Refining risk prediction in pediatric acute lymphoblastic leukemia through DNA methylation profiling.

Acute lymphoblastic leukemia (ALL) is the most prevalent cancer in children, and despite considerable progress in treatment outcomes, relapses still pose significant risks of mortality and long-term complications. To address this challenge, we employed a supervised machine learning technique, specifically random survival forests, to predict the risk of relapse and mortality using array-based DNA methylation data from a cohort of 763 pediatric ALL patients treated in Nordic countries. The relapse risk predictor (RRP) was constructed based on 16 CpG sites, demonstrating c-indexes of 0.667 and 0.677 in the training and test sets, respectively. The mortality risk predictor (MRP), comprising 53 CpG sites, exhibited c-indexes of 0.751 and 0.754 in the training and test sets, respectively. To validate the prognostic value of the predictors, we further analyzed two independent cohorts of Canadian (n = 42) and Nordic (n = 384) ALL patients. The external validation confirmed our findings, with the RRP achieving a c-index of 0.667 in the Canadian cohort, and the RRP and MRP achieving c-indexes of 0.529 and 0.621, respectively, in an independent Nordic cohort. The precision of the RRP and MRP models improved when incorporating traditional risk group data, underscoring the potential for synergistic integration of clinical prognostic factors. The MRP model also enabled the definition of a risk group with high rates of relapse and mortality. Our results demonstrate the potential of DNA methylation as a prognostic factor and a tool to refine risk stratification in pediatric ALL. This may lead to personalized treatment strategies based on epigenetic profiling.

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.

The prognostic impact of non-driver gene mutations and variant allele frequency in primary myelofibrosis.

Prognostic impact of non-MPN driver gene mutations in primary myelofibrosis. MIPSS70: Mutation-Enhanced International Prognostic Score System.

DNMT3A/TET2/ASXL1 Mutations are an Age-independent Thrombotic Risk Factor in Polycythemia Vera Patients: An Observational Study.

BACKGROUND: Polycythemia vera (PV) patients are classified as high or low thrombotic risk based on age and prior history of thrombosis. Despite adherence to treatment recommendations, vascular events remain frequent, leading us to question whether thrombotic risk stratification could be improved. We previously reported an association between thrombotic events and mutations in DTA genes (DNMT3A, TET2, and ASXL1). The objective of this study was to confirm this observation in a larger series of PV patients. METHODS: PV patients with a minimum follow-up of 3 years were recruited from 8 European centers. Medical history was searched for thrombotic event recorded at any time and next-generation sequencing carried out with a myeloid panel. Multivariable logistic regression evaluated the impact of variables on thrombotic risk. Kaplan-Meier thrombosis-free survival curves were compared by the log rank test. Associations in the total cohort were confirmed in a case-control study to exclude selection bias. RESULTS: Of the 136 patients recruited, 74 (56.1%) had a thrombotic event, with an incidence density of 2.83/100 person-years. In multivariable analysis, DTA mutation was a risk factor for thrombotic event, being predictive for shorter thrombosis-free survival in the whole cohort (p = 0.007), as well as in low-risk patients (p = 0.039) and older patients (p = 0.009), but not for patients with a prediagnostic event. A gender- and age-matched case-control study confirmed the increased risk of thrombotic event for PV patients with a DTA mutation. CONCLUSION: Our results support the use of molecular testing at diagnosis to help predict which PV patients are at higher risk of developing thrombosis.

Outcomes after intensive chemotherapy for secondary and myeloid-related changes acute myeloid leukemia patients aged 60 to 75 years old: a retrospective analysis from the PETHEMA registry.

Treatment options for patients with secondary acute myeloid leukemia (sAML) and AML with myeloid-related changes (AMLMRC) aged 60 to 75 years are scarce and unsuitable. A pivotal trial showed that CPX-351 improved complete remission with/without incomplete recovery (CR/CRi) and overall survival (OS) as compared with standard "3+7" regimens. We retrospectively analyze outcomes of 765 patients with sAML and AML-MRC aged 60 to 75 years treated with intensive chemotherapy, reported to the PETHEMA registry before CPX-351 became available. The CR/CRi rate was 48%, median OS was 7.6 months (95% confidence interval [CI]: 6.7-8.5) and event-free survival (EFS) 2.7 months (95% CI: 2-3.3), without differences between intensive chemotherapy regimens and AML type. Multivariate analyses identified age ≥70 years, Eastern Cooperative Oncology Group performance status ≥1 as independent adverse prognostic factors for CR/CRi and OS, while favorable/intermediate cytogenetic risk and NPM1 were favorable prognostic factors. Patients receiving allogeneic stem cell transplant (HSCT), autologous HSCT, and those who completed more consolidation cycles showed improved OS. This large study suggests that classical intensive chemotherapy could lead to similar CR/CRi rates with slightly shorter median OS than CPX-351.

