Search

Despite a detailed understanding of the genes mutated in myelodysplastic syndromes (MDS), diagnostic and treatment decisions for patients with MDS rely primarily on clinical and cytogenetic variables as considered by the Revised International Prognostic Scoring System (IPSS-R). Here we describe the recently developed Molecular IPSS (IPSS-M), a clinico-genomic risk stratification system that considers clinical, cytogenetic and genetic parameters; the implementation of a web portal to facilitate its adoption, a strategy to handle missing variables, and the worldwide utilization of the web calculator as a clinical support tool. The IPSS-M was trained on 2,957 clinically annotated diagnostic MDS samples profiled for mutations in 156 driver genes. To maximize the clinical applicability of the IPSS-M and account for missing genetic data (i.e genes missing from a sequencing panel), we implemented a strategy to calculate a risk score under three scenarios: best, worst and average. Last, we developed an online calculator as a standalone single-page web application using VueJs, and D3Js for the interactive visualizations, deployed through a CI/CD pipeline on AWS, where collection of anonymous usage analytics allows to track adoption and usability of the new proposed model. The model incorporates clinical, morphological, genetic variables informed by cytogenetics and constructed from the presence of oncogenic mutations in 31 genes. It delivers a unique risk score for each individual patient, as well as an assignment to one of six IPSS-M risk strata. Compared to the IPSS-R the IPSS-M re-stratified 46% of MDS patients. The model was validated in an external dataset of 754 MDS patients. We released an open-access IPSS-M web calculator available at https://mds-risk-model.com. By specifying the patient clinical and molecular profiles, the tool returns the patient-specific IPSS-M risk score and category, and the probability estimates over time for three clinical endpoints, i.e. leukemia free survival (LFS), overall survival, and incidence of leukemic transformation. Since its launch in June 2022, the calculator has been used by >6000 users in >75 countries, reaching a daily average of 100 users per day. Risks have been calculated for >45,000 patient profiles. 99.28% of the sessions initiated reach an IPSS-M score, suggesting that the calculator is intuitive and easy to use. We trained and validated the IPSS-M on 3,711 patients, a patient tailored risk stratification tool for patients with MDS that considers clinical, morphological and genetic variables inclusive of cytogenetics and mutations in one of 31 genes. The development of a web based tool was instrumental to the global dissemination of the model, enabling non-expert users to leverage the power of molecular biomarkers in risk stratification for patients with MDS. Citation Format: Elsa Bernard, Juan E. Arango Ossa, Heinz Tuechler, Peter L. Greenberg, Robert P. Hasserjian, Yasuhito Nannya, Sean M. Devlin, Maria Creignou, Philippe Pinel, Lily Monier, Juan S. Medina-Martinez, Dylan Domenico, Martin Jädersten, Ulrich Germing, Guillermo Sanz, Arjan A. van de Loosdrecht, Olivier Kosmider, Matilde Y. Follo, Felicitas Thol, Lurdes Zamora, Ronald F. Pinheiro, Andrea Pellagatti, Detlef Haase, Pierre Fenaux, Monika Belickova, Michael R. Savona, Virginia M. Klimek, Fabio P. Santos, Jacqueline Boultwood, Ioannis Kotsianidis, Valeria Santini, Francesc Solé, Uwe Platzbecker, Michael Heuser, Peter Valent, Kazuma Ohyashiki, Carlo Finelli, Maria Teresa Voso, Lee-Yung Shih, Michaela Fontenay, Joop H. Jansen, José Cervera, Norbert Gattermann, Benjamin L. Ebert, Rafael Bejar, Luca Malcovati, Mario Cazzola, Seishi Ogawa, Eva Hellström-Lindberg, Elli Papaemmanuil. Implementation and adoption of a web tool to support precision diagnostic and treatment decisions for patient with myelodysplastic syndromes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6168.

Tumor protein p53 (TP53) is the most frequently mutated gene in cancer1,2. In patients with myelodysplastic syndromes (MDS), TP53 mutations are associated with high-risk disease3,4, rapid transformation to acute myeloid leukemia (AML)5, resistance to conventional therapies6–8 and dismal outcomes9. Consistent with the tumor-suppressive role of TP53, patients harbor both mono- and biallelic mutations10. However, the biological and clinical implications of TP53 allelic state have not been fully investigated in MDS or any other cancer type. We analyzed 3,324 patients with MDS for TP53 mutations and allelic imbalances and delineated two subsets of patients with distinct phenotypes and outcomes. One-third of TP53-mutated patients had monoallelic mutations whereas two-thirds had multiple hits (multi-hit) consistent with biallelic targeting. Established associations with complex karyotype, few co-occurring mutations, high-risk presentation and poor outcomes were specific to multi-hit patients only. TP53 multi-hit state predicted risk of death and leukemic transformation independently of the Revised International Prognostic Scoring System (IPSS-R)11. Surprisingly, monoallelic patients did not differ from TP53 wild-type patients in outcomes and response to therapy. This study shows that consideration of TP53 allelic state is critical for diagnostic and prognostic precision in MDS as well as in future correlative studies of treatment response. Clinical sequencing across a large prospective cohort of patients with myelodysplasic syndrome uncovers distinct associations between the mono- and biallelic states of TP53 and clinical presentation

