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Journal articles on the topic 'Cancer genomics'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Siracusano, Salvatore, Riccardo Rizzetto, and Antonio Benito Porcaro. "Bladder cancer genomics." Urologia Journal 87, no. 2 (January 16, 2020): 49–56. http://dx.doi.org/10.1177/0391560319899011.

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Until recently, the treatment of bladder cancer, for several years, was limited to surgery and to immunotherapy or chemotherapy. Currently, the extensive analysis of molecular alterations has led to novel treatment approaches. The advent of polymerase chain reaction and genomic hybridization techniques has allowed to investigate alterations involved in bladder cancer at DNA level. By this way, bladder cancers can be classified as papillary or non-papillary based on genetic alterations with activation or mutations in FGFR3 papillary tumors and with inactivation or mutations involving TP53 and RB1 in non-papillary tumors. Recently, the patterns of gene expression allow to differentiate basal and luminal subtypes as reported in breast cancer. In particular, basal cancers are composed of squamous and sarcomatoid pathological findings, while luminal cancers are composed of papillary finding features and genetic mutations (FGFR3). In particular, specific investigative studies demonstrated that luminal cancers are associated with secondary muscle invasive cancer while basal tumors are related to advanced disease since they are often metastatic at diagnosis. Moreover, from therapeutic point of view, different researchers showed that mutations of DNA are related to the sensitivity of bladder cancer while performing cisplatin chemotherapy. In this prospective, the bladder cancer molecular subtyping classification might allow identifying the set of patients who can safely avoid neoadjuvant chemotherapy likely because of the low response to systemic chemotherapy (chemoresistant tumors). In this context, the Cancer Genome Atlas (TCGA) project has improved the knowledge of the molecular targets of invasive urothelial cancers allowing the researchers to propose hypothesis suggesting that agents targeting the genomic alterations may be an effective strategy in managing these cancers, which occur in about 68% of muscle invasive cancers. A future goal will be to combine treatment strategies of invasive bladder cancers according to their genetic mutational load defined by molecular pathology.

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NORRILD, BODIL, PER GULDBERG, and ELISABETH RALFKIAER. "Cancer genomics." APMIS 115, no. 10 (October 2007): 1037–38. http://dx.doi.org/10.1111/j.1600-0463.2007.apm_intro.xml.x.

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3

Lewin, Jeremy, and Lillian L. Siu. "Cancer genomics." Current Opinion in Oncology 27, no. 3 (May 2015): 250–57. http://dx.doi.org/10.1097/cco.0000000000000185.

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4

Weber, Barbara L. "Cancer genomics." Cancer Cell 1, no. 1 (February 2002): 37–47. http://dx.doi.org/10.1016/s1535-6108(02)00026-0.

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5

Mardis, Elaine. "Cancer Genomics." F1000Research 4 (October 28, 2015): 1162. http://dx.doi.org/10.12688/f1000research.6645.1.

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Modern cancer genomics has emerged from the combination of the Human Genome Reference, massively parallel sequencing, and the comparison of tumor to normal DNA sequences, revealing novel insights into the cancer genome and its amazing diversity. Recent developments in applying our knowledge of cancer genomics have focused on the utility of these data for clinical applications. The emergent results of this translation into the clinical setting already are changing the clinical care and monitoring of cancer patients.

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Nik-Zainal, S. "Abstract MS1-2: Genomics of DNA repair defects in breast cancer." Cancer Research 82, no. 4_Supplement (February 15, 2022): MS1–2—MS1–2. http://dx.doi.org/10.1158/1538-7445.sabcs21-ms1-2.

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Abstract While driver mutations in cancer genomes were the focus of cancer research for a long time, passenger mutational signatures - the imprints of DNA damage and DNA repair processes that have been operative during tumorigenesis - are also biologically informative. In this lecture, I provide an update of what has been uncovered in breast cancers in relation to genomic imprints of DNA repair defects and showcase how we have developed computational applications that we hope to translate toward clinical utility. Citation Format: S Nik-Zainal. Genomics of DNA repair defects in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr MS1-2.

