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Journal of Clinical Oncology logoLink to Journal of Clinical Oncology
. 2010 Aug 2;28(25):3937–3944. doi: 10.1200/JCO.2010.28.9538

Relationship Between Tumor Gene Expression and Recurrence in Four Independent Studies of Patients With Stage II/III Colon Cancer Treated With Surgery Alone or Surgery Plus Adjuvant Fluorouracil Plus Leucovorin

Michael J O'Connell 1,, Ian Lavery 1, Greg Yothers 1, Soonmyung Paik 1, Kim M Clark-Langone 1, Margarita Lopatin 1, Drew Watson 1, Frederick L Baehner 1, Steven Shak 1, Joffre Baker 1, J Wayne Cowens 1, Norman Wolmark 1
PMCID: PMC2940392  PMID: 20679606

Abstract

Purpose

These studies were conducted to determine the relationship between quantitative tumor gene expression and risk of cancer recurrence in patients with stage II or III colon cancer treated with surgery alone or surgery plus fluorouracil (FU) and leucovorin (LV) to develop multigene algorithms to quantify the risk of recurrence as well as the likelihood of differential treatment benefit of FU/LV adjuvant chemotherapy for individual patients.

Patients and Methods

We performed quantitative reverse transcription polymerase chain reaction (RT-qPCR) on RNA extracted from fixed, paraffin-embedded (FPE) tumor blocks from patients with stage II or III colon cancer who were treated with surgery alone (n = 270 from National Surgical Adjuvant Breast and Bowel Project [NSABP] C-01/C-02 and n = 765 from Cleveland Clinic [CC]) or surgery plus FU/LV (n = 308 from NSABP C-04 and n = 508 from NSABP C-06). Overall, 761 candidate genes were studied in C-01/C-02 and C-04, and a subset of 375 genes was studied in CC/C-06.

Results

A combined analysis of the four studies identified 48 genes significantly associated with risk of recurrence and 66 genes significantly associated with FU/LV benefit (with four genes in common). Seven recurrence-risk genes, six FU/LV-benefit genes, and five reference genes were selected, and algorithms were developed to identify groups of patients with low, intermediate, and high likelihood of recurrence and benefit from FU/LV.

Conclusion

RT-qPCR of FPE colon cancer tissue applied to four large independent populations has been used to develop multigene algorithms for estimating recurrence risk and benefit from FU/LV. These algorithms are being independently validated, and their clinical utility is being evaluated in the Quick and Simple and Reliable (QUASAR) study.

INTRODUCTION

Although adjuvant chemotherapy is the standard of care in stage III colon cancer, its routine use in patients with stage II colon cancer is controversial.110 The Quick and Simple and Reliable (QUASAR) study11 showed that adjuvant chemotherapy with fluorouracil (FU) plus leucovorin (LV) produces a small (approximately 3%) survival benefit in stage II colon cancer, which must be balanced with its toxicity, including toxic deaths (approximately 0.5%). This narrow therapeutic index underscores the importance of selecting the appropriate patients for adjuvant treatment.

In current practice, clinical and pathologic markers (ie, intestinal perforation/obstruction, pathologic stage T4, presence of lymphatic/vascular invasion, high tumor grade, < 12 nodes examined) can identify a minority of patients with stage II disease who have higher recurrence risk, but they do not adequately assess recurrence risk for individual patients. To address this issue, the use of molecular markers, such as microsatellite instability (MSI)/mismatch repair (MMR), LOH 18q, and levels of expression of individual genes or groups of genes1221 has been investigated. Some recent studies suggest MMR deficiency (ie, MSI high) may identify a small percentage (approximately 15%) of patients with stage II disease who receive little benefit from FU/LV.22 However, the clinical utility of these markers remains under study.23

Here, we report the application of the quantitative reverse transcription polymerase chain reaction (RT-qPCR) platform developed for the Oncotype DX Breast Cancer Assay (Genomic Health, Inc, Redwood City, CA)2426 in four independent colon cancer studies to generate the 12-gene recurrence score and 11-gene treatment score algorithms that, if validated, will quantify the risk of recurrence as well as the likelihood of differential treatment benefit of FU/LV adjuvant chemotherapy for individual patients with stage II colon cancer.

PATIENTS AND METHODS

Patients and Samples

Samples from four independent cohorts of patients with stage II or stage III colon cancer treated with surgery alone (National Surgical Adjuvant Breast and Bowel Project [NSABP] C-01/C-02 or Cleveland Clinic [CC] study) or surgery plus FU/LV (NSABP C-04, NSABP C-06) were studied (Appendix Table A1, online only; Appendix Fig A1, online only).2730 Prespecified criteria for being evaluable were as follows: eligibility for the parent clinical studies; availability of the fixed, paraffin-embedded (FPE) tumor block from initial diagnosis; presence of sufficient tumor (ie, ≥ 5% of tissue area occupied by invasive cancer cells in the guide hematoxylin and eosin slide); pathology diagnosis of colon adenocarcinoma (excluding signet ring carcinoma); adequate RNA to perform quantitative RT-qPCR analysis (≥ 1,069 ng for C-01/C-02 and C-04 and ≥ 587 ng for CC and C-06); and sufficient RNA quality by predefined metrics.

Sample Preparation

For each patient, RNA was extracted from three pooled 10-μm sections obtained from archived FPE colon tumor tissue. Nontumor elements were commonly identified on the guide hematoxylin and eosin slide reviewed for each patient and were removed by manual microdissection before transfer to the extraction tube.

Pathology, Assay Methods, Gene Selection, Reference Gene Normalization

Assessment of tumor grade was performed according to WHO criteria31 by an academic surgical pathologist with sub-specialty expertise in gastrointestinal pathology. The extracted RNA was quantified and then analyzed by RT-qPCR.32 For the C-01/C-02 and C-04 cohorts, two 384 well plates, which contained a total of 761 unique assays (ie, 761-gene panel), were used for each sample. With the exception of four assays (three K-ras mutations and one BRAF mutation), all assays were designed to detect the expression levels of wild-type genes. The panel of 761 candidate genes (Appendix Table A2, online only) was constructed from published gene expression profiling data and from biologic pathways identified as functionally important in colon cancer.1721 For the CC and C-06 cohorts, one 384-well plate, which contained 375 unique assays (ie, 375-gene panel), was used for each sample. The genes for the 375-gene panel were chosen from the 761-gene panel on the basis of the strength of the association of their level of expression with recurrence risk and chemotherapy benefit in the C-01/C-02 and C-04 studies. Gene expression measurements were normalized relative to five reference genes. Among the available samples across the four cohorts, only eight were excluded because of inadequate RT-qPCR expression.

MMR status was assessed by immunohistochemistry for MLH1 and MSH2 (which identify > 90% of the MMR-deficient tumors) on fixed, primary colon tumor tissue in the CC study.33

Blinding and Data Preparation

FPE tissue sections were prepared by either NSABP or CC personnel and were shipped to Genomic Health, Inc (Redwood City, CA), where the expression profiling was performed, blinded to the clinical data. The expression data and the clinical/pathology data were independently locked and then merged to construct the analysis data set for each study.

Study Design, Objectives, and End Points

The primary objective of all four studies was to identify genes associated with recurrence-free interval (RFI), defined as the time from surgery to first colon cancer recurrence. Deaths before recurrence were considered censoring events. Second primary cancers were considered neither events nor censoring events. Secondary end points were disease-free survival and overall survival.

Analysis Methods

Prespecified univariate (primary analysis) and multivariate relationships between clinical outcomes and categorical or continuous variables (eg, gene expression) were modeled using Cox proportional hazards regression.34 All baseline patient characteristics related to RFI (P < .20) were included in the multivariate analysis for a given study. Hazard ratios (HRs) were tested for significance using the likelihood ratio test.35 For univariate models of gene expression and RFI, an unadjusted P value less than .05 was considered significant. A test of interaction was used to identify genes that predict treatment benefit; because such tests have lower power compared with the main effect tests, an unadjusted P value of less than .10 was considered significant. No adjustment for multiplicity was applied. To estimate the false discovery rate (FDR), the Benjamini-Hochberg method was used within each study,36 and a permutation-based method37 was used across studies.

For each of the 375 genes assessed in the four studies, univariate t tests were performed to identify mean differences in gene expression between patients with stage II and stage III disease in each study. Additionally, Cox proportional hazards regression models of gene expression, stage, and the interaction of gene expression and stage, stratified by study, were examined, and a P value of less than .10 for interaction was considered significant. In the absence of strong evidence of stage differences, data across stages were combined for gene discovery and algorithm development.

To identify clusters of coexpressed genes and to facilitate the understanding of important biologic pathways, unsupervised hierarchical clustering of genes was performed using Pearson r as the distance measure for gene expression and the unweighted pair-group average as the amalgamation method.35 Similar results were obtained using other methods, such as principal component analysis.

A smaller subset of genes significantly and consistently related to risk of recurrence was identified by examining the results across studies. Multiple factors were considered for gene inclusion in algorithm development, including, but not limited to, the known role of the genes in important biologic pathways, analytic performance, and range of expression. The final gene panels and algorithms for prediction of recurrence risk (ie, recurrence score; Table 1) and chemotherapy benefit (ie, treatment score; Table 2) were derived as described in the text of the Appendix (online only).

Table 1.

Prediction of Recurrence Risk: Kaplan-Meier Estimates of Recurrence Risk at 3 Years and Associated 95% CIs from Bootstrap Analysis for Patients With Stage II Disease in Surgery-Alone Studies

Recurrence Risk Group Patients(median %) Risk of Recurrence at3 Years (%) 95% CI
Low (RS < 30) 25 8 5 to 12
Intermediate (RS 31-40) 39 11 7 to 15
High (RS ≥ 41) 37 25 18 to 32

Abbreviation: RS, recurrence score.

Table 2.

