Validation of mutated CEBPA bZIP as a distinct prognosis entity in acute myeloid leukemia: a study by the Spanish PETHEMA registry.

Not available.

Digital Microscopy Augmented by Artificial Intelligence to Interpret Bone Marrow Samples for Hematological Diseases.

Analysis of bone marrow aspirates (BMAs) is an essential step in the diagnosis of hematological disorders. This analysis is usually performed based on a visual examination of samples under a conventional optical microscope, which involves a labor-intensive process, limited by clinical experience and subject to high observer variability. In this work, we present a comprehensive digital microscopy system that enables BMA analysis for cell type counting and differentiation in an efficient and objective manner. This system not only provides an accessible and simple method to digitize, store, and analyze BMA samples remotely but is also supported by an Artificial Intelligence (AI) pipeline that accelerates the differential cell counting process and reduces interobserver variability. It has been designed to integrate AI algorithms with the daily clinical routine and can be used in any regular hospital workflow.

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.

Short-Chain Fatty Acid Production by Gut Microbiota Predicts Treatment Response in Multiple Myeloma.

PURPOSE: The gut microbiota plays important roles in health and disease. We questioned whether the gut microbiota and related metabolites are altered in monoclonal gammopathies and evaluated their potential role in multiple myeloma and its response to treatment. EXPERIMENTAL DESIGN: We used 16S rRNA sequencing to characterize and compare the gut microbiota of patients with monoclonal gammopathy of undetermined significance (n = 11), smoldering multiple myeloma (n = 9), newly diagnosed multiple myeloma (n = 11), relapsed/refractory multiple myeloma (n = 6), or with complete remission (n = 9). Short-chain fatty acids (SCFA) were quantified in serum and tested in cell lines. Relevant metabolites were validated in a second cohort of 62 patients. RESULTS: Significant differences in alpha- and beta diversity were present across the groups and both were lower in patients with relapse/refractory disease and higher in patients with complete remission after treatment. Differences were found in the abundance of several microbiota taxa across disease progression and in response to treatment. Bacteria involved in SCFA production, including Prevotella, Blautia, Weissella, and Agathobacter, were more represented in the premalignant or complete remission samples, and patients with higher levels of Agathobacter showed better overall survival. Serum levels of butyrate and propionate decreased across disease progression and butyrate was positively associated with a better response. Both metabolites had antiproliferative effects in multiple myeloma cell lines. CONCLUSIONS: We demonstrate that SCFAs metabolites and the gut microbiota associated with their production might have beneficial effects in disease evolution and response to treatment, underscoring its therapeutic potential and value as a predictor.

Clinical Outcomes of Patients with Multiple Myeloma after Daratumumab Failure.

Anti-CD38 monoclonal antibody (MoAB) therapy has significantly improved the prognosis of patients with multiple myeloma. However, not all patients sustain durable responses. We aimed to describe the natural history of patients relapsed or refractory (R/R) to CD38 MoAB therapy. We performed a single-center, retrospective analysis of the clinical characteristics and outcomes of 81 patients with multiple myeloma who progressed after treatment with daratumumab. Our cohort was heavily pretreated, with a median of two lines prior to daratumumab and only 17 patients received daratumumab as a first line. A total of 38.2% had received a previous autologous stem cell transplantation (ASCT), and 61.7% had received both an immunomodulatory drug (IMID) and a proteasome inhibitor (PI). The median overall survival (OS) was 21 months for the global cohort but it decreased to 14 months for triple-class refractory patients and 5 months for penta-refractory patients. Most of the patients (83.9%) received treatment after daratumumab progression, in many cases with second generation IMID or PI, but seven patients were treated with anti-BCMA therapy and three patients received CART therapy within a clinical trial. In conclusion, patients R/R to daratumumab represent an unmet clinical need with poor prognosis and in need of incorporation of new treatments.

Corrigendum: Personalized monitoring of circulating tumor DNA with a specific signature of trackable mutations after chimeric antigen receptor T-cell therapy in follicular lymphoma patients.

[This corrects the article DOI: 10.3389/fimmu.2023.1188818.].

Multidisciplinary management in chronic myeloid leukemia improves cardiovascular risk measured by SCORE.