Incidence, risk factors, and outcomes of second neoplasms in patients with acute promyelocytic leukemia: the PETHEMA-PALG experience.

The most important challenges in acute promyelocytic leukemia (APL) is preventing early death and reducing long-term events, such as second neoplasms (s-NPLs). We performed a retrospective analysis of 2670 unselected APL patients, treated with PETHEMA "chemotherapy based" and "chemotherapy free" protocols. Only de novo APL patients who achieved complete remission (CR) and completed the three consolidation cycles were enrolled into the analysis. Out of 2670 APL patients, there were 118 (4.4%) who developed s-NPLs with the median latency period (between first CR and diagnosis of s-NPL) of 48.0 months (range 2.8-231.1): 43.3 (range: 2.8-113.9) for s-MDS/AML and 61.7 (range: 7.1-231.1) for solid tumour. The 5-year CI of all s-NPLs was of 4.43% and 10 years of 7.92%. Among s-NPLs, there were 58 cases of s-MDS/AML, 3 cases of other hematological neoplasms, 57 solid tumours and 1 non-identified neoplasm. The most frequent solid tumour was colorectal, lung and breast cancer. Overall, the 2-year OS from diagnosis of s-NPLs was 40.6%, with a median OS of 11.1 months. Multivariate analysis identified age of 35 years (hazard ratio = 0.2584; p < 0.0001) as an independent prognostic factor for s-NPLs. There were no significant differences in CI of s-NPLs at 5 years between chemotherapy-based vs chemotherapy-free regimens (hazard ratio = 1.09; p = 0.932). Larger series with longer follow-up are required to confirm the potential impact of ATO+ATRA regimens to reduce the incidence of s-NPLs after front-line therapy for APL.

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.

Corrigendum: Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia.

[This corrects the article DOI: 10.3389/fonc.2022.968340.].

Presence of Myeloid Mutations in Patients with Chronic Myeloid Leukemia Increases Risk of Cardiovascular Event on Tyrosine Kinase Inhibitor Treatment.

For chronic myeloid leukemia (CML) patients with a known risk of cardiovascular events (CVE), imatinib is often recommended for first-line tyrosine kinase inhibitor (TKI) treatment rather than a second-generation TKI (2G-TKI) such as nilotinib or dasatinib. To date, very few studies have evaluated the genetic predisposition associated with CVE development on TKI treatment. In this retrospective study of 102 CML patients, 26 CVEs were reported during an average follow-up of over 10 years. Next-generation sequencing identified pathogenic/likely pathogenic mutations in genes associated with myeloid malignancies in 24.5% of the diagnostic samples analyzed. Patients with a recorded CVE had more myeloid mutations (0.48 vs. 0.14, p = 0.019) and were older (65.1 vs. 55.7 years, p = 0.016). Age ≥ 60 years and receiving a 2G-TKI in first-line were CVE risk factors. The presence of a pathogenic somatic myeloid mutation was an independent risk factor for CVE on any TKI (HR 2.79, p = 0.01), and significantly shortened the CV event-free survival of patients who received first-line imatinib (by 70 months, p = 0.011). Indeed, 62% of patients on imatinib with mutations had a CVE vs. the 19% on imatinib with a mutation and no CVE. In conclusion, myeloid mutations detectable at diagnosis increase CVE risk, particularly for patients on imatinib, and might be considered for first-line TKI choice.

Application of IPSET-thrombosis in 1366 Patients Prospectively Followed From the Spanish Registry of Essential Thrombocythemia.

The International Prognostic Score of thrombosis in Essential Thrombocythemia (IPSET-thrombosis) and its revised version have been proposed to guide thrombosis prevention strategies. We evaluated both classifications to prognosticate thrombosis in 1366 contemporary essential thrombocythemia (ET) patients prospectively followed from the Spanish Registry of ET. The cumulative incidence of thrombosis at 10 years, taking death as a competing risk, was 11.4%. The risk of thrombosis was significantly higher in the high-risk IPSET-thrombosis and high-risk revised IPSET-thrombosis, but no differences were observed among the lower risk categories. Patients allocated in high-risk IPSET-thrombosis (subdistribution hazard ratios [SHR], 3.7 [95% confidence interval, CI, 1.6-8.7]) and high-risk revised IPSET-thrombosis (SHR, 3.2 [95% CI, 1.4-7.45]) showed an increased risk of arterial thrombosis, whereas both scoring systems failed to predict venous thrombosis. The incidence rate of thrombosis in intermediate risk revised IPSET-thrombosis (aged >60 years, JAK2-negative, and no history of thrombosis) was very low regardless of the treatment administered (0.9% and 0% per year with and without cytoreduction, respectively). Dynamic application of the revised IPSET-thrombosis showed a low rate of thrombosis when patients without history of prior thrombosis switched to a higher risk category after reaching 60 years of age. In conclusion, IPSET-thrombosis scores are useful for identifying patients at high risk of arterial thrombosis, whereas they fail to predict venous thrombosis. Controlled studies are needed to determine the appropriate treatment of ET patients assigned to the non-high-risk categories.

