Računalno određivanje podtipova mišjih tumora dojke

master's thesis

• Title (english): Computational subtyping of mouse breast tumors
• Author: Ivna Ivanković
• Mentor: Marc Zapatka (mentor)
• Mentor: Rosa Karlić (mentor)
• Committee member: Rosa Karlić (predsjednik povjerenstva)
• Committee member: Sofia Ana Blažević (član povjerenstva)
• Committee member: Romana Gračan (član povjerenstva)
• Committee member: Biljana Balen (član povjerenstva)
• Granter: University of Zagreb; Faculty of Science; (Department of Biology); Zagreb
• Defense date and country: 2019-07-05, Croatia
• Scientific / art field, discipline and subdiscipline: NATURAL SCIENCES; Biology

Abstract

Istraživanja genske ekspresije tumora dojke metodom mikrocipova ukazala su na razlicite
molekularne portrete tumora na temelju kojih ih je moguce klasificirati u pet podtipova s razlicitim
klinickim ishodima: luminalni A, luminalni B, obogaceni receptorom HER2, bazalni
i tip nalik normalnom. PAM50 je molekularni klasifikator nastao reduciranjem proširenog
seta gena na 50 gena koji najznacajnije pridonose prepoznavanju podtipova. U paketu genefu
programskog jezika R implementirani su bioinformaticki algoritmi i genski potpisi za odreivanje
molekularnih podtipova tumora dojke, ukljucujuci i molekularni PAM50 klasifikator.
U ovom diplomskom radu modificiran je algoritam iz paketa genefu kako bi ulazni podaci bili
podaci dobiveni sekvenciranjem RNA umjesto podaci o genskoj ekspresiji dobiveni metodom
mikrocipova i kako bi odredio podtipove mišjih tumora u odnosu na ljudske tumore dojke.
Nadalje, nedavna istraživanja pokazala su da je moguce integrirati mišje i ljudske setove
podataka i tako informacije iz jednog seta koristiti za interpretaciju drugog. U ovom radu
primijenjena je kanonska korelacijska analiza iz paketa Seurat za integraciju mišjih i ljudskih
podataka dobivenih sekvenciranjem RNA tumora dojke i odreeni su podtipovi na temelju
genskog PAM50 seta. Usporeeni su rezultati odreivanja podtipova mišjih tumora dojke
dobiveni paketima genefu i Seurat, i procijenjena je tocnost dvaju neovisnih pristupa odreivanja
podtipova tumora dojke.

Abstract (english)

Studies of gene expression patterns of breast tumors derived from cDNA microarrays reported
their distinctive molecular portraits according to which tumors can be classified into five
intrinsic subtypes with distinct clinical outcomes: luminal A, luminal B, HER2 enriched,
basal and normal-like. PAM50 is a molecular classifier developed by minimizing expanded
intrinsic gene set to the top 50 genes that contribute to distinguishing intrinsic subtypes. The
R/Bioconductor package genefu implements bioinformatics algorithms and gene signatures
for molecular subtyping of breast cancer, including PAM50 molecular classifier. In this
project, an algorithm from package genefu was modified to subtype breast tumors using
RNA-Seq instead of microarray data as an input and then used to subtype RNA sequenced
mouse breast tumors in relation to human tumors. Furthermore, a recent study showed that
human and mouse data can be integrated using canonical correlation analysis from package
Seurat. The motivation for integrating diverse datasets lies in potential to use information
from one dataset for the interpretation of another. In this project canonical correlation
analysis was also used to integrate human and mouse bulk RNA-Seq data based on the set
of PAM50 genes and used to determine intrinsic breast tumor subtypes. Finally, results of
genefu and Seurat subtyping were compared and the performance of the two independent
approaches was assessed.

• Keywords: karcinom dojke; heterogenicnost tumora; PAM50; klasteriranje
• Keywords (english): breast cancer; tumor heterogeneity; PAM50; intrinsic subtypes; clustering
• Language: croatian
• URN:NBN: urn:nbn:hr:217:819133
• Study programme: Title: Molecular Biology; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra molekularne biologije (magistar/magistra molekularne biologije)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Open access; Embargo expiration date: 2020-07-05
• Created on: 2019-12-03 07:26:51