Introduction: Cardiovascular events are one of the main long-term complications in patients with chronic myeloid leukemia (CML) receiving treatment with tyrosine kinase inhibitors (TKIs). The proper choice of TKI and the adequate management of risk factors may reduce cardiovascular comorbidity in this population. Methods: This study evaluated the cardiovascular risk of a cohort of patients with CML at diagnosis and after follow-up in a specialized cardiovascular risk consultation. In order to do this, we performed data analysis from 35 patients who received TKIs and were referred to the aforementioned consultation between 2015 and 2018 at our center. Cardiovascular risk factors were analyzed separately, as well as integrated into the cardiovascular SCORE, both at diagnosis and at the last visit to the specialized consultation. Results: At the time of diagnosis, 60% had some type of risk factor, 20% had a high or very high risk SCORE, 40% had an intermediate risk, and 40% belonged to the low risk category. During follow-up, the main cardiovascular adverse event observed was hypertension (diagnosed in 8 patients, 23%). 66% of patients quit smoking, achieving control of blood pressure in 95%, diabetes in 50%, weight in 76%, and dyslipidemia in 92%. 5.7% of patients suffered a thrombotic event and a significant percentage of patients showed a reduction in their SCORE. Conclusion: Our study shows the benefit of controlling cardiovascular risk factors through follow-up in a specialized consultation for patients with CML treated with TKI.

Posttranslational splicing modifications as a key mechanism in cytarabine resistance in acute myeloid leukemia.

Despite the approval of several drugs for AML, cytarabine is still widely used as a therapeutic approach. However, 85% of patients show resistance and only 10% overcome the disease. Using RNA-seq and phosphoproteomics, we show that RNA splicing and serine-arginine-rich (SR) proteins phosphorylation were altered during cytarabine resistance. Moreover, phosphorylation of SR proteins at diagnosis were significantly lower in responder than non-responder patients, pointing to their utility to predict response. These changes correlated with altered transcriptomic profiles of SR protein target genes. Notably, splicing inhibitors were therapeutically effective in treating sensitive and resistant AML cells as monotherapy or combination with other approved drugs. H3B-8800 and venetoclax combination showed the best efficacy in vitro, demonstrating synergistic effects in patient samples and no toxicity in healthy hematopoietic progenitors. Our results establish that RNA splicing inhibition, alone or combined with venetoclax, could be useful for the treatment of newly diagnosed or relapsed/refractory AML.

Personalized monitoring of circulating tumor DNA with a specific signature of trackable mutations after chimeric antigen receptor T-cell therapy in follicular lymphoma patients.

Background: CART therapy has produced a paradigm shift in the treatment of relapsing FL patients. Strategies to optimize disease surveillance after these therapies are increasingly necessary. This study explores the potential value of ctDNA monitoring with an innovative signature of personalized trackable mutations. Method: Eleven FL patients treated with anti-CD19 CAR T-cell therapy were included. One did not respond and was excluded. Genomic profiling was performed before starting lymphodepleting chemotherapy to identify somatic mutations suitable for LiqBio-MRD monitoring. The dynamics of the baseline mutations (4.5 per patient) were further analyzed on 59 cfDNA follow-up samples. PET/CT examinations were performed on days +90, +180, +365, and every six months until disease progression or death. Results: After a median follow-up of 36 months, all patients achieved a CR as the best response. Two patients progressed. The most frequently mutated genes were CREBBP, KMT2D and EP300. Simultaneous analysis of ctDNA and PET/CT was available for 18 time-points. When PET/CT was positive, two out of four ctDNA samples were LiqBio-MRD negative. These two negative samples corresponded to women with a unique mesenteric mass in two evaluations and never relapsed. Meanwhile, 14 PET/CT negative images were mutation-free based on our LiqBio-MRD analysis (100%). None of the patients had a negative LiqBio-MRD test by day +7. Interestingly, all durably responding patients had undetectable ctDNA at or around three months after infusion. Two patients presented discordant results by PET/CT and ctDNA levels. No progression was confirmed in these cases. All the progressing patients were LiqBio-MRD positive before progression. Conclusion: This is a proof-of-principle for using ctDNA to monitor response to CAR T-cell therapy in FL. Our results confirm that a non-invasive liquid biopsy MRD analysis may correlate with response and could be used to monitor response. Harmonized definitions of ctDNA molecular response and pinpointing the optimal timing for assessing ctDNA responses are necessary for this setting. If using ctDNA analysis, we suggest restricting follow-up PET/CT in CR patients to a clinical suspicion of relapse, to avoid false-positive results.

Impact of IPSS-M implementation in real-life clinical practice.

