Готові списки джерел за темами / Tumor genomic

Добірка наукової літератури з теми "Tumor genomic"

Автор: Grafiati
Опубліковано: 9 вересня 2022
Оновлено: 18 вересня 2022

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Статті в журналах з теми "Tumor genomic"

1

Schmandt, R., and G. B. Mills. "Genomic components of carcinogenesis." Clinical Chemistry 39, no. 11 (November 1, 1993): 2375–85. http://dx.doi.org/10.1093/clinchem/39.11.2375.

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Abstract Many of the genes encoding growth factors, growth factor receptors, enzymes, and other effector molecules that regulate normal cell growth are designated protooncogenes. Oncogenes, those genes associated with cellular transformation, differ from their protooncogenic progenitors by being mutated, overexpressed, or expressed at inappropriate times or locations in the cell. One of the activities of growth factors is to prime cells to undergo programmed cell death, which is characterized by a series of morphologic changes called apoptosis. In normal cells, specific mediators must be activated or suppressed to bypass programmed cell death. In tumor cells, either the pathways leading to apoptosis are not functional or the mediators that normally "rescue" cells from this fate are overexpressed or constitutively activated. In addition to the biochemical pathways that drive cell division, there are others that limit cell proliferation; these, designated tumor suppressors, anti-oncogenes, or recessive oncogenes, must be inactivated in normal cells to allow passage through the cell cycle and cell proliferation. In contrast to oncogenes, which are overexpressed or activated in tumors, tumor-suppressor genes are frequently inactivated in tumor cells, either by mutation or deletion. Thus, in normal cells a series of checks and balances must be overcome to allow initiation and continuation of cell division. In tumors, these processes are aberrant, resulting in increased rates of cell division, increases in the proportion of cells in the cell cycle, or increased survival of activated cells. Therefore, tumor cells frequently accumulate genomic alterations, which may result in the activation of a particular array of oncogenes, the inactivation of specific tumor-suppressor genes, and the bypassing of programmed cell death. Trials of antitumor agents that act by exploiting the overexpression of oncogenes in tumors and of the biochemical pathways by which they mediate cell proliferation are currently underway.
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2

Ellsworth, Rachel E., Jeffrey A. Hooke, Craig D. Shriver, and Darrell L. Ellsworth. "Genomic Heterogeneity of Breast Tumor Pathogenesis." Clinical medicine. Oncology 3 (January 2009): CMO.S2946. http://dx.doi.org/10.4137/cmo.s2946.

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Pathological grade is a useful prognostic factor for stratifying breast cancer patients into favorable (low-grade, well-differentiated tumors) and less favorable (high-grade, poorly-differentiated tumors) outcome groups. Under the current system of tumor grading, however, a large proportion of tumors are characterized as intermediate-grade, making determination of optimal treatments difficult. In an effort to increase objectivity in the pathological assessment of tumor grade, differences in chromosomal alterations and gene expression patterns have been characterized in low-grade, intermediate-grade, and high-grade disease. In this review, we outline molecular data supporting a linear model of progression from low-grade to high-grade carcinomas, as well as contradicting genetic data suggesting that low-grade and high-grade tumors develop independently. While debate regarding specific pathways of development continues, molecular data suggest that intermediate-grade tumors do not comprise an independent disease subtype, but represent clinical and molecular hybrids between low-grade and high-grade tumors. Finally, we discuss the clinical implications associated with different pathways of development, including a new clinical test to assign grade and guide treatment options.
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Brastianos, Priscilla Kaliopi, Peleg Horowitz, Sandro Santagata, Robert T. Jones, Aaron McKenna, Keith Ligon, Emanuele Palescandolo, et al. "Genomic characterization of meningiomas." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 2020. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.2020.

