1
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Vitale R, Bugnon LA, Fenoy EL, Milone DH, Stegmayer G. Evaluating large language models for annotating proteins. Brief Bioinform 2024; 25:bbae177. [PMID: 38706315 PMCID: PMC11070647 DOI: 10.1093/bib/bbae177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/15/2024] [Accepted: 03/27/2024] [Indexed: 05/07/2024] Open
Abstract
In UniProtKB, up to date, there are more than 251 million proteins deposited. However, only 0.25% have been annotated with one of the more than 15000 possible Pfam family domains. The current annotation protocol integrates knowledge from manually curated family domains, obtained using sequence alignments and hidden Markov models. This approach has been successful for automatically growing the Pfam annotations, however at a low rate in comparison to protein discovery. Just a few years ago, deep learning models were proposed for automatic Pfam annotation. However, these models demand a considerable amount of training data, which can be a challenge with poorly populated families. To address this issue, we propose and evaluate here a novel protocol based on transfer learningṪhis requires the use of protein large language models (LLMs), trained with self-supervision on big unnanotated datasets in order to obtain sequence embeddings. Then, the embeddings can be used with supervised learning on a small and annotated dataset for a specialized task. In this protocol we have evaluated several cutting-edge protein LLMs together with machine learning architectures to improve the actual prediction of protein domain annotations. Results are significatively better than state-of-the-art for protein families classification, reducing the prediction error by an impressive 60% compared to standard methods. We explain how LLMs embeddings can be used for protein annotation in a concrete and easy way, and provide the pipeline in a github repo. Full source code and data are available at https://github.com/sinc-lab/llm4pfam.
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Affiliation(s)
- Rosario Vitale
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Leandro A Bugnon
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Emilio Luis Fenoy
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Diego H Milone
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
| | - Georgina Stegmayer
- Research Institute for Signals, Systems and Computational Intelligence sinc(i) (CONICET-UNL), Ciudad Universitaria, Santa Fe, Argentina
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2
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Kim J, Park SH, Lee H. PANCDR: precise medicine prediction using an adversarial network for cancer drug response. Brief Bioinform 2024; 25:bbae088. [PMID: 38487849 PMCID: PMC10940842 DOI: 10.1093/bib/bbae088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/09/2024] [Accepted: 02/16/2024] [Indexed: 03/18/2024] Open
Abstract
Pharmacogenomics aims to provide personalized therapy to patients based on their genetic variability. However, accurate prediction of cancer drug response (CDR) is challenging due to genetic heterogeneity. Since clinical data are limited, most studies predicting drug response use preclinical data to train models. However, such models might not be generalizable to external clinical data due to differences between the preclinical and clinical datasets. In this study, a Precision Medicine Prediction using an Adversarial Network for Cancer Drug Response (PANCDR) model is proposed. PANCDR consists of two sub-models, an adversarial model and a CDR prediction model. The adversarial model reduces the gap between the preclinical and clinical datasets, while the CDR prediction model extracts features and predicts responses. PANCDR was trained using both preclinical data and unlabeled clinical data. Subsequently, it was tested on external clinical data, including The Cancer Genome Atlas and brain tumor patients. PANCDR outperformed other machine learning models in predicting external test data. Our results demonstrate the robustness of PANCDR and its potential in precision medicine by recommending patient-specific drug candidates. The PANCDR codes and data are available at https://github.com/DMCB-GIST/PANCDR.
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Affiliation(s)
- Juyeon Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 61005, Gwangju, South Korea
| | - Sung-Hye Park
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, 03080, Seoul, South Korea
- Neuroscience Research Institute, Seoul National University College of Medicine, 03080, Seoul, South Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 61005, Gwangju, South Korea
- Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology, 61005, Gwangju, South Korea
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3
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Liu Q, Li G, Baladandayuthapani V. Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.03.560366. [PMID: 37873111 PMCID: PMC10592913 DOI: 10.1101/2023.10.03.560366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The pursuit of precision oncology heavily relies on large-scale genomic and pharmacological data garnered from preclinical cancer model systems such as cell lines. While cell lines are instrumental in understanding the interplay between genomic programs and drug response, it well-established that they are not fully representative of patient tumors. Development of integrative methods that can systematically assess the commonalities between patient tumors and cell-lines can help bridge this gap. To this end, we introduce the Integrative Principal Component Regression (iPCR) model which uncovers both joint and model-specific structured variations in the genomic data of cell lines and patient tumors through matrix decompositions. The extracted joint variation is then used to predict patient drug responses based on the pharmacological data from preclinical models. Moreover, the interpretability of our model allows for the identification of key driver genes and pathways associated with the treatment-specific response in patients across multiple cancers. We demonstrate that the outputs of the iPCR model can assist in inferring both model-specific and shared co-expression networks between cell lines and patients. We show that iPCR performs favorably compared to competing approaches in predicting patient drug responses, in both simulation studies and real-world applications, in addition to identifying key genomic drivers of cancer drug responses.
