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

Liu H, Kho AT, Kohane IS, Sun Y - PLoS Med. (2006)

Bottom Line: Kaplan-Meier survival analysis was conducted for lung adenocarcinoma patient subgroups identified via their developmental association.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.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.

View Article: PubMed Central - PubMed

Affiliation: Children's Hospital Informatics Program, Children's Hospital Boston, Boston, Massachusetts, United States of America. hongye.liu@childrens.harvard.edu

ABSTRACT

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.

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Mouse Lung Development Profiles in Temporal PC Representation, and the General Developmental Profile Segregation of Up- and Down-Regulated Genes in Human Lung Cancer(A) Expression profiles of all 3,590 unique genes during mouse lung development as represented in temporal PC1 and PC2. Each dot marks a gene.(B) Developmental profile examples of genes at the periphery of the disc-like scatter plot in (A) at 45° (π/4 radians starting at “3 o'clock”) rotational intervals.(C) Histograms of the mouse lung temporal PC1 coordinates of the 719 genes 2-fold significantly up- and down-regulated in any one of the four human lung cancer subtypes (χ2 = 168.338,p < 0.001, OR = 8.652).(D) The profiles of the top 100 genes (68 cancer up-regulated [green circles] and 32 cancer down-regulated [magenta circles]) composing the malignancy signature (seeResults) among all 3,590 mouse lung developmental gene profiles. Of the 68 cancer up-regulated genes, all but two are in the late developmental profile hemisphere. Of the 32 cancer down-regulated genes, all but two are in the early developmental profile hemisphere (χ2 = 82.5185,p < 0.001, OR = 544).
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pmed-0030232-g001: Mouse Lung Development Profiles in Temporal PC Representation, and the General Developmental Profile Segregation of Up- and Down-Regulated Genes in Human Lung Cancer(A) Expression profiles of all 3,590 unique genes during mouse lung development as represented in temporal PC1 and PC2. Each dot marks a gene.(B) Developmental profile examples of genes at the periphery of the disc-like scatter plot in (A) at 45° (π/4 radians starting at “3 o'clock”) rotational intervals.(C) Histograms of the mouse lung temporal PC1 coordinates of the 719 genes 2-fold significantly up- and down-regulated in any one of the four human lung cancer subtypes (χ2 = 168.338,p < 0.001, OR = 8.652).(D) The profiles of the top 100 genes (68 cancer up-regulated [green circles] and 32 cancer down-regulated [magenta circles]) composing the malignancy signature (seeResults) among all 3,590 mouse lung developmental gene profiles. Of the 68 cancer up-regulated genes, all but two are in the late developmental profile hemisphere. Of the 32 cancer down-regulated genes, all but two are in the early developmental profile hemisphere (χ2 = 82.5185,p < 0.001, OR = 544).

Mentions: In order to visualize the developmental profiles of 3,590 separate genes in a compact manner summarizing the large-scale developmental patterns, PCA [19,23,28] was applied on the temporal axis of the mouse lung development dataset of 3,590 mouse genes × ten developmental time points (Figure 1). PCA [29] reduces the feature space dimensionality—i.e., features such as genes or samples—of a multivariate dataset and identifies the most variationally informative features (variationally informative features are the main contributors to global variation in the data matrix [30]). The original data are rewritten as an equivalent set of coefficients relative to a new basis of PCs. Each PC is a linear combination of the original features (here samples and time stages) and represents a direction of extremal variance in the feature space. The first PC (PC1) captures the greatest amount of total variance in the dataset. PC2 captures the next greatest contribution to variance. The disc-shaped scatter plot inFigure 1A shows the 3,590 genes rewritten with respect to the two most important temporal PCs of the lung development dataset. Each dot marks a gene, and its coordinates indicate its expression pattern over the ten developmental time points. The high dot concentrations along the periphery of the left and right hemispheres represent gene clusters whose expression levels are high early in development and decrease monotonically with time and genes whose expression levels are low early in development and increase monotonically with time, respectively. The temporal PC1 coordinate of a gene is a qualitative indicator of its lung developmental profile (Figure 1B).


