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.
Proteomic profiling in liver samples using classification and regression tree algorithms is a promising technique to differentiate healthy subjects from CRLM patients and to classify the severity of CRLM patients.
We thank S Sabour for the interest expressed in our article [1] and we acknowledge his continu ous quest to improve the design of predictive studies through several letters to journal editors during the last years, to point out methodo logical issues, mistakes, misconceptions and misinterpretations [2][3][4].The results from our study showed that a single protein peak (7371 m/z) could differen tiate colorectal liver metastases patients from control patients with a sensitivity of 94.1% and a specificity of 100%. Also, with regard to colorectal liver metastases patients prognosis after surgery, we found that the algorithm that best differentiated favorable and unfavorable groups only needed two protein peaks (2970 and 2871 m/z) to discern prognosis with a sensitivity of 100% and a specificity of 90%. These promising results, which S Sabour seems to minimize, come from a preliminary study, are not at all clinically definitive (i.e., vali dated) and should thus encourage further validation studies as we clearly wrote in the 'Discussion'. We were fully aware of these design limitations from the initial moment we designed the study and that was the reason to necessarily include some words of caution in the 'Discussion'. Moreover, among other rea sons for this prudent statement we can include that some preliminary studies can give mis leading results without validation of the ini tial prediction (as S Sabour rightly points out) but also because validation studies (and not exploratory or preliminary studies like ours) are the ones that have the power to ascertain the strength of the relation and properly study the interaction between variables.
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