Prediction of Chemotherapy Benefit: Kaplan-Meier Estimates of Recurrence Risk at 3 Years by Treatment and FU/LV Benefit and Associated 95% CIs From Bootstrap Analysis for Patients With Stage II Disease

Chemotherapy Benefit Group* Recurrence at 3 Years
Result at 3 Years
Surgery Alone
Surgery + FU/LV
Risk (%) 95% CI Risk (%) 95% CI Benefit (%) 95% CI
Low 16 11 to 23 19 9 to 31 −3 −16 to 9
Intermediate 10 6 to 14 7 2 to 14 3 −5 to 10
High 15 10 to 20 7 2 to 13 8 0 to 15

Abbreviations: FU, fluorouracil; LV, leucovorin.

*

The chemotherapy benefit groups are defined using the recurrence score and treatment score calculated as described in Figures 3 and 4. The definition of the chemotherapy benefit groups is provided in Appendix Table A3 (online only).

Bootstrap methods38,39 were used to evaluate the extent to which recurrence risk differed among the recurrence risk groups defined by recurrence score for patients with stage II disease. A total of 1,000 bootstrap samples were drawn randomly with replacement from the pooled data set, taking variability between studies into account. Kaplan-Meier estimates of recurrence risk at 3 years were obtained for each recurrence risk group. Median recurrence risk estimates across all bootstrap samples and percentile CIs were reported. A similar approach was used to assess the results of the final multigene algorithm to predict FU/LV benefit, in which patients were divided in chemotherapy benefit groups on the basis of both their recurrence scores and treatment scores (Appendix Table A3, online only). Data were analyzed independently by the NSABP Biostatistical Center, Cleveland Clinic, and Genomic Health, Inc for individual studies. Analyses across four studies were conducted by Genomic Health.

RESULTS

The final numbers of evaluable patients were 270 in the C-01/C-02, 765 in the CC, 308 in the C-04, and 508 in the C-06 cohort. The outcomes and clinical/demographic characteristics of evaluable patients with tumor blocks were similar to those observed in the parent NSABP studies.

The baseline characteristics of the three NSABP cohorts were generally similar; patients from CC differed in age, percentage of right-sided tumors, number of lymph nodes examined, and percentage of stage II versus stage III disease (Appendix Table A1). Univariate Cox proportional hazards regression identified nodal status (0 positive nodes and ≥ 12 nodes examined, 0 positive nodes and < 12 nodes examined, 1 to 3 or ≥ 4 positive nodes) as the most significant clinical/pathologic predictor of RFI (P < .001) in all studies (Appendix Tables A4, A5, A6, and A7, online only; Appendix Figs A2A, A2B, A2C, and A2D, online only). T stage, available in adequate numbers of patients in CC, was associated with RFI (T4 v other; P = .003; Appendix Table A5). MMR was not associated with RFI in CC (P = .27; Appendix Table A5).

Univariate analysis identified 143 genes as significantly related to RFI in the C-01/C-02 cohort, 119 in the CC cohort, 143 in the C-04 cohort, and 169 in the C-06 cohort; 27%, 16%, 27%, and 11% of these genes, respectively, were expected to be false discoveries. When studies were pooled, the FDR was markedly lower. In the multivariate analysis, 43%, 74%, 50%, and 84% of the genes identified in the univariate analysis retained significance in C-01/C-02, CC, C-04, and C-06, respectively, and had similar HRs in both analyses (Appendix Figs A3 and A4, online only, for surgery-alone studies; similar results for studies of surgery + FU/LV not shown), which suggests that gene expression contributes information about recurrence beyond standard clinical and pathologic covariates. In these multivariate analyses, the contribution of nodal status was consistently statistically significant.

The relationship between gene expression, tumor stage, and RFI was investigated across studies. In univariate analyses, six of the 375 genes had significant (P < .05) mean differences in expression between patients with stage II and stage III disease in all four studies. Thirty-three genes had a significant interaction of gene expression and stage (P < .1) in Cox proportional hazards models stratified by study; 32 of these 33 genes were potential false positives (FDR = 97%), which suggests that interaction between gene expression and stage was weak. The coexpression of genes examined using cluster analysis was virtually identical in patients with stage II and stage III disease. Agreement between univariate HRs for patients with stage II and stage III disease is illustrated in Figure 1 for genes significantly associated with RFI in both surgery-alone studies and at least one study of surgery plus FU/LV. HRs were generally similar with overlapping CIs, with a few exceptions that could be chance findings due to multiplicity of testing across multiple genes and studies. These results support pooling data across stages for gene discovery and algorithm development.

Fig 1.

Fig 1.

Hazard ratio estimates and 95% CIs for gene expression from univariate Cox PH regression models of recurrence-free interval in patients on studies C-01/C-02, CC, C-04, and C-06 by tumor stage for the 48 genes that were significantly related to recurrence-free interval (A) in both of the surgery-only studies as well as (B) in at least one study of surgery + fluorouracil and leucovorin.

Recurrence risk genes were expected to have a similar relationship with RFI when measured in patients treated with surgery alone or surgery followed by FU/LV. A total of 48 (13%) of the 375 genes studied in all four development studies were significantly (P < .05) associated with RFI in both surgery alone studies and at least one study of surgery plus FU/LV. Fewer than one of these 48 genes is expected to be a false discovery. Cluster analysis identified two relatively distinct gene groups: a stromal gene group (containing several subgroups such as early response) and a cell cycle gene group (Fig 2). Higher expression of stromal genes (eg, BGN, FAP, GADD45B, and PAI) was associated with higher risk of recurrence, whereas higher expression of cell cycle genes (eg, Ki–67, MYBL2, and MCM2) was associated with lower risk of recurrence.

Fig 2.

Fig 2.

Unsupervised hierarchical clustering of the 48 genes significantly related to recurrence-free interval in both surgery-only studies and at least one study of surgery + fluorouracil and leucovorin using data from all four studies.

In contrast to recurrence risk genes, the genes predictive of differential FU/LV benefit are required to exhibit a different relationship with outcome (ie, different HRs) in patients treated with surgery alone compared with patients treated with surgery plus FU/LV. A total of 66 (18%) of 375 genes studied in all four development studies had interactions of gene expression and treatment that were significant at the less than .10 level if the data across the four studies were pooled (13 genes with P < .01; 45 genes with P < .05); four of these genes were also associated with risk of recurrence at the P < .05 level. Approximately 37 of these 66 genes are expected to be false discoveries; this was expected, given the lower statistical power associated with the analysis of interaction. Among 66 potentially predictive genes, there were a large number of genes involved in multiple stages of the cell cycle and apoptosis (ie, MAD2L1, AURKB, BIK, BUB1, CDC2), and higher expression was associated with greater differential benefit from FU/LV (Appendix Fig A5, online only). There were also a prominent stress response/hypoxia signature (ie, HSPE1, NR4A1, RhoB, HIF1A); multifunctional transcription factors (RUNX1, CREBBP, KLF5); and genes associated with wnt signaling (AXIN2 and LEF), MMR (MSH2 and MSH3), and angiogenesis (EFNB2). Higher expression of some of these genes (eg, RUNX1, CREBBP, KLF5, and EFNB2) was associated with lower benefit from FU/LV.

Seven of the 48 recurrence risk genes and six of the 66 chemotherapy benefit genes were selected to create the final recurrence score and treatment score algorithms (Appendix Figs A1 and A5; described in the Appendix). The results of bootstrap analyses to assess the predictive ability of the recurrence score are listed in Table 1 for patients with stage II disease treated with surgery alone (C-01/C-02 and CC cohorts). Patients were divided into three recurrence risk groups on the basis of the calculated recurrence score (ie, < 30, 30-40, and ≥ 41). The recurrence score separated the 632 patients with stage II disease into groups that had a sizable difference in estimates of risk of recurrence between the high- and the low-risk groups.

The results of bootstrap analyses to assess the predictive ability of the treatment score are listed in Table 2 for the 870 patients with stage II disease treated with surgery alone or surgery plus FU/LV. Patients were divided into three benefit groups (Appendix). For comparison, the overall 3-year risk of recurrence of patients with stage II disease was 14% for the surgery-alone group and was 10% for patients treated with surgery plus FU/LV. The correlation between the recurrence score and treatment score was relatively low (r = −0.4) in these studies, which suggests that the determinants of recurrence risk and differential FU/LV benefit may be distinct.

The performance of these algorithms was evaluated on the data set used for algorithm development; hence, the results in Tables 1 and 2 are likely to be optimistic. These algorithms will be validated on an independent data set of patients with stage II colon cancer from the QUASAR study.11

DISCUSSION

Our strategy for discovering genes related to recurrence risk and differential benefit with adjuvant FU/LV chemotherapy has been to perform multiple, large, independent studies to identify those genes most consistently and strongly related to clinical outcome (Appendix Fig A1).40,41 The results reported here are based on data from 1,851 patients, using standardized assay technology in a single laboratory. This approach is in contrast to algorithms developed from much larger numbers of genes and significantly smaller sample sizes.1721

We have identified 48 genes that have significant and similar relationships with RFI and 66 genes that have different relationships with RFI in patients treated with surgery alone compared with patients treated with surgery plus FU/LV: the former genes are likely to predict recurrence, whereas the latter genes are likely to predict differential benefit with adjuvant FU/LV therapy. A large proportion of these genes remain significantly associated with RFI after analysis is controlled for the effects of numerous clinical/pathologic covariates, including nodal status, which also contributed significantly to prediction of recurrence risk.

This report highlights several challenges to biomarker development in colon cancer. First, our ability to identify genes predictive of differential treatment benefit was limited by the lack of large, randomized clinical trials with tumor specimens (beyond the QUASAR validation trial). Second, the lower power of the test of interaction leads to a high FDR among the candidate predictive genes, of greater than 50% in our studies. Finally, the question of whether stage II and III disease are biologically similar or dissimilar is unresolved. However, for the vast majority of genes, we saw no strong difference between stages in the relationship of gene expression and RFI, and an additional analysis that compared patients who had stage II disease and ≥ 12 nodes examined with patients who had stage III disease confirmed these findings (data not shown).

