Cognitive Profile of Adults with Sickle Cell Disease - Cluster Analysis

Abstract Background: Published literature on cognitive functioning in adults with sickle cell disease (SCD) is sparse when compared to children. A few reports describe deficits in processing speed and executive functioning. Some studies suggest that these deficits are more severe in patients with silent cerebral infarcts (SCI). Even in the absence of radiological evidence of ischemic injury, some cognitive deficits have been depicted in adults . We hypothesize that in SCD adults, the cognitive profile varies with the presence of ischemic injury (SCI or overt stroke). The aims of this study were 1) to describe the neuropsychological profiles of SCD adults, and 2) to characterize clusters of patients with similar cognitive profiles. Methods: We conducted a retrospective analysis of all consecutive SCD adults who underwent comprehensive neuropsychological assessment during routine care at the UMGGR clinic at Henri Mondor Hospital, Créteil (France), between January 2017 and April 2021. The Montreal Cognitive Assessment (MoCA) and Hospital Anxiety and Depression Scale (HADS) were used for cognitive disorder, anxiety and depression screening, respectively. The cognitive battery combined standardized neuropsychological tests with established clinical utility and validity. Educational attainment was scored based on the number of years of schooling for the highest completed diploma. Principal component analysis was performed. ANOVA was used to compare patients' characteristics between clusters. Results: 80 patients, median age 36.5 [range 19-63] years were included. 40 (50.0%) were male. Genotype distribution was 62 patients (77.5%) with SS/Sbeta 0, 12 (15.0%) with SC and 6 (7.5%) with Sbeta +. On Principal Component Analysis, a 5-factor model presented the best fit (Bartlett's sphericity test (χ²(171)=1174; p<0.001)), explaining 71.8% of the variance in neuropsychological scores. The first factor encompassed tests specifically assessing visual attention/visual organization (right hemisphere). The second included tests for mental/cognitive control (frontal lobe), the third tests of selective inhibition/attention (fronto-parietal), the fourth tests for language/memory (left temporal lobe) and the last referred to shifting skill (sub-cortical loop). On hierarchical classification, 3 different clusters emerged: 32 patients in cluster 1, 32 in cluster 2 and 16 in cluster 3. Cluster 1 had a lower mean educational level (F(2,77) = 15,65; p<0,001). Cluster 1 showed the lowest mean MoCA score (20.0/30.0), relative to cluster 2 and 3 (24.6 and 26.4; p<0.001 and p<0.001, respectively). Cluster 1 patients presented deficits on all five factors. Cluster 2 patients compared to cluster 3 were altered in 4 factors (factors 1-4), but to a lesser extent than cluster 1. Processing speed was slower and some frontal-executive deficits were present in cluster 2 compared to cluster 3. There was no statistical difference between clusters in terms of ethnic origins. There was a trend for the presence of more cerebral vasculopathy in cluster 1 (chi2; p=0.06). Regarding stroke, 70% occurred during childhood in cluster 1, whereas 70% during adulthood in cluster 2, and 100% during adulthood in cluster 3. Conclusions: Overall, these results suggest at least three different cognitive profiles in adults with SCD: 1) few or no cognitive deficits (cluster 3), 2) some cognitive impairment with a sub-cortical cognitive profile (cluster 2) and 3) more global cognitive impairment with cortical/sub-cortical profile and specific deficits of memory, language and constructional praxis, depending on the location of prior overt neurological events (cluster 1). To reduce the long-term cognitive morbidity of SCD, patients can be identified by their distinct cognitive profiles and neurorehabilitation tailored to their unique profile should be applied. The large proportion of childhood stroke in patients with global cognitive impairment in contrast with majority of those with milder to no cognitive impairment having had their stroke in adulthood emphasize the crucial importance of preventing early childhood stroke and implementing early neurorehabilitation. Disclosures Forté: Canadian Hematology Society: Research Funding; Pfizer: Research Funding; Novartis: Honoraria. Kuo: Pfizer: Consultancy, Research Funding; Bluebird Bio: Consultancy; Novartis: Consultancy, Honoraria; Apellis: Consultancy; Alexion: Consultancy, Honoraria; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Celgene: Consultancy; Bioverativ: Membership on an entity's Board of Directors or advisory committees. Bartolucci: Jazz Pharma: Other: Lecture fees; AGIOS: Consultancy; Emmaus: Consultancy; GBT: Consultancy; F. Hoffmann-La Roche Ltd: Consultancy; Hemanext: Consultancy; INNOVHEM: Other: Co-founder; Bluebird: Consultancy, Research Funding; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Lecture fees, Steering committee, Research Funding; Fabre Foundation: Research Funding; Addmedica: Consultancy, Other: Lecture fees, Research Funding.

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Deficiency of RPS14 Beyond the Haploinsufficient Loss in Del(5q)

Blood ◽

10.1182/blood-2021-151926 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 2591-2591

Author(s):

Vera Adema ◽

Sunisa Kongkiatkamon ◽

Laura Palomo ◽

Wencke Walter ◽

Stephan Hutter ◽

...

Keyword(s):

