Somatic Mutations Improve Risk Classification By Cytogenetic Abnormalities in Patients with Myelodysplastic Syndrome after Hematopoietic Stem Cell Transplantation

Background: Hematopoietic stem cell transplantation (HSCT) offers potentially curative therapy for patients with myelodysplastic syndrome (MDS) but disease progression after HSCT remains a major reason for failure after transplant. Identification of risk factors for progression of MDS after HSCT would allow to identify target population for early initiation of preventive treatments to improve outcomes. Methods: Patients with a diagnosis of MDS who received first HSCT between 2013 and 2018 with available pre-transplant genetic profile obtained from next generation sequencing of genes were included for the retrospective analysis. Cytogenetic findings were categorized by the revised International Prognostic Scoring System (R-IPSS). Primary outcome of interest was risk of disease progression. Classification and regression tree (CART) analysis was performed to evaluate independent predictors on multivariate analysis using standard methods. Results: Of 378 MDS patients transplanted within the study period, 225 were eligible to be included in this analyses. As shown in the table 1, the study cohort was high risk; cytogenetic risk groups were very-poor and poor in 50 (23%) and 32 (15%) patients, respectively. At least one pathogenic mutation was identified in 215 (91%) of patients prior to transplant. Most frequently mutated genes included, TP53 (24%, 54/225), RAS pathway genes (NRAS, KRAS, FLT3, PTPN11 and KIT) (20%, 44/225), TET2 (16%, 37/172), ASXL1 (12%, 27/172) and DNMT3A (11%, 25/200). In our cohort, patients with very-poor cytogenetics had a high frequency of TP53 mutations (73%), and TP53 mutations occurred almost exclusively in patients with very-poor cytogenetics (76% v 7%; P < .001). That makes those two groups almost inseparable from each other. The median follow-up in 121 (54%) survivors was 24 months (range, 1.8 to 74 months). Of the 225 patients, 65 (29%) had disease progression after HSCT, with a median of 154 days to progression (range, 28 to 1196). By univariate analyses, presence of TP53 (HR, 3.2; CI, 1.9-5.4; P<.001), DNMT3A (HR, 2.6; CI, 1.5-4.7; P=.001), RAS pathway mutations (HR, 2.01; CI, 1.2-3.4; P=.01), therapy related MDS (HR, 2.05; CI, 1.2-3.5; P=.008), very poor risk cytogenetics (HR, 3.4; CI, 1.9-6.3; P<.002), and use of post-transplant cyclophosphamide (PTCy) (HR, 0.5; CI, 0.3-0.96; P=.003) were significant predictors of progression rate. As previously mentioned, we used CART analysis to evaluate independent predictors of progression. The results demonstrated that given the significant overlap with TP53 and very poor cytogenetics, when both variables were forced into the model, only very poor cytogenetics remained significant for progression. Based on CART analysis, 4 mutually exclusive risk groups for progression were identified (Figure 1): high risk (very poor risk cytogenetics or DNMT3Amut), intermediate risk (good, intermediate or poor risk cytogenetics/RAS-pathmut/DNMT3Awt), low risk (poor risk cytogenetics/RAS-pathwt /DNMT3Awt) and a very low risk group (very good, good or intermediate risk cytogenetics/RAS-pathwt /DNMT3Awt). The correlation between R-IPSS based cytogenetic risk and our identified risk groups is shown in table 2. This illustrates how the addition of molecular data upstaged 25% of the patients to a higher risk category as well as downstaged 23% of the patients to a lower risk category for disease progression when compared to the original R-IPSS classification. The cumulative incidence of disease progression at 2 years was 6% (reference), 26% (P=.005), 42% (P<.001) and 56% (P<.001) in very-low, low, intermediate and high risk groups, respectively (Figure 2). Within the risk groups identified, progression incidence was comparable by conditioning intensity and the use of PTCy. The actuarial 2-year progression-free survival for the defined 4 risk groups was, 69% (reference), 48% (HR, 2; P=.04), 38% (HR, 2.2; P=.009), 22% (HR, 3.2; P<.001) and 14% (HR, 4.8; P<.001), in very-low, low, intermediate and high-risk groups, respectively. Non-relapse mortality was similar across the identified risk groups. Conclusion: The proposed model, by incorporating DNMT3A and RAS pathway molecular mutation status to cytogenetic risk per R-IPSS, improves upon the classification of risk groups and enables the physician to better risk stratify and predict likelihood of progression after transplantation. Disclosures Garcia-Manero: Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; Novartis: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding. Popat:Bayer: Research Funding; Incyte: Research Funding; Jazz: Consultancy. Ciurea:Kiadis Pharma: Membership on an entity's Board of Directors or advisory committees, Other: stock holder; Spectrum: Membership on an entity's Board of Directors or advisory committees; Miltenyi: Research Funding; MolMed: Membership on an entity's Board of Directors or advisory committees. Kebriaei:Jazz: Consultancy; Pfizer: Honoraria; Kite: Honoraria; Amgen: Research Funding. Bashir:Imbrium: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Spectrum: Membership on an entity's Board of Directors or advisory committees; Kite: 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; StemLine: Research Funding; Acrotech: Research Funding; Celgene: Research Funding. Champlin:Actinium: Consultancy; Johnson and Johnson: Consultancy; Sanofi-Genzyme: Research Funding. Oran:Astex pharmaceuticals: Research Funding; AROG pharmaceuticals: Research Funding.

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Germline Genetic Variation of ASXL1 and BIM Predicts Response to Imatinib and Identifies a Subset of High Sokal Risk Patients with the Greatest Risk of Treatment Failure and Disease Progression

Blood ◽

10.1182/blood.v126.23.475.475 ◽

2015 ◽

Vol 126 (23) ◽

pp. 475-475 ◽

Cited By ~ 2

Author(s):

Justine E Marum ◽

Leanne Purins ◽

David T Yeung ◽

Wendy Tara Parker ◽

David J Price ◽

...

Keyword(s):