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2020 Background: Understanding the genetic alterations in cancer has lead to groundbreaking discoveries in targeted therapies. Meningiomas are among the most common primary brain tumors, with approximately 18,000 new cases diagnosed annually. Though certain genes have been associated with the development of meningiomas, most notably the tumor suppressor gene neurofibromatosis 2 (NF2), the genetic changes that drive meningiomas remain poorly understood. Our objective was to comprehensively characterize the somatic genetic alterations of meningiomas to gain insight into the molecular pathways that drive this disease. Methods: Fresh frozen specimens and paired blood were collected from 16 consented patients. DNA was extracted from regions of high tumor purity determined by evaluation of H&E slides. Whole-genome sequencing from 10 tumor-normal pairs and whole-exome sequencing from 6 tumor-normal pairs was carried out. We performed an unbiased screen for point mutations, insertions-deletions, rearrangements and copy-number changes across the exomes and genomes. Recurrent (potential driver) events were then analyzed with additional algorithms for statistical significance. Results: Alterations in the NF2 gene were present in 9 of 16 patients. Multiple novel rearrangements and recurrent non-NF2 mutations were also identified in the cohort. Massive genomic rearrangement termed chromothripsis was observed in chromosome 1 in one sample, which has never previously been described in meningiomas, and represents a potentially new mechanism of malignant transformation in this tumor type. Conclusions: While NF2 mutations appear to drive a majority of these tumors, our analysis has uncovered additional potential driver genes in meningiomas, particularly in those tumors negative for NF2 alterations. To our knowledge, this is the first study to comprehensively characterize the totality of somatic genetic alterations in meningiomas, and brings us closer to the development of new therapeutic targets for this disease.
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Garraway, Levi A. "Genomics-Driven Oncology: Framework for an Emerging Paradigm." Journal of Clinical Oncology 31, no. 15 (May 20, 2013): 1806–14. http://dx.doi.org/10.1200/jco.2012.46.8934.

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A majority of cancers are driven by genomic alterations that dysregulate key oncogenic pathways influencing cell growth and survival. However, the ability to harness tumor genetic information for its full clinical potential has only recently become manifest. Over the past several years, the convergence of discovery, technology, and therapeutic development has created an unparalleled opportunity to test the hypothesis that systematic knowledge of genomic information from individual tumors can improve clinical outcomes for many patients with cancer. Rigorous evaluation of this genomics-driven cancer medicine hypothesis will require many logistic innovations that are guided by overarching conceptual advances in tumor genomic profiling, data interpretation, clinical trial design, and the ethical return of genetic results to oncologists and their patients. The results of these efforts and the rigor with which they are implemented will determine whether and how comprehensive tumor genomic information may become incorporated into the routine care of patients with cancer.
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Chen, Mingjiu, Haitao Ma, Haoda Yu, Chen Chen, Pingping Dai, Zhiyi He, Pengcheng Li, et al. "Genomic heterogeneity of multifocal NSCLC." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e21595-e21595. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e21595.

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e21595 Background: Distinguishing multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) remains a common diagnostic dilemma but critical for developing a therapeutic strategy. Methods: In this study, we analyse genomic features of 584 tissue samples from 258 NSCLC patients with > 1 surgically-resected tumor. NGS was performed using panels of 1021/59 genes. Results: Among 80 patients with definite diagnosis, 23 patients (46 tumors) were diagnosed with IPM. And 57 patients (145 tumors) were MPLC, consisting of 53 synchronous and 3 metachronous tumor pairs. Among 23 IPM tumor pairs, we identified at least one shared somatic mutation. By contrast, 93%(53/57) MPLC tumor pairs exhibited entirely unique mutation profiles in each tumor. Of 57 MPLCs, 4(7%) had no driver alteration ( EGFR, KRAS, BRAF, ALK, ERBB2, MET Exon 14). 9(16%) had a driver in only one of the tumors. In the remaining 44 MPLCs, 40 had unique driver mutation in each tumor. 50%(20/40) had distinct EGFR mutations, the most common combination was L858R and delins in exon 19. Specifically, 8(20%) patients resided ≥3 unique driver mutations simultaneously. This observation indicated that multiple lesions in the same individual can be driven by distinct molecular events. In contrast, 21 available IPM tumor pairs shared the same driver mutation. Pathogenic germline mutations were also analysed. Two were found in 2 MPLCs, involving PLAB2 and BLM, which were both null variants. While there were no pathogenic germline mutations found in IPMs. Regarding the MSI status, all samples from either MPLCs and IPMs displayed MSS, unexpectedly. To further verify the findings above, the remaining 393 samples with uncertain diagnosis were classified into group M (no shared mutation, 218 samples /97 patients) and I (≥1 shared mutation, 175 samples/81 patients). We find the similarity results among all available patients, driver mutation profiles exhibited completely unique and multiple in group M and fully consistent in group I. Conclusions: Taken together, our analyses indicated that the genomic heterogeneity of multifocal NSCLC may be a potential strategy for differentiating IPM and MPLC. This may hold implications for prioritizing therapeutic strategies.
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Pawloski, Jacob A., Hassan A. Fadel, Yi-Wen Huang, and Ian Y. Lee. "Genomic Biomarkers of Meningioma: A Focused Review." International Journal of Molecular Sciences 22, no. 19 (September 23, 2021): 10222. http://dx.doi.org/10.3390/ijms221910222.

