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The prognostic landscape of interactive biological processes presents treatment responses in cancer

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Title: The prognostic landscape of interactive biological processes presents treatment responses in cancer
Authors: He, B
Gao, R
Lv, D
Wen, Y
Song, L
Wang, X
Lin, S
Huang, Q
Deng, Z
Wang, Z
Yan, M
Zheng, F
Lam, EW-F
Kelley, KW
Li, Z
Liu, Q
Item Type: Journal Article
Abstract: Background Differential gene expression patterns are commonly used as biomarkers to predict treatment responses among heterogeneous tumors. However, the link between response biomarkers and treatment-targeting biological processes remain poorly understood. Here, we develop a prognosis-guided approach to establish the determinants of treatment response. Methods The prognoses of biological processes were evaluated by integrating the transcriptomes and clinical outcomes of ~26,000 cases across 39 malignancies. Gene-prognosis scores of 39 malignancies (GEO datasets) were used for examining the prognoses, and TCGA datasets were selected for validation. The Oncomine and GEO datasets were used to establish and validate transcriptional signatures for treatment responses. Findings The prognostic landscape of biological processes was established across 39 malignancies. Notably, the prognoses of biological processes varied among cancer types, and transcriptional features underlying these prognostic patterns distinguished response to treatment targeting specific biological process. Applying this metric, we found that low tumor proliferation rates predicted favorable prognosis, whereas elevated cellular stress response signatures signified resistance to anti-proliferation treatment. Moreover, while high immune activities were associated with favorable prognosis, enhanced lipid metabolism signatures distinguished immunotherapy resistant patients. Interpretation These findings between prognosis and treatment response provide further insights into patient stratification for precision treatments, providing opportunities for further experimental and clinical validations.
Issue Date: 1-Mar-2019
Date of Acceptance: 31-Jan-2019
URI: http://hdl.handle.net/10044/1/67325
DOI: 10.1016/j.ebiom.2019.01.064
ISSN: 2352-3964
Publisher: Elsevier
Start Page: 120
End Page: 133
Journal / Book Title: EBioMedicine
Volume: 41
Copyright Statement: © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Sponsor/Funder: Cancer Research UK
Breast Cancer Care & Breast Cancer Now
Breast Cancer Care & Breast Cancer Now
Breast Cancer Care & Breast Cancer Now
Medical Research Council (MRC)
Funder's Grant Number: 12011
2012NovemberPhD016
2014NovPhD326
2012MayPR070
MR/N012097/1
Keywords: Science & Technology
Life Sciences & Biomedicine
Medicine, General & Internal
Medicine, Research & Experimental
General & Internal Medicine
Research & Experimental Medicine
Prognosis
Biological processes
Treatment response
Cell-proliferation
Immune processes
IMMUNE CHECKPOINT BLOCKADE
GENE-EXPRESSION
PREDICTS RESPONSE
PD-1 BLOCKADE
T-CELLS
ANTI-PD-1
SENSITIVITY
RESISTANCE
RESOURCE
GLIOBLASTOMA
Biological processes
Cell-proliferation
Immune processes
Prognosis
Treatment response
Antineoplastic Agents
Biomarkers, Tumor
Cell Line, Tumor
Datasets as Topic
Drug Resistance, Neoplasm
Humans
Neoplasms
Transcriptome
Treatment Outcome
Cell Line, Tumor
Humans
Neoplasms
Antineoplastic Agents
Treatment Outcome
Drug Resistance, Neoplasm
Transcriptome
Datasets as Topic
Biomarkers, Tumor
Publication Status: Published
Online Publication Date: 2019-02-22
Appears in Collections:Department of Surgery and Cancer