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Riesco-Eizaguirre, Garcilaso, and Pilar Santisteban. "ENDOCRINE TUMOURS: Advances in the molecular pathogenesis of thyroid cancer: lessons from the cancer genome." European Journal of Endocrinology 175, no. 5 (November 2016): R203—R217. http://dx.doi.org/10.1530/eje-16-0202.

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Thyroid cancer is the most common endocrine malignancy giving rise to one of the most indolent solid cancers, but also one of the most lethal. In recent years, systematic studies of the cancer genome, most importantly those derived from The Cancer Genome Altas (TCGA), have catalogued aberrations in the DNA, chromatin, and RNA of the genomes of thousands of tumors relative to matched normal cellular genomes and have analyzed their epigenetic and protein consequences. Cancer genomics is therefore providing new information on cancer development and behavior, as well as new insights into genetic alterations and molecular pathways. From this genomic perspective, we will review the main advances concerning some essential aspects of the molecular pathogenesis of thyroid cancer such as mutational mechanisms, new cancer genes implicated in tumor initiation and progression, the role of non-coding RNA, and the advent of new susceptibility genes in thyroid cancer predisposition. This look across these genomic and cellular alterations results in the reshaping of the multistep development of thyroid tumors and offers new tools and opportunities for further research and clinical development of novel treatment strategies.

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8

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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9

Caulfield, Mark. "6 Translating genomics for clinical benefit." Postgraduate Medical Journal 95, no. 1130 (November 21, 2019): 686.3–686. http://dx.doi.org/10.1136/postgradmedj-2019-fpm.6.

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The UK 100,000 Genomes Project has focussed on transforming genomic medicine in the National Health Service using whole genome sequencing in rare disease, cancer and infection. Genomics England partnering with the NHS established 13 Genomic Medicine Centres, the NHS whole genome sequencing centre and the Genomics England Clinical Interpretation Partnership (3337 researchers from 24 countries). We sequenced the 100,000th genome on the 5th December 2019 and completed an initial analysis for participants in July 2019. Alongside these genomes we have assembled a longitudinal life course dataset for research and diagnosis including 2.6 billion clinical data points for the 3000 plus researchers to work on to drive up the value of the genomes for direct healthcare. In parallel we have partnered the NHS to establish one of the world’s most advanced Genomic Medicine Service where we re-evaluated 300,000 genomic tests and upgraded 25% of tests to newer technologies with an annual review. The Department of Health have announced the ambition to undertake 5 million genome analyses over the next 5 years focused on new areas tractable to health gain.

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10

Parikh, Ankur R. "Lung Cancer Genomics." Acta Medica Academica 48, no. 1 (June 26, 2019): 78. http://dx.doi.org/10.5644/ama2006-124.244.

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<p>The landscape of lung cancer treatment is rapidly evolving with the use of genomic testing which helps identify specific mutations or resistance mutations for these heterogenous tumors. Advanced lung cancer has a very poor prognosis but identifying other treatment options based on genomic profiling of the tumor can lead to improved outcomes. Evidence of benefit for genomic testing in lung cancer has now resulted in this test becoming part of national guidelines. There are challenges with genomic testing which need to be understood as well as understanding how to apply test results. These results can help identify treatment options or may serve as predictors to respond to specific therapies.</p><p><strong>Conclusion.</strong> In the current era of precision medicine, it is imperative clinicians be familiar with genomic testing and be able to offer it to their cancer patients, specifically those with advanced lung cancer.</p>

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11

Onyango, Patrick. "Genomics and cancer." Current Opinion in Oncology 14, no. 1 (January 2002): 79–85. http://dx.doi.org/10.1097/00001622-200201000-00014.

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12

Simon, Richard M. "Personalized Cancer Genomics." Annual Review of Statistics and Its Application 5, no. 1 (March 7, 2018): 169–82. http://dx.doi.org/10.1146/annurev-statistics-031017-100609.

