Runaas, Lyndsey

Abstract Allogeneic hematopoietic cell transplantation (allo-HCT) is a potentially curative treatment for many hematological malignancies, but graft-versus-host disease (GVHD) is a common complication. Low gut microbiome diversity is associated with higher GVHD risk and shorter survival in multiple studies. Recently, the BMT CTN 1703 clinical trial demonstrated superiority of a GVHD-prophylaxis regimen including post-transplant cyclophosphamide (PTCy) compared to the standard prophylaxis (tacrolimus and methotrexate, Tac/MTX) in terms of GVHD-free, relapse-free survival at one year among reduced intensity conditioning allo-HCT recipients. However, the effect of PTCy on the gut microbiome and its association with clinical outcome have not been described. Here, we report on a companion randomized clinical controlled trial (BMT CTN 1801), which collected 2575 longitudinal stool samples from 304 study participants. Samples were obtained up to weekly up to day 84 post allo-HCT and at less frequent intervals thereafter, up to 2 years. Microbiome diversity and absolute microbial load were lower in the PTCy group compared to the Tac/MTX group on days 14–28 post-HCT. However, diversity at the timepoint closest to neutrophil engraftment was not significantly associated with non-relapse mortality after one year or other clinical outcomes, contrary to expectations from previous studies. Microbial domination events, when a single species exceeds 30% relative abundance, were comparable across treatment arms and reflected both pathogen blooms as well as less severe disruptions of the microbial community. Clostridium scindens and secondary bile acid metabolism pathways were less prevalent in the PTCy arm than in the Tac/MTX arm post-HCT, yet presence of secondary bile acid metabolism pathways was associated with a lower risk of chronic GVHD. Given that PTCy was associated with a greater disruption of the microbiome as measured by diversity, absolute microbial abundance, and bile acid metabolism capability, but better clinical outcomes overall, these data suggest that the importance of the microbiome in modulating the host immune systems after allo-HCT is specific to different types of GVHD prophylaxis.

Abstract Background: A randomized multicenter trial (BMT CTN 1703) demonstrated that after reduced intensity conditioning HCT, PT-Cy-based GVHD prophylaxis led to significant reductions in acute and chronic GVHD, but was associated with significantly more Grade 2 (moderate) infections and similar overall survival vs Tac/MTX. A linked mechanistic study (BMT CTN 1801) found that PT-Cy led to pan-T cell depletion and reduced T cell receptor (TCR) repertoire diversity vs Tac/MTX. To discover the molecular mechanisms driving the association of PT-Cy with increased infections, we examined pathogen-specific T cell reconstitution in BMT CTN 1801 patients (n=165 PT-Cy, n=159 Tac/MTX). Methods: We performed deep TCR sequencing on 2,369 blood samples collected pre-HCT (recipient baseline) and on Days +7, 14, 28, 63, 100, 180, 270, 365, 730, and from the graft infusion product. We then applied a novel set of TCR classifiers (Adaptive Biotechnologies) capable of identifying pathogen-specific clones associated with CMV, EBV, SARS-CoV-2, HSV-1, HSV-2, parvovirus, RSV, influenza A, rhinovirus, adenovirus, HHV-6B, norovirus and Toxoplasma. Single-cell (sc) TCR/RNA-sequencing on a sample subset (n=26) was performed to determine the phenotype of pathogen-specific T cells. Results. By linking scRNA-seq with pathogen-specific TCR barcoding, we identified CD4 and CD8 memory T cells as their predominant T cell reservoir. We found that the total number of pathogen-specific singleton TCRs was significantly lower with PT-Cy, starting early post-HCT and persisting through 2 years. For example, at Day +14, PT-Cy patients had 7.8-fold fewer pathogen-specific singleton TCRs vs Tac/MTX (mean of 6.9 vs 54.1, p<0.001). Consistent differences between PT-Cy and Tac/MTX were observed for TCRs specific for each pathogen individually. Patients who developed Grade 3 (severe) viral infections had significantly fewer pathogen-specific singleton TCRs at Day +14 compared to those who had no viral infections (mean of 4.0 vs 32.5, p=0.03). Leveraging intrinsic TCR barcoding of the HCT infusion (“graft”) and of the patient's baseline samples (“recipient”), we could assign the graft/recipient origin of the pathogen-specific cells with high fidelity. This analysis revealed pronounced differences in the origin of pathogen-specific T cells with PT-Cy vs Tac/MTX, especially prior to Day +100. The absolute number of pathogen-specific TCRs originating from the graft was significantly higher with Tac/MTX vs PT-Cy. This difference was observed from day +7 through 6 months post-HCT, with up to 7.1-fold more graft-derived pathogen-specific TCRs in Tac/MTX vs PT-Cy (p<0.001 at each timepoint before day +100). Within patients who received Tac/MTX, the balance of graft vs recipient pathogen-specific clones significantly favored graft clones (for example, a mean of 14.9 graft vs 8.6 recipient pathogen-specific TCRs at Day+14, p=0.02). By contrast, with PT-Cy, in addition to having substantially fewer pathogen-specific TCRs overall, the proportion of these TCRs was not significantly different between graft and recipient clones (mean of 2.1 graft vs 3.3 recipient pathogen-specific TCRs at Day+14, p=0.42). These data uncover that, in addition to the reduction in pathogen-specific T cells with PT-Cy, there was a relative skewing of the anti-microbial T cell reservoir towards recipient cells that persisted after conditioning. Conclusions: PT-Cy led to in vivo T cell depletion across all T cell subsets, which prominently included clones tracked from the allograft itself into the HCT recipient, with a relative sparing of recipient T cell clones that survived transplant conditioning. While the mechanism for relative sparing of recipient cells has not yet been determined, these conditioning-exposed cells may be less proliferative than newly infused graft T cells. The depletion of graft T cells included CD4 and CD8 memory cells specific for infections, which may have contributed to relative deficits in protective immunity against infection in patients receiving PT-Cy. Importantly, these data suggest that with PT-Cy, persisting recipient TCRs may play a key role in preserving anti-infectious immunity. Taken together, these results provide a mechanistic basis for key post-transplant clinical outcomes of the landmark BMT CTN 1703 study. They also underscore the importance of recipient immune status with PT-Cy, creating the opportunity for targeted infection risk assessment in these patients.

