Quantitative modeling and analysis of drug screening data for personalized cancer medicine

Forskningsoutput: AvhandlingDoktorsavhandlingSamling av artiklar

Sammanfattning

Despite recent progress in the field of molecular medicine, the treatment and cure of complex diseases such as cancer remains a challenge. Development of resistance to first-line chemotherapy is a common cause of current anticancer treatment failure. To deal with this problem, the personalized medicine (PM) approach has been adapted toward more targeted cancer research and management. The PM approach is based on each patient s genetic, epigenetic and drug response profiling, which is used to design the best treatment option for the given patient. As the PM approach is increasingly being adopted in clinical practice, there is an urgent need for computational models and data mining methods that allow fast processing and analysis of the massive relevant profiling datasets. High-throughput drug screening enables systematic profiling of cellular responses to a wide collection of oncology compounds and their combinations, hence providing an unbiased strategy for personalized drug treatment selection. However, screening experiments with patient-derived cell samples often results in high-dimensional data matrices, with inherent sources of noise. This complicates many downstream analyses, such as the detection of differential drug activity or understanding the mechanisms behind drug sensitivity and resistance in a given patient. To meet these challenges, a computational pipeline for drug response profiling was developed in this thesis. The pipeline was based on a novel metric to quantify drug response, called the drug sensitivity score (DSS). Further, by combining the normalized drug response profile of each cancer sample with a global drug-target interaction network, a target addiction score (TAS) was developed to de-convolute the selective protein targets and obtain knowledge on their functional importance. Finally, delta scoring was developed to quantify drug combination effects and to address the problem of the clonal evolution of cancer, which often leads to resistance to mono therapies. This novel computational pipeline improves understanding of cancer development and translates compound activities into informed treatment choices for clinicians. As exemplified in two case studies of adult acute myeloid leukemia (AML) and adult granulosa cell tumor (AGCT), the models developed here have the potential to significantly contribute to the effective analysis of data from individual cancer patients and from pan-cancer cell line panels. Hence, these models will play a substantial role in future personalized cancer treatment strategies and the selection of effective treatment options for individual cancer patients.
Originalspråkengelska
UtgivningsortHelsinki
Förlag
Tryckta ISBN978-951-51-2965-9
Elektroniska ISBN978-951-51-2966-6
StatusPublicerad - 2017
MoE-publikationstypG5 Doktorsavhandling (artikel)

Vetenskapsgrenar

  • 3111 Biomedicinska vetenskaper
  • 3122 Cancersjukdomar

Citera det här

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title = "Quantitative modeling and analysis of drug screening data for personalized cancer medicine",
abstract = "Despite recent progress in the field of molecular medicine, the treatment and cure of complex diseases such as cancer remains a challenge. Development of resistance to first-line chemotherapy is a common cause of current anticancer treatment failure. To deal with this problem, the personalized medicine (PM) approach has been adapted toward more targeted cancer research and management. The PM approach is based on each patient s genetic, epigenetic and drug response profiling, which is used to design the best treatment option for the given patient. As the PM approach is increasingly being adopted in clinical practice, there is an urgent need for computational models and data mining methods that allow fast processing and analysis of the massive relevant profiling datasets. High-throughput drug screening enables systematic profiling of cellular responses to a wide collection of oncology compounds and their combinations, hence providing an unbiased strategy for personalized drug treatment selection. However, screening experiments with patient-derived cell samples often results in high-dimensional data matrices, with inherent sources of noise. This complicates many downstream analyses, such as the detection of differential drug activity or understanding the mechanisms behind drug sensitivity and resistance in a given patient. To meet these challenges, a computational pipeline for drug response profiling was developed in this thesis. The pipeline was based on a novel metric to quantify drug response, called the drug sensitivity score (DSS). Further, by combining the normalized drug response profile of each cancer sample with a global drug-target interaction network, a target addiction score (TAS) was developed to de-convolute the selective protein targets and obtain knowledge on their functional importance. Finally, delta scoring was developed to quantify drug combination effects and to address the problem of the clonal evolution of cancer, which often leads to resistance to mono therapies. This novel computational pipeline improves understanding of cancer development and translates compound activities into informed treatment choices for clinicians. As exemplified in two case studies of adult acute myeloid leukemia (AML) and adult granulosa cell tumor (AGCT), the models developed here have the potential to significantly contribute to the effective analysis of data from individual cancer patients and from pan-cancer cell line panels. Hence, these models will play a substantial role in future personalized cancer treatment strategies and the selection of effective treatment options for individual cancer patients.",
keywords = "Antineoplastic Agents, +pharmacology, Dose-Response Relationship, Drug, Drug Therapy, Combination, Drug Delivery Systems, Drug Resistance, Neoplasm, +drug effects, High-Throughput Screening Assays, Leukemia, Myeloid, Acute, +drug therapy, Models, Biological, Models, Theoretical, Neoplasm Recurrence, Local, Granulosa Cell Tumor, Precision Medicine, 3111 Biomedicine, 3122 Cancers",
author = "Bhagwan Yadav",
note = "M1 - 68 s. + liitteet Volume: Proceeding volume:",
year = "2017",
language = "English",
isbn = "978-951-51-2965-9",
publisher = "[B. Yadav]",
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}

Quantitative modeling and analysis of drug screening data for personalized cancer medicine. / Yadav, Bhagwan.