A prognostic model based on gene expression parameters predicts a better response to bortezomib-containing immunochemotherapy in diffuse large B-cell lymphoma.

Diffuse Large B-cell Lymphoma (DLBCL) is the most common type of aggressive lymphoma. Approximately 60% of fit patients achieve curation with immunochemotherapy, but the remaining patients relapse or have refractory disease, which predicts a short survival. Traditionally, risk stratification in DLBCL has been based on scores that combine clinical variables. Other methodologies have been developed based on the identification of novel molecular features, such as mutational profiles and gene expression signatures. Recently, we developed the LymForest-25 profile, which provides a personalized survival risk prediction based on the integration of transcriptomic and clinical features using an artificial intelligence system. In the present report, we studied the relationship between the molecular variables included in LymForest-25 in the context of the data released by the REMoDL-B trial, which evaluated the addition of bortezomib to the standard treatment (R-CHOP) in the upfront setting of DLBCL. For this, we retrained the machine learning model of survival on the group of patients treated with R-CHOP (N=469) and then made survival predictions for those patients treated with bortezomib plus R-CHOP (N=459). According to these results, the RB-CHOP scheme achieved a 30% reduction in the risk of progression or death for the 50% of DLBCL patients at higher molecular risk (p-value 0.03), potentially expanding the effectiveness of this treatment to a wider patient population as compared with other previously defined risk groups.

Clinical outcomes after CPX-351 in patients with high-risk acute myeloid leukemia: A comparison with a matched cohort from the Spanish PETHEMA registry.

BACKGROUND: CPX-351 is approved for the treatment of therapy related acute myeloid leukemia (t-AML) and AML with myelodysplastic related changes (MRC-AML). The benefits of this treatment over standard chemotherapy has not been addressed in well matched cohorts of real-life patients. METHODS: Retrospective analysis of AML patients treated with CPX-351 as per routine practice. A propensity score matching (PSM) was used to compare their main outcomes with those observed in a matched cohort among 765 historical patients receiving intensive chemotherapy (IC), all of them reported to the PETHEMA epidemiologic registry. RESULTS: Median age of 79 patients treated with CPX-351 was 67 years old (interquartile range 62-71), 53 were MRC-AML. The complete remission (CR) rate or CR without recovery (CRi) after 1 or 2 cycles of CPX-351 was 52%, 60-days mortality 18%, measurable residual disease <0.1% in 54% (12 out of 22) of them. Stem cell transplant (SCT) was performed in 27 patients (34%), median OS was 10.3 months, and 3-year relapse incidence was 50%. Using PSM, we obtained two comparable cohorts treated with CPX-351 (n = 52) or IC (n = 99), without significant differences in CR/CRi (60% vs. 54%) and median OS (10.3 months vs. 9.1 months), although more patients were bridged to SCT in the CPX-351 group (35% vs. 12%). The results were confirmed when only 3 + 7 patients were included in the historical cohort. In multivariable analyses, SCT was associated with better OS (HR 0.33 95% CI: 0.18-0.59), p < 0.001. CONCLUSION: Larger post-authorization studies may provide evidence of the clinical benefits of CPX-351 for AML in the real-life setting.

Toxicity of Asciminib in Real Clinical Practice: Analysis of Side Effects and Cross-Toxicity with Tyrosine Kinase Inhibitors.

(1) Background: Despite the prognostic improvements achieved with tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML), a minority of patients still fail TKIs. The recent introduction of asciminib may be a promising option in intolerant patients, as it is a first-in-class inhibitor with a more selective mechanism of action different from the ATP-competitive inhibition that occurs with TKIs. Therefore, our goal was to analyze toxicities shown with asciminib as well as to study cross-toxicity with previous TKIs. (2) Methods: An observational, multicenter, retrospective study was performed with data from 77 patients with CML with therapeutic failure to second-generation TKIs who received asciminib through a managed-access program (MAP) (3) Results: With a median follow-up of 13.7 months, 22 patients (28.5%) discontinued treatment: 32% (7/22) due to intolerance and 45% (10/22) due to resistance. Fifty-five percent of the patients reported adverse effects (AEs) with asciminib and eighteen percent grade 3-4. Most frequent AEs were: fatigue (18%), thrombocytopenia (17%), anemia (12%), and arthralgias (12%). None of the patients experienced cardiovascular events or occlusive arterial disease. Further, 26%, 25%, and 9% of patients required dose adjustment, temporary suspension, or definitive discontinuation of treatment, respectively. Toxicities under asciminib seemed lower than with prior TKIs for anemia, cardiovascular events, pleural/pericardial effusion, diarrhea, and edema. Cross-toxicity risk was statistically significant for thrombocytopenia, anemia, neutropenia, fatigue, vomiting, and pancreatitis. (4) Conclusion: Asciminib is a molecule with a good safety profile and with a low rate of AEs. However, despite its new mechanism of action, asciminib presents a risk of cross-toxicity with classical TKIs for some AEs.