Genetic Variation ◽

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Risk Groups ◽

Front Line ◽

Advisory Committees ◽

Poor Risk ◽

Significant Difference ◽

Selection Of

Abstract Background: Scoring systems at CML diagnosis, such as Sokal risk, provide important response prediction for imatinib (IM) treated patients (pts). Specific treatment policies have been suggested for high risk pts to optimize otherwise inferior outcomes. However, responses among pts with high risk are heterogeneous and new biomarkers are required to facilitate rational selection of optimal therapy. Biological factors, such as germline genetic variation, may play a role in therapy response dynamics. We aimed to identify predictive biomarkers of response to IM at CML diagnosis to aid selection of front line therapy for optimal treatment outcomes. Methods: Targeted amplicon sequencing using a custom Ion AmpliSeq panel and the Ion Proton was performed for 35 genes: 10 BCL2 family genes involved in TKI initiated apoptosis (including BIM, BAD and BCL2); 5 drug metabolism genes; and 20 genes implicated in hematologic malignancies (including ASXL1 and TET2). Genotypes were determined for 200 candidate single nucleotide variants (SNPs) for 528 front line IM and 83 front line NIL treated pts. For the IM pts, baseline variables were assessed for association with outcome: Sokal risk, age, gender, assigned IM dose (400, 600 or 800 mg); and genotype. Results: SNPs significantly associated with outcome in univariate analyses were assessed in multivariate models with the other baseline variables. The Sokal risk, ASXL1 rs4911231 and BIM rs686952 SNPs were independent predictors of 12 mo MMR, 48 mo MR4, MR4.5 and failure free survival (FFS, loss of any response, death, progression to AP/BC). For the ASXL1 SNP, the homozygous T genotype (155/508 evaluable pts, 30%), and for the BIM SNP, the A allele (249/507 evaluable pts, 49%) were associated with superior outcomes. We explored the additive effect of combining the genotypes of the ASXL1 and BIM SNPs on outcome. Three risk groups were readily identified (defined in Fig): Good (16% of evaluable pts), Average (46%) and Poor (37%). There were significant differences in the cumulative incidence of 3 mo EMR, 12 mo MMR, and 48 mo MR4, MR4.5 and FFS, as stratified by these SNP risk groups in IM treated pts (Table and Fig A). No significant association was found for progression to AP/BC or survival for any baseline variable. To examine the predictive power of SNP genotype within the high Sokal risk group, high risk pts were stratified by SNP genotype group. Significant differences were observed for EMR, MMR, MR4, MR4.5 and FFS (Table and Figure B), demonstrating the ability of the SNP genotype within high Sokal risk pts to predict response. Moreover, high Sokal risk pts harboring a poor risk SNP genotype had a significantly higher risk of progression to AP/BC vs high Sokal risk pts with an average/good risk genotype, 12% vs 2% (P =.03). The impact of SNP genotype risk on achieving 12 mo MMR was examined in the 83 pts treated with frontline NIL (median 24 mo follow up). In contrast to the significant difference observed for IM pts, there was no significant difference for NIL pts: 75% vs 73% vs 64% for good, average and poor risk, respectively, P =.34, suggesting the poor risk conferred by genotype may be abrogated by more potent TKI. Conclusion: Our data suggest inherent genetic variation contributes to the heterogeneity of response to IM. An intronic SNP in BIM, a key initiator of TKI induced apoptosis, and a synonymous SNP in ASXL1 exon 12, a region commonly mutated in hematologic cancers, were strong biomarkers of IM response. The mechanism by which these SNPs affect response awaits further clinical and experimental evaluation. Among pts with high Sokal risk, the genotype of these 2 SNPs delineated response and identified a good risk subgroup where more potent TKI may not be required for optimal outcomes. Assessment of genetic variation at diagnosis may contribute to a prognostic score that will allow for optimization of therapy. Disclosures Yeung: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: travel grant international meeting, Research Funding. Hughes:Bristol-Myers Squibb: Honoraria, Research Funding; ARIAD: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Branford:BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Ariad: Research Funding; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Qiagen: Membership on an entity's Board of Directors or advisory committees.

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Phase II Trial of the Combination of Ixazomib, Lenalidomide, and Dexamethasone in High-Risk Smoldering Multiple Myeloma

Blood ◽

10.1182/blood-2018-99-117871 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 804-804 ◽

Cited By ~ 7

Author(s):

Mark Bustoros ◽

Chia-jen Liu ◽

Kaitlen Reyes ◽

Kalvis Hornburg ◽

Kathleen Guimond ◽

...

Keyword(s):

Multiple Myeloma ◽

Bone Marrow ◽

High Risk ◽

Disease Progression ◽

Board Of Directors ◽

Rna Sequencing ◽

Research Funding ◽

Response Rate ◽

Advisory Committees ◽

Equity Ownership

Abstract Background. This study aimed to determine the progression-free survival and response rate using early therapeutic intervention in patients with high-risk smoldering multiple myeloma (SMM) using the combination of ixazomib, lenalidomide, and dexamethasone. Methods. Patients enrolled on study met eligibility for high-risk SMM based on the newly defined criteria proposed by Rajkumar et al., Blood 2014. The treatment plan was designed to be administered on an outpatient basis where patients receive 9 cycles of induction therapy of ixazomib (4mg) at days 1, 8, and 15, in combination with lenalidomide (25mg) at days 1-21 and Dexamethasone at days 1, 8, 15, and 22. This induction phase is followed by ixazomib (4mg) and lenalidomide (15mg) maintenance for another 15 cycles. A treatment cycle is defined as 28 consecutive days, and therapy is administered for a total of 24 cycles total. Bone marrow samples from all patients were obtained before starting therapy for baseline assessment, whole exome sequencing (WES), and RNA sequencing of plasma and bone marrow microenvironment cells. Moreover, blood samples were obtained at screening and before each cycle to isolate cell-free DNA (cfDNA) and circulating tumor cells (CTCs). Stem cell collection is planned for all eligible patients. Results. In total, 26 of the planned 56 patients were enrolled in this study from February 2017 to April 2018. The median age of the patients enrolled was 63 years (range, 41 to 73) with 12 males (46.2%). Interphase fluorescence in situ hybridization (iFISH) was successful in 18 patients. High-risk cytogenetics (defined as the presence of t(4;14), 17p deletion, and 1q gain) were found in 11 patients (61.1%). The median number of cycles completed was 8 cycles (3-15). The most common toxicities were fatigue (69.6%), followed by rash (56.5%), and neutropenia (56.5%). The most common grade 3 adverse events were hypophosphatemia (13%), leukopenia (13%), and neutropenia (8.7%). One patient had grade 4 neutropenia during treatment. Additionally, grade 4 hyperglycemia occurred in another patient. As of this abstract date, the overall response rate (partial response or better) in participants who had at least 3 cycles of treatment was 89% (23/26), with 5 Complete Responses (CR, 19.2%), 9 very good partial responses (VGPR, 34.6%), 9 partial responses (34.6%), and 3 Minimal Responses (MR, 11.5%). None of the patients have shown progression to overt MM to date. Correlative studies including WES of plasma cells and single-cell RNA sequencing of the bone microenvironment cells are ongoing to identify the genomic and transcriptomic predictors for the differential response to therapy as well as for disease evolution. Furthermore, we are analyzing the cfDNA and CTCs of the patients at different time points to investigate their use in monitoring minimal residual disease and disease progression. Conclusion. The combination of ixazomib, lenalidomide, and dexamethasone is an effective and well-tolerated intervention in high-risk smoldering myeloma. The high response rate, convenient schedule with minimal toxicity observed to date are promising in this patient population at high risk of progression to symptomatic disease. Further studies and longer follow up for disease progression are warranted. Disclosures Bustoros: Dava Oncology: Honoraria. Munshi:OncoPep: Other: Board of director. Anderson:C4 Therapeutics: Equity Ownership; Celgene: Consultancy; Bristol Myers Squibb: Consultancy; Takeda Millennium: Consultancy; Gilead: Membership on an entity's Board of Directors or advisory committees; Oncopep: Equity Ownership. Richardson:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Oncopeptides: Membership on an entity's Board of Directors or advisory committees; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; BMS: Research Funding; Janssen: 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. Ghobrial:Celgene: Consultancy; Takeda: Consultancy; Janssen: Consultancy; BMS: Consultancy.