Abstract Introduction: Graft Failure (GF) is a rare but devastating outcome of HCT. Primary GF (PGF), defined as a failure of neutrophil recovery by Day+28, is straightforward to diagnose. Diagnosing secondary GF (SGF) is more challenging, with its broad time-range, multiple confounding diagnoses, and lack of predictive biomarkers. In BMT CTN1703/1801, we analyzed patients receiving RIC HCT for heme malignancies with either Tac/MTX (n=159) or PT-Cy (n=165) GVHD prophylaxis. SGF was defined as donor chimerism <5% after initial donor engraftment. With Tac/MTX, there were 3 PGF and 1 SGF diagnoses. With PT-Cy, there were 4 PGF and 6 SGF. Median SGF diagnosis was Day +64 (range: Day+28-215). There were too few Tac/MTX patients to analyze SGF, but sufficient events with PT-Cy. To identify PT-Cy SGF predictors, we leveraged lymphocyte, T, B, and NK cell reconstitution analysis. In SGF we found an early, profound deficit in the reconstitution of all major lymphocyte populations, including total lymphocytes, T, B, and NK cells, as early as Day+28. This enabled the modeling of a SGF risk classifier based on the Absolute Lymphocyte Count (ALC). Methods: Clinical ALC measurements were performed on all SGF patients (n=6) and on non-GF controls with ALCs available (146 of 155 non-GF patients). Flow cytometry was performed on all PT-Cy SGF patients (n = 6) and from a subset of non-GF controls (n = 18: controls were chosen as patients without relapse or severe GVHD, to reduce confounders introduced by immunologic interventions, and for whom all samples, including from the graft infusion, were available). T, B, and NK counts, as well as T cell subsets, were compared (using Welch's T test) on Days 7, 14, 21, 28, 42, 63, 98, 180, 270, 365, 730, with SGF patients censored on the day of GF diagnosis. To interrogate the optimal ALC cutoff, the cumulative incidence of SGF was computed at Days +28 and +42, with death without GF as a competing risk. Results: We have previously demonstrated that, compared to Tac/MTX, PT-Cy patients exhibited an early decrease in reconstitution of all T cell populations (with normal NK and B cell reconstitution). Here we focused specifically on PT-Cy patients with or without SGF. Graft CD34 counts/kg were not different between SGF patients and non-GF patients (mean CD34/kg = 1.38 x10e6 (SGF) vs 0.67 x10e6 (non-GF, p = 0.61) However, even amidst the overarching early suppression in T cell reconstitution with PT-Cy, SGF patients could be easily distinguished from the larger PT-Cy cohort, based on more profound deficits in ALC, T, B and NK cells reconstitution, measured using 2 strategies: (1) SGF patients demonstrated significant early (Day +28) quantitative defects in the reconstitution of the ALC (mean +/- SEM 262+/-32 cells/µL (non-GF) vs 60 +/- 25 (SGF, p<0.0001), CD4 T cells (62 +/- 12 cells/µL vs 19+/-15, p=0.048), CD8 T cells (15 +/-3 cells/µL vs 3.5 +/-1 p=0.0009), all CD8 T cell subpopulations, as well as NK cells (116+/-31 cells/µL vs 2.9+/-1.4 cells/µL, p= 0.002) and B cells (3.5 +/-1.4 cells/µL vs 0.16+/-0.07 p =0.03). (2) In SGF, there was a significant deficit in the rate of rise of all major lymphocyte populations between Days 28-42-60 vs non-GF, including CD4 T cells (p<0.0001), CD8 T cells (p = 0.0002), B cells (p<0.0001), and NK cells (p =0.048). These discoveries suggested that a classifier could be identified to risk-stratify patients for SGF. We explored an ALC cutpoint, amenable to standard clinical lab analysis. A statistically significant threshold was identified at both Days+28 and +42, with Day+42 being most predictive: A threshold of 120 cells/µL was identified as optimal, with landmark analysis documenting a SGF rate of 34.7% below the cutpoint, and SGF of 1% above it (HR = 45.9, 95% CI 5.7 - 366). Conclusions: Despite the small number of events, PT-Cy patients with SGF demonstrated a distinctive reconstitution trajectory that encompassed an early, substantial, and sustained deficit in all lymphocyte counts, as well as a lack of their longitudinal expansion. This enabled the discovery of a candidate ALC biomarker cutpoint at Day+42 that could distinguish patients who were more likely to develop SGF. If confirmed, these data could generate a predictive biomarker for SGF, which would enable the design of trials evaluating early interventions (e.g. CD34+ boosts, DLI, modification of immunosuppression) to improve outcomes for these patients.