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4
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Toseef M, Olayemi Petinrin O, Wang F, Rahaman S, Liu Z, Li X, Wong KC. Deep transfer learning for clinical decision-making based on high-throughput data: comprehensive survey with benchmark results. Brief Bioinform 2023:bbad254. [PMID: 37455245 DOI: 10.1093/bib/bbad254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/04/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023] Open
Abstract
The rapid growth of omics-based data has revolutionized biomedical research and precision medicine, allowing machine learning models to be developed for cutting-edge performance. However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task. Transfer learning is a promising tool that bridges the gap between data domains by transferring knowledge from the source to the target domain. Researchers have proposed transfer learning to predict clinical outcomes by leveraging pre-clinical data (mouse, zebrafish), highlighting its vast potential. In this work, we present a comprehensive literature review of deep transfer learning methods for health informatics and clinical decision-making, focusing on high-throughput molecular data. Previous reviews mostly covered image-based transfer learning works, while we present a more detailed analysis of transfer learning papers. Furthermore, we evaluated original studies based on different evaluation settings across cross-validations, data splits and model architectures. The result shows that those transfer learning methods have great potential; high-throughput sequencing data and state-of-the-art deep learning models lead to significant insights and conclusions. Additionally, we explored various datasets in transfer learning papers with statistics and visualization.
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Affiliation(s)
- Muhammad Toseef
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | | | - Fuzhou Wang
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Saifur Rahaman
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Zhe Liu
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Jilin, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Hong Kong SAR
- Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong SAR
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5
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Yuan S, Chen YC, Tsai CH, Chen HW, Shieh GS. Feature selection translates drug response predictors from cell lines to patients. Front Genet 2023; 14:1217414. [PMID: 37519889 PMCID: PMC10382684 DOI: 10.3389/fgene.2023.1217414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 06/26/2023] [Indexed: 08/01/2023] Open
Abstract
Targeted therapies and chemotherapies are prevalent in cancer treatment. Identification of predictive markers to stratify cancer patients who will respond to these therapies remains challenging because patient drug response data are limited. As large amounts of drug response data have been generated by cell lines, methods to efficiently translate cell-line-trained predictors to human tumors will be useful in clinical practice. Here, we propose versatile feature selection procedures that can be combined with any classifier. For demonstration, we combined the feature selection procedures with a (linear) logit model and a (non-linear) K-nearest neighbor and trained these on cell lines to result in LogitDA and KNNDA, respectively. We show that LogitDA/KNNDA significantly outperforms existing methods, e.g., a logistic model and a deep learning method trained by thousands of genes, in prediction AUC (0.70-1.00 for seven of the ten drugs tested) and is interpretable. This may be due to the fact that sample sizes are often limited in the area of drug response prediction. We further derive a novel adjustment on the prediction cutoff for LogitDA to yield a prediction accuracy of 0.70-0.93 for seven drugs, including erlotinib and cetuximab, whose pathways relevant to anti-cancer therapies are also uncovered. These results indicate that our methods can efficiently translate cell-line-trained predictors into tumors.
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Affiliation(s)
- Shinsheng Yuan
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan
| | - Yen-Chou Chen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Chi-Hsuan Tsai
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Huei-Wen Chen
- College of Medicine, Graduate Institute of Toxicology, National Taiwan University, Taipei, Taiwan
| | - Grace S. Shieh
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan
- Data Science Degree Program, Academia Sinica and National Taiwan University, Taipei, Taiwan
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6
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Dou B, Zhu Z, Merkurjev E, Ke L, Chen L, Jiang J, Zhu Y, Liu J, Zhang B, Wei GW. Machine Learning Methods for Small Data Challenges in Molecular Science. Chem Rev 2023; 123:8736-8780. [PMID: 37384816 PMCID: PMC10999174 DOI: 10.1021/acs.chemrev.3c00189] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.
