Kuvaus

Cancer is a leading cause of death worldwide, and its incidence is increasing due to modern lifestyle that prolonged human life. All cancers originate from a single cell that had acquired genetic aberrations enabling uncontrolled proliferation. Each cancer is unique in its aberrant genetic makeup, which defines, to large extent, its biology, aggressiveness, and vulnerabilities to different treatments. Furthermore, the genetic makeup of each cancer is heterogeneous among its constituent cancer cells, and dynamic with the ability to evolve in order to preserve the survival of cancer cells. Sequencing technologies are currently producing massive amounts of data that, with the help of specialized computational methods, can revolutionize our knowledge on cancer. A key question in cancer research is how to personalize the treatment of cancer patients, so that each cancer is treated according to its molecular characteristics. The first study in this thesis takes a step in that direction through a proposed novel molecular classification system of diffuse large B-cell lymphoma (DLBCL), which is the most common hematological malignancy in adults. The suggested classification, derived from the integrative analysis of gene expression and DNA mutations, stratifies DLBCL into four groups with distinct biology, genetic landscapes, and clinical outcome. These subtypes could help identify patients at high risk who may benefit from an altered treatment plan. Understanding the genomic evolution of cancer that transforms a typically curable primary tumor into an incurable drug-resistant metastasis is another aspect of cancer research under intensive investigation. The second study in this thesis investigates the spreading patterns of metastasis in breast cancer, which is the most common cancer in women. Using phylogenetic analysis of somatic mutations from longitudinal breast cancer samples, the metastasis routes were uncovered. The study revealed that breast cancer spreads either in parallel from primary tumor to multiple distant sites, or linearly from primary tumor to a distant site, and then from that to another. However, in all cases, axillary lymph nodes did not mediate the spreading to distant sites. This provided a genetic-based evidence on the redundancy of lymph node dissection in breast cancer management. Towards a genetic-based diagnostics in cancer, the computational methods used to detect genetic aberrations need to be evaluated for their accuracy. The third study in this thesis performs a comparison of methods for detecting somatic copy number alterations from cancer samples. The study evaluated several commonly used methods for two different sequencing platforms using simulated and real cancer data. The results provided an overview of the weaknesses of the different methods that could be methodologically improved. Altogether, this thesis gives an overview on the field of computational cancer genomics and presents three studies that exemplify the clinical relevance of computational research.
Alkuperäiskielienglanti
Valvoja/neuvonantaja
  • Hautaniemi, Sampsa, Valvoja
JulkaisupaikkaHelsinki
Kustantaja
Painoksen ISBN978-951-51-5190-2
Sähköinen ISBN978-951-51-5191-9
TilaJulkaistu - 2019
OKM-julkaisutyyppiG5 Tohtorinväitöskirja (artikkeli)