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Meningiomas represent a phenotypically and genetically diverse group of tumors which often behave in ways that are not simply explained by their pathologic grade. The genetic landscape of meningiomas has become a target of investigation as tumor genomics have been found to impact tumor location, recurrence risk, and malignant potential. Additionally, targeted therapies are being developed that in the future may provide patients with personalized chemotherapy based on the genetic aberrations within their tumor. This review focuses on the most common genetic mutations found in meningiomas of all grades, with an emphasis on the impact on tumor location and clinically relevant tumor characteristics. NF-2 and the non-NF-2 family of genetic mutations are summarized in the context of low-grade and high-grade tumors, followed by a comprehensive discussion regarding the genetic and embryologic basis for meningioma location and phenotypic heterogeneity. Finally, targeted therapies based on tumor genomics currently in use and under investigation are reviewed and future avenues for research are suggested. The field of meningioma genomics has broad implications on the way meningiomas will be treated in the future, and is gradually shifting the way clinicians approach this diverse group of tumors.
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MacConaill, Laura E. "Existing and Emerging Technologies for Tumor Genomic Profiling." Journal of Clinical Oncology 31, no. 15 (May 20, 2013): 1815–24. http://dx.doi.org/10.1200/jco.2012.46.5948.

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Ongoing global genome characterization efforts are revolutionizing our knowledge of cancer genomics and tumor biology. In parallel, information gleaned from these studies on driver cancer gene alterations—mutations, copy number alterations, translocations, and/or chromosomal rearrangements—can be leveraged, in principle, to develop a cohesive framework for individualized cancer treatment. These possibilities have been enabled, to a large degree, by revolutionary advances in genomic technologies that facilitate systematic profiling for hallmark cancer genetic alterations at increasingly fine resolutions. Ongoing innovations in existing genomics technologies, as well as the many emerging technologies, will likely continue to advance translational cancer genomics and precision cancer medicine.
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de Moor, Janet S., Stacy W. Gray, Sandra A. Mitchell, Carrie N. Klabunde, and Andrew N. Freedman. "Oncologist Confidence in Genomic Testing and Implications for Using Multimarker Tumor Panel Tests in Practice." JCO Precision Oncology, no. 4 (September 2020): 620–31. http://dx.doi.org/10.1200/po.19.00338.

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PURPOSE The evolution of precision oncology increasingly requires oncologists to incorporate genomic testing into practice. Yet, providers’ confidence with genomic testing is poorly documented. This article describes medical oncologists’ confidence with genomic testing and the association between genomic confidence and test use. METHODS We used data from the 2017 National Survey of Precision Medicine in Cancer Treatment to characterize oncologists’ confidence with genomic testing. Genomic confidence was examined separately by type of test user: next-generation sequencing (NGS) only, gene expression (GE) only, both NGS and GE, or nonuser. Predictors of genomic confidence were examined with multinomial logistic regression. The association between genomic confidence and test use was examined with multivariable linear regression. RESULTS More than 75% of genomic test users were either moderately or very confident about using results from multimarker tumor panel tests to guide patient care. Confidence with using multimarker tumor panel tests was highest among both NGS and GE test users, with 60.1% very confident in using test results, and lowest among NGS-only test users, with 38.2% very confident in using test results. Oncologists were most confident in using single-gene tests and least confident in using whole-genome or -exome sequencing to guide patient care. Genomic confidence was positively associated with self-reported test use. In adjusted models, training in genomics, larger patient volume, and treating patients with solid tumors predicted higher genomic confidence. Onsite pathology services and receipt of electronic medical record alerts for genomic testing predicted lower genomic confidence. CONCLUSION Oncologists’ confidence varies by testing platform, patient volume, genomic training, and practice infrastructure. Research is needed to identify modifiable factors that can be targeted to enhance provider confidence with genomic testing.
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Starr, Jason Scott, Kabir Mody, Ali Roberts, and Pashtoon Murtaza Kasi. "Circulating tumor DNA analysis of neuroendocrine tumors." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e15698-e15698. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e15698.