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13

Van Loo, Peter, and Peter J. Campbell. "ABSOLUTE cancer genomics." Nature Biotechnology 30, no. 7 (July 2012): 620–21. http://dx.doi.org/10.1038/nbt.2293.

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14

Futreal, P. Andrew, Arek Kasprzyk, Ewan Birney, James C. Mullikin, Richard Wooster, and Michael R. Stratton. "Cancer and genomics." Nature 409, no. 6822 (February 2001): 850–52. http://dx.doi.org/10.1038/35057046.

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15

Falchook, Aaron D., and Joel E. Tepper. "Rectal cancer genomics." Seminars in Colon and Rectal Surgery 25, no. 1 (March 2014): 13–18. http://dx.doi.org/10.1053/j.scrs.2013.09.010.

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16

Chang, David K., Sean M. Grimmond, and Andrew V. Biankin. "Pancreatic cancer genomics." Current Opinion in Genetics & Development 24 (February 2014): 74–81. http://dx.doi.org/10.1016/j.gde.2013.12.001.

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17

Li, Paul E., and Peter S. Nelson. "Prostate cancer genomics." Current Urology Reports 2, no. 1 (February 2001): 70–78. http://dx.doi.org/10.1007/s11934-001-0028-6.

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18

Liu, Yining, Scott F. Cummins, and Min Zhao. "A Genomics Resource for 12 Edible Seaweeds to Predict Seaweed-Secreted Peptides with Potential Anti-Cancer Function." Biology 11, no. 10 (October 4, 2022): 1458. http://dx.doi.org/10.3390/biology11101458.

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Seaweeds are multicellular marine macroalgae with natural compounds that have potential anticancer activity. To date, the identification of those compounds has relied on purification and assay, yet few have been documented. Additionally, the genomes and associated proteomes of edible seaweeds that have been identified thus far are scattered among different resources and with no systematic summary available, which hinders the development of a large-scale omics analysis. To enable this, we constructed a comprehensive genomics resource for the edible seaweeds. These data could be used for systematic metabolomics and a proteome search for anti-cancer compound and peptides. In brief, we integrated and annotated 12 publicly available edible seaweed genomes (8 species and 268,071 proteins). In addition, we integrate the new seaweed genomic resources with established cancer bioinformatics pipelines to help identify potential seaweed proteins that could help mitigate the development of cancer. We present 7892 protein domains that were predicted to be associated with cancer proteins based on a protein domain–domain interaction. The most enriched protein families were associated with protein phosphorylation and insulin signalling, both of which are recognised to be crucial molecular components for patient survival in various cancers. In addition, we found 6692 seaweed proteins that could interact with over 100 tumour suppressor proteins, of which 147 are predicted to be secreted proteins. In conclusion, our genomics resource not only may be helpful in exploring the genomics features of these edible seaweed but also may provide a new avenue to explore the molecular mechanisms for seaweed-associated inhibition of human cancer development.

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19

Samuel, Nardin, and Thomas J. Hudson. "Translating Genomics to the Clinic: Implications of Cancer Heterogeneity." Clinical Chemistry 59, no. 1 (January 1, 2013): 127–37. http://dx.doi.org/10.1373/clinchem.2012.184580.

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BACKGROUND Sequencing of cancer genomes has become a pivotal method for uncovering and understanding the deregulated cellular processes driving tumor initiation and progression. Whole-genome sequencing is evolving toward becoming less costly and more feasible on a large scale; consequently, thousands of tumors are being analyzed with these technologies. Interpreting these data in the context of tumor complexity poses a challenge for cancer genomics. CONTENT The sequencing of large numbers of tumors has revealed novel insights into oncogenic mechanisms. In particular, we highlight the remarkable insight into the pathogenesis of breast cancers that has been gained through comprehensive and integrated sequencing analysis. The analysis and interpretation of sequencing data, however, must be considered in the context of heterogeneity within and among tumor samples. Only by adequately accounting for the underlying complexity of cancer genomes will the potential of genome sequencing be understood and subsequently translated into improved management of patients. SUMMARY The paradigm of personalized medicine holds promise if patient tumors are thoroughly studied as unique and heterogeneous entities and clinical decisions are made accordingly. Associated challenges will be ameliorated by continued collaborative efforts among research centers that coordinate the sharing of mutation, intervention, and outcomes data to assist in the interpretation of genomic data and to support clinical decision-making.