Our strategy has led to the discovery of recurrence risk genes that can be confidently associated with clinical outcome and are generally different from the genes identified in our breast cancer studies (with the exceptions of Ki-67 and MYBL2)42 or the genes previously reported in colon cancer.1721 Higher expression of cell cycle genes was associated with an increased RFI; this observation is similar to that reported for Ki-67 in colon cancer43,44 but is opposite to the relationship observed in breast cancer studies.42 The association of stromal gene expression with colon cancer recurrence provides an elegant molecular explanation for Dukes' original observation that invasion is the critical characteristic that should be used in staging colorectal cancer.45,46

Some of the genes that were identified as predictive of chemotherapy benefit are not unexpected. Sensitivity to FU should be affected by factors related to the level of proliferation (ie, cell-cycle related genes), the induction of apoptosis and hypoxia,47 FU metabolism, and MMR.48 However, it is not clear why the expression of other genes, such as GJB2 or HES6, which are associated with gap junction communication and notch signaling respectively, would predict FU benefit. The quantitative expression of the genes related to FU activation/metabolism (TS, DPD, TP)15,4954 or to the markers (hMLH1, hMSH2) associated with MMR5557 were not associated with differential FU/LV benefit in our studies; current guidelines by the American Society of Clinical Oncology conclude that there is insufficient evidence to recommend the use of these markers as predictors of response to therapy.23

This report describes our process for identifying genes that can be used to estimate recurrence risk and differential FU/LV chemotherapy benefit on the basis of the relationship of quantitative gene expression at the time of diagnosis and clinical outcome in patients with stage II/III colon cancer treated with surgery or surgery plus FU/LV. The results of these four studies have been used to develop a multigene assay (Figs 3 and 4) for prediction of recurrence risk and differential benefit of adjuvant FU/LV chemotherapy that can be used to divide patients with stage II colon cancer into groups with different likelihoods of recurrence and treatment benefit. The requirement for external validation is being addressed in QUASAR,11 a large, independent study of patients with stage II colon cancer randomly assigned to surgery alone or to surgery followed by adjuvant FU/LV chemotherapy.

Fig 3.

Fig 3.

Recurrence score gene panel and algorithm. The recurrence score that is based on 12 genes (seven cancer-related genes and five reference genes) is derived from the reference-normalized expression measurements in four steps and is scaled from 0 to 100. First, expression of each gene is normalized relative to the expression of the five reference genes (ie, ATP5E, GPX1, PGK1, UBB, and VDAC2). Reference-normalized expression measurements range from two to 15, with a 1-unit increase reflecting approximately twice as much input RNA. Expression of individual genes is given a threshold as follows: if FAP, Ki-67 or MYBL2 measurement is less than 6.5 CT, it is considered to be 6.5; if GADD45B measurement is less than 5 CT, then it is considered to be 5. Genes are grouped on the basis of function and/or correlated expression. Second, the stromal and cell cycle group scores are calculated as averages of the reference-normalized, individual gene expression measurements as follows: stromal group score = (BGN + FAP + INHBA) ÷ 3; cell cycle group score = (Ki-67 + C-MYC + MYBL2) ÷ 3. Third, the unscaled recurrence score (RSu) is calculated with the use of coefficients that are defined on the basis of regression analysis of gene expression and recurrence in four development studies: RSu = + 0.15 × stromal group score − 0.30 × cell cycle group score + 0.15 × GADD45B. A plus sign indicates that increased expression is associated with increased risk of recurrence, and a minus sign indicates that increased expression is associated with decreased risk of recurrence. Fourth, the recurrence score (RS) is rescaled as follows: RS = 44 × (RSu + 0.82); if RS is less than 0, then RS = 0, and if RS is greater than 100, then RS = 100.

Fig 4.

Fig 4.

Treatment score gene panel and algorithm. The treatment score that is based on 11 genes (six cancer-related genes and the same five reference genes; Fig 3) is derived from the reference-normalized expression measurements in three steps and is scaled from 0 to 100. First, expression of each gene is normalized relative to the expression of the five reference genes (ATP5E, GPX1, PGK1, UBB, and VDAC2). Expression of individual genes is given a threshold as follows: if MAD2L1 or RUNX1 measurement is less than 5.5 CT, it is considered to be 5.5; if BIK measurement is less than 6 CT, then it is considered to be 6; and if EFNB2 measurement is less than 5 CT, then it is considered to be 5. Second, the unscaled treatment score (TSu) is calculated with the use of coefficients that are defined on the basis of regression analysis of gene expression and chemotherapy benefit in four development studies: TSu = −0.3 × EFNB2 − 0.04 × RUNX1 + 0.1 × MAD2L1 + 0.3 × BIK + 0.1 × AXIN2 + 0.1 × HSPE1. A plus sign indicates that increased expression is associated with increased chemotherapy benefit, and a minus sign indicates that increased expression is associated with decreased chemotherapy benefit. Third, the treatment score (TS) is rescaled as follows: TS = 37 × (TSu − 1); if TS is less than 0, then TS = 0, and if TS is greater than 100, then TS = 100.

Acknowledgment

We thank Barbara C. Good, PhD, Director of Scientific Publications for the National Surgical Adjuvant Breast and Bowel Project, for editorial assistance; and Meike Labusch, Angela Chen, Anhthu Nguyen, Bhavin Padhiar, Debjani Dutta, Jayadevi Krishnakumar, Jennie Jeong, Jenny Wu, Hyun Soo Son, Mei-Lan Liu, Mylan Pho, Ranjana Ambannavar, Lauren Intagliata, Freda Lane, James Hackett, and Jeanne Yue from Genomic Health, Inc, for their contributions to sample and data processing and data analysis.

Appendix

Development of the Colon Cancer Recurrence Score and Treatment Score Assays

To develop a tumor gene expression assay for use with tumor blocks that are routinely prepared following surgery, we used a multistep approach.

First, a real-time reverse transcriptase polymerase chain reaction method to quantify the expression of hundreds of genes in RNA isolated from three 10-μm sections of fixed, paraffin-embedded tumor tissue was developed.24

Second, we selected 761 candidate genes from the published literature, genomic databases, pathway analysis, and from microarray-based gene expression profiling experiments performed with fresh frozen tissue.1721 The 761 candidate genes are listed in Appendix Table A2 (online only).

Third, we performed four independent clinical studies in a total of 1,851 patients with colon cancer to test the relationship between the expression of the candidate genes and time to recurrence. We reasoned that in any single gene expression study a considerable number of genes may correlate with outcome as a result of chance alone. To identify true positives, we performed multiple independent studies to identify whether expression of any of the candidate genes correlated with recurrence across the studies. We hypothesized that the genes most highly correlated with recurrence would survive evaluation across diverse patients and treatments, and we selected heterogeneous populations for development of the gene list. After National Surgical Adjuvant Breast and Bowel Project (NSABP) C-01/C-02 and NSABP C-04 were conducted, the gene list was reduced to 375 genes on the basis of the strength of the relationship between gene expression and recurrence risk as well as the association of gene expression with chemotherapy benefit; and the NSABP C-06 patients and Cleveland Clinic cohorts were studied with 375 genes (Appendix Table A2). There were 48 genes for which expression was associated with recurrence in three of four studies at an unadjusted P value less than .05, and 66 genes had an interaction of gene expression and chemotherapy treatment significant at an unadjusted P < .1.

Fourth, we used the results from the four development studies to select the final gene panels and to design algorithms to compute a recurrence score (RS) and a treatment score (TS) for each tumor sample. The list of gene candidates was narrowed down using a number of considerations that included, but were not limited to, the strength of the associations with recurrence, the consistency of performance across studies, consistency of performance in patients with stage II and stage III disease, and analytic performance. The correlation of the expression of the genes with respect to each other was analyzed by unsupervised cluster analysis and by principal component analysis. Two major gene groups were identified among the prognostic genes: a stromal group (containing extracellular matrix genes, such as BGN, FAP, INHBA, and SPARC; early response genes, such as GADD45B; and invasion genes, such as PAI), and a cell cycle group (genes such as Ki-67, MYBL2, MCM2). Increased expression of stromal genes was associated with increased risk of recurrence, whereas increased expression of cell cycle genes was associated with decreased risk of recurrence. Among the 66 predictive genes, there were several multifunctional transcription factors (RUNX1, TCF1, CREBBP, KLF5) and genes involved in cell cycle and apoptosis (eg, MAD2L1, AURKB, BIK, TOP2A, BUB1, CDC2), hypoxia/stress response (HSPE1, NR4A1, RhoB, HIF1A, CREBBP, EPAS), wnt signaling (AXIN 2 and LEF), mismatch repair (MSH2 and MSH3), and angiogenesis (EFNB2). Analyses were then performed to determine the appropriate number of terms to include in the model and the functional forms of the variables. For this purpose, we used correlation analysis, dimension reduction (including stepwise variable selection and classification and regression trees), Martingale residual analysis, and bootstrap resampling. Multiple analyses across the four studies were conducted to determine whether models with a larger number of genes were better able to predict risk of recurrence and/or chemotherapy benefit and whether having fewer genes resulted in loss of robustness. We observed that relatively parsimonious models with anywhere from six to 10 recurrence-risk genes and five to six chemotherapy-benefit genes were adequate. The selection of the final seven recurrence-risk and six chemotherapy-benefit genes was based primarily on the strength of their performance across all studies and the consistency of primer/probe performance in the assay.

Fig A1.

Fig A1.

Outline of the strategy for determining relationships between tumor gene expression, disease recurrence, and differential benefit from fluorouracil (FU) plus leucovorin (LV). NSABP, National Surgical Adjuvant Breast and Bowel Project; QUASAR, Quick and Simple and Reliable.

Fig A2.

Fig A2.