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Affiliation(s)
- Bozheng Dou
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Zailiang Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Ekaterina Merkurjev
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Lu Ke
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Long Chen
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jian Jiang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yueying Zhu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Jie Liu
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Bengong Zhang
- Research Center of Nonlinear Science, School of Mathematical and Physical Sciences,Wuhan Textile University, Wuhan 430200, P, R. China
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824, United States
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7
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Hostallero DE, Wei L, Wang L, Cairns J, Emad A. Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:535-550. [PMID: 36775056 PMCID: PMC10787192 DOI: 10.1016/j.gpb.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/28/2022] [Accepted: 01/31/2023] [Indexed: 02/12/2023]
Abstract
Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.
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Affiliation(s)
- David Earl Hostallero
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, QC H2S, Canada
| | - Lixuan Wei
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Liewei Wang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Junmei Cairns
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.
| | - Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, QC H2S, Canada; The Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, QC H3A, Canada.
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8
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Zheng Z, Chen J, Chen X, Huang L, Xie W, Lin Q, Li X, Wong K. Enabling Single-Cell Drug Response Annotations from Bulk RNA-Seq Using SCAD. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2204113. [PMID: 36762572 PMCID: PMC10104628 DOI: 10.1002/advs.202204113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 12/09/2022] [Indexed: 06/18/2023]
Abstract
The single-cell RNA sequencing (scRNA-seq) quantifies the gene expression of individual cells, while the bulk RNA sequencing (bulk RNA-seq) characterizes the mixed transcriptome of cells. The inference of drug sensitivities for individual cells can provide new insights to understand the mechanism of anti-cancer response heterogeneity and drug resistance at the cellular resolution. However, pharmacogenomic information related to their corresponding scRNA-Seq is often limited. Therefore, a transfer learning model is proposed to infer the drug sensitivities at single-cell level. This framework learns bulk transcriptome profiles and pharmacogenomics information from population cell lines in a large public dataset and transfers the knowledge to infer drug efficacy of individual cells. The results suggest that it is suitable to learn knowledge from pre-clinical cell lines to infer pre-existing cell subpopulations with different drug sensitivities prior to drug exposure. In addition, the model offers a new perspective on drug combinations. It is observed that drug-resistant subpopulation can be sensitive to other drugs (e.g., a subset of JHU006 is Vorinostat-resistant while Gefitinib-sensitive); such finding corroborates the previously reported drug combination (Gefitinib + Vorinostat) strategy in several cancer types. The identified drug sensitivity biomarkers reveal insights into the tumor heterogeneity and treatment at cellular resolution.
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Affiliation(s)
- Zetian Zheng
- Department of Computer ScienceCity University of Hong KongKowloonHong Kong
| | - Junyi Chen
- The Laboratory of Data Discovery for Health (D²4H), Hong Kong Science ParkNew TerritoriesHong Kong
| | - Xingjian Chen
- Department of Computer ScienceCity University of Hong KongKowloonHong Kong
| | - Lei Huang
- Department of Computer ScienceCity University of Hong KongKowloonHong Kong
| | - Weidun Xie
- Department of Computer ScienceCity University of Hong KongKowloonHong Kong
| | - Qiuzhen Lin
- College of Computer Science and Software Engineering, Shenzhen UniversityShenzhenChina
| | - Xiangtao Li
- School of Artificial IntelligenceJilin UniversityJilinChina
| | - Ka‐Chun Wong
- Department of Computer ScienceCity University of Hong KongKowloonHong Kong
- Shenzhen Research InstituteCity University of Hong KongShenzhenChina
- Hong Kong Institute for Data ScienceCity University of Hong KongKowloonHong Kong
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9
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Partin A, Brettin TS, Zhu Y, Narykov O, Clyde A, Overbeek J, Stevens RL. Deep learning methods for drug response prediction in cancer: Predominant and emerging trends. Front Med (Lausanne) 2023; 10:1086097. [PMID: 36873878 PMCID: PMC9975164 DOI: 10.3389/fmed.2023.1086097] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/23/2023] [Indexed: 02/17/2023] Open
Abstract
Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.
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Affiliation(s)
- Alexander Partin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Thomas S. Brettin
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Yitan Zhu
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Oleksandr Narykov
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Austin Clyde
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Jamie Overbeek
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
| | - Rick L. Stevens
- Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States
- Department of Computer Science, The University of Chicago, Chicago, IL, United States
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10
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Hosna A, Merry E, Gyalmo J, Alom Z, Aung Z, Azim MA. Transfer learning: a friendly introduction. JOURNAL OF BIG DATA 2022; 9:102. [PMID: 36313477 PMCID: PMC9589764 DOI: 10.1186/s40537-022-00652-w#sec5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 09/19/2022] [Indexed: 06/07/2024]
Abstract
Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.