Abstract Despite concerns about the toxicity of allogeneic hematopoietic cell transplantation (alloHCT) in older patients, prospective data characterizing prevalence or risk stratification for geriatric morbidity such as disability or frailty are limited. We prospectively assessed the prognostic impact of the novel composite health assessment risk model (CHARM), a score established to predict 1-year nonrelapse mortality (NRM), among 1105 patients aged ≥60 years enrolled on the Bone Marrow Transplant Clinical Trials Network Study 1704. Secondary end points were assessed post-alloHCT at day 100 (D100), D180, and D365 in multivariable models adjusted with predetermined clinical variables. Among alloHCT survivors, the prevalence of disability by instrumental activities of daily living (IADL), frailty by the Physical Frailty Phenotype, and physical function impairment by Patient Reported Measurement Information System (PROMIS) was highest at D100 and lower on D180 and D365. Higher CHARM scores were independently associated with greater disability (coefficient, −0.64; 95% confidence interval [CI], −0.85 to −0.43; P< .001), increased frailty (coefficient, 0.19; CI, 0.081-0.31; P< .001), worse PROMIS physical function, greater PROMIS depression, increased serious organ toxicity by D100, more cognitive decline at D100, and higher mortality after acute graft-versus-host disease (GVHD) but not significantly associated with PROMIS anxiety or acute GVHD. Higher CHARM scores predicted worse disability-free survival (odds ratio [OR], 2.03; CI, 1.66-2.48; P< .001) and lower frailty-free survival (OR, 2.00; CI, 1.61-2.49). In summary, CHARM is an independent prognostic scoring system not only for NRM but also for geriatric morbidity and functional limitation–free survival through 1 year after alloHCT. Pre-alloHCT CHARM is a novel tool to aid shared decision-making for older patients. This trial was registered at www.clinicaltrials.gov as #NCT03992352.

Abstract Allogeneic hematopoietic cell transplantation (allo-HCT) is potentially curative for older adults with hematologic malignancies. Concerns on nonrelapse mortality (NRM) in older adults limit allo-HCT utilization. We executed a prospective, observational study BMT-CTN 1704 (Blood and Marrow Transplant Clinical Trials Network) enrolling allo-HCT recipients aged ≥60 years from 49 centers in the United States. We analyzed associations between 13 measurements of older adult health and NRM within 1 year to construct a comprehensive health assessment risk model (primary-CHARM) using multivariate Fine-Gray model and grouped penalized variable selection. Two machine learning (ML) models (Cox and pseudo-value boosting) were also explored. Models’ performances were compared using area under the curve (AUC), with bootstrap and cross-validation sampling to correct for optimism, decision curve analysis (DCA), calibration, and Brier scores. Among 1105 patients with median age of 67 (range, 60-82) years who received allo-HCT, NRM was 14.4% and overall survival (OS) 71.7% at 1 year. Factors statistically selected for inclusion in primary-CHARM were higher comorbidity burden, lower albumin, higher C-reactive protein, older age, higher weight-loss percentage, lower patient-reported performance score, and cognitive impairment. Primary-CHARM scores were independently associated with higher NRM (hazard ratio [HR], 2.72; P < .0001) and worse OS (HR, 2.09; P < .0001). Bootstrap bias–corrected AUC for primary-CHARM was 0.591. Comparing primary-CHARM with HCT-comorbidity index and 2 ML-CHARM models, calibration, Brier score, and DCA analysis favored primary-CHARM. Primary-CHARM, with mostly simple and readily available parameters, risk stratifies older adults for allo-HCT. Adopting primary-CHARM in practice may promote broader use of HCT by quantifying risk and enhance the design of strategies to improve outcomes. This trial was registered at www.ClinicalTrials.gov as #NCT03992352.