Gene Expression ◽

Board Of Directors ◽

Linear Model ◽

Research Funding ◽

Review Committee ◽

Advisory Committees ◽

Tp53 Mutations ◽

Model Based ◽

Independent Review ◽

Cluster 2

Abstract The prevailing theory in del(5q) is that haploinsuffciency (HI) stemming from deletion and not simply LOH (loss of heterozygosity) is the culprit in clonal evolution. To date no haploinsufficient gene has been found to be the leukemogenic factor conveying growth advantage, but various other genes have been found to be important for phenotypic features or for propensity to acquire subsequent specific lesions. RPS14 is an example of such a gene, particularly in patients (pts) with isolated del(5q), responsible for macrocytic anemia and erythroid dysplasia and a propensity for acquisition of TP53 mutations. We hypothesized that RPS14 downmodulation and its consequences may be more common than del(5q) and it is frequent pathophysiologic feature in MDS. We first analyzed the genomic and expression profile of 170 pts with del(5q) and 825 diploid for 5q. We developed a new analytic pipeline to identify the most HI genes present in a large number of del(5q) pts. Genes within CDR (common deleted region) were classified as HI from the linear model fit if (i) clonality vs. gene expression slope from the isolated del(5q) was negative and FDR<.05; and (ii) effect of del(5q) at 50% clonality vs. other cases was negative and FDR<.05. A total of 62 genes met these criteria for linear-model based genes HI status, with a further 5 genes dropping due to low expression. Gene expression for these 57 HI genes among del(5q) samples was adjusted to 50%-clonality using the slopes from the estimated linear model to remove clonal heterogeneity. After applying model-based sparse clustering approach on all cohort, we obtained 7 clusters (Figure 1). As expected, del(5q) cases clustered together and showed consistent HI of 5q marker gene expression. Cluster-1 (n=146) included almost all del(5q) cases, except for 8 "mis-categorized" patients. It was characterized by low risk MDS (LR-MDS), presence of anemia/neutropenia and low mutational burden, with TP53 being the most commonly mutated gene and the only cluster with CSNK1A1 mutations. The remaining non-del(5q) patients were grouped in 6 clusters. Diploid cluster-2 (n=133) featured a normal karyotype, frequent ASXL1 and TET2 mutations, and profound down-modulation of RPS14 in all the patients included in the cluster (vs. other diploid pts). While the median RPS14 expression in cluster-1 (del(5q) cluster, with 50% adjusted clonality) was 7.29 (range 4.68-8.82 Log 2CPM), cluster-2 exhibited a median RPS14 expression of 6.12 Log 2CPM (range: 4.91-7.31 Log 2CPM). Clusters-3, -4, -5 (n=138, 90, 94, respectively) included most of the high risk MDS (HR-MDS). Cluster-3 was enriched for thrombocytopenia and SRSF2 mutations; cluster-4 for anemia, thrombocytopenia and ASXL1 and SRSF2 mutations. Cluster-5 was characterized by pancytopenia and frequent ASXL1 mutations and CK (complex karyotype). Cluster-6 (n=66) and -7 (n=233) contained the majority of non-del(5q) LR-MDS. When we analyzed the RPS14 expression in these clusters based on the RPS14 expression in cluster 2 we found 13% (n=18), 21% (n=19), 9% (n=8), 14% (n=9), 7% (n=16) of low RPS14 expressors in cluster-3, -4, -5, -6, -7, respectively. Cluster-2 showed a similar percentage of patients with anemia, and thrombocytopenia vs. Cluster-1 (69 vs. 50%, 23 vs. 30%; respectively). The mutational profile included a higher frequency of mutations for SRSF2 (29 vs. 0%), NRAS/KRAS (22% vs. 4%), ASXL1 (40 vs. 15%), TET2 (35 vs. 15%), and JAK2 (17 vs. 6%). These results indicate a more proliferative molecular spectrum of RPS14 downregulated cluster-2 than del(5q)-cluster-1, but RPS14 downmodulation did not lead to acquisition of TP53 mutations (4% vs. 76%). Considering all non-del(5q) RPS14 low expressors (n=186), only 3% of the cases had TP53 mutations. Since TP53 and CSNK1A1 mutations were characteristic of cluster-1 we studied interactions with HI RPS14. HI RPS14 in del(5q) and diploid low expressors showed a decreased expression of CDKN1A (P<.001) in comparison to the non-HI or low RPS14. We also found that CSNK1A1 mutations were not found outside of del(5q) pts, CSNK1A1 low expressors coincided with RPS14 low expressors. In conclusion, RPS14 expression defect is more widespread than del(5q) in MDS. However, only del(5q) RPS14 HI pts are prone to harbor TP53 and CSNK1A1 mutations; a group of diploid pts with low RPS14 and CSNK1A1 expressions might mimic some del5q features and could potentially respond to similar treatments. Figure 1 Figure 1. Disclosures 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. Carraway: AbbVie: Other: Independent review committee; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Other: Independent review committee; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Agios: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astex: Other: Independent review committee; Stemline: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Celgene, a Bristol Myers Squibb company: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Maciejewski: Bristol Myers Squibb/Celgene: Consultancy; Regeneron: Consultancy; Novartis: Consultancy; Alexion: Consultancy.

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Single Cell ATAC Lineage Deconvolution Reveals Overlapping Subclones in Epigenetically Distinct AML Samples

Blood ◽

10.1182/blood-2021-154517 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 2381-2381

Author(s):

Deepika Dilip ◽

Richard Koche ◽

Kamal Menghrajani ◽

Ari Melnick ◽

Olivier Elemento ◽

...

Keyword(s):

Dimensionality Reduction ◽

Single Cell ◽

Board Of Directors ◽

Research Funding ◽

Single Cells ◽

Great Promise ◽

Sequencing Analysis ◽

Advisory Committees ◽

High Degree ◽

Cluster 2

Abstract Introduction: Acute Myeloid Leukemia (AML) is a biologically diverse disease. Expanded mutation panels and novel epigenetic assays are identifying an increasing number of putative AML subtypes beyond the traditional 'Good', 'Intermediate', and 'Poor' risk designations. Although these approaches show great promise, identifying the relevant underlying disease biology remains difficult. Single cell studies highlight this difficulty, showing dynamic interactions between multiple subclones, each with its own set of cooperating mutations interfering with normal hematopoiesis. We have previously shown that bulk ATAC data can be used to 'deconvolve' and identify hematopoietic state in AML samples. Here we extend this work, showing that this approach can be used to identify normal hematopoietic states with a high degree of accuracy. In addition, we show that AML samples that appear different in bulk actually contain overlapping lineage characteristics at the single cell level. Methods: Single cell ATAC-seq count files were downloaded from GSE74310, GSE96769 as well as corresponding bulk ATAC-seq count files from GSE74912, GSE96771. These data were generated by flow sorting normal specimens into well-known stages of hematopoiesis followed by either bulk or single cell ATAC-seq. A set of AML samples was processed by both single cell and bulk ATAC as well. Bulk ATAC data was normalized using DESeq2 followed by variance stabilizing transformation. Single cell data was processed and normalized using the Seurat pipeline with default parameters. A common peak atlas was created for each dataset, and peaks characteristic of each stage of hematopoiesis were selected using a modified Kruskal-Wallis statistic and optimized using a set of well-characterized in-vitro sample mixtures. Lineage deconvolution was performed using a non-negative least squares regression comparing each unknown sample to the set of normal hematopoietic states. Results: Dimensionality reduction of single cell ATAC-seq using uniform manifold approximation and projection (UMAP) largely recapitulates stages of hematopoiesis used to sort the samples (Figure 1a). Single cell lineage deconvolution is able to identify the purity of these populations more precisely (Figure 1b), with HSC, MPP, LMPP, CLP, GMP, MEP, and Monocytic stages showing relatively pure lineage characteristics. In contrast, the CMP stage appears to be composed of a heterogeneous population, as has been previously shown. Dimensionality reduction of bulk ATAC-seq data using Principle Component Analysis (PCA) illustrates distinct stages of hematopoiesis, and separates the AML samples into two groups (Figure 2c). To further analyze these groups, bulk lineage deconvolution was performed, showing that cluster 1 (purple) has a more differentiated appearance characterized by GMP and Monocyte lineages while cluster 2 also reflects earlier stages of hematopoiesis including HSC, MPP, and LMPP (Figure 2d). One sample from each cluster (highlighted in red in figure 2c,d) was evaluated using single cell ATAC-seq. Lineage deconvolution on the component cells illustrates substantial lineage characteristic overlap between subclones of these samples, with lineage based hierarchical clustering generating two clusters with mixed sample origin (Figure 2e). These clusters are separated into more and less differentiated lineage groups, with the cluster 2 sample cells more commonly having an HSC or MPP dominant lineage. However, some cluster 1 cells do have HSC or MPP lineage features as well, which is reflected by the poor association of cluster with sample (Fisher's exact p=0.8). Conclusions: Lineage deconvolution can be performed on single cell ATAC-seq data with a high degree of precision on normal samples and illustrates clonal lineage heterogeneity in malignant specimens not previously appreciated in bulk sequencing analysis. Analysis of greater numbers of samples and cells are needed to draw general conclusions, but the approach shows promise as a means of computationally identifying or sorting normal single cells and more precisely characterizing leukemias. Figure 1 Figure 1. Disclosures Melnick: Janssen Pharmaceuticals: Research Funding; Sanofi: Research Funding; Daiichi Sankyo: Research Funding; Epizyme: Consultancy; Constellation: Consultancy; KDAC Pharma: Membership on an entity's Board of Directors or advisory committees. Elemento: Johnson and Johnson: Research Funding; Volastra Therapeutics: Consultancy, Other: Current equity holder, Research Funding; Eli Lilly: Research Funding; Janssen: Research Funding; One Three Biotech: Consultancy, Other: Current equity holder; Champions Oncology: Consultancy; Freenome: Consultancy, Other: Current equity holder in a privately-held company; Owkin: Consultancy, Other: Current equity holder; AstraZeneca: Research Funding. Levine: Lilly: Honoraria; Gilead: Honoraria; Janssen: Consultancy; Morphosys: Consultancy; Astellas: Consultancy; Roche: Honoraria, Research Funding; Incyte: Consultancy; Amgen: Honoraria; Celgene: Research Funding; Isoplexis: Membership on an entity's Board of Directors or advisory committees; C4 Therapeutics: Membership on an entity's Board of Directors or advisory committees; Prelude: Membership on an entity's Board of Directors or advisory committees; Auron: Membership on an entity's Board of Directors or advisory committees; Ajax: Membership on an entity's Board of Directors or advisory committees; Zentalis: Membership on an entity's Board of Directors or advisory committees; Mission Bio: Membership on an entity's Board of Directors or advisory committees; Imago: Membership on an entity's Board of Directors or advisory committees; QIAGEN: Membership on an entity's Board of Directors or advisory committees. Glass: GLG: Consultancy.

