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. 2008 Dec 9;105(49):19432-7.
doi: 10.1073/pnas.0806674105. Epub 2008 Dec 2.

A genomic approach to colon cancer risk stratification yields biologic insights into therapeutic opportunities

Katherine S Garman  1 , Chaitanya R Acharya, Elena Edelman, Marian Grade, Jochen Gaedcke, Shivani Sud, William Barry, Anna Mae Diehl, Dawn Provenzale, Geoffrey S Ginsburg, B Michael Ghadimi, Thomas Ried, Joseph R Nevins, Sayan Mukherjee, David Hsu, Anil Potti
Affiliations

A genomic approach to colon cancer risk stratification yields biologic insights into therapeutic opportunities

Katherine S Garman et al. Proc Natl Acad Sci U S A. .

Erratum in

  • Proc Natl Acad Sci U S A. 2009 Apr 21;106(16):6878

Retraction in

Abstract

Gene expression profiles provide an opportunity to dissect the heterogeneity of solid tumors, including colon cancer, to improve prognosis and predict response to therapies. Bayesian binary regression methods were used to generate a signature of disease recurrence in patients with resected early stage colon cancer validated in an independent cohort. A 50-gene signature was developed that effectively distinguished early stage colon cancer patients with a low or high risk of disease recurrence. RT-PCR analysis of the 50-gene signature validated 9 of the top 10 differentially expressed genes. When applied to two independent validation cohorts of 55 and 73 patients, the 50-gene model accurately predicted recurrence. Standard Kaplan-Meier survival analysis confirmed the prognostic accuracy (P < 0.01, log rank), as did multivariate Cox proportional hazard models. We tested potential targeted therapeutic options for patients at high risk for disease recurrence and found a clinically important relationship between sensitivity to celecoxib, LY-294002 (PI3kinase inhibitor), retinol, and sulindac in colon cancer cell lines expressing the poor prognostic phenotype (P < 0.01, t test), which performed better than standard chemotherapy (5-FU and oxaliplatin). We present a genomic strategy in early stage colon cancer to identify patients at highest risk of recurrence. An ability to move beyond current staging by refining the estimation of prognosis in early stage colon cancer also has implications for individualized therapy.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Development of 50-gene predictor of Recurrence. (A) Consort diagram. To develop a genomic predictor of colon cancer recurrence, we made use of a training cohort of 52 patient samples representing clinical Stage 1 and Stage 2, for which gene expression data were available to develop a prognostic model. Two independent datasets of 55 and 73 samples were used as validation cohorts. (B) Development of the 50-Gene Predictor of Recurrence. (Top) Shown are the heat-map of the samples used to develop the metagene model (the training set) with blue and red representing extremes of expression with visually apparent differences in gene expression. Samples from patients who remained diseased free are on the left (n = 43), and samples from patients who had disease recurrence are on the right (n = 7). (Middle) Shown is a comparison individual and mean Recurrence Scores, or the probability of recurrence as predicted by the 50-gene model, by group as compared by t test (P < 0.001). (Bottom) Shown is the ROC curve identifying the Recurrence Score of 0.76 as the optimal cut-point to be used to classify samples in the validation set. The area under the curve (AUC) of 0.94 further confirms the robustness of the 50-gene model.
Fig. 2.
Fig. 2.
Validation of the 50-gene predictor of colon cancer recurrence. (A) Independent validation of the prognostic model: the scatter plot compares individual and mean Recurrence Scores for the MEXP-1224 cohort (n = 55) grouped by actual recurrence (P = 0.002, t test). (B) Blinded validation of the prognostic model: the scatter plot demonstrates a comparison of mean Recurrence Scores for the cohort (n = 73) grouped by actual recurrence (P = 0.007, t test; 90% sensitivity). (C) The Kaplan–Meier survival analysis demonstrates time to recurrence for the two groups: the blue curve represents those patients predicted to remain disease-free by the model, and the red curve represents those predicted to have recurrence.
Fig. 3.
Fig. 3.
RT-PCR validation. In vitro RT-PCR assay of the top 10 differentially expressed genes demonstrates concordance in 9 of 10 genes between the PCR results and the 50-gene microarray-based signature. Data are presented as a comparison between the gene coefficients (specific to each gene in the Bayesian model) of the candidate genes and the log of the RQ values for the respective genes in the RT-PCR experiments.
Fig. 4.
Fig. 4.
In vitro validation of candidate drug sensitivity. (A) The left panel shows the mutational events seen in the cohort of colon cancer cell lines, sorted based on the probability of recurrence based on the 50-gene model (blue: lowest risk score, red: highest risk score). (Right) Shown are the results of experiments performed using 14 colon cancer cell lines. Gene expression data from these cancer cell lines was used to classify them according to Recurrence Scores (blue: low risk, red: high risk), using the 50-gene model. The cell lines were then treated with specific drugs identified as candidate agents by using a connectivity map analysis of the 50-gene model. For each drug, sensitivity as measured by cell death using cell proliferation assays is shown in the figure and compared between groups. The cell lines with high Recurrence Scores appear to be more sensitive to drug than those with low Recurrence Scores, demonstrating that treatment modalities involving the use of celecoxib, LY294002 (PI3Kinase inhibitor) and retinol may be beneficial in patients with colon cancer who are at high risk for disease recurrence. (B) (Left) Shown is the change in Recurrence Score after exposure to PI3Kinase (LY294002) and COX2 inhibition (celecoxib). An ANOVA analysis demonstrates a significant difference between pretreatment and post treatment (with LY294002, celecoxib) recurrence scores in colon cancer cell lines. (Right) Traditional chemotherapy agents (5-FU and oxaliplatin) do not show a significantly greater predilection for inhibiting growth in the cell lines with a high recurrence score. (C) A histogram shows that all of the cell lines demonstrate a decrease in recurrence score post treatment, indicating a reversal of the high-risk phenotype after exposure to LY294002 or celecoxib, with DLD-1 showing the greatest sensitivity to reversal and COLO-320 showing the least effect. In comparison, the effect of 5-FU and oxaliplatin is inconsistent across the cell lines.
Fig. 5.
Fig. 5.
Clinical application of the 50-gene predictor of early-stage colon cancer recurrence. The schema of a proposed clinical trial that would further validate the prognostic ability of the 50-gene predictor in patients with stage II colon cancer, first identifying low risk patients and then those with high recurrence scores receive adjuvant chemoprevention.
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Comment in

  • Findings of research misconduct.
    [No authors listed] [No authors listed] NIH Guide Grants Contracts. 2015 Nov 20:NOT-OD-16-021. NIH Guide Grants Contracts. 2015. PMID: 26601329 Free PMC article. No abstract available.
  • Findings of Research Misconduct.
    [No authors listed] [No authors listed] Fed Regist. 2015 Nov 9;80(216):69230-69231. Fed Regist. 2015. PMID: 27737266 Free PMC article. No abstract available.

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