(A) Kaplan-Meier plot of recurrence-free interval by nodal status on National Surgical Adjuvant Breast and Bowel Project (NSABP) C-01/C-02: 0 positive nodes and ≥ 12 nodes examined (n = 62), 0 positive nodes and less than 12 nodes examined (n = 64), 1 to 3 positive nodes (n = 94), or ≥ 4 positive nodes (n = 41). (B) Kaplan-Meier plot of recurrence-free interval by nodal status at the Cleveland Clinic: 0 positive nodes and ≥ 12 nodes examined (n = 387), 0 positive nodes and less than 12 nodes examined (n = 117), 1 to 3 positive nodes (n = 201), or ≥ 4 positive nodes (n = 60). (C) Kaplan-Meier plot of recurrence-free interval by nodal status on NSABP C-04: 0 positive nodes and ≥ 12 nodes examined (n = 66), 0 positive nodes and less than 12 nodes examined (n = 68), 1 to 3 positive nodes (n = 114), or ≥ 4 positive nodes (n = 56). (D) Kaplan-Meier plot of recurrence-free interval by nodal status on NSABP C-06: 0 positive nodes and ≥ 12 nodes examined (n = 119), 0 positive nodes and less than 12 nodes examined (n = 116), 1 to 3 positive nodes (n = 189), or ≥ 4 positive nodes (n = 84).

Fig A3.

Fig A3.

Agreement of the univariate and multivariate hazard ratios (HRs) for 143 genes significantly related to recurrence-free interval for patients on the National Surgical Adjuvant Breast and Bowel Project C-01/C02 study.

Fig A4.

Fig A4.

Agreement of the univariate and multivariate hazard ratios (HRs) for 119 genes significantly related to recurrence-free interval for patients on the Cleveland Clinic study.

Fig A5.

Fig A5.

Unsupervised hierarchical clustering of the 66 genes with significant gene by treatment interaction using data from all four studies.

Table A1.

Demographics and Baseline Medical Characteristics of Four Study Cohorts

Characteristic No. of Patients Patients by Study Cohort
NSABPC-01/C-02
NSABP C-04
Cleveland Clinic
NSABP C-06
No. % No. % No. % No. %
Sex
    Female 869 129 47.8 144 46.8 350 45.8 246 48.4
    Male 982 141 52.2 164 53.2 415 54.2 262 51.6
Age, years
    ≥ 60 1,209 164 60.7 144 46.8 603 78.8 298 58.7
    < 60 642 106 39.3 164 53.2 162 21.2 210 41.3
Tumor location
    Left 405 69 25.6 68 22.1 153 20.0 115 22.6
    Rectosigmoid 635 96 35.6 110 35.7 252 32.9 177 34.8
    Right 786 95 35.2 122 39.6 360 47.1 209 41.1
    Multiple or unknown 25 10 3.7 8 2.6 0 0.0 7 1.4
Surgical procedure
    Colectomy/hemicolectomy 1,196 184 68.1 181 58.8 526 68.8 305 60.0
    Segmental/anterior resection 563 56 20.7 109 35.4 205 26.8 193 38.0
    Other/unknown 92 30 11.1 18 5.8 34 4.4 10 2.0
No. of nodes examined
    < 12 700 129 50.2 154 51.2 175 22.9 242 47.7
    ≥ 12 1,130 128 49.8 147 48.8 590 77.1 265 52.3
No. of positive nodes
    0 1,007 131 49.2 137 44.6 504 65.9 235 46.3
    1-3 598 94 35.3 114 37.1 201 26.3 189 37.2
    ≥ 4 241 41 15.4 56 18.2 60 7.8 84 16.5
Nodal status of examined nodes
    0 of < 12 examined 365 64 24.5 68 22.4 117 15.3 116 22.8
    0 of ≥ 12 examined 634 62 23.8 66 21.7 387 50.6 119 23.4
    1-3 overall 598 94 36.0 114 37.5 201 26.3 189 37.2
    ≥ 4 overall 241 41 15.7 56 18.4 60 7.8 84 16.5
Tumor stage
    II 1,007 131 48.5 137 44.5 504 65.9 235 46.3
    III 844 139 51.5 171 55.5 261 34.1 273 53.7
Tumor grade
    High 460 66 24.5 48 15.6 173 22.7 173 34.1
    Low 1,386 203 75.5 260 84.4 588 77.3 335 65.9
Mucinous status
    Mucinous 295 23 8.5 23 7.5 146 19.1 103 20.3
    Not mucinous 1,556 247 91.5 285 92.5 619 80.9 405 79.7

Abbreviations: NSABP, National Surgical Adjuvant Breast and Bowel Project.

Table A2.