Tieteenalat

  • 3111 Biolääketieteet
  • 1184 Genetiikka, kehitysbiologia, fysiologia

Lainaa tätä

Alkodsi, Amjad. / Computational investigation of cancer genomes. Helsinki : [A. Alkodsi], 2019. 50 Sivumäärä
@phdthesis{a11dbee3e84e42ae9041592d91a48473,
title = "Computational investigation of cancer genomes",
abstract = "Cancer is a leading cause of death worldwide, and its incidence is increasing due to modern lifestyle that prolonged human life. All cancers originate from a single cell that had acquired genetic aberrations enabling uncontrolled proliferation. Each cancer is unique in its aberrant genetic makeup, which defines, to large extent, its biology, aggressiveness, and vulnerabilities to different treatments. Furthermore, the genetic makeup of each cancer is heterogeneous among its constituent cancer cells, and dynamic with the ability to evolve in order to preserve the survival of cancer cells. Sequencing technologies are currently producing massive amounts of data that, with the help of specialized computational methods, can revolutionize our knowledge on cancer. A key question in cancer research is how to personalize the treatment of cancer patients, so that each cancer is treated according to its molecular characteristics. The first study in this thesis takes a step in that direction through a proposed novel molecular classification system of diffuse large B-cell lymphoma (DLBCL), which is the most common hematological malignancy in adults. The suggested classification, derived from the integrative analysis of gene expression and DNA mutations, stratifies DLBCL into four groups with distinct biology, genetic landscapes, and clinical outcome. These subtypes could help identify patients at high risk who may benefit from an altered treatment plan. Understanding the genomic evolution of cancer that transforms a typically curable primary tumor into an incurable drug-resistant metastasis is another aspect of cancer research under intensive investigation. The second study in this thesis investigates the spreading patterns of metastasis in breast cancer, which is the most common cancer in women. Using phylogenetic analysis of somatic mutations from longitudinal breast cancer samples, the metastasis routes were uncovered. The study revealed that breast cancer spreads either in parallel from primary tumor to multiple distant sites, or linearly from primary tumor to a distant site, and then from that to another. However, in all cases, axillary lymph nodes did not mediate the spreading to distant sites. This provided a genetic-based evidence on the redundancy of lymph node dissection in breast cancer management. Towards a genetic-based diagnostics in cancer, the computational methods used to detect genetic aberrations need to be evaluated for their accuracy. The third study in this thesis performs a comparison of methods for detecting somatic copy number alterations from cancer samples. The study evaluated several commonly used methods for two different sequencing platforms using simulated and real cancer data. The results provided an overview of the weaknesses of the different methods that could be methodologically improved. Altogether, this thesis gives an overview on the field of computational cancer genomics and presents three studies that exemplify the clinical relevance of computational research.",
keywords = "Lymphoma, Large B-Cell, Diffuse, +classification, +genetics, Breast Neoplasms, Neoplasm Invasiveness, Neoplasm Metastasis, Neoplasms, Multiple Primary, Neoplasms, Second Primary, Lymph Nodes, Patient-Specific Modeling, Whole Genome Sequencing, Whole Exome Sequencing, Computational Biology, +methods, Precision Medicine, 3111 Biomedicine, 1184 Genetics, developmental biology, physiology",
author = "Amjad Alkodsi",
note = "M1 - 50 s. + liitteet",
year = "2019",
language = "English",
isbn = "978-951-51-5190-2",
publisher = "[A. Alkodsi]",
address = "Finland",

}

Computational investigation of cancer genomes. / Alkodsi, Amjad.

Helsinki : [A. Alkodsi], 2019. 50 s.

Tutkimustuotos: OpinnäyteVäitöskirjaArtikkelikokoelma

TY - THES

T1 - Computational investigation of cancer genomes

AU - Alkodsi, Amjad

N1 - M1 - 50 s. + liitteet

PY - 2019

Y1 - 2019

N2 - Cancer is a leading cause of death worldwide, and its incidence is increasing due to modern lifestyle that prolonged human life. All cancers originate from a single cell that had acquired genetic aberrations enabling uncontrolled proliferation. Each cancer is unique in its aberrant genetic makeup, which defines, to large extent, its biology, aggressiveness, and vulnerabilities to different treatments. Furthermore, the genetic makeup of each cancer is heterogeneous among its constituent cancer cells, and dynamic with the ability to evolve in order to preserve the survival of cancer cells. Sequencing technologies are currently producing massive amounts of data that, with the help of specialized computational methods, can revolutionize our knowledge on cancer. A key question in cancer research is how to personalize the treatment of cancer patients, so that each cancer is treated according to its molecular characteristics. The first study in this thesis takes a step in that direction through a proposed novel molecular classification system of diffuse large B-cell lymphoma (DLBCL), which is the most common hematological malignancy in adults. The suggested classification, derived from the integrative analysis of gene expression and DNA mutations, stratifies DLBCL into four groups with distinct biology, genetic landscapes, and clinical outcome. These subtypes could help identify patients at high risk who may benefit from an altered treatment plan. Understanding the genomic evolution of cancer that transforms a typically curable primary tumor into an incurable drug-resistant metastasis is another aspect of cancer research under intensive investigation. The second study in this thesis investigates the spreading patterns of metastasis in breast cancer, which is the most common cancer in women. Using phylogenetic analysis of somatic mutations from longitudinal breast cancer samples, the metastasis routes were uncovered. The study revealed that breast cancer spreads either in parallel from primary tumor to multiple distant sites, or linearly from primary tumor to a distant site, and then from that to another. However, in all cases, axillary lymph nodes did not mediate the spreading to distant sites. This provided a genetic-based evidence on the redundancy of lymph node dissection in breast cancer management. Towards a genetic-based diagnostics in cancer, the computational methods used to detect genetic aberrations need to be evaluated for their accuracy. The third study in this thesis performs a comparison of methods for detecting somatic copy number alterations from cancer samples. The study evaluated several commonly used methods for two different sequencing platforms using simulated and real cancer data. The results provided an overview of the weaknesses of the different methods that could be methodologically improved. Altogether, this thesis gives an overview on the field of computational cancer genomics and presents three studies that exemplify the clinical relevance of computational research.

