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master's thesis
Računalno određivanje podtipova mišjih tumora dojke
Zagreb: University of Zagreb, Faculty of Science, 2019. urn:nbn:hr:217:819133
Ivanković, Ivna
University of Zagreb
Faculty of Science
Department of Biology

Institutional repository: Repository of the Faculty of Science

Cite this document

Ivanković, I. (2019). Računalno određivanje podtipova mišjih tumora dojke (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:819133

Ivanković, Ivna. "Računalno određivanje podtipova mišjih tumora dojke." Master's thesis, University of Zagreb, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:217:819133

Ivanković, Ivna. "Računalno određivanje podtipova mišjih tumora dojke." Master's thesis, University of Zagreb, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:217:819133

Ivanković, I. (2019). 'Računalno određivanje podtipova mišjih tumora dojke', Master's thesis, University of Zagreb, Faculty of Science, accessed 26 May 2024, https://urn.nsk.hr/urn:nbn:hr:217:819133

Ivanković I. Računalno određivanje podtipova mišjih tumora dojke [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2019 [cited 2024 May 26] Available at: https://urn.nsk.hr/urn:nbn:hr:217:819133

I. Ivanković, "Računalno određivanje podtipova mišjih tumora dojke", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:217:819133

Metadata
TitleRačunalno određivanje podtipova mišjih tumora dojke
Title (english)Computational subtyping of mouse breast tumors
AuthorIvna Ivanković
MentorMarc Zapatka (mentor)
MentorRosa Karlić (mentor)
Committee memberRosa Karlić (predsjednik povjerenstva)
Committee memberSofia Ana Blažević (član povjerenstva)
Committee memberRomana Gračan (član povjerenstva)
Committee memberBiljana Balen (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Biology)
Zagreb
Defense date and country2019-07-05, 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
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:819133
Study programmeTitle: Molecular Biology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra molekularne biologije (magistar/magistra molekularne biologije)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Embargo expiration date: 2020-07-05
Terms of useLicence In copyright icon
Created on2019-12-03 07:26:51