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e15698 Background: Neuroendocrine tumors (NETs) and carcinomas (NECs) are a diverse group of tumors with an equally diverse biology and clinical behavior. Data on tissue-based genomic profiling of NETs exists, however, there is limited data using circulating tumor DNA (ctDNA) technology. We sought out to characterize NETs via ctDNA to identify genomic alterations. Methods: 27 patients with metastatic NET/NEC with 32 total plasma samples were analyzed using Guardant360 ctDNA assay. Breakdown of NET/NEC by location: 14 pancreatic NET (pNET), 11 NEC, 1 small bowel NET, 1 lung NET. The ctDNA test detects single nucleotide variants in 54-73 genes, copy number amplifications, fusions, and indels in selected genes. Results: Of the 27 patients, 19 (70%) had a detectable genomic alteration. The detectable (non-synonymous) alterations are as follows: TP53 (n = 14, 70%), NF1 (n = 8, 40%), EGFR (n = 5, 25%), BRCA2 (n = 4, 20%), KRAS (n = 4, 20%), ARID1A (n = 3, 15%), CDK6 (n = 3, 15%), ALK (n = 3, 15%), MET (n = 2, 10%), PTEN (n = 2, 10%), BRAF (n = 2, 10%), MTOR (n = 2, 10%) AKT1 (n = 1), BRCA1 (n = 1), CCND2 (n = 1), CCNE1 (n = 1), CTNNB1 (n = 1), ESR1 (n = 1), FGFR2 (n = 1), HRAS (n = 1), IDH1 (n = 1), KIT (n = 1), MYC (n = 1), NOTCH1 (n = 1), NRAS (n = 1), PDGFRA (n = 1), RAF1 (n = 1), RB1 (n = 1), SMAD4 (n = 1), STK11 (n = 1), TSC1 (n = 1), ERBB2 (n = 1), PIK3CA (n = 1). Conclusions: This experience highlights the feasibility of ctDNA to help identify genomic alterations in this patient population. Further studies incorporating ctDNA testing in this patient population are warranted.
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Surrey, Lea F., Minjie Luo, Fengqi Chang, and Marilyn M. Li. "The Genomic Era of Clinical Oncology: Integrated Genomic Analysis for Precision Cancer Care." Cytogenetic and Genome Research 150, no. 3-4 (2016): 162–75. http://dx.doi.org/10.1159/000454655.

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Genomic alterations are important biological markers for cancer diagnosis and prognosis, disease classification, risk stratification, and treatment selection. Chromosomal microarray analysis (CMA) and next-generation sequencing (NGS) technologies are superb new tools for evaluating cancer genomes. These state-of-the-art technologies offer high-throughput, highly accurate, targeted and whole-genome evaluation of genomic alterations in tumor tissues. The application of CMA and NGS technologies in cancer research has generated a wealth of useful information about the landscape of genomic alterations in cancer and their implications in cancer care. As the knowledge base in cancer genomics and genome biology grows, the focus of research is now shifting toward the clinical applications of these technologies to improve patient care. Although not yet standard of care in cancer, there is an increasing interest among the cancer genomics communities in applying these new technologies to cancer diagnosis in the Clinical Laboratory Improvement Amendments (CLIA)-certified laboratories. Many clinical laboratories have already started adopting these technologies for cancer genomic analysis. We anticipate that CMA and NGS will soon become the major diagnostic means for cancer genomic analysis to meet the increasing demands of precision cancer care.
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Дисертації з теми "Tumor genomic"

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Qiao, Yi. "Tumor subclone structure reconstruction with genomic variation data." Thesis, Boston College, 2014. http://hdl.handle.net/2345/bc-ir:104182.

