Supervised multivariate analysis of sequence groups to identify specificity determining residues

<p>Abstract</p> <p>Background</p> <p>Proteins that evolve from a common ancestor can change functionality over time, and it is important to be able identify residues that cause this change. In this paper we show how a supervised multivariate statistical method, Between Group Analysis (BGA), can be used to identify these residues from families of proteins with different substrate specifities using multiple sequence alignments.</p> <p>Results</p> <p>We demonstrate the usefulness of this method on three different test cases. Two of these test cases, the Lactate/Malate dehydrogenase family and Nucleotidyl Cyclases, consist of two functional groups. The other family, Serine Proteases consists of three groups. BGA was used to analyse and visualise these three families using two different encoding schemes for the amino acids.</p> <p>Conclusion</p> <p>This overall combination of methods in this paper is powerful and flexible while being computationally very fast and simple. BGA is especially useful because it can be used to analyse any number of functional classes. In the examples we used in this paper, we have only used 2 or 3 classes for demonstration purposes but any number can be used and visualised.</p

Phase II trial of imatinib mesylate in patients with metastatic melanoma

Metastatic melanoma cells express a number of protein tyrosine kinases (PTKs) that are considered to be targets for imatinib. We conducted a phase II trial of imatinib in patients with metastatic melanoma expressing at least one of these PTKs. Twenty-one patients whose tumours expressed at least one PTK (c-kit, platelet-derived growth factor receptors, c-abl, or abl-related gene) were treated with 400 mg of imatinib twice daily. One patient with metastatic acral lentiginous melanoma, containing the highest c-kit expression among all patients, had dramatic improvement on positron emission tomographic scan at 6 weeks and had a partial response lasting 12.8 months. The responder had a substantial increase in tumour and endothelial cell apoptosis at 2 weeks of treatment. Imatinib was fairly well tolerated: no patient required treatment discontinuation because of toxicity. Fatigue and oedema were the only grade 3 or 4 toxicities that occurred in more than 10% of the patients. Imatinib at the studied dose had minimal clinical efficacy as a single-agent therapy for metastatic melanoma. However, based on the characteristics of the responding tumour in our study, clinical activity of imatinib, specifically in patients with melanoma with certain c-kit aberrations, should be examined

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

Palivové články pro motorová vozidla

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