The survival of patients undergoing hematopoietic cell transplantation (HCT) from unrelated donors for acute leukemia exhibits considerable variation, even after stringent genetic matching. To improve the donor selection process, we attempted to create an algorithm to quantify the likelihood of survival to 5 years after unrelated donor HCT for acute leukemia, based on the clinical characteristics of the donor selected. All standard clinical variables were included in the model, which also included average leukocyte telomere length of the donor based on its association with recipient survival in severe aplastic anemia, and links to multiple malignancies. We developed a multivariate classifier that assigned a Preferred or NotPreferred label to each prospective donor based on the survival of the recipient. In a previous analysis using a resampling method, recipients with donors labeled Preferred experienced clinically compelling better survival compared with those labeled NotPreferred by the test. However, in a pivotal validation study in an independent cohort of 522 patients, the overall survival of the Preferred and NotPreferred donor groups was not significantly different. Although machine learning approaches have successfully modeled other biological phenomena and have led to accurate predictive models, our attempt to predict HCT outcomes after unrelated donor transplantation was not successful.

Recent studies suggest improved survival in patients with severe aplastic anemia receiving hematopoietic cell transplant (HCT) from unrelated donors with longer telomeres. Here, we tested whether this effect is generalizable to patients with acute leukemia. From the Center for International Blood and Marrow Transplant Research (CIBMTR®) database, we identified 1,097 patients who received 8/8 HLA matched unrelated HCT for acute myeloid leukemia (AML) or acute lymphocytic leukemia (ALL) between 2004 and 2012 with myeloablative conditioning, and had pre-HCT blood sample from the donor in CIBMTR repository. The median age at HCT for recipients was 40 years (range=<1-68), and 32 years for donors (range=18-61). We used qPCR for relative telomere length measurement, and Cox proportional hazard models for statistical analyses. In a discovery cohort of 300 patients, longer donor RTL (>25th percentile) was associated with reduced risks of relapse (HR=0.62, p=0.05) and acute graft-versus-host disease II-IV (HR=0.68, p=0.05), and possibly with a higher probability of neutrophil engraftment (HR=1.3, p=0.06). However, these results did not replicate in two validation cohorts of 297 and 488 recipients. There was one exception; a higher probability of neutrophil engraftment was observed in one validation cohort (HR=1.24, p=0.05). In a combined analysis of the three cohorts, no statistically significant associations (all p>0.1) were found between donor RTL and any outcomes.