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Thesis advisor: Gabor Marth
Unlike normal tissue cells, which contain identical copies of the same genome, tumors are composed of genetically divergent cell subpopulations, or subclones. The abilities to identify the number of subclones, their frequencies within the tumor mass, and the evolutionary relationships among them are crucial in understanding the basis of tumorigenesis, drug response, relapse, and metastasis. It is also essential information for informed, personalized therapeutic decisions. Studies have attempted to reconstruct subclone structure by identifying distinct allele frequency distribution modes at a handful of somatic single nucleotide variant loci, but this question was not adequately addressed with computational means at the start of this dissertation work, and recent efforts either enforce certain assumptions or resort to statistical procedure which cannot guarantee the complete landscape of solution space. This dissertation present a computational framework that examines somatic variation events, such as copy number changes, loss of heterozygosity, or point mutations, in order to identify the underlying subclone structure. Chapter 2 discuss the presence of intra-tumoral heterogeneity, and for historical interest, a method to reconstruct the parsimonious solution based on simplifying assumptions in tumor micro-evolution process. Analysis results on clinical datasets concerning Ovarian Serious Carcinoma and Intracranal Germ Cell Tumor based on this method, which confirmed the genomic complexity, are also presented. Due to the reason that the linkage information i.e. whether two mutations are co-localizing in the same cancer cell is lost during tissue homogenization and DNA fragmentation, common sample preparation steps used in whole genome profiling techniques, often there are more than one subclone model capable of explaining the observation. Chapter 3 describes an extended method that is able to search for all models consistent with the observation. Consequently, the solution to a specific input dataset is then a set of possible subclone structures. The method then trim this solution space in the case that more than one sample from the same patient are available, such as the primary and relapse tumor pairs. Furthermore, a statistical framework is developed that, when further trimming is not possible, predicts whether two mutations are co-localizing in the same subclone. The formal definition on the problem of subclone structure reconstruction, as well as techniques to pre-process various types of genomic variation data are given given here as well. Results on the analysis of published and novel datasets, ranging from cancer types including Acute Myeloid Leukemia, Sinonasal Undifferenciated Carcinoma and Ovarian Serious Carcinoma, and data types including whole genome sequencing, copy number array, single nucleotide polymorphism array and single nucleotide variant calls with deep sequencing are also included. They show that the method is applicable to these wide range of cancer and data types, able to independently replicate the published conclusion based on manual reasoning, and gain novel insights into the pattern of tumor recurrence and chemoresistance. It also shows that the method can be valuable in prioritizing variants for function study
Thesis (PhD) — Boston College, 2014
Submitted to: Boston College. Graduate School of Arts and Sciences
Discipline: Biology
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2

Srinivasan, Seetha V. "Loss of the RB tumor suppressor contributes to genomic instability." Cincinnati, Ohio : University of Cincinnati, 2002. http://rave.ohiolink.edu/etdc/view.cgi?acc_num=ucin1212166350.

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Thesis (Ph.D.)--University of Cincinnati, 2008.
Advisor: Erik S. Knudsen. Title from electronic thesis title page (viewed Sep. 8, 2008). Keywords: RB; cell cycle; DNA replication; mitosis; p53; ploidy; genome integrity. Includes abstract. Includes bibliographical references.
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SRINIVASAN, SEETHA V. "Loss of the RB tumor suppressor contributes to genomic instability." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1212166350.

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Barbee, Bonnie. "Genomic Heterogeneity of Glioblastoma: A Comparison of the Enhancing Tumor Core and the Brain Around the Tumor." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/603560.

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Holcomb, Ilona Noelani. "Genomic profiling of prostate cancer within and beyond the primary tumor /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/10282.

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Rooney, Michael Steven. "Integrative genomic approaches to dissecting host-tumor and host-pathogen immune processes." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98722.