In the current World Health Organization (WHO)-classification, therapy-related myelodysplastic syndromes (t-MDS) are categorized together with therapy-related acute myeloid leukemia (AML) and t-myelodysplastic/myeloproliferative neoplasms into one subgroup independent of morphologic or prognostic features. Analyzing data of 2087 t-MDS patients from different international MDS groups to evaluate classification and prognostication tools we found that applying the WHO classification for p-MDS successfully predicts time to transformation and survival (both p

In the version of this article initially published, the affiliation provided for author Matteo Giovanni Della Porta (37Cancer Center, Humanitas Research Hospital & Humanitas University, Milan, Italy) was incorrect, and a second affiliation was missing. The correct affiliations are as follows (with subsequent affiliations renumbered accordingly): 37Humanitas Clinical and Research Center–IRCCS, Humanitas Cancer Center, Milan, Italy. 38Department of Biomedical Sciences, Humanitas University, Milan, Italy. The errors have been corrected in the HTML and PDF versions of the article.

In myelodysplastic syndromes (MDSs), the evolution of risk for disease progression or death has not been systematically investigated despite being crucial for correct interpretation of prognostic risk scores. In a multicenter retrospective study, we described changes in risk over time, the consequences for basal prognostic scores, and their potential clinical implications. Major MDS prognostic risk scoring systems and their constituent individual predictors were analyzed in 7212 primary untreated MDS patients from the International Working Group for Prognosis in MDS database. Changes in risk of mortality and of leukemic transformation over time from diagnosis were described. Hazards regarding mortality and acute myeloid leukemia transformation diminished over time from diagnosis in higher-risk MDS patients, whereas they remained stable in lower-risk patients. After approximately 3.5 years, hazards in the separate risk groups became similar and were essentially equivalent after 5 years. This fact led to loss of prognostic power of different scoring systems considered, which was more pronounced for survival. Inclusion of age resulted in increased initial prognostic power for survival and less attenuation in hazards. If needed for practicability in clinical management, the differing development of risks suggested a reasonable division into lower- and higher-risk MDS based on the IPSS-R at a cutoff of 3.5 points. Our data regarding time-dependent performance of prognostic scores reflect the disparate change of risks in MDS subpopulations. Lower-risk patients at diagnosis remain lower risk whereas initially high-risk patients demonstrate decreasing risk over time. This change of risk should be considered in clinical decision making.

The role of bone marrow dysplastic erythroid precursors (EP) in both MDS and AML has been the subject of considerable debate over the past several decades. We have analyzed a large series of adults with MDS and focused on whether any% of EP identified in the bone marrow aspirates of over 1400 patients selected from the Dusseldorf, Germany adult MDS Registry has prognostic relevance. The data was examined for varying% of blasts, the WHO prognostic MDS subtypes and the IPSS-R. We did not identify any adjustment of bone marrow blast percentage by%EP, incuding the 50% rule" developed by the FAB leukemia working group, to have a meaningful impact on outcome, either leukemic risk of progression or overall survival. There was a trend for a%EP

In patients with myelodysplastic syndromes (MDS) the impact of the percentage of erythroid precursors in the bone marrow has been the subject of considerable debate, especially with regard to prognosis. We examined the prognostic impact of the percentage of erythroid cells in the bone marrow (bmery) in 2453 primary untreated MDS patients in a retrospective multi-center analysis. Bmery were quantified in bone marrow smears at the time of diagnosis and were correlated with overall survival (OS) and AML evolution. We identified three distinct risk categories: " = 10% bmery" (poor), "11-25 or45% bmery" (intermediate), and "26-45% bmery" (good) with distinct OS of 23, 40 and 48 months, respectively. The percentage of bmery showed prognostic significance concerning OS (Dxy = 0.08, p 0.001) and AML-free survival (Dxy = 0.15, p 0.001). Considering the IPSS-R by stratification, the Dxy were 0.09 for survival, and 0.18 for transformation (p 0.001). Added to the IPSS-R, bmery enhances the prognostic power for both survival (Dxy = 0.39) and time to AML (Dxy = 0.59). Survival and time to AML differ in MDS according to the percentage of bmery. The best outcome was found in those who had normal or near normal bmery counts. Moreover, adding bmery as differentiating feature to the IPSS-R may enhance its prognostic significance.

Clinical features of myelodysplastic syndromes (MDS) could be influenced by many factors, such as disease intrinsic factors (e.g., morphologic, cytogenetic, molecular), extrinsic factors (e.g, management, environment), and ethnicity. Several previous studies have suggested such differences between Asian and European/USA countries. In this study, to elucidate potential differences in primary untreated MDS between Japanese (JPN) and Caucasians (CAUC), we analyzed the data from a large international database collected by the International Working Group for Prognosis of MDS (300 and 5838 patients, respectively). JPN MDS were significantly younger with more severe cytopenias, and cytogenetic differences: less del(5q) and more +1/+1q, -1/del(1p), der(1;7), -9/del(9q), del(16q), and del(20q). Although differences in time to acute myeloid leukemia transformation did not occur, a significantly better survival in JPN was demonstrated, even after the adjustment for age and FAB subtypes, especially in lower, but not in higher prognostic risk categories. Certain clinical factors (cytopenias, blast percentage, cytogenetic risk) had different impact on survival and time to transformation to leukemia between the two groups. Although possible confounding events (e.g., environment, diet, and access to care) could not be excluded, our results indicated the existence of clinically relevant ethnic differences regarding survival in MDS between JPN and CAUC patients. The good performance of the IPSS-R in both CAUC and JP patients underlines that its common risk model is adequate for CAUC and JP., Leukemia Research, 73, pp.51-57; 2018