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Affiliation(s)
- Asmaul Hosna
- Department of Computer Science, Asian University for Women, 20/A M. M. Ali Road, Chittogram, Bangladesh
| | - Ethel Merry
- Department of Computer Science, Asian University for Women, 20/A M. M. Ali Road, Chittogram, Bangladesh
| | - Jigmey Gyalmo
- Department of Computer Science, Asian University for Women, 20/A M. M. Ali Road, Chittogram, Bangladesh
| | - Zulfikar Alom
- Department of Computer Science, Asian University for Women, 20/A M. M. Ali Road, Chittogram, Bangladesh
| | - Zeyar Aung
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Mohammad Abdul Azim
- Department of Computer Science, Asian University for Women, 20/A M. M. Ali Road, Chittogram, Bangladesh
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11
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Hosna A, Merry E, Gyalmo J, Alom Z, Aung Z, Azim MA. Transfer learning: a friendly introduction. JOURNAL OF BIG DATA 2022; 9:102. [PMID: 36313477 PMCID: PMC9589764 DOI: 10.1186/s40537-022-00652-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 09/19/2022] [Indexed: 05/28/2023]
Abstract
Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.
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Affiliation(s)
- Asmaul Hosna
- Department of Computer Science, Asian University for Women, 20/A M. M. Ali Road, Chittogram, Bangladesh
| | - Ethel Merry
- Department of Computer Science, Asian University for Women, 20/A M. M. Ali Road, Chittogram, Bangladesh
| | - Jigmey Gyalmo
- Department of Computer Science, Asian University for Women, 20/A M. M. Ali Road, Chittogram, Bangladesh
| | - Zulfikar Alom
- Department of Computer Science, Asian University for Women, 20/A M. M. Ali Road, Chittogram, Bangladesh
| | - Zeyar Aung
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Mohammad Abdul Azim
- Department of Computer Science, Asian University for Women, 20/A M. M. Ali Road, Chittogram, Bangladesh
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12
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Yingtaweesittikul H, Wu J, Mongia A, Peres R, Ko K, Nagarajan N, Suphavilai C. CREAMMIST: an integrative probabilistic database for cancer drug response prediction. Nucleic Acids Res 2022; 51:D1242-D1248. [PMID: 36259664 PMCID: PMC9825458 DOI: 10.1093/nar/gkac911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/18/2022] [Accepted: 10/11/2022] [Indexed: 01/30/2023] Open
Abstract
Extensive in vitro cancer drug screening datasets have enabled scientists to identify biomarkers and develop machine learning models for predicting drug sensitivity. While most advancements have focused on omics profiles, cancer drug sensitivity scores precalculated by the original sources are often used as-is, without consideration for variabilities between studies. It is well-known that significant inconsistencies exist between the drug sensitivity scores across datasets due to differences in experimental setups and preprocessing methods used to obtain the sensitivity scores. As a result, many studies opt to focus only on a single dataset, leading to underutilization of available data and a limited interpretation of cancer pharmacogenomics analysis. To overcome these caveats, we have developed CREAMMIST (https://creammist.mtms.dev), an integrative database that enables users to obtain an integrative dose-response curve, to capture uncertainty (or high certainty when multiple datasets well align) across five widely used cancer cell-line drug-response datasets. We utilized the Bayesian framework to systematically integrate all available dose-response values across datasets (>14 millions dose-response data points). CREAMMIST provides easy-to-use statistics derived from the integrative dose-response curves for various downstream analyses such as identifying biomarkers, selecting drug concentrations for experiments, and training robust machine learning models.
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Affiliation(s)
| | - Jiaxi Wu
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Aanchal Mongia
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Rafael Peres
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | - Karrie Ko
- Genome Institute of Singapore, A*STAR, Singapore, Singapore
| | | | - Chayaporn Suphavilai
- To whom correspondence should be addressed. Tel: +65 86213683; Fax: +65 68088292;
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13
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Identification of phenocopies improves prediction of targeted therapy response over DNA mutations alone. NPJ Genom Med 2022; 7:58. [PMID: 36253482 PMCID: PMC9576758 DOI: 10.1038/s41525-022-00328-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 09/29/2022] [Indexed: 11/09/2022] Open
Abstract
DNA mutations in specific genes can confer preferential benefit from drugs targeting those genes. However, other molecular perturbations can “phenocopy” pathogenic mutations, but would not be identified using standard clinical sequencing, leading to missed opportunities for other patients to benefit from targeted treatments. We hypothesized that RNA phenocopy signatures of key cancer driver gene mutations could improve our ability to predict response to targeted therapies, despite not being directly trained on drug response. To test this, we built gene expression signatures in tissue samples for specific mutations and found that phenocopy signatures broadly increased accuracy of drug response predictions in-vitro compared to DNA mutation alone, and identified additional cancer cell lines that respond well with a positive/negative predictive value on par or better than DNA mutations. We further validated our results across four clinical cohorts. Our results suggest that routine RNA sequencing of tumors to identify phenocopies in addition to standard targeted DNA sequencing would improve our ability to accurately select patients for targeted therapies in the clinic.
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14
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Trastulla L, Noorbakhsh J, Vazquez F, McFarland J, Iorio F. Computational estimation of quality and clinical relevance of cancer cell lines. Mol Syst Biol 2022; 18:e11017. [PMID: 35822563 PMCID: PMC9277610 DOI: 10.15252/msb.202211017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 12/12/2022] Open
Abstract
Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene-drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine-learning-based directions that could resolve some of the arising discrepancies.
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Affiliation(s)
| | - Javad Noorbakhsh
- Broad Institute of MIT and HarvardCambridgeMAUSA
- Present address:
Kojin TherapeuticsBostonMAUSA
| | - Francisca Vazquez
- Broad Institute of MIT and HarvardCambridgeMAUSA
- Department of Medical OncologyDana‐Farber Cancer InstituteBostonMAUSA
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15
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Kong J, Ha D, Lee J, Kim I, Park M, Im SH, Shin K, Kim S. Network-based machine learning approach to predict immunotherapy response in cancer patients. Nat Commun 2022; 13:3703. [PMID: 35764641 PMCID: PMC9240063 DOI: 10.1038/s41467-022-31535-6] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 06/22/2022] [Indexed: 11/08/2022] Open
Abstract
Immune checkpoint inhibitors (ICIs) have substantially improved the survival of cancer patients over the past several years. However, only a minority of patients respond to ICI treatment (~30% in solid tumors), and current ICI-response-associated biomarkers often fail to predict the ICI treatment response. Here, we present a machine learning (ML) framework that leverages network-based analyses to identify ICI treatment biomarkers (NetBio) that can make robust predictions. We curate more than 700 ICI-treated patient samples with clinical outcomes and transcriptomic data, and observe that NetBio-based predictions accurately predict ICI treatment responses in three different cancer types-melanoma, gastric cancer, and bladder cancer. Moreover, the NetBio-based prediction is superior to predictions based on other conventional ICI treatment biomarkers, such as ICI targets or tumor microenvironment-associated markers. This work presents a network-based method to effectively select immunotherapy-response-associated biomarkers that can make robust ML-based predictions for precision oncology.
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Affiliation(s)
- JungHo Kong
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 37673, Korea
| | - Doyeon Ha
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 37673, Korea
| | - Juhun Lee
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 37673, Korea
| | - Inhae Kim
- ImmunoBiome Inc., Pohang, 37666, Korea
| | - Minhyuk Park
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 37673, Korea
| | - Sin-Hyeog Im
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 37673, Korea
- ImmunoBiome Inc., Pohang, 37666, Korea
- Institute of Convergence Science, Yonsei University, Seoul, 03722, Korea
| | - Kunyoo Shin
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 37673, Korea
- Institute of Convergence Science, Yonsei University, Seoul, 03722, Korea
| | - Sanguk Kim
- Department of Life Sciences, Pohang University of Science and Technology, Pohang, 37673, Korea.
- Institute of Convergence Science, Yonsei University, Seoul, 03722, Korea.
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16
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Kowald A, Barrantes I, Möller S, Palmer D, Murua Escobar H, Schwerk A, Fuellen G. Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives. Brief Bioinform 2022; 23:6572661. [PMID: 35453145 PMCID: PMC9116218 DOI: 10.1093/bib/bbac133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 02/16/2022] [Accepted: 03/21/2022] [Indexed: 01/14/2023] Open
Abstract
Accurate transfer learning of clinical outcomes from one cellular context to another, between cell types, developmental stages, omics modalities or species, is considered tremendously useful. When transferring a prediction task from a source domain to a target domain, what counts is the high quality of the predictions in the target domain, requiring states or processes common to both the source and the target that can be learned by the predictor reflected by shared denominators. These may form a compendium of knowledge that is learned in the source to enable predictions in the target, usually with few, if any, labeled target training samples to learn from. Transductive transfer learning refers to the learning of the predictor in the source domain, transferring its outcome label calculations to the target domain, considering the same task. Inductive transfer learning considers cases where the target predictor is performing a different yet related task as compared with the source predictor. Often, there is also a need to first map the variables in the input/feature spaces and/or the variables in the output/outcome spaces. We here discuss and juxtapose various recently published transfer learning approaches, specifically designed (or at least adaptable) to predict clinical (human in vivo) outcomes based on preclinical (mostly animal-based) molecular data, towards finding the right tool for a given task, and paving the way for a comprehensive and systematic comparison of the suitability and accuracy of transfer learning of clinical outcomes.
