Callisia N Clarke1, Michael S Lee2, Wei Wei3, Ganiraju Manyam3, Zhi-Qin Jiang4, Yiling Lu5, Jeffrey Morris3, Bradley Broom3, David Menter4, Eduardo Vilar-Sanchez6, Kanwal Raghav4, Cathy Eng4, George J Chang7, Iris Simon8, Rene Bernards9, Michael Overman4, Gordon B Mills5, Dipen Maru10, Scott Kopetz4. 1. Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI, USA. cnclarke@mcw.edu. 2. Division of Hematology and Oncology, University of North Carolina, Chapel Hill, NC, USA. 3. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 4. Department of GI Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 5. Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 6. Department of Clinical Cancer Prevention, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 7. Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. 8. Agendia BV, Amsterdam, The Netherlands. 9. Division of Molecular Carcinogenesis, Cancer Systems Biology Centre and Cancer Genomics Centre, The Netherlands Cancer Institute, Amsterdam, The Netherlands. 10. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Abstract
BACKGROUND: The directed study of the functional proteome in colorectal cancer (CRC) has identified critical protein markers and signaling pathways; however, the prognostic relevance of many of these proteins remains unclear. METHODS: We determined the prognostic implications of the functional proteome in 263 CRC tumor samples from patients treated at MD Anderson Cancer Center (MDACC) and 462 patients from The Cancer Genome Atlas (TCGA) to identify patterns of protein expression that drive tumorigenesis. A total of 163 validated proteins were analyzed by reverse phase protein array (RPPA). Unsupervised hierarchical clustering of the tumor proteins from the MDACC cohort was performed, and clustering was validated using RPPA data from TCGA CRC. Cox regression was used to identify predictors of tumor recurrence. RESULTS: Clustering revealed dichotomization, with subtype A notable for a high epithelial-mesenchymal transition (EMT) protein signature, while subtype B was notable for high Akt/TSC/mTOR pathway components. Survival data were only available for the MDACC cohort and were used to evaluate prognostic relevance of these protein signatures. Group B demonstrated worse relapse-free survival (hazard ratio 2.11, 95% confidence interval 1.04-4.27, p = 0.039), although there was no difference in known genomic drivers between the two proteomic groups. Proteomic grouping and stage were significant predictors of recurrence on multivariate analysis. Eight proteins were found to be significant predictors of tumor recurrence on multivariate analysis: Collagen VI, FOXO3a, INPP4B, LcK, phospho-PEA15, phospho-PRAS40, Rad51, phospho-S6. CONCLUSION: CRC can be classified into distinct subtypes by proteomic features independent of common oncogenic driver mutations. Proteomic analysis has identified key biomarkers with prognostic importance, however these findings require further validation in an independent cohort.
BACKGROUND: The directed study of the functional proteome in colorectal cancer (CRC) has identified critical protein markers and signaling pathways; however, the prognostic relevance of many of these proteins remains unclear. METHODS: We determined the prognostic implications of the functional proteome in 263 CRC tumor samples from patients treated at MD Anderson Cancer Center (MDACC) and 462 patients from The Cancer Genome Atlas (TCGA) to identify patterns of protein expression that drive tumorigenesis. A total of 163 validated proteins were analyzed by reverse phase protein array (RPPA). Unsupervised hierarchical clustering of the tumor proteins from the MDACC cohort was performed, and clustering was validated using RPPA data from TCGA CRC. Cox regression was used to identify predictors of tumor recurrence. RESULTS: Clustering revealed dichotomization, with subtype A notable for a high epithelial-mesenchymal transition (EMT) protein signature, while subtype B was notable for high Akt/TSC/mTOR pathway components. Survival data were only available for the MDACC cohort and were used to evaluate prognostic relevance of these protein signatures. Group B demonstrated worse relapse-free survival (hazard ratio 2.11, 95% confidence interval 1.04-4.27, p = 0.039), although there was no difference in known genomic drivers between the two proteomic groups. Proteomic grouping and stage were significant predictors of recurrence on multivariate analysis. Eight proteins were found to be significant predictors of tumor recurrence on multivariate analysis: Collagen VI, FOXO3a, INPP4B, LcK, phospho-PEA15, phospho-PRAS40, Rad51, phospho-S6. CONCLUSION: CRC can be classified into distinct subtypes by proteomic features independent of common oncogenic driver mutations. Proteomic analysis has identified key biomarkers with prognostic importance, however these findings require further validation in an independent cohort.
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