ALBERT

Description: Background Therapy-related Myelodysplastic Syndromes (t-MDS) are those MDS occurring after cytotoxic and/or radiation therapy administered for a prior neoplastic or non-neoplastic disorder. Their prognosis is generally very poor. The commonly used risk prognostic models for MDS (IPSS and IPSS-R) are not validated in this entity as they were developed after the exclusion of therapy-related cases (Greenberg et al. Blood 1997; Greenberg et al. Blood 2012). Aims The main aims of this study are: a) to report clinical findings and overall survival on 233 patients with t-MDS, and to compare them with a large series of de novo cases; b) to test if IPSS-R is applicable to t-MDS patients. Patients and methods The study is based on the Spanish Registry for MDS, a retrospective database that includes more than 10000 cases. The investigators were asked to fill in a questionnaire regarding prior disease (PD) and prior therapy in those cases reported to be t-MDS. Herein are described the clinical features and overall survival of the first 233 cases with the required information, and compared with patients with de novo MDS from a single center series (n=725). Log Rank test was applied to asses IPSS-R in t-MDS group. Results The 233 reported patients were diagnosed between January 1993 and February 2014. The series includes 104 women (44,6%) and 129 men (55,4%). One hundred and two patients (43.9%) had a primary hematologic malignancy, 119 (51%) had a solid tumor, and 12 (5.1%) received cytotoxic therapy for autoimmune disorders. Ninety eight patients (42.6%) received only chemotherapy (CT), 45 (19.6%) received only radiotherapy (RT), 44 (19.1%) received combined modality treatment (CMT), and 43 (18.6%) received an autologous stem cell transplantation (ASCT). The median time of latency between PD and diagnosis in t-MDS group was 4.56 years (range: 0.03-29.63) in patients previously treated with CT or CMT, significantly lower than the observed after RT (8.54; range 0.83-23.02) or ASCT (8.64; range 2.87-28.32) groups (p=0.023 and p Disclosures No relevant conflicts of interest to declare.

Description: Background: The International Prognostic Scoring System (IPSS) for MDS has recently been revised (IPSS-R). However both scoring systems were developed after exclusion of therapy-related cases and data on its usefulness in treatment-related MDS (tMDS) is limited. Aims and Methods: We analyzed 1837 pts from Spanish, German, Swiss, Austrian, US, Italian, and Dutch centers diagnosed 1975-2015. Complete data to calculate the IPSS/-R was available in 1511 pts. The impact of prognostic features was analyzed by uni- and multivariable models and estimated by a measure of concordance for censored data (Dxy). Results: Median age was 68 years. 1% of pts had 5q-syndrome, 13% RCUD, 4% RARS, 27% RCMD/-RS, 18% RAEB 1, 18% RAEB 2, 4% CMML 1, 2% CMML 2, 3% MDS-U, and 7% AML (RAEB-T) according to WHO-classification. Regarding cytogenetics 38% exhibited good, 14% intermediate, and 48% poor-risk according to IPSS, and 2% very good, 36% good, 17% intermediate, 15% poor, and 31% very poor according to IPSS-R. Prognostic risk groups were 12% IPSS low, 34% int 1, 36% int 2, and 18% high, while the IPSS-R was very low in 8%, low in 20%, intermediate in 17%, high in 23%, and very high in 32%. The most frequent primary diseases were NHL 28%, breast cancer 16%, myeloma 6%, prostate cancer 6%, Hodgkins disease 5%, and 4% gastrointestinal tumors. Patients received chemotherapy in 75% and radiotherapy in 47%. Regarding chemotherapeutic drugs, most pts received combination regimens containing alkylating agents in 65%, topoisomerase inhibitors in 44%, antitubulin agents in 26%, and antimetabolites in 26%. Median follow-up from MDS diagnosis was 59 months, median survival 16 months. Since a disease altering treatment is, at least in higher risk disease, which is overrepresented in tMDS, standard of care, we decided to analyze treated as well as untreated pts to avoid a selection bias. This included stem cell transplantation in 16% with a median survival of 24 months. Features with influence on survival and time to AML in univariable analysis included FAB, WHO, IPSS, IPSS-R, cytogenetics, hb, platelets, marrow and peripheral blasts, ferritin, LDH, fibrosis, ß2-microglobulin, and use of alkylating agents for the treatment of primary disease. For hemoglobin, platelets, LDH, fibrosis, and ß2-microglobulin the influence was stronger on survival. Year of diagnosis, age, gender, neutrophil count, WBC, use of chemo or radiotherapy as well as other chemotherapeutic agents had no marked influence on both outcomes. According to our results, both the IPSS (Dxy 0.29 for survival, 0.32 for AML) and IPSS-R (Dxy 0.34, 0.32 for AML) perform moderately in tMDS, but not as well as in primary MDS (pMDS). Therefore, existing prognostic models need to be adjusted to tMDS. However, this appears to be not without difficulties. The scores tested, as well as most prognostic variables themselves perform inferior compared to pMDS. It becomes even more complicated since tMDS in itself is even more heterogeneous than pMDS. Scores and variables perform differently depending on the primary disease or therapy. The IPSS/-R and its variables perform for example better in pts with solid tumors compared to hematologic diseases or in pts who have received radio- instead of chemotherapy, but also in pts after prostate compared to breast cancer. In addition to the integration of further variables, new cutoffs, or the weighting of existing variables, we are currently testing the possibility of separate score versions for different tMDS subgroups. Separate score versions for survival and time to AML would also give differing weights to most features. Hemoglobin, platelets and cytogenetics would get more weight for survival, while marrow blasts would be more important regarding AML. Conclusion: In contrast to early descriptions of tMDS, with poor risk cytogenetics in the vast majority of pts and a uniformly poor prognosis, surprisingly we find good risk karyotypes in a relatively large number of pts. Although, poor risk cytogenetics are still overrepresented, this indicates, different types of tMDS exist. Our analysis shows that many variables exhibit prognostic influence in tMDS and the IPSS or preferably IPSS-R can be applied in these pts. However, the prognostic power of both scores is inferior compared to pMDS, making an optimized tMDS score reasonable. Currently data from further IWG centers is integrated in our database and further analyses are performed to propose a tMDS specific score. Figure 1. Figure 1. Disclosures Komrokji: Novartis: Research Funding, Speakers Bureau; Pharmacylics: Speakers Bureau; Incyte: Consultancy; Celgene: Consultancy, Research Funding. Sekeres:TetraLogic: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Steensma:Celgene: Consultancy; Incyte: Consultancy; Amgen: Consultancy; Onconova: Consultancy. Valent:Novartis: Consultancy, Honoraria, Research Funding; Ariad: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria; Pfizer: Honoraria; Celgene: Honoraria. Platzbecker:Boehringer: Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Esteve:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