Background In patients with Myelodysplastic Syndromes (MDS), TP53 mutations associate with high-risk presentation, complex karyotype, acute myeloid leukemia (AML) progression and poor response to hematopoietic stem cell transplantation. These associations highlight the relevance of TP53 as a prognostic and predictive biomarker. Consistent with its role as a tumor suppressor, bi-allelic targeting of the TP53 locus is a frequent but not an obligatory event. Despite the central role of TP53 in MDS, the clinical implications of TP53 mutations in the context of allelic state have not been extensively studied. Methods Under the auspices of the International Working Group for Prognosis in MDS, we sequenced a representative cohort of 3,324 peri-diagnosis MDS patients on a next generation sequencing (NGS) panel optimized for myeloid disease. Conventional G-banding analysis (CBA) was available for 2,931 patients. Focal (~3MB) gains and deletions and regions of NGS-derived copy-neutral loss of heterozygosity (cnLOH) were assessed using an in-house algorithm CNACS. Putative oncogenic mutations in TP53 were characterized by consideration of normal controls and established population databases. A validation cohort of 1,120 samples with independent but comparable molecular and clinical annotation was sourced from a compendium of Japanese MDS data to include JALSG-MDS212, JMDP registry, and regional registries. Results NGS-derived ploidy alterations and CBA show a high genome-wide concordance. From NGS profiles, 11% of patients (n=360) are subject to cnLOH, of which 80 target the TP53 locus. We characterize 490 TP53 mutations in 380 patients, representing 11% of the cohort. Amongst those patients, 22% (n=85) and 21% (n=78) have a deletion or a cnLOH involving the TP53 locus, respectively. Taken together, these segregate patients into two TP53 states: a mono-allelic state where one wild type allele remains (33% of TP53 mutated patients, n=126); and a multi-hit state where TP53 is altered multiple times by either mutations, deletions or cnLOH (67% of TP53 mutated patients, n=254). We find that TP53 state shapes clinical presentation and outcomes. Mono-allelic TP53 patients present with more favorable disease than multi-hit TP53 patients: they are less cytopenic, have lower bone marrow blasts (median 4 vs. 9%, p Conclusions TP53 is a natural candidate for incorporation in molecularly informed risk stratification schemas (molecular IPSS-R). We show that TP53 state rather than mutation alone is an independent diagnostic and prognostic biomarker in MDS. We propose that ascertainment of TP53 state is critical in prospective clinical sequencing for risk estimation, disease monitoring and future correlative research into predictors of response to established and investigational therapies. Disclosures Bernard: Celgene: Research Funding. Hasserjian:Jazz Pharmaceuticals: Consultancy; Promedior, Inc.: Consultancy. Germing:Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Jazz Pharmaceuticals: Honoraria; Amgen: Honoraria. Cargo:Celgene: Research Funding. Santini:Acceleron: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Menarini: Membership on an entity's Board of Directors or advisory committees; Johnson & Johnson: Honoraria; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Honoraria, Membership on an entity's Board of Directors or advisory committees. Kotsianidis:Celgene: Research Funding. Takaori-Kondo:Pfizer: Honoraria; Chugai: Research Funding; Janssen: Honoraria; Kyowa Kirin: Research Funding; Takeda: Research Funding; Ono: Research Funding; Bristol-Myers Squibb: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria. Savona:Selvita: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Karyopharm Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; AbbVie: Membership on an entity's Board of Directors or advisory committees; Boehringer Ingelheim: Patents & Royalties; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Incyte Corporation: Membership on an entity's Board of Directors or advisory committees, Research Funding; TG Therapeutics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Sunesis: Research Funding. Ades:Takeda: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Silence Therapeutics: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Helsinn Healthcare: Membership on an entity's Board of Directors or advisory committees; Agios: Membership on an entity's Board of Directors or advisory committees; Jazz: Membership on an entity's Board of Directors or advisory committees; Abbvie: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding. Neuberg:Pharmacyclics: Research Funding; Madrigal Pharmaceuticals: Equity Ownership; Celgene: Research Funding. Stevenson:Celgene: Research Funding. Fenaux:Jazz: Honoraria, Research Funding; Astex: Honoraria, Research Funding; Aprea: Research Funding; Celgene Corporation: Honoraria, Research Funding. Platzbecker:Novartis: Consultancy, Honoraria; Abbvie: Consultancy, Honoraria; Celgene: Consultancy, Honoraria. Heuser:Synimmune: Research Funding; Bayer Pharma AG, Berlin: Research Funding. Valent:Blueprint: Research Funding; Pfizer: Honoraria; Celgene: Honoraria; Novartis: Consultancy, Honoraria, Research Funding; Deciphera: Honoraria, Research Funding. Miyazaki:Nippon-Shinyaku: Honoraria; Dainippon-Sumitomo: Honoraria; Otsuka: Honoraria; Chugai: Research Funding; Novartis: Honoraria; Kyowa-Kirin: Honoraria. Finelli:Novartis: Consultancy, Speakers Bureau; Celgene Corporation: Consultancy, Research Funding, Speakers Bureau; Janssen: Consultancy, Speakers Bureau. Atsuta:CHUGAI PHARMACEUTICAL CO., LTD.: Honoraria; Kyowa Kirin Co., Ltd: Honoraria. Gattermann:Novartis: Honoraria; Takeda: Research Funding; Alexion: Research Funding. Ebert:Broad Institute: Other: Contributor to a patent filing on this technology that is held by the Broad Institute.; Celgene: Research Funding; Deerfield: Research Funding. Bejar:Celgene: Consultancy; Takeda Pharmaceuticals: Research Funding; AbbVie/Genentech: Consultancy, Honoraria; Astex/Otsuka: Consultancy; Modus Outcomes: Consultancy; Daiichi-Sankyo: Consultancy. Greenberg:Notable Labs: Research Funding; Celgene: Research Funding; Genentech: Research Funding; H3 Biotech: Research Funding; Aprea: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees. Ogawa:Qiagen Corporation: Patents & Royalties; ChordiaTherapeutics, Inc.: Consultancy, Equity Ownership; RegCell Corporation: Equity Ownership; Dainippon-Sumitomo Pharmaceutical, Inc.: Research Funding; Kan Research Laboratory, Inc.: Consultancy; Asahi Genomics: Equity Ownership. Papaemmanuil:Celgene: Research Funding.

To the editor: The recent article by Arber et al[1][1] detailing the 2016 revision of the World Health Organization (WHO) classification of myeloid malignancies and AML was timely and germane. Regarding myelodysplastic syndromes (MDS), the authors indicate diagnostic criteria that include levels of

The current classification system for Myelodysplastic Syndromes lumps all therapy-related (tMDS) into one subgroup assuming all tMDS had the same poor prognosis. We have put together a database including 2032 patients with a diagnosis of tMDS from several different IWG centers and the MDS clinical research consortium. With the idea of developing an individual scoring system for tMDS, we decided to start by optimizing the cytogenetic part of the IPSSR. First, we did an extensive review of karyotypes. Finally, 1245 patients had complete data and correct ISCN formula to be used for score development. We could show regarding karyotypes there are very limited differences between primary and tMDS. Mainly the distribution of risk groups differs with complex occurring more (37%) and normal karyotypes occurring less frequent, although still accounting for 30%. There are few exceptions that are relatively special for tMDS, like translocations including 11q23. A few karyotypes are less frequent; therefore, we could not evaluate the value of IPSS-R cytogenetics for all karyotypes. However, if we apply IPSS-R cytogenetics to our patient cohort, we can separate 5 different risk groups as in pMDS. We tested the performance of the score by using the Dxy. As main endpoint we chose transformation-free survival giving better information about the severity of the disease compared to the single endpoints survival and AML transformation that where calculated for completeness as well. The Dxy for the IPSS-R cytogenetic part is 0.31 for transformation-free survival. This indicates an effective prognostic performance although not as good as in pMDS. Several attempts were done to develop a tMDS specific cytogenetic score. The best draft scoring component achieves a Dxy of 0.33. Counting the number of aberrations achieves a score of 0.30. If normal clone present or not is added, the performance of this very simple model is improved with a Dxy of 0.32. As we could show, all these different approaches lead to a comparable performance. One can argue that still regarding a few karyotypes the prognostic impact is slightly different between p and tMDS (e.g. +8). On the other hand, the most practical approach seems to be to adopt the original cytogenetic part of the IPSS-R for further score development since clinicians do not need to use different scoring systems for different MDS subtypes. While the final analyses for the development of a tMDS specific risk score are currently under way, extensive calculations regarding the performance of different scores like WHO- (Dxy 0.24), FAB-classification (Dxy 0.19), WPSS-R (Dxy 0.35), IPSS-R (Dxy 0.37), and IPSS-R+age (Dxy 0.36), show all these systems can separate different risk groups within our cohort. However, these results also show an inferior performance of the scoring systems in t compared to pMDS. There are multiple possible reasons for this. The most important seem to be tMDS patients are often not cured from the primary disease and its disease specific risk of death should ideally be considered. Unfortunately, we don't have that data. And second, we included treated as well as untreated patients. It seems not to be feasible otherwise since the selection bias for old unfit patients would be unacceptable. We could show already in pMDS that the score performances are considerably worse if we analyze treated patients and the score performance in our cohort is better if limited to untreated patients. To conclude, we can say existing classification and scoring systems work in tMDS and can separate groups with clearly different risk for death and transformation. Although we could not develop a tMDS specific cytogenetic score this could be seen positively since it underlines tMDS do not seem to be much different regarding disease specific risk. This should initiate a discussion of a revision of the WHO-classification and encourage clinicians to use the existing tools for risk assessment and treatment decisions. A simple solution could be to use the WHO classification for pMDS and precede each subgroup with a t, like tMDS-SLD, and so on. Such an approach would be of importance for patients falsely classified as tMDS. After all this classification is done according to anamnestic information only and sporadic cases cannot be excluded. Until now, in the first analyzes performed with the final tMDS-database, we did not find any indication that risk factors established in pMDS would lose or change their meaning in tMDS. Figure. Figure. Disclosures Komrokji: Celgene: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Novartis: Honoraria, Speakers Bureau. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Opsona: Membership on an entity's Board of Directors or advisory committees. List:Celgene: Research Funding. Roboz:Orsenix: Consultancy; Eisai: Consultancy; Novartis: Consultancy; Celltrion: Consultancy; Astex Pharmaceuticals: Consultancy; Argenx: Consultancy; Janssen Pharmaceuticals: Consultancy; Jazz Pharmaceuticals: Consultancy; Argenx: Consultancy; Janssen Pharmaceuticals: Consultancy; Pfizer: Consultancy; Cellectis: Research Funding; Daiichi Sankyo: Consultancy; Sandoz: Consultancy; Otsuka: Consultancy; Daiichi Sankyo: Consultancy; Eisai: Consultancy; Pfizer: Consultancy; Roche/Genentech: Consultancy; Novartis: Consultancy; Celltrion: Consultancy; Celgene Corporation: Consultancy; Cellectis: Research Funding; Orsenix: Consultancy; Aphivena Therapeutics: Consultancy; Otsuka: Consultancy; Jazz Pharmaceuticals: Consultancy; Sandoz: Consultancy; Roche/Genentech: Consultancy; Aphivena Therapeutics: Consultancy; AbbVie: Consultancy; Bayer: Consultancy; Bayer: Consultancy; Astex Pharmaceuticals: Consultancy; Celgene Corporation: Consultancy; AbbVie: Consultancy. Döhner:Jazz: Consultancy, Honoraria; Astex Pharmaceuticals: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Seattle Genetics: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Research Funding; Astex Pharmaceuticals: Consultancy, Honoraria; AROG Pharmaceuticals: Research Funding; Pfizer: Research Funding; Sunesis: Consultancy, Honoraria, Research Funding; Celator: Consultancy, Honoraria; Agios: Consultancy, Honoraria; Celator: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Astellas: Consultancy, Honoraria; Bristol Myers Squibb: Research Funding; Amgen: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Pfizer: Research Funding; Novartis: Consultancy, Honoraria, Research Funding; AbbVie: Consultancy, Honoraria; Sunesis: Consultancy, Honoraria, Research Funding; Celgene: Consultancy, Honoraria, Research Funding; Jazz: Consultancy, Honoraria; AbbVie: Consultancy, Honoraria. Valent:Pfizer: Honoraria; Novartis: Honoraria; Incyte: Honoraria. Platzbecker:Celgene: Research Funding. Lübbert:TEVA: Other: Study drug; Celgene: Other: Travel Support; Cheplapharm: Other: Study drug; Janssen: Honoraria, Research Funding. Díez-Campelo:Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Stauder:Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Teva: Research Funding. Germing:Janssen: Honoraria; Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding.

Although 20% of children and adolescents in Europe suffer from overt mental health problems, their illness-related service utilisation is often unknown. If at all, existing research has only addressed the health care sector while services requirements in mental health care go far beyond the health care system, including the social, the educational and the criminal justice system.This paper aims at describing the service contact patterns of children and adolescents within and outside the health care sector before they are admitted to a child and adolescent mental health hospital. Additionally, we evaluate the private out-of-pocket payments that occur for primary carers.A cohort of consecutive admissions to a child and adolescent hospital in Austria was prospectively analysed. We collected data on service use and out-of-pocket expenses before hospital admission from primary carers through face-to-face interviews using an adapted version of the European Child and Adolescent Mental Health Service Receipt Inventory (EU-CAMHSRI). Clinical data came from validated questionnaires (CBCL, YSR) and from the anamnestic documentation.Ninety percent from a cohort of 441 patients had some contact with services or took medication before they were admitted to hospital. Most often, services in the health care outpatient setting were used. Outside of the health care system, support in school, as well as counselling services, were used most frequently, whereas the persons hardly sought support in living or employment. Roughly 32,400 per 100 patients was spent privately, yet these out-of pocket expenses were very unevenly distributed. Service use and out-of-pocket spending increased with social status and were gender-specific. The more severe external behaviour symptoms were, the more non-health care services were used.Mentally ill children and adolescents use a broad range of services across sectors before admission to hospital. Service use is associated with specific symptoms of the disease, yet not with the diagnosis. For some carers, this is linked to considerable financial burden because many of those services are only partly publicly funded or are not part of the health sector. A limitation of the study is the uncertainty of self-reports. Furthermore, we do not know whether the services used were needs-based and effective, and what the utilisation patterns of non-hospitalised children and adolescents are.Mental health policy for children and adolescents in Austria needs to focus on how to organise a needs-oriented and coordinated services mix across different sectors that is equally accessible regardless of the patients' socio-economic background.To support planning, further research on the factors that predict service use and on the cost-effectiveness of services is required.

A risk-adapted treatment strategy is mandatory for myelodysplastic syndromes (MDS). We refined the World Health Organization (WHO)-classification-based Prognostic Scoring System (WPSS) by determining the impact of the newer clinical and cytogenetic features, and we compared its prognostic power to that of the revised International Prognostic Scoring System (IPSS-R). A population of 5326 untreated MDS was considered. We analyzed single WPSS parameters and confirmed that the WHO classification and severe anemia provide important prognostic information in MDS. A strong correlation was found between the WPSS including the new cytogenetic risk stratification and WPSS adopting original criteria. We then compared WPSS with the IPSS-R prognostic system. A highly significant correlation was found between the WPSS and IPSS-R risk classifications. Discrepancies did occur among lower-risk patients in whom the number of dysplastic hematopoietic lineages as assessed by morphology did not reflect the severity of peripheral blood cytopenias and/or increased marrow blast count. Moreover, severe anemia has higher prognostic weight in the WPSS versus IPSS-R model. Overall, both systems well represent the prognostic risk of MDS patients defined by WHO morphologic criteria. This study provides relevant in formation for the implementation of risk-adapted strategies in MDS.

The International Prognostic Scoring Sytem (IPSS) is an important standard for ssessing prognosis of primary untreated adult patients with myelodysplastic syndromes (MDS). To refine the IPSS, MDS patient databases from international institutions were coalesced to assemble a much larger combined database (Revised-IPSS [IPSS-R], n = 7012, IPSS, n = 816) for analysis. Multiple statistically weighted clinical features were used to generate a prognostic categorization model. Bone marrow cytogenetics, marrow blast percentage, and cytopenias remained the basis of the new system. Novel components of the current analysis included: 5 rather than 3 cytogenetic prognostic subgroups with specific and new classifications of a number of less common cytogenetic subsets, splitting the low marrow blast percentage value, and depth of cytopenias. This model defined 5 rather than the 4 major prognostic categories that are present in the IPSS. Patient age, performance status, serum ferritin, and lactate dehydrogenase were significant additive features for survival but not for acute myeloid leukemia transformation. This system comprehensively integrated the numerous known clinical features into a method analyzing MDS patient prognosis more precisely than the initial IPSS. As such, this IPSS-R should prove beneficial for predicting the clinical outcomes of untreated MDS patients and aiding design and analysis of clinical trials in this disease.

Purpose The International Prognostic Scoring System (IPSS) remains the most commonly used system for risk classification in myelodysplastic syndromes (MDSs). The IPSS gives more weight to blast count than to cytogenetics. However, previous publications suggested that cytogenetics are underweighted in the IPSS. Here we investigate the prognostic impact of cytogenetic subgroups compared with that of bone marrow blast count in a large, multicentric, international patient cohort. Patients and Methods In total, 2,351 patients with MDS who have records in the German-Austrian and the MD Anderson Cancer Center databases were included and analyzed in univariate and multivariate models regarding overall survival and risk of transformation to acute myeloid leukemia (AML). The data were analyzed separately for patients treated with supportive care without specific therapy, with AML-like chemotherapy, or with other therapy regimens (low-dose chemotherapy, demethylating agents, immune modulating agents, valproic acid, and cyclosporine). Results The prognostic impact of poor-risk cytogenetic findings (as defined by the IPSS classification) on overall survival was as unfavorable as an increased (> 20%) blast count. The hazard ratio (compared with an abnormal karyotype or a bone marrow blast count < 5%) was 3.3 for poor-risk cytogenetics, 4.8 for complex abnormalities harboring chromosomes 5 and/or 7, and 3.1 for a blast count of 21% to 30% (P < .01 for all categories). The predictive power of the IPSS cytogenetic subgroups was unaffected by type of therapy given. Conclusion The independent prognostic impact of poor-risk cytogenetics on overall survival is equivalent to the impact of high blast counts. This finding should be considered in the upcoming revision of the IPSS.