Listing of the 761 Candidate Genes

No. Gene Name Sequence ID 375 Gene Panel No. Gene Name Sequence ID 375 Gene Panel
1 ABCB1 NM_000927.2 Yes 384 IL6ST NM_002184.2 Yes
2 ABCC5 NM_005688.1 Yes 385 IL-8 NM_000584.2
3 ABCC6 NM_001171.2 Yes 386 ILT-2 NM_006669.1
4 A-Catenin NM_001903.1 387 IMP-1 NM_006546.2
5 ACP1 NM_004300.2 388 IMP2 NM_006548.3
6 ADAM10 NM_001110.1 389 ING1L NM_001564.1
7 ADAM17 NM_003183.3 390 ING5 NM_032329.4
8 ADAMTS12 NM_030955.2 Yes 391 INHA NM_002191.2
9 ADPRT NM_001618.2 392 INHBA NM_002192.1 Yes
10 AGXT NM_000030.1 393 INHBB NM_002193.1
11 AKAP12 NM_005100.2 Yes 394 IRS1 NM_005544.1 Yes
12 AKT1 NM_005163.1 395 ITGA3 NM_002204.1
13 AKT2 NM_001626.2 396 ITGA4 NM_000885.2
14 AKT3 NM_005465.1 Yes 397 ITGA5 NM_002205.1 Yes
15 AL137428 AL137428.1 398 ITGA6 NM_000210.1
16 ALCAM NM_001627.1 Yes 399 ITGA7 NM_002206.1
17 ALDH1A1 NM_000689.1 400 ITGAV NM_002210.2 Yes
18 ALDOA NM_000034.2 401 ITGB1 NM_002211.2 Yes
19 AMFR NM_001144.2 Yes 402 ITGB3 NM_000212.1 Yes
20 ANGPT2 NM_001147.1 Yes 403 ITGB4 NM_000213.2 Yes
21 ANTXR1 NM_032208.1 Yes 404 ITGB5 NM_002213.3
22 ANXA1 NM_000700.1 Yes 405 KCNH2 iso a/b NM_000238.2
23 ANXA2 NM_004039.1 Yes 406 KCNH2 iso a/c NM_172057.1 Yes
24 ANXA5 NM_001154.2 Yes 407 KCNK4 NM_016611.2
25 AP-1 NM_002228.2 408 KDR NM_002253.1
26 APC NM_000038.1 Yes 409 Ki-67 NM_002417.1 Yes
27 APEX-1 NM_001641.2 410 KIAA0125 NM_014792.2
28 APG-1 NM_014278.2 Yes 411 KIF22 NM_007317.1 Yes
29 APN NM_001150.1 412 KIF2C NM_006845.2
30 APOC1 NM_001645.3 413 KIFC1 XM_371813.1 Yes
31 AREG NM_001657.1 Yes 414 Kitlng NM_000899.1
32 ARG NM_005158.2 415 KLF5 NM_001730.3 Yes
33 ARHF NM_019034.2 416 KLF6 NM_001300.4 Yes
34 ATOH1 NM_005172.1 417 KLK10 NM_002776.1 Yes
35 ATP5A1 NM_004046.3 Yes 418 KLK6 NM_002774.2 Yes
36 ATP5E NM_006886.2 Yes 419 KLRK1 NM_007360.1 Yes
37 AURKB NM_004217.1 Yes 420 KNTC2 NM_006101.1
38 Axin 2 NM_004655.2 Yes 421 K-ras NM_033360.2 Yes
39 axin1 NM_003502.2 Yes 422 K-ras mutant 1 GHI_k-ras_mut1 Yes
40 BAD NM_032989.1 Yes 423 K-ras mutant 2 GHI_k-ras_mut2 Yes
41 BAG1 NM_004323.2 424 K-rasmutant 3 GHI_k-ras_mut3 Yes
42 BAG2 NM_004282.2 425 KRAS2 NM_004985.3
43 BAG3 NM_004281.2 426 KRT19 NM_002276.1
44 Bak NM_001188.1 427 KRT8 NM_002273.1 Yes
45 Bax NM_004324.1 Yes 428 LAMA3 NM_000227.2 Yes
46 BBC3 NM_014417.1 429 LAMB3 NM_000228.1
47 BCAS1 NM_003657.1 430 LAMC2 NM_005562.1 Yes
48 B-Catenin NM_001904.1 Yes 431 LAT NM_014387.2 Yes
49 Bcl2 NM_000633.1 432 LCN2 NM_005564.2
50 BCL2L10 NM_020396.2 433 LDLRAP1 NM_015627.1
51 BCL2L11 NM_138621.1 Yes 434 LEF NM_016269.2 Yes
52 BCL2L12 NM_138639.1 435 LGALS3 NM_002306.1 Yes
53 Bclx NM_001191.1 436 LGMN NM_001008530
54 BCRP NM_004827.1 437 LILRB3 NM_006864.1
55 BFGF NM_007083.1 438 LMNB1 NM_005573.1 Yes
56 BGN NM_001711.3 Yes 439 LMYC NM_012421.1 Yes
57 BID NM_001196.2 440 LOX NM_002317.3 Yes
58 BIK NM_001197.3 Yes 441 LOXL2 NM_002318.1 Yes
59 BIN1 NM_004305.1 442 LRP5 NM_002335.1 Yes
60 BLMH NM_000386.2 Yes 443 LRP6 NM_002336.1 Yes
61 BMP2 NM_001200.1 444 LY6D NM_003695.2
62 BMP4 NM_001202.2 445 MAD NM_002357.1
63 BMP7 NM_001719.1 446 MAD1L1 NM_003550.1 Yes
64 BMPR1A NM_004329.2 447 MAD2L1 NM_002358.2 Yes
65 BRAF NM_004333.1 Yes 448 MADH2 NM_005901.2 Yes
66 Braf Mutant 1 GHI_BRAF_mut4 Yes 449 MADH4 NM_005359.3 Yes
67 BRCA1 NM_007295.1 Yes 450 MADH7 NM_005904.1 Yes
68 BRCA2 NM_000059.1 Yes 451 MAP2 NM_031846.1
69 BRK NM_005975.1 452 MAP2K1 NM_002755.2
70 BTF3 NM_001207.2 453 MAP3K1 XM_042066.8
71 BTRC NM_033637.2 454 MAPK14 NM_139012.1
72 BUB1 NM_004336.1 Yes 455 Maspin NM_002639.1 Yes
73 BUB1B NM_001211.3 456 MAX NM_002382.3
74 BUB3 NM_004725.1 457 MCM2 NM_004526.1 Yes
75 C20 orf1 NM_012112.2 Yes 458 MCM3 NM_002388.2 Yes
76 C20ORF126 NM_030815.2 Yes 459 MCM6 NM_005915.2 Yes
77 C8orf4 NM_020130.2 Yes 460 MCP1 NM_002982.1 Yes
78 CA9 NM_001216.1 461 MDK NM_002391.2
79 c-abl NM_005157.2 462 MDM2 NM_002392.1
80 Cad17 NM_004063.2 Yes 463 MGAT5 NM_002410.2 Yes
81 CALD1 NM_004342.4 Yes 464 MGMT NM_002412.1
82 CAPG NM_001747.1 Yes 465 mGST1 NM_020300.2
83 CAPN1 NM_005186.2 466 MMP1 NM_002421.2 Yes
84 CASP8 NM_033357.1 467 MMP12 NM_002426.1
85 CASP9 NM_001229.2 Yes 468 MMP2 NM_004530.1 Yes
86 CAT NM_001752.1 469 MMP7 NM_002423.2 Yes
87 CAV1 NM_001753.3 Yes 470 MMP9 NM_004994.1 Yes
88 CBL NM_005188.1 471 MRP1 NM_004996.2
89 CCL20 NM_004591.1 472 MRP2 NM_000392.1
90 CCL3 NM_002983.1 473 MRP3 NM_003786.2 Yes
91 CCNA2 NM_001237.2 Yes 474 MRP4 NM_005845.1
92 CCNB1 NM_031966.1 Yes 475 MRPL40 NM_003776.2
93 CCNB2 NM_004701.2 476 MSH2 NM_000251.1 Yes
94 CCND1 NM_001758.1 477 MSH3 NM_002439.1 Yes
95 CCND3 NM_001760.2 478 MSH6 NM_000179.1
96 CCNE1 NM_001238.1 479 MT3 NM_005954.2
97 CCNE2 NM_057749.1 Yes 480 MTA1 NM_004689.2
98 CCNE2variant 1 NM_057749var1 Yes 481 MUC1 NM_002456.1 Yes
99 CCR7 NM_001838.2 Yes 482 MUC2 NM_002457.1 Yes
100 CD105 NM_000118.1 483 MUC5B XM_039877.11
101 CD134 NM_003327.1 484 MUTYH NM_012222.1
102 CD18 NM_000211.1 Yes 485 MVP NM_017458.1
103 CD24 NM_013230.1 Yes 486 MX1 NM_002462.2
104 CD28 NM_006139.1 487 MXD4 NM_006454.2
105 CD31 NM_000442.1 488 MYBL2 NM_002466.1 Yes
106 CD34 NM_001773.1 489 MYH11 NM_002474.1 Yes
107 CD3z NM_000734.1 Yes 490 MYLK NM_053025.1 Yes
108 CD44E X55150 Yes 491 NAT2 NM_000015.1
109 CD44s M59040.1 Yes 492 NAV2 NM_182964.3 Yes
110 CD44v3 AJ251595v3 493 NCAM1 NM_000615.1 Yes
111 CD44v6 AJ251595v6 Yes 494 NDE1 NM_017668.1
112 CD68 NM_001251.1 Yes 495 NDRG1 NM_006096.2
113 CD80 NM_005191.2 Yes 496 NDUFS3 NM_004551.1
114 CD82 NM_002231.2 497 NEDD8 NM_006156.1 Yes
115 CD8A NM_171827.1 498 NEK2 NM_002497.1 Yes
116 CD9 NM_001769.1 499 NF2 NM_000268.2
117 CDC2 NM_001786.2 Yes 500 NFKBp50 NM_003998.1 Yes
118 CDC20 NM_001255.1 Yes 501 NFKBp65 NM_021975.1
119 cdc25A NM_001789.1 502 NISCH NM_007184.1
120 CDC25B NM_021874.1 503 Nkd-1 NM_033119.3 Yes
121 CDC25C NM_001790.2 Yes 504 NMB NM_021077.1
122 CDC4 NM_018315.2 Yes 505 NMBR NM_002511.1
123 CDC42 NM_001791.2 506 NME1 NM_000269.1 Yes
124 CDC42BPA NM_003607.2 Yes 507 NOS3 NM_000603.2
125 CDC6 NM_001254.2 Yes 508 NOTCH1 NM_017617.2 Yes
126 CDCA7variant 2 NM_145810.1 Yes 509 NOTCH2 NM_024408.2
127 CDH1 NM_004360.2 Yes 510 NPM1 NM_002520.2
128 CDH11 NM_001797.2 Yes 511 NR4A1 NM_002135.2 Yes
129 CDH3 NM_001793.3 Yes 512 NRG1 NM_013957.1
130 CDK2 NM_001798.2 513 NRP1 NM_003873.1 Yes
131 CDX1 NM_001804.1 514 NRP2 NM_003872.1 Yes
132 Cdx2 NM_001265.2 Yes 515 NTN1 NM_004822.1
133 CEACAM1 NM_001712.2 516 NUFIP1 NM_012345.1
134 CEACAM6 NM_002483.2 517 ODC1 NM_002539.1 Yes
135 CEBPB NM_005194.2 Yes 518 OPN NM_000582.1 Yes
136 CEGP1 NM_020974.1 519 ORC1L NM_004153.2
137 CENPA NM_001809.2 Yes 520 OSM NM_020530.3
138 CENPE NM_001813.1 521 OSMR NM_003999.1 Yes
139 CENPF NM_016343.2 Yes 522 P14ARF S78535.1 Yes
140 CES2 NM_003869.4 523 p16-INK4 L27211.1 Yes
141 CGA NM_001275.2 524 p21 NM_000389.1 Yes
142 CGB NM_000737.2 Yes 525 p27 NM_004064.1
143 CHAF1B NM_005441.1 526 P53 NM_000546.2
144 CHD2 NM_001271.1 527 p53R2 AB036063.1 Yes
145 CHFR NM_018223.1 Yes 528 PADI4 NM_012387.1
146 Chk1 NM_001274.1 Yes 529 PAI1 NM_000602.1 Yes
147 Chk2 NM_007194.1 530 Pak1 NM_002576.3
148 CIAP1 NM_001166.2 531 PARC NM_015089.1
149 cIAP2 NM_001165.2 Yes 532 PCAF NM_003884.3
150 c-kit NM_000222.1 533 PCNA NM_002592.1 Yes
151 CKS1B NM_001826.1 534 PDGFA NM_002607.2 Yes
152 CKS2 NM_001827.1 Yes 535 PDGFB NM_002608.1 Yes
153 Claudin 4 NM_001305.2 Yes 536 PDGFC NM_016205.1 Yes
154 CLDN1 NM_021101.3 Yes 537 PDGFD NM_025208.2 Yes
155 CLDN7 NM_001307.3 Yes 538 PDGFRa NM_006206.2 Yes
156 CLIC1 NM_001288.3 Yes 539 PDGFRb NM_002609.2
157 CLTC NM_004859.1 Yes 540 PFN1 NM_005022.2
158 CLU NM_001831.1 541 PFN2 NM_053024.1 Yes
159 cMet NM_000245.1 Yes 542 PGK1 NM_000291.1 Yes
160 c-myb NM_005375.1 Yes 543 PI3K NM_002646.2 Yes
161 cMYC NM_002467.1 Yes 544 PI3KC2A NM_002645.1
162 CNN NM_001299.2 545 PIK3CA NM_006218.1
163 COL1A1 NM_000088.2 Yes 546 PIM1 NM_002648.2
164 COL1A2 NM_000089.2 Yes 547 Pin1 NM_006221.1
165 COPS3 NM_003653.2 548 PKD1 NM_000296.2
166 COX2 NM_000963.1 549 PKR2 NM_002654.3 Yes
167 COX3 MITO_COX3 550 PLA2G2A NM_000300.2
168 CP NM_000096.1 551 PLAUR NM_002659.1
169 CRBP NM_002899.2 552 PLK NM_005030.2 Yes
170 CREBBP NM_004380.1 Yes 553 PLK3 NM_004073.2 Yes
171 CRIP2 NM_001312.1 554 PLOD2 NM_000935.2
172 cripto NM_003212.1 Yes 555 PMS1 NM_000534.2
173 CRK(a) NM_016823.2 556 PMS2 NM_000535.2
174 CRMP1 NM_001313.1 557 PPARG NM_005037.3
175 CRYAB NM_001885.1 Yes 558 PPID NM_005038.1
176 CSEL1 NM_001316.2 Yes 559 PPM1D NM_003620.1 Yes
177 CSF1 NM_000757.3 Yes 560 PPP2R4 NM_178001.1
178 CSK (SRC) NM_004383.1 561 PR NM_000926.2
179 c-Src NM_005417.3 Yes 562 PRDX2 NM_005809 Yes
180 CTAG1B NM_001327.1 563 PRDX3 NM_006793.2
181 CTGF NM_001901.1 Yes 564 PRDX4 NM_006406.1 Yes
182 CTHRC1 NM_138455.2 Yes 565 PRDX6 NM_004905.2
183 CTLA4 NM_005214.2 566 PRKCA NM_002737.1 Yes
184 CTNNBIP1 NM_020248.2 567 PRKCB1 NM_002738.5 Yes
185 CTSB NM_001908.1 Yes 568 PRKCD NM_006254.1
186 CTSD NM_001909.1 569 PRKR NM_002759.1
187 CTSH NM_004390.1 570 pS2 NM_003225.1 Yes
188 CTSL NM_001912.1 Yes 571 PTCH NM_000264.2 Yes
189 CTSL2 NM_001333.2 572 PTEN NM_000314.1 Yes
190 CUL1 NM_003592.2 573 PTGER3 NM_000957.2 Yes