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Affiliation(s)
- Axel Kowald
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Israel Barrantes
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Steffen Möller
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Daniel Palmer
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Hugo Murua Escobar
- Department of Medicine, Clinic III, Hematology, Oncology, Palliative Medicine, Rostock University Medical Center, Rostock, Germany
| | | | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany.,Centre for Transdisciplinary Neurosciences Rostock, Research Focus Oncology and Ageing of Individuals and Society, Interdisciplinary Faculty, Rostock, Germany
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17
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Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:7842566. [PMID: 35434134 PMCID: PMC9010213 DOI: 10.1155/2022/7842566] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 01/29/2022] [Accepted: 03/06/2022] [Indexed: 02/07/2023]
Abstract
Purpose Artificial intelligence (AI) techniques are used in precision medicine to explore novel genotypes and phenotypes data. The main aims of precision medicine include early diagnosis, screening, and personalized treatment regime for a patient based on genetic-oriented features and characteristics. The main objective of this study was to review AI techniques and their effectiveness in neoplasm precision medicine. Materials and Methods A comprehensive search was performed in Medline (through PubMed), Scopus, ISI Web of Science, IEEE Xplore, Embase, and Cochrane databases from inception to December 29, 2021, in order to identify the studies that used AI methods for cancer precision medicine and evaluate outcomes of the models. Results Sixty-three studies were included in this systematic review. The main AI approaches in 17 papers (26.9%) were linear and nonlinear categories (random forest or decision trees), and in 21 citations, rule-based systems and deep learning models were used. Notably, 62% of the articles were done in the United States and China. R package was the most frequent software, and breast and lung cancer were the most selected neoplasms in the papers. Out of 63 papers, in 34 articles, genomic data like gene expression, somatic mutation data, phenotype data, and proteomics with drug-response which is functional data was used as input in AI methods; in 16 papers' (25.3%) drug response, functional data was utilized in personalization of treatment. The maximum values of the assessment indicators such as accuracy, sensitivity, specificity, precision, recall, and area under the curve (AUC) in included studies were 0.99, 1.00, 0.96, 0.98, 0.99, and 0.9929, respectively. Conclusion The findings showed that in many cases, the use of artificial intelligence methods had effective application in personalized medicine.
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18
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Mourragui SMC, Loog M, Vis DJ, Moore K, Manjon AG, van de Wiel MA, Reinders MJT, Wessels LFA. Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning. Proc Natl Acad Sci U S A 2021; 118:e2106682118. [PMID: 34873056 PMCID: PMC8670522 DOI: 10.1073/pnas.2106682118] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2021] [Indexed: 12/13/2022] Open
Abstract
Preclinical models have been the workhorse of cancer research, producing massive amounts of drug response data. Unfortunately, translating response biomarkers derived from these datasets to human tumors has proven to be particularly challenging. To address this challenge, we developed TRANSACT, a computational framework that builds a consensus space to capture biological processes common to preclinical models and human tumors and exploits this space to construct drug response predictors that robustly transfer from preclinical models to human tumors. TRANSACT performs favorably compared to four competing approaches, including two deep learning approaches, on a set of 23 drug prediction challenges on The Cancer Genome Atlas and 226 metastatic tumors from the Hartwig Medical Foundation. We demonstrate that response predictions deliver a robust performance for a number of therapies of high clinical importance: platinum-based chemotherapies, gemcitabine, and paclitaxel. In contrast to other approaches, we demonstrate the interpretability of the TRANSACT predictors by correctly identifying known biomarkers of targeted therapies, and we propose potential mechanisms that mediate the resistance to two chemotherapeutic agents.
