A new functional prognostic model has demonstrated potential in forecasting progression-free survival (PFS) for patients with relapsed/refractory chronic lymphocytic leukemia (CLL) undergoing treatment with ibrutinib and venetoclax. According to a study published in HemaSphere, the model, constructed from six laboratory features of leukemic cells and their microenvironment, surpassed existing clinical scores.
The model integrates ex vivo sensitivity to three drugs, two ratios of protein expression or phosphorylation, and the percentage of CD8-positive T cells. Researchers observed that a higher percentage of these T cells correlated with an raised risk of progression, whereas greater drug sensitivity was linked to improved PFS.
Moving Beyond Traditional Prognostic Indicators
Historically, CLL prognostic markers such as unmutated IGHV and TP53 aberrations were established during the chemoimmunotherapy era. However, with the introduction of targeted therapies like ibrutinib and venetoclax, these markers may no longer suffice. The study’s authors noted that fixed-duration targeted combinations have achieved deep responses even in patients with these traditional markers.
A reevaluation of the International Prognostic Index for CLL (CLL-IPI) revealed that targeted therapies outperformed chemoimmunotherapy across all risk groups. In the phase 2 VISION/HO141 trial of ibrutinib plus venetoclax, the estimated 4-year PFS was 81% overall, with a median follow-up of 51.7 months.
Model Development
To create the functional prognostic model, researchers examined peripheral blood mononuclear cells from 177 patients with relapsed/refractory CLL enrolled in the VISION/HO141 trial. This high-risk cohort included 17% with del(17p), 25% with mutated TP53, and 63% with unmutated IGHV.
Cells underwent ex vivo testing against 95 single agents and 87 drug combinations, along with flow cytometry profiling and immunophenotyping. The final model incorporated six features: sensitivity to VEGFR-2/EGFR inhibition, JAK1/2 plus Bcl-2 inhibition, and Bruton tyrosine kinase (BTK) plus PI3K inhibition, as well as the BCL-2 to BTK (pY223) ratio, p90RSK (pS380) to PLCγ2 (pY759) ratio, and CD8-positive T-cell proportion.
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Performance and Validation
In the test set, the functional prognostic model attained a Harrell C-index of 0.766 and a time-averaged area under the receiver operating characteristic curve (ROC-AUC) of 0.812. In a blinded validation set, the C-index reached 0.815, and the ROC-AUC was 0.805.
The model effectively differentiated between high- and low-risk groups in a combined test and validation subset of 66 patients with complete data for established scores, unlike the CLL-IPI. However, the authors noted some overfitting due to the relatively small sample size and the model’s reliance on predefined functional profiles.
The model’s capacity to predict treatment response in an external cohort treated with umbralisib indicates its potential applicability beyond the original study’s specific drugs. Nonetheless, further validation in larger, independent cohorts and prospective clinical trials is essential before clinical implementation.
The authors concluded that while the model holds promise, its retrospective nature and the need for additional validation are limitations. As targeted therapies advance, such models may significantly contribute to personalizing treatments for individual patients, ultimately enhancing outcomes in relapsed/refractory CLL. They highlighted that the model’s predictive capabilities could be instrumental in selecting the most effective therapies for patients based on their unique biological profiles.
In the context of evolving treatment environments, the integration of functional prognostic models into clinical practice could represent a significant step forward. By leveraging laboratory features of leukemic cells and their microenvironment, these models offer a more subtle approach to predicting treatment outcomes. The successful validation of this model in external cohorts would mark a substantial advancement in the management of relapsed/refractory CLL, providing clinicians with a valuable tool to optimize patient care.