Thesis topic proposal
Transcriptional adaptation and minimal regulatory networks in cancer: systems biology models for a better understandig of mutant phenotypes


Institute: Semmelweis University, Budapest
theoretical medicine
Doctoral School of Pathological Sciences

Thesis supervisor: Endre Sebestyén
Location of studies (in Hungarian): SE
Abbreviation of location of studies: SE

Description of the research topic:

Transcriptional adaptation and minimal regulatory networks in cancer: systems biology models for a better understanding of mutant phenotypes

The proposed research topic is focusing on understanding the effect of genetic redundancy, degeneracy and essentiality on disease causing mutations using computational tools. Eukaryotic gene regulatory networks are noisy, complex, and depending on cell or tissue type, specific genes might not be needed at all. Although there is a consensus on the genetic origins of cancer, and the role of driver mutations in cancer development, progression, metastasis and drug resistance, the interpretation of the effect of mutations is not always straightforward. Stochastic gene expression patterns, genetic compensatory mechanisms and the redundancy and degeneracy of gene regulatory networks all lead to complications. Understanding and predicting the final phenotypic effect of driver mutations is complex and prone to problems. Our aim is to have a better understanding of the effect of various mutations, and clarify their diagnostic, predictive and prognostic utility.

Using large-scale biological datasets from the Sequence Read Archive, the Cancer Genome Atlas, Blueprint Epigenome and similar projects, characterizing thousands of samples and multiple cancer types, we will investigate how gene regulatory networks change in cancer, and what are the most essential genes or biological functions required even for a cancer cell. As essentiality seems to be the property of biological function and not specific genes, the exact mutation status of a single gene might not matter as long as there are redundant or degenerate elements compensating for changes. Additionally, the mechanism of transcriptional adaptation might compensate for loss-of-function mutations in cancer. Loss-of-function mutations still lead to transcribed RNA, that is later removed by nonsense mediated decay and other mechanisms. However, recent data has shown that these RNA decay mechanisms leads to expression upregulation in genes with similar sequences.

We will describe pan-cancer and cancer-specific essential biological functions using uniformly reanalyzed genome and exome sequencing data from various cancer types. We will also investigate if transcriptional adaptation can be detected in different tumors. Data from hypermutated cancer samples will be especially valuable, as they might have more than 100 mutations per megabase, leading to a large number of mutated genes, promoters, enhancers and other functional genomic regions. Cancer genome and transcriptome data will be complemented by data from healthy tissue or cell types, characterized on a large scale by the GTEx or GEUVADIS projects. While investigating biological questions, we will also build novel computational pipelines and software tools to analyze high-throughput sequencing results.

Additional information: https://su-compbio.github.io/positions

Deadline for application: 2020-12-06

2020. X. 20.
ODT online ülés
Az ODT következő, online ülésére 2020. november 6-án 10.00 órakor kerül sor.

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