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Related Concept Videos

Disorders of Leukocytes01:27

Disorders of Leukocytes

Leukocyte disorders can lead to either leukopenia, characterized by an abnormally low leukocyte count, or leukocytosis, marked by a very high leukocyte number.
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The T and B lymphocytes of the adaptive immune system develop from common lymphoid progenitor cells in the bone marrow. These progenitors give rise to precursors that eventually develop into both T and B lymphocytes. As these precursors mature, they gain the ability to detect and respond to foreign antigens in the body, a process known as immunocompetence. Additionally, these precursors acquire self-tolerance, a process that ensures they do not react to self-antigens. This intricate system...

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Modeling Chemotherapy Resistant Leukemia In Vitro
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Predicting relapse in acute lymphoblastic leukemia.

Marc S Schwartz1, Lori S Muffly2

  • 1University of Colorado Anschutz School of Medicine, Aurora, CO, USA.

Leukemia & Lymphoma
|August 31, 2024
PubMed
Summary
This summary is machine-generated.

Predicting relapse in acute lymphoblastic leukemia (ALL) is crucial. Measurable residual disease (MRD) and genomics help identify high-risk patients for intensified therapy and relapse prevention.

Keywords:
ALLAcute lymphoblastic leukemiaMRD

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Area of Science:

  • Hematology
  • Oncology
  • Clinical Trials

Background:

  • Outcomes for acute lymphoblastic leukemia (ALL) have improved, but relapse remains a challenge.
  • Approximately 20% of pediatric and 50% of adult ALL patients face relapse after frontline chemotherapy.

Purpose of the Study:

  • To review robust clinical predictors of relapse in ALL.
  • To focus on measurable residual disease (MRD) and genomics for relapse prediction.
  • To discuss the application of prognostic tools in various clinical settings.

Main Methods:

  • Review of clinical predictors for ALL relapse.
  • Focus on measurable residual disease (MRD) assessment.
  • Analysis of genomic data for prognostic insights.

Main Results:

  • Measurable residual disease (MRD) and genomics are key predictors of ALL relapse.
  • These tools aid in stratifying patients for risk-adapted therapy.
  • Prognostic tools are applicable across different treatment phases.

Conclusions:

  • Accurate prediction of ALL relapse is essential for improving patient outcomes.
  • MRD and genomic profiling enable personalized treatment strategies.
  • Early identification of relapse risk facilitates timely intervention and novel therapy incorporation.