Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma

Xiaoyong Ge; Zaoqu Liu; Siyuan Weng; Hui Xu; Yuyuan Zhang; Long Liu; Qin Dang; Chunguang Guo; Richard Beatson; Jinhai Deng; Xinwei Han

doi:10.1016/j.csbj.2022.06.064

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma

Xiaoyong Ge, Zaoqu Liu^*, Siyuan Weng, Hui Xu, Yuyuan Zhang, Long Liu, Qin Dang, Chunguang Guo, Richard Beatson, Jinhai Deng, Xinwei Han

^*Corresponding author for this work

Comprehensive Cancer Centre

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Background: Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). Methods: A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. Results: Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. Conclusion: We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

Original language	English
Pages (from-to)	3449-3460
Number of pages	12
Journal	Computational and Structural Biotechnology Journal
Volume	20
DOIs	https://doi.org/10.1016/j.csbj.2022.06.064
Publication status	Published - Jan 2022

Keywords

Immune landscapes
Lung adenocarcinoma
Molecular subtypes
Pharmacogenomics
Precision medication
Therapeutic responses

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1016/j.csbj.2022.06.064

Cite this

@article{4f789a580fb34930815c06bbed5ec1ad,

title = "Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma",

abstract = "Background: Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). Methods: A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. Results: Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. Conclusion: We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.",

keywords = "Immune landscapes, Lung adenocarcinoma, Molecular subtypes, Pharmacogenomics, Precision medication, Therapeutic responses",

author = "Xiaoyong Ge and Zaoqu Liu and Siyuan Weng and Hui Xu and Yuyuan Zhang and Long Liu and Qin Dang and Chunguang Guo and Richard Beatson and Jinhai Deng and Xinwei Han",

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s)",

year = "2022",

month = jan,

doi = "10.1016/j.csbj.2022.06.064",

language = "English",

volume = "20",

pages = "3449--3460",

journal = "Computational and Structural Biotechnology Journal",

issn = "2001-0370",

publisher = "Research Network of Computational and Structural Biotechnology",

}

TY - JOUR

T1 - Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma

AU - Ge, Xiaoyong

AU - Liu, Zaoqu

AU - Weng, Siyuan

AU - Xu, Hui

AU - Zhang, Yuyuan

AU - Liu, Long

AU - Dang, Qin

AU - Guo, Chunguang

AU - Beatson, Richard

AU - Deng, Jinhai

AU - Han, Xinwei

PY - 2022/1

Y1 - 2022/1

N2 - Background: Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). Methods: A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. Results: Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. Conclusion: We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

AB - Background: Pharmacogenomics is crucial for individualized drug therapy and plays an increasingly vital role in precision medicine decision-making. However, pharmacogenomics-based molecular subtypes and their potential clinical significance remain primarily unexplored in lung adenocarcinoma (LUAD). Methods: A total of 2065 samples were recruited from eight independent cohorts. Pharmacogenomics data were generated from the profiling of relative inhibition simultaneously in mixtures (PRISM) and the genomics of drug sensitivity in cancer (GDSC) databases. Multiple bioinformatics approaches were performed to identify pharmacogenomics-based subtypes and find subtype-specific properties. Results: Three reproducible molecular subtypes were found, which were independent prognostic factors and highly associated with stage, survival status, and accepted molecular subtypes. Pharmacogenomics-based subtypes had distinct molecular characteristics: S-Ⅰ was inflammatory, proliferative, and immune-evasion; S-Ⅱ was proliferative and genetics-driven; S-III was metabolic and methylation-driven. Finally, our study provided subtype-guided personalized treatment strategies: Immune checkpoint blockers (ICBs), doxorubicin, tipifarnib, AZ628, and AZD6244 were for S-Ⅰ; Cisplatin, camptothecin, roscovitine, and A.443654 were for S-Ⅱ; Docetaxel, paclitaxel, vinorelbine, and BIBW2992 were for S-III. Conclusion: We provided a novel molecular classification strategy and revealed three pharmacogenomics-based subtypes for LUAD patients, which uncovered potential subtype-related and patient-specific therapeutic strategies.

KW - Immune landscapes

KW - Lung adenocarcinoma

KW - Molecular subtypes

KW - Pharmacogenomics

KW - Precision medication

KW - Therapeutic responses

UR - http://www.scopus.com/inward/record.url?scp=85133916261&partnerID=8YFLogxK

U2 - 10.1016/j.csbj.2022.06.064

DO - 10.1016/j.csbj.2022.06.064

M3 - Article

AN - SCOPUS:85133916261

SN - 2001-0370

VL - 20

SP - 3449

EP - 3460

JO - Computational and Structural Biotechnology Journal

JF - Computational and Structural Biotechnology Journal

ER -

Integrative pharmacogenomics revealed three subtypes with different immune landscapes and specific therapeutic responses in lung adenocarcinoma

Abstract

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this