Classification of endocrine resistant breast cancers from transcriptomic datasets using multi-gene signatures
| dc.contributor.advisor | Cameron, David | |
| dc.contributor.advisor | Morgan, Sarah | |
| dc.contributor.author | Larionov, Alexey | |
| dc.date.accessioned | 2024-04-10T14:29:10Z | |
| dc.date.available | 2024-04-10T14:29:10Z | |
| dc.date.issued | 2012-09 | |
| dc.description.abstract | Breast cancer is the most frequent cancer in women in developed countries. Endocrine treatment is indicated to the majority of breast cancer patients. However, in some cases it does not work despite the current clinical indications. Eventually the resistance may develop in many of those who initially respond. Re-analysis of available breast cancer transcriptomic datasets using new multi-gene signatures associated with endocrine resistance may help to understand and overcome endocrine resistance. The goal of this project was to develop a bioinformatics pipeline to (i) select endocrine resistant cases from the available breast cancer datasets and (ii) classify the selected cases by multiple multi-gene signatures. The pipeline has been successfully designed and applied for classification of endocrineresistant samples from 9 breast cancer datasets using 7 transcriptional signatures. The obtained results have been presented in a dedicated web site. The pipeline consists of: Procedures for a manually curated selection of relevant datasets and signatures; Procedures for semi-automatic data pre-processing, allowing cross-platform analysis; A new, fully automated, classification algorithm (Iterative Consensus PAM). The main features of the developed classification algorithm include: It is based on un-supervised partitioning; It allows for “non-classifiable” samples; The procedure does not require a training set; The procedure can be used in a cross-platform context (Affymetrix & Illumina). The developed pipeline and web site may constitute a prototype for a future web-hub collecting (i) data on endocrine-resistant breast cancer specimens, (ii) collecting multigene signatures relevant to endocrine resistance and (iii) providing tools to apply the signatures to the data. The web-repository could provide a tool to integrate the data and signatures and to produce new clinical and biological knowledge about endocrine resistance in breast cancer. | en_UK |
| dc.description.coursename | Applied Bioinformatics | en_UK |
| dc.description.sponsorship | Biotechnology and Biological (BBSRC) | en_UK |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/21181 | |
| dc.language.iso | en_UK | en_UK |
| dc.publisher | Cranfield University | en_UK |
| dc.publisher.department | Cranfield Health | en_UK |
| dc.subject | Endocrine | en_UK |
| dc.subject | breast cancer transcriptomic datasets | en_UK |
| dc.subject | endocrine resistant | en_UK |
| dc.subject | multi-gene signatures | en_UK |
| dc.subject | breast cancer | en_UK |
| dc.subject | bioinformatics pipeline | en_UK |
| dc.title | Classification of endocrine resistant breast cancers from transcriptomic datasets using multi-gene signatures | en_UK |
| dc.type | Thesis or dissertation | en_UK |
| dc.type.qualificationlevel | Masters | en_UK |
| dc.type.qualificationname | MSc | en_UK |