Helsinki : [B. Yadav], 2017. 68 s.

Forskningsoutput: AvhandlingDoktorsavhandlingSamling av artiklar

TY - THES

T1 - Quantitative modeling and analysis of drug screening data for personalized cancer medicine

AU - Yadav, Bhagwan

N1 - M1 - 68 s. + liitteet Volume: Proceeding volume:

PY - 2017

Y1 - 2017

N2 - Despite recent progress in the field of molecular medicine, the treatment and cure of complex diseases such as cancer remains a challenge. Development of resistance to first-line chemotherapy is a common cause of current anticancer treatment failure. To deal with this problem, the personalized medicine (PM) approach has been adapted toward more targeted cancer research and management. The PM approach is based on each patient s genetic, epigenetic and drug response profiling, which is used to design the best treatment option for the given patient. As the PM approach is increasingly being adopted in clinical practice, there is an urgent need for computational models and data mining methods that allow fast processing and analysis of the massive relevant profiling datasets. High-throughput drug screening enables systematic profiling of cellular responses to a wide collection of oncology compounds and their combinations, hence providing an unbiased strategy for personalized drug treatment selection. However, screening experiments with patient-derived cell samples often results in high-dimensional data matrices, with inherent sources of noise. This complicates many downstream analyses, such as the detection of differential drug activity or understanding the mechanisms behind drug sensitivity and resistance in a given patient. To meet these challenges, a computational pipeline for drug response profiling was developed in this thesis. The pipeline was based on a novel metric to quantify drug response, called the drug sensitivity score (DSS). Further, by combining the normalized drug response profile of each cancer sample with a global drug-target interaction network, a target addiction score (TAS) was developed to de-convolute the selective protein targets and obtain knowledge on their functional importance. Finally, delta scoring was developed to quantify drug combination effects and to address the problem of the clonal evolution of cancer, which often leads to resistance to mono therapies. This novel computational pipeline improves understanding of cancer development and translates compound activities into informed treatment choices for clinicians. As exemplified in two case studies of adult acute myeloid leukemia (AML) and adult granulosa cell tumor (AGCT), the models developed here have the potential to significantly contribute to the effective analysis of data from individual cancer patients and from pan-cancer cell line panels. Hence, these models will play a substantial role in future personalized cancer treatment strategies and the selection of effective treatment options for individual cancer patients.

AB - Despite recent progress in the field of molecular medicine, the treatment and cure of complex diseases such as cancer remains a challenge. Development of resistance to first-line chemotherapy is a common cause of current anticancer treatment failure. To deal with this problem, the personalized medicine (PM) approach has been adapted toward more targeted cancer research and management. The PM approach is based on each patient s genetic, epigenetic and drug response profiling, which is used to design the best treatment option for the given patient. As the PM approach is increasingly being adopted in clinical practice, there is an urgent need for computational models and data mining methods that allow fast processing and analysis of the massive relevant profiling datasets. High-throughput drug screening enables systematic profiling of cellular responses to a wide collection of oncology compounds and their combinations, hence providing an unbiased strategy for personalized drug treatment selection. However, screening experiments with patient-derived cell samples often results in high-dimensional data matrices, with inherent sources of noise. This complicates many downstream analyses, such as the detection of differential drug activity or understanding the mechanisms behind drug sensitivity and resistance in a given patient. To meet these challenges, a computational pipeline for drug response profiling was developed in this thesis. The pipeline was based on a novel metric to quantify drug response, called the drug sensitivity score (DSS). Further, by combining the normalized drug response profile of each cancer sample with a global drug-target interaction network, a target addiction score (TAS) was developed to de-convolute the selective protein targets and obtain knowledge on their functional importance. Finally, delta scoring was developed to quantify drug combination effects and to address the problem of the clonal evolution of cancer, which often leads to resistance to mono therapies. This novel computational pipeline improves understanding of cancer development and translates compound activities into informed treatment choices for clinicians. As exemplified in two case studies of adult acute myeloid leukemia (AML) and adult granulosa cell tumor (AGCT), the models developed here have the potential to significantly contribute to the effective analysis of data from individual cancer patients and from pan-cancer cell line panels. Hence, these models will play a substantial role in future personalized cancer treatment strategies and the selection of effective treatment options for individual cancer patients.

KW - Antineoplastic Agents

KW - +pharmacology

KW - Dose-Response Relationship, Drug

KW - Drug Therapy, Combination

KW - Drug Delivery Systems

KW - Drug Resistance, Neoplasm

KW - +drug effects

KW - High-Throughput Screening Assays

KW - Leukemia, Myeloid, Acute

KW - +drug therapy

KW - Models, Biological

KW - Models, Theoretical

KW - Neoplasm Recurrence, Local

KW - Granulosa Cell Tumor

KW - Precision Medicine

KW - 3111 Biomedicine

KW - 3122 Cancers

M3 - Doctoral Thesis

SN - 978-951-51-2965-9

PB - [B. Yadav]

CY - Helsinki

ER -