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An Integrative multi-lineage model of variation in leukopoiesis and acute myelogenous leukemia

Overview of attention for article published in BMC Systems Biology, August 2017
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2 tweeters

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4 Dimensions

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13 Mendeley
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Title
An Integrative multi-lineage model of variation in leukopoiesis and acute myelogenous leukemia
Published in
BMC Systems Biology, August 2017
DOI 10.1186/s12918-017-0469-2
Pubmed ID
Authors

Joyatee M. Sarker, Serena M. Pearce, Robert P. Nelson, Tamara L. Kinzer-Ursem, David M. Umulis, Ann E. Rundell

Abstract

Acute myelogenous leukemia (AML) progresses uniquely in each patient. However, patients are typically treated with the same types of chemotherapy, despite biological differences that lead to differential responses to treatment. Here we present a multi-lineage multi-compartment model of the hematopoietic system that captures patient-to-patient variation in both the concentration and rates of change of hematopoietic cell populations. By constraining the model against clinical hematopoietic cell recovery data derived from patients who have received induction chemotherapy, we identified trends for parameters that must be met by the model; for example, the mitosis rates and the probability of self-renewal of progenitor cells are inversely related. Within the data-consistent models, we found 22,796 parameter sets that meet chemotherapy response criteria. Simulations of these parameter sets display diverse dynamics in the cell populations. To identify large trends in these model outputs, we clustered the simulated cell population dynamics using k-means clustering and identified thirteen 'representative patient' dynamics. In each of these patient clusters, we simulated AML and found that clusters with the greatest mitotic capacity experience clinical cancer outcomes more likely to lead to shorter survival times. Conversely, other parameters, including lower death rates or mobilization rates, did not correlate with survival times. Using the multi-lineage model of hematopoiesis, we have identified several key features that determine leukocyte homeostasis, including self-renewal probabilities and mitosis rates, but not mobilization rates. Other influential parameters that regulate AML model behavior are responses to cytokines/growth factors produced in peripheral blood that target the probability of self-renewal of neutrophil progenitors. Finally, our model predicts that the mitosis rate of cancer is the most predictive parameter for survival time, followed closely by parameters that affect the self-renewal of cancer stem cells; most current therapies target mitosis rate, but based on our results, we propose that additional therapeutic targeting of self-renewal of cancer stem cells will lead to even higher survival rates.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 15%
Student > Bachelor 2 15%
Student > Ph. D. Student 2 15%
Student > Doctoral Student 1 8%
Lecturer 1 8%
Other 4 31%
Unknown 1 8%
Readers by discipline Count As %
Engineering 3 23%
Mathematics 2 15%
Biochemistry, Genetics and Molecular Biology 1 8%
Nursing and Health Professions 1 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Other 4 31%
Unknown 1 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 27 August 2017.
All research outputs
#6,973,171
of 11,662,003 outputs
Outputs from BMC Systems Biology
#503
of 988 outputs
Outputs of similar age
#137,543
of 262,784 outputs
Outputs of similar age from BMC Systems Biology
#8
of 18 outputs
Altmetric has tracked 11,662,003 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 988 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 43rd percentile – i.e., 43% 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 262,784 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.