SF3B1 is the most frequently mutated splicing factor in cancer. Mutations in SF3B1 likely confer clonal advantages to cancer cells but they may also confer vulnerabilities that can be therapeutically ...targeted. SF3B1 cancer mutations can be maintained in homozygosis in C. elegans, allowing synthetic lethal screens with a homogeneous population of animals. These mutations cause alternative splicing (AS) defects in C. elegans, as it occurs in SF3B1-mutated human cells. In a screen, we identified RNAi of U2 snRNP components that cause synthetic lethality with sftb-1/SF3B1 mutations. We also detected synthetic interactions between sftb-1 mutants and cancer-related mutations in uaf-2/U2AF1 or rsp-4/SRSF2, demonstrating that this model can identify interactions between mutations that are mutually exclusive in human tumors. Finally, we have edited an SFTB-1 domain to sensitize C. elegans to the splicing modulators pladienolide B and herboxidiene. Thus, we have established a multicellular model for SF3B1 mutations amenable for high-throughput genetic and chemical screens.
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.
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.
Summary
Background
Metabolically healthy obesity (MHO) shows a reduced risk compared with obese patients with adverse metabolic conditions. Lean people suffering some metabolic derangements also have ...non‐alcoholic fatty liver disease (NAFLD)‐related outcomes compared with non‐obese subjects with a few metabolic risks.
Aim
To define the impact of the metabolic status on the NAFLD‐related outcomes, beyond the presence of obesity.
Methods
We designed a multicentre cross‐sectional study, including 1058 biopsy‐proven NAFLD patients. Metabolically healthy status was strictly defined by the lack of metabolic risk factors (diabetes mellitus, low HDL, hypertriglyceridemia, arterial hypertension). Non‐alcoholic steatohepatitis (NASH) and significant fibrosis (F2‐F4) were identified by liver biopsy. Chronic kidney disease epidemiology collaboration equation was calculated for kidney function and the atherogenic index of plasma (AIP) for cardiovascular risk.
Results
Metabolically healthy (OR 1.88; P = 0.050) and unhealthy obesity (OR 3.47: P < 0.0001), and unhealthy non‐obesity (OR 3.70; P < 0.0001) were independently associated with NASH together with homeostatic model assessment (HOMA), ALT, and platelets. Significant fibrosis was more frequently observed in the presence of adverse metabolic conditions in obese (OR 3.89; P = 0.003) and non‐obese patients (OR 3.92; P = 0.002), and independently associated with platelets, albumin, ALT, HOMA, and age. The number of metabolic factors determined the risk of NASH and significant fibrosis. Glomerular filtration rate was lower in unhealthy (91.7 ± 18) than healthy metabolism (95.6 ± 17) (P = 0.007). AIP was higher in adverse metabolic conditions (P = 0.0001). Metabolically unhealthy non‐obesity showed higher liver damage (NASH 55.8% vs 42.4%; P < 0.05; significant fibrosis 31.7% vs 11.4%; P < 0.0001) and cardiovascular risk (P < 0.0001) than healthy obesity.
Conclusions
Metabolic unhealthy status showed a greater impact on NASH, significant fibrosis, kidney dysfunction, and atherogenic profile than obesity. However, metabolically healthy obesity was not a full healthy condition. We should focus our messages especially on patients with adverse metabolic conditions.
INTRODUCTION
Anemia is the most frequent cytopenia in lower-risk MDS. Erythropoietic-stimulating agents (ESAs) are commonly used in these patients. The use of ÒclassicalÓ parameters (EPO and ferritin ...levels) and the revised IPSS (IPSS-R) has been proposed1 (SantiniÕs score) to predict response to ESAs and overall survival (OS) among patients with lower risk MDS by IPSS and a favorable Nordic group score2.
OBJECTIVES
The main objective of the study was to evaluate overall response rate (ORR) to ESAs and OS according to the proposed SantiniÕs score in an independent and large cohort of anemic lower risk MDS patients receiving treatment with ESAs.
