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Cancers
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Modeling Strategies for Drug Response Prediction in Cancer
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Modeling Strategies for Drug Response Prediction in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 6113

Special Issue Editors

Prof. Dr. Rick L. Stevens

E-Mail Website
Guest Editor
1. Department of Computer Science, University of Chicago, Chicago, IL 60637, USA
2. Computing Environment and Life Sciences Directorate, Argonne National Laboratory, Lemont, IL 60439, USA
Interests: artificial intelligence; machine learning; deep learning; cancer; biology; bioinformatics; drug response
Dr. Yitan Zhu

E-Mail Website
Co-Guest Editor
Computing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA
Interests: machine learning; deep learning; statistical modeling; image analysis; bioinformatics; cancer genomics; precision oncology; drug development

Special Issue Information

Dear Colleagues,

In concert with the Cancers journal and in response to the surge in deep learning-based drug response prediction research, we are developing a Special Issue entitled “Modeling Strategies for Drug Response Prediction in Cancer”. The Special Issue will focus on cancer drug response with an anticipated publication date of July 31, 2023. Both original research articles and comprehensive reviews are welcomed. Potential topics include:

  • Techniques for evaluating and comparing models that go beyond examining simple error metrics;
  • Transfer learning from cell-lines to PDXs and other clinically relevant cancer models or patients;
  • Improving prediction performance by expanding data types used for prediction, e.g., molecular, clinical, and image data;
  • Multi-modal models integrating multiple types of cancer and drug information;
  • Modeling approaches that emphasize interpretability and clinically actionable models;
  • Modeling approaches with connections to novel high-throughput data generation methods;
  • Model uncertainty and clinical relevance investigation;
  • Reinforcement learning and active learning strategies to drive experimental designs;
  • Biologically inspired models and hybrid models integrating treatment mechanisms;
  • Approaches that address hard-to-model issues, such as immune response, prior treatments, side effects, tumor micro-environments, combination of chemo and radiation therapy, etc.
  • Pan-cancer pan-drug models vs. tumor- or drug-specific models; and
  • Single-drug vs. drug combination models.

We encourage you to respond to this call.

Prof. Dr. Rick L. Stevens
Guest Editor

Dr. Yitan Zhu
Co-Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • cancer
  • biology
  • bioinformatics
  • drug response

Published Papers (5 papers)

