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Books on the topic 'Tumor genomic'

Author: Grafiati
Published: 9 September 2022
Last updated: 18 September 2022

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1

Simon, Richard M. Genomic clinical trials and predictive medicine. Cambridge: Cambridge University Press, 2012.

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2

Cancer genome and tumor microenvironment. New York: Springer, 2010.

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3

Thomas-Tikhonenko, Andrei, ed. Cancer Genome and Tumor Microenvironment. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0711-0.

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4

Thomas-Tikhonenko, Andrei. Cancer genome and tumor microenvironment. New York: Springer, 2010.

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5

Zhang, Xuewu. Omics technologies in cancer biomarker discovery. Austin, Tex: Landes Bioscience, 2011.

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6

Parker, James N., and Philip M. Parker. Von Hippel-Lindau syndrome: A bibliography and dictionary for physicians, patients, and genome researchers [to internet references]. San Diego, CA: ICON Health Publications, 2007.

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7

Yosef, Shiloh, and SpringerLink (Online service), eds. The DNA Damage Response: Implications on Cancer Formation and Treatment. Dordrecht: Springer Netherlands, 2009.

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8

Yegnasubramanian, Srinivasan. Modern Molecular Biology: Approaches for Unbiased Discovery in Cancer Research. New York, NY: Springer Science+Business Media, LLC, 2010.

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9

Maher, Christopher J., and Elaine R. Mardis. Genomic Landscape of Cancer. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0004.

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The study of cancer genomics has advanced rapidly during the last decade due to the development of next generation or massively parallel technology for DNA sequencing. The resulting knowledge is transforming the understanding of both inherited (germline) genetic susceptibility and the somatic changes in tumor tissue that drive abnormal growth and progression. The somatic alterations in tumor tissue vary depending on the type of cancer and its characteristic “genomic landscape.” New technologies have increased the speed and lowered the cost of DNA sequencing and have enabled high-volume characterization of RNA, DNA methylation, DNA-protein complexes, DNA conformation, and a host of other factors that, when altered, can contribute to the development and/or progression of the cancer. Technologic advances have greatly expanded research on somatic changes in tumor tissue, revealing both the singularity of individual cancer genomes and the commonality of genetic alterations that drive cancer in different tissues.
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10

Sherman, Mark E., Melissa A. Troester, Katherine A. Hoadley, and William F. Anderson. Morphological and Molecular Classification of Human Cancer. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0003.

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Accurate and reproducible classification of tumors is essential for clinical management, cancer surveillance, and studies of pathogenesis and etiology. Tumor classification has historically been based on the primary anatomic site or organ in which the tumor occurs and on its morphologic and histologic phenotype. While pathologic criteria are useful in predicting the average behavior of a group of tumors, histopathology alone cannot accurately predict the prognosis and treatment response of individual cancers. Traditional measures such as tumor stage and grade do not take into account molecular events that influence tumor aggressiveness or changes in the tumor composition during treatment. This chapter provides a primer on approaches that use pathology and molecular biology to classify and subclassify cancers. It describes the features of carcinomas, sarcomas, and malignant neoplasms of the immune system and blood, as well as various high-throughput genomic platforms that characterize the molecular profile of tumors.
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11

Thun, Michael J., Martha S. Linet, James R. Cerhan, Christopher A. Haiman, and David Schottenfeld. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0001.

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This Introduction provides a broad overview of the scientific advances and crosscutting developments that increasingly influence epidemiologic research on the causes and prevention of cancer. High-throughput technologies have identified the molecular “driver” events in tumor tissue that underlie the multistage development of many types of cancer. These somatic (largely acquired) alterations disrupt normal genetic and epigenetic control over cell maintenance, division and survival. Tumor classification is also changing to reflect the genetic and molecular alterations in tumor tissue, as well as the anatomic, morphologic, and histologic phenotype of the cancer. Genome-wide association studies (GWAS) have identified more than 700 germline (inherited) genetic loci associated with susceptibility to various forms of cancer, although the risk estimates for almost all of these are small to modest and their exact location and function remain to identified. Advances in genomic and other “OMIC” technologies are identifying biomarkers that reflect internal exposures, biological processes and intermediate outcomes in large population studies. While research in many of these areas is still in its infancy, mechanistic and molecular assays are increasingly incorporated into etiologic studies and inferences about causation. Other sections of the book discuss the global public health impact of cancer, the growing list of exposures known to affect cancer risk, the epidemiology of over 30 types of cancer by tissue of origin, and preventive interventions that have dramatically reduced the incidence rates of several major cancers.
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12

