Publication details
Non-invasive prognostic protein biomarker signatures associated with colorectal cancer
| Authors | |
|---|---|
| Year of publication | 2015 |
| Type | Article in Periodical |
| Magazine / Source | EMBO MOLECULAR MEDICINE |
| MU Faculty or unit | |
| Citation | |
| web | http://embomolmed.embopress.org/content/embomm/7/9/1153.full.pdf |
| Doi | http://dx.doi.org/10.15252/emmm.201404874 |
| Field | Oncology and hematology |
| Keywords | colorectal cancer; prognostic protein biomarker; targeted proteomics |
| Attached files | |
| Description | The current management of colorectal cancer (CRC) would greatly benefit from non-invasive prognostic biomarkers indicative of clinicopathological tumor characteristics. Here, we employed targeted proteomic profiling of 80 glycoprotein biomarker candidates across plasma samples of a well-annotated patient cohort with comprehensive CRC characteristics. Clinical data included 8-year overall survival, tumor staging, histological grading, regional localization, and molecular tumor characteristics. The acquired quantitative proteomic dataset was subjected to the development of biomarker signatures predicting prognostic clinical endpoints. Protein candidates were selected into the signatures based on significance testing and a stepwise protein selection, each within 10-fold cross-validation. A six-protein biomarker signature of patient outcome could predict survival beyondclinical stage and was able to stratify patients into groups of better and worse prognosis. We further evaluated the performance of the signature on the mRNA level and assessed its prognostic value in the context of previously published transcriptional signatures. Additional signatures predicting regional tumor localization and disease dissemination were also identified. The integration ofrich clinical data, quantitative proteomic technologies, and tailored computational modeling facilitated the characterization of these signatures in patient circulation. These findings highlight the value ofa simultaneous assessment of important prognostic disease characteristics within a single measurement. |