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Author:Laajala, Teemu Daniel
Title:Analysis of tumor growth experiments using mixed-effects modeling
Syöpäkasvatuskokeiden analysointi sekamalleilla
Publication type:Master's thesis
Publication year:2012
Pages:[8] + 80      Language:   eng
Department/School:Tietojenkäsittelytieteen laitos
Main subject:Informaatiotekniikka   (T-61)
Supervisor:Lähdesmäki, Harri
Instructor:Aittokallio, Tero ; Corander, Jukka
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto  919   | Archive
Keywords:mixed-effects models
xenograft
tumor growth experiment
pre-clinical study
animal models
power analysis
EM-algorithm
model based categorization
time series
missing values
sekamallit
ksenografti
syöpätutkimus
esikliininen koe
eläinmalli
tilastollinen voima
EM-algoritmi
mallipohjainen kategoriointi
aikasarja
puuttuvat arvot
Abstract (eng): Tumor growth experiments used in pre-clinical cancer drug development often result in challenging response profiles.
An implanted human cancer cell line inside a genetically standardized, immune-deficient mouse can shrink spontaneously in a control group or grow aggressively in a treatment group, even if the treatment otherwise appears to block the effective growth of the implanted cancer cells.
Such experiments have previously been analysed using simple statistical methods such as comparison of the end-point tumor volume between the treatment and control groups through a t-test.

We designed a mixed-effects modelling framework that explores hidden subgroups of the data and seeks to connect the identified groups to established tumor biomarkers.
Suitable response and model transformations were proposed to be able to model a wide range of tumor growth experiments.
Cross-validation as well as other model validation and diagnostic tools were utilized for motivating the suggested model choice.
In addition, various approaches were proposed for the purposes of developing better experiment protocols in the future.

The modelling procedure and conclusions made from the statistical inference were shown to be coherent with the previously published studies and the model parameters were assessed to have meaningful interpretation.
The biological motivation of the identified categories was shown to be feasible through several biomarkers.
The current work provides a fruitful base for future model development.
Abstract (fin): Esikliiniset syöpäkokeet johtavat usein heterogeenisiin kasvuprofiileihin.
Istutettu syöpäsolukasvain saattaa spontaanisti pienentyä immunokatoisissa geneettisesti standardoiduissa kontrollihiirissä ja toisaalta yksittäinen tuumori saattaa kasvaa aggressiivisesti hoitoryhmässä, vaikka hoito vaikuttaisi muuten pysäyttävän syöpäkasvainten suurentumisen.
Tällaisten kasvatuskokeiden tilastollinen analyysi on suoritettu monissa julkaisuissa yksinkertaisilla menetelmillä, esimerkiksi vertailemalla viimeisen aikapisteen tuumorivolyymeja t-testillä eri hoitoryhmien yli.

Kehitimme sekamalleihin perustuvan menetelmän, joka etsii syöpien alatyyppejä niiden kasvuprofiilien perusteella.
Menetelmä pyrkii yhdistämään löydetyt alatyypit alalla käytettyihin biomarkkereihin.
Mallin muokkausta ja vasteiden muunnoksia esitetään erilaisiin tilanteisiin, jotta mahdollisimman monenlaisia syöpäkokeita pystyttäisiin analysoimaan kehitetyllä lähestymistavalla.
Ristiinvalidointia sekä muuta mallin diagnostiikkaa käytetään hyväksi perusteltaessa valittua mallin määrittelyä.
Lisäksi testataan erilaisia menetelmiä parantaa käytettyä koeasetelmaa sovitetun mallin perusteella.

Malliin perustuva päättely rinnastetaan aiempiin julkaisuihin.
Sovitetun mallin parametreja tutkitaan biologisen esitiedon perusteella sekä osoitetaan mallilla olevan järkevä yhteys käytettyyn koeasetelmaan sekä kasvatuskokeisiin liittyviin hypoteeseihin. löydetyt kategoriat linkitetään alan biomarkkereihin ja esitetään erilaisia tulevaisuuden näkökulmia syöpäkokeiden mallinkehitykseen.
ED:2012-05-14
INSSI record number: 44492
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