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Research

18 pages, 2598 KiB  
Article
A Comprehensive Investigation of Active Learning Strategies for Conducting Anti-Cancer Drug Screening
by Priyanka Vasanthakumari, Yitan Zhu, Thomas Brettin, Alexander Partin, Maulik Shukla, Fangfang Xia, Oleksandr Narykov, Michael Ryan Weil and Rick L. Stevens
Cancers 2024, 16(3), 530; https://doi.org/10.3390/cancers16030530 - 26 Jan 2024
Cited by 1 | Viewed by 763
Abstract
It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response [...] Read more.
It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method. Full article
(This article belongs to the Special Issue Modeling Strategies for Drug Response Prediction in Cancer)
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19 pages, 3650 KiB  
Article
Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models
by Oleksandr Narykov, Yitan Zhu, Thomas Brettin, Yvonne A. Evrard, Alexander Partin, Maulik Shukla, Fangfang Xia, Austin Clyde, Priyanka Vasanthakumari, James H. Doroshow and Rick L. Stevens
Cancers 2024, 16(1), 50; https://doi.org/10.3390/cancers16010050 - 21 Dec 2023
Cited by 2 | Viewed by 1175
Abstract
Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been [...] Read more.
Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs. Full article
(This article belongs to the Special Issue Modeling Strategies for Drug Response Prediction in Cancer)
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15 pages, 2289 KiB  
Article
Applying Natural Language Processing to Single-Report Prediction of Metastatic Disease Response Using the OR-RADS Lexicon
by Lydia Elbatarny, Richard K. G. Do, Natalie Gangai, Firas Ahmed, Shalini Chhabra and Amber L. Simpson
Cancers 2023, 15(20), 4909; https://doi.org/10.3390/cancers15204909 - 10 Oct 2023
Cited by 1 | Viewed by 797
Abstract
Generating Real World Evidence (RWE) on disease responses from radiological reports is important for understanding cancer treatment effectiveness and developing personalized treatment. A lack of standardization in reporting among radiologists impacts the feasibility of large-scale interpretation of disease response. This study examines the [...] Read more.
Generating Real World Evidence (RWE) on disease responses from radiological reports is important for understanding cancer treatment effectiveness and developing personalized treatment. A lack of standardization in reporting among radiologists impacts the feasibility of large-scale interpretation of disease response. This study examines the utility of applying natural language processing (NLP) to the large-scale interpretation of disease responses using a standardized oncologic response lexicon (OR-RADS) to facilitate RWE collection. Radiologists annotated 3503 retrospectively collected clinical impressions from radiological reports across several cancer types with one of seven OR-RADS categories. A Bidirectional Encoder Representations from Transformers (BERT) model was trained on this dataset with an 80–20% train/test split to perform multiclass and single-class classification tasks using the OR-RADS. Radiologists also performed the classification to compare human and model performance. The model achieved accuracies from 95 to 99% across all classification tasks, performing better in single-class tasks compared to the multiclass task and producing minimal misclassifications, which pertained mostly to overpredicting the equivocal and mixed OR-RADS labels. Human accuracy ranged from 74 to 93% across all classification tasks, performing better on single-class tasks. This study demonstrates the feasibility of the BERT NLP model in predicting disease response in cancer patients, exceeding human performance, and encourages the use of the standardized OR-RADS lexicon to improve large-scale prediction accuracy. Full article
(This article belongs to the Special Issue Modeling Strategies for Drug Response Prediction in Cancer)
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18 pages, 3562 KiB  
Article
Interpreting the Mechanism of Synergism for Drug Combinations Using Attention-Based Hierarchical Graph Pooling
by Zehao Dong, Heming Zhang, Yixin Chen, Philip R. O. Payne and Fuhai Li
Cancers 2023, 15(17), 4210; https://doi.org/10.3390/cancers15174210 - 22 Aug 2023
Cited by 3 | Viewed by 1315
Abstract
Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed [...] Read more.
Synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations, the major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusions of AI models untransparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in real-world human–AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and the mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, a self-attention-based node and edge pool (henceforth SANEpool), that can compute the attention score (importance) of genes and connections based on the genomic features and topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. Experiments on various well-adopted drug-synergy-prediction datasets demonstrate that (1) the SANEpool model has superior predictive ability to generate accurate synergy score prediction, and (2) the sub-molecular networks detected by the SANEpool are self-explainable and salient for identifying synergistic drug combinations. Full article
(This article belongs to the Special Issue Modeling Strategies for Drug Response Prediction in Cancer)
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17 pages, 8976 KiB  
Article
Unlocking the Potential of Kinase Targets in Cancer: Insights from CancerOmicsNet, an AI-Driven Approach to Drug Response Prediction in Cancer
by Manali Singha, Limeng Pu, Gopal Srivastava, Xialong Ni, Brent A. Stanfield, Ifeanyi K. Uche, Paul J. F. Rider, Konstantin G. Kousoulas, J. Ramanujam and Michal Brylinski
Cancers 2023, 15(16), 4050; https://doi.org/10.3390/cancers15164050 - 10 Aug 2023
Cited by 2 | Viewed by 1238
Abstract
Deregulated protein kinases are crucial in promoting cancer cell proliferation and driving malignant cell signaling. Although these kinases are essential targets for cancer therapy due to their involvement in cell development and proliferation, only a small part of the human kinome has been [...] Read more.
Deregulated protein kinases are crucial in promoting cancer cell proliferation and driving malignant cell signaling. Although these kinases are essential targets for cancer therapy due to their involvement in cell development and proliferation, only a small part of the human kinome has been targeted by drugs. A comprehensive scoring system is needed to evaluate and prioritize clinically relevant kinases. We recently developed CancerOmicsNet, an artificial intelligence model employing graph-based algorithms to predict the cancer cell response to treatment with kinase inhibitors. The performance of this approach has been evaluated in large-scale benchmarking calculations, followed by the experimental validation of selected predictions against several cancer types. To shed light on the decision-making process of CancerOmicsNet and to better understand the role of each kinase in the model, we employed a customized saliency map with adjustable channel weights. The saliency map, functioning as an explainable AI tool, allows for the analysis of input contributions to the output of a trained deep-learning model and facilitates the identification of essential kinases involved in tumor progression. The comprehensive survey of biomedical literature for essential kinases selected by CancerOmicsNet demonstrated that it could help pinpoint potential druggable targets for further investigation in diverse cancer types. Full article
(This article belongs to the Special Issue Modeling Strategies for Drug Response Prediction in Cancer)
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