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Proposal of a Novel Latin American International Prognostic Index (LATAM-IPI) for Diffuse Large B-Cell Lymphoma (DLBCL) By the Grupo Para El Estudio De Linfoproliferativos En Latino-America (GELL)

Blood ◽

10.1182/blood-2020-142836 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 25-27

Author(s):

Luis Villela Villela ◽

Ana Ramirez-Ibarguen ◽

Brady E Beltran ◽

Camila Peña ◽

Denisse A. Castro ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Scoring Systems ◽

Risk Groups ◽

Training Set ◽

Extranodal Involvement ◽

Advisory Committees ◽

Internal Validation ◽

Biological Variables

Introduction. There are different scoring systems to differentiate risk groups in patients with DLBCL treated with chemoimmunotherapy. Those systems have used the same 5 variables (age, performance status, LDH, stage, extranodal involvement) for 27 years. However, LATAM data have not been included in the development of previous scoring systems. It is important to mention that novel biological variables, such as albumin, beta-2-microglobulin (B2M) and platelet/lymphocyte ratio (PLR), have been reported and could improve discrimination (Villela et al. Blood 2019; 134Suppl_1: 1613). Therefore, we carried out a large, multinational study to develop and validate a LATAM-IPI score. Methods. This is a retrospective cohort of 1030 patients with a diagnosis of DLBCL treated with standard chemoimmunotherapy with curative intent between 2010 and 2018. Data were obtained from 8 LATAM countries: Argentina, Colombia, Chile, Guatemala, Mexico, Paraguay, Peru, and Venezuela. The five classic IPI variables (age, ECOG, extranodal involvement, LDH, stage) were analyzed and albumin and PLR were added (Villela et al. Blood 2019; 134Suppl_1: 1613). B2M was not included because it was not requested regularly in all countries. Development of LATAM-IPI: The training set consisted of 85% of the sample, randomly selected, and the remaining 15% was reserved for internal validation. Using the training set, the univariate and multivariate association between clinical prognostic factors and OS was analyzed fitting Cox proportional-hazard models. Outcomes. Clinical characteristics of the training (n=878) and internal validation (n=151) cohorts are shown in Table 1. There were no statistical differences in baseline characteristics between the cohorts. The median follow-up for the whole cohort was 36 months (IQR: 11-57). When exploring the classic IPI variables on the training set, all variables were associated with high risk of mortality [age 65-74, Hazard Ratio (HR) 1.24, 95% CI 0.96 to 1.58, p=0.08; age ≥75, HR 1.71, 95% CI 1.28 to 2.28, p=0.0003), ECOG (≥ 2, HR=2, 95% CI 1.61 to 2.53; p<0.0001), EN (≥2, HR=1.53, 95% CI 1.18 to 1.97; p=0.0012), stage (III/IV, HR=2.1, 95% CI 1.64 to 2.69; p<0.0001) and LDH (ratio 1.1-2.9, HR=1.55, 95% CI 1.22 to 1.97; p=0.0003; ratio ≥3, HR= 2.68, 95% CI 1.93 to 3.7, p<0.0001). Similarly, the biological variables Albumin (≤3.5 mg/dL, HR 2.37, 95% CI 1.9 to 2.95, p<0.0001) and PLR (≥273, HR= 1.52, 95% CI 1.23 to 1.87; p=0.0001) were associated with high risk of death. Next, these variables were evaluated by multivariate analysis. The independent variables were albumin (<3.5 mg/dL, HR 1.84, 95% CI 1.45 to 2.3, p<0.0001, 1 point), LDH (ratio 1.1 to 2.9, HR 1.30, 95% CI 1.02 to 1.67, p=0.03, 1 point; ratio ≥3, HR=1.84, 95% CI 1.31 to 2.5, p=0.0004, 2 points), advanced stage (HR 1.65, 95% CI 1.27 to 2.13, p=0.0001, 1 point), age (≥75, HR= 1.51, 95% CI 1.15 to 1.98, p=0.003, 1 point), and ECOG (≥2, HR 1.40, 95% CI 1.10 to 1.77, p=0.005). Now, for the development of LATAM-IPI, the groups were distributed as follows: 0 points, low; 1-3 points, intermediate; 4-6 points, high risk. There were no differences in the distribution of the risk groups between training and validation sets (Table 2). In the learning cohort, the 5-year OS rates for low, intermediate and high risk were 81%, 63% and 33%, respectively (p<0.0001). In the validation cohort, the 5-year OS rates for low, intermediate and high risk were 81%, 63% and 44%, respectively (p=0.02) (Figure 1). Conclusions: Using multinational learning and validation cohorts including over 1,000 DLBCL patients treated with standard chemoimmunotherapy in LATAM, we developed a novel LATAM-IPI score using age ≥75 years, ECOG ≥2, advanced stage, LDH ratio (1.1-29 and ≥3) and albumin <3.5 mg/dl. Next steps are to disseminate our results with other involved researchers in LATAM to prospectively assess and reproduce our results. We expect this score will help to further define the prognosis of DLBCL patients in LATAM. Disclosures Villela: amgen: Speakers Bureau; Roche: Other: advisory board, Speakers Bureau. Idrobo:Janssen: Honoraria, Speakers Bureau; Amgen: Honoraria, Speakers Bureau; Abbvie: Honoraria, Speakers Bureau; Tecnofarma: Honoraria, Speakers Bureau; Takeda: Honoraria, Speakers Bureau. Gomez-Almaguer:Amgen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AbbVie: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene/BMS: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Roche: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Castillo:Janssen: Consultancy, Research Funding; TG Therapeutics: Research Funding; Kymera: Consultancy; Abbvie: Research Funding; Beigene: Consultancy, Research Funding; Pharmacyclics: Consultancy, Research Funding.

