Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine

Hojin Moon; Evan Lee

doi:doi:10.11648/j.ijdsa.20210703.12

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Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine

Hojin Moon, Evan Lee

Published in International Journal of Data Science and Analysis (Volume 7, Issue 3)

Received: 1 February 2021 Accepted: 8 February 2021 Published: 8 May 2021

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Abstract

Surgical resection only remains the standard choice for the treatment of early-stage non-small cell lung cancer (NSCLC) patients. Preliminary studies suggest that the application of adjuvant chemotherapy with surgery (ACT) is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. However, at an individual level, not all patients may benefit from ACT. Given the well-known adverse effects and toxicity of ACT, finding the patients that are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to develop a statistical decision support algorithm to find predictive genomic biomarkers and identify subgroups of patients who benefit from ACT. Cox regression models are trained using a randomized controlled trial gene expression data from the National Center for Biotechnology Information (NCBI) utilizing explicit treatment interaction terms. To handle high dimensions inherent in gene expression data, a regularized Cox regression model with lasso penalty is applied to find the most significant interacting markers. Risk scores are estimated from the proposed model and are used to stratify patients into a high risk or low risk group respective to ACT treatment. After applying the model to an independent validation genomic data set, we show that patients who underwent the recommended treatment according to their risk group estimated by our proposed algorithm exhibit a slightly higher survival rate than those who do not.

Published in	International Journal of Data Science and Analysis (Volume 7, Issue 3)
DOI	10.11648/j.ijdsa.20210703.12
Page(s)	60-68
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Decision Support Algorithm, Genomic Markers, Regularized Cox Model, Subgroup Analysis, Survival Analysis

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APA Style

Hojin Moon, Evan Lee. (2021). Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine. International Journal of Data Science and Analysis, 7(3), 60-68. https://doi.org/10.11648/j.ijdsa.20210703.12

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ACS Style

Hojin Moon; Evan Lee. Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine. Int. J. Data Sci. Anal. 2021, 7(3), 60-68. doi: 10.11648/j.ijdsa.20210703.12

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AMA Style

Hojin Moon, Evan Lee. Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine. Int J Data Sci Anal. 2021;7(3):60-68. doi: 10.11648/j.ijdsa.20210703.12

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@article{10.11648/j.ijdsa.20210703.12,
  author = {Hojin Moon and Evan Lee},
  title = {Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {3},
  pages = {60-68},
  doi = {10.11648/j.ijdsa.20210703.12},
  url = {https://doi.org/10.11648/j.ijdsa.20210703.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210703.12},
  abstract = {Surgical resection only remains the standard choice for the treatment of early-stage non-small cell lung cancer (NSCLC) patients. Preliminary studies suggest that the application of adjuvant chemotherapy with surgery (ACT) is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. However, at an individual level, not all patients may benefit from ACT. Given the well-known adverse effects and toxicity of ACT, finding the patients that are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to develop a statistical decision support algorithm to find predictive genomic biomarkers and identify subgroups of patients who benefit from ACT. Cox regression models are trained using a randomized controlled trial gene expression data from the National Center for Biotechnology Information (NCBI) utilizing explicit treatment interaction terms. To handle high dimensions inherent in gene expression data, a regularized Cox regression model with lasso penalty is applied to find the most significant interacting markers. Risk scores are estimated from the proposed model and are used to stratify patients into a high risk or low risk group respective to ACT treatment. After applying the model to an independent validation genomic data set, we show that patients who underwent the recommended treatment according to their risk group estimated by our proposed algorithm exhibit a slightly higher survival rate than those who do not.},
 year = {2021}
}