Machine Learning Improves Risk Stratification in Myelofibrosis: An Analysis of the Spanish Registry of Myelofibrosis.

Myelofibrosis (MF) is a myeloproliferative neoplasm (MPN) with heterogeneous clinical course. Allogeneic hematopoietic cell transplantation remains the only curative therapy, but its morbidity and mortality require careful candidate selection. Therefore, accurate disease risk prognostication is critical for treatment decision-making. We obtained registry data from patients diagnosed with MF in 60 Spanish institutions (N = 1386). These were randomly divided into a training set (80%) and a test set (20%). A machine learning (ML) technique (random forest) was used to model overall survival (OS) and leukemia-free survival (LFS) in the training set, and the results were validated in the test set. We derived the AIPSS-MF (Artificial Intelligence Prognostic Scoring System for Myelofibrosis) model, which was based on 8 clinical variables at diagnosis and achieved high accuracy in predicting OS (training set c-index, 0.750; test set c-index, 0.744) and LFS (training set c-index, 0.697; test set c-index, 0.703). No improvement was obtained with the inclusion of MPN driver mutations in the model. We were unable to adequately assess the potential benefit of including adverse cytogenetics or high-risk mutations due to the lack of these data in many patients. AIPSS-MF was superior to the IPSS regardless of MF subtype and age range and outperformed the MYSEC-PM in patients with secondary MF. In conclusion, we have developed a prediction model based exclusively on clinical variables that provides individualized prognostic estimates in patients with primary and secondary MF. The use of AIPSS-MF in combination with predictive models that incorporate genetic information may improve disease risk stratification.

CNL and aCML should be considered as a single entity based on molecular profiles and outcomes.

Chronic neutrophilic leukemia (CNL) and atypical chronic myeloid leukemia (aCML) are rare myeloid disorders that are challenging with regard to diagnosis and clinical management. To study the similarities and differences between these disorders, we undertook a multicenter international study of one of the largest case series (CNL, n = 24; aCML, n = 37 cases, respectively), focusing on the clinical and mutational profiles (n = 53 with molecular data) of these diseases. We found no differences in clinical presentations or outcomes of both entities. As previously described, both CNL and aCML share a complex mutational profile with mutations in genes involved in epigenetic regulation, splicing, and signaling pathways. Apart from CSF3R, only EZH2 and TET2 were differentially mutated between them. The molecular profiles support the notion of CNL and aCML being a continuum of the same disease that may fit best within the myelodysplastic/myeloproliferative neoplasms. We identified 4 high-risk mutated genes, specifically CEBPA (ß = 2.26, hazard ratio [HR] = 9.54, P = .003), EZH2 (ß = 1.12, HR = 3.062, P = .009), NRAS (ß = 1.29, HR = 3.63, P = .048), and U2AF1 (ß = 1.75, HR = 5.74, P = .013) using multivariate analysis. Our findings underscore the relevance of molecular-risk classification in CNL/aCML as well as the importance of CSF3R mutations in these diseases.

Evaluation of the Stellae-123 prognostic gene expression signature in acute myeloid leukemia.

Risk stratification in acute myeloid leukemia (AML) has been extensively improved thanks to the incorporation of recurrent cytogenomic alterations into risk stratification guidelines. However, mortality rates among fit patients assigned to low or intermediate risk groups are still high. Therefore, significant room exists for the improvement of AML prognostication. In a previous work, we presented the Stellae-123 gene expression signature, which achieved a high accuracy in the prognostication of adult patients with AML. Stellae-123 was particularly accurate to restratify patients bearing high-risk mutations, such as ASXL1, RUNX1 and TP53. The intention of the present work was to evaluate the prognostic performance of Stellae-123 in external cohorts using RNAseq technology. For this, we evaluated the signature in 3 different AML cohorts (2 adult and 1 pediatric). Our results indicate that the prognostic performance of the Stellae-123 signature is reproducible in the 3 cohorts of patients. Additionally, we evidenced that the signature was superior to the European LeukemiaNet 2017 and the pediatric clinical risk scores in the prediction of survival at most of the evaluated time points. Furthermore, integration with age substantially enhanced the accuracy of the model. In conclusion, Stellae-123 is a reproducible machine learning algorithm based on a gene expression signature with promising utility in the field of AML.

Correction: Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia.

[This corrects the article DOI: 10.1371/journal.pone.0247093.].