Literature DB >> 22157289

Emerging technologies for improved stratification of cancer patients: a review of opportunities, challenges, and tools.

Wisut Lamlertthon1, Michele C Hayward, David Neil Hayes.   

Abstract

Cancer is a heterogeneous collection of diseases with wild variation in etiology, pathogenesis, response to therapy, and prognosis. Sources of variation are frequently obscure. Current practice attempts to classify tumors by tissue of origin and extent of disease through staging such that more risky tumors can be managed with more aggressive treatments. Modest inroads have been made with biomarkers to further characterize groups of tumors with important characteristics such as response to selected drugs. However, biomarker-driven decisions are relatively few when examining the maze of clinical decisions in the care of cancer patients. Against this backdrop, waves of researchers have unleashed a vast array of new technologies, with the goal of better characterization of the inherent diversity of tumors. This review outlines the use of cancer biomarkers and emerging technologies to stratify patients with a focus on the challenges and opportunities of next-generation nucleic acid sequencing approaches in oncology.

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Year:  2011        PMID: 22157289     DOI: 10.1097/PPO.0b013e31823bd1f8

Source DB:  PubMed          Journal:  Cancer J        ISSN: 1528-9117            Impact factor:   3.360


  3 in total

Review 1.  Genetic Landscape of Human Papillomavirus-Associated Head and Neck Cancer and Comparison to Tobacco-Related Tumors.

Authors:  D Neil Hayes; Carter Van Waes; Tanguy Y Seiwert
Journal:  J Clin Oncol       Date:  2015-09-08       Impact factor: 44.544

2.  Personalized genomic results: analysis of informational needs.

Authors:  Tara J Schmidlen; Lisa Wawak; Rachel Kasper; J Felipe García-España; Michael F Christman; Erynn S Gordon
Journal:  J Genet Couns       Date:  2014-02-03       Impact factor: 2.537

3.  ReQON: a Bioconductor package for recalibrating quality scores from next-generation sequencing data.

Authors:  Christopher R Cabanski; Keary Cavin; Chris Bizon; Matthew D Wilkerson; Joel S Parker; Kirk C Wilhelmsen; Charles M Perou; J S Marron; D Neil Hayes
Journal:  BMC Bioinformatics       Date:  2012-09-04       Impact factor: 3.169

  3 in total

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