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20

Daly, P. A. "Cancer genetics or cancer genomics in the era of genomic medicine?" Annals of Oncology 14 (June 2003): iii19—iii25. http://dx.doi.org/10.1093/annonc/mdg743.

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21

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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22

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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23

Upadhyay, Tejal, and Samir Patel. "Identifying Subtypes of Cancer Using Genomic Data by Applying Data Mining Techniques." International Journal of Natural Computing Research 8, no. 3 (July 2019): 55–64. http://dx.doi.org/10.4018/ijncr.2019070104.

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This article is about the study of genomics structures and identifying cancer types from it. It divides into six parts. The first part is about the introduction of cancer, types of cancers, how cancer arises, etc. The second part is about the genomic study and how cancer is related to that, which features are used for the study. The third part is about the software which the authors have used to study these genomic structures, which data sets are used, and what is the final output for this study. The fourth part shows the proposed algorithm for the study. The fifth part shows the data preprocessing and clustering. Different preprocessing and clustering algorithms are used. The sixth part shows the results and conclusion with a future scope. The genomics data which is used by this article is taken from the Cancer Genome Atlas data portal which is freely available. Some applied imputation techniques fill up for the missing values and important features are extracted. Different clustering algorithms are applied on genome dataset and results are generated.

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24

Allen, Daniel. "Cancer nursing and genomics." Cancer Nursing Practice 20, no. 2 (March 1, 2021): 17–19. http://dx.doi.org/10.7748/cnp.20.2.17.s10.

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25

Deshmukh, PriyankaDeepak, Preeti Nair, AkhilM Trivedi, and JoshuaShaji Thomas. "Oral cancer and genomics." Journal of the International Clinical Dental Research Organization 13, no. 2 (2021): 86. http://dx.doi.org/10.4103/jicdro.jicdro_23_21.

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26

Fong, Kwun M., Rayleen V. Bowman, and Ian A. Yang. "Genomics of lung cancer." Journal of Thoracic Disease 9, no. 2 (February 2017): E155—E157. http://dx.doi.org/10.21037/jtd.2017.02.29.

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27

Sulaieva, O. N., D. O. Shapochka, O. E. Stakhovskiy, O. Ya Zahoruiko, N. V. Stefiniv, T. A. Stepanova, and D. A. Rozhkova. "GENOMICS OF BLADDER CANCER." Fiziolohichnyĭ zhurnal 66, no. 2-3 (May 28, 2020): 83–92. http://dx.doi.org/10.15407/fz66.2-3.083.

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28

Mardis, Elaine R. "Next-generation cancer genomics." Genome Biology 11, Suppl 1 (2010): I3. http://dx.doi.org/10.1186/gb-2010-11-s1-i3.

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29

Vinall, M., and K. Offit. "Genomics and Cancer Prevention." MD Conference Express 13, no. 6 (July 1, 2013): 8–9. http://dx.doi.org/10.1177/155989771306002.

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30

Martin, Ronald F. "Cancer Screening and Genomics." Surgical Clinics of North America 95, no. 5 (October 2015): xiii—xiv. http://dx.doi.org/10.1016/j.suc.2015.07.002.

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31

Arthanareeswaran, V. K. A., P. Tenke, B. Koves, and B. Kovacs. "Genomics in prostate cancer." European Urology Supplements 14, no. 6 (October 2015): e1290. http://dx.doi.org/10.1016/s1569-9056(15)30327-4.

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32

Borczuk, A. C., R. L. Toonkel, and C. A. Powell. "Genomics of Lung Cancer." Proceedings of the American Thoracic Society 6, no. 2 (April 15, 2009): 152–58. http://dx.doi.org/10.1513/pats.200807-076lc.

