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Cancer Biology & Biomarkers

A selection of articles from European Pharmaceutical Review covering Cancer Biology and Biomarkers:

 

Drug Discovery and Development

Novel medicines development for cancer treatment

24 June 2010  •  Hans Winkler, Global Head Oncology & Biomarker Programs, Ortho Biotech Oncology Research & Development, Johnson & Johnson

The pharmaceutical industry has reached a critical phase in its evolution. The cost and time to develop novel medicines has become unsustainable3. Reasons for this may include a much higher demand on evidence of safety and efficacy, rapidly increasing costs of contract research and the tremendous pressure on pricing and reimbursement from regulators and payers expecting higher benefit rates than the current average for newly launched products. Concomitant with these pressures we observe an ever decreasing R&D productivity which acerbates the problem2.In this context, it is important to remind ourselves of the task to be achieved. First, a suitable target has to be identified whose inhibition will result in major effects on tumour growth and survival.

Figure 1 Cell undergoing senescence. Cell showing characteristics of cellular senescence: binucleated and flat and enlarged morphology

Cellular senescence as an anti-tumour mechanism

24 June 2010  •  Amancio Carnero, Scientist, Seville Biomedical Research Institute (IBIS/HUVR), Spanish National Research Council

One of the critical steps in human carcinogenesis is cellular immortalisation, a process in which cells must escape senescence and acquire an infinite lifespan. In the absence of immortalisation, although a cell might undergo malignant transformation, it could not proliferate indefinitely. Furthermore, it has been clearly established in vitro and in vivo that cellular senescence is a tumour suppressor mechanism induced by oncogenic stress. Normal somatic cells grown in culture cease to proliferate, senesce, after a finite number of divisions.

Finding novel targets for anticancer target discovery

9 May 2010  •  Wolfgang Link, Experimental Therapeutics Program, Centro Nacional de Investigaciones Oncologicas (CNIO)

Advances in our understanding of the molecular basis of cancer together with novel approaches to interfere with signal transduction pathways have opened new horizons for anticancer target discovery. In particular, the image based large scale analysis of cellular phenotypes that arise from genetic or chemical perturbations paved the way for the identification and validation of disease relevant molecular targets independent of preconceived notions of mechanistic relationships.

DNA sequencing

Applying statistical inference in genomics with evidence based pathways: Towards elucidating new functional correlations of biomarkers

22 February 2010  •  Peter Ghazal, Professor of Molecular Genetics and Biomedicine, University of Edinburgh and Head of Division of Pathway Medicine and Associate Director of Centre for Systems Biology, Edinburgh, Al Ivens, Head of Data Analysis, Fios Genomics Ltd and Thorsten Forster, Statistical Bioinformatician, Division of Pathway Medicine, University of Edinburgh

In conventional pharmacogenomic studies, genetic polymorphisms (including single nucleotide and copy number variations) are elucidated from case-control distribution of individuals usually representing ethnicity, severity of disease, and positive or negative response to treatment. However, the interpretation of a single genetic marker in this context is complicated, as the same marker may lead to multiple different phenotypes. Likewise, similar phenotypes can arise under different genetic backgrounds (models). This problem has led to the emergence of integrative approaches for combining statistical inference methods with bioinformatics-based text-mining, sequencing and expression analyses. An increasingly popular approach based on network analysis is challenged by the fact that a certain amount of the interpretation of the data is left to the subjective choices of the user. More rigorous statistical methods can be applied. However, these methods are also challenged by the fact that the developed measures of significance are provided outside the context of biology. In this perspective, we outline statistical methods and the development of evidence-based pathway analysis for identifying new biomarker correlations.

 

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