Objectives: The IPSS-M is a recently published score for risk stratification in myelodysplastic syndromes (MDS), based on clinical and molecular data. We aimed to evaluate its relevance on treatment choice in a real-life setting. Methods: We retrospectively collected clinical, cytogenetic and molecular data from 166 MDS patients. We calculated IPSS-R and IPSS-M scores and compared Overall Survival (OS) and Leukemia Free Survival (LFS). We also analyzed which patients would have been affected by the re-stratification in terms of clinical management. Results: We found that 86.1% of the patients had at least one genetic alteration. The most frequent mutated genes were SF3B1 (25.9%), DNMT3A (16.8%) and ASXL1 (14.4%). IPSS-M re-stratified 48.2% of the patients, of which 16.9% were downgraded and 31.3% were upgraded. IPSS-M improved outcome prediction, with a Harrell's c-index of 0.680 vs 0.626 for OS and 0.801 vs 0.757 for LFS. In 22.2% of the cohort, the reclassification of the IPSS-M could potentially affect clinical management; 17.4% of the patients would be eligible for treatment intensification and 4.8% for treatment reduction. Conclusions: IPSS-M implementation in clinical practice could imply different treatment approaches in a significant number of patients. Our work validates IPSS-M in an external cohort and confirms its applicability in a real-life setting.

Comparison of the 2022 and 2017 European LeukemiaNet risk classifications in a real-life cohort of the PETHEMA group.

Next-Generation Sequencing is needed for the accurate genetic risk stratification of acute myeloid leukemia according to European LeukemiaNet (ELN) guidelines. We validated and compared the 2022 ELN risk classification in a real-life cohort of 546 intensively and 379 non-intensively treated patients. Among fit patients, those aged ≥65 years old showed worse OS than younger regardless risk classification. Compared with the 2017 classification, 14.5% of fit patients changed the risk with the 2022 classification, increasing the high-risk group from 44.3% to 51.8%. 3.7% and 0.9% FLT3-ITD mutated patients were removed from the favorable and adverse 2017 categories respectively to 2022 intermediate risk group. We suggest that midostaurin therapy could be a predictor for 3 years OS (85.2% with vs. 54.8% without midostaurin, P = 0.04). Forty-seven (8.6%) patients from the 2017 intermediate group were assigned to the 2022 adverse-risk group as they harbored myelodysplasia (MDS)-related mutations. Patients with one MDS-related mutation did not reach median OS, while patients with ≥2 mutations had 13.6 months median OS (P = 0.002). Patients with TP53 ± complex karyotype or inv(3) had a dismal prognosis (7.1 months median OS). We validate the prognostic utility of the 2022 ELN classification in a real-life setting providing supportive evidences to improve risk stratification guidelines.

Janus kinase inhibitor ruxolitinib in combination with nilotinib and prednisone in patients with myelofibrosis (RuNiC study): A phase Ib, multicenter study.

This phase Ib, non-randomized, open-label study evaluates the safety and tolerability of ruxolitinib in combination with nilotinib and prednisone in patients with naïve or ruxolitinib-resistant myelofibrosis (MF). A total of 15 patients with primary or secondary MF received the study treatment; 13 patients had received prior ruxolitinib treatment (86.7%). Eight patients completed seven cycles (53.3%) and six patients completed twelve cycles of treatment (40%). All the patients experienced at least one adverse event (AE) during the study (the most common AEs were hyperglycemia, asthenia, and thrombocytopenia), and 14 patients registered at least one treatment-related AE (the most common treatment-related AEs were hyperglycemia (22.2%; three grade 3 cases). Five treatment-related serious AEs (SAEs) were reported in two patients (13.3%). No deaths were registered throughout the study. No dose-limiting toxicity was observed. Four out of fifteen (27%) patients experienced a 100% spleen size reduction at Cycle 7, and two additional patients achieved a >50% spleen size reduction, representing an overall response rate of 40% at Cycle 7. In conclusion, the tolerability of this combination was acceptable, and hyperglycemia was the most frequent treatment-related AE. Ruxolitinib in combination with nilotinib and prednisone showed relevant clinical activity in patients with MF. This trial was registered with EudraCT Number 2016-005214-21.

Real-life disease monitoring in follicular lymphoma patients using liquid biopsy ultra-deep sequencing and PET/CT.

