MRD in CLL: some answers, many questions

The kappa/lambda ratio of surface immunoglobulin light chain as a valuable parameter for MRD assessment in CLL with atypical immunophenotype

In recent years, the significance of detecting minimal/measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL) has increased due to the availability of highly effective therapeutic agents. Flow cytometry provides notable cost-effectiveness and immediacy, with an expected sensitivity level of approximately 10-4. The critical aspect of MRD detection via flow cytometry lies in accurately defining the region containing tumor cells. However, a subset of CLL, known as CLL with atypical immunophenotype, exhibits a distinct cell surface marker expression pattern that can make MRD detection challenging, because these markers often resemble those of normal B cells. To enhance the sensitivity of MRD detection in such atypical cases of CLL, we have capitalized on the observation that cell surface immunoglobulin (sIg) light chains tend to be expressed at a higher level in this subtype. For every four two-dimensional plots of cell surface markers, we used a plot to evaluate the expression of sIg kappa/lambda light chains and identified regions where the kappa/lambda ratio of sIg light chains deviated from a designated threshold within the putative CLL cell region. Using this method, we could detect atypical CLL cells at a level of 10-4. We propose this method as an effective MRD assay.

Investigation of fatty acid metabolism in chronic lymphocytic leukemia to guide clinical outcome and therapy

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the West. With CLL's heterogeneity, some people still develop disease refractory and relapse despite advances in treatment. Thus, early diagnosis and treatment of high-risk CLL patients is critical. Fatty acid (FA) metabolism contributes to tumorigenesis, progression, and therapy resistance through enhanced lipid synthesis, storage, and catabolism. In this study, we aimed to construct a prognostic model to improve the risk stratification of CLL and reveal the link between FA metabolism and CLL. The differentially expressed FA metabolism-related genes (FMGs) in CLL were filtered through univariate Cox regression analysis based on public databases. Functional enrichment was examined using prognostic FA metabolism-related gene enrichment analysis. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) estimated immune infiltration score and immune-related pathways. Pearson's correlation analysis investigated FA metabolism-related genes and drug sensitivity. A novel prognostic model was built using least absolute shrinkage and selection operator (LASSO) Cox algorithms. This validation cohort included 36 CLL patients from our center. We obtained CLL RNA microarray profiles from public databases and identified 15 prognostic-related FMGs. CLL patients were divided into two molecular clusters based on the expression of FMGs. The Kaplan-Meier analysis revealed a significant difference in TFS (P < 0.001) and OS (P < 0.001) between the two clusters. KEGG functional analysis showed that several pathways were enriched, including the chemokine and immune-related signaling pathways. In the training and validation cohorts, patients with higher FA metabolism-related prognostic index (FAPI) levels had worse outcomes. Finally, a novel nomogram prognostic model including CLL international prognostic index (CLL-IPI) was constructed, exhibiting reliable effectiveness and accuracy. In conclusion, we established a reliable predictive signature based on FA metabolism-related genes and constructed a novel nomogram prognostic model, supporting the potential preclinical implications of FA metabolism in CLL research.

Computational flow cytometry provides accurate assessment of measurable residual disease in chronic lymphocytic leukaemia

Undetectable measurable residual disease (MRD) is associated with favourable clinical outcomes in chronic lymphocytic leukaemia (CLL). While assessment is commonly performed using multiparameter flow cytometry (MFC), this approach is associated with limitations including user bias and expertise that may not be widely available. Implementation of unsupervised clustering algorithms in the laboratory can address these limitations and have not been previously reported in a systematic quantitative manner. We developed a computational pipeline to assess CLL MRD using FlowSOM. In the training step, a self-organising map was generated with nodes representing the full breadth of normal immature and mature B cells along with disease immunophenotypes. This map was used to detect MRD in multiple validation cohorts containing a total of 456 samples. This included an evaluation of atypical CLL cases and samples collected from two different laboratories. Computational MRD showed high correlation with expert analysis (Pearson's r > 0.99 for typical CLL). Binary classification of typical CLL samples as either MRD positive or negative demonstrated high concordance (>98%). Interestingly, computational MRD detected disease in a small number of atypical CLL cases in which MRD was not detected by expert analysis. These results demonstrate the feasibility and value of automated MFC analysis in a diagnostic laboratory.