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

Liu H, Kho AT, Kohane IS, Sun Y - PLoS Med. (2006)

Mouse Lung Development Profiles in Temporal PC Representation, and the General Developmental Profile Segregation of Up- and Down-Regulated Genes in Human Lung Cancer(A) Expression profiles of all 3,590 unique genes during mouse lung development as represented in temporal PC1 and PC2. Each dot marks a gene.(B) Developmental profile examples of genes at the periphery of the disc-like scatter plot in (A) at 45° (π/4 radians starting at “3 o'clock”) rotational intervals.(C) Histograms of the mouse lung temporal PC1 coordinates of the 719 genes 2-fold significantly up- and down-regulated in any one of the four human lung cancer subtypes (χ2 = 168.338,p < 0.001, OR = 8.652).(D) The profiles of the top 100 genes (68 cancer up-regulated [green circles] and 32 cancer down-regulated [magenta circles]) composing the malignancy signature (seeResults) among all 3,590 mouse lung developmental gene profiles. Of the 68 cancer up-regulated genes, all but two are in the late developmental profile hemisphere. Of the 32 cancer down-regulated genes, all but two are in the early developmental profile hemisphere (χ2 = 82.5185,p < 0.001, OR = 544).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC1483910&req=5

pmed-0030232-g001: Mouse Lung Development Profiles in Temporal PC Representation, and the General Developmental Profile Segregation of Up- and Down-Regulated Genes in Human Lung Cancer(A) Expression profiles of all 3,590 unique genes during mouse lung development as represented in temporal PC1 and PC2. Each dot marks a gene.(B) Developmental profile examples of genes at the periphery of the disc-like scatter plot in (A) at 45° (π/4 radians starting at “3 o'clock”) rotational intervals.(C) Histograms of the mouse lung temporal PC1 coordinates of the 719 genes 2-fold significantly up- and down-regulated in any one of the four human lung cancer subtypes (χ2 = 168.338,p < 0.001, OR = 8.652).(D) The profiles of the top 100 genes (68 cancer up-regulated [green circles] and 32 cancer down-regulated [magenta circles]) composing the malignancy signature (seeResults) among all 3,590 mouse lung developmental gene profiles. Of the 68 cancer up-regulated genes, all but two are in the late developmental profile hemisphere. Of the 32 cancer down-regulated genes, all but two are in the early developmental profile hemisphere (χ2 = 82.5185,p < 0.001, OR = 544).
Mentions: In order to visualize the developmental profiles of 3,590 separate genes in a compact manner summarizing the large-scale developmental patterns, PCA [19,23,28] was applied on the temporal axis of the mouse lung development dataset of 3,590 mouse genes × ten developmental time points (Figure 1). PCA [29] reduces the feature space dimensionality—i.e., features such as genes or samples—of a multivariate dataset and identifies the most variationally informative features (variationally informative features are the main contributors to global variation in the data matrix [30]). The original data are rewritten as an equivalent set of coefficients relative to a new basis of PCs. Each PC is a linear combination of the original features (here samples and time stages) and represents a direction of extremal variance in the feature space. The first PC (PC1) captures the greatest amount of total variance in the dataset. PC2 captures the next greatest contribution to variance. The disc-shaped scatter plot inFigure 1A shows the 3,590 genes rewritten with respect to the two most important temporal PCs of the lung development dataset. Each dot marks a gene, and its coordinates indicate its expression pattern over the ten developmental time points. The high dot concentrations along the periphery of the left and right hemispheres represent gene clusters whose expression levels are high early in development and decrease monotonically with time and genes whose expression levels are low early in development and increase monotonically with time, respectively. The temporal PC1 coordinate of a gene is a qualitative indicator of its lung developmental profile (Figure 1B).

Bottom Line: Kaplan-Meier survival analysis was conducted for lung adenocarcinoma patient subgroups identified via their developmental association.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.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.

View Article: PubMed Central - PubMed

Affiliation: Children's Hospital Informatics Program, Children's Hospital Boston, Boston, Massachusetts, United States of America. hongye.liu@childrens.harvard.edu

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus