We evaluated the prognostic relevance of several clinical and laboratory parameters in 226 Mayo Clinic patients with chronic myelomonocytic leukemia (CMML): 152 (67%) males and median age 71 years. At a median follow-up of 15 months, 166 (73%) deaths and 33 (14.5%) leukemic transformations were documented. In univariate analysis, significant risk factors for survival included anemia, thrombocytopenia, increased levels of white blood cells, absolute neutrophils, absolute monocyte count (AMC), absolute lymphocytes, peripheral blood and bone marrow blasts, and presence of circulating immature myeloid cells (IMCs). Spliceosome component (P=0.4) and ASXL1 mutations (P=0.37) had no impact survival. On multivariable analysis, increased AMC (>10 × 10(9)/l, relative risk (RR) 2.5, 95% confidence interval (CI) 1.7-3.8), presence of circulating IMC (RR 2.0, 95% CI 1.4-2.7), decreased hemoglobin (<10 g/dl, RR 1.6, 99% CI 1.2-2.2) and decreased platelet count (<100 × 10(9)/l, RR 1.4, 99% CI 1.0-1.9) remained significant. Using these four risk factors, a new prognostic model for overall (high risk, RR 4.4, 95% CI 2.9-6.7; intermediate risk, RR 2.0, 95% CI 1.4-2.9) and leukemia-free survival (high risk, RR 4.9, 95% CI 1.9-12.8; intermediate risk, RR 2.6, 95% CI 1.1-5.9) performed better than other conventional risk models and was validated in an independent cohort of 268 CMML patients.

Ruxolitinib is a JAK1/2 inhibitor that is effective in managing symptoms and splenomegaly related to myelofibrosis (MF). Unfortunately, many patients must discontinue ruxolitinib, at which time treatment options are not well defined. In this study, we investigated salvage treatment options and clinical outcomes among MF patients who received and discontinued ruxolitinib outside the context of a clinical trial. Among 145 patients who received ruxolitinib, 23 died while on treatment, 58 remained on treatment at time of analysis, leaving 64 people available for analysis. Development of cytopenias was the most common reason for discontinuation (38%) after median treatment time of 3.8 months (mo). The majority of patients received some form of salvage therapy after ruxolitinib discontinuation (n = 42; 66%), with allogeneic hematopoietic stem cell transplant (alloHSCT) (n = 17), being most commonly employed. Lenalidomide, thalidomide, hydroxyurea, interferon, and danazol were used with similar frequency. The response rate to salvage treatment was 26% (8 responses) and responses were most often seen with lenalidomide or thalidomide. Improved outcomes were observed in patients who underwent alloHSCT or received salvage therapy compared to those who did not receive additional therapy. Median overall survival (OS) after ruxolitinib discontinuation was 13 months. These findings show that salvage therapy can provide clinical responses after ruxolitinib discontinuation; however, these responses are rare and outcomes in this patient population are poor. This represents an area of unmet clinical need in MF.

PURPOSE Patients with myelodysplastic syndromes (MDS) have a survival that can range from months to decades. Prognostic systems that incorporate advanced analytics of clinical, pathologic, and molecular data have the potential to more accurately and dynamically predict survival in patients receiving various therapies. METHODS A total of 1,471 MDS patients with comprehensively annotated clinical and molecular data were included in a training cohort and analyzed using machine learning techniques. A random survival algorithm was used to build a prognostic model, which was then validated in external cohorts. The accuracy of the proposed model, compared with other established models, was assessed using a concordance (c)index. RESULTS The median age for the training cohort was 71 years. Commonly mutated genes included SF3B1, TET2, and ASXL1. The algorithm identified chromosomal karyotype, platelet, hemoglobin levels, bone marrow blast percentage, age, other clinical variables, seven discrete gene mutations, and mutation number as having prognostic impact on overall and leukemia-free survivals. The model was validated in an independent external cohort of 465 patients, a cohort of patients with MDS treated in a prospective clinical trial, a cohort of patients with paired samples at different time points during the disease course, and a cohort of patients who underwent hematopoietic stem-cell transplantation. CONCLUSION A personalized prediction model on the basis of clinical and genomic data outperformed established prognostic models in MDS. The new model was dynamic, predicting survival and leukemia transformation probabilities at different time points that are unique for a given patient, and can upstage and downstage patients into more appropriate risk categories.

Established prognostic tools in patients with myelodysplastic syndromes (MDS) were largely derived from untreated patient cohorts. Although azanucleosides are standard therapies for higher-risk (HR)-MDS, the relative prognostic performance of existing prognostic tools among patients with HR-MDS receiving azanucleoside therapy is unknown. In the MDS Clinical Research Consortium database, we compared the prognostic utility of the International Prognostic Scoring System (IPSS), revised IPSS (IPSS-R), MD Anderson Prognostic Scoring System (MDAPSS), World Health Organization-based Prognostic Scoring System (WPSS) and the French Prognostic Scoring System (FPSS) among 632 patients who presented with HR-MDS and were treated with azanucleosides as the first-line therapy. Median follow-up from diagnosis was 15.7 months. No prognostic tool predicted the probability of achieving an objective response. Nonetheless, all five tools were associated with overall survival (OS, P = 0.025 for the IPSS, P = 0.011 for WPSS and P < 0.001 for the other three tools). The corrected Akaike Information Criteria, which were used to compare OS with the different prognostic scoring systems as covariates (lower is better) were 4138 (MDAPSS), 4156 (FPSS), 4196 (IPSS-R), 4186 (WPSS) and 4196 (IPSS). Patients in the highest-risk groups of the prognostic tools had a median OS from diagnosis of 11 – 16 months and should be considered for up-front transplantation or experimental approaches.

While therapy-related (t)-myelodysplastic syndromes (MDS) have worse outcomes than de novo MDS (d-MDS), some t-MDS patients have an indolent course. Most MDS prognostic models excluded t-MDS patients during development. The performances of the International Prognostic Scoring System (IPSS), revised IPSS (IPSS-R), MD Anderson Global Prognostic System (MPSS), WHO Prognostic Scoring System (WPSS) and t-MDS Prognostic System (TPSS) were compared among patients with t-MDS. Akaike information criteria (AIC) assessed the relative goodness of fit of the models. We identified 370 t-MDS patients (19%) among 1950 MDS patients. Prior therapy included chemotherapy alone (48%), chemoradiation (31%), and radiation alone in 21%. Median survival for t-MDS patients was significantly shorter than for d-MDS (19 vs 46 months, P<0.005). All models discriminated survival in t-MDS (P<0.005 for each model). Patients with t-MDS had a significantly higher hazard of death relative to d-MDS in every risk model, and had inferior survival compared to patients with d-MDS within all risk group categories. AIC Scores (lower is better) were 2316 (MPSS), 2343 (TPSS), 2343 (IPSS-R), 2361 (WPSS) and 2364 (IPSS). In conclusion, subsets of t-MDS patients with varying clinical outcomes can be identified using conventional risk stratification models. The MPSS, TPSS and IPSS-R provide the best predictive power.