Date of download: 5/29/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer JAMA. 2008;299(13): doi: /jama The model can be found at The numbers at risk refer to number of patients at risk entering each 25- month interval. Low ( 50) risk categories are based on prespecified risk scores generated using a computerized clinicopathological prognostic model based on age, comorbidities, estrogen receptor status, tumor grade, tumor size, and lymph node status. Figure Legend:

Date of download: 5/29/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer JAMA. 2008;299(13): doi: /jama D1 indicates initial discovery data set. Columns on heatmap represent patient samples. Rows are representative pathways that were evaluated for deregulation: WH, wound healing; TNF, tumor necrosis factor; IGS, invasiveness gene signature; EPI, epigenetic stem cell; CIN, chromosomal instability. Cluster assignments are based on the dominant expression pattern, where ≥50% of the samples express the phenotype (pathways activated) that drives the cluster formation. Pairwise cluster comparisons using Kaplan- Meier survival plots yield the associated statistical significance (log-rank P ≤.05) of the clusters with unique biology associated with them in terms of pathway activation. Statistically nonsignificant comparisons of prognostic clusters (log-rank P =.05) are not reported. Clusters 1, 4, and 5 have prognostic significance. Clusters 1 and 5 represent patients with intermediate and good prognosis, respectively, and cluster 4 represents patients with the worst prognosis. Figure Legend:

Date of download: 5/29/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer JAMA. 2008;299(13): doi: /jama D1 indicates initial discovery data set. Columns on heatmap represent patient samples. Rows are representative pathways that were evaluated for deregulation: WH, wound healing; TNF, tumor necrosis factor; IGS, invasiveness gene signature; EPI, epigenetic stem cell; CIN, chromosomal instability. Cluster assignments are based on the dominant expression pattern, where ≥50% of the samples express the phenotype (pathways activated) that drives the cluster formation. Pairwise cluster comparisons using Kaplan- Meier survival plots yield the associated statistical significance (log-rank P ≤.05) of the clusters with unique biology associated with them in terms of pathway activation. Statistically nonsignificant comparisons of prognostic clusters (log-rank P =.05) are not reported. Clusters 2 and 3 have prognostic significance. Cluster 2 represents patients with good prognosis and cluster 3 represents patients with poor prognosis. Figure Legend:

Date of download: 5/29/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer JAMA. 2008;299(13): doi: /jama D1 indicates initial discovery data set. Columns on heatmap represent patient samples. Rows are representative pathways that were evaluated for deregulation: WH, wound healing; TNF, tumor necrosis factor; IGS, invasiveness gene signature; EPI, epigenetic stem cell; CIN, chromosomal instability. Cluster assignments are based on the dominant expression pattern, where ≥50% of the samples express the phenotype (pathways activated) that drives the cluster formation. Pairwise cluster comparisons using Kaplan- Meier survival plots yield the associated statistical significance (log-rank P ≤.05) of the clusters with unique biology associated with them in terms of pathway activation. Statistically nonsignificant comparisons of prognostic clusters (log-rank P =.05) are not reported. Clusters 1, 4, and 5 have prognostic significance. Clusters 1 and 5 represent patients with good to intermediate prognosis, and cluster 4 represents patients with poor prognosis. Figure Legend:

Date of download: 5/29/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer JAMA. 2008;299(13): doi: /jama D2 indicates validation data set. Columns on heatmap represent patient samples. Rows are representative pathways that were evaluated for deregulation: WH, wound healing; TNF, tumor necrosis factor; IGS, invasiveness gene signature; EPI, epigenetic stem cell; CIN, chromosomal instability. Low-risk cohort: patterns of oncogenic pathway activation and tumor biology/microenvironment deregulation are shown as clusters (clusters 1 and 2) representing prognostic subphenotypes of the low-risk cohort illustrating that the patterns of pathway activation identified in the initial discovery data set (D1) are reproducible in D2. In this case, Kaplan-Meier survival analysis illustrates prognostic clusters (clusters 1 and 2), with No. of patients at risk reported at 25-month intervals of follow- up. Intermediate-risk cohort: Kaplan-Meier survival analysis illustrates prognostic clusters (clusters 1 and 5), along with their respective patterns of oncogenic pathway and tumor biology/microenvironment deregulation shown as a heatmap. High-risk cohort: Kaplan-Meier survival analysis demonstrates the prognostic significance of cluster 1 and cluster 3 along with their patterns of oncogenic pathway and tumor biology/microenvironment deregulation as a heatmap. Patterns observed in D2 are identical to the patterns of pathway activation observed in the prognostic clusters (within the high-risk cohort) identified in D1. Red color in the heatmaps indicates a high probability of deregulation and blue indicates a low probability of deregulation. The cluster numbers in D2 are not the same expression patterns as clusters defined in D1. Figure Legend:

Date of download: 5/29/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer JAMA. 2008;299(13): doi: /jama Scatterplots depict classification of prognostic clusters based on the first 3 principal components (based on principal component analysis, the results of which contribute to most of the variation in a data set) to demonstrate that the poor prognostic clusters in D1 (red dots) and D2 (blue dots) are similar, while also being clearly distinct from the good prognostic cluster in D1 (light blue dots). Each dot represents a specific sample in that cluster with respect to the first 3 principal components. Each axis of the 3-dimensional scatterplot represents the amount of variance as represented by that principal component. The first 3 principal components present the maximum amount of variation in a data set. Figure Legend:

Date of download: 5/29/2016 Copyright © 2016 American Medical Association. All rights reserved. From: Gene Expression Signatures, Clinicopathological Features, and Individualized Therapy in Breast Cancer JAMA. 2008;299(13): doi: /jama In each instance, chemosensitivity predictions were plotted such that a high probability of sensitivity (or response) is indicated by red and a low probability of sensitivity (or resistance) is indicated by blue. Cluster designations indicate previously determined clusters of pathway patterns (see Figures 2, 3, and 4). Relative sensitivity patterns to cytotoxic agents used in breast cancer were identified from Kruskal-Wallis 1-way analysis of variance followed by Dunn's posttest performed on the statistically significant prognostic clusters; only results from Dunn’s posttest at P<.05 are shown here. The chemosensitivity patterns as observed in the heatmap are used to determine whether the statistically significant (P<.05) prognostic clusters are resistant or sensitive to that particular cytotoxic agent. Figure Legend: