Purpose: Although the anti–EGF receptor (EGFR) monoclonal antibody cetuximab is an effective strategy in colorectal cancer therapy, its clinical use is limited by intrinsic or acquired resistance. Alterations in the “sphingolipid rheostat”—the balance between the proapoptotic molecule ceramide and the mitogenic factor sphingosine-1-phosphate (S1P)—due to sphingosine kinase 1 (SphK1) overactivation have been involved in resistance to anticancer-targeted agents. Moreover, cross-talks between SphK1 and EGFR-dependent signaling pathways have been described. Experimental design: We investigated SphK1 contribution to cetuximab resistance in colorectal cancer, in preclinical in vitro/in vivo models, and in tumor specimens from patients. Results: SphK1 was found overexpressed and overactivated in colorectal cancer cells with intrinsic or acquired resistance to cetuximab. SphK1 contribution to resistance was supported by the demonstration that SphK1 inhibition by N,N-dimethyl-sphingosine or silencing via siRNA in resistant cells restores sensitivity to cetuximab, whereas exogenous SphK1 overexpression in sensitive cells confers resistance to these agents. Moreover, treatment of resistant cells with fingolimod (FTY720), a S1P receptor (S1PR) antagonist, resulted in resensitization to cetuximab both in vitro and in vivo, with inhibition of tumor growth, interference with signal transduction, induction of cancer cells apoptosis, and prolongation of mice survival. Finally, a correlation between SphK1 expression and cetuximab response was found in colorectal cancer patients. Clin Cancer Res; 19(1); 138–47. ©2012 AACR.

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide. It has been estimated that more than one-third of patients are diagnosed when CRC has already spread to the lymph nodes. One out of five patients is diagnosed with metastatic CRC. The stage of diagnosis influences treatment outcome and survival. Notwithstanding the recent advances in multidisciplinary management and treatment of CRC, patients are still reluctant to undergo screening tests because of the associated invasiveness and discomfort (e.g., colonoscopy with biopsies). Moreover, the serological markers currently used for diagnosis are not reliable and, even if they were useful to detect disease recurrence after treatment, they are not always detected in patients with CRC (e.g., CEA). Recently, translational research in CRC has produced a wide spectrum of potential biomarkers that could be useful for diagnosis, treatment, and follow-up of these patients. The aim of this review is to provide an overview of the newer noninvasive or minimally invasive biomarkers of CRC. Here, we discuss imaging and biomolecular diagnostics ranging from their potential usefulness to obtain early and less-invasive diagnosis to their potential implementation in the development of a bespoke treatment of CRC.

BACKGROUND: Epidermal growth factor receptor (EGFR) mutations are reliably detected by referral laboratories, even if most lung cancer cytology specimens sent to such laboratories contain very few cells. However, EGFR mutations may be distributed heterogeneously within tumors, thereby raising concerns that mutations detected on cytology are not representative of the entire tumor and, thus, are less reliable in predicting response to tyrosine kinase inhibitor (TKI) treatment than mutations detected on histology. To address this issue, the authors reviewed their clinical practice archives and compared the outcome of TKI treatment among patients who were selected by cytology versus patients who were selected by histology.METHODS: From July 2010 to July 2012, 364 cytology samples and 318 histology samples were received. Exon 19 deletions and the L858R point mutation in exon 21, detected by fragment assay and TaqMan assay, respectively, were confirmed by direct sequencing; discrepancies were resolved by cloning polymerase chain reaction products. The response rate (RR) and progression‐free survival (PFS) at 12 months (range, 3‐34 months) were evaluable in 13 EGFR‐mutated patients who were selected for treatment by cytology and 13 patients who were selected by histology.RESULTS: The mutation rate was similar in histology samples (8.5%) and cytology samples (8.8%). The RR (54%) and PFS (9.2 months) were similar in histologically selected patients and cytologically selected patients (RR, 62%; PFS, 8.6 months; P = .88). The disease control rate (responsive plus stable disease) was 92% in histologically selected patients and 100% in cytologically selected patients.CONCLUSIONS: EGFR mutations detected on cytology specimens by a centralized laboratory can predict TKI treatment response equally well as mutations identified on histology samples. Cancer (Cancer Cytopathol) 2013;121:552–560. © 2013 American Cancer Society.

Microarray technology was used to profile miRNA expression in primary tumor and stromal tissue from paraffin embedded material of 51 patients with colorectal cancer. 26 miRNAs resulted differentially expressed with at least 2-fold change in tumor tissue with respect to stroma (16 more expressed in the tumor and 10 more expressed in the stroma). 10/26 were confirmed as differentially expressed at qRTPCR: miR-200c-3p, miR-141-3p, miR-200b-3p, miR-200a-3p, miR-1246, miR-92a-3p, miR-194-5p, miR-192-5p, miR-3651-5p, and miR-574-3p. No significant association was found between miRNA expressions and stage at diagnosis, site of primary tumor, first site of metastasis, progression-free, or overall survival.

Purpose: To assess the association of RAS mutation status and radiomics-derived data by Contrast Enhanced-Magnetic Resonance Imaging (CE-MRI) in liver metastases. Materials and Methods: 76 patients (36 women and 40 men; 59 years of mean age and 36–80 years as range) were included in this retrospective study. Texture metrics and parameters based on lesion morphology were calculated. Per-patient univariate and multivariate analysis were made. Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analysis, pattern recognition approaches with features selection approaches were considered. Results: Significant results were obtained for texture features while morphological parameters had not significant results to classify RAS mutation. The results showed that using a univariate analysis was not possible to discriminate accurately the RAS mutation status. Instead, considering a multivariate analysis and classification approaches, a KNN exclusively with texture parameters as predictors reached the best results (AUC of 0.84 and an accuracy of 76.9% with 90.0% of sensitivity and 67.8% of specificity on training set and an accuracy of 87.5% with 91.7% of sensitivity and 83.3% of specificity on external validation cohort). Conclusions: Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status.