Unravelling Lung Cancer Heterogeneity and Associated Therapeutic Responses Using In Vivo and Ex Vivo Model Systems

December 7th, 2018

The thesis work of M.Sc. Ashwini Nagaraj focused on investigating the role of  tumor cell of origin in establishing lung cancer phenotypic and functional heterogeneity using genetically engineered lung cancer mouse models. The thesis was supervised by Dr. Emmy Vershuren and Dr. Denis Kainov.

Ashwini's thesis demonstrate that the tumor cell of origin is crucial in determining survival, lung cancer subtype, and associated immune microenvironment in the mouse model used. In addition, it ighlights that beyond understanding lung cancer at the genetic level, investigation of histotype-specific etiology, and spatially distributed functions including oncogenic signaling activities is important for designing effective therapies. 

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Ash­wini Naga­ra­j’s doc­toral thesis high­lights the im­port­ance of strat­i­fy­ing cancer ac­cord­ing to eti­ology-re­lated phen­o­types.

Ashwini Nagaraj's e-thesis.

Sakrepatna Nagaraj
Research Program Systems Oncology
Field of science Biochemistry, cell and molecular biology

Seven shades of tamoxifen resistance - Molecular mechanisms of drug resistance in breast cancer

October 23rd, 2018

M.Sc. Susanne Hultsch's thesis work focuses on the cellular events that precede the development of resistance to one the most important breast cancer treatments, tamoxifen. The thesis was supervised by Sara Kangaspeska, Olli Kallioniemi and Vilja Pietiäinen.

The overall goal of Susanne’s thesis was to create and utilize breast cancer cell line models to explore the resistance mechanisms of breast cancer and find potential prognostic markers as well as drug vulnerabilities. Susanne says that even after "70 years of research, drug resistance is still a problem and we need to learn more about mechanisms of it and vulnerabilities associated with these mechanisms to be able to treat drug-resistant cancers".

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Susanne Hultsch’s dis­ser­ta­tion provides new in­sights into the com­plex mech­an­isms of drug res­ist­ance

Susanne Hultsch's e-thesis.

Improving Precision in Therapies for Hematological Malignancies

October 12th, 2018

M.Sc. Muntasir Mamun Majumder's thesis focused on accelerating the adoption of precision medicine practices in myeloma and other hematological cancers. The thesis was supervised by Caroline Heckman and Krister Wennerberg

The main goal of Mamun's thesis was to identify individualised treatment decisions and to facilitate drug discovery efforts for multiple myeloma. The research covered all the key aspects of precision medicine from method development, patient stratification and biomarker identification to validating the clinical utility of the findings.

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Mun­tasir Mamun Majum­der’s thesis es­tab­lishes the next level of pre­ci­sion medicine for my­el­oma.

Majumder Mamun Muntasir's e-thesis.

Machine Learning for Systems Pharmacology

October 5th, 2018

 M.Sc. Anna Cichonska's thesis work was supervised by Tero Aittokallio and Matti Pirinen (FIMM) and Professor Juho Rousu (Aalto University). Anna received an "Aalto Univeristy dissertation award 2019" on her thesis.

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Anna Cichonska's e-thesis.

Targeting Key Survival Signaling Pathways for The Treatment of Leukemia

September 28th, 2018

M.Sc. Heikki Kuusanmäki's thesis approached the challenges of transforming the new knowledge on leukemia genetics into clinically actionable strategies by combining genetic profiling with high-throughput drug sensitivity testing in patient derived leukemia cells. The thesis work was supervised by Caroline Heckman and Satu Mustjoki.

The main objective of Heikki's thesis was to identify novel driver mutations in two rare leukemia types, large granular lymphocyte (LGL) leukemia and T-cell acute lymphoblastic leukemia (T-ALL). Furthermore, he wanted to develop a new flow cytometry-based drug screening assay to assess cell population specific drug responses in heterogenous leukemia samples.

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Heikki Kuusan­mäki’s dis­ser­ta­tion veri­fies the im­port­ance of cell sur­vival sig­nal­ing path­ways in leuk­emia.

Heikki Kuusanmäki's e-thesis.

Postdoctoral researcher
Institute for Molecular Medicine Finland
Field of science Biomedicine

Clonal evolution and heterogeneity of cancer in the context of individualized medicine

August 24th, 2018

M.Sc. Poojitha Ojamies' thesis work focused on developing sequencing and bioinformatics methods to study spatial and temporal tumor heterogeneity and evolution of cancer subclones. The thesis was supervised by Prof. Olli Kallioniemi and Dr. Maija Wolff.

The goal of Poojitha’s thesis was to comprehensively investigate cancer evolution and heterogeneity as well as understand drug responses at a subclone level in the context of individualised cancer treatment. Her thesis project is part of FIMM’s “Individualised systems medicine in cancer” Grand Challenge programme.

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Poojitha Ojam­ies’ dis­ser­ta­tion provides valu­able in­sights on treat­ing cancer.

Poojitha Ojamies' e-thesis.

Computer vision for tissue characterization and outcome prediction in cancer

August 24th, 2018

M.Sc. (Tech) Riku Turkki's thesis focused on investigating the use of computer vision for tissue characterisation and patient outcome prediction in cancer. The thesis work was supervised by Johan Lundin and Nina Linder.