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Affiliation(s)
- Soufiane M C Mourragui
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands
| | - Marco Loog
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands
- Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark
| | - Daniel J Vis
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Kat Moore
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Anna G Manjon
- Division of Cell Biology, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands
| | - Mark A van de Wiel
- Epidemiology and Biostatistics, Amsterdam University Medical Center, 1105 AZ Amsterdam, The Netherlands
- Medical Research Council Biostatistics Unit, Cambridge University, Cambridge CB2 0SR, United Kingdom
| | - Marcel J T Reinders
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands;
- Leiden Computational Biology Center, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
| | - Lodewyk F A Wessels
- Division of Molecular Carcinogenesis, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, The Netherlands;
- Department of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 XE Delft, The Netherlands
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19
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Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00408-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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20
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Rydzewski NR, Peterson E, Lang JM, Yu M, Laura Chang S, Sjöström M, Bakhtiar H, Song G, Helzer KT, Bootsma ML, Chen WS, Shrestha RM, Zhang M, Quigley DA, Aggarwal R, Small EJ, Wahl DR, Feng FY, Zhao SG. Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures. NPJ Genom Med 2021; 6:76. [PMID: 34548481 PMCID: PMC8455625 DOI: 10.1038/s41525-021-00239-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/23/2021] [Indexed: 12/14/2022] Open
Abstract
We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.
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Affiliation(s)
| | - Erik Peterson
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Joshua M Lang
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Menggang Yu
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
| | - S Laura Chang
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
| | - Martin Sjöström
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
| | - Hamza Bakhtiar
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Gefei Song
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Kyle T Helzer
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Matthew L Bootsma
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - William S Chen
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
| | | | - Meng Zhang
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
| | - David A Quigley
- Helen Diller Family Comprehensive Cancer Center, UCSF, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA
| | - Rahul Aggarwal
- Helen Diller Family Comprehensive Cancer Center, UCSF, San Francisco, CA, USA
- Division of Hematology and Oncology, Department of Medicine, UCSF, San Francisco, CA, USA
| | - Eric J Small
- Helen Diller Family Comprehensive Cancer Center, UCSF, San Francisco, CA, USA
- Division of Hematology and Oncology, Department of Medicine, UCSF, San Francisco, CA, USA
| | - Daniel R Wahl
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
| | - Felix Y Feng
- Department of Radiation Oncology, UCSF, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, UCSF, San Francisco, CA, USA
- Division of Hematology and Oncology, Department of Medicine, UCSF, San Francisco, CA, USA
- Department of Urology, UCSF, San Francisco, CA, USA
| | - Shuang G Zhao
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA.
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA.
- William S. Middleton Memorial Veterans Hospital, Madison, WI, USA.
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21
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Sharifi-Noghabi H, Jahangiri-Tazehkand S, Smirnov P, Hon C, Mammoliti A, Nair SK, Mer AS, Ester M, Haibe-Kains B. Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models. Brief Bioinform 2021; 22:6348324. [PMID: 34382071 DOI: 10.1093/bib/bbab294] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/29/2021] [Accepted: 07/10/2021] [Indexed: 11/13/2022] Open
Abstract
The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. The application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.
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Affiliation(s)
- Hossein Sharifi-Noghabi
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.,Vancouver Prostate Center, Vancouver, British Columbia, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Soheil Jahangiri-Tazehkand
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Petr Smirnov
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Casey Hon
- Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Anthony Mammoliti
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | | | - Arvind Singh Mer
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
| | - Martin Ester
- School of Computing Science, Simon Fraser University, Burnaby, British Columbia, Canada.,Vancouver Prostate Center, Vancouver, British Columbia, Canada
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Princess Margaret Cancer Centre, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada.,University of Toronto, Toronto, Ontario, Canada
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22
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Peres da Silva R, Suphavilai C, Nagarajan N. TUGDA: task uncertainty guided domain adaptation for robust generalization of cancer drug response prediction from in vitro to in vivo settings. Bioinformatics 2021; 37:i76-i83. [PMID: 34000002 PMCID: PMC8275325 DOI: 10.1093/bioinformatics/btab299] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/27/2021] [Indexed: 11/21/2022] Open
Abstract