Introduction: The BMT CTN 1704 study developed and validated the CHARM that stratified risk for non-relapse mortality and overall mortality among older adults, performing better than the HCT-CI alone (Artz A et al, Blood. 2023;142:109) and equally to two machine learning models. CHARM assigns a total score for 7 health variables: increasing age, higher HCT-CI scores, lower albumin, higher C-reactive protein, higher percent of weight loss over the preceding year, lower patient-reported performance status scores, and lower cognitive score per Montreal cognitive assessment (MoCA). A CHARM calculator is available at:https://cibmtr.org/CIBMTR/OffNav/DevSandbox/CHARM-Risk-NRM-Calculator No multi-institutional prospective data exist on functional trajectories and morbidity after HCT in older patients. We now report on the association of CHARM to trajectories of secondary morbidity outcomes among allo-HCT survivors of this large, prospective study. Patients and Methods: Allo-HCT candidates, aged ≥60 years (yrs), were enrolled (n=1226) from 49 centers in the US between 2019 - 2021. The primary analysis includes 1105 patients proceeding to allo-HCT on study and secondary endpoints were assessed at day (D) 100, 180 and 365 except MoCA and organ toxicity were restricted to D100 and frailty had inadequate data for D100. A sequential multiple imputation strategy was implemented to impute endpoints for survivors at each time point with missing data. Analyses on multiply imputed datasets were conducted and the results combined using Rubin's rule. Associations between CHARM scores and secondary outcomes were analyzed using a multivariable Cox, Fine-Gray, Generalized Estimating Equations, and logistic regression model for survival, competing risks, continuous, and binary outcomes, respectively, with latter two focused on surviving patients. Models were adjusted for other variables including conditioning intensity, graft-versus-host disease (GVHD) prophylaxis regimen, disease-risk index, donor-recipient gender match, ethnicity, baseline value of the dependent variable, and visit timepoints. Results: Higher CHARM scores were associated with development of serious organ toxicities by D100 (OR: 2.05, [1.52-2.78], p<0.0001) and ≥2 worsening score on the MoCA (odds ratio (OR) 1.55 [1.16-2.1], p=0.003). Among survivors at all timepoints, higher CHARM scores were associated with greater disability by instrumental activities of daily living (IADL) (Slope -0.640 [-0.433-0.846], p<0.001) and worsening Patient-Reported Outcomes Measurement Reporting System (PROMIS) physical function (Slope -0.981 [-0.057 - -1.904], p=0.037), depression (Slope 0.763 [0.042-1.484], p=0.038) and in a lesser magnitude anxiety (Slope 0.659, p=0.076). Among survivors at D180 and D365, higher CHARM scores were associated with worse frailty (Slope 0.193 [0.081-0.305], p<0.001). CHARM scores were not associated with development of acute GVHD grades 2-4 or 3-4 but were associated with post-GVHD increased mortality (HR: 1.61, [1.25-2.08], p=0.0002). Higher CHARM scores are associated with a lower incidence of chronic GVHD (HR: 0.83, p=0.026), likely due to the effect of CHARM on the competing risk of death leaving fewer patients at risk for chronic GVHD. Conclusions: The novel primary CHARM, originally developed to predict risks of NRM, also predicts worse frailty, disability, cognitive decline, and serious organ toxicities; outcomes that are critically important to older recipients of allo-HCT. CHARM therefore informs risks of transplant morbidity, separate from risks of developing acute GVHD. Results further support adopting CHARM in practice to counsel patients, expedite HCT referrals for lower risk CHARM, and design trials for high CHARM score patients.

Introduction: Allogeneic hematopoietic cell transplantation (HCT) remains the only therapy for long-term disease control for many high-risk hematologic malignancies (HM). HCT use in older HM patients (pts) continues to increase. However, concerns of excessive transplant-related morbidity and non-relapse mortality (NRM) limit referrals and broader application of curative intent HCT. The HCT-comorbidity index (HCT-CI) was initially developed to improve risk-stratification of NRM. More recently, geriatric assessment (GA) and biomarkers have emerged as promising additional tools that may refine estimates of these risks. We hypothesized that a combination of health assessments by GA and biomarkers would constitute a robust and valid model, CHARM, for accurate personalized estimation of one-year (1-yr) NRM. Here, we report the results of the largest, first of its kind, prospective study of older recipients of HCT through the Blood and Marrow Transplant Clinical Trials Network (BMT CTN) 1704 study (NCT03992352). Patients and Methods: Adults aged ≥60 years (yrs) with HM institutionally eligible for HCT were enrolled (n=1226) from 49 centers in the US between 2019 - 2021. Within 21 days of conditioning, 13 prospectively identified older pt.-specific health variables, informed from prior studies, were collected: age, HCT-CI, % of weight loss over the past yr, pt reported Karnofsky performance status, PROMIS physical function scale, instrumental activities of daily living (IADL), number of falls, PROMIS depression scale, number of prescribed medications, 4-meter walk speed, cognition by the Montreal Cognitive Assessment (MoCA), C-reactive protein (CRP) and albumin. The CHARM score to predict NRM within 1 yr was built using a multivariable (MVA) Fine-Gray model, with multiple imputation to handle modest missingness in covariates and grouped penalized variable selection using SCAD to identify variables to retain in the model. Final variables were used as continuous. The model further considered adjustment for conditioning intensity, CMV serostatus, and donor type. Area under the ROC curve (AUC) was calculated to validate the CHARM and compare to HCT-CI alone, with bootstrap sampling to correct for optimism in the within-sample AUC. MVA of overall survival (OS) within 1 yr used the CHARM score and adjusted for other significant variables derived from stepwise regression. Results: The primary analysis included 1105 pts who were confirmed eligible and proceeded to transplant. Median age was 67 (range 60-82) and 32% were ≥ 70 yrs. AML (45%) and MDS (30%) were the most common indications and matched donors the most frequent donor type at 72%. Reduced intensity conditioning with fludarabine (flu)-melphalan (38%) and flu-busulfan (20%) were the most common regimens. Baseline geriatric vulnerabilities among evaluable pts were frequent including slow walk speed (<0.8 meter/second) in 22%, any IADL limitation in 37%, and cognitive impairment (MoCA < 23) in 12%. 