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Cancer progression modeling using static sample data.

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

Bottom Line: 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.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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Related in: MedlinePlus

Overall survival of 12 breast cancer subgroups detected in the METABRIC data. Cluster 11 contained only three samples and so was omitted.
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Fig6: Overall survival of 12 breast cancer subgroups detected in the METABRIC data. Cluster 11 contained only three samples and so was omitted.

Mentions: To examine the relationship between clinical outcome and the groups on the two major paths to malignancy, we used the Kaplan–Meier method [48] to plot overall survival (OS). Figure 6 illustrates a clear trend of worsening survival function associated with progression along the major trajectory through luminal types to basal or HER2+ tumors (cluster 8 through cluster 6, and cluster 1 to either cluster 3 or 5). As would be expected, each cluster, or node, on this linear path generally had a worse OS index than the preceding cluster. Interestingly, cluster 9, located at the start of the linear path between normal samples (no OS data) and the first luminal-type cluster (cluster 8), has a worse OS function than downstream cluster 8. Similar associations with outcome have been reported for this group in other studies [49]. Cluster 9 was classified as normal-like by PAM50 labeling, and there has been conjecture about whether this is an artifact of contamination by high levels of normal tissue in this early stage tumor [50]. The position of the cluster on the progression model may support that notion. A thorough histological investigation of this class of tumors would be needed to resolve this issue. A more plausible explanation is that cluster 9 is connected with cluster 10, which was classed as basal and had a poor OS as shown in Figure 6. This means a subset of tumors in cluster 9 can bypass luminal intermediates and progress to either cluster 10 or 8 directly. Another pattern to note from the Kaplan–Meier plot is the OS data for cluster 4 (Figure 6). This cluster is located at the bifurcation point on the linear model. The OS function for cluster 4 is similar to that for the basal and HER2+ tumors early on, and continues to mimic HER2+ throughout the survival analysis. This implies that pivotal gene activities associated with outcome are acquired at this stage prior to final commitment to a basal or a HER2+ phenotype.Figure 6


Cancer progression modeling using static sample data.

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

Overall survival of 12 breast cancer subgroups detected in the METABRIC data. Cluster 11 contained only three samples and so was omitted.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig6: Overall survival of 12 breast cancer subgroups detected in the METABRIC data. Cluster 11 contained only three samples and so was omitted.
Mentions: To examine the relationship between clinical outcome and the groups on the two major paths to malignancy, we used the Kaplan–Meier method [48] to plot overall survival (OS). Figure 6 illustrates a clear trend of worsening survival function associated with progression along the major trajectory through luminal types to basal or HER2+ tumors (cluster 8 through cluster 6, and cluster 1 to either cluster 3 or 5). As would be expected, each cluster, or node, on this linear path generally had a worse OS index than the preceding cluster. Interestingly, cluster 9, located at the start of the linear path between normal samples (no OS data) and the first luminal-type cluster (cluster 8), has a worse OS function than downstream cluster 8. Similar associations with outcome have been reported for this group in other studies [49]. Cluster 9 was classified as normal-like by PAM50 labeling, and there has been conjecture about whether this is an artifact of contamination by high levels of normal tissue in this early stage tumor [50]. The position of the cluster on the progression model may support that notion. A thorough histological investigation of this class of tumors would be needed to resolve this issue. A more plausible explanation is that cluster 9 is connected with cluster 10, which was classed as basal and had a poor OS as shown in Figure 6. This means a subset of tumors in cluster 9 can bypass luminal intermediates and progress to either cluster 10 or 8 directly. Another pattern to note from the Kaplan–Meier plot is the OS data for cluster 4 (Figure 6). This cluster is located at the bifurcation point on the linear model. The OS function for cluster 4 is similar to that for the basal and HER2+ tumors early on, and continues to mimic HER2+ throughout the survival analysis. This implies that pivotal gene activities associated with outcome are acquired at this stage prior to final commitment to a basal or a HER2+ phenotype.Figure 6

Bottom Line: 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.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