Although a lot of progress is made in cancer diagnostics and outcome prediction, histological analysis of tumour tissue still has a key role. Manual histological evaluation is time-consuming and prone to subjective assessment. In his thesis, Riku tackled this challenge by developing computer vision aided processes and testing their applicability for clinical purposes.  The main aim of his dissertation was to investigate the use of computer vision for tissue characterisation and patient outcome prediction in cancer.

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Riku Turkki’s doc­toral thesis demon­strates the po­ten­tial of com­puter vis­ion in analysis of cancer his­to­path­o­logy.

Riku Turkki's e-thesis.

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Functional testing of urological cancer models by RNAi and drug libraries

August 13th, 2018

M.Sc. Khalid Saeed’s thesis work focused on identifying novel future treatment strategies for prostate, renal and kidney cancer. His thesis was supervised by Olli Kallioniemi, Päivi Östling and Taija af Hällström.

In his thsesis, Khalid’s was aimed to generate molecular profile and drug testing data using patient-derived cancer cells. His proof-of-concept studies show that this kind of data can be used to help clinicians in treatment decision and to facilitate the early selection of the best drug candidates for clinical development.

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Khalid Saeed’s dis­ser­ta­tion shows why it pays to be an early ad­op­ter.

Khalid Saeed's e-thesis.

Molecular Effects of Obesity and Related Metabolic Risk Factors – A Transcriptomics and Metabolomics Approach

June 8th, 2018

M.Sc., MBA Maheswary Muniandy’s thesis focused on studying obesity using omics data and bioinformatics tools. The thesis work was supervised by Dr. Miina Ollikainen and Prof. Kirsi Pietiläinen.

The global obesity epidemic is worsening in most parts of the world and the implications of obesity regarding both personal health and health-economics are enormous. Understanding the molecular mechanisms leading to and caused by obesity could help in designing personalised treatment to support weight-loss. In her thesis Maheswary tried to gain understanding of the complex, multifactorial biology behind obesity using bioinformatics tools.

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Ma­heswary Mu­ni­andy’s dis­ser­ta­tion re­veals the many faces of obesity.

Maheswary Munidandy's e-thesis.

Postdoctoral researcher
Research Program for Clinical and Molecular Metabolism
Field of science Public health care science, environmental and occupational health

Integrative bioinformatics of functional and genomic profiles for cancer systems medicine

June 8th, 2018

M.Sc. Alok Jaiswal’s thesis focused on developing novel computational models and approaches for data analysis to facilitate extracting reproducible and meaningful information from the genomic and functional cancer datasets. The thesis work was supervised by Tero Aittokallio and Jing Tang.

The goal of Alok's thesis was to provide information about the genes driving cancer that could be utilised in identifying promising cancer drug candidates and predictive biomarkers. He developed an approach to remove noise from genome-wide RNAi screens and also integrated genomic profiles with the RNAi screen data to be able to predict major cancer driver genes and their synthetic lethal partners.

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Alok Jaiswal’s dis­ser­ta­tion demon­strates the im­pact of com­pu­ta­tional ap­proaches on data in­teg­ra­tion in cancer research.

Alok Jaiswal's e-thesis.

Analysis of somatic mutations in leukemias

May 21st, 2018

M.Sc. Samuli Eldfors' thesis focused on identifying clinically important somatic mutations contributing to the development and treatment response of different leukemias. The thesis work was supervised by Dr. Caroline Heckman and Prof. Olli Kallioniemi.

In his thesis, Samuli has identified somatic mutations in three leukemia types and analyzed the biological and clinical significance of these mutations. A major part of the work has been the development of a computational workflow and the application of bioinformatics approaches to detect mutations based on whole exome sequencing data.

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Sam­uli Eld­fors’ dis­ser­ta­tion demon­strates the clin­ical sig­ni­fic­ance of so­matic muta­tions in leuk­emias.

Samuli Eldfors' e-thesis.

In Search of Improved Outcome Prediction of Prostate Cancer – A Biological and Clinical Approach

May 11th, 2018

The thesis of M.Sc. Andrew Erickson focused on identifying novel biological and clinical factors that could be utilized in the outcome prediction of prostate cancer. The thesis was supervised by Tuomas Mirtti and Antti Rannikko.

In the first part his thesis work, Andrew focused on outcomes of low-risk active surveillance patients. And in the second part of the thesis, the focus was on the long-term outcome of primarily treated patients.

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An­drew Er­ick­son’s dis­ser­ta­tion im­proves the out­come pre­dic­tion of pro­state cancer.

Andrew Erickson's e-thesis.  

Association and interplay of genetic and epigenetic variants in smoking behavior

April 20th, 2018

The thesis of M.Sc. Richa Gupta focused on studying the links between the smoking behaviour and genetic as well as epigenetic variation. The thesis work was supervised by Jaakko Kaprio and Miina Ollikainen

Smoking is the single greatest preventable cause of death in the world today. In her thesis, Richa utilised several different omics data, including genetics (assessed by single nucleotide polymorphisms), epigenetics (assessed by DNA methylation), as well as transcriptomics data  to identify novel associations and validating the involvement of known candidate genes in smoking behaviour.

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Richa Gupta’s dis­ser­ta­tion demon­strates the in­ter­play between the gen­ome and epi­gen­ome in smoking.

Richa Gupta's e-thesis


Bioinformatic identification of disease driver networks using functional profiling data

March 23rd, 2018

The thesis of M.Sc. Agnieszka Szwajda was supervised my FIMM-EMBL Group Leaders Tero Aittokallio and Krister Wennerberg.

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Agnieszka Szwajda's e-thesis.