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Network-Based Matching of Patients and Targeted Therapies for Precision Oncology
The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible – allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitiv...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7301202/ https://www.ncbi.nlm.nih.gov/pubmed/31797633 |
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author | Liu, Qingzhi Ha, Min Jin Bhattacharyya, Rupam Garmire, Lana Baladandayuthapani, Veerabhadran |
author_facet | Liu, Qingzhi Ha, Min Jin Bhattacharyya, Rupam Garmire, Lana Baladandayuthapani, Veerabhadran |
author_sort | Liu, Qingzhi |
collection | PubMed |
description | The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible – allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients’ responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the “missing link” between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies. |
format | Online Article Text |
id | pubmed-7301202 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73012022020-06-18 Network-Based Matching of Patients and Targeted Therapies for Precision Oncology Liu, Qingzhi Ha, Min Jin Bhattacharyya, Rupam Garmire, Lana Baladandayuthapani, Veerabhadran Pac Symp Biocomput Article The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible – allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients’ responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the “missing link” between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies. 2020 /pmc/articles/PMC7301202/ /pubmed/31797633 Text en http://creativecommons.org/licenses/by-nc/4.0/ Open Access chapter published by World Scientific Publishing Company and distributed under the terms of the Creative Commons Attribution Non-Commercial (CC BY-NC) 4.0 License. |
spellingShingle | Article Liu, Qingzhi Ha, Min Jin Bhattacharyya, Rupam Garmire, Lana Baladandayuthapani, Veerabhadran Network-Based Matching of Patients and Targeted Therapies for Precision Oncology |
title | Network-Based Matching of Patients and Targeted Therapies for Precision Oncology |
title_full | Network-Based Matching of Patients and Targeted Therapies for Precision Oncology |
title_fullStr | Network-Based Matching of Patients and Targeted Therapies for Precision Oncology |
title_full_unstemmed | Network-Based Matching of Patients and Targeted Therapies for Precision Oncology |
title_short | Network-Based Matching of Patients and Targeted Therapies for Precision Oncology |
title_sort | network-based matching of patients and targeted therapies for precision oncology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7301202/ https://www.ncbi.nlm.nih.gov/pubmed/31797633 |
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