Literature DB >> 19931196

Personalized medicine: individualized care of cancer patients.

Scott Ely1.   

Abstract

For most cancer patients today, therapy is chosen and implemented on a watch-and-wait basis. Although an individual's clinical information is used to decide which regimen is likely to work best, we still employ only data referring to outcomes of groups of patients. Currently, an individual patient's biologic data is rarely employed in a systematic way to predict the best course of therapy. However, the advent of low-cost individual genomic and proteomic analysis provides hope that we are entering a new era of personalized, patient-specific care. This article is an analysis of the current real-life clinical use of individual patient data, a discussion of barriers to personalization, and examples of current success.

Entities:  

Mesh:

Year:  2009        PMID: 19931196     DOI: 10.1016/j.trsl.2009.08.001

Source DB:  PubMed          Journal:  Transl Res        ISSN: 1878-1810            Impact factor:   7.012


  7 in total

1.  Personalizing Stem Cell Research and Therapy: The Arduous Road Ahead or Missed Opportunity?

Authors:  S A Patel; C C King; P K Lim; U Habiba; M Dave; R Porecha; P Rameshwar
Journal:  Curr Pharmacogenomics Person Med       Date:  2010-03-01

2.  Preappointment testing for BRAF/KIT mutation in advanced melanoma: a model in molecular data delivery for individualized medicine.

Authors:  Taofic Mounajjed; Char L Brown; Therese K Stern; Annette M Bjorheim; Andrew J Bridgeman; Kandelaria M Rumilla; Robert R McWilliams; Thomas J Flotte
Journal:  Hum Pathol       Date:  2014-07-30       Impact factor: 3.466

3.  Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.

Authors:  Simone Mocellin; Jeff Shrager; Richard Scolyer; Sandro Pasquali; Daunia Verdi; Francesco M Marincola; Marta Briarava; Randy Gobbel; Carlo Rossi; Donato Nitti
Journal:  PLoS One       Date:  2010-08-10       Impact factor: 3.240

4.  Shifting from population-wide to personalized cancer prognosis with microarrays.

Authors:  Li Shao; Xiaohui Fan; Ningtao Cheng; Leihong Wu; Haoshu Xiong; Hong Fang; Don Ding; Leming Shi; Yiyu Cheng; Weida Tong
Journal:  PLoS One       Date:  2012-01-25       Impact factor: 3.240

5.  An overview of machine learning methods for monotherapy drug response prediction.

Authors:  Farzaneh Firoozbakht; Behnam Yousefi; Benno Schwikowski
Journal:  Brief Bioinform       Date:  2022-01-17       Impact factor: 11.622

6.  Personalized cardiac regeneration by stem cells-Hype or hope?

Authors:  Ulrich Marc Becher; Vedat Tiyerili; Dirk Skowasch; Georg Nickenig; Nikos Werner
Journal:  EPMA J       Date:  2011-03-05       Impact factor: 6.543

7.  KRAS mutations: variable incidences in a Brazilian cohort of 8,234 metastatic colorectal cancer patients.

Authors:  Carlos Gil Ferreira; Veronica Aran; Ilana Zalcberg-Renault; Ana Paula Victorino; Jonas H Salem; Martin H Bonamino; Fernando M Vieira; Mariano Zalis
Journal:  BMC Gastroenterol       Date:  2014-04-10       Impact factor: 3.067

  7 in total

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