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Impact of Polypharmacy and Potentially Inappropriate Medications Among Older Adults with Blood Cancers

Blood ◽

10.1182/blood-2021-148731 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 4089-4089

Author(s):

Tammy T. Hshieh ◽

Clark Dumontier ◽

Tim Jaung ◽

Nupur E. Bahl ◽

Emily S. Magnavita ◽

...

Keyword(s):

Older Adults ◽

Cognitive Impairment ◽

Board Of Directors ◽

Research Funding ◽

Potentially Inappropriate Medications ◽

Advisory Committees ◽

Delayed Recall ◽

Nccn Guidelines ◽

Risk Scale ◽

Active Cancer

Abstract Background. Polypharmacy and potentially inappropriate medications (PIMs) are common among older adults with blood cancer and can lead to adverse effects and poor outcomes. Polypharmacy is commonly defined as taking ≥5 or ≥8 medications, depending on the population. PIMs can cause adverse side effects for certain patients, e.g. diphenhydramine and benzodiazepines. We sought to define the prevalence of polypharmacy and PIMs in older adults with blood cancers, and to examine the association between both with cognitive impairment and frailty in this population. Methods. From February 2015 to November 2019, all transplant-ineligible patients ages 75 and older who presented for initial consultation for hematologic malignancy at the Dana-Farber Cancer Institute (Boston, MA) were approached by a research assistant (RA) for a 15-minute screening geriatric assessment. The RA assessed 42 aging-related health deficits using patient-reported and objective performance measures spanning the domains of function, cognition, comorbidity, and mobility. Patients were determined to be frail, pre-frail or robust via two approaches: deficit accumulation approach (Rockwood, JGMedSci 2007) and phenotypic approach (Fried, JGMedSci 2001). Cognition was measured using the delayed recall section of the Montreal Cognitive Assessment (MoCA; Nasreddine, JAGS 2005) and Clock-In-Box test (CIB; Chester, Am J Med 2011). In addition, we collected data via electronic medical record review of all prescribed and over-the-counter medications patients were taking at the time of initial consultation. These data were reconciled and reviewed for quality by two board-certified geriatricians. The geriatricians identified 2 types of PIMs: anticholinergic PIMs per the Anticholinergic Risk Scale (Rudolph, Arch Intern Med 2008) and cancer-specific PIMs per the National Cancer Care Network Medications of Concern (NCCN Older Adult Oncology 2020). For patients recommended for active cancer treatment, the association between polypharmacy and PIMs with frailty was assessed using ordinal logistic regression. The association between polypharmacy and PIMs with cognitive impairment (by MoCA delayed recall and CIB) was assessed using logistic regression. All models controlled for age, gender, and comorbidity (via Charlson Comorbidity Index). Results. In this patient cohort (N=785), 286 (36%) were female with 240 (30%) in the leukemia disease group, 272 (35%) lymphoma and 273 (35%) multiple myeloma. 603 (77%) patients had polypharmacy (≥5 medications) and 421 (54%) were taking ≥8 medications. 201 (25%) patients were taking at least one PIM based on the Anticholinergic Risk Scale (Rudolph) and 343 (44%) based on the NCCN guidelines. Overall, 131 (17%) were frail, 457 (58%) pre-frail and 197 (25%) robust. 541 (69%) patients had Charlson Co-morbidity Index ≥3; 111 (14%) patients had "probable" cognitive impairment by MoCA Delayed Recall and 147 (19%) had "probable" cognitive impairment by CIB. In the 468 (60%) patients on active cancer treatment, there was an association of frailty with polypharmacy defined by a cutoff of ≥8 (adjusted odds ratio [aOR]=2.82, 95% confidence interval [CI] 1.92-4.17), but not ≥5 medications (aOR=1.42, 95% CI 0.91-2.22; Table 1). With each additional medication on a patient's medication list, their odds of being more frail increased by 8% (aOR=1.08, 95% CI 1.04-1.12). With each one-point increase on the Anticholinergic Risk Scale, odds of being more frail increased by 19% (aOR=1.19, 95% CI 1.03-1.39). With each additional PIM based on NCCN guidelines, odds of being more frail increased by 65% (aOR=1.65, 95% CI 1.34-2.04). Polypharmacy and PIMs were not associated with cognitive impairment by either MoCA Delayed Recall or CIB. Conclusion. Polypharmacy and PIMs are prevalent among older patients with blood cancers and are strongly associated with frailty but not cognitive impairment, independent of comorbidity. Increasing number of anticholinergic and especially cancer-specific PIMs have a stronger association with frailty compared to increasing number of medications in general. Our findings highlight that the types of medications contributing to polypharmacy may be more important than number of total medications. This suggests the need for streamlined ways of identifying specific PIMs in practice to deprescribe medications that may be associated with cumulative harm in older adults with cancer. Figure 1 Figure 1. Disclosures Stone: Aprea: Consultancy; Boston Pharmaceuticals: Consultancy; BerGen Bio: Membership on an entity's Board of Directors or advisory committees; Arog: Consultancy, Research Funding; Amgen: Membership on an entity's Board of Directors or advisory committees; OncoNova: Consultancy; Syndax: Membership on an entity's Board of Directors or advisory committees; Syntrix/ACI: Membership on an entity's Board of Directors or advisory committees; Jazz: Consultancy; Macrogenics: Consultancy; Novartis: Consultancy, Research Funding; Glaxo Smith Kline: Consultancy; Innate: Consultancy; Janssen: Consultancy; Elevate Bio: Membership on an entity's Board of Directors or advisory committees; Foghorn Therapeutics: Consultancy; Gemoab: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Research Funding; Actinium: Membership on an entity's Board of Directors or advisory committees; Bristol Meyers Squibb: Consultancy; Celgene: Consultancy; Abbvie: Consultancy; Syros: Membership on an entity's Board of Directors or advisory committees; Takeda: Consultancy. Soiffer: Rheos Therapeutics, USA: Consultancy; Kiadis, Netherlands: Membership on an entity's Board of Directors or advisory committees; Juno Therapeutics, USA: Other: Data Safety Monitoring Board; Precision Biosciences, USA: Consultancy; Jazz Pharmaceuticals, USA: Consultancy; Takeda: Consultancy; Jasper: Consultancy; Gilead, USA: Other: Career Development Award Committee; NMPD - Be the Match, USA: Membership on an entity's Board of Directors or advisory committees.

