Diagnostic, Prognostic and Predictive Markers in Leukemia

About this Research Topic

Background

Cancer biomarkers have transformed cancer treatment, enabling significant advancements in diagnosis, therapeutics, and patient outcomes. Leukemia is a clinically and genetically heterogeneous disease marked by the clonal expansion of abnormally differentiated hematopoietic progenitors. Through molecular profiling, genetic testing, and personalized medicine, oncologists can now identify and tailor treatment for leukemia patients according to the specific molecular characteristics of each patient.

Advances in next-generation sequencing for detecting genetic mutations have enhanced the diagnosis and treatment of leukemia. However, only a small proportion of patients benefit, as actionable targets are infrequent, and the disease exhibits significant clonal diversity. This research topic explores recent advances in leukemia biomarkers and future strategies for better management of the disease.

Risk classification using prediction models that integrate multi-omics molecular and clinical data shows potential for improved stratification of leukemia patients in the future. Machine learning and deep learning approaches have already offered valuable insights and a deeper understanding of disease development. As sequencing technologies and machine learning algorithms continue to advance, leukemia is set to move towards multi-omics precision medicine in the near future. Deep single-cell multi-omic profiling holds promise for identifying markers of leukemia subsets as well as overcoming drug resistance in relapsed or refractory cases. Recent advances in single-cell transcriptomics and proteomics have revealed markers that differentiate leukemic subsets with varying proliferative capacities, and validated leukemia self-renewal gene expression at the single-cell level. Proliferation and self-renewal are distinct processes in immunophenotypically-defined leukemic stem cells, with targeting self-renewal being crucial for enhancing the likelihood of cure. Identifying single-cell biomarkers specific to self-renewing leukemic stem cells could aid in diagnosing minimal residual disease and improve disease management.

Mitochondrial mutation profiling has emerged as a potential prognostic marker in leukemia. Moreover, leukemic blast development, proliferation, and survival depend on the tumor microenvironment, where phenotypically diverse cells alter immune responses to help tumor cells evade elimination. Studying the tumor microenvironment, particularly immune cells, and developing prediction models based on tumor microenvironment specific characteristics could enhance responses to immunotherapy and improve prognosis. Furthermore, emerging functional technologies like Dynamic BH3 profiling assays allow for quantitative phenotypic analysis by exposing patient-derived tumor cells to treatments and evaluating drug sensitivity, providing useful markers for therapy response.

We welcome Original Research Papers, Reviews, and Mini-Review Articles from preclinical and clinical studies that include but are not limited to the following topics:

1) Diagnostic markers for leukemia including AML, ALL, CML, CLL
2) Biomarkers and molecular markers for prognosis of patients with leukemia
3) Predictive markers for response to therapy including chemotherapy and targeted therapy
4) Markers for predicting response to immunotherapy
5) Markers to detect minimal residual disease
6) Functional markers for response to therapy

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• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Leukemia, Metabolic Vulnerabilities, Prognostic Markers, Predictive Markers, Stress Response Pathways, Biomarkers, Machine Learning, Risk Models, AML, ALL, CML, CLL

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