Metastatic Tissue Proteomic Profiling Predicts 5-Year Outcomes in Patients with Colorectal Liver Metastases

Abstract

Colorectal cancer (CRC) is one of the most common cancers in the developed countries, and nearly 70% of patients with CRC develop colorectal liver metastases (CRLMs). During the last decades, several scores have been proposed to predict recurrence after CRLM resection. However, these risk scoring systems do not accurately reflect the prognosis of these patients. Therefore, this investigation was designed to identify a proteomic profile in human hepatic tumor samples to classify patients with CRLM as “mild” or “severe” based on the 5-year survival. The study was performed on 85 CRLM tumor samples. Firstly, to evaluate any distinct tumor proteomic signatures between mild and severe CRLM patients, a training group of 57 CRLM tumor samples was characterized by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, and a classification and regression tree (CART) analysis was subsequently performed. Finally, 28 CRLM tumor samples were used to confirm and validate the results obtained. Based on all the protein peaks detected in the training group, the CART analysis was generated, and four peaks were considered to be the most relevant to construct a diagnostic algorithm. Indeed, the multivariate model yielded a sensitivity of 85.7% and a specificity of 86.1%, respectively. In addition, the receiver operating characteristic (ROC) curve showed an excellent diagnostic accuracy to discriminate mild from severe CRLM patients (area under the ROC: 0.903). Finally, the validation process yielded a sensitivity and specificity of 68.8% and 83.3%, respectively. We identified a proteomic profile potentially useful to determine the prognosis of CRLM patients based on the 5-year survival.

Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models

“…The most important predictor for CRLM patients is the mutant rat sarcoma virus (RAS) oncogene, which is part of almost 30% of modern models (n = 21, 29.6%). Regarding other molecular biomarkers, Marfa et al used the CART analysis to construct a proteomic signature in a series of 85 patients, which differentiates mild from severe cases (based on predicted OS) according to the four most significant protein peaks [81]. Another emerging predictor is differentially expressed genes (DEGs), or differentially expressed exosomal miRNAs, which has already been used in five studies [22,44,59,62,68].…”

Biomarkers in Colorectal Cancer: The Role of Translational Proteomics Research

“…The major site of metastases in colorectal cancer is the liver, occurring in 20–35% of patients at the time of diagnosis and in nearly 70% of patients during the course of the disease (70). Marfà et al (71) used a high-throughput proteomic technique to predict 5-year survival in patients with colorectal cancer who developed liver metastases. Human hepatic tumor samples were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI), and a classification and regression tree analysis was done posteriorly.…”

“…Human hepatic tumor samples were analyzed by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI), and a classification and regression tree analysis was done posteriorly. This approach allowed the identification of four relevant protein peaks and the construction of an algorithm that revealed an excellent diagnostic accuracy in differentiating mild from severe colorectal liver metastases patients (71).…”