In the present study, we screened 84 Follicular Lymphoma patients for somatic mutations suitable as liquid biopsy MRD biomarkers using a targeted next-generation sequencing (NGS) panel. We found trackable mutations in 95% of the lymph node samples and 80% of the liquid biopsy baseline samples. Then, we used an ultra-deep sequencing approach with 2 · 10-4 sensitivity (LiqBio-MRD) to track those mutations on 151 follow-up liquid biopsy samples from 54 treated patients. Positive LiqBio-MRD at first-line therapy correlated with a higher risk of progression both at the interim evaluation (HRINT 11.0, 95% CI 2.10-57.7, p = 0.005) and at the end of treatment (HREOT, HR 19.1, 95% CI 4.10-89.4, p < 0.001). Similar results were observed by PET/CT Deauville score, with a median PFS of 19 months vs. NR (p < 0.001) at the interim and 13 months vs. NR (p < 0.001) at EOT. LiqBio-MRD and PET/CT combined identified the patients that progressed in less than two years with 88% sensitivity and 100% specificity. Our results demonstrate that LiqBio-MRD is a robust and non-invasive approach, complementary to metabolic imaging, for identifying FL patients at high risk of failure during the treatment and should be considered in future response-adapted clinical trials.

Molecular Landscape and Validation of New Genomic Classification in 2668 Adult AML Patients: Real Life Data from the PETHEMA Registry.

Next-Generation Sequencing (NGS) implementation to perform accurate diagnosis in acute myeloid leukemia (AML) represents a major challenge for molecular laboratories in terms of specialization, standardization, costs and logistical support. In this context, the PETHEMA cooperative group has established the first nationwide diagnostic network of seven reference laboratories to provide standardized NGS studies for AML patients. Cross-validation (CV) rounds are regularly performed to ensure the quality of NGS studies and to keep updated clinically relevant genes recommended for NGS study. The molecular characterization of 2856 samples (1631 derived from the NGS-AML project; NCT03311815) with standardized NGS of consensus genes (ABL1, ASXL1, BRAF, CALR, CBL, CEBPA, CSF3R, DNMT3A, ETV6, EZH2, FLT3, GATA2, HRAS, IDH1, IDH2, JAK2, KIT, KRAS, MPL, NPM1, NRAS, PTPN11, RUNX1, SETBP1, SF3B1, SRSF2, TET2, TP53, U2AF1 and WT1) showed 97% of patients having at least one mutation. The mutational profile was highly variable according to moment of disease, age and sex, and several co-occurring and exclusion relations were detected. Molecular testing based on NGS allowed accurate diagnosis and reliable prognosis stratification of 954 AML patients according to new genomic classification proposed by Tazi et al. Novel molecular subgroups, such as mutated WT1 and mutations in at least two myelodysplasia-related genes, have been associated with an adverse prognosis in our cohort. In this way, the PETHEMA cooperative group efficiently provides an extensive molecular characterization for AML diagnosis and risk stratification, ensuring technical quality and equity in access to NGS studies.

Genetic Alterations in Members of the Proteasome 26S Subunit, AAA-ATPase (PSMC) Gene Family in the Light of Proteasome Inhibitor Resistance in Multiple Myeloma.

For the treatment of Multiple Myeloma, proteasome inhibitors are highly efficient and widely used, but resistance is a major obstacle to successful therapy. Several underlying mechanisms have been proposed but were only reported for a minority of resistant patients. The proteasome is a large and complex machinery. Here, we focus on the AAA ATPases of the 19S proteasome regulator (PSMC1-6) and their implication in PI resistance. As an example of cancer evolution and the acquisition of resistance, we conducted an in-depth analysis of an index patient by applying FISH, WES, and immunoglobulin-rearrangement sequencing in serial samples, starting from MGUS to newly diagnosed Multiple Myeloma to a PI-resistant relapse. The WES analysis uncovered an acquired PSMC2 Y429S mutation at the relapse after intensive bortezomib-containing therapy, which was functionally confirmed to mediate PI resistance. A meta-analysis comprising 1499 newly diagnosed and 447 progressed patients revealed a total of 36 SNVs over all six PSMC genes that were structurally accumulated in regulatory sites for activity such as the ADP/ATP binding pocket. Other alterations impact the interaction between different PSMC subunits or the intrinsic conformation of an individual subunit, consequently affecting the folding and function of the complex. Interestingly, several mutations were clustered in the central channel of the ATPase ring, where the unfolded substrates enter the 20S core. Our results indicate that PSMC SNVs play a role in PI resistance in MM.

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.