Predicting Prognosis in Chronic Lymphocytic Leukemia in the Contemporary Targeted Therapy Era: Where Do We Stand?

Chronic lymphocytic leukemia (CLL) prognostication plays a pivotal role in managing patient care, guiding therapeutic decisions, and predicting clinical outcomes. Historically, clinical staging systems such as the Rai and Binet classifications formed the cornerstone of CLL prognostication. However, with the advent of targeted therapies and advancements in molecular biology, the understanding of CLL has significantly evolved, warranting a re-evaluation of traditional prognostic models.

The widespread clinical adoption of these older models has been limited due to their relatively low accuracy. Comparative analyses suggest that approximately 30% of predictions made by existing models are incorrect, underscoring the urgent need for more refined and precise tools. This implies a shift from disease-centric models, which offer static predictions based on population averages, to patient-centric models, which provide dynamic, individualized prognostication.

Furthermore, the emergence of targeted therapies has introduced novel prognostic factors that extend beyond traditional clinical parameters, significantly influencing both progression-free survival (PFS) and overall survival (OS). These factors include discontinuation of targeted agents due to adverse events, transformation of the disease (e.g., Richter syndrome), and patient-specific characteristics such as polypharmacy. A particularly high-risk subgroup consists of patients who experience disease progression after treatment with both Bruton's tyrosine kinase (BTK) and B-cell lymphoma 2 (BCL2) inhibitors, commonly referred to as “double refractory” cases. Additionally, genetic mutations that confer primary resistance to these therapies further define a poor-risk population.

This evolving landscape signifies a transition in CLL prognostication from reliance on traditional clinical staging systems to more advanced models that incorporate molecular and genetic markers, along with treatment-related factors. These advancements hold the potential to substantially enhance the personalization of therapeutic strategies. However, significant challenges remain, including limited access to comprehensive genetic testing and the need for standardized minimal residual disease (MRD) monitoring. Addressing these issues will be essential for the effective clinical application of these advanced prognostic tools.

This Research Topic welcomes original research articles, reviews, and perspectives that cover a range of sub-themes, including but not limited to:

- Development of machine learning prognostic models for CLL
- Incorporating patient-related factors in prognostic models for CLL
- Assessing the prognostic value of post-treatment factors
- Improving the accuracy of prognostic models for CLL

Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.

Stefano Molica received honoraria from Janssen, AbbVie, and AstraZeneca.

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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
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: CLL, Prognostic factors, Predictive factors, Prognostic models, Targeted Therapy

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