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A Laboratory Based Scoring System Predicts Early Treatment in Rai 0/Binet a CLL

Blood ◽

10.1182/blood-2018-99-112162 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 4399-4399

Author(s):

Jared A Cohen ◽

Francesca Maria Rossi ◽

Riccardo Bomben ◽

Lodovico Terzi-di-Bergamo ◽

Pietro Bulian ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Scoring System ◽

Research Funding ◽

Early Stage ◽

Recursive Partitioning ◽

Risk Groups ◽

Advisory Committees ◽

Early Stage Disease ◽

Training Cohort

Abstract Introduction: Observation is the standard of care for asymptomatic early stage chronic lymphocytic leukemia (CLL) however these cases follow a heterogenous course. Recent studies show novel biomarkers can delineate indolent from aggressive early stage disease and current clinical trials are exploring the role of early intervention in high risk cases. Although several scoring systems have been established in CLL, most are designed for overall survival, do not circumscribe early stage disease, and require cumbersome calculations relying on extensive laboratory and clinical information. Aim: We propose a novel laboratory-based prognostic calculator to risk stratify time to first treatment (TTFT) in early stage CLL and guide candidate selection for early intervention. Methods: We included 1574 cases of early stage CLL in an international cohort from Italy, the United Kingdom and the United States using a training-validation model. Patient information was obtained from participating centers in accordance with the Declaration of Helsinki. The training cohort included 478 Rai 0 cases from a multicenter Italian cohort, all referred to a single center (Clinical and Experimental Onco-Hematology Unit of the Centro Riferimento Oncologico in Aviano, IT) for immunocytogenetic lab analyses. Considering TTFT as an endpoint, we evaluated 8 variables (age>65, WBC>32K, 17p-, 11q-, +12, IGHV status, CD49d+, gender) with univariate and multivariate Cox regression internally validated using bootstrapping procedures. FISH thresholds were 5% for 11q-, and +12 and 10% for 17p-. Cases were categorized according to the hierarchical model proposed by Dohner. IGHV status was considered unmutated at ≥98%. CD49d+ was set at >30%. WBC cutoff of >32K was established by maximally selected log rank analysis. Variables were weighted based on the proportion of their normalized hazard ratios rounded to the nearest whole integer. We used recursive partitioning for risk-category determination and Kaplan-Meier analysis to generate survival curves. We compared the concordance index (C-index) of our model with the CLL international prognostic index (CLL-IPI) for 381/478 cases in the training cohort with available beta-2-microglobulin data and for all validation cohorts. We used 3 independent single-center cohorts for external validation. Results: The training cohort had 478 cases of Rai 0 CLL with a median (95% CI) TTFT of 124 months (m) (104-183m). Five prognostic variables emerged with respect to TTFT, and each assigned a point value of 1 or 2 according to their respective normalized HR values as follows: 17p-, and UM IGHV (2 pts); 11q-, +12, and WBC>32K (1 pt). We identified three risk groups, based on point cut-offs of 0, 1-2, and 3-5 established by recursive partitioning analysis with a median (95% CI) TTFT of 216m (216-216m), 104m (93-140m) and 58m (44-68m) (p<0.0001, C-index 0.75) for the low, intermediate, and high-risk groups, respectively (figure 1). A comparison with the CLL-IPI was possible in 381 cases with available beta-2-microglobulin data. In this subset, the C-index was 0.75 compared to 0.68 when patient risk groups were split according to the CLL-IPI. The scoring system was then validated in 3 independent cohorts of early stage CLL: i) Gemelli Hospital in Rome, IT provided 144 Rai 0 cases. Median (95% CI) TTFT was 86m (80-94m, 95% CI). Median (95% CI) TTFT for the low, intermediate and high-risk groups was 239m (239-239m), 98m (92-132m) and 85m (60-109m) respectively (p=0.002 between low and intermediate groups, p=0.09 between intermediate and high groups; C-index 0.64 v 0.60 for CLL-IPI). ii) Cardiff University Hospital in Wales, UK provided 395 Binet A cases. Median (95% CI) TTFT was 74 m (67-81m) overall and NR, 111m (97-146m) and 70m (29-114m) for the low, intermediate and high-risk groups respectively (p<0.001 between low and intermediate groups, p=0.009 between intermediate and high groups; C-index 0.63 v 0.63 for CLL-IPI). iii) Mayo Clinic in Rochester, MN provided 557 Rai 0 cases. Median (95% CI) TTFT was 127m (96m-NR) overall and NR, 76m (64m-NR) and 36m (31-59m) for the low, intermediate and high-risk groups respectively (p<0.0001; C-index 0.72 v 0.68 for CLL-IPI). Conclusion: We present a novel laboratory-based scoring system for Rai 0/Binet A CLL to aid case selection in risk-adapted treatment for early disease. Further comparison to existing indices is needed to verify its utility in the clinical setting. Disclosures Zaja: Novartis: Honoraria, Research Funding; Takeda: Honoraria; Abbvie: Honoraria; Celgene: Honoraria, Research Funding; Amgen: Honoraria; Janssen: Honoraria; Sandoz: Honoraria. Fegan:Roche: Honoraria; Napp: Honoraria; Janssen: Honoraria; Gilead Sciences, Inc.: Honoraria; Abbvie: Honoraria. Pepper:Cardiff University: Patents & Royalties: Telomere measurement patents. Parikh:AstraZeneca: Honoraria, Research Funding; Janssen: Research Funding; MorphoSys: Research Funding; Abbvie: Honoraria, Research Funding; Gilead: Honoraria; Pharmacyclics: Honoraria, Research Funding. Kay:Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees.

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Prognostic Value of Cpss Cytogenetic Risk Classification in Patients with CMML after Allogeneic Hematopoietic Stem Cell Transplantation: A Retrospective Multicenter Study of the Chronic Malignancies Working Party of the EBMT

Blood ◽

10.1182/blood-2018-99-115981 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2182-2182

Author(s):

Christian Koenecke ◽

Dirk-Jan Eikema ◽

Sheree Hazelaar ◽

Dietrich W. Beelen ◽

Victoria Potter ◽

...

Keyword(s):