MOTIVATION Large-scale cancer omics studies have highlighted the diversity of patient molecular profiles and the importance of leveraging this information to deliver the right drug to the right patient at the right time. Key challenges in learning predictive models for this include the high-dimensionality of omics data and heterogeneity in biological and clinical factors affecting patient response. The use of multi-task learning techniques has been widely explored to address dataset limitations for in vitro drug response models, while domain adaptation (DA) has been employed to extend them to predict in vivo response. In both of these transfer learning settings, noisy data for some tasks (or domains) can substantially reduce the performance for others compared to single-task (domain) learners, i.e. lead to negative transfer (NT). RESULTS We describe a novel multi-task unsupervised DA method (TUGDA) that addresses these limitations in a unified framework by quantifying uncertainty in predictors and weighting their influence on shared feature representations. TUGDA's ability to rely more on predictors with low-uncertainty allowed it to notably reduce cases of NT for in vitro models (94% overall) compared to state-of-the-art methods. For DA to in vivo settings, TUGDA improved over previous methods for patient-derived xenografts (9 out of 14 drugs) as well as patient datasets (significant associations in 9 out of 22 drugs). TUGDA's ability to avoid NT thus provides a key capability as we try to integrate diverse drug-response datasets to build consistent predictive models with in vivo utility. AVAILABILITYAND IMPLEMENTATION https://github.com/CSB5/TUGDA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rafael Peres da Silva
- School of Computing, National University of Singapore, 117417 Singapore, Singapore.,Genome Institute of Singapore, A*STAR, 138672 Singapore, Singapore
| | | | - Niranjan Nagarajan
- School of Computing, National University of Singapore, 117417 Singapore, Singapore.,Genome Institute of Singapore, A*STAR, 138672 Singapore, Singapore.,Yong Loo Lin School of Medicine, National University of Singapore, 119228 Singapore, Singapore
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23
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Park S, Soh J, Lee H. Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data. BMC Bioinformatics 2021; 22:269. [PMID: 34034645 PMCID: PMC8152321 DOI: 10.1186/s12859-021-04146-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 04/22/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Predicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data tends to hinder performance improvement. In this study, we aimed to develop a new method, which can effectively reduce the large dimension of data, based on the supervised deep learning model for predicting drug response. RESULTS We proposed a novel method called Supervised Feature Extraction Learning using Triplet loss (Super.FELT) for drug response prediction. Super.FELT consists of three stages, namely, feature selection, feature encoding using a supervised method, and binary classification of drug response (sensitive or resistant). We used multi-omics data including mutation, copy number aberration, and gene expression, and these were obtained from cell lines [Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), and Cancer Therapeutics Response Portal (CTRP)], patient-derived tumor xenografts (PDX), and The Cancer Genome Atlas (TCGA). GDSC was used for training and cross-validation tests, and CCLE, CTRP, PDX, and TCGA were used for external validation. We performed ablation studies for the three stages and verified that the use of multi-omics data guarantees better performance of drug response prediction. Our results verified that Super.FELT outperformed the other methods at external validation on PDX and TCGA and was good at cross-validation on GDSC and external validation on CCLE and CTRP. In addition, through our experiments, we confirmed that using multi-omics data is useful for external non-cell line data. CONCLUSION By separating the three stages, Super.FELT achieved better performance than the other methods. Through our results, we found that it is important to train encoders and a classifier independently, especially for external test on PDX and TCGA. Moreover, although gene expression is the most powerful data on cell line data, multi-omics promises better performance for external validation on non-cell line data than gene expression data. Source codes of Super.FELT are available at https://github.com/DMCB-GIST/Super.FELT .
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Affiliation(s)
- Sejin Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Jihee Soh
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Hyunju Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea.
- Graduate School of Artificial Intelligence, Gwangju Institute of Science and Technology, Gwangju, South Korea.
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24
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Born J, Manica M, Oskooei A, Cadow J, Markert G, Rodríguez Martínez M. PaccMann RL: De novo generation of hit-like anticancer molecules from transcriptomic data via reinforcement learning. iScience 2021; 24:102269. [PMID: 33851095 PMCID: PMC8022157 DOI: 10.1016/j.isci.2021.102269] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 01/11/2021] [Accepted: 03/01/2021] [Indexed: 02/07/2023] Open
Abstract
With the advent of deep generative models in computational chemistry, in-silico drug design is undergoing an unprecedented transformation. Although deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the cellular environment of target diseases. Bridging systems biology and drug design, we present a reinforcement learning method for de novo molecular design from gene expression profiles. We construct a hybrid Variational Autoencoder that tailors molecules to target-specific transcriptomic profiles, using an anticancer drug sensitivity prediction model (PaccMann) as reward function. Without incorporating information about anticancer drugs, the molecule generation is biased toward compounds with high predicted efficacy against cell lines or cancer types. The generation can be further refined by subsidiary constraints such as toxicity. Our cancer-type-specific candidate drugs are similar to cancer drugs in drug-likeness, synthesizability, and solubility and frequently exhibit the highest structural similarity to compounds with known efficacy against these cancer types.
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Affiliation(s)
- Jannis Born
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | | | - Ali Oskooei
- IBM Research Europe, 8803 Rüschlikon, Switzerland
| | - Joris Cadow
- IBM Research Europe, 8803 Rüschlikon, Switzerland
| | - Greta Markert
- IBM Research Europe, 8803 Rüschlikon, Switzerland
- Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zürich, Switzerland
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