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33

Bahcall, Orli. "FunSeq for cancer genomics." Nature Genetics 45, no. 11 (October 29, 2013): 1273. http://dx.doi.org/10.1038/ng.2819.

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34

Shah, Muhammad A., Emily L. Denton, Lihua Liu, and Matthieu Schapira. "ChromoHub V2: cancer genomics." Bioinformatics 30, no. 4 (December 6, 2013): 590–92. http://dx.doi.org/10.1093/bioinformatics/btt710.

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35

Shen, Hongbing. "Progress of cancer genomics." Thoracic Cancer 6, no. 5 (June 2, 2015): 557–60. http://dx.doi.org/10.1111/1759-7714.12281.

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36

Liu, Edison T. "Functional genomics of cancer." Current Opinion in Genetics & Development 18, no. 3 (June 2008): 251–56. http://dx.doi.org/10.1016/j.gde.2008.07.014.

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37

Sandoval, Juan, and Manel Esteller. "Cancer epigenomics: beyond genomics." Current Opinion in Genetics & Development 22, no. 1 (February 2012): 50–55. http://dx.doi.org/10.1016/j.gde.2012.02.008.

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38

Edgren, Henrik, and Olli Kallioniemi. "Integrated breast cancer genomics." Cancer Cell 10, no. 6 (December 2006): 453–54. http://dx.doi.org/10.1016/j.ccr.2006.11.007.

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Shlien, Adam, David Malkin, and Uri Tabori. "Translational Childhood Cancer Genomics." JAMA Oncology 2, no. 3 (March 1, 2016): 384. http://dx.doi.org/10.1001/jamaoncol.2015.5076.

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Zhao, Xiaomin, Xuying LI, Yi Liu, Kathleen Calzone, Juan Xu, Xueling Xiao, and Honghong Wang. "Genetic and genomic nursing competency among nurses in tertiary general hospitals and cancer hospitals in mainland China: a nationwide survey." BMJ Open 12, no. 12 (December 2022): e066296. http://dx.doi.org/10.1136/bmjopen-2022-066296.

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ObjectivesTo explore genetic/genomic nursing competency and associated factors among nurses from tertiary general and specialist cancer hospitals in mainland China and compare the competencies of nurses from the two types of hospitals.Design and settingA cross-sectional survey was conducted from November 2019 to January 2020, wherein 2118 nurses were recruited from 8 tertiary general hospitals and 4 cancer hospitals in mainland China. We distributed electronic questionnaires to collect data on nurses’ demographics, work-related variables and genomic nursing competency.Participants2118 nurses were recruited via a three-stage stratified cluster sampling method.ResultsMore than half (59.1%, 1252/2118) of the participants reported that their curriculum included genetics/genomics content. The mean nurses’ genomic knowledge score was 8.30/12 (95% CI=8.21 to 8.39). Only 5.4% had always collected a complete family history in the past 3 months. Compared with general hospital nurses, slightly more cancer hospital nurses (75.6% vs 70.6%, p=0.010) recognised the importance of genomics, while there was no significant difference in the knowledge scores (8.38 vs 8.21, p>0.05). Gender (β=0.06, p=0.005), years of clinical nursing (β=−0.07, p=0.002), initial level of nursing education (β=0.10, p<0.001), membership of the Chinese Nursing Association (β=0.06, p=0.004), whether their curriculum included genetics/genomics content (β=0.08, p=0.001) and attitude towards becoming more educated in genetics/genomics (β=0.25, p<0.001) were significantly associated with the nurses’ genomic knowledge score.ConclusionThe levels of genomic knowledge among mainland Chinese nurses in tertiary hospitals were moderate. The overall genomic competency of cancer hospital nurses was comparable to that of general hospital nurses. Further genomic training is needed for nurses in China to increase their genomic competency and accelerate the integration of genomics into nursing practice.

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Devitt, Michael E., and Robert Dreicer. "Evolving Role of Genomics in Genitourinary Neoplasms." Acta Medica Academica 48, no. 1 (June 26, 2019): 68. http://dx.doi.org/10.5644/ama2006-124.243.

