Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method

Huang, Haihui; Dai, Jingguo; Liang, Yong

doi:10.1159/000495826

Cited by 12 publications

(8 citation statements)

References 46 publications

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“…However, due to the technical and ethical challenges of screening individuals against many drugs [5], such models are either trained for only a handful of drugs [6] or are trained using preclinical samples such as 2D cancer cell line cultures (CCLs) [7][8][9][10]. In spite of the success of these methods in predicting the drug response of left-out preclinical samples using models trained on preclinical samples, they have had limited success in predicting the CDR of real patients [9,11], with some exceptions [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

et al. 2020

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Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and molecular profiles obtained prior to administration of the drug, can play a significant role in individualized medicine. Machine learning models have the potential to address this issue but training them requires data from a large number of patients treated with each drug, limiting their feasibility. While large databases of drug response and molecular profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear whether preclinical samples can be used to predict CDR of real patients. We designed a systematic approach to evaluate how well different algorithms, trained on gene expression and drug response of CCLs, can predict CDR of patients. Using data from two large databases, we evaluated various linear and non-linear algorithms, some of which utilized information on gene interactions. Then, we developed a new algorithm called TG-LASSO that explicitly integrates information on samples' tissue of origin with gene expression profiles to improve prediction performance. Our results showed that regularized regression methods provide better prediction performance. However, including the network information or common methods of including information on the tissue of origin did not improve the results. On the other hand, TG-LASSO improved the predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. Additionally, TG-LASSO identified genes associated with the drug response, including known targets and pathways involved in the drugs' mechanism of action. Moreover, genes identified by TG-LASSO for multiple drugs in a tissue were associated with patient survival. In summary, our analysis suggests that preclinical samples can be used to predict CDR of patients and identify biomarkers of drug sensitivity and survival.

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Section: Introductionmentioning

confidence: 99%

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

et al. 2020

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“…However, due to the technical and ethical challenges of screening individuals against many drugs [5], such models are either trained for only a handful of drugs [6] or are trained using preclinical samples such as 2D cancer cell line cultures (CCLs) [7–10]. In spite of the success of these methods in predicting the drug response of left-out preclinical samples using models trained on preclinical samples, they have had limited success in predicting the CDR of real patients [9, 11], with some exceptions [12–14].…”

Section: Introductionmentioning

confidence: 99%

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Huang

Bhope

Lim

et al. 2019

Preprint

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1Prediction of clinical drug response (CDR) of cancer patients, based on their clinical and 2 molecular profiles obtained prior to administration of the drug, can play a significant role 3 in individualized medicine. Machine learning models have the potential to address this 4 issue, but training them requires data from a large number of patients treated with each 5 drug, limiting their feasibility. While large databases of drug response and molecular 6 profiles of preclinical in-vitro cancer cell lines (CCLs) exist for many drugs, it is unclear 7 whether preclinical samples can be used to predict CDR of real patients. 8 9We designed a systematic approach to evaluate how well different algorithms, trained on 10 gene expression and drug response of CCLs, can predict CDR of patients. Using data from 11 two large databases, we evaluated various linear and non-linear algorithms, some of 12 which utilized information on gene interactions. Then, we developed a new algorithm 13 called TG-LASSO that explicitly integrates information on samples' tissue of origin with 14 gene expression profiles to improve prediction performance. Our results showed that 15 regularized regression methods provide significantly accurate prediction. However, 16including the network information or common methods of including information on the 17 tissue of origin did not improve the results. On the other hand, TG-LASSO improved the 18 predictions and distinguished resistant and sensitive patients for 7 out of 13 drugs. 19Additionally, TG-LASSO identified genes associated with the drug response, including 20 known targets and pathways involved in the drugs' mechanism of action. Moreover, 21 utilizes a new approach for explicitly incorporating the tissue of origin of samples in the 43 prediction task. Our results show that TG-LASSO outperforms all other algorithms and can 44 accurately distinguish resistant and sensitive patients for the majority of the tested drugs. 45Follow-up analysis reveal that this method can also identify biomarkers of drug sensitivity 46 in each cancer type. 47

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“…At this stage, statements about the current performance of ML-based DRP methods overall are difficult to make. In the clinic, molecular patient data are already an established input for treatment decisions [ 152 ], and the potential of ML-based DRP models to predict tumoral responses to anticancer drugs has been established [ 30 , 44 , 46 , 58 , 67 , 71 , 101 ]. However, ML-based DRP seems to be rarely used in clinical practice.…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…Huang et al. [ 58 ] proposed a logistic regression model that is additionally constrained based on biomolecular networks.…”

Section: Drp Modelsmentioning

confidence: 99%

An overview of machine learning methods for monotherapy drug response prediction

Firoozbakht

Yousefi

Schwikowski

2021

Briefings in Bioinformatics

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For an increasing number of preclinical samples, both detailed molecular profiles and their responses to various drugs are becoming available. Efforts to understand, and predict, drug responses in a data-driven manner have led to a proliferation of machine learning (ML) methods, with the longer term ambition of predicting clinical drug responses. Here, we provide a uniquely wide and deep systematic review of the rapidly evolving literature on monotherapy drug response prediction, with a systematic characterization and classification that comprises more than 70 ML methods in 13 subclasses, their input and output data types, modes of evaluation, and code and software availability. ML experts are provided with a fundamental understanding of the biological problem, and how ML methods are configured for it. Biologists and biomedical researchers are introduced to the basic principles of applicable ML methods, and their application to the problem of drug response prediction. We also provide systematic overviews of commonly used data sources used for training and evaluation methods.

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Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method

Cited by 12 publications

References 46 publications

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

Tissue-guided LASSO for prediction of clinical drug response using preclinical samples

An overview of machine learning methods for monotherapy drug response prediction

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