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Анотація:
Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 243-263).
Two parallel research efforts were pursued. First, we conducted a systematic exploration of how the genomic landscape of cancer shapes and is shaped by anti-tumor immunity. Using large-scale genomic data sets of solid tissue tumor biopsies, we quantified the cytolytic activity of the local immune infiltrate and identified associated properties across 18 tumor types. The number of predicted MHC Class I-associated neoantigens was correlated with cytolytic activity and was lower than expected in colorectal and other tumors, suggesting immune-mediated elimination. We identified recurrently mutated genes that showed positive association with cytolytic activity, including beta-2- microglobulin (B2M), HLA-A, -B and -C and Caspase 8 (CASP8), highlighting loss of antigen presentation and blockade of extrinsic apoptosis as key strategies of resistance to cytolytic activity. Genetic amplifications were also associated with high cytolytic activity, including immunosuppressive factors such as PDL1/2 and ALOX12B/15B. Our genetic findings thus provide evidence for immunoediting in tumors and uncover mechanisms of tumor-intrinsic resistance to cytolytic activity. Second, we combined measurements of protein production and degradation and mRNA dynamics so as to build a quantitative genomic model of the differential regulation of gene expression in lipopolysaccharide-stimulated mouse dendritic cells. Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels. Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for more than half of the absolute change in protein molecules in the cell. Thus, the proteome is regulated by transcriptional induction for newly activated cellular functions and by protein lifecycle changes for remodeling of preexisting functions.
by Michael Steven Rooney.
Ph. D.
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Fishler, Kristen B. S. "“It’s the Wild, Wild West Out There” Experiences of a Multidisciplinary Genomic Breast Cancer Tumor Board Implementing Tumor Sequencing in Clinical Care." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1525169475571341.

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O'Connor, Brian Daniel. "Analysis of high level patterns in genomic data from protein thermostability to tumor biology /." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1383484301&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Pereira, Carolina Ruivo 1986. "Genomic profile of tumorgrafts identifies B2M as a novel tumor suppressor gene in lung cancer." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/482055.

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El cáncer de pulmón es la forma más mortal de cáncer en el mundo. Recientemente, el estudio del perfil genómico a larga escala de tumores humanos ha impulsado el desarrollo de drogas que tienen como diana terapéutica genes alterados. Dado que las terapias dirigidas son escasas, el descubrimiento de nuevos genes implicados en cáncer de pulmón con relevancia clínica es crucial. Por eso, este proyecto tuvo como base la secuenciación de exomas y transcriptomas de xenotransplantes de pulmón. La pureza tumoral alcanzada durante el injerto fue fundamental, sobre todo para identificar delecciones homocigóticas y amplificaciones génicas. El gen B2M (β2-microglobulina), encontrado inactivado en 5% de los tumores pulmonares, se caracterizó. Su pérdida genética se correlacionó con bajos niveles de infiltración intratumoral por linfocitos T citotóxicos. Además, la β2-microglobulina se asoció a supervivencia en pacientes tratados con agentes anti-PD1/PD-L1, evidenciando su rol potencial el predecir respuestas a inmunoterapias en neoplasias pulmonares.
Lung cancer is the deadliest form of cancer worldwide. Recently, the large-scale genomic profiling of human tumors has fueled the development of efficient anticancer agents that target the activity of mutated genes. Given that directed therapies are still very scarce, the discovery of novel lung cancer-related genes with potential relevance within the clinical context is imperative. Thus, this project consisted on coupling high-throughput sequencing strategies (exomes and transcriptomes) with the use of lung tumorgrafts. The high tumor purity achieved through the engraftment was crucial, particularly to identify homozygous deletions and gene amplifications. The B2M gene (β2-microglobulin), found to be mutated in 5% of lung tumors, was characterized. Its genetic loss was correlated to lower cytotoxic T-cell intratumoral infiltration, probably impairing the immune-mediated tumor eradication. Moreover, β2-microglobulin was associated with survival in patients treated with anti-PD-1/PD-L1 agents, highlighting a potential role in predicting response to immunologically-based therapies in lung cancer.
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Patrick, James Lambert. "Computer Aided Analysis of Restriction Landmark Genomic Scanning Images from Tumor and Cell Line Models." University of Toledo Health Science Campus / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=mco1196365469.

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Книги з теми "Tumor genomic"

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

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2

Thomas-Tikhonenko, Andrei. 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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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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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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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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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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Частини книг з теми "Tumor genomic"

1

Rassekh, Shahrad Rod, and Evica Rajcan-Separovic. "Comparative Genomic Hybridization of Wilms’ tumor." In Methods in Molecular Biology, 249–65. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-281-0_16.

