ColoPrint®: A Gene Expression Profile that Identifies Patients at High Risk of Metastasis

Recently many independent studies have demonstrated that molecular staging (or genomic profiling) holds promise in predicting the long-term outcome of any one individual based on the gene expression profile of their cancer at diagnosis. Inherent to this approach is the hypothesis that every cancer contains informative gene expression signatures that, at the time of diagnosis, can portend the biologic behavior over time. The power of using microarray gene analysis for predicting the prognosis of stage II and III colon cancer patients has been demonstrated in several clinical studies, validating the use of prognostic profiles for clinical management of colon cancer patients.  Agendia has developed a prognostic profile for colon cancer that is currently being commercialized into a diagnostic assay for clinical utilization (ColoPrint). The development path follows Agendia’s successful commercialization of MammaPrint; the first genomic profile to achieve the stringent FDA IVDMIA clearance and widely used in the clinic for the prognosis of recurrence in early stage breast cancer patients thereby helping physicians to make more personalized therapeutic recommendations for each individual.(1-6)

Using microarray technology and tumor classification methods, a subset of genes was identified that are predictive for the risk of recurrence of stage II and III colon cancer. Tumor tissue from a training cohort of 188 patients was collected for the gene signature development. Median follow-up was 65.1months during which 51 patients experienced a recurrence of their disease. Gene expression was measured on Agilent 44K Whole Genome oligonucleotide microarrays. This classifier (or prognostic profile) was applied to an independent validation cohort of 208 colon cancer patients with Stage II and III cancer with known follow-up. In this patient cohort, approximately two-thirds of all stage II patients were predicted to have a low risk with a false negative rate of ~9%. The profile was further validated in published datasets (n=322) and showed also here a significant separation of low risk and high risk patients.(7)  The prognostic profile was translated into a robust and standardized test (ColoPrint). The validation in additional 233 retrospective samples from stage II and stage III colon cancer patients was recently presented at the ASCO Annual Meeting and confirmed the earlier validation studies.(8)

In the validation sets, Coloprint was more powerful than clinical factors recommended by ASCO for selecting high risk stage II patients. Furthermore, mulitivariate analysis indicated that a combination of ColoPrint and selected clinical variables could even more powerful and accurate in identifying high risk patients.  A more detailed comparison between ColoPrint and clinical parameters will be addressed in the PARSC Clinical Trial for Early Stage Colon Cancer Patients.  In summary,  ColoPrint can accurately predict the prognosis of stage II and III colon cancer patients and facilitates the identification of stage II patients who may be safely managed without adjuvant chemotherapy.


Click here for more information on Agendia’s PARSC Clinical Trial for Stage II & III Colon Cancer Patients. 


References
  1. Eschrich S, Yang I, Bloom G et al. (2005) Molecular Staging for Survival Predication of Colorectal Cancer Patients. JCO 15;3526-35
  2. Barrier A, Boelle PY, Roser F et al. (2006) Stage II colon cancer prognosis prediction by tumor gene expression profiling. J Clin Oncol. 24(29):4685-913) 
  3. Lin YH, Friederichs J, Black MA et al. (2007) Multiple gene expression classifiers from different array platforms predict poor prognosis of colorectal cancer. Clin Cancer Res. 13.498-5074) 
  4. van 't Veer LJ, Dai H, van de Vijver MJ et al. (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530-5365) 
  5. Buyse M, Loi S, van’t Veer LJ et al. (2006) Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer J Natl Cancer Inst. 98(17):1183-926) 
  6. Glas AM, Floore A, Delahaye LJ et al. (2006) Converting a breast cancer microarray signature into a high-throughput diagnostic test.  BMC Genomics. 2006 Oct 30;7:2787) 
  7. Salazar et al (2009) Annals of Oncology Supplement 7;vii16 and manuscript submitted
  8. Rosenberg et al. (2010) Journal of Clinical Oncology Vol 28 Supplement 15; 264