Stem Cell ◽

High Risk ◽

Hematopoietic Stem Cell Transplantation ◽

Stem Cell Transplantation ◽

Board Of Directors ◽

Cumulative Incidence ◽

Research Funding ◽

Advisory Committees ◽

Hematopoietic Stem ◽

Allogeneic Hsct

Abstract Introduction: The only curative treatment approach for patients with Chronic Myelomonocytic Leukemia (CMML) is allogeneic hematopoietic stem cell transplantation (HSCT), but disease relapse after transplantation is a major concern. Predictors for disease outcome after HSCT are limited. However, unfavorable cytogenetic abnormalities have been shown to serve as predictors for relapse after transplantation. The aim of this large multicentric, international study was to retrospectively determine the impact of cytogenetic information according to the CMML-specific prognostic scoring system (CPSS) on outcome after allogeneic HSCT. Patients and Methods: Patients were selected from the EBMT database who had received a first allogeneic HSCT for the treatment of CMML between 2000 and 2015. 268 centers participated into this study. In total, 1503 patients were included. Impact of CPSS-cytogenetic classification was analyzed regarding overall survival (OS) and cumulative incidence of relapse and non-relapse mortality after HSCT (gray test). Results: 488 female (32.5%) and 1013 male (67.5%) patients were included to the study. Median age at HSCT was 57.6 years (range 0.3-75.4). At time of HSCT, only 422 (28.1%) patients were in complete remission, whereas 1004 (66.8%) had active disease (77 missing). Matched related donor HSCT was performed in 35.7% of the patients, matched unrelated donor HSCT in 57.6%, mismatched related in 3.3% and mismatched unrelated in 3.4%. Bone marrow (12.6%), peripheral blood (84.3%), or both (0.3%) served as the stem cell graft. Cord blood was used as a graft in 2.8%. Myeloablative preparative regimens wereused in 223 patients (15.0%), and less intensive regimens were given to 1268 patients (85.0%). Median survival of patients included into this study was 52.2 months. 637 patients had sufficient cytogenetic information according to CPSS (866 missing), complete relapse information was available in 1385 patients. 143 patients could be categorized into CPSS-high, 85 in intermediate and 375 in low risk cytogenetics, respectively. In univariate analysis high risk CPSS cytogenetic information was found to be strongly associated with OS (low 38% (32-44%), intermediate 41% (30-53%), high 26% (18-34%)), and higher cumulative incidence of relapse (low 40% (35-46%), intermediate 42% (30-54%), high 48% (39-56%)), but not with non relapse mortality (low 28% (23-33%), intermediate 25% (16-35%), high 30% (22-38%)) at 60 months (Figure 1). Conclusion: In this international, multicentric analysis we show that CMML patients with high-risk cytogenetics had significantly worse OS after HSCT than patients with intermediate or low risk cytogenetics according to CPSS. New therapeutic strategies to prevent relapse after HSCT in CMML patients with high-risk cytogenetics are needed. Disclosures Koenecke: Amgen: Consultancy; abbvie: Consultancy; BMS: Consultancy; Roche: Consultancy. Beelen:Medac: Consultancy, Other: Travel Support. Finke:Novartis: Consultancy, Honoraria, Other: travel grants, Research Funding; Riemser: Consultancy, Honoraria, Research Funding; Medac: Consultancy, Honoraria, Other: travel grants, Research Funding; Neovii: Consultancy, Honoraria, Other: travel grants, Research Funding. Niederwieser:Novartis: Research Funding; Miltenyi: Speakers Bureau. Chalandon:Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel costs. Ganser:Novartis: Membership on an entity's Board of Directors or advisory committees. Kobbe:Amgen: Honoraria, Research Funding; Roche: Honoraria, Research Funding; Celgene: Honoraria, Other: Travel Support, Research Funding.

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Combining Information Regarding Chromosomal Aberrations t(4;14), Del(17p13) and the Copy Number of 1q21 with the International Staging System Classification Allows Stratification of Myeloma Patients Undergoing Autologous Stem Cell Transplantation: Results From the HOVON-65/GMMG HD4 Trial

Blood ◽

10.1182/blood.v118.21.332.332 ◽

2011 ◽

Vol 118 (21) ◽

pp. 332-332

Author(s):

Kai Neben ◽

Henk M. Lokhorst ◽

Anna Jauch ◽

Uta Bertsch ◽

Thomas Hielscher ◽

...

Keyword(s):

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Prognostic Value ◽

Risk Group ◽

Staging System ◽

Risk Groups ◽

Low Risk ◽

Advisory Committees ◽

International Staging System

Abstract Abstract 332 PURPOSE : In Multiple Myeloma (MM), the combination of serum beta-2-microglobulin level with serum albumin concentration has been proposed as an outcome predictor in the International Staging System (ISS). More recently, subgroups of MM defined by genetic and cytogenetic abnormalities have been associated with unique biologic, clinical, and prognostic features. PATIENTS AND METHODS: We analyzed the prognostic value of 12 chromosomal abnormalities by fluorescent in situ hybridization (FISH) in a series of 354 MM patients treated within the HOVON-65/GMMG-HD4 trial. Patients with newly diagnosed MM were randomized to receive either three cycles of VAD (arm A; vincristine, adriamycin, dexamethasone) or PAD (arm B; bortezomib, adriamycin, dexamethasone). All patients underwent autologous stem cell transplantation (ASCT) followed by maintenance therapy with thalidomide 50 mg daily (arm A) or bortezomib 1.3 mg/m2 once every 2 weeks (arm B), respectively. In addition, a second cohort of patients was analyzed as a control group (n=462), undergoing ASCT at the University of Heidelberg between September 1994 and December 2010. RESULTS: For the entire group of patients treated within the HOVON-65/GMMG-HD4 trial, we identified 233 patients with 2 copies (67.7%), 95 patients with 3 copies (27.6%) and 16 patients (4.7%) with more than three copies of the chromosomal region 1q21. In addition to del(17p13) and t(4;14), we added +1q21 (>3 copies) to the group of high-risk aberrations, since the outcome of these patients was almost as poor as it was observed for patients with del(17p13). Subsequently, we analyzed whether combining the ISS score with information on the presence of high-risk aberrations could improve the prognostic value with regard to patients' outcome. A combination of the presence or absence of del(17p13), t(4;14), or +1q21 (>3 copies) with the ISS score allowed patients to be stratified into three distinct groups: low-risk [absence of del(17p13)/t(4;14)/+1q21 (>3 copies) and ISS I], high-risk [presence of del(17p13)/t(4;14)/+1q21 (>3 copies) and ISS II/III], and intermediate-risk (all remaining patients). Most of the patients belonged to the low- (33%) and intermediate-risk (49%) groups, whereas 18% were allocated to the high-risk group. The median PFS times for the low-, intermediate-, and high-risk groups were 41.9 months, 31.1 months (HR=1.7; p=0.0018) and 18.7 months (HR=3.6; p<0.0001), respectively. The 3yr-overall survival (OS) decreased from 94% in the low-risk group to 80% (HR=4.6; p=0.0001) and 43% (HR=12.8; p<0.0001) in the intermediate- and high-risk groups, respectively. These results were confirmed in the independent cohort of patients: From date of first ASCT, the median PFS times for the low-, intermediate-, and high-risk groups were 43.3 months, 23.0 months (HR=1.5; p=0.015) and 13.8 months (HR=2.4; p=0.0003), respectively. The 4yr-OS decreased from 84% in the low-risk group to 71% (HR=2.1; p=0.0043) and 49% (HR=3.84; p<0.0001) in the intermediate- and high-risk groups, respectively. CONCLUSION: In our series, the ISS/FISH-based score/algorithm predicted PFS and OS much better than the ISS alone. Our results with molecular cytogenetic techniques may already have implications for the risk-adapted clinical management of patients with MM particularly in younger patients. Disclosures: van de Velde: Ortho Biotech Oncology Research & Development: Employment. Sonneveld:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen-Cilag: Membership on an entity's Board of Directors or advisory committees, Research Funding; Onyx: Membership on an entity's Board of Directors or advisory committees, Research Funding; Millennium: Membership on an entity's Board of Directors or advisory committees.

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Older Age As a Prognostic Factor for Overall Survival for Multiple Myeloma Patients Undergoing Upfront Autologous Hematopoietic Stem Cell Transplantation

Blood ◽

10.1182/blood-2018-99-114200 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2144-2144

Author(s):

David M. Cordas Dos Santos ◽

Rima M. Saliba ◽

Romil Patel ◽

Qaiser Bashir ◽

Chitra Hosing ◽

...