AB - Cancer is a leading cause of death worldwide, and its incidence is increasing due to modern lifestyle that prolonged human life. All cancers originate from a single cell that had acquired genetic aberrations enabling uncontrolled proliferation. Each cancer is unique in its aberrant genetic makeup, which defines, to large extent, its biology, aggressiveness, and vulnerabilities to different treatments. Furthermore, the genetic makeup of each cancer is heterogeneous among its constituent cancer cells, and dynamic with the ability to evolve in order to preserve the survival of cancer cells. Sequencing technologies are currently producing massive amounts of data that, with the help of specialized computational methods, can revolutionize our knowledge on cancer. A key question in cancer research is how to personalize the treatment of cancer patients, so that each cancer is treated according to its molecular characteristics. The first study in this thesis takes a step in that direction through a proposed novel molecular classification system of diffuse large B-cell lymphoma (DLBCL), which is the most common hematological malignancy in adults. The suggested classification, derived from the integrative analysis of gene expression and DNA mutations, stratifies DLBCL into four groups with distinct biology, genetic landscapes, and clinical outcome. These subtypes could help identify patients at high risk who may benefit from an altered treatment plan. Understanding the genomic evolution of cancer that transforms a typically curable primary tumor into an incurable drug-resistant metastasis is another aspect of cancer research under intensive investigation. The second study in this thesis investigates the spreading patterns of metastasis in breast cancer, which is the most common cancer in women. Using phylogenetic analysis of somatic mutations from longitudinal breast cancer samples, the metastasis routes were uncovered. The study revealed that breast cancer spreads either in parallel from primary tumor to multiple distant sites, or linearly from primary tumor to a distant site, and then from that to another. However, in all cases, axillary lymph nodes did not mediate the spreading to distant sites. This provided a genetic-based evidence on the redundancy of lymph node dissection in breast cancer management. Towards a genetic-based diagnostics in cancer, the computational methods used to detect genetic aberrations need to be evaluated for their accuracy. The third study in this thesis performs a comparison of methods for detecting somatic copy number alterations from cancer samples. The study evaluated several commonly used methods for two different sequencing platforms using simulated and real cancer data. The results provided an overview of the weaknesses of the different methods that could be methodologically improved. Altogether, this thesis gives an overview on the field of computational cancer genomics and presents three studies that exemplify the clinical relevance of computational research.

KW - Lymphoma, Large B-Cell, Diffuse

KW - +classification

KW - +genetics

KW - Breast Neoplasms

KW - Neoplasm Invasiveness

KW - Neoplasm Metastasis

KW - Neoplasms, Multiple Primary

KW - Neoplasms, Second Primary

KW - Lymph Nodes

KW - Patient-Specific Modeling

KW - Whole Genome Sequencing

KW - Whole Exome Sequencing

KW - Computational Biology

KW - +methods

KW - Precision Medicine

KW - 3111 Biomedicine

KW - 1184 Genetics, developmental biology, physiology

M3 - Doctoral Thesis

SN - 978-951-51-5190-2

PB - [A. Alkodsi]

CY - Helsinki

ER -

Alkodsi A. Computational investigation of cancer genomes. Helsinki: [A. Alkodsi], 2019. 50 s.