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Kendal, Wayne S., and Philip Frost. "Genomic Instability, Tumor Heterogeneity and Progression." In Advances in Experimental Medicine and Biology, 1–4. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4899-5037-6_1.

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El-Ashry, Dorraya, Marija Balic, and Richard J. Cote. "Circulating Tumor Cells: Enrichment and Genomic Applications." In Genomic Applications in Pathology, 73–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96830-8_6.

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Balic, Marija, and Richard J. Cote. "Circulating Tumor Cells: Enrichment and Genomic Applications." In Genomic Applications in Pathology, 71–84. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0727-4_5.

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Watson, Geoffrey Alan, Kirsty Taylor, and Lillian L. Siu. "Innovation and Advances in Precision Medicine in Head and Neck Cancer." In Critical Issues in Head and Neck Oncology, 355–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63234-2_24.

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AbstractThe clinical utility of precision medicine through molecular characterization of tumors has been demonstrated in some malignancies, especially in cases where oncogenic driver alterations are identified. Next generation sequencing data from thousands of patients with head and neck cancers have provided vast amounts of information about the genomic landscape of this disease. Thus far, only a limited number of genomic alterations have been druggable, such as NTRK gene rearrangements in salivary gland cancers (mainly mammary analogue secretory carcinoma), NOTCH mutations in adenoid cystic cancers, HRAS mutations in head and neck squamous cell cancers, and even a smaller number of these have reached regulatory approval status. In order to expand the scope of precision medicine in head and neck cancer, additional evaluation beyond genomics is necessary. For instance, there is increasing interest to perform transcriptomic profiling for target identification. Another advance is in the area of functional testing such as small interfering RNA and drug libraries on patient derived cell cultures. Liquid biopsies to detect specific tumor clones or subclones, or viral sequences such as HPV, are of great interest to enable non-invasive tracking of response or resistance to treatment. In addition, precision immuno-oncology is a tangible goal, with a growing body of knowledge on the interactions between the host immunity, the tumor and its microenvironment. Immuno-oncology combinations that are tailored to immunophenotypes of the host-tumor-microenvironment triad, personalized cancer vaccines, and adoptive cell therapies, among others, are in active development. Many therapeutic possibilities and opportunities lie ahead that ultimately will increase the reality of precision medicine in head and neck cancer.
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Gasch, Christin, Klaus Pantel, and Sabine Riethdorf. "Whole Genome Amplification in Genomic Analysis of Single Circulating Tumor Cells." In Whole Genome Amplification, 221–32. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2990-0_15.

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7

Giaretti, Walter. "Aneuploidy and Heterogeneity Mechanisms in Human Colorectal Tumor Progression." In Genomic Instability and Immortality in Cancer, 53–68. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5365-6_4.

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Cardiff, Robert D. "The Tumor Pathology of Genetically Engineered Mice: Genomic Pathology." In Genetically Engineered Mice for Cancer Research, 133–80. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-0-387-69805-2_7.

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Noiville, Christine, and Florence Bellivier. "Biological Sample Collection in the Era of Genomic Medicine: A New Example of a Public Commons?" In Public Regulation of Tumor Banks, 211–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90563-1_18.

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Riaz, Ramish, Shah Rukh Abbas, and Maria Shabbir. "Adapting the Foreign Soil: Factors Promoting Tumor Metastasis." In 'Essentials of Cancer Genomic, Computational Approaches and Precision Medicine, 171–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1067-0_8.

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Тези доповідей конференцій з теми "Tumor genomic"

1

Apple, Annie, Kevin Neuzil, Hannah Phelps, and Harold N. Lovvorn. "Race Disparities in Genomic Alterations Within Wilms Tumor Specimens." In AAP National Conference & Exhibition Meeting Abstracts. American Academy of Pediatrics, 2021. http://dx.doi.org/10.1542/peds.147.3_meetingabstract.950.

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2

Lorber, Thomas, Tanja Dietsche, Joël Gsponer, Alexander Rufle, Michael Barret, Lukas Sommer, Katharina Glatz, Christian Ruiz, and Lukas Bubendorf. "Abstract 3167: Deciphering the genomic heterogeneity in malignant melanoma by genomic profiling of clonal tumor populations." In Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1538-7445.am2013-3167.