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<p>The aim of this article is to review the current role of genomic testing in the risk, prognosis, and treatment of genitourinary malignancies. The authors selected guidelines, publications, and abstracts relevant to the current and emerging role of genomics in genitourinary cancers. The risk of developing genitourinary cancer can be stratified based on genomic data. Prostate cancer has the strongest degree of heritability, with <em>BRCA1/2 </em>and <em>HOXB13 </em>mutations playing a role in familial disease. Genomic data is on the verge of informing treatment decisions across genitourinary cancers. mCRPC has diverse genomic alterations that represent potential therapeutic targets, including alterations in the AR pathway, DNA damage and repair pathways, cell cycle pathways, PI3K pathway, and Wnt signaling. Genomic alterations in clear cell renal cell carcinoma can inform prognosis and mutations in mTOR pathways predict response to mTOR inhibitors. Urothelial carcinoma can be classified into different subtypes based on gene expression profiling, which provides prognostic information and predicts response to chemotherapy and immunotherapy. Specific mutations have been identified that predict response to therapy including <em>ERCC2 </em>mutations and cisplatin, DNA damage and repair mutations and checkpoint inhibitors, and <em>FGFR3 </em>mutations and FGFR tyrosine kinase inhibitors such as erdafitinib.</p><p><strong>Conclusion. </strong>Genitourinary malignancies have not felt the impact of genomic data as greatly as other cancer types. The majority of benefit lies in identifying patients at high risk of genitourinary cancer. Fortunately, breakthroughs are on the horizon that will result in a greater incorporation of genomic information into treatment decisions for patients with genitourinary cancer.</p>

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Nik-Zainal, S., L. Alexandrov, D. Wedge, P. Van Loo, K. Raine, D. R. Jones, P. A. Futreal, P. J. Campbell, and M. R. Stratton. "604 Cancer Genomics, Epigenetics and Genomic Instability. Mutational Processes Shaping the Genomes of Twenty-one Breast Cancers." European Journal of Cancer 48 (July 2012): S144. http://dx.doi.org/10.1016/s0959-8049(12)71258-3.

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43

Kundra, Ritika, Hongxin Zhang, Robert Sheridan, Sahussapont Joseph Sirintrapun, Avery Wang, Angelica Ochoa, Manda Wilson, et al. "OncoTree: A Cancer Classification System for Precision Oncology." JCO Clinical Cancer Informatics, no. 5 (March 2021): 221–30. http://dx.doi.org/10.1200/cci.20.00108.

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PURPOSE Cancer classification is foundational for patient care and oncology research. Systems such as International Classification of Diseases for Oncology (ICD-O), Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), and National Cancer Institute Thesaurus (NCIt) provide large sets of cancer classification terminologies but they lack a dynamic modernized cancer classification platform that addresses the fast-evolving needs in clinical reporting of genomic sequencing results and associated oncology research. METHODS To meet these needs, we have developed OncoTree, an open-source cancer classification system. It is maintained by a cross-institutional committee of oncologists, pathologists, scientists, and engineers, accessible via an open-source Web user interface and an application programming interface. RESULTS OncoTree currently includes 868 tumor types across 32 organ sites. OncoTree has been adopted as the tumor classification system for American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange (GENIE), a large genomic and clinical data-sharing consortium, and for clinical molecular testing efforts at Memorial Sloan Kettering Cancer Center and Dana-Farber Cancer Institute. It is also used by precision oncology tools such as OncoKB and cBioPortal for Cancer Genomics. CONCLUSION OncoTree is a dynamic and flexible community-driven cancer classification platform encompassing rare and common cancers that provides clinically relevant and appropriately granular cancer classification for clinical decision support systems and oncology research.

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Slovak, Ryan, Meaghan Dendy Case, and Hyun S. Kim. "Genomics and Interventional Oncology in Primary Liver Cancer." Digestive Disease Interventions 04, no. 01 (March 2020): 053–59. http://dx.doi.org/10.1055/s-0040-1708533.