1-yr NRM was lower than expected at 14.4% with 1-yr OS of 72%. In the MVA model, albumin, CRP, HCT-CI, and weight loss were independently associated with NRM (Table) and comprised the CHARM. Of note, increasing age did not significantly affect NRM. The CHARM had within sample and bootstrap corrected AUC of 0.641 and 0.607 whereas the HCT-CI alone achieved corresponding AUC values of 0.601 and 0.598. NRM by tertiles is shown in Figure. Each point increase in the CHARM (median -2.2504, range -3.6327 to 0.1381), was associated with a hazard ratio (HR) of 2.83 (95% CI 2.12 - 3.78, p < 0.0001) for NRM. The CHARM also predicted overall mortality within 1-yr with a HR of 2.04 (1.65- 2.53, p <0.0001) per one point increase, independent of the disease risk index, which had a HR of 1.5 (95% CI 1.12 - 1.99, p =0.0059) for high/very high vs low/intermediate categories. Conclusions: We report on the first prospectively established tool, CHARM, to risk-stratify older adult HCT comprised of simple and readily available parameters in transplant and oncology clinics. Adopting CHARM in practice may promote HCT referrals and access for older pts, given low risks of NRM among CHARM lower 2/3 rd tertiles. Identifying high-risk CHARM pts pre-HCT will promote developing novel strategies to reduce NRM. Further analysis of the impacts of the 13 health assessment variables on geriatric morbidity such as disability trajectories is in-process.

Background: BMT CTN 1703 was a Phase 3 trial comparing Tac/MTX (n = 217) to post-transplant cyclophosphamide (PT-Cy)/Tac/MMF (n = 214) GVHD prophylaxis after reduced intensity conditioning unrelated donor hematopoietic cell transplant (HCT). PT-Cy significantly improved GVHD-free Relapse-Free Survival (Bolanos-Meade et al NEJM 2023). However overall survival was not significantly different and the # of Grade 2 infections was higher with PT-Cy. To investigate its biologic underpinnings 1703 was linked to a mechanistic study, BMT CTN 1801, which co-enrolled 324 pts (159 CNI/MTX 165 PT-Cy). The resulting dataset enabled an analysis of post-HCT T cell reconstitution at an unprecedented level of detail. Methods: Samples were collected from the HCT infusion and on D+7, 14, 28, 63, 98, 180, 270, 1- and 2-yr. We extracted DNA (2,225 samples) and performed Adaptive Biotech β-strand T Cell Receptor (TCR) Immunosequencing (TCR-Seq). We profiled 44,077 (median) TCRs/sample resulting in 215,155,719 T cells analyzed. We evaluated multiple TCR diversity measures longitudinally and before/after cGVHD and infections. Each measure evaluates a specific aspect of TCR diversity, including Simpsons Clonality (focuses on the most expanded clones) Diversity Slope (mid-range clones) Richness (both mid-range and less-expanded clones) and Singletons (the TCR-Seq equivalent of naïve T cells). This analysis was robust to the full range (1000-50,000) of down-sampled TCRs sequenced. Results: We identified major differences in TCR reconstitution in Tac/MTX vs PT-Cy that begin early and persist through 2 yr post-HCT. These resulted in a significantly less diverse TCR repertoire with PT-Cy with substantial implications for its efficacy and toxicity. The most salient differences are: (1) PT-Cy caused significant in-vivo T cell depletion early post-HCT (% T cells/total WBC at D+28: 8.2% (Tac/MTX) vs 2.2% (PT-Cy) p=2.2x10 -23). (2) The remaining donor T cells in PT-Cy undergo substantial clonal expansion ( Fig 1) resulting in significantly constrained TCR diversity measured by Clonality, Slope, Richness, Singletons (not shown) and Singletons/Richness ( Fig 2) which began early (D+14) and persisted for 2 yr. (3) Linked T cell depletion and clonal expansion resulted in a TCR diversity bottleneck after PT-Cy ( Fig 1). Due to the bottleneck, while the fraction of T cells originating from the transplant infusion at =/> 1 yr was similar between arms (18% vs 24%, p=0.7) there were significantly fewer Singleton TCRs in PT-Cy, suggesting a reduction in naïve T cells ( Fig 2), an outcome that has critical consequences for both cGVHD and infection. Thus while cGVHD onset with Tac/MTX was linked to a significant drop in TCR diversity due to singleton clonal expansion (median log-2 fold change in singletons with Tac/MTX = -0.25 p = 0.01) this drop was not observed with PT-Cy (log-2 fold change = -0.05, p = 0.1). Along with the significantly decreased # singletons at all time-points in PT-Cy ( Fig 2) this suggests that while cGVHD after Tac/MTX is linked to substantial naïve T cell expansion, the same is not true with PT-Cy, identifying a novel mechanism for cGVHD control with PT-Cy. The substantially fewer singletons in PT-Cy vs Tac/MTX may also increase the risk of Grade 2+ infections. T cell responses to new infections rely on antigen recognition/effector maturation of naïve (singleton TCR) T cells. After infection, the TCR repertoire became less diverse for both Tac/MTX and PT-Cy (log-2-fold singleton change -0.17 and -1.2 p = 0.0006 and 0.01) indicating that singletons in both cohorts were capable of expanding to pathogens. However the markedly decreased # singletons in PT-Cy at all timepoints was associated with significantly fewer singletons prior to infection (mean 3979 at 5K read-depth for Tac/MTX vs 2820 for PT-Cy p<0.001). This singleton deficit would place PT-Cy patients at higher risk of missing the critical mass of TCRs required to respond effectively to infections, consistent with their increased rate of Gr 2 infections. Conclusions: We performed longitudinal TCR-Seq on >200 million T cells from 324 HCT patients, representing the largest post-HCT TCR-Seq analysis in the history of the field, and enabling novel insights into TCR dynamics. These data identify a previously unappreciated global TCR diversity bottleneck with PT-Cy that simultaneously protects patients from cGVHD and places them at higher risk of infectious complications.

