Validation of the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes in chronic myelomonocytic leukaemia: A novel approach for improved risk stratification.

Chronic myelomonocytic leukaemia (CMML) is a rare haematological disorder characterized by monocytosis and dysplastic changes in myeloid cell lineages. Accurate risk stratification is essential for guiding treatment decisions and assessing prognosis. This study aimed to validate the Artificial Intelligence Prognostic Scoring System for Myelodysplastic Syndromes (AIPSS-MDS) in CMML and to assess its performance compared with traditional scores using data from a Spanish registry (n = 1343) and a Taiwanese hospital (n = 75). In the Spanish cohort, the AIPSS-MDS accurately predicted overall survival (OS) and leukaemia-free survival (LFS), outperforming the Revised-IPSS score. Similarly, in the Taiwanese cohort, the AIPSS-MDS demonstrated accurate predictions for OS and LFS, showing superiority over the IPSS score and performing better than the CPSS and molecular CPSS scores in differentiating patient outcomes. The consistent performance of the AIPSS-MDS across both cohorts highlights its generalizability. Its adoption as a valuable tool for personalized treatment decision-making in CMML enables clinicians to identify high-risk patients who may benefit from different therapeutic interventions. Future studies should explore the integration of genetic information into the AIPSS-MDS to further refine risk stratification in CMML and improve patient outcomes.

Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes.

Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems.

Novel Mutation Hotspots within Non-Coding Regulatory Regions of the Chronic Lymphocytic Leukemia Genome.

Mutations in non-coding DNA regions are increasingly recognized as cancer drivers. These mutations can modify gene expression in cis or by inducing high-order chormatin structure modifications with long-range effects. Previous analysis reported the detection of recurrent and functional non-coding DNA mutations in the chronic lymphocytic leukemia (CLL) genome, such as those in the 3' untranslated region of NOTCH1 and in the PAX5 super-enhancer. In this report, we used whole genome sequencing data produced by the International Cancer Genome Consortium in order to analyze regions with previously reported regulatory activity. This approach enabled the identification of numerous recurrently mutated regions that were frequently positioned in the proximity of genes involved in immune and oncogenic pathways. By correlating these mutations with expression of their nearest genes, we detected significant transcriptional changes in genes such as PHF2 and S1PR2. More research is needed to clarify the function of these mutations in CLL, particularly those found in intergenic regions.

A Three-Gene Expression Signature Identifies a Cluster of Patients with Short Survival in Chronic Lymphocytic Leukemia.

Chronic lymphocytic leukemia (CLL) is a lymphoproliferative disorder characterized by its heterogeneous clinical evolution. Despite the discovery of the most frequent cytogenomic drivers of disease during the last decade, new efforts are needed in order to improve prognostication. In this study, we used gene expression data of CLL samples in order to discover novel transcriptomic patterns associated with patient survival. We observed that a 3-gene expression signature composed of SCGB2A1, KLF4, and PPP1R14B differentiate a group of circa 5% of cases with short survival. This effect was independent of the main cytogenetic markers of adverse prognosis. Finally, this finding was reproduced in an independent retrospective cohort. We believe that this small gene expression pattern will be useful for CLL prognostication and its association with CLL response to novel drugs should be explored in the future.

The association of germline variants with chronic lymphocytic leukemia outcome suggests the implication of novel genes and pathways in clinical evolution.

BACKGROUND: Chronic Lymphocytic Leukemia (CLL) is the most frequent lymphoproliferative disorder in western countries and is characterized by a remarkable clinical heterogeneity. During the last decade, multiple genomic studies have identified a myriad of somatic events driving CLL proliferation and aggressivity. Nevertheless, and despite the mounting evidence of inherited risk for CLL development, the existence of germline variants associated with clinical outcomes has not been addressed in depth. METHODS: Exome sequencing data from control leukocytes of CLL patients involved in the International Cancer Genome Consortium (ICGC) was used for genotyping. Cox regression was used to detect variants associated with clinical outcomes. Gene and pathways level associations were also calculated. RESULTS: Single nucleotide polymorphisms in PPP4R2 and MAP3K4 were associated with earlier treatment need. A gene-level analysis evidenced a significant association of RIPK3 with both treatment need and survival. Furthermore, germline variability in pathways such as apoptosis, cell-cycle, pentose phosphate, GNα13 and Nitric oxide was associated with overall survival. CONCLUSION: Our results support the existence of inherited conditionants of CLL evolution and points towards genes and pathways that may results useful as biomarkers of disease outcome. More research is needed to validate these findings.

Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns.

Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in western countries. CLL evolution is frequently indolent, and treatment is mostly reserved for those patients with signs or symptoms of disease progression. In this work, we used RNA sequencing data from the International Cancer Genome Consortium CLL cohort to determine new gene expression patterns that correlate with clinical evolution.We determined that a 290-gene expression signature, in addition to immunoglobulin heavy chain variable region (IGHV) mutation status, stratifies patients into four groups with notably different time to first treatment. This finding was confirmed in an independent cohort. Similarly, we present a machine learning algorithm that predicts the need for treatment within the first 5 years following diagnosis using expression data from 2,198 genes. This predictor achieved 90% precision and 89% accuracy when classifying independent CLL cases. Our findings indicate that CLL progression risk largely correlates with particular transcriptomic patterns and paves the way for the identification of high-risk patients who might benefit from prompt therapy following diagnosis.

Short-term improvement of endothelial function in rituximab-treated rheumatoid arthritis patients refractory to tumor necrosis factor alpha blocker therapy.

OBJECTIVE: Cardiovascular disease is the major cause of excessive mortality in rheumatoid arthritis (RA) and endothelial dysfunction plays a key role in atherosclerosis. The aim of the present study was to assess whether rituximab therapy was able to improve endothelial function in RA patients refractory to tumor necrosis factor alpha (TNFalpha) blockers. METHODS: Six consecutive RA patients (5 women; age range 55-79 years) with active disease refractory to TNFalpha inhibitor therapy were studied. Patients received intravenous rituximab (1 course, consisting of 2 infusions of 1,000 mg each separated by 2 weeks). Flow-mediated endothelium-dependent vasodilatation (FMD%) and endothelium-independent vasodilatation (postnitroglycerin) were measured at day 0 prior to the first rituximab infusion, at week 2 (before the second infusion), and at month 6. RESULTS: At week 2, a dramatic increase in FMD% values was observed in all patients (mean +/- SD 7.02 +/- 2.31%, median 7.29%, range 3.2-9.75%) compared with those observed before the first infusion (mean +/- SD 3.35 +/- 1.58%, median 3.04%, range 1.69-5.89%). In addition, at month 6, FMD% values in all patients (mean +/- SD 7.66 +/- 1.73%, median 7.64%, range 5.61-9.98%) were greater than those found before the first infusion (P = 0.03). The dramatic improvement of FMD% was associated with a significant decrease in C-reactive protein level and Disease Activity Score in 28 joints. CONCLUSION: Our study demonstrates an active effect of rituximab on endothelial function in RA patients refractory to TNFalpha blockers.

Erythropoietin as adjuvant to pre-operative autologous blood donation in total hip arthroplasty: new algorithm for use.

Erythropoietin (rhEPO) has been used in different surgical procedures as a method for saving allogeneic blood, with variable efficacy. Forty consecutive patients entered the pre-operative autologous blood donation (PABD) program, and during donations hemoglobin fell below 115 g/l; they received rhEPO 40,000 U every week for three or four weeks (group 1). As control group, 35 consecutive patients who entered the PABD program were studied; during donations, hemoglobin levels in these patients fell below 115 g/l, but rhEPO was not administered (group 2). Pre-surgery hemoglobin levels were higher in patients who received rhEPO (134 g/l vs. 121 g/l; p<0.0002), and an average of 3.47 doses were administered. The number of transfused autologous units was 1.6 in group 1 and 2.1 in group 2 (p<0.05), while the number of allogeneic units was 0.9 and 0.1, respectively (p<0.0005), so that only 5% of patients treated with rhEPO required some allogeneic unit, as compared to 40% of those who did not receive rhEPO (p<0.0005). There were no relevant adverse effects, but in two patients from group 1, rhEPO treatment had to be discontinued because the level of hemoglobin exceeded 150 g/l. The mean duration of hospital admission was shorter in the patients who received rhEPO than in those who only underwent PABD (8 days vs. 11.8 days; NS). When adequately used, rhEPO is an effective and safe alternative to the use of allogeneic blood.