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AbstractPersonalized medicine is revolutionizing oncologic care. Molecular and imaging “fingerprinting” of cancer through genomics, radiomics, and radiogenomics has allowed for the meticulous characterization of many forms of malignancy, including primary liver cancers. With this data, treatments are being developed that precisely target and exploit key variations in individual tumors. As these methods continue to evolve, interventional oncologists are well positioned to capitalize on the advances being made. This article will provide a concise overview of the genomic, radiomic, and radiogenomic research on hepatocellular carcinoma and intrahepatic cholangiocarcinoma, in addition to discussions on how precision medicine would relate to interventional oncology.

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45

Carpenter, Seren, and R. Steven Conlan. "Clinical Functional Genomics." Cancers 13, no. 18 (September 15, 2021): 4627. http://dx.doi.org/10.3390/cancers13184627.

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Functional genomics is the study of how the genome and its products, including RNA and proteins, function and interact to affect different biological processes. The field of functional genomics includes transcriptomics, proteomics, metabolomics and epigenomics, as these all relate to controlling the genome leading to expression of particular phenotypes. By studying whole genomes—clinical genomics, transcriptomes and epigenomes—functional genomics allows the exploration of the diverse relationship between genotype and phenotype, not only for humans as a species but also in individuals, allowing an understanding and evaluation of how the functional genome ‘contributes’ to different diseases. Functional variation in disease can help us better understand that disease, although it is currently limited in terms of ethnic diversity, and will ultimately give way to more personalized treatment plans.

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Zutter, Mary M., Kenneth J. Bloom, Liang Cheng, Ian S. Hagemann, Jill H. Kaufman, Alyssa M. Krasinskas, Alexander J. Lazar, et al. "The Cancer Genomics Resource List 2014." Archives of Pathology & Laboratory Medicine 139, no. 8 (December 1, 2014): 989–1008. http://dx.doi.org/10.5858/arpa.2014-0330-cp.

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Context Genomic sequencing for cancer is offered by commercial for-profit laboratories, independent laboratory networks, and laboratories in academic medical centers and integrated health networks. The variability among the tests has created a complex, confusing environment. Objective To address the complexity, the Personalized Health Care (PHC) Committee of the College of American Pathologists proposed the development of a cancer genomics resource list (CGRL). The goal of this resource was to assist the laboratory pathology and clinical oncology communities. Design The PHC Committee established a working group in 2012 to address this goal. The group consisted of site-specific experts in cancer genetic sequencing. The group identified current next-generation sequencing (NGS)–based cancer tests and compiled them into a usable resource. The genes were annotated by the working group. The annotation process drew on published knowledge, including public databases and the medical literature. Results The compiled list includes NGS panels offered by 19 laboratories or vendors, accompanied by annotations. The list has 611 different genes for which NGS-based mutation testing is offered. Surprisingly, of these 611 genes, 0 genes were listed in every panel, 43 genes were listed in 4 panels, and 54 genes were listed in 3 panels. In addition, tests for 393 genes were offered by only 1 or 2 institutions. Table 1 provides an example of gene mutations offered for breast cancer genomic testing with the annotation as it appears in the CGRL 2014. Conclusions The final product, referred to as the Cancer Genomics Resource List 2014, is available as supplemental digital content.

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Kumar, R., A. Horvath, R. Mazumder, M. Toi, F. Sato, M. R. Pillai, L. Costa, et al. "The Global Cancer Genomics Consortium's Second Annual Symposium: Genomics Medicine in Cancer Research." Genes & Cancer 4, no. 5-6 (April 17, 2013): 196–200. http://dx.doi.org/10.1177/1947601913484582.

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Gray, Stacy W., Sarah E. Gollust, Deanna Alexis Carere, Clara A. Chen, Angel Cronin, Sarah S. Kalia, Huma Q. Rana, et al. "Personal Genomic Testing for Cancer Risk: Results From the Impact of Personal Genomics Study." Journal of Clinical Oncology 35, no. 6 (February 20, 2017): 636–44. http://dx.doi.org/10.1200/jco.2016.67.1503.