Background Disease karyotype is the parameter with the strongest prognostic impact in the IPSS-R. Around 50% of MDS patients have a normal karyotype. Conventional G-banding cytogenetics has a resolution of 5-10 Mb. SNP microarrays is a genomic array with a higher resolution than cytogenetics and allows the detection of small copy number alterations (CNA) and absence of heterozygosity (AOH). Our hypothesis is that in MDS patients with normal karyotype small cryptic alterations might exist, undetectable by conventional karyotyping, but with an impact on patient's outcome. Our aim is to study a series of MDS patients with normal karyotype belonging to the IWG-PM consortium (MDS Foundation) by SNP microarrays. Herein, we describe the preliminary findings as recruitment is ongoing and complete results will be presented at the meeting. Patients and methods We have recruited microarray data of 125 MDS patients with normal karyotype from centers belonging to the IWG-PM of the MDS Foundation. All cases included were studied with SNP array from Affymetrix®. We collected clinical, biological and microarray data and we studied their impact on overall survival (OS) and acute myeloid leukemia (AML) evolution in 73 patients. The median age was 67.8 years (21.8-92.1), 51.2% were male and the most frequent subtype according 2008 WHO classification was refractory cytopenia with multilineage dysplasia (41.6%), followed by refractory anemia with excess of blasts-1 (RAEB-1) and -2, 18.4% and 16.8%, respectively. Median values at diagnosis were, for hemoglobin: 9.3g/dL, leukocytes count: 3.6x109/L, platelet count: 139x109/L, percentage of polymorphonuclear cells: 47%, and bone marrow blasts: 3.6%. Nineteen patients were analyzed with their matched germ-line control sample. The statistical analysis was performed with SPSS and R software. As level of significance, 5% (two-sided) was chosen. No adjustment for multiple testing was applied. Results Fifty nine cases were found to be altered by microarrays (47.2%), with a median number of alterations of 1 (range: 1-8). CNA were detected in 36.8% of cases, in 8 cases CNA were larger than 5 Mb. AOH were detected in 18.4% of cases. The median size of the affected genome by CNA was 116 Kb, adding up CNA plus AOH was 24745.05Kb. There were significant differences, in distribution of FAB diagnosis and median percentage of bone marrow blasts, in patients altered by SNP arrays in comparison to patients with normal findings by this technique. The impact on the outcome has been studied in 73 patients. The median OS was 35.6 months. The results of SNP array analysis showed that alterations detected by microarrays had no significant impact on outcome (P=0.237 for OS and P=0.807 for AML evolution), but the median OS is higher in cases with a "real" (neither visible by cytogenetics nor submicroscopic alterations) normal karyotype (53.48 vs. 25.90 months). Of note, the presence of AOH has a strong impact on OS (53.48 vs. 16.13 months, P=0.004), less on AML (P=0.086); also its size, greater than 25Mb, has an impact on OS (P=0.012) and AML evolution (P=0.050). To compensate for differing risk distributions, we performed analyses adjusting for IPSS-R. Adjusted for risk, the microarray result adds prognostic power on OS (P=0.010) and AML evolution (P=0.059). As it was observed, the presence of AOH had prognostic impact on OS (P=0.008) and AML evolution (P=0.017). The affected genome size showed a significant impact on OS (P=0.006) and AML (P=0.024), four categories were defined: 0 Kb, >0-500 Kb, >500-20000 Kb and >20000 Kb. Conclusions MDS patients with normal karyotypes and alterations detected by genomic microarrays have lower OS and shorter time to AML compared to those without CNA. While not significant in marginal analysis, it can be demonstrated if adjustment for IPSS-R is performed. Alterations and, particularly, AOH have an independent prognostic impact and add prognostic power to IPSS-R. Acknowledgments Financial support: This work was supported, in part, by a grant from the Instituto de Salud Carlos III, Ministerio de Sanidad y Consumo, Spain (PI 11/02010); Red Temática de Investigación Cooperativa en Cáncer (RTICC, FEDER) (RD12/0036/0044); 2014 SGR225 (GRE) Generalitat de Catalunya; Fundació Internacional Josep Carreras, Obra Social “La Caixa”, Celgene Spain and NHRI-EX103-10003NI,Taiwan. Special thanks to Dr. Al-Ali, Dr. Bäsecke, members of Grupo Español de SMD (GESMD) and MDS Foundation. Disclosures No relevant conflicts of interest to declare.

14 Revised International Prognostic Scoring System (IPSS-R), developed by the International Prognostic Working Group for Prognosis in MDS (IWG-PM) P. Greenberg, H. Tuechler, J. Schanz, F. Sole, J.M. Bennett, G. Garcia-Manero, A. Levis, L. Malcovati, M. Cazzola, G. Sanz, J. Cermak, C. Fonatsch, M. LeBeau, M. Slovak, O. Krieger, M. Luebbert, S. Magalhaes, Y. Miyazaki, M. Pfeilstocker, M. Sekeres, J. Maciejewski, R. Stauder, S. Tauro, A. van de Loosdrecht, U. Germing, P. Fenaux, D. Haase. Hematology, Stanford University Cancer Center, Stanford, CA, USA; Hanusch Hospital, Vienna, Austria; University of Goettingen, Goettingen, Germany; Hospital del Mar, Barcelona, Spain; Wilmot Cancer Center, Rochester, NY, MD Anderson Cancer Center, Houston, TX, USA; Arrigo Hospital, Alessandria, University of Pavia Medical School, Pavia, Italy; Hematology, Hospital Universitario La Fe, Valencia, Spain; Institute of Hematology and Blood Transfusion, Prague, Czech Republic; Medical University of Vienna, Vienna, Austria; Hematology/Oncology, University of Chicago Cancer Research Center, Chicago, IL, Quest Diagnostics, Chantilly, VA, USA; Elisabethinen Hospital, Linz, Austria; University of Freiburg, Freiburg, Germany; Federal University of Ceara, Fortaleza, Brazil; Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan; Cleveland Clinic Foundation, Cleveland, OH, USA; Medical University of Innsbruck, Innsbruck, Austria; University of Dundee, Dundee, UK; VU Medical Center, Amsterdam, The Netherlands; Hematology/Oncology, University of Duesseldorf, Duesseldorf, Germany; Hopital Avicennes, Paris, France

