Abstract
CRC cancer is one of the deadliest diseases in Western countries. In order to develop prognostic biomarkers for CRC (colorectal cancer) aggressiveness, we analyzed retrospectively 267 CRC patients via a novel, multidimensional biomarker platform. Using nanofluidic technology for qPCR analysis and quantitative fluorescent immunohistochemistry for protein analysis, we assessed 33 microRNAs, 124 mRNAs and 9 protein antigens. Analysis was conducted in each single dimension (microRNA, gene or protein) using both the multivariate Cox model and Kaplan-Meier method. Thereafter, we simplified the censored survival data into binary response data (aggressive vs. non aggressive cancer). Subsequently, we integrated the data into a diagnostic score using sliced inverse regression for sufficient dimension reduction. Accuracy was assessed using area under the receiver operating characteristic curve (AUC). Single dimension analysis led to the discovery of individual factors that were significant predictors of outcome. These included seven specific microRNAs, four genes, and one protein. When these factors were quantified individually as predictors of aggressive disease, the highest demonstrable area under the curve (AUC) was 0.68. By contrast, when all results from single dimensions were combined into integrated biomarkers, AUCs were dramatically increased with values approaching and even exceeding 0.9. Single dimension analysis generates statistically significant predictors, but their predictive strengths are suboptimal for clinical utility. A novel, multidimensional integrated approach overcomes these deficiencies. Newly derived integrated biomarkers have the potential to meaningfully guide the selection of therapeutic strategies for individual patients while elucidating molecular mechanisms driving disease progression.
Highlights
CRC is one of the deadliest diseases worldwide
Thereafter, we analyzed the top combinations in the testing set and we found 15 multidimensional biomarkers (MB) which showed area under the curve (AUC) values
Discoveries of effective biomarkers that can guide therapeutic decisions are ambitiously sought in the hopes of achieving the best possible outcomes, minimizing not necessary and toxic procedures
Summary
CRC is one of the deadliest diseases worldwide. Caucasian patients with local, regional, or metastatic disease exhibit a 5-year survival rate of 66%, 44%, and 4%, respectively [1]. In the last two decades, median overall survival has increased significantly with the introduction of new cytotoxic agents and biologic therapies. The response to such treatments depends on molecular determinants whose elucidation has been the focus of intense and productive research efforts. Development of novel biomarkers that can reliably identify patients at high risk for disease progression and death would be especially useful in determining the clinical circumstances where adjuvant chemotherapy is warranted. In the absence of a robust clinical predictor of disease outcome, the decision to treat or not to treat stage II patients with 5FU cannot rest on objective and firm criteria. Identified predictive biomarkers which had shown great promise in this arena including telomerase, transforming growth factors (TGFa and TGFb), epidermal growth factors (erbB2 and erbB3) and mucin (MUC1 and MUC2) have disappointed in studies of clinical utility [4]
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