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TY  - JOUR
T1  - Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine
AU  - Hojin Moon
AU  - Evan Lee
Y1  - 2021/05/08
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210703.12
DO  - 10.11648/j.ijdsa.20210703.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 60
EP  - 68
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210703.12
AB  - Surgical resection only remains the standard choice for the treatment of early-stage non-small cell lung cancer (NSCLC) patients. Preliminary studies suggest that the application of adjuvant chemotherapy with surgery (ACT) is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. However, at an individual level, not all patients may benefit from ACT. Given the well-known adverse effects and toxicity of ACT, finding the patients that are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to develop a statistical decision support algorithm to find predictive genomic biomarkers and identify subgroups of patients who benefit from ACT. Cox regression models are trained using a randomized controlled trial gene expression data from the National Center for Biotechnology Information (NCBI) utilizing explicit treatment interaction terms. To handle high dimensions inherent in gene expression data, a regularized Cox regression model with lasso penalty is applied to find the most significant interacting markers. Risk scores are estimated from the proposed model and are used to stratify patients into a high risk or low risk group respective to ACT treatment. After applying the model to an independent validation genomic data set, we show that patients who underwent the recommended treatment according to their risk group estimated by our proposed algorithm exhibit a slightly higher survival rate than those who do not.
VL  - 7
IS  - 3
ER  -

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Author Information

Hojin Moon

Department of Mathematics and Statistics, California State University, Long Beach, California, the United States
Evan Lee

Yale University, New Haven, Connecticut, the United States

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APA Style

Hojin Moon, Evan Lee. (2021). Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine. International Journal of Data Science and Analysis, 7(3), 60-68. https://doi.org/10.11648/j.ijdsa.20210703.12

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ACS Style

Hojin Moon; Evan Lee. Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine. Int. J. Data Sci. Anal. 2021, 7(3), 60-68. doi: 10.11648/j.ijdsa.20210703.12

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AMA Style

Hojin Moon, Evan Lee. Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine. Int J Data Sci Anal. 2021;7(3):60-68. doi: 10.11648/j.ijdsa.20210703.12

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@article{10.11648/j.ijdsa.20210703.12,
  author = {Hojin Moon and Evan Lee},
  title = {Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine},
  journal = {International Journal of Data Science and Analysis},
  volume = {7},
  number = {3},
  pages = {60-68},
  doi = {10.11648/j.ijdsa.20210703.12},
  url = {https://doi.org/10.11648/j.ijdsa.20210703.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20210703.12},
  abstract = {Surgical resection only remains the standard choice for the treatment of early-stage non-small cell lung cancer (NSCLC) patients. Preliminary studies suggest that the application of adjuvant chemotherapy with surgery (ACT) is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. However, at an individual level, not all patients may benefit from ACT. Given the well-known adverse effects and toxicity of ACT, finding the patients that are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to develop a statistical decision support algorithm to find predictive genomic biomarkers and identify subgroups of patients who benefit from ACT. Cox regression models are trained using a randomized controlled trial gene expression data from the National Center for Biotechnology Information (NCBI) utilizing explicit treatment interaction terms. To handle high dimensions inherent in gene expression data, a regularized Cox regression model with lasso penalty is applied to find the most significant interacting markers. Risk scores are estimated from the proposed model and are used to stratify patients into a high risk or low risk group respective to ACT treatment. After applying the model to an independent validation genomic data set, we show that patients who underwent the recommended treatment according to their risk group estimated by our proposed algorithm exhibit a slightly higher survival rate than those who do not.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine
AU  - Hojin Moon
AU  - Evan Lee
Y1  - 2021/05/08
PY  - 2021
N1  - https://doi.org/10.11648/j.ijdsa.20210703.12
DO  - 10.11648/j.ijdsa.20210703.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 60
EP  - 68
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20210703.12
AB  - Surgical resection only remains the standard choice for the treatment of early-stage non-small cell lung cancer (NSCLC) patients. Preliminary studies suggest that the application of adjuvant chemotherapy with surgery (ACT) is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. However, at an individual level, not all patients may benefit from ACT. Given the well-known adverse effects and toxicity of ACT, finding the patients that are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to develop a statistical decision support algorithm to find predictive genomic biomarkers and identify subgroups of patients who benefit from ACT. Cox regression models are trained using a randomized controlled trial gene expression data from the National Center for Biotechnology Information (NCBI) utilizing explicit treatment interaction terms. To handle high dimensions inherent in gene expression data, a regularized Cox regression model with lasso penalty is applied to find the most significant interacting markers. Risk scores are estimated from the proposed model and are used to stratify patients into a high risk or low risk group respective to ACT treatment. After applying the model to an independent validation genomic data set, we show that patients who underwent the recommended treatment according to their risk group estimated by our proposed algorithm exhibit a slightly higher survival rate than those who do not.
VL  - 7
IS  - 3
ER  -

Copy | Download