Tzortzatou, Stathopoulou Fotini, ed. Genome and proteome in oncology. New York: Nova Science Publishers, 2005.

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13

Ali-Fehmi, Rouba, and Eman Abdulfatah. Biological Aspects and Clinical Applications of Serum Biomarkers in Ovarian Cancer. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190248208.003.0002.

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Ovarian cancer, the most aggressive gynecological malignancy, presents at advanced stages with metastatic disease. Diagnosis at an early stage is the most important determinant of survival; however, the majority of patients are asymptomatic at early stages and the current diagnostic tools used in clinics show limited success in early detection and hence the need for new diagnostic biomarkers. With the advance of techniques in genomic and proteomics, numerous biomarkers are emerging which may serve as a platform for early detection of ovarian cancer. These include gene-, protein-, miRNAs, and metabolite- based biomarkers. Examples of gene-based biomarkers include HE4, FLOR1, p16INK4a, BRCA1, BRCA2, MLH1, and MSH2. Protein- based biomarkers include leptin, prolactin, osteopontin, IGF-II, and MIF. This chapter discusses the serum tumor markers (CA-125) in current use for screening, diagnosis and monitoring of ovarian cancer as well as the novel biomarkers that are under investigation and validation.
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14

Seyfried, Thomas N., and Laura M. Shelton. Metabolism-Based Treatments to Counter Cancer. Edited by Jong M. Rho. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780190497996.003.0012.

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Accumulating evidence indicates that cancer is a type of mitochondrial metabolic disease. Chronic damage to mitochondria causes a gradual shift in cellular energy metabolism from respiration to fermentation. Consequently, fermentable metabolites become the drivers of cancer. Mitochondrial injury can explain the long-standing “oncogenic paradox,” and all major hallmarks of cancer including genomic instability. Restriction of fermentable fuels therefore becomes a viable therapeutic strategy for cancer management. The ketogenic diet (KD) is a metabolic therapy that lowers blood glucose and elevates blood ketone bodies. Ketone bodies are a “super fuel” for functional mitochondria, but cannot be metabolized efficiently by tumor mitochondria. The efficacy of KDs for cancer management can be enhanced when used together with drugs and procedures (such as hyperbaric oxygen therapy) (that further target fermentation. Therapeutic ketosis can represent an alternative, nontoxic strategy for managing and preventing a broad range of cancers while reducing healthcare costs.
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15

M, Hampton Garret, and Sikora Karol, eds. Genomics in cancer drug discovery and development. Amsterdam: Elsevier/AP, 2007.

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16

Genomics and Models of Nerve Sheath Tumors. MDPI, 2020. http://dx.doi.org/10.3390/books978-3-03943-490-9.

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17

Dabbs, David J. Diagnostic Immunohistochemistry: Theranostic and Genomic Applications. Elsevier - Health Sciences Division, 2018.

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18

New Research on Genomic Instability. Nova Biomedical Books, 2007.

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19

J, Dabbs David, ed. Diagnostic immunohistochemistry: Diagnostic, theranostic, and genomic applications. 3rd ed. Philadelphia, PA: Saunders /Elsevier, 2009.

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20

(Editor), Wei Zhang, and Gregory N. Fuller (Editor), eds. Genomic and Molecular Neuro-Oncology. Jones & Bartlett Publishers, 2003.

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21

(Editor), Garret Hampton, Karol Sikora (Editor), George F. Vande Woude (Series Editor), and George Klein (Series Editor), eds. Genomics in Cancer Drug Discovery and Development, Volume 96 (Advances in Cancer Research). Academic Press, 2006.

