochemical signaling molecules are essential for proper brain function and their dysfunction is involved in several mental disorders. Therefore, the accurate detection and monitoring of these substances are crucial in brain studies. Neurotransmitters are present in the nervous system at very low concentrations, and they mixed with many other biochemical molecules and minerals, thus making their selective detection and measurement difficult. Although numerous techniques to do so have been proposed in the literature, neurotransmitter monitoring in the brain is still a challenge and the subject of ongoing research. This article reviews the current advances and trends in neurotransmitters detection techniques, including in vivo sampling and imaging techniques, electrochemical and nano-object sensing techniques for in vitro and in vivo detection, as well as spectrometric, analytical and derivatization-based methods mainly used for in vitro research. The document analyzes the strengths and weaknesses of each method, with the aim to offer selection guidelines for neuro-engineering research.

scholarly journals MOLI: multi-omics late integration with deep neural networks for drug response prediction

2019 ◽  
Vol 35 (14) ◽  
pp. i501-i509 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Olga Zolotareva ◽  
Colin C Collins ◽  
Martin Ester
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Prediction Accuracy ◽  
Drug Response ◽  
Deep Neural Networks ◽  
Response Prediction ◽  
Supplementary Information ◽  
Precision Oncology

Abstract Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI’s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI’s high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Biomaterial Screening Approach Reveals Microenvironmental Mechanisms of Drug Resistance

2017 ◽  
Author(s):  
Alyssa D. Schwartz ◽  
Lauren E. Barney ◽  
Lauren E. Jansen ◽  
Thuy V. Nguyen ◽  
Christopher L. Hall ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Systems Biology ◽  
Drug Screening ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Mek Inhibitor ◽  
Tumor Burden ◽  
Adaptive Resistance

TOC FigureDrug response screening, gene expression, and kinome signaling were combined across biomaterial platforms to combat adaptive resistance to sorafenib.Insight BoxWe combined biomaterial platforms, drug screening, and systems biology to identify mechanisms of extracellular matrix-mediated adaptive resistance to RTK-targeted cancer therapies. Drug response was significantly varied across biomaterials with altered stiffness, dimensionality, and cell-cell contacts, and kinome reprogramming was responsible for these differences in drug sensitivity. Screening across many platforms and applying a systems biology analysis were necessary to identify MEK phosphorylation as the key factor associated with variation in drug response. This method uncovered the combination therapy of sorafenib with a MEK inhibitor, which decreased viability on and within biomaterials in vitro, but was not captured by screening on tissue culture plastic alone. This combination therapy also reduced tumor burden in vivo, and revealed a promising approach for combating adaptive drug resistance.AbstractTraditional drug screening methods lack features of the tumor microenvironment that contribute to resistance. Most studies examine cell response in a single biomaterial platform in depth, leaving a gap in understanding how extracellular signals such as stiffness, dimensionality, and cell-cell contacts act independently or are integrated within a cell to affect either drug sensitivity or resistance. This is critically important, as adaptive resistance is mediated, at least in part, by the extracellular matrix (ECM) of the tumor microenvironment. We developed an approach to screen drug responses in cells cultured on 2D and in 3D biomaterial environments to explore how key features of ECM mediate drug response. This approach uncovered that cells on 2D hydrogels and spheroids encapsulated in 3D hydrogels were less responsive to receptor tyrosine kinase (RTK)-targeting drugs sorafenib and lapatinib, but not cytotoxic drugs, compared to single cells in hydrogels and cells on plastic. We found that transcriptomic differences between these in vitro models and tumor xenografts did not reveal mechanisms of ECM-mediated resistance to sorafenib. However, a systems biology analysis of phospho-kinome data uncovered that variation in MEK phosphorylation was associated with RTK-targeted drug resistance. Using sorafenib as a model drug, we found that co-administration with a MEK inhibitor decreased ECM-mediated resistance in vitro and reduced in vivo tumor burden compared to sorafenib alone. In sum, we provide a novel strategy for identifying and overcoming ECM-mediated resistance mechanisms by performing drug screening, phospho-kinome analysis, and systems biology across multiple biomaterial environments.

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