The Implications of Colorectal Cancer Molecular Biology in Clinical Practice

Serum Neutrophil Gelatinase-Associated Lipocalin in Patients with Colorectal Liver Metastases: Preliminary Results of An Exploratory Prospective Study

Purpose

To quantify preoperative serum neutrophil gelatinase-associated lipocalin (NGAL) levels in patients undergoing curative resection of colorectal liver metastases and to assess the relationship between NGAL levels and prognostic features in these patients.

Methods

From April 2005 to August 2007, 32 patients operated on for first curative resection of colorectal liver metastases underwent determination of preoperative serum NGAL. Patients were divided into four homogeneous clinical groups and into two risk groups based on their clinical risk scores. NGAL levels were corrected by simultaneous creatinine levels to avoid bias due to renal failure.

Results

Higher values of corrected NGAL levels (CNL) were found in patients of the high-risk group (94.53±56.18 vs 57.87±24.49, p=0.014). Patients with more than three tumor nodules had higher values of CNL compared to patients with three or fewer nodules (101.78±56.35 vs 58.57±27.24, p=0.008). Patients with disease involving both hepatic lobes had higher CNL levels than those with involvement of a single lobe (106.5±59.13 vs 59.01±26.69, p=0.005). Patients with higher clinical risk scores had significantly higher CNL.

Conclusions

CNL are associated with the considered prognostic clinical factors and scores, suggesting a possible role for CNL as a prognosis-related indicator and a neoplastic tissue volume marker in patients with colorectal liver metastases.

Prognostic prediction by liver tissue proteomic profiling in patients with colorectal liver metastases

AIM

To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients.

MATERIALS & METHODS

Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surface-enhanced laser desorption/ionization TOF-MS proteomic profiles from cryopreserved CRLM (patients) and normal liver tissue (controls).

RESULTS

The protein peak 7371 m/z showed the clearest differences between CRLM and control groups (94.1% sensitivity, 100% specificity, p < 0.001). The algorithm that best differentiated favorable and unfavorable groups combined 2970 and 2871 m/z protein peaks (100% sensitivity, 90% specificity).

CONCLUSION

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.

Clinical and prognostic usefulness of serum proteomic profile in hepatic colorectal metastases: a pilot prospective study

PURPOSE

To analyze the use of proteomic profiles to discriminate healthy from patients with colorectal liver metastases (CLM) and to predict neoplastic recurrence after CLM resection.

METHODS

From April 2005 to October 2008, 70 patients operated for first curative resection of CLM and 60 healthy controls underwent determination of preoperative serum proteomic profile. We performed a preliminary training with patients and controls and obtained a classification system based on these patients' proteomic profiles training. The system was then tested about the ability to predict the colon versus rectum origin, metachronous or synchronous appearance, risk of recurrence after CLM resection and whether a sample was from a control or a CLM patient.

RESULTS

Sensitivity, specificity, positive and negative predictive values for detecting CLM patients were 75, 100, 100 and 54.6 %, respectively. Best CLM appearance time identification was 50 % and primary tumor origin identification was 62.5 %. Best classifications of neoplastic recurrence within the first year after CLM resection and during the follow-up period were 47.5 and 45 %, respectively. Larger training sets and prevalence-based training sets led to better classification of patients and characteristics.

CONCLUSION

Proteomic profiles are a promising tool for discriminating CLM patients from healthy patients and for predicting neoplastic recurrence.