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Single Cell Deconvolution of Myeloblasts Reveals Depletion of HSCs and Expansion of Inflammatory Granulocyte-Monocyte Progenitors (GMPs) Associated with Adverse Outcomes in Chronic Myelomonocytic Leukemia (CMML)

Blood ◽

10.1182/blood-2021-151348 ◽

2021 ◽

Vol 138 (Supplement 1) ◽

pp. 319-319

Author(s):

Abhishek Dhawan ◽

Meghan Ferrall-Fairbanks ◽

Brian Johnson ◽

Hannah Newman ◽

Virginia Volpe ◽

...

Keyword(s):

Flow Cytometry ◽

Single Cell ◽

Disease Progression ◽

Board Of Directors ◽

Rna Sequencing ◽

Research Funding ◽

Adverse Outcomes ◽

Advisory Committees ◽

Myeloid Neoplasms ◽

Cluster 2

Abstract Myeloblasts are associated with adverse outcomes and define transformation to acute myeloid leukemia in all chronic myeloid neoplasms. Myeloblasts represent hematopoietic stem and progenitor cells (HSPCs) that express CD34, but are never resolved into stem and progenitor subpopulations during clinical evaluation. Therefore, how expansion of myeloblasts reshapes the HSPC compartment and its impact on clinical outcomes remains undefined. To address this important feature of disease progression, we transcriptionally and immunophenotypically mapped CD34 + HSPCs at single cell resolution for 66 samples from 45 patients with CMML. Single cell-RNA sequencing was performed on 137,578 CD34 + enriched HSPCs from 39 CMML samples and integrated with 63,672 publicly available CD34 + normal HSPCs (Fig A). We overlaid each CMML sample on a pseudotime projection of differentiation trajectories from normal samples to establish sample-specific aberrancies in HSPC states. This mapping classified samples into HSPC-biased groups of monocyte (mono)-bias, megakaryocyte erythroid (ME)-bias, and normal-like, respectively enriched for GMP, MEP, and HSC transcriptional signatures (Fig B). These groups were associated with distinct clinical genomic characteristics and were congruent with patient-specific bulk sequencing. For example, ME biased cases had statistically higher hemoglobin and mono-bias cases were associated with adverse survival, inflammatory clinical correlates, and RAS pathway mutations (Fig C). Importantly, we identified significant depletion of HSC across CMML that was most pronounced in the mono-bias group. This was validated by flow cytometry in 26 CD34 + enriched samples, which showed HSC numbers decreased as myeloblasts expanded and disease progressed (Fig D,E). The mono-biased group strongly correlated to the fraction of cells that were transcriptionally enriched for cytokine receptor (CR) signaling (cluster 2, Fig F). These cluster 2 cells constituted a subset of GMPs that could be identified by CD120b expression based on COMET analysis (Fig F), were depleted after therapy in sequential samples, and were associated with high CTNNB1 and low IRF8 expression, suggesting that they are self-renewing GMPs as previously reported in murine models (Herault Nature 2017). To validate the clinical relevance of CR signaling in HSPCs, we established a CR high-parameter flow cytometry panel by prioritizing CRs from primary CMML CD34 + RNA-sequencing data and quantified their expression using PE-conjugated antibodies to screen CR expression and density. This led to a 30-parameter panel that accounted for CR co-expression, spectral overlap, enabled us to both map CRs on HSCs, CMPs, MEPs, and GMPs, and calculate the CR Shannon diversity in 26 CMML and 5 normal controls (Fig G). Patients with CD120b + GMPs had inferior survival, were associated with higher-risk, proliferative disease, and higher CR diversity (Fig H). Further, increased CR diversity was associated with inferior survival across all HSPC compartments. Given the expansion of GMPs in mono-biased patients, we hypothesized that prior periods of stress-induced hematopoiesis (SIH) could contribute to the development of this adverse HSPC differentiation trajectory during disease progression. We modeled SIH by performing BMT experiments with NRAS Q61R/WT bone marrow cells and controls as RAS mutations were associated with a mono-bias state. These experiments identified a depletion of HSC and expansion of CD120b + GMPs compared to controls recapitulating the HSPC compartment in human mono-biased cases (Fig I,J). We modeled the impact of SIH in human CMML by chronically treating RAS mutated CMML PDX models with LPS or vehicle and similarly observed HSC depletion and CD120b + GMP expansion in LPS-treated mice (Fig K,L). Our data suggests that HSC depletion is a characteristic of myeloblast expansion during disease progression. Further, even in a disease with homogenous hematopoietic output (monocytosis), progenitor expansion of HSPCs can occur in three distinct skewed states. The mono-biased state is associated with poor outcomes and can be recapitulated by modeling SIH in CMML. PDX studies are ongoing to validate these results and the effects of SIH on survival. Deconvolution of HSPCs at single cell resolution of other myeloid neoplasms and strategies to mitigate triggers of SIH to prevent the mono-biased state should be explored. Figure 1 Figure 1. Disclosures Komrokji: Acceleron: Consultancy; AbbVie: Consultancy; Taiho Oncology: Membership on an entity's Board of Directors or advisory committees; PharmaEssentia: Membership on an entity's Board of Directors or advisory committees; Geron: Consultancy; Jazz: Consultancy, Speakers Bureau; BMSCelgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Sallman: Intellia: Membership on an entity's Board of Directors or advisory committees; Agios: 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, Speakers Bureau; Syndax: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite: Membership on an entity's Board of Directors or advisory committees; Shattuck Labs: Membership on an entity's Board of Directors or advisory committees; Magenta: Consultancy; Takeda: Consultancy; Aprea: Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Membership on an entity's Board of Directors or advisory committees; Incyte: Speakers Bureau. Bejar: Gilead: Consultancy, Honoraria; Takeda: Research Funding; Aptose Biosciences, Inc.: Current Employment, Current equity holder in publicly-traded company; Silence Therapeutics: Consultancy; Astex: Consultancy; Epizyme: Consultancy, Honoraria; BMS: Consultancy, Research Funding. Padron: BMS: Research Funding; Incyte: Research Funding; Kura: Research Funding; Blueprint: Honoraria; Taiho: Honoraria; Stemline: Honoraria.

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System-Level Disease-Driven Immune Signatures in Patients with Diffuse Large B-Cell Lymphoma Associated with Poor Survival

Blood ◽

10.1182/blood-2019-131359 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 2897-2897

Author(s):

Astrid Tschan-Plessl ◽

Eivind Heggernes Ask ◽

Thea Johanne Gjerdingen ◽

Michelle Saetersmoen ◽

Hanna Julie Hoel ◽

...

