FGFR4 Arg388 allele correlates with tumour thickness and FGFR4 protein expression with survival of melanoma patients

A single nucleotide polymorphism in the gene for FGFR4 (−Arg388) has been associated with progression in various types of human cancer. Although fibroblast growth factors (FGFs) belong to the most important growth factors in melanoma, expression of FGF receptor subtype 4 has not been investigated yet. In this study, the protein expression of this receptor was analysed in 137 melanoma tissues of different progression stages by immunohistochemistry. FGFR4 protein was expressed in 45% of the specimens and correlated with pTNM tumour stages (UICC, P=0.023 and AJCC, P=0.046), presence of microulceration (P=0.009), tumour vascularity (P=0.001), metastases (P=0.025), number of primary tumours (P=0.022), overall survival (P=0.047) and disease-free survival (P=0.024). Furthermore, FGFR4 Arg388 polymorphism was analysed in 185 melanoma patients by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The Arg388 allele was detected in 45% of the melanoma patients and was significantly associated with tumour thickness (by Clark's level of invasion (P=0.004) and by Breslow in mm (P=0.02)) and the tumour subtype nodular melanoma (P=0.002). However, there was no correlation of the FGFR4 Arg388 allele with overall and disease-free survival. In conclusion, the Arg388 genotype and the protein expression of FGFR4 may be potential markers for progression of melanoma

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases