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Updated: Jul 13, 2026
08:31
Acellular and Cellular Lung Model to Study Tumor Metastasis
Published on: August 19, 2018
Predicting survival within the lung cancer histopathological hierarchy using a multi-scale genomic model of
Hongye Liu1, Alvin T Kho, Isaac S Kohane
1Children's Hospital Informatics Program, Children's Hospital Boston, Boston, Massachusetts, United States of America. hongye.liu@childrens.harvard.edu
Plos Medicine
|June 28, 2006
Summary
Lung cancer molecular profiles mirror mouse lung development, offering prognostic insights. Earlier genomic alignment with lung development predicts poorer patient survival, aiding cancer classification.
Area of Science:
- Comparative genomics
- Developmental biology
- Cancer research
Background:
- Histopathologic heterogeneity complicates lung cancer diagnosis and prognosis.
- Need for a clinically relevant molecular classification of lung cancer.
Purpose of the Study:
- To develop a molecular classification of lung cancer using a developmental framework.
- To investigate the prognostic significance of molecular similarities between human lung cancer and developing mouse lung.
Main Methods:
- Comparative analysis of molecular profiles from human lung cancer subtypes and normal lung tissue with a mouse lung development model.
- Principal component analysis in temporal and genomic domains.
- Development of a classification algorithm based on a multi-scale developmental framework.
- Kaplan-Meier survival analysis for patient subgroups.
Main Results:
- Identified multi-scale genomic similarities between human lung cancer subtypes and developing mouse lung with prognostic implications.
- Found a significant association between the position of human lung cancer cases on the mouse lung development trajectory and patient survival.
- Earlier genomic association with lung development predicted poorer prognosis.
- Discovered a gene set enriched for pyrimidine metabolism and cell-adhesion functions relevant to lung development and oncogenesis.
Conclusions:
- Murine lung development provides a data-driven, biologically informed framework for understanding human lung cancer mechanisms and clinical outcomes.
- Molecular dynamics of lung development offer insights into lung cancer biology and prognosis.
- The study highlights the potential of developmental biology models for cancer research.
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