Abstract 2772 Poster Board II-748 Introduction: The IPSS-Score, published by Greenberg et al. (1997), defines the gold standard in risk stratification of patients with MDS. Since its implementation in 1997 based on 816 patients with primary MDS, the knowledge concerning the prognostic impact of distinct abnormalities increased extensively. The present study proposes a new and comprehensive cytogenetic scoring system based on an international data collection of 3803 patients, originating from the German-Austrian (GA)-, the International Risk analysis workshop (IMRAW)- and the Spanish Cytogenetics working group (GCECGH). Additionally, 53 cases of rare abnormalities were contributed by the International Cytogenetics Working Group of the MDS Foundation (ICWG), resulting in total number of 3856 pts. As compared to our previous reports, the data set was substantially enlarged by adding the GCECGH cases and data quality was improved by updating the clinical and survival data; allowing the analysis of the prognostic impact for isolated abnormalities exclusively to assure a maximum accurateness. Furthermore, multivariate analysis was refined by including peripheral cytopenias. Materials and Methods: Inclusion criteria were defined as follows: Primary MDS, age >=16, and bone marrow blasts Results: In total, 20 cytogenetic subgroups matching the inclusion criteria were detected. Abnormalities were grouped as normal (n=1522, 52.5% of all cases), single (1 abnormality), double (2 abnormalities) or complex (>=3 abnormalities). Single abnormalities found were: del(5q) (176, 6.1%); -7/7q- (59, 2.0%); +8 (130, 4.5%); del(20q) (48, 1.7%), -Y (46, 2.1%); der(1;7)(q10;p10)/t(1;7)(var;var) (10, 0.3%); der(3)(q21)/der(3)(q26) (10, 0.3%); del(11q) (19, 0.7%); del(12p) (17, 0.6%); i(17)(q10) (11, 0.4%); +19 (10, 0.3%), +21 (10, 0.3%) and any other single (150, 5.2%). Double abnormalities were stratified into 3 subgroups: double including del(5q) (45, 1.6%); double including -7/7q- (31; 1.1%) and any other double (98, 3.4%). As reported (Haase et al. Blood 2008), complex karyotypes were sub-divided into 2 groups: Karyotypes with 3 abnormalities (59, 2.0%) vs. >3 abnormalities (188, 6.5%). Finally, 20 pts. (0.7%) displayed cytogenetically unrelated clones. According to OS and AML-t, abnormalities were classified to 4 prognostic subgroups: good (normal, del(5q), double incl. del(5q), der(1;7)(q10;p10)/t(1;7)(var;var), del(11q), del(12p), +19, del(20q), -Y); int-1 (any other double, +8, i(17)(q10), +21, any other single, independent clones); int-2 (double incl. -7/7q-, der(3)(q21)/der(3)(q26), -7/7q-, complex 3 abnormalities) and poor (complex >3 abnormalities). Median survival was 50.6 months for good (n=1936), 25.7 months for int-1 (n=451), 16.0 months for int-2 (n=177) and 5.7 months for poor (n=188) and AML-t was 71.9 months for good (n=1681), 14.7 months for int-1 (n=384), 9.8 months for int-2 (n=148) and 3.4 months for poor (n=159). Differences in OS and AML-t were highly significant (p Conclusions: In summary, we were able to generate a solid database for a revised cytogenetic scoring system, which can serve as the cytogenetic model for the upcoming revision of the IPSS. Acknowledgments: The authors like to thank the MDS-Foundation for its support. Disclosures: No relevant conflicts of interest to declare.

In Myelodysplastic Syndromes (MDS) the International Prognostic Scoring System (IPSS) is the golden standard to assess the patients risk profile. Although only three parameters were included in the score, namely the medullary blast cell count, the karyotype and the amount of cytopenias, multiple risk factors, among others age and gender, were discussed in its original publication (Greenberg et al., Blood89,6,1997:p2079–88). Their additional predictive importance was stated there, but not formally quantified. In a retrospective multicenter analysis (for sample details see below) the modulating impact of age, gender and FAB classification on the IPSS was studied and individualized score values of the IPSS for each age*gender*FAB group were estimated (see table below). While without consideration of age, gender and FAB classification the risk of a patient at the four IPSS levels is best represented by the values 0 to 3, these values vary considerably between age*gender*FAB subgroups, as e.g. an IPSS ‘low’ scoring female patient 66 years), gender and FAB classification (RA/RARS vs RAEB/RAEBT/CMML), as well as interactions between IPSS and gender, and age and FAB, expressed by the formula RR = ipss*.44 + sex*−.27+age66*.47+fab_gr*.41+(ipss:sex)*.36+(age66:fab_gr)−.60. The IPSS/gender interaction shows that the IPSS differentiates much better in women than in men, the age/FAB interaction underlines that the FAB classification has more relevance in younger as compared to older patients. Sample details: In a series of 897 primary MDS patients treated with supportive care only, median survival was 47 months and the median follow up 56 months. The median age was 68 years. According to the FAB classification the distribution and the median survival duration were as follows: RA 341 pts, 70 mo; RARS 152 pts, 77 mo; RAEB 172 pts 20 mo; CMML 148 pts, 25 mo; RAEBT 84 pts, 11 mo. According to the IPSS the distribution and the median survival duration were as follows: low 268 pts (29,9%), 86 mo; int-1 285 pts (31,8%), 55 mo; int-2 158 pts (17,6%), 23 mo; high 186 pts (20,7%), 13 mo. These results are concordant with other published data, especially the original publication of the IPSS. Conclusion: The reported table and formula are useful and convenient to substantially enhance the precision of the IPSS in predicting the risk of MDS patients. IPSS Low Int-1 Int-2 High n.a. not applicaple all patients 0.0 1.0 2.0 3.0 male,66,RA/RARS 1.3 1.9 2.5 n.a. female,>66,RA/RARS −0.5 0.9 2.3 n.a. male,66,RAEB/RAEBT/CMML n.a. 2.2 2.8 3.3 female,>66,RAEB/RAEBT/CMML n.a. 1.1 2.5 4.0