Keyword(s):

B Cell ◽

Board Of Directors ◽

Research Funding ◽

Cell Lymphoma ◽

B Cell Lymphoma ◽

Healthy Controls ◽

Protein Profiles ◽

Advisory Committees ◽

Immune Signatures ◽

Cluster 2

Global gene expression profiling of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) has revealed broad innate immune signatures that distinguish the heterogeneous disease subtypes and correlate with good treatment outcome. However, we still lack tools to identify the relatively large group of patients that are refractory to initial therapy and have a dismal prognosis. Here, we used mass cytometry and serum profiling in a systems-level approach to analyze immune responses in 36 patients with aggressive B cell lymphoma and age- and sex-matched healthy controls. Stochastic neighbor embedding (t-SNE) analysis of protein profiles divided patients into two distinct clusters, with cluster 2 representing patients with a more severe deviation in their protein expression compared to healthy controls. Patients in cluster 2 showed a more dramatic perturbation of their immune cell repertoires with expansion of myeloid-derived suppressor cells (MDSCs), increased T cell differentiation and significantly higher expression of metabolic markers such as GLUT-1 and activation markers, including Ki67, CD38 and PD-1. An extended analysis of serum protein profiles in two independent cohorts (n=69 and n=80 patients, respectively) revealed that that the identified systemic immune signatures were linked to poor progression free survival (PFS) and inferior overall survival (OS). Immune monitoring during chemo-immunotherapy showed that most patients normalized their serum protein profiles. Notably, non-responding patients retained higher than normal expression of several proteins, including PD-L1, CD70, IL-18, granzyme A and CD83. These studies demonstrate distinct patterns of disease-driven alterations in the systemic immune response of DLBCL patients that are associated with poor survival and persist in patients who are refractory to therapy. Figure 1 System-level immune signatures associated with poor prognosis in DLBCL. A) Altered serum profiles in patients compared to healthy controls. Two clusters of patients were identified based on t-SNE analysis of serum profiles. B) Patients in cluster 2 had bulky disease and B symptoms. C) t-SNE map of all patients (n=36) and controls (n=17). Relative abundance of cells from healthy controls and patients in all areas of the t-SNE clustering, highlighting cell subsets that are larger or smaller in patients compared to healthy donors. Colors indicate the difference in kernel density estimation of the t-SNE data for patients and healthy controls. D) Abundance of monocytic myeloid-derived suppressor cells as percentage of all CD45+ cells in healthy donors and the two patient clusters. White, Healthy controls; Blue, Cluster 1; Red, Cluster 2. E) Major phenotypic differences between patient clusters shown as mean mass intensity (MMI) or percent positive cells for selected markers (CD38 and PD-1) across multiple subsets. White, Healthy controls; Blue, Cluster 1; Red, Cluster 2. F-G) Overall survival in patients with serologically defined immune signatures belonging to cluster 1 or 2. H) Abundance of serum proteins in patients that stayed in remission (n=24) compared to those that did not (n=6). Figure 1 Disclosures Olweus: Gilead Kite: Research Funding; Intellia: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Wahlin:Roche and Gilead: Consultancy. Fehniger:Cyto-Sen Therapeutics: Consultancy; Horizon Pharma PLC: Other: Consultancy (Spouse). Holte:Novartis: Honoraria, Other: Advisory board. Kolstad:Nordic Nanovector: Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck: Research Funding. Malmberg:Fate Therapeutics, Inc.: Consultancy, Research Funding; Vycellix: Consultancy, Membership on an entity's Board of Directors or advisory committees.

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Evidence of Educational Bias in Cognitive Screening of Adults with Sickle Cell Disease: Comparison of Available Tools and Possible Strategies for Mitigation

Blood ◽

10.1182/blood-2020-143169 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 13-14

Author(s):

Stéphanie Forté ◽

Maryline Couette ◽

Damien Oudin Doglioni ◽

Denis Soulieres ◽

Kevin H.M. Kuo ◽

...

Keyword(s):

Cognitive Impairment ◽

Sickle Cell Disease ◽

Educational Attainment ◽

Board Of Directors ◽

Research Funding ◽

Cognitive Screening ◽

Cell Disease ◽

Advisory Committees ◽

Visuospatial Task ◽

Level Of Education

Background: Cognitive impairment is a dreaded complication of sickle cell disease (SCD) that impacts quality of life, school performance and employment. In 2020, the American Society of Hematology issued a strong recommendation that clinicians supervising the care of adults with SCD conduct surveillance for cognitive impairment using simplified signaling questions (DeBaun, 2020). However, guidance on the optimal screening strategy is lacking and several available tools are biased by language and education. The Rowland Universal Dementia Assessment Scale (RUDAS) was specifically designed for cognitive screening in multicultural populations (Storey, 2004). In the general elderly population, RUDAS is less biased by education than the Montreal Cognitive Assessment (MoCA) (Naqvi, 2015). Hypothesis: In adults with SCD, performance on the RUDAS is less influenced by educational attainment when compared to the MoCA. Our primary aim was to estimate the prevalence of suspected cognitive impairment using RUDAS and MoCA in adult SCD patients. The secondary aims were to examine for the presence of educational bias and to develop mitigation strategies in case of such a bias. Methods: Study design: cross-sectional study at UMGRR clinic at Henri Mondor Hospital, Créteil (France). Inclusion criteria: out-patients ≥18 years-old; all SCD phenotypes. Exclusion criteria: inability to obtain informed consent and/or follow study instructions. Intervention: Cognitive screening was performed using the RUDAS (translated to French by Philippe Desmarais), MoCA (third alternative version) and an additional visuospatial task of copying overlapping triangles (from the French BEC96 assessment). RUDAS and MoCA scores <28 and <26, respectively, were considered suggestive of cognitive impairment per previous studies (Basic, 2009 and Nasredinne, 2005) and patients were referred for definite neuropsychological evaluation. Survey on demographics and screening for depression and anxiety using Hospital Anxiety Depression Scale (HADS) were completed by the participants. Educational attainment was scored based on the number of years of schooling for the highest completed diploma. Statistical plan: linear regression was performed to identify possible associations between RUDAS, MoCA and social determinants of health. Results: Among the first 45 consecutive adult SCD patients undergoing routine cognitive screening, the median age was 39 (range 19-67). RUDAS and MoCA scores suggestive of mild cognitive impairment were found in 33/45 (73.3%) and 29/45 (64.4%) participants, respectively. There was a strong correlation between both tests (r=0.48, p=0.001). Both RUDAS and MoCA scores increased significantly with increasing level of education (r=0.36, p=0.015 and r=0.39, p=0.007, respectively), but were not significantly influenced by the HADS score. RUDAS and MoCA test items most biased by education were visuoconstructional tasks. Tasks assessing executive functioning and language were also biased in MoCA. Substituting the 3D visuospatial task of the RUDAS by a 2D task reduced the educational bias (r=0.20, p=0.045). Adding 1 point for highest level of education £ 12 years after kindergarten did significantly mitigate the effect of education on the RUDAS but only partially for the MoCA (r=0.23, p=0.131 and r=0.30, p=0.047). Conclusions: Overall, these results suggest there is an educational bias in the neurocognitive screening of adult SCD patients using available tools such as the RUDAS and MoCA. Although RUDAS was less biased overall, visuospatial assessment remained biased. The task often considered more "culture-fair" is still subject to the impact of educational potential (Statucka, 2019). We provide different strategies to mitigate education bias when assessing with RUDAS. Thus, the RUDAS adjusted by the educational level allows to systematically identify SCD patients in need of comprehensive neurocognitive testing. Prospective validation is ongoing. Disclosures Forté: Canadian Hematology Society: Research Funding; Pfizer - Global Medical Grants: Research Funding. Soulieres:Novartis: Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees. Kuo:Pfizer: Consultancy, Research Funding; Celgene: Consultancy; Alexion: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Bioverativ: Membership on an entity's Board of Directors or advisory committees; Agios: Consultancy, Membership on an entity's Board of Directors or advisory committees; Bluebird Bio: Consultancy; Apellis: Consultancy. Bartolucci:Roche: Consultancy; Innovhem: Other; AGIOS: Consultancy; Bluebird: Consultancy; Emmaus: Consultancy; Addmedica: Research Funding; Fabre Foundation: Research Funding; Novartis: Research Funding; Bluebird: Research Funding; GBT: Consultancy; ADDMEDICA: Consultancy; HEMANEXT: Consultancy; Novartis: Consultancy.