191 CUL4A NM_003589.1 Yes 574 PTHLH NM_002820.1
192 CXCL12 NM_000609.3 Yes 575 PTHR1 NM_000316.1
193 CXCR4 NM_003467.1 Yes 576 PTK2 NM_005607.3
194 CYBA NM_000101.1 577 PTK2B NM_004103.3
195 CYP1B1 NM_000104.2 Yes 578 PTP4A3 NM_007079.2
196 CYP2C8 NM_000770.2 Yes 579 PTP4A3 v2 NM_032611.1 Yes
197 CYP3A4 NM_017460.3 Yes 580 PTPD1 NM_007039.2
198 CYR61 NM_001554.3 Yes 581 PTPN1 NM_002827.2
199 DAPK1 NM_004938.1 Yes 582 PTPRF NM_002840.2
200 DCC NM_005215.1 583 PTPRJ NM_002843.2 Yes
201 DCC_exons18-23 X76132_18-23 584 PTPRO NM_030667.1
202 DCC_exons6-7 X76132_6-7 585 PTTG1 NM_004219.2
203 DCK NM_000788.1 586 RAB32 NM_006834.2 Yes
204 DDB1 NM_001923.2 587 RAB6C NM_032144.1
205 DET1 NM_017996.2 588 RAC1 NM_006908.3
206 DHFR NM_000791.2 Yes 589 RAD51C NM_058216.1
207 DHPS NM_013407.1 590 RAD54L NM_003579.2 Yes
208 DIABLO NM_019887.1 591 RAF1 NM_002880.1 Yes
209 DIAPH1 NM_005219.2 592 RALBP1 NM_006788.2 Yes
210 DICER1 NM_177438.1 593 RANBP2 NM_006267.3 Yes
211 DKK1 NM_012242.1 Yes 594 ranBP7 NM_006391.1
212 DLC1 NM_006094.3 Yes 595 RANBP9 NM_005493.2
213 DPYD NM_000110.2 Yes 596 RAP1GDS1 NM_021159.3
214 DR4 NM_003844.1 Yes 597 RARA NM_000964.1
215 DR5 NM_003842.2 598 RARB NM_016152.2
216 DRG1 NM_004147.3 599 RASSF1 NM_007182.3
217 DSP NM_004415.1 600 RBM5 NM_005778.1
218 DTYMK NM_012145.1 601 RBX1 NM_014248.2 Yes
219 DUSP1 NM_004417.2 Yes 602 RCC1 NM_001269.2 Yes
220 DUSP2 NM_004418.2 603 REG4 NM_032044.2 Yes
221 DUT NM_001948.2 Yes 604 RFC NM_003056.1
222 DYRK1B NM_004714.1 605 RhoB NM_004040.2 Yes
223 E2F1 NM_005225.1 Yes 606 rhoC NM_175744.1 Yes
224 EDN1 NM_001955.1 607 RIZ1 NM_012231.1
225 EFNA1 NM_004428.2 Yes 608 RNF11 NM_014372.3
226 EFNA3 NM_004952.3 609 ROCK1 NM_005406.1 Yes
227 EFNB1 NM_004429.3 610 ROCK2 NM_004850.3 Yes
228 EFNB2 NM_004093.2 Yes 611 RPLPO NM_001002.2
229 EFP NM_005082.2 Yes 612 RPS13 NM_001017.2 Yes
230 EGFR NM_005228.1 613 RRM1 NM_001033.1 Yes
231 EGLN1 NM_022051.1 614 RRM2 NM_001034.1 Yes
232 EGLN3 NM_022073.2 Yes 615 RTN4 NM_007008.1
233 EGR1 NM_001964.2 Yes 616 RUNX1 NM_001754.2 Yes
234 EGR3 NM_004430.2 Yes 617 RXRA NM_002957.3
235 EI24 NM_004879.2 Yes 618 S100A1 NM_006271.1 Yes
236 EIF4E NM_001968.1 Yes 619 S100A2 NM_005978.2
237 EIF4EL3 NM_004846.1 Yes 620 S100A4 NM_002961.2 Yes
238 ELAVL1 NM_001419.2 Yes 621 S100A8 NM_002964.3
239 EMP1 NM_001423.1 Yes 622 S100A9 NM_002965.2
240 EMR3 NM_032571.2 623 S100P NM_005980.2 Yes
241 EMS1 NM_005231.2 624 SAT NM_002970.1 Yes
242 ENO1 NM_001428.2 Yes 625 SBA2 NM_018639.3 Yes
243 EP300 NM_001429.1 626 SDC1 NM_002997.1
244 EPAS1 NM_001430.3 Yes 627 SEMA3B NM_004636.1
245 EpCAM NM_002354.1 628 SEMA3F NM_004186.1
246 EPHA2 NM_004431.2 629 SEMA4B NM_020210.1 Yes
247 EPHB2 NM_004442.4 Yes 630 SFRP2 NM_003013.2 Yes
248 EPHB4 NM_004444.3 631 SFRP4 NM_003014.2 Yes
249 EphB6 NM_004445.1 Yes 632 SGCB NM_000232.1 Yes
250 EPM2A NM_005670.2 633 SHC1 NM_003029.3 Yes
251 ErbB3 NM_001982.1 634 SHH NM_000193.2
252 ERCC1 NM_001983.1 635 SI NM_001041.1 Yes
253 ERCC2 NM_000400.2 636 Siah-1 NM_003031.2
254 EREG NM_001432.1 Yes 637 SIAT4A NM_003033.2 Yes
255 ERK1 Z11696.1 638 SIAT7B NM_006456.1
256 ERK2 NM_002745.1 639 SIM2 NM_005069.2 Yes
257 ESPL1 NM_012291.1 Yes 640 SIN3A NM_015477.1
258 EstR1 NM_000125.1 641 SIR2 NM_012238.3 Yes
259 ETV4 NM_001986.1 642 SKP1A NM_006930.2
260 F3 NM_001993.2 Yes 643 SKP2 NM_005983.2 Yes
261 FABP4 NM_001442.1 Yes 644 SLC25A3 NM_213611.1 Yes
262 FAP NM_004460.2 Yes 645 SLC2A1 NM_006516.1
263 fas NM_000043.1 646 SLC31A1 NM_001859.2 Yes
264 fasl NM_000639.1 647 SLC5A8 NM_145913.2
265 FASN NM_004104.4 Yes 648 SLC7A5 NM_003486.4
266 FBXO5 NM_012177.2 Yes 649 SLPI NM_003064.2 Yes
267 FBXW7 NM_033632.1 650 SMARCA3 NM_003071.2 Yes
268 FDXR NM_004110.2 651 SNAI1 NM_005985.2
269 FES NM_002005.2 652 SNAI2 NM_003068.3 Yes
270 FGF18 NM_003862.1 Yes 653 SNRPF NM_003095.1 Yes
271 FGF2 NM_002006.2 Yes 654 SOD1 NM_000454.3 Yes
272 FGFR1 NM_023109.1 655 SOD2 NM_000636.1 Yes
273 FGFR2 isoform 1 NM_000141.2 656 SOS1 NM_005633.2 Yes
274 FHIT NM_002012.1 657 SOX17 NM_022454.2
275 FIGF NM_004469.2 658 SPARC NM_003118.1 Yes
276 FLJ12455 NM_022078.1 659 SPINT2 NM_021102.1 Yes
277 FLJ20712 AK000719.1 660 SPRY1 AK026960.1 Yes
278 FLT1 NM_002019.1 661 SPRY2 NM_005842.1 Yes
279 FLT4 NM_002020.1 662 SR-A1 NM_021228.1
280 FOS NM_005252.2 Yes 663 ST14 NM_021978.2 Yes
281 FOXO3A NM_001455.1 Yes 664 STAT1 NM_007315.1
282 FPGS NM_004957.3 Yes 665 STAT3 NM_003150.1
283 FRP1 NM_003012.2 666 STAT5A NM_003152.1
284 FST NM_006350.2 Yes 667 STAT5B NM_012448.1 Yes
285 Furin NM_002569.1 668 STC1 NM_003155.1 Yes
286 FUS NM_004960.1 669 STK11 NM_000455.3
287 FUT1 NM_000148.1 670 STK15 NM_003600.1 Yes
288 FUT3 NM_000149.1 671 STMN1 NM_005563.2
289 FUT6 NM_000150.1 Yes 672 STMY3 NM_005940.2 Yes
290 FXYD5 NM_014164.4 673 STS NM_000351.2
291 FYN NM_002037.3 Yes 674 SURV NM_001168.1 Yes
292 FZD1 NM_003505.1 Yes 675 TAGLN NM_003186.2 Yes
293 FZD2 NM_001466.2 676 TBP NM_003194.1
294 FZD6 NM_003506.2 677 TCF-1 NM_000545.3 Yes
295 G1P2 NM_005101.1 678 TCF-7 NM_003202.2
296 GADD45 NM_001924.2 679 TCF7L1 NM_031283.1
297 GADD45B NM_015675.1 Yes 680 TCF7L2 NM_030756.1
298 GADD45G NM_006705.2 681 TCFL4 NM_170607.2
299 GAGE4 NM_001474.1 682 TEK NM_000459.1
300 GBP1 NM_002053.1 683 TERC U86046.1 Yes
301 GBP2 NM_004120.2 Yes 684 TERT NM_003219.1
302 G-Catenin NM_002230.1 Yes 685 TFF3 NM_003226.1 Yes
303 GCLC NM_001498.1 686 TGFA NM_003236.1
304 GCLM NM_002061.1 687 TGFB2 NM_003238.1 Yes
305 GCNT1 NM_001490.3 Yes 688 TGFB3 NM_003239.1 Yes
306 GDF15 NM_004864.1 689 TGFBI NM_000358.1 Yes
307 GIT1 NM_014030.2 Yes 690 TGFBR1 NM_004612.1 Yes
308 GJA1 NM_000165.2 Yes 691 TGFBR2 NM_003242.2 Yes
309 GJB2 NM_004004.3 Yes 692 THBS1 NM_003246.1 Yes
310 GPX1 NM_000581.2 Yes 693 THY1 NM_006288.2 Yes
311 GPX2 NM_002083.1 694 TIMP1 NM_003254.1 Yes
312 Grb10 NM_005311.2 Yes 695 TIMP2 NM_003255.2 Yes
313 GRB14 NM_004490.1 696 TIMP3 NM_000362.2 Yes
314 GRB2 NM_002086.2 697 TJP1 NM_003257.1
315 GRB7 NM_005310.1 698 TK1 NM_003258.1 Yes
316 GRIK1 NM_000830.2 699 TLN1 NM_006289.2 Yes
317 GRO1 NM_001511.1 700 TMEPAI NM_020182.3 Yes
318 GRP NM_002091.1 701 TMSB10 NM_021103.2 Yes
319 GRPR NM_005314.1 Yes 702 TMSB4X NM_021109.2 Yes
320 GSK3B NM_002093.2 Yes 703 TNC NM_002160.1
321 GSTA3 NM_000847.3 704 TNF NM_000594.1
322 GSTM1 NM_000561.1 705 TNFRSF5 NM_001250.3
323 GSTM3 NM_000849.3 706 TNFRSF6B NM_003823.2
324 GSTp NM_000852.2 Yes 707 TNFSF4 NM_003326.2
325 GSTT1 NM_000853.1 Yes 708 TOP2A NM_001067.1 Yes
326 H2AFZ NM_002106.2 Yes 709 TOP2B NM_001068.1
327 HB-EGF NM_001945.1 Yes 710 TP NM_001953.2 Yes
328 hCRA a U78556.1 Yes 711 TP53BP1 NM_005657.1 Yes
329 HDAC1 NM_004964.2 Yes 712 TP53BP2 NM_005426.1 Yes
330 HDAC2 NM_001527.1 713 TP53I3 NM_004881.2
331 HDGF NM_004494.1 714 TRAG3 NM_004909.1 Yes
332 hENT1 NM_004955.1 715 TRAIL NM_003810.1 Yes
333 Hepsin NM_002151.1 716 TS NM_001071.1 Yes
334 HER2 NM_004448.1 Yes 717 TST NM_003312.4
335 Herstatin AF177761.2 718 TUBA1 NM_006000.1 Yes
336 HES6 NM_018645.3 Yes 719 TUBB NM_001069.1
337 HGF M29145.1 720 TUFM NM_003321.3 Yes
338 HIF1A NM_001530.1 Yes 721 TULP3 NM_003324.2
339 HK1 NM_000188.1 722 tusc4 NM_006545.4
340 HLA-DPB1 NM_002121.4 723 UBB NM_018955.1 Yes
341 HLA-DRA NM_019111.3 724 UBC NM_021009.2
342 HLA-DRB1 NM_002124.1 725 UBE2C NM_007019.2 Yes
343 HLA-G NM_002127.2 Yes 726 UBE2M NM_003969.1 Yes
344 HMGB1 NM_002128.3 727 UBL1 NM_003352.3
345 hMLH NM_000249.2 728 UCP2 NM_003355.2
346 HNRPAB NM_004499.2 Yes 729 UGT1A1 NM_000463.2
347 HNRPD NM_031370.2 Yes 730 UMPS NM_000373.1 Yes
348 HoxA1 NM_005522.3 731 UNC5A XM_030300.7
349 HoxA5 NM_019102.2 Yes 732 UNC5B NM_170744.2 Yes
350 HOXB13 NM_006361.2 Yes 733 UNC5C NM_003728.2
351 HOXB7 NM_004502.2 Yes 734 upa NM_002658.1 Yes
352 HRAS NM_005343.2 Yes 735 UPP1 NM_003364.2 Yes
353 HSBP1 NM_001537.1 736 VCAM1 NM_001078.2
354 HSD17B1 NM_000413.1 737 VCL NM_003373.2 Yes
355 HSD17B2 NM_002153.1 Yes 738 VCP NM_007126.2 Yes
356 HSPA1A NM_005345.4 Yes 739 VDAC1 NM_003374.1
357 HSPA1B NM_005346.3 Yes 740 VDAC2 NM_003375.2 Yes
358 HSPA4 NM_002154.3 741 VDR NM_000376.1
359 HSPA5 NM_005347.2 742 VEGF NM_003376.3 Yes
360 HSPA8 NM_006597.3 Yes 743 VEGF_altsplice1 AF486837.1 Yes
361 HSPB1 NM_001540.2 744 VEGF_altsplice2 AF214570.1 Yes
362 HSPCA NM_005348.2 745 VEGFB NM_003377.2 Yes
363 HSPE1 NM_002157.1 Yes 746 VEGFC NM_005429.2 Yes
364 HSPG2 NM_005529.2 Yes 747 VIM NM_003380.1 Yes
365 ICAM1 NM_000201.1 748 WIF NM_007191.2 Yes
366 ICAM2 NM_000873.2 Yes 749 WISP1 NM_003882.2 Yes
367 ID1 NM_002165.1 750 WNT2 NM_003391.1 Yes
368 ID2 NM_002166.1 751 Wnt-3a NM_033131.2
369 ID3 NM_002167.2 Yes 752 Wnt-5a NM_003392.2
370 ID4 NM_001546.2 Yes 753 Wnt-5b NM_032642.2
371 IFIT1 NM_001548.1 754 WWOX NM_016373.1
372 IGF1 NM_000618.1 Yes 755 XPA NM_000380.2
373 IGF1R NM_000875.2 756 XPC NM_004628.2
374 IGF2 NM_000612.2 757 XRCC1 NM_006297.1
375 IGFBP2 NM_000597.1 758 YB-1 NM_004559.1
376 IGFBP3 NM_000598.1 Yes 759 YWHAH NM_003405.2
377 IGFBP5 NM_000599.1 Yes 760 zbtb7 NM_015898.2
378 IGFBP6 NM_002178.1 761 ZG16 NM_152338.1
379 IGFBP7 NM_001553 Yes
380 IHH NM_002181.1
381 IL10 NM_000572.1
382 IL1B NM_000576.2
383 IL6 NM_000600.1

