Literature DB >> 20693306

Personalized medicine: marking a new epoch in cancer patient management.

Maria Diamandis1, Nicole M A White, George M Yousef.   

Abstract

Personalized medicine (PM) is defined as "a form of medicine that uses information about a person's genes, proteins, and environment to prevent, diagnose, and treat disease." The promise of PM has been on us for years. The suite of clinical applications of PM in cancer is broad, encompassing screening, diagnosis, prognosis, prediction of treatment efficacy, patient follow-up after surgery for early detection of recurrence, and the stratification of patients into cancer subgroup categories, allowing for individualized therapy. PM aims to eliminate the "one size fits all" model of medicine, which has centered on reaction to disease based on average responses to care. By dividing patients into unique cancer subgroups, treatment and follow-up can be tailored for each individual according to disease aggressiveness and the ability to respond to a certain treatment. PM is also shifting the emphasis of patient management from primary patient care to prevention and early intervention for high-risk individuals. In addition to classic single molecular markers, high-throughput approaches can be used for PM including whole genome sequencing, single-nucleotide polymorphism analysis, microarray analysis, and mass spectrometry. A common trend among these tools is their ability to analyze many targets simultaneously, thus increasing the sensitivity, specificity, and accuracy of biomarker discovery. Certain challenges need to be addressed in our transition to PM including assessment of cost, test standardization, and ethical issues. It is clear that PM will gradually continue to be incorporated into cancer patient management and will have a significant impact on our health care in the future.
© 2010 AACR.

Entities:  

Mesh:

Year:  2010        PMID: 20693306     DOI: 10.1158/1541-7786.MCR-10-0264

Source DB:  PubMed          Journal:  Mol Cancer Res        ISSN: 1541-7786            Impact factor:   5.852


  53 in total

1.  Structural requirements of research tissue banks derived from standardized project surveillance.

Authors:  E Herpel; N Koleganova; B Schreiber; B Walter; C V Kalle; P Schirmacher
Journal:  Virchows Arch       Date:  2012-06-01       Impact factor: 4.064

Review 2.  Predicting response to epigenetic therapy.

Authors:  Marianne B Treppendahl; Lasse S Kristensen; Kirsten Grønbæk
Journal:  J Clin Invest       Date:  2014-01-02       Impact factor: 14.808

3.  Has discovery-based cancer research been a bust?

Authors:  R J Epstein
Journal:  Clin Transl Oncol       Date:  2013-09-04       Impact factor: 3.405

4.  A comprehensively molecular haplotype-resolved genome of a European individual.

Authors:  Eun-Kyung Suk; Gayle K McEwen; Jorge Duitama; Katja Nowick; Sabrina Schulz; Stefanie Palczewski; Stefan Schreiber; Dustin T Holloway; Stephen McLaughlin; Heather Peckham; Clarence Lee; Thomas Huebsch; Margret R Hoehe
Journal:  Genome Res       Date:  2011-08-03       Impact factor: 9.043

Review 5.  Biobanking residual tissues.

Authors:  Peter H J Riegman; Evert-Ben van Veen
Journal:  Hum Genet       Date:  2011-08-04       Impact factor: 4.132

6.  Review of massively parallel DNA sequencing technologies.

Authors:  Sowmiya Moorthie; Christopher J Mattocks; Caroline F Wright
Journal:  Hugo J       Date:  2011-10-27

7.  Tumor-infiltrating γδT cells predict prognosis and adjuvant chemotherapeutic benefit in patients with gastric cancer.

Authors:  Jieti Wang; Chao Lin; He Li; Ruochen Li; Yifan Wu; Hao Liu; Heng Zhang; Hongyong He; Weijuan Zhang; Jiejie Xu
Journal:  Oncoimmunology       Date:  2017-07-24       Impact factor: 8.110

8.  Improved decision making for prioritizing tumor targeting antibodies in human xenografts: Utility of fluorescence imaging to verify tumor target expression, antibody binding and optimization of dosage and application schedule.

Authors:  Michael Dobosz; Ute Haupt; Werner Scheuer
Journal:  MAbs       Date:  2016-09-23       Impact factor: 5.857

9.  Proteomic classification of acute leukemias by alignment-based quantitation of LC-MS/MS data sets.

Authors:  Eric J Foss; Dragan Radulovic; Derek L Stirewalt; Jerald Radich; Olga Sala-Torra; Era L Pogosova-Agadjanyan; Shawna M Hengel; Keith R Loeb; H Joachim Deeg; Soheil Meshinchi; David R Goodlett; Antonio Bedalov
Journal:  J Proteome Res       Date:  2012-09-11       Impact factor: 4.466

10.  Emerging trends in cancer care: health plans' and pharmacy benefit managers' perspectives on changing care models.

Authors:  Rhonda Greenapple
Journal:  Am Health Drug Benefits       Date:  2012-07
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