東華大學圖書館 |

Quantitative Proteomics to Support Translational Cancer Research.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Quantitative Proteomics to Support Translational Cancer Research./
作者:	Hoffman, Melissa.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	212 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
標題:	Biology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10831258
ISBN:	9780438279551

Quantitative Proteomics to Support Translational Cancer Research.
Hoffman, Melissa.

Quantitative Proteomics to Support Translational Cancer Research. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 212 p.

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--University of South Florida, 2018.

Altered signaling pathways, which are mediated by post-translational modifications and changes in protein expression levels, are key regulators of cancer initiation, progression, and therapeutic escape. Many aspects of cancer progression, including early carcinogenesis and immediate response to drug treatment, are beyond the scope of genetic profiling and non-invasive monitoring techniques. Global protein profiling of cancer cell line models, tumor tissues, and biofluids (e.g. serum or urine) using mass spectrometry-based proteomics produces novel biological insights, which support improved patient outcomes. Recent technological advances resulting in next-generation mass spectrometry instrumentation and improved bioinformatics workflows have led to unprecedented measurement reproducibility as well as increased depth and coverage of the human proteome. It is now possible to interrogate the cancer proteome with quantitative proteomics to identify prognostic cancer biomarkers, stratify patients for treatment, identify new therapeutic targets, and elucidate drug resistance mechanisms. There are, however, numerous challenges associated with protein measurements. Biological samples have a high level of complexity and wide dynamic range, which is even more pronounced in samples used for non-invasive disease monitoring, such as serum. Cancer biomarkers are generally found in low abundance compared to other serum proteins, particularly at early stages of disease where cancer detection would make the biggest impact on improving patient survival. Additionally, the large-scale datasets generally require bioinformatics expertise to produce useful biological insights. These difficulties converge to create obstacles for down-stream clinical translation. This dissertation research demonstrates how proteomics is applied to develop new resources and generate novel workflows to improve protein quantification in complex biosamples, which could improve translation of cancer research to benefit patient care. The studies described in this dissertation move from assessment of quantitative mass spectrometry platforms, to analytical assay development and validation, and ending with personalized biomarker development applied to patient samples.

ISBN: 9780438279551Subjects--Topical Terms:

522710
Biology.

Quantitative Proteomics to Support Translational Cancer Research.
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500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 $a Adviser: John Koomen.
502 $a Thesis (Ph.D.)--University of South Florida, 2018.
520 $a Altered signaling pathways, which are mediated by post-translational modifications and changes in protein expression levels, are key regulators of cancer initiation, progression, and therapeutic escape. Many aspects of cancer progression, including early carcinogenesis and immediate response to drug treatment, are beyond the scope of genetic profiling and non-invasive monitoring techniques. Global protein profiling of cancer cell line models, tumor tissues, and biofluids (e.g. serum or urine) using mass spectrometry-based proteomics produces novel biological insights, which support improved patient outcomes. Recent technological advances resulting in next-generation mass spectrometry instrumentation and improved bioinformatics workflows have led to unprecedented measurement reproducibility as well as increased depth and coverage of the human proteome. It is now possible to interrogate the cancer proteome with quantitative proteomics to identify prognostic cancer biomarkers, stratify patients for treatment, identify new therapeutic targets, and elucidate drug resistance mechanisms. There are, however, numerous challenges associated with protein measurements. Biological samples have a high level of complexity and wide dynamic range, which is even more pronounced in samples used for non-invasive disease monitoring, such as serum. Cancer biomarkers are generally found in low abundance compared to other serum proteins, particularly at early stages of disease where cancer detection would make the biggest impact on improving patient survival. Additionally, the large-scale datasets generally require bioinformatics expertise to produce useful biological insights. These difficulties converge to create obstacles for down-stream clinical translation. This dissertation research demonstrates how proteomics is applied to develop new resources and generate novel workflows to improve protein quantification in complex biosamples, which could improve translation of cancer research to benefit patient care. The studies described in this dissertation move from assessment of quantitative mass spectrometry platforms, to analytical assay development and validation, and ending with personalized biomarker development applied to patient samples.
520 $a As an example, four different quantitative mass spectrometry acquisition platforms are explored and comparisons of their ability to quantify low abundance peptides in a complex background are explored. Lung cancers frequently have aberrant signaling resulting in increased kinase activity and targetable signaling hubs; kinase inhibitors have been successfully developed and implemented clinically. Therefore, changes in amounts of kinase peptides in the complex background of peptides from all ATP-utilizing enzymes in a lung cancer cell line model after kinase inhibitor treatment was selected as a model system. Traditional mass spectrometry platforms, data dependent acquisition and multiple reaction monitoring, are compared to the two newer methods, data independent acquisition and parallel reaction monitoring. Relative quantification is performed across the four methods and analytical performance as well as downstream applications, including drug target identification and elucidation of signaling changes. Liquid chromatography--multiple reaction monitoring (LC-MRM) was selected for development of multiplexed quantitative assays based on superior sensitivity and fast analysis times, allowing for larger peptide panels. Method comparison results also provide guidelines for quantitative proteomics platform selection for translational cancer researchers.
520 $a Next, a multiplexed quantitative LC-MRM assay targeting a panel of 30 RAS signaling proteins was developed and described. Over 30% of all human cancers have a RAS mutation and these cancers are generally aggressive and limited treatment options, leading to poor patient prognosis. Many targeted inhibitors have successfully shut down RAS signaling, leading to tumor regression, however, acquired drug resistance is common. The multiplexed LC-MRM assays characterized and validated are a publically available resource for cancer researchers to interrogate the RAS signal transduction network. Feasibility has been demonstrated in cell line models in order to identify signaling changes that confer BRAF inhibitor resistance and biomarkers of sensitivity to treatment. This analytical LC-MRM panel could support meaningful development of new therapeutic options and identification of companion biomarkers, with the end goal of improving patient outcomes.
520 $a Multiplexed LC-MRM assays developed for personalized disease biomarkers using an integrated multi-omics approach are described for Multiple Myeloma, an incurable malignancy with poor patient outcomes. This disease is characterized by clonal expansion of the plasma cells in the bone marrow, which secrete a monoclonal immunoglobulin, or M-protein. Clinical treatment decisions are based on multiple semi-quantitative assays that require manual evaluation. In the clinic, minimal residual disease quantification methods, including multi-parameter flow cytometry and immunohistochemistry, are applied to bone marrow aspirates, which is a highly invasive technique that does not provide a systemic evaluation of the disease. To address these issues, we hypothesized that unique variable region peptides could be identified and LC-MRM assays developed specific to each patient's M-protein to improve specificity and sensitivity in non-invasive disease monitoring. A proteogenomics approach was used to design personalized assays for each patient to monitor their disease progression, which demonstrate improved specificity and up to a 500-fold increase in sensitivity compared to current clinical methods. (Abstract shortened by ProQuest.).
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