Table A3.

Definitions of the Chemotherapy Benefit Groups

Chemotherapy Benefit Group X = 0.859exp[1.839×RSu+3.526+1.781xTSu]−0.859exp[1.839×RSu]
Low X less than 2%
Intermediate X greater than or equal to 2% and less than 6%
High X greater than or equal to 6%

NOTE. The unscaled recurrence score and unscaled treatment score were used in combination to determine a chemotherapy benefit group for each patient, as the absolute chemotherapy benefit was also a function of baseline recurrence risk.

Table A4.

Relationship Between Baseline Patient Characteristics and RFI in NSABP C-01/C-02 According to Cox Regression Analysis

Variable No. of Patients HR 95% CI P
Female v male 270 0.82 0.57 to 1.19 .299
Age, per 1 year increase 270 1.00 0.99 to 1.02 .702
Age ≥ 60 v < 60 years 270 1.02 0.70 to 1.48 .929
Location of tumor 270 .006
    Right v left 2.46 1.45 to 4.19
    Rectosigmoid v left 1.76 1.02 to 3.04
    Multiple/unknown v left 2.21 0.82 to 5.93
Protocol (C-02 v C-01) 270 0.65 0.42 to 1.02 .052
Surgery + BCG v surgery only 270 1.37 0.94 to 2.00 .107
No. of nodes examined 270 .335
    < 12 v ≥ 12 1.18 0.81 to 1.73
    Unknown v≥ 12 1.81 0.82 to 4.00
No. of positive nodes 266 < .001
    1-3 v 0 1.73 1.13 to 2.65
    ≥ 4 v 0 2.94 1.80 to 4.78
Nodal involvement status 261 < .001
    0 of < 12 examined v 0 of ≥ 12 examined 2.45 1.27 to 4.72
    1-3 v 0 of ≥ 12 examined 2.79 1.50 to 5.19
    ≥ 4 v 0 of ≥ 12 examined 4.74 2.44 to 9.20
Stage III v II 270 2.10 1.43 to 3.08 < .001
Tumor grade at GHI
    First reading high v low 270 1.35 0.88 to 2.09 .180
    Second reading high v low 269 1.51 1.01 to 2.26 .054
Surgery 270 .346
    Segmental/anterior resection v colectomy/hemicolectomy 1.27 0.82 to 1.96
    Other/unknown v colectomy/hemicolectomy 0.78 0.42 to 1.48
Mucinous tumor 270 1.64 0.92 to 2.93 .115
Surgery year 270 0.95 0.88 to 1.02 .151

Abbreviations: RFI, recurrence-free interval; NSABP, National Surgical Adjuvant Breast and Bowel Project; HR, hazard ratio; BCG, Bacillus Calmette-Guérin; GHI, Genomic Health, Inc.

Table A5.

