Immune biomarkers to predict SARS-CoV-2 vaccine effectiveness in patients with hematological malignancies

There is evidence of reduced SARS-CoV-2 vaccine effectiveness in patients with hematological malignancies. We hypothesized that tumor and treatment-related immunosuppression can be depicted in peripheral blood, and that immune profiling prior to vaccination can help predict immunogenicity. We performed a comprehensive immunological characterization of 83 hematological patients before vaccination and measured IgM, IgG, and IgA antibody response to four viral antigens at day +7 after second-dose COVID-19 vaccination using multidimensional and computational flow cytometry. Health care practitioners of similar age were the control group (n = 102). Forty-four out of 59 immune cell types were significantly altered in patients; those with monoclonal gammopathies showed greater immunosuppression than patients with B-cell disorders and Hodgkin lymphoma. Immune dysregulation emerged before treatment, peaked while on-therapy, and did not return to normalcy after stopping treatment. We identified an immunotype that was significantly associated with poor antibody response and uncovered that the frequency of neutrophils, classical monocytes, CD4, and CD8 effector memory CD127low T cells, as well as naive CD21+ and IgM+D+ memory B cells, were independently associated with immunogenicity. Thus, we provide novel immune biomarkers to predict COVID-19 vaccine effectiveness in hematological patients, which are complementary to treatment-related factors and may help tailoring possible vaccine boosters

Evaluation of the outcomes of newly diagnosed patients with high-risk myelodysplastic syndrome according to the initial therapeutical strategies chosen in usual clinical practice

Myelodysplastic syndromes (MDS) are a heterogeneous group of diseases without a care standard and show variability in treatment outcomes. This Spanish, observational, prospective study ERASME (CEL-SMD-2012-01) assessed the evolution of newly diagnosed and treatment-naïve high-risk MDS patients (according to IPPS-R). 204 patients were included: median age 73.0 years, 54.4% males, 69.6% 0-1 ECOG, and 94.6% with comorbidities. Active treatment was the most common strategy (52.0%) vs. stem cell transplantation (25.5%) and supportive care/watchful-waiting (22.5%). Overall (median) event-free survival was 7.9 months (9.1, 8.3, and 5.3); progression-free survival: 10.1 months (12.9, 12.8, and 4.3); and overall survival: 13.8 months (15.4, 14.9; 8.4), respectively, with significant differences among groups. Adverse events (AEs) of ≥3 grade were reported in 72.6% of patients; serious AEs reported in 60.6%. 33.1% of patients died due to AEs. Three patients developed second primary malignant neoplasms (median: 8.2 months). Our study showed better outcomes in patients receiving active therapy early after diagnosis

Clinical and biological significance of isolated Y chromosome loss in myelodysplastic syndromes and chronic myelomonocytic leukemia. A report from the Spanish MDS Group

On behalf of the Spanish MDS Group.Isolate loss of chromosome Y (-Y) in myelodysplastic syndromes (MDS) is associated to a better outcome but it is also well described as an age-related phenomenon. In this study we aimed to analyze the prognostic impact of −Y in the context of the IPSS-R cytogenetic classification, evaluate the clinical significance of the percentage of metaphases with isolated −Y, and test whether finding −Y may predispose to over-diagnose MDS in patients with borderline morphological features. We evaluated 3581 male patients from the Spanish MDS Registry with a diagnosis of MDS or chronic myelomonocytic leukemia (CMML). −Y was identified in 177 patients (4.9%). Compared with the 2246 male patients with normal karyotype, −Y group showed a reduced risk of leukemic transformation that did not translate into a survival advantage. The overall survival and the risk of leukemic transformation were not influenced by the percentage of metaphases with −Y. The −Y group was not enriched in patients with minor morphologic traits of dysplasia, suggesting that the better outcome in the −Y group cannot be explained by enrichment in cases misdiagnosed as MDS. In conclusion, our results support the current recommendation of classifying patients with −Y within the very good risk category of the IPSS-R for MDS and rule out a selection bias as a possible explanation of this better outcome. An analysis of the molecular basis of MDS with isolated −Y would be of interest as it may provide a biological basis of protection against progression to acute leukemia.This study was supported in part by research funding from the CIBERONC Consortium grant CB16/12/00284 (“Instituto de Salud Carlos III”).Peer Reviewe

Therapy-Related MDS Can be Separated into Different Risk-Groups According to Tools for Classification and Prognostication of Primary MDS

Abstract 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

Frequency and Prognostic Significance of Cytogenetic Abnormalities in 1269 Patients with Therapy-Related Myelodysplastic Syndrome - a Study of the International Working Group (IWG-PM) for Myelodysplastic Syndromes (MDS)