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3

Yang, Chieh-Hsiang, Elke A. Jarboe, Jason Gertz, Katherine E. Varley, C. Matthew Peterson, and Margit M. Janát-Amsbury. "Abstract 648: Integrated genomic characterization of endometrial cancer tumor grafts: a step toward genomic-guided treatment." In Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.am2016-648.

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4

Chokshi, Chirayu, David Tieu, Kevin Brown, Chitra Venugopal, Lina Liu, Laura Kuhlman, Katherine Chan, et al. "Abstract PR009: The functional genomic landscape of recurrent glioblastoma." In Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; October 19-20, 2020. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/2326-6074.tumimm20-pr009.

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5

Subramanian, Ayshwarya, Stanley Shackney, and Russell Schwartz. "Inference of tumor phylogenies from genomic assays on heterogeneous samples." In the 2nd ACM Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2147805.2147824.

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Catalanotti, Claudia, Sarah Garcia, Kamila Belhocine, Vijay Kumar, Zeljko Dzakula, Andrew Price, Shamoni Maheshwar, et al. "Abstract 3400: Characterizing genomic variation and tumor heterogeneity in cancer." In Proceedings: AACR Annual Meeting 2018; April 14-18, 2018; Chicago, IL. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.am2018-3400.

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7

White, Chris, Lysa-Anne Volpe, Luping Chen, Anupreet Bal, Michael Jackson, John Foulke, and Fang Tian. "Abstract 1571: Tumor cell panels: New tools in genomic era." In Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1538-7445.am2013-1571.

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8

Roman, Theodore, Brenda Xiao, and Russell Schwartz. "Abstract 974: Automating deconvolution of heterogeneous bulk tumor genomic data." In Proceedings: AACR Annual Meeting 2017; April 1-5, 2017; Washington, DC. American Association for Cancer Research, 2017. http://dx.doi.org/10.1158/1538-7445.am2017-974.

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Roman, Theodore, Lu Xie, and Russell Schwartz. "Abstract 849: Improved geometric deconvolution of bulk tumor genomic data." In Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.am2016-849.

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10

Lorber, Thomas, Tanja Dietsche, Valeria Perrina, Michael Barret, Kathrin Glatz, Christian Ruiz, and Lukas Bubendorf. "Abstract 3424: Deciphering the genomic heterogeneity and evolution in malignant melanoma by genomic profiling of clonal tumor populations." In Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-3424.

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Звіти організацій з теми "Tumor genomic"

1

Wagle, Nikhil. Tumor Genomic Profiling in Breast Cancer Patients Using Targeted Massively Parallel Sequencing. Fort Belvoir, VA: Defense Technical Information Center, January 2014. http://dx.doi.org/10.21236/ada598724.

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2

Park, Jae-Hyun. Influence of the Tumor Microenvironment on Genomic Changes Conferring Chemoresistance in Breast Cancer. Fort Belvoir, VA: Defense Technical Information Center, April 2013. http://dx.doi.org/10.21236/ada580419.

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3

Moritz, Robert. Development of Advanced Technologies for Complete Genomic and Proteomic Characterization of Quantized Human Tumor Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2014. http://dx.doi.org/10.21236/ada614224.

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4

Moritz, Robert. Development of Advanced Technologies for Complete Genomic and Proteomic Characterization of Quantized Human Tumor Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada583585.

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5

Foltz, Gregory. Development of Advanced Technologies for Complete Genomic and Proteomic Characterization of Quantized Human Tumor Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2013. http://dx.doi.org/10.21236/ada583639.

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Foltz, Gregory. Development of Advanced Technologies for Complete Genomic and Proteomic Characterization of Quantized Human Tumor Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada574964.

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Van Drogen, Audrey, and Charles H. Spruck. The Role of hCDC4 as a Tumor Suppressor Gene in Genomic Instability Underlying Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, November 2006. http://dx.doi.org/10.21236/ada463404.

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Moritz, Robert. Development of Advanced Technologies for Complete Genomic and Proteomic Characterization of Quantized Human Tumor Cells. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada573716.

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9

Cobbs, Charles. Development of Advanced Technologies for Complete Genomic and Proteomic Characterization of Quantized Human Tumor Cells. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ada622404.

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10

Collins, Colin C. A Genomics Approach to Tumor Gemome Analysis. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada410900.

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