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The Myelofibrosis Symptom Burden (MF-SB): An International Phenotypic Cluster Analysis of 329 Patients

Blood ◽

10.1182/blood.v120.21.1731.1731 ◽

2012 ◽

Vol 120 (21) ◽

pp. 1731-1731

Author(s):

Holly Lynn Geyer ◽

Amylou C. Dueck ◽

Robyn M. Emanuel ◽

Jean-Jacques Kiladjian ◽

Stefanie Slot ◽

...

Keyword(s):

Cluster Analysis ◽

Board Of Directors ◽

Research Funding ◽

Symptom Burden ◽

Symptom Clusters ◽

Costal Margin ◽

Spleen Size ◽

Cognitive Complaints ◽

Advisory Committees ◽

Cluster 2

Abstract Abstract 1731 Background: Symptom burden in primary, post-ET and post-PV myelofibrosis (MF) is frequently severe and correlates with a poor prognosis. However, symptom manifestations are heterogeneous with variable presence of specific symptoms, splenomegaly and cytopenias. We sought to identify the spectrum and features of MF symptomatic phenotypes by cluster analysis of prospectively gathered information on MF symptoms and disease features. Methods: Data was collected among an international cohort of subjects with MF. Data included demographics, disease features and completion of the Brief Fatigue Inventory (BFI) and Myeloproliferative Neoplasm Symptom Assessment Form Total Symptom Score (MPN-SAF TSS) (Blood 2011; 118:401–408). Surveyed symptoms addressed key disease features on a 0 (absent) to 10 (worst-imaginable) scale. Cluster development was based on consideration of r-squared in hierarchical clustering using Ward's linkage. Final cluster assignment was based on the nonhierarchical k-means method. Comparisons between symptom clusters were based on ANOVA and chi-squared tests. Results: Subject Demographic and Disease Characteristics: Data from 329 prospectively enrolled persons with MF was collected (Chinese 102, French 54, German 19, Italian 22, Dutch 45, English 51, Spanish 29, Swedish 7) including 223 PMF, 67 post-ET MF and 39 post-PV MF patients. Participants were of typical age (mean 59) and gender (47% F). Among all participants, four natural symptom clusters were identified (Figure 1). Among clusters, disease features including leukopenia, thrombocytopenia, and enlarged spleen varied significantly between clusters (P<0.05). Cluster 1: The “Fatigue Dominant with Few Lab Abnormalities” Profile (n=150 (46%; 69% PMF, 20% post-ET MF, 11% post-PV MF)). Cluster 1, the largest, is characterized by fatigue-dominant complaints in the setting of the lowest overall MPN-SAF TSS and highest proportion of males (59%). Individuals among this group have the lowest prevalence of laboratory abnormalities (65% total; anemia, 67%; thrombocytopenia, 20%) or clinical deficiencies including enlarged spleen (average 6.0 cm below costal margin), prior thrombosis (9%), prior hemorrhage (5%) or prior RBC-transfusions (20.4%). Interestingly, individuals in this group are most likely to have had prior splenectomy (5.8%). Cluster 2: The “Cognitive Complaints with Enlarged Spleen” Cluster (n=105 (32%; 65% PMF, 20% post-ET MF, 15% post-PV MF)). Cluster 2 is the 2nd largest cluster. Subjects have relatively few abnormal lab values (67% vs 65%–77%) but have high severity of fatigue, sexual difficulties, insomnia, inactivity and reduced QOL. These individuals have the largest spleen size (8.7cm below costal margin). Cluster 3: The “Nighttime and Cognitive Complaints” Group (n=53 (16%; 64% PMF, 25% post-ET MF, 11% post-PV MF)). Cluster 3 is the smallest cluster. Subjects have many cognitive and nighttime-related complaints including sexual difficulties, night sweats, insomnia, and concentration problems. Subjects with post-ET MF are predominant. This cluster also has the 2ndsmallest spleen size (7 cm) or history of prior thrombosis (9.6%), hemorrhage (7.8%) or requirement for transfusions (21.2%). Cluster 4: The “Severe Fatigue with Few End-organ Complaints” Cluster (n=21 (6%; 81% PMF, 14% post-ET MF, 5% post-PV MF)). Cluster 4 is the most symptomatic cohort with the highest proportion of subjects with PM. There is a lower frequency of end-organ complaints including abdominal pain, cough, and headaches. Symptoms including sexual difficulties, sad mood and insomnia are predominant. No subjects had prior splenectomy. Subjects also have the highest prevalence of prior thrombosis (29%), hemorrhage (14%), and transfusions (43%). Additionally, this cohort has the largest prevalence of lab abnormalities (77%) with thrombocytopenia (71%), leukopenia (41%) and anemia (41%). Conclusion: This analysis will allow us to examine a new framework for evaluating persons with MF using symptom profiles and is the 1st cluster evaluation of MF. Lab and physical findings contrast significantly between symptom clusters indicating these phenotypic symptoms likely result from etiological factors present in specific disease phenotypes. Future studies should evaluate whether there is a correlation between cluster profiles, prognosis and genotype. Disclosures: Kiladjian: Celgene: Research Funding; Novartis: Honoraria, Research Funding; Shire: Honoraria. Roy:Novartis, BMS: Speakers Bureau. Harrison:Novartis: Honoraria, Research Funding, Speakers Bureau; YM Bioscience: Consultancy, Honoraria; Sanofi Aventis: Honoraria; Shire: Honoraria, Research Funding. Vannucchi:Novartis: Membership on an entity's Board of Directors or advisory committees. Passamonti:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria, Membership on an entity's Board of Directors or advisory committees. Mesa:Incyte: Research Funding; Lilly: Research Funding; Sanofi: Research Funding; NS Pharma: Research Funding; YM Bioscience: Research Funding.

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Distinct Gene Expression Signatures in Patients with Richter's Syndrome and Chronic Lymphocytic Leukemia with Prior Exposure to Ibrutinib

Blood ◽

10.1182/blood-2020-136172 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 30-31

Author(s):

Hanyin Wang ◽

Shulan Tian ◽

Qing Zhao ◽

Wendy Blumenschein ◽

Jennifer H. Yearley ◽

...

