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Cancer progression modeling using static sample data.

Sun Y, Yao J, Nowak NJ, Goodison S - Genome Biol. (2014)

Bottom Line: We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data.Our findings support a linear, branching model for breast cancer progression.An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.

View Article: PubMed Central - PubMed

ABSTRACT
As molecular profiling data continues to accumulate, the design of integrative computational analyses that can provide insights into the dynamic aspects of cancer progression becomes feasible. Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples. We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression. An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.

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Seven key genes (AURKA, PLAU, STAT1, VEGF, CASP3, ESR1, and ERBB2) representing proliferation, tumor invasion/metastasis, immune response, angiogenesis, apoptosis phenotypes, and ER and HER2 signaling, respectively, were mapped onto the two major progression branches of the METABRIC model. For ease of presentation in one plot, the expression level of each gene was normalized into the interval of [0,1]. The small interval between normal and luminal A represents normal-like.
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Fig8: Seven key genes (AURKA, PLAU, STAT1, VEGF, CASP3, ESR1, and ERBB2) representing proliferation, tumor invasion/metastasis, immune response, angiogenesis, apoptosis phenotypes, and ER and HER2 signaling, respectively, were mapped onto the two major progression branches of the METABRIC model. For ease of presentation in one plot, the expression level of each gene was normalized into the interval of [0,1]. The small interval between normal and luminal A represents normal-like.

Mentions: Next, we investigated the change in gene expression of some key genes during breast cancer progression by mapping them onto the model derived from the more comprehensive METABRIC dataset. Our initial interrogation mapped the expression of seven key genes (AURKA, PLAU, STAT1, VEGF, CASP3, ESR1 and ERBB2), which represent described hallmarks of cancer, namely proliferation, tumor invasion and metastasis, immune response, angiogenesis, apoptosis, and estrogen (ER) and HER2 signaling, respectively [59]. The resulting plots revealed the change of expression of these genes along both linear paths, normal to HER2+ (N-H, Additional file 1: Figure S17) and normal to basal (N-B, Additional file 1: Figure S18). By normalizing the expression levels, we were able to visualize the changes on a single overlay plot (Figure 8). The curves were generated using the polynomial curve fitting method and the degree parameter was estimated through tenfold cross-validation [40]. Once again, we label the axis with PAM50 subtype labels to indicate an approximation of tumor subtypes for ease of discussion.Figure 8


Cancer progression modeling using static sample data.

Sun Y, Yao J, Nowak NJ, Goodison S - Genome Biol. (2014)

Seven key genes (AURKA, PLAU, STAT1, VEGF, CASP3, ESR1, and ERBB2) representing proliferation, tumor invasion/metastasis, immune response, angiogenesis, apoptosis phenotypes, and ER and HER2 signaling, respectively, were mapped onto the two major progression branches of the METABRIC model. For ease of presentation in one plot, the expression level of each gene was normalized into the interval of [0,1]. The small interval between normal and luminal A represents normal-like.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4196119&req=5

Fig8: Seven key genes (AURKA, PLAU, STAT1, VEGF, CASP3, ESR1, and ERBB2) representing proliferation, tumor invasion/metastasis, immune response, angiogenesis, apoptosis phenotypes, and ER and HER2 signaling, respectively, were mapped onto the two major progression branches of the METABRIC model. For ease of presentation in one plot, the expression level of each gene was normalized into the interval of [0,1]. The small interval between normal and luminal A represents normal-like.
Mentions: Next, we investigated the change in gene expression of some key genes during breast cancer progression by mapping them onto the model derived from the more comprehensive METABRIC dataset. Our initial interrogation mapped the expression of seven key genes (AURKA, PLAU, STAT1, VEGF, CASP3, ESR1 and ERBB2), which represent described hallmarks of cancer, namely proliferation, tumor invasion and metastasis, immune response, angiogenesis, apoptosis, and estrogen (ER) and HER2 signaling, respectively [59]. The resulting plots revealed the change of expression of these genes along both linear paths, normal to HER2+ (N-H, Additional file 1: Figure S17) and normal to basal (N-B, Additional file 1: Figure S18). By normalizing the expression levels, we were able to visualize the changes on a single overlay plot (Figure 8). The curves were generated using the polynomial curve fitting method and the degree parameter was estimated through tenfold cross-validation [40]. Once again, we label the axis with PAM50 subtype labels to indicate an approximation of tumor subtypes for ease of discussion.Figure 8

Bottom Line: We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data.Our findings support a linear, branching model for breast cancer progression.An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.

View Article: PubMed Central - PubMed

ABSTRACT
As molecular profiling data continues to accumulate, the design of integrative computational analyses that can provide insights into the dynamic aspects of cancer progression becomes feasible. Here, we present a novel computational method for the construction of cancer progression models based on the analysis of static tumor samples. We demonstrate the reliability of the method with simulated data, and describe the application to breast cancer data. Our findings support a linear, branching model for breast cancer progression. An interactive model facilitates the identification of key molecular events in the advance of disease to malignancy.

Show MeSH
Related in: MedlinePlus