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1.
Figure 1

Figure 1. From: A robust prognostic signature for hormone-positive node-negative breast cancer.

Duplicate analysis showing approximate relationship between studies in analysis. The VennMaster diagram shows the approximate overlap between GEO datasets used in the current study. Three studies show zero overlap while the other six show significant overlap.

Obi L Griffith, et al. Genome Med. 2013;5(10):92-92.
2.
Figure 4

Figure 4. From: A robust prognostic signature for hormone-positive node-negative breast cancer.

Risk group threshold determination. The distribution of RFRS scores was determined for patients in the training dataset (n = 325) comparing those with a known relapse (right side; in blue) versus those with no known relapse (left side; in red). As expected, patients without a known relapse tend to have a higher predicted likelihood of relapse (by RFRS) and vice versa. Mixed model clustering was used to identify thresholds (0.333 and 0.606) for defining low-, intermediate-, and high-risk groups as indicated.

Obi L Griffith, et al. Genome Med. 2013;5(10):92-92.
3.
Figure 2

Figure 2. From: A robust prognostic signature for hormone-positive node-negative breast cancer.

Sample breakdown. A total of 858 samples passed all filtering steps including 487 samples with 10-year follow-up data (213 relapse; 274 no relapse). The remaining 371 samples had insufficient follow-up for 10-year classification analysis but were retained for use in survival analysis. The 858 samples were broken into two-thirds training and one-third testing sets resulting in: a training set of 572 samples for use in survival analysis and 325 samples with 10-year follow-up (143 relapse; 182 no relapse) for classification analysis; and a testing set of 286 samples for use in survival analysis and 162 samples with 10-year follow-up (70 relapse; 92 no relapse) for classification analysis.

Obi L Griffith, et al. Genome Med. 2013;5(10):92-92.
4.
Figure 6

Figure 6. From: A robust prognostic signature for hormone-positive node-negative breast cancer.

Estimated likelihood of relapse at 10 years for any RFRS value. The likelihood of relapse was calculated in the training dataset (n = 505) for 50 RFRS intervals (from 0 to 1). A smooth curve was fitted using a loess function and 95% confidence intervals plotted to represent the error in the fit. Short vertical marks just above the x axis, one for each patient, represent the distribution of RFRS values observed in the training data. Thresholds for risk groups are indicated. The plot shows a linear relationship between RFRS and likelihood of relapse at 10 years with the likelihood ranging from approximately 0 to 40%.

Obi L Griffith, et al. Genome Med. 2013;5(10):92-92.
5.
Figure 7

Figure 7. From: A robust prognostic signature for hormone-positive node-negative breast cancer.

Gene Ontology categorization of 17-gene model. A Gene Ontology (GO) categorization was performed using DAVID to identify the associated GO biological processes for the 17-gene model. A VennMaster diagram represents the approximate overlap between GO terms. To simplify, redundant terms were grouped together. Genes in the 17-gene list are involved in a wide range of biological processes known to be involved in breast cancer biology including cell cycle, hormone response, cell death, DNA repair, transcription regulation, wound healing, and others. Since the eight-gene set is entirely contained in the 17-gene set it would be involved in many of the same processes.

Obi L Griffith, et al. Genome Med. 2013;5(10):92-92.
6.
Figure 5

Figure 5. From: A robust prognostic signature for hormone-positive node-negative breast cancer.

Likelihood of relapse according to RFRS group. Kaplan-Meier survival analysis shows a significant difference in relapse-free survival for low-, intermediate-, and high-risk groups as defined by: (A) the full-gene-set model on training data (n = 572, P = 3.95E-11); (B) the eight-gene-set model on independent test data (n = 286, P = 2.84E-05); (C) the eight-gene-set model on independent NKI data (n = 89, P = 0.004); (D) the 17-gene-set model on independent METABRIC data (n = 315, P <1.99E-04). Note, for panel A, the risk scores and corresponding groups for samples used in classifier training (n = 325) were assigned from internal OOB cross-validation. Only those patients not used in initial training (training data without 10-year follow-up; test data) were assigned a risk score and group by de novo classification. Significance between risk groups was determined by Kaplan-Meier log rank test (with test for linear trend).

Obi L Griffith, et al. Genome Med. 2013;5(10):92-92.
7.
Figure 3

Figure 3. From: A robust prognostic signature for hormone-positive node-negative breast cancer.

Heatmap showing top 100 probe sets after k-means clustering (k = 20). Training data (n = 325) were clustered by expression value using k-means clustering (k = 20) for the top 100 probe sets identified by random forest classification variable importance. The first color side bar on the left indicates cluster number and the second indicates relative variable importance within the cluster (darker blue = greater importance). The top side bars indicate risk group (low, intermediate, and high from left to right) and relapse status (red = relapse; yellow = no relapse). Genes (probe sets) are indicated on the right axis. Genes highlighted in yellow represent the primary genes in the model (best in each cluster). Genes not highlighted represent alternates to primary genes in each cluster. Genes highlighted in pink represent genes excluded from the model because of probe set sequence ambiguity or status as a hypothetical protein.

Obi L Griffith, et al. Genome Med. 2013;5(10):92-92.

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