Keyword(s):

Gene Expression ◽

B Cell ◽

Board Of Directors ◽

Research Funding ◽

Cell Activation ◽

Expression Profiles ◽

Lymphocytic Leukemia ◽

B Cell Activation ◽

Advisory Committees ◽

Upregulated Genes

Introduction: Richter's syndrome (RS) represents transformation of chronic lymphocytic leukemia (CLL) into a highly aggressive lymphoma with dismal prognosis. Transcriptomic alterations have been described in CLL but most studies focused on peripheral blood samples with minimal data on RS-involved tissue. Moreover, transcriptomic features of RS have not been well defined in the era of CLL novel therapies. In this study we investigated transcriptomic profiles of CLL/RS-involved nodal tissue using samples from a clinical trial cohort of refractory CLL and RS patients treated with Pembrolizumab (NCT02332980). Methods: Nodal samples from 9 RS and 4 CLL patients in MC1485 trial cohort were reviewed and classified as previously published (Ding et al, Blood 2017). All samples were collected prior to Pembrolizumab treatment. Targeted gene expression profiling of 789 immune-related genes were performed on FFPE nodal samples using Nanostring nCounter® Analysis System (NanoString Technologies, Seattle, WA). Differential expression analysis was performed using NanoStringDiff. Genes with 2 fold-change in expression with a false-discovery rate less than 5% were considered differentially expressed. Results: The details for the therapy history of this cohort were illustrated in Figure 1a. All patients exposed to prior ibrutinib before the tissue biopsy had developed clinical progression while receiving ibrutinib. Unsupervised hierarchical clustering using the 300 most variable genes in expression revealed two clusters: C1 and C2 (Figure 1b). C1 included 4 RS and 3 CLL treated with prior chemotherapy without prior ibrutinib, and 1 RS treated with prior ibrutinib. C2 included 1 CLL and 3 RS received prior ibrutinib, and 1 RS treated with chemotherapy. The segregation of gene expression profiles in samples was largely driven by recent exposure to ibrutinib. In C1 cluster (majority had no prior ibrutinb), RS and CLL samples were clearly separated into two subgroups (Figure 1b). In C2 cluster, CLL 8 treated with ibrutinib showed more similarity in gene expression to RS, than to other CLL samples treated with chemotherapy. In comparison of C2 to C1, we identified 71 differentially expressed genes, of which 34 genes were downregulated and 37 were upregulated in C2. Among the upregulated genes in C2 (majority had prior ibrutinib) are known immune modulating genes including LILRA6, FCGR3A, IL-10, CD163, CD14, IL-2RB (figure 1c). Downregulated genes in C2 are involved in B cell activation including CD40LG, CD22, CD79A, MS4A1 (CD20), and LTB, reflecting the expected biological effect of ibrutinib in reducing B cell activation. Among the 9 RS samples, we compared gene profiles between the two groups of RS with or without prior ibrutinib therapy. 38 downregulated genes and 10 upregulated genes were found in the 4 RS treated with ibrutinib in comparison with 5 RS treated with chemotherapy. The top upregulated genes in the ibrutinib-exposed group included PTHLH, S100A8, IGSF3, TERT, and PRKCB, while the downregulated genes in these samples included MS4A1, LTB and CD38 (figure 1d). In order to delineate the differences of RS vs CLL, we compared gene expression profiles between 5 RS samples and 3 CLL samples that were treated with only chemotherapy. RS samples showed significant upregulation of 129 genes and downregulation of 7 genes. Among the most significantly upregulated genes are multiple genes involved in monocyte and myeloid lineage regulation including TNFSF13, S100A9, FCN1, LGALS2, CD14, FCGR2A, SERPINA1, and LILRB3. Conclusion: Our study indicates that ibrutinib-resistant, RS-involved tissues are characterized by downregulation of genes in B cell activation, but with PRKCB and TERT upregulation. Furthermore, RS-involved nodal tissues display the increased expression of genes involved in myeloid/monocytic regulation in comparison with CLL-involved nodal tissues. These findings implicate that differential therapies for RS and CLL patients need to be adopted based on their prior therapy and gene expression signatures. Studies using large sample size will be needed to verify this hypothesis. Figure Disclosures Zhao: Merck: Current Employment. Blumenschein:Merck: Current Employment. Yearley:Merck: Current Employment. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Parikh:Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Kenderian:Sunesis: Research Funding; MorphoSys: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Gilead: Research Funding; BMS: Research Funding; Tolero: Research Funding; Lentigen: Research Funding; Juno: Research Funding; Mettaforge: Patents & Royalties; Torque: Consultancy; Kite: Research Funding; Novartis: Patents & Royalties, Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ding:DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees.

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Identifying Factors That Predict for Unplanned Readmissions for Acute Myeloid Leukemia Patients Receiving Consolidation Cytarabine Based Therapies

Blood ◽

10.1182/blood-2019-128807 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 3433-3433

Author(s):

Caitlin Siebenaller ◽

Madeline Waldron ◽

Kelly Gaffney ◽

Brian P. Hobbs ◽

Ran Zhao ◽

...

Keyword(s):