Abstract Background: Outcomes of patients with relapsed/refractory AML (RR-AML) have remained poor. Therapy with venetoclax based combinations in this setting leads to CR/CRi rates of 21-49% (DiNardo CD et al. Am J Hematol 2018, Aldoss I et al. Haematologica 2018, Stahl M et al. Blood Adv 2020). Preclinical studies with BCL-2 inhibitors indicate potential mechanisms of drug resistance including overexpression of the anti-apoptotic protein MCL-1 (Konopleva et al. Cancer Cell. 2006). Pevonedistat is a first in class inhibitor of Nedd8 activating enzyme that induces the pro-apoptotic protein NOXA leading to neutralization of MCL-1 and apoptosis. Preclinical studies evaluating the combination of pevonedistat and venetoclax against AML cell lines have demonstrated a synergistic effect (Knorr KL et al. Cell Death Differ. 2015). Hence, we designed a phase I study to assess the safety and tolerability of adding pevonedistat to the combination of azacitidine and venetoclax in patients with RR-AML. Study design and methods: We conducted a phase I study with the combination of pevonedistat, venetoclax and azacitidine in patients with RR-AML. Patients aged 18 years or above with morphologically documented RR-AML, ECOG performance status 0-2 and adequate organ function were eligible. Major exclusion criteria were isolated extramedullary relapse, hematopoietic cell transplantation (HCT) within 100 days of enrollment, and active acute GVHD. Previous therapy with hypomethylating agents (HMA) or venetoclax was not an exclusion criterion. The dose escalation phase was conducted using 3+3 design. Treatment included azacitidine (75 mg/m 2 daily x 7 days), venetoclax (400 mg daily x 28 days), and pevonedistat in escalating doses (10-20 mg/m 2 IV days 1,3,5 of each cycle) with a cycle length of 28 days. Pevonedistat was given at 10 mg/m 2 dose in cohort 1, 15 mg/m 2 dose in cohort 2 and 20 mg/m 2 dose in cohort 3. The primary endpoint is to determine the recommended phase 2 dose (RP2D) and toxicity profile of pevonedistat, azacitidine and venetoclax. Other endpoints included determination of response rates, duration of response, survival, pharmacokinetics, correlation of response rates with AML genomic profile, correlation of pretreatment levels of BCL2, BCLXL, MCL1, BAX or BAK with response, determination of changes in NOXA (PMAIP1) mRNA and protein expression pre-and post-pevonedistat treatment, evaluation of BH3 mimetic profiling on bone marrow samples by flow cytometry and assessing the sensitivity of leukemia and leukemic stem/progenitor cells to pevonedistat ex vivo. Results: Thirteen patients participated in the dose escalation phase, 12 of whom were evaluable. Median age was 69 years (61-91), 30.8% had secondary/therapy related AML, 69.2% with adverse risk disease, 53.8% previously received venetoclax/HMA and 23.1% had relapse after prior allogeneic hematopoietic cell transplantation (HCT) (Table 1). Seven patients were enrolled into cohort 1 (pevonedistat 10 mg/m 2 dose) of which one was not evaluable, three patients enrolled into cohort 2 (pevonedistat 15 mg/m 2 dose), and three patients enrolled into cohort 3 (pevonedistat 20 mg/m 2 dose). Grade 3 or higher AEs included febrile neutropenia (23%), infection (15%), anemia (38%), neutropenia (54%), thrombocytopenia (38%). There was 1 dose limiting toxicity (DLT) in cohort 1 (atrial fibrillation) that triggered cohort expansion. However, subsequent patients did not experience DLT despite planned dose escalation. Of the 12 evaluable total patients, CR/CRi was observed in 5 (41.6%) patients. Notably, patients with RR-AML who were venetoclax/HMA naïve had a CR/CRi 83.3% (5/6 patients). The response rates for each cohort are summarized in Table 2. Three of the five patients with CR/CRi (60%) achieved MRD negativity by flow cytometry. Four patients who achieved CR/CRi proceeded to allogeneic HCT after therapy. Median OS of the cohort was 5.4 months (1.8-14) and median OS was not reached in patients with CR/CRi. Conclusions: The addition of pevonedistat to venetoclax and azacitidine backbone is safe and well tolerated in patients with RR-AML. Dose escalation yielded encouraging efficacy in venetoclax/HMA naïve RR-AML patients. The study is currently in the dose expansion phase. Details on correlative studies examining mechanisms of therapeutic efficacy and resistance will be reported in the main meeting. Figure 1 Figure 1. Disclosures Guru Murthy: Cardinal health: Honoraria; TG Therapeutics: Other: Advisory board meeting; DAVA Oncology: Honoraria; CancerExpertNow: Honoraria; Qessential: Honoraria; Techspert: Consultancy; Curio