IMPROVING PRECISION IN THERAPIES FOR HEMATOLOGICAL MALIGNANCIES

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http://urn.fi/URN:ISBN:978-951-51-4553-6
Title: IMPROVING PRECISION IN THERAPIES FOR HEMATOLOGICAL MALIGNANCIES
Author: Majumder, Muntasir Mamun
Contributor: University of Helsinki, Faculty of Biological and Environmental Sciences, Institute for Molecular Medicine Finland (FIMM)
Doctoral Programme in Biomedicin
Publisher: Helsingin yliopisto
Date: 2018-10-12
Belongs to series: Dissertationes Scholae Doctoralis Ad Sanitatem Investigandam Universitatis Helsinkiensis 66/2018 - URN:ISSN:2342-317X
URI: http://urn.fi/URN:ISBN:978-951-51-4553-6
http://hdl.handle.net/10138/246099
Thesis level: Doctoral dissertation (article-based)
Abstract: Unlike the traditional trial and error approach, precision medicine strategies exploit specific genetic defects and deregulated signaling pathways within cancer cells and may target unique cellular phenotypes to match therapies to patients. Multiple myeloma displays enormous genetic complexity and heterogeneity, which may be ascribed to the diversity of responses observed in patients sharing identical prognostic markers or disease stage. An ambitious aim has been to identify predictive biomarkers that forecast the treatment outcome and detect patient subgroups that are likely to respond. Additionally, characterizing the diverse effects of small molecules on nonmalignant hematopoietic cells is required to understand potential off-target effects and drug interactions, which could further improve the precision of treatment and thus the outcome in patients. In this study, we comprehensively assessed responses to 142 anticancer therapies in 100 patient samples and integrated their responses with genomic, transcriptomic, and clinical profiles, generating a rich pharmacogenetic resource for connecting myeloma genotype to drug responses. An unsupervised clustering of drug responses identified four therapeutically relevant myeloma patient subgroups with a significant variation observed in their ex vivo sensitivity to therapies, genomic composition, and clinical outcome. An acquired sensitivity to signaling inhibitors was associated with a clinically aggressive disease and a higher mutational load in those patients. Fourteen percent of patients displayed cross resistance to nearly all tested drugs and were characterized by a higher expression of the drug resistance transporter genes ABCB1 and ABCC3, genes participating in cell adhesion, and cytokines. Utilizing matched multi-assay data from myeloma patient derived cells, we elucidated indicators of drug sensitivity and identified approved drugs that could potentially be repurposed for myeloma. Midostaurin sensitivity was detected in 43% of relapsed patients harboring mutations in TP53 and FAM46C. Four patients who were treated with tailored therapies based on preclinical evidence from this study achieved meaningful and objective responses, providing clinical evidence that ex vivo responses could reflect treatment outcome in patients. Signaling and molecular heterogeneity in cell lineages are incurred during hematopoiesis, which dictates the phenotype of blood cells and thus influences their cellular response to drug treatments. We developed and utilized a high-content, multi-parametric flow cytometry assay to determine the diversity of responses in 11 hematopoietic cell types to 71 small molecules and compared their basal signaling state and protein abundances. We discovered that hematological cell populations exhibit distinct drug responses that are tied to their cellular lineages. Venetoclax exhibited dose-dependent cell selectivity toward lymphocyte lineages. From a comparison of sensitivity profiles in healthy and malignant cells in 281 patient samples from diverse hematological malignancies, we found that drug response detected in the cell of origin is predictive of its response in its malignant state. The findings presented in this thesis demonstrate that deep molecular profiling and functional testing provides powerful tools that are complementary to each other for stratifying patients into subgroups, generating mechanistic biomarkers, and individualizing treatments in patients, which lies at the heart of precision medicine.
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