METHODS
Data from 530 anemic patients with low/int1 risk IPSS de novo MDS (according to FAB and WHO criteria) and sufficient follow-up data available were recorded in Spresas3 (SPanish Registry of Erythropoietic Stimulating Agents Study from GESMD). Two hundred and twenty six patients (42.6% of the patients) were selected according to specific criteria regarding the published SantiniÕs score1: Hb level </=10 g/dL, serum erythropoietin (EPO) <500 mU/mL and ESA (EPO alfa or B 40000-60000IU/week, or darbepoetin 150-300 ug/week). Applying 1 point to each of the following unfavorable variables for response to ESA, EPO>200mU/mL(=1), serum ferritin (SF) >350 ng/mL(=1) and IPSS-R very low=0, low=1, intermediate=2 and high=3) yielded a score ranging from 0 to 5. ESAs response rate and overall survival were analysed according to these score. Response to treatment was evaluated according to IWG 2006 response criteria and a multivariate logistic regression analysis was used to identify independent predictors of erythroid response (ER). OS were defined as the time between diagnosis and the corresponding event or last follow up (Feb 2015) and were analyzed using univariable and multivariable Cox proportional hazards regression methods.
RESULTS
Median age was 77 years (interquartile range IQR 25%-75%: 71-83 y), median Hb level at start of treatment was 10 g/dL (IQR25-75: 9-10), median EPO level was 90 (IQR25-75: 27,25-108) and median ferritin level was 338,5 (IQR25-75: 146,5-568,75). Among 139 patients with this data available, 85 patients (61,1%) were RBC transfusion dependent before ESAs treatment. Median time from diagnosis to ESAs treatment was 82 (IQR25-75: 27-353) days. According to the IPSS, 68.6% (N=155) and 31.4% (N=71) were in low and Int-1 risk groups, respectively. Regarding IPSS-R, 23% (N=52), 66.8% (N=151), 9.7% (N=22) and 0.4% (N=1) were in very low, low, intermediate and high risk, respectively. ORR to ESA treatment was 71.2% (N=161), with a median duration of response of 2.06 years. Prognosis factors of ER showed a trend toward to a higher ER among patients in the lower IPSS-R (P>0.05), low IPSS (p=0.039) and lower EPO levels (p<0.0001) while in multivariate analysis EPO level was confirmed as most significant variable associated to ER (p<0.001).
According to SantiniÕs score, 11.5%(N=26), 42.9%(N=97), 25.8%(N=81), 8%(N=18) and 1.8%(N=4) of the patients were in the 0, 1, 2 3 and 4 score. Erythroid response was better for patients in the lower scores, with response rates of 73.1%, 82.5%, 65.4%, 50% and 0%, for patients in 0, 1, 2, 3 and 4 score, respectively (p<0.001, figure 1). After a median follow up of 3.1 years, median OS from diagnosis was 4.99 years. Interestingly, median OS from diagnosis was clearly related to SantiniÕs score (10.7 years, 6.7y, 4.9y, 3.7y and 6.7y for patients with 0, 1, 2, 3, and 4 points, respectively, p=0.041, Figure 2) whereas median OS from start of ESAs showed also some trend (p=0.26).
CONCLUSIONS
The present study confirms that SantiniÕs score is useful to identify patients with a higher probability of response to ESAs and better OS among lower risk MDS patients with an expected favorable response to ESA according to Nordic group score.
Spresas study was partly supported by Janssen
1.-Santini et al, Blood 122(13), 2013.
2.-Hellstršm-Lindberg, Br J Haematol 120(6), 2003.
3.-D'ez Campelo, EHA 2015 meeting, P244.
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Díez Campelo:Novartis: Research Funding, Speakers Bureau; Janssen: Research Funding; Celgene: Research Funding, Speakers Bureau. Off Label Use: Use of erythropoietic stimulating agents for anemia in patients with myelodysplastic syndromes. Ramos:JANSSEN: Honoraria, Membership on an entity's Board of Directors or advisory committees; AMGEN: Consultancy, Honoraria; NOVARTIS: Consultancy, Honoraria; CELGENE: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Falantes:Celgene: Honoraria. Garcia:Celgene: Research Funding. Sanz:JANSSEN CILAG: Honoraria, Research Funding, Speakers Bureau.
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