Description: Clonal evolution is considered as a hallmark of progression in chronic lymphocytic leukemia (CLL). Next-generation sequencing technologies have expanded our knowledge of genetic abnormalities in CLL and enabled to describe marked clonal changes. The acquisition of driver mutations accompanied by selectively neutral passenger changes may be essential to understand the transformation from diagnosis to later more aggressive stages. However, the role of genetic mutations and clonal evolution during the clinical progression prior any therapy is still largely unknown. Longitudinal studies analyzing CLL patients repeatedly before intervening treatment are currently scarce. Patients and methods: We examined the exomes from 35 CLL patients in 2 time-points. Two groups of patients were characterized: (i)patients with progression (n=26) in which we analyzed samples taken from an early stable stage (inactive disease) and during clinical progression (active disease), but before treatment (median of time to first treatment=2.7 years); (ii)patients without progression with a stable inactive disease until last follow-up (n=9) (median follow-up=5.25 years). We also compared patients that gained new cytogenetic aberration detected by FISH in the 2nd time-point with those who did not. Sequencing libraries were prepared using TruSeq Exome Enrichment and sequenced by Illumina HiSeq1000 (84X). Somatic mutation calling was performed by a standardized bioinformatics pipeline. Thereafter, driver mutations were identified using the Cancer Genome Interpreter (https://www.cancergenomeinterpreter.org), a novel tool that identifies variants that are already validated as oncogenic and predicts the effect of the mutations of unknown significance. Results: We identified 397 somatic mutations in 364 different genes, ranging from 4 to 26 mutations per patient. Among them, 58 driver mutations were identified, being SF3B1 (6/35, 17.1%), TP53 (4/35, 11.4%) and NOTCH1 (4/35, 11.4%) the most common mutated genes. Comparing progressive vs. stable group, CLL patients with clinical progression showed a higher intra-tumoral heterogeneity than cases without progression (median of somatic mutations=14[4-26] vs. 9[5-14]). Comparing both tumoral time-points in the same patient, we identified a total of 11 acquired driver mutations and 7 mutations increasing its allele frequency in more than double in the 2nd time-point respect to the 1st one. All of them were detected in patients with clinical progression. Interestingly, TP53 and BIRC3 exhibited recurrently acquired mutations (detected each one in 2 cases). Three driver mutations in cancer genes not yet known for CLL (DHX9, GNAQ and HDAC2) were also acquired. Within CLL progressive patients (n=26), we observed clonal evolution characterized by acquired cytogenetic aberration in 9 cases. In patients with progression but no cytogenetic aberration gained at the 2nd moment (n=17), we detected that almost half of them (7/17) showed clonal evolution by acquired or doubled driver mutations. In the remaining patients with clinical progression but without any clonal evolution (n=10), 6 cases showed a driver mutation of CLL genes associated with bad prognosis (SF3B1, TP53, NOTCH1 or RPS15) already at first time-point. In the stable group (n=9), none acquired or doubled mutation was detected. However, clonal evolution characterized by acquired cytogenetic aberration was observed in 4/9 stable patients: two of them acquired 13q- whereas the other two acquired 11q-. Within stable patients without clonal evolution (n=5), we detected one case with a driver mutation in SF3B1 already at 1st time-point (follow-up=5 years). Conclusion: Clonal evolution represents a central feature of tumor progression in CLL. Our data show that the disease is evolving during time even in stable patients without any clinical signs of disease activity. In progressive patients, the disease evolution is accompanied by new appearance or accumulation of driver mutations and cytogenetic aberrations. Moreover, progressive patients that showed less or no changes during time bore typical CLL drivers at the first time-point. Funding: Seventh Framework Programme (NGS-PTL/2012-2015/no.306242); Ministry of Education, Youth and Sports (2013-2015, no.7E13008; CEITEC 2020 (LQ1601)); AZV-MZ-CR 15-31834A-4/2015 and TACR (TEO2000058/2014-2019); PI15/01471; Junta de Castilla y León (MHS). Disclosures No relevant conflicts of interest to declare.