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Purpose Significant concerns exist regarding the potential for unwarranted behavior changes and the overuse of health care resources in response to direct-to-consumer personal genomic testing (PGT). However, little is known about customers’ behaviors after PGT. Methods Longitudinal surveys were given to new customers of 23andMe (Mountain View, CA) and Pathway Genomics (San Diego, CA). Survey data were linked to individual-level PGT results through a secure data transfer process. Results Of the 1,042 customers who completed baseline and 6-month surveys (response rate, 71.2%), 762 had complete cancer-related data and were analyzed. Most customers reported that learning about their genetic risk of cancers was a motivation for testing (colorectal, 88%; prostate, 95%; breast, 94%). No customers tested positive for pathogenic mutations in highly penetrant cancer susceptibility genes. A minority of individuals received elevated single nucleotide polymorphism-based PGT cancer risk estimates (colorectal, 24%; prostate, 24%; breast, 12%). At 6 months, customers who received elevated PGT cancer risk estimates were not significantly more likely to change their diet, exercise, or advanced planning behaviors or engage in cancer screening, compared with individuals at average or reduced risk. Men who received elevated PGT prostate cancer risk estimates changed their vitamin and supplement use more than those at average or reduced risk (22% v 7.6%, respectively; adjusted odds ratio, 3.41; 95% CI, 1.44 to 8.18). Predictors of 6-month behavior include baseline behavior (exercise, vitamin or supplement use, and screening), worse health status (diet and vitamin or supplement use), and older age (advanced planning, screening). Conclusion Most adults receiving elevated direct-to-consumer PGT single nucleotide polymorphism-based cancer risk estimates did not significantly change their diet, exercise, advanced care planning, or cancer screening behaviors.

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Cakiroglu, Ece, and Serif Senturk. "Genomics and Functional Genomics of Malignant Pleural Mesothelioma." International Journal of Molecular Sciences 21, no. 17 (September 1, 2020): 6342. http://dx.doi.org/10.3390/ijms21176342.

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Malignant pleural mesothelioma (MPM) is a rare, aggressive cancer of the mesothelial cells lining the pleural surface of the chest wall and lung. The etiology of MPM is strongly associated with prior exposure to asbestos fibers, and the median survival rate of the diagnosed patients is approximately one year. Despite the latest advancements in surgical techniques and systemic therapies, currently available treatment modalities of MPM fail to provide long-term survival. The increasing incidence of MPM highlights the need for finding effective treatments. Targeted therapies offer personalized treatments in many cancers. However, targeted therapy in MPM is not recommended by clinical guidelines mainly because of poor target definition. A better understanding of the molecular and cellular mechanisms and the predictors of poor clinical outcomes of MPM is required to identify novel targets and develop precise and effective treatments. Recent advances in the genomics and functional genomics fields have provided groundbreaking insights into the genomic and molecular profiles of MPM and enabled the functional characterization of the genetic alterations. This review provides a comprehensive overview of the relevant literature and highlights the potential of state-of-the-art genomics and functional genomics research to facilitate the development of novel diagnostics and therapeutic modalities in MPM.

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Palumbo, Elisa, and Antonella Russo. "Chromosome Imbalances in Cancer: Molecular Cytogenetics Meets Genomics." Cytogenetic and Genome Research 150, no. 3-4 (2016): 176–84. http://dx.doi.org/10.1159/000455804.

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Genomic instability is a hallmark of cancer, and it is well-known that in several cancers the karyotype is unstable and rapidly evolving. Molecular cytogenetics has contributed to the description and interpretation of cancer karyotypes, in particular through multicolor FISH approaches which can define even complex chromosome rearrangements. The introduction of genome-wide methods has made available a powerful set of tools with higher resolution than cytogenetics, thus appropriate to comprehend the huge variability of cancer cells. This review focuses on novel findings deriving from the combination of cytogenetic and genomic approaches in cancer research.

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