Novel mechanisms of resistance to EGFR inhibitory drugs in non-small cell lung cancer

EGFR activating mutations are present in 10-40% of non-small cell lung cancer. Such mutations render tumour cells sensitive to EGFR tyrosine kinase inhibitors (EGFR TKIs), with responses of up to 80% in populations selected for the presence of an activating mutation. Unfortunately, almost all patients develop resistance after about a year. Clinically described mechanisms of resistance include the presence of a secondary mutation (T790M) in EGFR which prevents EGFR TKIs binding to the EGF receptor, and amplification MET which permits survival signalling via the ERBB3 receptor. However in 30% of cases, the mechanism of acquired resistance to EGFR TKIs is still unknown. My aim was to carry out a genome-wide siRNA screen to identify novel mechanisms of resistance to EGFR TKIs. I identified two genes that have not been implicated in EGFR TKI resistance previously, NF1 and DEPTOR, which are negative regulators of RAS and mTOR respectively. Depletion of NF1 or DEPTOR leads to increased resistance to EGFR TKIs via upregulation of MAPK signalling by direct and indirect mechanisms

Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

<p>Abstract</p> <p>Background</p> <p>Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.</p> <p>Results</p> <p>We proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different scales in the graphs. We introduced a niching strategy that reinforces diversity through the population and avoided trapping of the algorithm in one local minimum in the early steps of learning. We show the limited effect of the mutation operator when niching is applied. Finally, we compared our best evolutionary approach with various well known learning algorithms (MCMC, K2, greedy search, TPDA, MMHC) devoted to BN structure learning.</p> <p>Conclusion</p> <p>We studied the behaviour of an evolutionary approach enhanced by niching for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model. These results were obtained for the learning of a bio-realistic network and, more importantly, on various small datasets. This is a suitable approach for learning transcriptional regulatory networks from real datasets without prior knowledge.</p

Integrative genomic and transcriptomic characterization of papillary carcinomas of the breast

Papillary carcinoma (PC) is a rare type of breast cancer, which comprises three histologic subtypes: encapsulated PC (EPC), solid PC (SPC) and invasive PC (IPC). Microarray-based gene expression and Affymetrix SNP 6.0 gene copy number profiling, and RNA-sequencing revealed that PCs are luminal breast cancers that display transcriptomic profiles distinct from those of grade- and estrogen receptor (ER)-matched invasive ductal carcinomas of no special type (IDC-NSTs), and that the papillary histologic pattern is unlikely to be underpinned by a highly recurrent expressed fusion gene or a highly recurrent expressed mutation. Despite displaying similar patterns of gene copy number alterations, significant differences in the transcriptomic profiles of EPCs, SPCs and IPCs were found, and may account for their different histologic features