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Article details
Title
Molecular Testing in Myelodysplastic Syndromes for the Practicing Oncologist: Will the Progress Fulfill the Promise?
Published in
Oncologist, July 2015
DOI 10.1634/theoncologist.2015-0067
Pubmed ID
Authors
Abstract

: Myelodysplastic syndromes (MDS) are heterogeneous hematopoietic neoplasms that are driven by somatically acquired genetic mutations and epigenetic alterations. Accurate risk stratification is essential for delivery of risk-adaptive therapeutic interventions. The current prognostic tools sum the impact of clinical, pathologic, and laboratory parameters. Newer technologies with next-generation targeted deep sequencing and whole-genome and -exome sequencing have identified several recurrent mutations that play a vital role in the pathophysiology of MDS and the impact of these genetic changes on disease phenotype. Equally important, well-annotated databases of MDS patients with paired clinicopathologic and genetic data have enabled better understanding of the independent prognostic impact of several molecular mutations on important clinical endpoints such as overall survival and probability of leukemic progression. Cumulative evidence suggests that genomic data can also be used clinically to aid with the diagnosis, prognosis, prediction of response to specific therapies, and the development of novel and rationally targeted therapies. However, the optimal use of this mutational profiling remains a work in progress and currently there is no standard set of genes or techniques that are recommended for routine use in the clinic. In this review, we discuss the genomic revolution and its impact on our understanding of MDS biology and risk stratification. We also discuss the current role and the challenges of the application of genetic mutational data into daily clinical practice and how future research could help improve the prognostication precision and specific therapy selection for patients with MDS. Heterogeneity in clinical outcomes of MDS is partly related to interpatient variability of recurrent somatic mutations that drive disease phenotype and progression. Although clinical risk stratification tools have functioned well in prognostication for patients with MDS, their ability to predict clinical benefits of specific MDS therapies is limited. Molecular testing shows promise in aiding diagnosis, risk stratification, and therapy-specific benefit prediction for MDS patients. Nonetheless, logistical issues related to assay performance standardization, validation, interpretation, and development of guidelines for how to use the results to inform clinical decisions are yet to be resolved.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Netherlands 1 3%
Unknown 37 97%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Bachelor 7 18%
Student > Ph. D. Student 7 18%
Researcher 6 16%
Student > Master 2 5%
Lecturer > Senior Lecturer 1 3%
Other 3 8%
Unknown 12 32%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 10 26%
Biochemistry, Genetics and Molecular Biology 7 18%
Pharmacology, Toxicology and Pharmaceutical Science 2 5%
Nursing and Health Professions 2 5%
Agricultural and Biological Sciences 1 3%
Other 4 11%
Unknown 12 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 August 2015.
All research outputs
#27,724,172
of 34,358,242 outputs
Outputs from Oncologist
#4,251
of 4,872 outputs
Outputs of similar age
#220,130
of 298,888 outputs
Outputs of similar age from Oncologist
#44
of 56 outputs
Altmetric has tracked 34,358,242 research outputs across all sources so far. This one is in the 16th percentile – i.e., 16% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,872 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 298,888 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.