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Statistical and algorithmic developments for the analysis of Triple Negative Breast Cancers

Abstract : Throughout the world and among the different types of cancer, breast cancer is one of the most prevalent ones. It can be subdivided in several types among which the triple negative invasive ductal breast carcinoma (TNBC). TNBC is one of the most aggressive types of breast cancer: it is associated to a poor prognosis and there is still no targeted therapy for this type of tumor. In this context, we aim to discover deregulated genes and signaling pathways in human TNBC using high-throughput omic data of well-characterized breast tumors to identify potential therapeutic targets. My work can be divided in two main parts. First, I developed methods for the analysis of genomic data: I proposed a method (ITALICS) for the normalization of Affymetrix SNP 100K and 500K arrays, worked on the segmentation of DNA copy number profiles, proposed new algorithms and new statistical tools to assess the stability of segmentation and derive exact formulation of several model selection criteria and proposed an improved and faster dynamic programming algorithm that recovers the best segmentation exactly with respect to the quadratic loss. Next, I worked on the analysis of the omic data. The first step of my analysis was to plan the experimental design of the omic experiments. I then analyzed the transcriptomic data using already developed and available tools. I sought to better characterize the distinctness of TNBC at the tran- scriptomic level and its overlap with immunohistochemistry data. I worked at the gene and pathway level to identify genes and pathways of interest. Finally, I analyzed the genomic data using the tools and methods that I have developed.
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https://pastel.archives-ouvertes.fr/pastel-00593939
Contributor : Guillem Rigaill <>
Submitted on : Wednesday, May 18, 2011 - 10:52:01 AM
Last modification on : Monday, October 19, 2020 - 11:03:16 AM
Long-term archiving on: : Saturday, December 3, 2016 - 6:13:33 PM

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  • HAL Id : pastel-00593939, version 1

Citation

Guillem Rigaill. Statistical and algorithmic developments for the analysis of Triple Negative Breast Cancers. Applications [stat.AP]. AgroParisTech, 2010. English. ⟨NNT : 2010AGPT0066⟩. ⟨pastel-00593939⟩

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