Frequent alterations in p16/CDKN2A identified by immunohistochemistry and FISH in chordoma

The expression of p16/CDKN2A, the second most commonly inactivated tumour suppressor gene in cancer, is lost in the majority of chordomas. However, the mechanism(s) leading to its inactivation and contribution to disease progression have only been partially addressed using small patient cohorts. We studied 384 chordoma samples from 320 patients by immunohistochemistry and found that p16 protein was lost in 53% of chordomas and was heterogeneously expressed in these tumours. To determine if CDKN2A copy number loss could explain the absence of p16 protein expression we performed fluorescence in situ hybridisation (FISH) for CDKN2A on consecutive tissue sections. CDKN2A copy number status was altered in 168 of 274 (61%) of samples and copy number loss was the most frequent alteration acquired during clinical disease progression. CDKN2A homozygous deletion was always associated with p16 protein loss but only accounted for 33% of the p16‐negative cases. The remaining immunonegative cases were associated with disomy (27%), monosomy (12%), heterozygous loss (20%) and copy number gain (7%) of CDKN2A, supporting the hypothesis that loss of protein expression might be achieved via epigenetic or post‐transcriptional regulatory mechanisms. We identified that mRNA levels were comparable in tumours with and without p16 protein expression, but other events including DNA promoter hypermethylation, copy number neutral loss of heterozygosity and expression of candidate microRNAs previously implicated in the regulation of CDKN2A expression were not identified to explain the protein loss. The data argue that p16 loss in chordoma is commonly caused by a post‐transcriptional regulatory mechanism that is yet to be defined

A statistical approach to estimating the strength of cell-cell interactions under the differential adhesion hypothesis

International audienceBACKGROUND: The Differential Adhesion Hypothesis (DAH) is a theory of the organization of cells within a tissue which has been validated by several biological experiments and tested against several alternative computational models. RESULTS: In this study, a statistical approach was developed for the estimation of the strength of adhesion, incorporating earlier discrete lattice models into a continuous marked point process framework. This framework allows to describe an ergodic Markov Chain Monte Carlo algorithm that can simulate the model and reproduce empirical biological patterns. The estimation procedure, based on a pseudo-likelihood approximation, is validated with simulations, and a brief application to medulloblastoma stained by beta-catenin markers is given. CONCLUSION: Our model includes the strength of cell-cell adhesion as a statistical parameter. The estimation procedure for this parameter is consistent with experimental data and would be useful for high-throughput cancer studies