Keyword(s):

Stem Cell ◽

High Risk ◽

Board Of Directors ◽

Research Funding ◽

Older Age ◽

Stage Ii ◽

Advisory Committees ◽

Post Transplant ◽

Cart Analysis ◽

Significant Difference

Abstract Background High-dose chemotherapy and autologous hematopoietic stem cell transplantation (auto-HCT) is considered the standard of care for newly diagnosed, transplant-eligible multiple myeloma (MM) patients. Due to improvements in induction, stem cell mobilization, and dose adjustment of the conditioning regimen, auto-HCT is increasingly used in older MM patients, with several retrospective analyses showing similar clinical outcomes compared to younger patients. Methods To further confirm these results, we performed a single-center retrospective analysis of MM patients undergoing auto-HCT between January 2006 and December 2016. Patients were divided into two groups: older (> 70 years) and younger (≤ 70 years). Results 1128 patients (182 older, 946 younger) were included in this analysis. Patient characteristics are summarized in the attached Table. More patients (59% vs. 45%, p = 0.01) in the older cohort had ISS stage II or III disease. Older cohort was more likely to receive reduced-dose melphalan (140 mg/m²) as conditioning regimen (32% vs 3%, p = <0.0001). There was no significant difference in high-risk cytogenetics, induction regimens, and response to induction, or post-transplant maintenance between the older and younger cohorts. The overall median follow-up among survivors was 49 months in the older and 52 months in the younger group. One-hundred-day non-relapse mortality (NRM) was 2/182 (1.1%) and 6/946 (0.6%) (p = 0.5) in the older and younger groups, respectively. However, 1-year NRM was significantly higher in the older vs. younger cohort (7 /182 (4%; unknown 3, pneumonia or respiratory failure 4) vs. 9/946 (1%; unknown 2, pneumonia or respiratory failure 4, cardiac failure 3), HR 4.1, p = 0.005). Post-transplant, 75 (41%) and 431 (45%) achieved complete remission (CR) in the older and younger groups, respectively (p = 0.29). There was no significant difference in the rate of disease progression post-transplant between older (31%) and younger (30%) groups (p = 0.3). The 5-year progression free survival (PFS) was 24% and 37% in the older and younger groups, respectively (HR 1.3, p = 0.02). Similarly, 5-year overall survival (OS) was 56% and 73% in the older and younger groups (HR 1.8, p = <0.001). In univariate analyses, age > 70 years, high-risk cytogenetics, serum creatinine level > 2 mg/dl and ISS stage III were associated with worse PFS and OS. In contrast, melphalan 200 mg/m² for conditioning and achievement of CR after induction therapy were associated with better PFS and OS. These 6 factors were studied in multivariate analyses using a classification and regression tree (CART) method. In CART analysis for PFS, ISS stage II or III, and high-risk cytogenetics were associated with shorter PFS. Similarly, in CART analysis for OS, older age (> 69 years), ISS stage II or III, and high-risk cytogenetics were associated with a shorter OS. Conclusion In this large single-center analysis, there was no difference in 100-day NRM, CR rates and the risk of progression after auto-HCT between the older and the younger patients. However, older age was associated with a shorter PFS and OS due to increased NRM. On multivariate CART analysis, ISS stage II or III and high-risk cytogenetics were associated with a worse PFS and OS, while age > 69 years was associated with a worse OS only. The impact of comorbidities on NRM is being evaluated in ongoing analyses. Disclosures Lee: Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies Corporation: Consultancy; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees; Chugai Biopharmaceuticals: Consultancy; Takeda Oncology: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees. Patel:Abbvie: Research Funding; Takeda: Research Funding; Poseida Therapeutics, Inc.: Research Funding; Celgene: Research Funding. Thomas:Bristol Myers Squibb Inc.: Research Funding; Celgene: Research Funding; Acerta Pharma: Research Funding; Amgen Inc: Research Funding; Array Pharma: Research Funding. Orlowski:BioTheryX, Inc: Consultancy, Membership on an entity's Board of Directors or advisory committees; Poseida: Research Funding; Bristol Myers Squibb: Consultancy; Genentech: Consultancy; Millenium Pharmaceuticals: Consultancy, Research Funding; Janssen Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Champlin:Otsuka: Research Funding; Sanofi: Research Funding.

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Crowdsourced High-Risk Classifiers for Multiple Myeloma Patients Commonly Identify PHF19 As a Robust Progression Biomarker

Blood ◽

10.1182/blood-2019-127362 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4370-4370

Author(s):

Michael J Mason ◽

Carolina D. Schinke ◽

Christine Eng ◽

Fadi Towfic ◽

Fred Gruber ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

High Risk ◽

Disease Progression ◽

Board Of Directors ◽

Research Funding ◽

Plasma Cells ◽

Employment Equity ◽

Advisory Committees ◽

Equity Ownership

Multiple myeloma (MM) is a hematological malignancy of terminally differentiated plasma cells residing within the bone marrow with 25,000-30,000 patients diagnosed in the United States each year. The disease's clinical course depends on a complex interplay chromosomal abnormalities and mutations within plasma cells and patient socio-demographic factors. Novel treatments extended the time to disease progression and overall survival for the majority of patients. However, a subset of 15%-20% of MM patients exhibit an aggressive disease course with rapid disease progression and poor overall survival regardless of treatment. Accurately predicting which patients are at high-risk is critical to designing studies with a better understanding of myeloma progression and enabling the discovery of novel therapeutics that extend the progression free period of these patients. To date, most MM risk models use patient demographic data, clinical laboratory results and cytogenetic assays to predict clinical outcome. High-risk associated cytogenetic alterations include deletion of 17p or gain of 1q as well as t(14;16), t(14;20), and most commonly t(4,14), which leads to juxtaposition of MMSET with the immunoglobulin heavy chain locus promoter, resulting in overexpression of the MMSET oncogene. While cytogenetic assays, in particular fluorescence in situ hybridization (FISH), are widely available, their risk prediction is sub-optimal and recently developed gene expression based classifiers predict more accurately rapid progression. To investigate possible improvements to models of myeloma risk, we organized the Multiple Myeloma DREAM Challenge, focusing on predicting high-risk, defined as disease progression or death prior to 18 months from diagnosis. This effort combined 4 discovery datasets providing participants with clinical, cytogenetic, demographic and gene expression data to facilitate model development while retaining 4 additional datasets, whose clinical outcome was not publicly available, in order to benchmark submitted models. This crowd-sourced effort resulted in the unbiased assessment of 171 predictive algorithms on the validation dataset (N = 823 unique patient samples). Analysis of top performing methods identified high expression of PHF19, a histone methyltransferase, as the gene most strongly associated with disease progression, showing greater predictive power than the expression level of the putative high-risk gene MMSET. We show that a simple 4 feature model composed of age, stage and the gene expression of PHF19 and MMSET is as accurate as much larger published models composed of over 50 genes combined with ISS and age. Results from this work suggest that combination of gene expression and clinical data increases accuracy of high risk models which would improve patient selection in the clinic. Disclosures Towfic: Celgene Corporation: Employment, Equity Ownership. Dalton:MILLENNIUM PHARMACEUTICALS, INC.: Honoraria. Goldschmidt:Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; John-Hopkins University: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Mundipharma: Research Funding; Amgen: Consultancy, Research Funding; Chugai: Honoraria, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Molecular Partners: Research Funding; MSD: Research Funding; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Adaptive Biotechnology: Membership on an entity's Board of Directors or advisory committees; Janssen: Consultancy, Research Funding; Dietmar-Hopp-Stiftung: Research Funding; John-Hopkins University: Research Funding. Avet-Loiseau:takeda: Consultancy, Other: travel fees, lecture fees, Research Funding; celgene: Consultancy, Other: travel fees, lecture fees, Research Funding. Ortiz:Celgene Corporation: Employment, Equity Ownership. Trotter:Celgene Corporation: Employment, Equity Ownership. Dervan:Celgene: Employment. Flynt:Celgene Corporation: Employment, Equity Ownership. Dai:M2Gen: Employment. Bassett:Celgene: Employment, Equity Ownership. Sonneveld:SkylineDx: Research Funding; Takeda: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; BMS: Honoraria; Amgen: Honoraria, Research Funding. Shain:Amgen: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; AbbVie: Research Funding; Takeda: Membership on an entity's Board of Directors or advisory committees; Sanofi Genzyme: Membership on an entity's Board of Directors or advisory committees; Adaptive Biotechnologies: Consultancy. Munshi:Abbvie: Consultancy; Takeda: Consultancy; Oncopep: Consultancy; Celgene: Consultancy; Adaptive: Consultancy; Amgen: Consultancy; Janssen: Consultancy. Morgan:Bristol-Myers Squibb, Celgene Corporation, Takeda: Consultancy, Honoraria; Celgene Corporation, Janssen: Research Funding; Amgen, Janssen, Takeda, Celgene Corporation: Other: Travel expenses. Walker:Celgene: Research Funding. Thakurta:Celgene: Employment, Equity Ownership.

