Predicting survival within the lung cancer histopathological hierarchy using a multi-scale genomic model of development

被引:61
|
作者
Liu, Hongye
Kho, Alvin T.
Kohane, Isaac S.
Sun, Yao
机构
[1] Childrens Hosp, Childrens Hosp Informat Program, Boston, MA 02115 USA
[2] Childrens Hosp, Dept Newborn Med, Boston, MA 02115 USA
[3] Harvard Univ, MIT, Div Hlth Sci & Technol, Cambridge, MA 02138 USA
关键词
D O I
10.1371/journal.pmed.0030232
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: The histopathologic heterogeneity of lung cancer remains a significant confounding factor in its diagnosis and prognosis-spurring numerous recent efforts to find a molecular classification of the disease that has clinical relevance. Methods and Findings: Molecular profiles of tumors from 186 patients representing four different lung cancer subtypes (and 17 normal lung tissue samples) were compared with a mouse lung development model using principal component analysis in both temporal and genomic domains. An algorithm for the classification of lung cancers using a multi-scale developmental framework was developed. Kaplan-Meier survival analysis was conducted for lung adenocarcinoma patient subgroups identified via their developmental association. We found multi-scale genomic similarities between four human lung cancer subtypes and the developing mouse lung that are prognostically meaningful. Significant association was observed between the localization of human lung cancer cases along the principal mouse lung development trajectory and the corresponding patient survival rate at three distinct levels of classical histopathologic resolution: among different lung cancer subtypes, among patients within the adenocarcinoma subtype, and within the stage I adenocarcinoma subclass. The earlier the genomic association between a human tumor profile and the mouse lung development sequence, the poorer the patient's prognosis. Furthermore, decomposing this principal lung development trajectory identified a gene set that was significantly enriched for pyrimidine metabolism and cell-adhesion functions specific to lung development and oncogenesis. Conclusions: From a multi-scale disease modeling perspective, the molecular dynamics of murine lung development provide an effective framework that is not only data driven but also informed by the biology of development for elucidating the mechanisms of human lung cancer biology and its clinical outcome.
引用
收藏
页码:1090 / 1102
页数:13
相关论文
共 50 条
  • [1] MULTI-SCALE MODEL OF BLADDER CANCER DEVELOPMENT
    Kashdan, Eugene
    Bunimovich-Mendrazitsky, Svetlana
    DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS, 2011, 31 : 803 - 812
  • [2] Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network
    Sheikh, Taimoor Shakeel
    Lee, Yonghee
    Cho, Migyung
    CANCERS, 2020, 12 (08) : 1 - 21
  • [3] Multi-scale model predicting friction of crystalline materials
    Torche, Paola C.
    Silva, Andrea
    Kramer, Denis
    Polcar, Tomas
    Hovorka, Ondrej
    ADVANCED MATERIALS INTERFACES, 2022, 9 (04):
  • [4] Multi-scale feature fusion for histopathological image categorisation in breast cancer
    Wang, Yingping
    Deng, Xing
    Shao, Haijian
    Jiang, Yingtao
    COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION, 2023, 11 (06): : 2350 - 2362
  • [5] A multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer
    Kim, Gihyeon
    Park, Young Mi
    Yoon, Hyun Jung
    Choi, Jang-Hwan
    PEERJ COMPUTER SCIENCE, 2023, 9
  • [6] A Stochastic Multi-Scale Model for Predicting MEMS Stiction Failure
    Hoang, T. V.
    Wu, L.
    Paquay, S.
    Golinval, J. -C.
    Arnst, M.
    Noels, L.
    MICRO AND NANOMECHANICS, VOL 5, 2017, : 1 - 8
  • [7] Complex material flow problems: a multi-scale model hierarchy and particle methods
    S. Göttlich
    A. Klar
    S. Tiwari
    Journal of Engineering Mathematics, 2015, 92 : 15 - 29
  • [8] Complex material flow problems: a multi-scale model hierarchy and particle methods
    Goettlich, S.
    Klar, A.
    Tiwari, S.
    JOURNAL OF ENGINEERING MATHEMATICS, 2015, 92 (01) : 15 - 29
  • [9] PKMT-Net: A pathological knowledge-inspired multi-scale transformer network for subtype prediction of lung cancer using histopathological images
    Zhao, Zhilei
    Guo, Shuli
    Han, Lina
    Zhou, Gang
    Jia, Jiaoyu
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2025, 106
  • [10] An Integrated Multi-scale Model for Breast Cancer Histopathological Image Classification with Joint Colour-Texture Features
    Gupta, Vibha
    Bhavsar, Arnav
    COMPUTER ANALYSIS OF IMAGES AND PATTERNS: 17TH INTERNATIONAL CONFERENCE, CAIP 2017, PT II, 2017, 10425 : 354 - 366