Board Of Directors ◽

Myeloid Leukemia ◽

Research Funding ◽

Smoking Status ◽

Consolidation Therapy ◽

Peripheral Vascular ◽

Consolidation Chemotherapy ◽

Advisory Committees ◽

History Of ◽

Acute Myeloid

Background: Younger patients (pts) with acute myeloid leukemia (AML) who enter a remission after intensive induction chemotherapy routinely receive at least one cycle of consolidation therapy with high dose cytarabine (HiDAC). This is commonly administered over a five-day inpatient stay, after which pts are discharged home as their blood counts nadir. It is thus a natural consequence of therapy that readmission for febrile neutropenia (FN) occurs, which can impact measures of quality and value in this population. Precise descriptions of incidence, type, and severity of infection, if identified, are lacking, and thus it is unknown to what standard cancer centers should be held for anticipated readmission. We measured these rates, and attempted to identify predictive factors for readmission. Methods: Adult AML pts ≥ 18 years of age who received at least one cycle of HiDAC consolidation (1000-3000 mg/m2 for six doses) in 2009-2019 were included. Our primary aim was to identify predictive factors for readmission after the first cycle of consolidation chemotherapy. The following pt characteristics and co-morbid conditions were analyzed: age, gender, body mass index (BMI), smoking status, AML cytogenetic risk status, history of diabetes, peripheral vascular disease, cardiovascular disease, chronic pulmonary disease, hepatic impairment, and other cancers. Secondary aims included: estimating rates of all-cause readmissions among all HiDAC cycles, defining the rate of FN readmissions, estimating rates of intensive care unit (ICU) admissions, clinical (e.g., probable pneumonia per imaging) and microbiologically-documented infections, prophylactic (ppx) medications used, and mortality. Statistical analyses interrogated potential risk factors for evidence of association with hospital readmission after the first cycle of consolidation chemotherapy. Results: We identified 182 AML pts who fit inclusion criteria. The median age was 50 years (range 19-73); 55% were female and 45% were male. Statistical analyses revealed no association with readmission after cycle 1 for cytogenetic risk (p=0.85), history of heart failure (p= 0.67), chronic pulmonary disease (p=1), connective tissue disease (p=0.53), cerebrovascular accident (p=0.63), diabetes (p=0.63), gender (p=0.07), history of lymphoma (p=0.53), other solid tumors (p=0.53), liver disease (p=1), myocardial infarction (p=0.71), peripheral vascular disease (p=1), or smoking status (p= 0.52). For 480 HiDAC cycles analyzed (88% at 3000 mg/m2), the overall readmission rate was 50% (242/480), of which 85% (205/242) were for FN. Those readmissions which were not FN were for cardiac complications (chest pain, EKG changes), non-neutropenic fevers or infections, neurotoxicity, bleeding or clotting events, or other symptoms associated with chemotherapy (nausea/vomiting, pain, etc.). Median time to FN hospital admission was 18 days (range 6-27) from the start of HiDAC. Of the 205 FN readmissions, 57% had documented infections. Of these infections, 41% were bacteremia, 23% fungal, 16% sepsis, 12% other bacterial, and 8% viral. Of 480 HiDAC cycles, ppx medications prescribed included: 92% fluoroquinolone (442/480), 81% anti-viral (389/480), 30 % anti-fungal (142/480), and 3% colony stimulating factor (14/480). Only 7% (14/205) of FN readmissions resulted in an ICU admission, and 1% (3/205) resulted in death. Conclusions: Approximately half of patients treated with consolidation therapy following intensive induction therapy can be expected to be readmitted to the hospital. The majority of FN readmissions were associated with clinical or microbiologically documented infections and are not avoidable, however ICU admission and death associated with these complications are rare. Readmission of AML pts following HiDAC is expected, and therefore, should be excluded from measures of value and quality. Disclosures Waldron: Amgen: Consultancy. Hobbs:Amgen: Research Funding; SimulStat Inc.: Consultancy. Advani:Macrogenics: Research Funding; Abbvie: Research Funding; Kite Pharmaceuticals: Consultancy; Pfizer: Honoraria, Research Funding; Amgen: Research Funding; Glycomimetics: Consultancy, Research Funding. Nazha:Incyte: Speakers Bureau; Abbvie: Consultancy; Daiichi Sankyo: Consultancy; Jazz Pharmacutical: Research Funding; Novartis: Speakers Bureau; MEI: Other: Data monitoring Committee; Tolero, Karyopharma: Honoraria. Gerds:Imago Biosciences: Research Funding; Roche: Research Funding; Celgene Corporation: Consultancy, Research Funding; Pfizer: Consultancy; CTI Biopharma: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Sierra Oncology: Research Funding. Sekeres:Syros: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium: Membership on an entity's Board of Directors or advisory committees. Mukherjee:Partnership for Health Analytic Research, LLC (PHAR, LLC): Consultancy; McGraw Hill Hematology Oncology Board Review: Other: Editor; Projects in Knowledge: Honoraria; Celgene Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Bristol-Myers Squibb: Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees.

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Comparable Outcomes between Unrelated Donor (8/8 or 7/8 matched) and Haploidentical Donor for Allogeneic Stem Cell Transplantation in Adult Patients with Severe Aplastic Anemia

Blood ◽

10.1182/blood-2019-130419 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 4619-4619

Author(s):

Jee Yon Shin ◽

Sung-Soo Park ◽

Gi June Min ◽

Silvia Park ◽

Sung-Eun Lee ◽

...

Keyword(s):

Board Of Directors ◽

Research Funding ◽

Acute Gvhd ◽

Chronic Gvhd ◽

Advisory Committees ◽

Sibling Donor ◽

Alternative Donor ◽

Matched Sibling ◽

Grade Ii

Background Either allogeneic hematopoietic stem cell transplantation (SCT) from HLA-matched sibling donor or immunosuppressive therapy (IST) has been recommended as one of the standard treatments for severe aplastic anemia (SAA). Regarding only 30% of chance finding HLA‐matched sibling donor, SCT from an alternative donor including unrelated (URD) or haplo-identical related donor (HAPLO) is considered to be a treatment option after failure to IST in patients who lack of a HLA-matched sibling donor. The aim of this study was to compare the outcomes of URD SCT and HAPLO SCT for SAA patients. Method Consecutive 152 adult patients with SAA who received first SCT between March 2002 and May 2018 were included: 73 of HLA-well-matched (8/8) URD (WM-URD), 34 of HLA-mismatched URD (MM-URD), and 45 of HAPLO. With the intention to have a follow-up period at least 1 year, data were analyzed at May 2019. A conditioning regimen with total body irradiation (TBI) and cyclophosphamide was used for URD-SCT, whereas that with TBI and fludarabine was administered for HAPLO-SCT (Lee et al, BBMT 2011;17:101, Park et al, BBMT 2017;23:1498, Lee et al, Am J Hematol 2018;93:1368). The combination of tacrolimus and methotrexate were used as graft-versus-host disease (GVHD) prophylaxis. Results The median follow-up was 53.4 (range, 0.2-174.1) months. The median age of URD and HAPLO cohort was 30 (range 18-59) and 34 (range 18-59) years, respectively. Except for one and three patients who failed respective a neutrophil and platelet engraftment, other patients achieved neutrophil and platelet engraftments with median 11 and 15 days for WM-URD, 13 and 16.5 days for MM-URD, and 12 and 14 days for HAPLO, respectively. The five-years overall survival (OS), failure-free survival (FFS), and cumulative incidences (CIs) of graft-failure and transplant-related mortality were similar among three groups: 88.3%, 85.5%, 2.7%, and 11.7% for WM-URD; 81.7%, 81.7%, 0%, and 18.3% for MM-URD, and 86.3%, 84.1%, 6.7%, and 9.2% for HAPLO. The 180-days CI of grade II-IV acute GVHD in WM-URD, MM-URD and HAPLO were 35.6%, 52.9%, and 28.9%, respectively; and moderate to severe chronic GVHD were 28.7%, 38.7% and 11.8% in respective cohort. The CI of grade II-IV acute GVHD and moderate to severe chronic GVHD were significantly higher in MM-URD than those in HAPLO (both, p=0.026). ATG is the only factor affecting both grade II-IV acute GVHD (Hazard ratio 0.511, p=0.01) and moderate to severe chronic GVHD (Hazard ratio 0.378, p=0.003) in multivariate analysis. Other complications including CMV DNAemia, hemorrhagic cystitis, invasive fungal disease, secondary malignancy, and sinusoidal obstruction syndrome were similar among three groups. Survival outcomes of a subgroup of ≥ 2 allele MM-URD (n=16) extracted form MM-URD were inferior that of other donor types (n=136): 75.0% vs. 86.9% (p=0.163) for 5-year OS and 75.0% vs. 84.7% (p=0.272) for 5-year FFS. Conclusion This study shows that there were no significant differences between alternative donor sources in the absence of suitable matched sibling donor. Host/donor features and urgency of transplant should drive physician towards the best choice among alternative donor sources for SAA patients treated with SCT. However, selection of ≥ 2 allele MM-URD should not be recommended due to high incidence of GVHD and inferior outcomes. Figure Disclosures Kim: Celgene: Consultancy, Honoraria; Astellas: Consultancy, Honoraria; Hanmi: Consultancy, Honoraria; AGP: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; SL VaxiGen: Consultancy, Honoraria; Novartis: Consultancy; Amgen: Honoraria; Chugai: Honoraria; Yuhan: Honoraria; Sanofi-Genzyme: Honoraria, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Handok: Honoraria; Janssen: Honoraria; Daiichi Sankyo: Honoraria, Membership on an entity's Board of Directors or advisory committees; BL & H: Research Funding; Otsuka: Honoraria. Lee:Alexion: Consultancy, Honoraria, Research Funding; Achillion: Research Funding.

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