Sciences: Honoraria; Guidepoint: Consultancy. Abedin: AltruBio: Research Funding; Helsinn: Research Funding; Amgen: Honoraria; Pfizer: Research Funding; Agios: Honoraria; Actinium: Research Funding; Astellas Pharma Inc.: Research Funding. Litzow: Jazz: Other: Advisory Board; Pluristem: Research Funding; Actinium: Research Funding; Amgen: Research Funding; Astellas: Research Funding; AbbVie: Research Funding; Omeros: Other: Advisory Board; Biosight: Other: Data monitoring committee. Atallah: Novartis: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Research Funding; BMS: Honoraria, Speakers Bureau; Takeda: Consultancy, Research Funding; Abbvie: Consultancy, Speakers Bureau; Amgen: Consultancy.

Abstract Introduction: The gut microbiome is a potentially modifiable factor in treatment related outcomes in allogeneic hematopoietic cell transplant (HCT). Prior studies have linked pre- or mid-treatment gut microbiome diversity with risk for treatment related morbidity and mortality. However, these studies have been limited by the inclusion of one or only a few institutions and the lack of longitudinal sampling with high quality metadata. These limitations complicate the interpretation of microbiome alterations over the course of HCT. Methods: To overcome these, we devised and implemented a large-scale biospecimen collection protocol in conjunction with BMT CTN 1703, a randomized, multicenter, Phase III trial of tacrolimus/methotrexate vs. post-transplant cyclophosphamide/tacrolimus/mycophenolate mofetil in reduced intensity conditioning (RIC) allogeneic HCT (NCT03959241). Patients enrolled on 1703 were optionally co-enrolled in the companion immune and microbiome profiling study, 1801 (MI-IMMUNE). This involved blood, urine and stool sampling before conditioning (PCON), then weekly starting on day 0 through day 77 (through day 84 for blood), and on days 98, 180, 270, 365 and 730. For all enrolled participants where the donor consented, residual donor cells were saved from the empty hematopoietic stem cell product bag for later analysis. Additionally, the protocol included one-time blood, urine and stool sample collection from consented matched related donors (MRDs) prior to stem cell collection. Starting with protocol version 4.0 on February 1, 2021, participation in 1801 stool collection was required for the first six out of eighteen sample collection timepoints. Participation in later stool and urine timepoint collections remained optional. Here we review the feasibility of creating a multi-institutional biobank. Additionally, we assess the success of our strategies by calculating sample collection compliance and standard deviation in compliance across centers for each timepoint. Results: On June 18, 2021, BMT CTN 1703/1801 closed to accrual with 431 patients enrolled on 1703; 323 patients from 36 centers were co-enrolled on 1801. 304 (94%) provided study samples, making this the largest prospective microbiome and immune profiling study in HCT patients to date. As of July 6, 2021, 3,683 blood, 2,668 urine, and 2,098 stool samples had been collected. Across the first 6 timepoints for all participating centers, blood, urine and stool sample collection averaged 93%, 82%, and 74% compliance, respectively. Of the 99 (30%) patients enrolled on 1801 with a MRD, 34 (34%) donors consented to sample collection. Sample collection compliance was lower for MRDs than for patients on the study with 76%, 74%, and 62% of expected blood, urine and stool samples collected, respectively, from this group. For stool collection exclusively, a median of 5 samples were collected per patient across the first 6 timepoints (median of 6 across all timepoints) with 93 (31%) of patients completing a full sample set through Day 28. 139 (46%) patients provided at least one sample after day 28; these represented 37% of the total samples collected to date. The PCON sample, which provides an important measure of pre-treatment gut microbiome diversity, had the third highest compliance with 74% of patients providing a sample. Surprisingly, Day 28 had the lowest compliance (66%) and highest standard deviation (37%) possibly because this timepoint often falls around the time of hospital discharge. Between PCON and day 28, the standard deviation between sites in the average collection compliance (24%) and number of samples collected per patient (1.1) was small indicating the successful adoption of stool collection across institutions. Table 1 summarizes sample collection statistics. Conclusion: Overall this study has resulted in a large, novel biobank of blood, urine and stool samples from