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Microenvironment Profiling in Myelodysplastic Syndromes and Its Relationship to Clonal Hematopoiesis and Disease Progression

Blood ◽

10.1182/blood-2021-150104 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 4650-4650

Author(s):

Antonieta Molero Yordi ◽

Bárbara Tazón ◽

Laura Gallur ◽

Silvia Saumell ◽

Tamara Jimenez ◽

...

Keyword(s):

Bone Marrow ◽

T Cells ◽

High Risk ◽

Disease Progression ◽

Board Of Directors ◽

Myelodysplastic Syndromes ◽

Research Funding ◽

Statistical Significance ◽

Advisory Committees ◽

Heterogeneous Distribution

Abstract Background: Immune dysregulation and somatic gene mutations are known prognostics factors in myelodysplastic syndromes (MDS). Moreover, impaired cytotoxicity and a decrease in mature natural killer cells (NK) have been related to higher risk characteristics. Also, killer immunoglobulin-like receptor (KIR) expression and haplotype have been associated with overall survival in MDS. The overexpression of inflammatory cytokines, produced by the clonal cell, plays a role in the immune environment. Early MDS presented increase apoptosis, whereas high risk MDS shows a downregulation of pro-apoptotic cytokines indicating decreased immune surveillance. The importance of the interaction of the immune populations and the malignant clone is not entirely understood. The aim of this study was to characterize how the microenvironment regulates the malignant clone and to describe the different immune landscape in MDS bone marrow. Methods: We prospectively studied 50 MDS patients, 12 idiopathic cytopenia of unknown significance (ICUS) and 4 healthy donors (HD). We analyzed different immune cells in bone marrow: NK (CD3CD56+CD16+/CD56+CD16-/CD56-CD16+) and their activating (NKp46, NKp30, NKG2C, NKG2D, NKp44, DNAM) and inhibitory receptors (TIGIT, NKG2A, Irp60, and PD1) as well as their ligands (HLA-ABC, MICA-B, CD155, PD-L1). We also assessed myeloid-derived suppressor cells (MDSC), differentiating granulocytic (Gr-MDSC: CD11b +CD33 +HLA-DR -CD15 +CD14 -) from monocytic (Mo-MDSC: CD11b +CD33 +HLA-DRlow/CD15 +CD14 +). Also, T cells subpopulations in peripheral blood with the following markers (CD3/CD4/ CD8/CCR7/CD45RA/ CD27/CD28/CD279/CD57/CXCR3/CCR6). Molecular analysis by NGS using the Oncomine Myeloid Research Assay (ThermoFisher Scientific) included 40 genes associated with myeloid malignancies. Also, we determined the KIR haplotype by NGS. For the study of cytokines concentrations, we used the Luminex® platform with ThermoFisher commercial kit ProcartaPlex TM Multiplex immunoassay. Results: A total of 66 samples were tested. Patient's median age was 74 years-old and 44% were female (other details in table1). Compared to ICUS, we found in MDS patients a decrease of TCD4+PD1+ T cells (MDS 26.23% vs ICUS 41.23%, p=0.022), effector TCD8+ cells (MDS 15.74% vs ICUS 45.69%, p= 0.02) and in TNK (MDS 1.72% vs ICUS 7.8%, p= 0.04). Regarding NK cells, we observed a decrease in mature NK (CD56dimCD16+) in MDS compared to ICUS, which did not reach statistical significance (MDS 15.25% vs ICUS 79.16%; p=0.104). As for NK receptors, we observed a significant decrease in NKG2C (MDS 4.94%, vs HD 28.35% p=0.039) and KIR2DS4 (MDS 16.56% vs HD 91.18%; p=0.036) expression in MDS. In the study of ligands, a significant loss of MIC-A/B in MDS vs. controls (MDS 0.42% vs HD 6.96%, p=0.034) was detected. Regarding cytotoxicity, a higher expression of perforin in MDS and ICUS compared with HD (35%, 42.65% vs. 11.93% respectively; p=0.033) was showed. A 33% of patients presented with KIR A haplotype, with no differences in the immunological profile between haplotypes. In terms of MDSC, we observed a trend to higher expression in MDS compared to controls (MDS 1.58%, ICUS 0.15% vs HD 0.18% p=0.10). Of these patients, 4 required treatment and 1 progressed to AML. We found mutations in 34 (85%) of MDS, of these, 27 (79.4%) had more than 2, with 38% of patients with abnormal cytogenetic (including 14.7% complex karyotype). Mutated patients had more MDSC than unmutated patients (0.95% vs 0.01%, p=0.001) and a trend to lower CD56dimCD16+ expression in mutated patients compared with unmutated MDS (24.7% vs. 91.57%, respectively, p=0.058). Finally, in the cytokines analysis, an increased level of IL-10 in high-risk compared to low and intermediate patients (2 pg/ml vs. 1 pg/ml, p=0.04) was demonstrated, 16 (53%) had high concentrations of IL10 > 40 pg/ml, 8 (26.6%) had more than 2 mutations and 3 (10%) had a single TP53 mutation. Conclusions: Our analysis showed a heterogeneous distribution of the different immune populations. We found a decreased mature NK and increased MDSC in mutated patients. Further analyses should be performed to describe independent factors that may affect disease progression. Figure 1 Figure 1. Disclosures Molero Yordi: Oryzon Genomics: Consultancy. Salamero: Pfizer: Consultancy; BMS/Celgene: Consultancy, Honoraria; Novartis: Consultancy, Honoraria. Diez-Campelo: Takeda Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; BMS: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Prosper: Oryzon: Honoraria; BMS-Celgene: Honoraria, Research Funding; Janssen: Honoraria. Bosch: Roche: Membership on an entity's Board of Directors or advisory committees, Other: Travel; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees, Other: Travel; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel; TAKEDA: Membership on an entity's Board of Directors or advisory committees, Other: Travel. Valcarcel: SANOFI: Consultancy, Honoraria, Speakers Bureau; SOBI: Consultancy, Honoraria, Speakers Bureau; JAZZ: Consultancy, Honoraria, Speakers Bureau; AMGEN: Consultancy, Honoraria, Speakers Bureau; NOVARTIS: Consultancy, Honoraria, Speakers Bureau; ASTELLAS: Consultancy, Honoraria, Speakers Bureau; TAKEDA: Consultancy, Honoraria, Speakers Bureau; BMS: Consultancy, Honoraria, Speakers Bureau; CELGENE: Consultancy, Honoraria, Speakers Bureau.

