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

Huang, Edward W.; Bhope, Ameya; Lim, Jing; Sinha, Saurabh; Emad, Amin

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https://hdl.handle.net/2142/106056 Copy

Description

Title

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

Author(s)

• Huang, Edward W.
• Bhope, Ameya
• Lim, Jing
• Sinha, Saurabh
• Emad, Amin

Issue Date

2020-01-22

Keyword(s)

• Gene expression
• Drug administration
• Drug information
• Algorithms
• Machine learning algorithms
• Routes of administration
• Drug screening
• Forecasting

Date of Ingest

2020-01-23T16:36:36Z

Abstract

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.

Publisher

PLoS

Series/Report Name or Number

PLoS Computational Biology; vol. 16, no. 1, 2020

Type of Resource

text

Genre of Resource

Article

Language

en

Permalink

http://hdl.handle.net/2142/106056

DOI

https://doi.org/10.1371/journal.pcbi.1007607

Copyright and License Information

Copyright 2020 Edward W. Huang, Ameya Bhope, Jing Lim, Saurabh Sinha, and Amin Emad

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