patients undergoing RIC allogeneic HCT at 36 centers across the US. This will serve as a valuable resource for investigating the role of the gut microbiome in long term health outcomes following HCT. Although the results of 1801 are forthcoming given ongoing sample collection, the size and composition of the biobank to date clearly demonstrate the feasibility of implementing multi-institutional stool collection. This study represents a critical step towards the large-scale adoption of microbiome sampling as a diagnostic tool. Figure 1 Figure 1. Disclosures Chhabra: GSK: Honoraria. Clark: Kadmon: Consultancy. Horowitz: Mesoblast: Research Funding; Shire: Research Funding; Vertex: Research Funding; Stemcyte: Research Funding; Vor Biopharma: Research Funding; Janssen: Research Funding; Miltenyi Biotech: Research Funding; Kiadis: Research Funding; Sobi: Research Funding; Kite/Gilead: Research Funding; Pfizer, Inc: Research Funding; Jazz Pharmaceuticals: Research Funding; Magenta: Consultancy, Research Funding; Medac: Research Funding; Novartis: Research Funding; GlaxoSmithKline: Research Funding; Daiicho Sankyo: Research Funding; Xenikos: Research Funding; Omeros: Research Funding; Orca Biosystems: Research Funding; Pharmacyclics: Research Funding; Regeneron: Research Funding; Tscan: Research Funding; Takeda: Research Funding; CSL Behring: Research Funding; Genentech: Research Funding; Gamida Cell: Research Funding; Chimerix: Research Funding; Bristol-Myers Squibb: Research Funding; bluebird bio: Research Funding; Astellas: Research Funding; Amgen: Research Funding; Allovir: Consultancy; Actinium: Research Funding; Sanofi: Research Funding; Seattle Genetics: Research Funding. Jenq: Microbiome DX: Consultancy; Merck: Consultancy; Prolacta: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kaleido: Consultancy, Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Research Funding; Seres: Consultancy, Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees, Patents & Royalties, Research Funding; LisCure: Consultancy, Membership on an entity's Board of Directors or advisory committees; MaaT Pharma: Consultancy, Membership on an entity's Board of Directors or advisory committees; Karius: Consultancy. Levine: Equillium Bio: Membership on an entity's Board of Directors or advisory committees; Incyte: Consultancy, Research Funding; Kamada: Research Funding; Biogen: Research Funding; Omeros: Membership on an entity's Board of Directors or advisory committees; Symbio: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Talaris Therapeutics: Membership on an entity's Board of Directors or advisory committees; Viracor: Patents & Royalties: GVHD biomarker patent with royalties from Viracor; Mesoblast: Consultancy, Research Funding; X4 Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees. Murthy: CRISPR Therapeutics: Research Funding. Riches: ATARA Biotherapeutics: Other: Payment; BioIntelect: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Other: Payment. Sung: Merck: Research Funding; Novartis: Research Funding; Enterome: Research Funding; Seres: Research Funding; AVROBIO: Consultancy; Abbott Nutrition: Honoraria; Clasado: Other: Research Product; DSM: Other: Research Product. Al Malki: Neximmune: Consultancy; Jazz Pharmaceuticals, Inc.: Consultancy; Rigel Pharma: Consultancy; Hansa Biopharma: Consultancy; CareDx: Consultancy. Rezvani: Kaleido: Other: One-time scientific advisory board; Nohla Therapeutics: Other: One-time scientific advisory board; Pharmacyclics-Abbvie: Research Funding; US Department of Justice: Consultancy. Bolaños-Meade: Incyte Corp: Consultancy. Holtan: Incyte: Consultancy, Research Funding; Generon: Consultancy. Saber: Govt. COI: Other. Hamadani: Sanofi, Genzyme, AstraZeneca, BeiGene: Speakers Bureau; Janssen, Incyte, ADC Therapeutics, Omeros, Morphosys, Kite: Consultancy; Takeda, Spectrum Pharmaceuticals and Astellas Pharma: Research Funding. Kean: Bluebird Bio: Research Funding; Bristol Myers Squibb: Patents & Royalties: From clinical trial data, Research Funding; Vertex: Consultancy; Novartis: Consultancy; Gilead: Research Funding; Regeneron: Research Funding; EMD Serono: Consultancy. Perales: Cidara: Honoraria; Servier: Honoraria; Incyte: Honoraria, Other; Equilium: Honoraria; Takeda: Honoraria; Novartis: Honoraria, Other; Nektar Therapeutics: Honoraria, Other; NexImmune: Honoraria; MorphoSys: Honoraria; Omeros: Honoraria; Karyopharm: Honoraria; Sellas Life Sciences: Honoraria; Merck: Honoraria; Miltenyi Biotec: Honoraria, Other; Medigene: Honoraria; Bristol-Myers Squibb: Honoraria; Kite/Gilead: Honoraria, Other; Celgene: Honoraria.