Abstract To develop a prognostic scoring system tailored for therapy-related myelodysplastic syndromes (tMDS), we put together a database containing 1933 patients (pts) with tMDS from Spanish, German, Swiss, Austrian, US, Italian, and Dutch centers diagnosed between 1975-2015. Complete data to calculate the IPSS and IPSS-R were available in 1603 pts. Examining different scoring systems, we found that IPSS and IPSS-R do not risk stratify tMDS as well as they do primary MDS (pMDS), thereby supporting the need for a tMDS-specific score (Kuendgen et al., ASH 2015). The current analysis focuses on cytogenetic information as a potential component of a refined tMDS score, based on this large, unique patient cohort. Of the 1933 pts, 477 had normal karyotype (KT), 197 had missing cytogenetics, while 467 had a karyotype not readily interpretable. Incomplete karyotype descriptions will be reedited for the final evaluation. Of the remaining 1269 pts the most frequent cytogenetic abnormalities (abn) were: -7, del(5q), +mar, +8, del(7q), -5, del(20q), -17, -18, -Y, del(12p), -20, and +1 with >30 cases each. Frequencies are shown in Table 1. Some abn were observed mostly or solely within complex KTs, such as monosomies, except -7. Others, like del(20q) or -Y, are mainly seen as single or double abn, while del(5q), -7, or del(7q) are seen in complex as well as non-complex KTs. The cytogenetic profile overlapped with that of pMDS (most frequent abn: del(5q), -7/del(7q), +8, -18/del(18q), del(20q), -5, -Y, -17/del(17p), +21, and inv(3)/t(3q) (Schanz et al, JCO 2011)), with notable differences including overrepresentation of complete monosomies, a higher frequency of -7 or t(11q23), and a more frequent occurrence of cytogenetic subtypes in complex KTs, which was especially evident in del(5q) occurring as a single abn in 16%, compared to 70% within a complex KT. IPSS-R cytogenetic groups were distributed as follows: Very Good (2%), Good (35%), Int (17%), Poor (15%), Very Poor (32%). Regarding the number of abn (including incomplete KT descriptions) roughly 30% had a normal KT, 20% 1, 10% 2, and 40% ≥3 abn, compared to pMDS: 55% normal KT, 29% 1, 10% 2, and 6% ≥3 abn. To be evaluable for prognostic information, abn should occur in a minimum of 10 pts. As a single aberration this was the case for -7, +8, del(5q), del(20q), del(7q), -Y, and t(11;varia) (q23;varia). Of particular interest, there was no apparent prognostic difference between -7 and del(7q); del(5q) as a single abn was associated with a relatively good survival, while the prognosis was poor with the first additional abn; t(11q23) occurred primarily as a single abn and was associated with an extremely poor prognosis, and prognosis of pts with ≥4 abn was dismal independent of composition (Table 1). To develop a more biologically meaningful scoring system containing homogeneous and prognostically stable groups, we will further combine subgroups with different abn leading to the same cytogenetic consequences. For example, deletions, unbalanced translocations, derivative chromosomes, dicentric chromosomes of 17p, and possibly -17 all lead to a loss of genetic material at the short arm of this respective chromosome affecting TP53. Further information might be derived from analyses of the minimal common deleted regions. For some abn, like del(11q), del(3p), and del(9q), this can be refined to one chromosome band only (table 1). Conclusion: Development of a robust scoring system for all subtypes of tMDS is challenging using existing variables. This focused analysis on the cytogenetic score component shows that favorable KTs are evident in a substantial proportion of pts, in contrast to historic data describing unfavorable cytogenetics in the majority of pts. Although complex and monosomal KTs are overrepresented, this suggests the existence of distinct tMDS-subtypes, although some of these cases might not be truly therapy-induced despite a history of cytotoxic treatment. The next steps will be to analyze the prognosis of the different groups, develop a tMDS cytogenetic score, and examine minimal deleted regions to identify candidate genes for development of tMDS, as well as to describe the possible influence of different primary diseases and treatments (radio- vs chemotherapy, different drugs) on induction of cytogenetic subtypes. Our detailed analysis of tMDS cytogenetics should reveal important prognostic information and is likely to help understand mechanisms of MDS development. Disclosures Komrokji: Novartis: Consultancy, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Sole:Celgene: Membership on an entity's Board of Directors or advisory committees. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium/Takeda: Membership on an entity's Board of Directors or advisory committees. Roboz:Cellectis: Research Funding; Agios, Amgen, Amphivena, Astex, AstraZeneca, Boehringer Ingelheim, Celator, Celgene, Genoptix, Janssen, Juno, MEI Pharma, MedImmune, Novartis, Onconova, Pfizer, Roche/Genentech, Sunesis, Teva: Consultancy. Steensma:Amgen: Consultancy; Genoptix: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Millenium/Takeda: Consultancy; Ariad: Equity Ownership. Schlenk:Pfizer: Honoraria, Research Funding; Amgen: Research Funding. Valent:Amgen: Honoraria; Deciphera Pharmaceuticals: Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Ariad: Honoraria, Research Funding; Deciphera Pharmaceuticals: Research Funding. Giagounidis:Celgene Corporation: Consultancy. Giagounidis:Celgene Corporation: Consultancy. Platzbecker:Celgene Corporation: Honoraria, Research Funding; TEVA Pharmaceutical Industries: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Janssen-Cilag: Honoraria, Research Funding; Amgen: Honoraria, Research Funding. Lübbert:Janssen-Cilag: Other: Travel Funding, Research Funding; Celgene: Other: Travel Funding; Ratiopharm: Other: Study drug valproic acid