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Use of Monoclonal Antibody Therapy in Hematologic Patients with Mild-to-Moderate COVID-19: A Retrospective Single-Center Experience

Blood ◽

10.1182/blood-2021-154187 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 3037-3037

Author(s):

Idoroenyi Amanam ◽

Janny M. Yao ◽

Alfredo Puing ◽

Ni-Chun Tsai ◽

Diana Samuels ◽

...

Keyword(s):

Monoclonal Antibody ◽

High Risk ◽

Disease Progression ◽

Board Of Directors ◽

Median Time ◽

Active Treatment ◽

Research Funding ◽

Patient Characteristics ◽

Advisory Committees ◽

Privately Held

Abstract Background: In November 2020, the U.S. Food and Drug Administration (FDA) issued emergency use authorization (EUA) for monoclonal antibody (mAb) therapy in patients with mild to moderate COVID-19 who are at high risk for disease progression. These mAbs reduce the risk of hospitalization in the general population. However, its efficacy and safety in immunocompromised hematology patients are not known. Methods: From November 9th, 2020, until February 28th, 2021, all adult hematology patients with mild to moderate COVID-19 disease who received monoclonal antibodies within 10 days of symptoms onset were included. Patients who were asymptomatic, had severe or critical COVID-19 disease, or were hospitalized at the time of COVID-19 diagnosis were excluded. Baseline demographic, clinical outcomes, and hematologic-related data were extracted. All statistical analysis was performed using SAS statistical software. Results: Thirty-eight hematology patients with mild to moderate COVID-19 disease who received mAb therapy under EUA were included in this study. Thirty (79%) patients received bamlanivimab and 8 (21%) casirivimab-imdevimab. Baseline characteristics prior to mAB administration include: 53% female, median age of 51 years (range: 21-80), with 18% above 65 years old. Twenty-eight (74%) patients received cellular therapy: 18 (47%) had undergone allogeneic hematopoietic cell transplantation (HCT), 9 (24%) autologous HCT, and 1 (3%) chimeric antigen receptor T-cell (CAR T) therapy. Among the 17 patients who had COVID-19 disease after HCT, the median time to COVID-19 diagnosis was 22.8 months (range: 2.6-274.4) from HCT to COVID-19 diagnosis. Twelve out of 17 (71%) alloHCT patients were being managed for active graft-vs-host disease (GvHD) at the time of COVID-19 diagnosis (chronic GVHD: n=11 [mild: 4, moderate: 4, severe: 3], acute GVHD (grade 2): n=1). Ten (59%) alloHCT patients were on immunosuppressant therapy at the time of COVID-19 diagnosis. Fifteen (39%) patients were on active treatment for their hematologic malignancy (HM) at the time of COVID-19 diagnosis with a mean of 3 previous lines of treatment (range: 1-6). Additional patient characteristics are shown in Table 1. mAb therapy under EUA was well tolerated in this patient population with only 1 (3%) patient having experienced an adverse reaction characterized as headache. Four (11%) patients were hospitalized due to COVID-19, and 2 (5%) progressed to severe disease. All four patients had received bamlanivimab. The median time for hospitalization from diagnosis of COVID-19 to admission date was 8 days (range: 1-20) while median time from mAB infusion to hospitalization was 7.5 days (range: 0-17). One patient (3%) died within 30 days of COVID-19 diagnosis; the cause of death was COVID-19 disease. Most patients (n=34, 89%) ultimately tested negative for SARS-CoV-2 by PCR after mAb infusion. 34% of patients (n=13) cleared the virus within 2 weeks of receiving mAb infsuion. The median time to clearance of viral shedding was 25.5 days (range: 7-138). After mAb infusion, most patients (10/15; 67%) who were previously on active treatment for HM prior to diagnosis of COVID-19 resumed therapy for their HM with a median delay of 21.5 days (range: 12-42). We observed a significant difference in hospitalization was amongst patients who received a HCT vs. non-HCT (0%, 0/26 and 36%, 4/11 respectively; p<0.01). None of the other patient characteristics, which included: gender, ethnicity, age, BMI, smoking, obesity, chronic kidney disease, diabetes mellitus, hypertension, coronary vascular disease, and lung disease, were associated with significantly increased rate of hospitalization. Conclusion: This study demonstrates that SARS-COV2 specific mAb use in malignant hematology patients under EUA was safe and may reduce hospitalization as reported in the literature amongst those at high risk for disease progression. Thus, the access to SARS-COV2 mAb in this population who is at increased risk for complications from SARS-COV2 infection is critical in reducing progression to severe COVID-19 disease and hospitalization. Figure 1 Figure 1. Disclosures Ali: Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; CTI BioPharma: Membership on an entity's Board of Directors or advisory committees; BMS: Speakers Bureau. Aribi: Seagen: Consultancy. Artz: Radiology Partners: Other: Spouse has equity interest in Radiology Partners, a private radiology physician practice. Koller: Novartis: Consultancy. Nikolaenko: Rafael Pharmaceuticals: Research Funding; Pfizer: Research Funding. Shouse: Beigene: Honoraria; Kite Pharma: Speakers Bureau. Stein: Amgen: Consultancy, Speakers Bureau; Celgene: Speakers Bureau; Stemline: Speakers Bureau. Marcucci: Abbvie: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings. Forman: Mustang Bio: Consultancy, Current holder of individual stocks in a privately-held company; Lixte Biotechnology: Consultancy, Current holder of individual stocks in a privately-held company; Allogene: Consultancy. Dadwal: AlloVir: Research Funding; Merck: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astellas: Speakers Bureau; Shire/Takeda: Research Funding; Aseptiscope: Consultancy; Janssen: Other: Investigator; Karius: Other: Investigator. Al Malki: CareDx: Consultancy; Rigel Pharma: Consultancy; Jazz Pharmaceuticals, Inc.: Consultancy; Neximmune: Consultancy; Hansa Biopharma: Consultancy.

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