Relationship Between Baseline Patient Characteristics and RFI in CC Study According to Cox Regression Analysis

Variable No. of Patients HR 95% CI P
Female v male 765 0.93 0.67 to 1.28 .653
Age, per 1 year increase 765 1.02 1.00 to 1.03 .010
Age ≥ 60 v < 60 years 765 1.49 0.98 to 2.26 .054
Location of tumor 765 .609
    Right v left 1.25 0.80 to 1.95
    Rectosigmoid v left 1.15 0.71 to 1.85
No. of nodes examined
    < 12 v ≥ 12 765 1.66 1.17 to 2.36 .006
No. of positive nodes 765 < .001
    1-3 v 0 2.03 1.42 to 2.91
    ≥ 4 v 0 4.22 2.67 to 6.67
Nodal involvement status 765 < .001
    0 of < 12 examined v 0 of ≥ 12 examined 1.50 0.90 to 2.50
    1-3 v 0 of ≥ 12 examined 2.26 1.53 to 3.33
    ≥ 4 v0 of ≥ 12 examined 4.69 2.89 to 7.60)
Stage III v II 765 2.43 1.76 to 3.36 < .001
GHI tumor grade high v Low 765 0.87 0.61 to 1.26 .460
CC tumor grade high vLow 761 1.28 0.89 to 1.86 .195
Surgery 765 .947
    Segmental/anterior resection v colectomy/hemicolectomy 0.94 0.65 to 1.36
    Other/unknown v colectomy/hemicolectomy 0.94 0.41 to 2.15
Mucinous tumor 765 0.64 0.40 to 1.02 .049
Surgery year 765 .131
    1986-1990 v 1981-1985 0.90 0.59 to 1.38
    1991-1995 v 1981-1985 0.56 0.34 to 0.94
    1996-2000 v 1981-1985 0.88 0.57 to 1.35
T stage T4 v T1, T2, and T3 765 1.74 1.22 to 2.48 .003
Fixative 765 .090
    Hollandes v formalin 1.53 0.91 to 2.59
    Zenkers or Bouins v formalin 2.06 1.06 to 4.02
MMR deficient v proficient 712 0.75 0.45 to 1.27 .266
Surgeon 765 .742

Abbreviations: RFI, recurrence-free interval; CC, Cleveland Clinic; HR, hazard ratio; GHI, Genomic Health, Inc; MMR, mismatch repair.

Table A6.

Relationship Between Baseline Patient Characteristics and RFI in NSABP C-04 Study According to Cox Regression Analysis

Variable No. of Patients HR 95% CI P
Age ≥ 60 v < 60 years 308 0.76 0.50 to 1.15 .191
Age, per 1 year increase 308 0.99 0.97 to 1.01 .240
Female v male 308 0.81 0.53 to 1.23 .322
Race 308 .470
    Black v white 0.65 0.28 to 1.49
    Other v white 1.26 0.51 to 3.12
Location of tumor 308 .972
    Right v left 1.13 0.65 to 1.97
    Rectosigmoid v left 1.11 0.63 to 1.95
    Multiple/unknown v left 0.95 0.22 to 4.09
Surgery 308 .836
    Segmental/anterior resection v colectomy/hemicolectomy 1.13 0.73 to 1.75
    Other/unknown v colectomy/hemicolectomy 0.96 0.38 to 2.40
Tumor grade by GHI
    First reading 308 1.37 0.81 to 2.32 .260
    Second reading 308 1.04 0.64 to 1.68 .884
No. of nodes examined 308 .245
    < 12 v ≥ 12 1.37 0.90 to 2.10
    Unknown v ≥ 12 1.97 0.61 to 6.39
Tumor stage III v II 308 1.44 0.94 to 2.21 .089
No. of positive nodes 307 .002
    1-3 v 0 1.06 0.64 to 1.73
    ≥ 4 v 0 2.43 1.47 to 4.04
Nodes involvement 304 < .001
    0 of < 12 examined v 0 of ≥ 12 examined 2.27 1.11 to 4.66
    1-3 v 0 of ≥ 12 examined 1.66 0.83 to 3.32
    ≥ 4 v 0 of ≥ 12 examined 3.83 1.90 to 7.72
Mucinous tumor 308 1.53 0.77 to 3.04 .255
Surgery date, quarters 308 1.00 0.88 to 1.13 .975
Surgery year 308 0.98 0.63 to 1.53 .924

Abbreviations: RFI, recurrence-free interval; NSABP, National Surgical Adjuvant Breast and Bowel Project; HR, hazard ratio; GHI, Genomic Health, Inc.

Table A7.

Relationship Between Baseline Patient Characteristics and RFI in NSABP C-06 Study According to Cox Regression Analysis

Variable No. of Patients HR 95% CI P
Female v male 508 1.14 0.80 to 1.61 .463
Age, per 1 year increase 508 1.00 0.99 to 1.02 .632
Age ≥ 60 v < 60 years 508 1.13 0.79 to 1.62 .485
Ethnicity 508 .492
    Black v white 0.98 0.53 to 1.82
    Other/unknown v white 0.53 0.17 to 1.68
Location of tumor 508 .805
    Right v left 1.10 0.70 to 1.73
    Rectosigmoid v left 0.95 0.59 to 1.53
    Multiple/unknown v left 0.55 0.08 to 4.07
No. of nodes examined
    < 12 v ≥ 12 507 1.09 0.77 to 1.30 .632
Tumor stage III v II 508 2.84 1.91 to 4.22 < .001
T stage* 508 .241
    T1 v T3 1.51 0.37 to 6.11
    T2 v T3 0.83 0.41 to 1.70
No. of positive nodes 508 < .001
    1-3 v 0 2.19 1.41 to 3.38
    ≥ 4 v 0 4.49 2.84 to 7.09
Nodal involvement 508 <.001
    0 of < 12 examined v 0 of ≥ 12 examined 1.70 0.84 to 3.41
    1-3 v 0 of ≥ 12 examined 2.91 1.59 to 5.35
    ≥ 4 v 0 of ≥ 12 examined 5.98 3.21 to 11.1
GHI tumor grade
    First reading 508 0.95 0.63 to 1.42 .794
    Second reading 508 1.08 0.75 to 1.56 .668
Surgery 508 .424
    Segmental/anterior resection v resection v colectomy/hemicolectomy 0.89 0.61 to 1.28
    Other/unknown v colectomy/hemicolectomy 1.80 0.66 to 4.92
Mucinous tumor 508 0.71 0.44 to 1.14 .144
Surgery date, quarters 508 0.97 0.90 to 1.05 .427
Surgery year 508 0.88 0.66 to 1.17 .367

Abbreviations: RFI, recurrence-free interval; NSABP, National Surgical Adjuvant Breast and Bowel Project; HR, hazard ratio; GHI, Genomic Health, Inc.

*

Data insufficient to estimate T4 effect.

Footnotes

See accompanying editorial on page 3904

Supported by Public Health Service Grants No. U10-CA-37377, U10-CA-69974, U10-CA-12027, and U10-CA-69651 from the National Cancer Institute, National Institutes of Health, Department of Health and Human Services, and by Genomic Health, Inc.

Presented in part at the 42nd Annual Meeting of the American Society of Clinical Oncology, June 2-6, 2006, Atlanta, GA, and at the Annual Gastrointestinal Cancers Symposium of the American Society of Clinical Oncology, January 25-27, 2008, Orlando, FL.

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

Clinical trial information can be found for the following: NCT00427570.

AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST

Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a “U” are those for which no compensation was received; those relationships marked with a “C” were compensated. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors.

Employment or Leadership Position: Kim M. Clark-Langone, Genomic Health, Inc (C); Margarita Lopatin, Genomic Health, Inc (C); Drew Watson, Genomic Health, Inc (C); Frederick L. Baehner, Genomic Health, Inc (C); Steven Shak, Genomic Health, Inc (C); Joffre Baker, Genomic Health, Inc (C); J. Wayne Cowens, Genomic Health, Inc (C) Consultant or Advisory Role: Michael J. O'Connell, Genomic Health, Inc (U) Stock Ownership: Kim M. Clark-Langone, Genomic Health, Inc; Margarita Lopatin, Genomic Health, Inc; Drew Watson, Genomic Health, Inc; Steven Shak, Genomic Health, Inc; Joffre Baker, Genomic Health, Inc; J. Wayne Cowens, Genomic Health, Inc Honoraria: Greg Yothers, Genomic Health, Inc Research Funding: None Expert Testimony: None Other Remuneration: None

AUTHOR CONTRIBUTIONS

Conception and design: Michael J. O'Connell, Ian Lavery, Soonmyung Paik, Margarita Lopatin, Frederick L. Baehner, Steven Shak, Joffre Baker, J. Wayne Cowens, Norman Wolmark

Financial support: Steven Shak

Administrative support: Soonmyung Paik, J. Wayne Cowens, Norman Wolmark

Provision of study materials or patients: Ian Lavery, Soonmyung Paik

Collection and assembly of data: Ian Lavery, Greg Yothers, Soonmyung Paik, Kim M. Clark-Langone, Drew Watson, Frederick L. Baehner, Steven Shak, J. Wayne Cowens

Data analysis and interpretation: Michael J. O'Connell, Ian Lavery, Greg Yothers, Soonmyung Paik, Kim M. Clark-Langone, Margarita Lopatin, Drew Watson, Steven Shak, Joffre Baker, J. Wayne Cowens

Manuscript writing: Michael J. O'Connell, Greg Yothers, Soonmyung Paik, Kim M. Clark-Langone, Margarita Lopatin, Frederick L. Baehner, Steven Shak, Joffre Baker, J. Wayne Cowens

Final approval of manuscript: Michael J. O'Connell, Ian Lavery, Greg Yothers, Soonmyung Paik, Kim M. Clark-Langone, Margarita Lopatin, Drew Watson, Frederick L. Baehner, Steven Shak, Joffre Baker, J. Wayne Cowens, Norman Wolmark

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