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22

(Editor), Garret Hampton, Karol Sikora (Editor), George F. Vande Woude (Series Editor), and George Klein (Series Editor), eds. Genomics in Cancer Drug Discovery and Development, Volume 96 (Advances in Cancer Research). Academic Press, 2006.

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23

New Insights in the Genetics and Genomics of Adrenocortical Tumors and Pheochromocytomas. MDPI, 2022. http://dx.doi.org/10.3390/books978-3-0365-3357-5.

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24

B, Fisher Paul, ed. Cancer genomics and proteomics: Methods and protocols. Totowa, N.J: Humana Press, 2007.

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25

Merl, Dan, Joseph Lucas, Joseph Nevins, Haige Shen, and Mike West. Trans-study projection of genomic biomarkers in analysis of oncogene deregulation and breast cancer. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.6.

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This article focuses on the use of Bayesian concepts and methods in the trans-study projection of genomic biomarkers for the analysis of oncogene deregulation in breast cancer. The objective of the study is to determine the extent to which patterns of gene expression associated with experimentally induced oncogene pathway deregulation can be used to investigate oncogene pathway activity in real human cancers. This is often referred to as the in vitro to in vivo translation problem, which is addressed using Bayesian sparse factor regression analysis for model-based translation and refinement of in vitro generated signatures of oncogene pathway activity into the domain of human breast tumour tissue samples. The article first provides an overview of the role of oncogene pathway deregulation in human cancers before discussing the details of modelling and data analysis. It then considers the findings based on biological evaluation and Bayesian pathway annotation analysis.
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26

Sand, Michael. MicroRNAs in malignant tumors of the skin: First steps of tiny players in the skin to a new world of genomic medicine. Springer, 2018.

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27

Rezvani, Mojgan. Genomics-based characterization of tumor suppressor genes in the cardiovascular system: A role for adenomatosis polyposis coli gene in human cardiovascular development and disease. 2001.

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28

Tangen, Catherine M., Marian L. Neuhouser, and Janet L. Stanford. Prostate Cancer. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0053.

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Prostate cancer is the most common solid tumor and the second leading cause of cancer-related mortality in American men. Worldwide, prostate cancer ranks second and fifth as a cause of cancer and cancer deaths, respectively. Despite the international burden of disease due to prostate cancer, its etiology is unclear in most cases. Established risk factors include age, race/ancestry, and family history of the disease. Prostate cancer has a strong heritable component, and genome-wide association studies have identified over 110 common risk-associated genetic variants. Family-based sequencing studies have also found rare mutations (e.g., HOXB13) that contribute to prostate cancer susceptibility. Numerous environmental and lifestyle factors (e.g., obesity, diet) have been examined in relation to prostate cancer incidence, but few modifiable exposures have been consistently associated with risk. Some of the variability in results may be related to etiological heterogeneity, with different causes underlying the development of distinct disease subgroups.
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29

Cerhan, James R., Claire M. Vajdic, and John J. Spinelli. The Non-Hodgkin Lymphomas. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190238667.003.0040.

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The non-Hodgkin lymphomas (NHL) are a heterogeneous group of over forty lymphoid neoplasms that have undergone a major redefinition over the last twenty-five years, in part due to advances in immunology and genetics as well as implementation of the WHO classification system. NHLs are considered clonal tumors of B-cells, T-cells, or natural killer (NK) cells arrested at various stages of differentiation, regardless of whether they present in the blood (lymphoid leukemia) or lymphoid tissues (lymphoma). In the United States, the age-standardized NHL incidence rate (per 100,000) doubled from 1973 (10.2) to 2004 (21.4) and then stabilized, while five-year relative survival rates improved from 42% in 1973 to 70% in 2004. Established risk factors for NHL or specific NHL subtypes include infectious agents (HTLV-1, HIV, EBV, HHV8, HCV, H. pylori), immune dysregulation (primary immunodeficiency, transplantation, autoimmunity, and immunosuppressive drugs), family history of lymphoma, and common genetic variants identified by genome-wide association studies.
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