Introduction. A recent study showed that receiving a hematopoietic cell transplant (HCT) from donors with long leukocyte relative telomere length (RTL) was associated with improved survival in patients with severe aplastic anemia who primarily received bone marrow grafts at age <40 years. Here, we tested the effect of donor RTL on post-HCT outcomes in a large cohort of patients with acute leukemia. Methods. From the Center for International Blood and Marrow Transplant Research (CIBMTR®) database, we identified 1,097 patients who received unrelated HCT for acute myeloid leukemia (AML) or acute lymphocytic leukemia (ALL) and fulfilled the following criteria: 1) HCT between 2004 and 2012, 2) available pre-HCT blood sample for the donor, 3) 8/8 HLA matching, and 3) myeloablative conditioning. We used qPCR to measure RTL; 1084 samples completed testing (597 measured at Telomere Diagnostics, Inc. and 487 at the Cancer Genomics Research Laboratory, NCI). Telomere Diagnostics samples were randomly divided into discovery (n=300) and validation (n=297) cohorts; the NCI samples served as a 2nd validation cohort (N=500). Cox proportional hazard models were used for statistical analyses. All analyses were adjusted for donor age together with clinical factors that met statistical inclusion criteria in a multivariate model. Donor RTL was categorized into short (≤25th percentile based on cohort-specific distribution), or long otherwise. Follow-up ended in November 2014. Results. All patient demographics, disease- and HCT-related characteristics were similar in the discovery, 1st, and 2nd validation cohorts with the exception of graft source (peripheral blood stem cells in 67%, 60%, and 70%, respectively, p=0.008), and year of HCT (0%, 0%, and 37% between 2008-2012, respectively, p<0.001). Results from the discovery cohort suggested that long donor RTL may be associated with: 1) improved overall survival (HR=0.73, 95% CI=0.52-1.03, p=0.08), and disease-free survival (HR=0.73, 95% CI=0.52-1.03, p=0.07); 2) higher probability of neutrophil engraftment (HR=1.3, 95% CI=0.99-1.71, p=0.06), and 3) decreased risk of relapse (HR=0.61, 95% CI=0.38-1.00, p=0.05) and acute graft-versus-host disease II-IV (HR=0.68, 95% CI=0.46-1.0, p=0.05). However, none of these results were replicated in the two validation cohorts with the exception of a higher probability of neutrophil engraftment observed in the 2nd (HR=1.24, 95% CI=1.0-1.54, p=0.05), but not the 1st validation (HR=0.84, 95% CI=0.63-1.11, p=0.21). All three cohorts were combined for a final exploratory analysis and no associations (all p>0.3) were found between donor telomere length and any outcomes. Conclusions. Donor telomere length is not associated with outcomes in unrelated donor HCT for patients with acute leukemia. Table. Impact of donor RTL comparing the three longest quartiles to the shortest quartile. Table. Impact of donor RTL comparing the three longest quartiles to the shortest quartile. Disclosures Loftus: Telomere Diagnostics, Inc.: Consultancy. Friedman:Telomere Diagnostics, Inc.: Consultancy. Buturovic:Clinical Persona: Consultancy. Blauwkamp:Telomere Diagnostics, Inc.: Employment. Shelton:Telomere Diagnostics, Inc.: Employment.

Donor telomere length was recently shown to be associated with survival in severe aplastic anemia patients [1] who undergo unrelated donor HCT. We hypothesize that telomere length may also be clinically useful in selecting donors for acute leukemia patients who undergo HCT. To that end, we are developing predictive models, based on leukocyte telomere length together with clinical variables as inputs, and overall survival as the clinical outcome. The models classify donors as either "preferred" or "not preferred," to provide a decision making tool that can be used to select donors when multiple donors are available. We describe development of the classifier models using survival as the primary outcome and explore the predictive ability of the models for relapse and treatment-related mortality. Whole blood samples and clinical information were obtained from the Center for International Blood and Marrow Transplant Research (CIBMTR) for 300 acute leukemia patients who underwent unrelated donor bone marrow or peripheral blood stem cell transplantation, 2004-2008. Inclusion criteria for adult and pediatric patients included: acute myeloid leukemia (AML) and acute lymphocytic leukemia (ALL) of any stage; 8/8 HLA-matched; and myeloablative conditioning. The samples were processed in Telomere Diagnostic's Clinical Laboratory Improvement Amendments (CLIA) certified laboratory, using a qPCR method to obtain relative leukocyte telomere length from the donor blood samples. We used a multivariate analysis looking at donor telomere length and 30 other variables to identify a subset of donors that resulted in improved outcomes in the respective patients. With this approach, we derived an algorithm that identified 268 patients with substantially improved overall survival ("preferred group") compared to the entire study group ("standard of care group"). Preferred cases identified by the algorithm exhibited an improvement in median survival of 55.7 months vs standard of care (97 mos. median survival for preferred, vs. 41.3 mos. for standard of care, as shown in Fig. 1. Five-year overall survival was 59% for the preferred group, and 44.9% for the standard of care group. We performed additional analysis to create multivariate predictive models, which we call "classifiers," for donor selection. Rather than creating just a single classifier, we created six classifiers that provide a range of stringency levels (13%-80%). Classifiers with high stringency yield a smaller fraction of preferred donors to non-preferred donors; classifiers with low stringency yield a higher fraction of preferred donors to non-preferred donors. Using this graded approach makes differences in donor quality apparent, even where the recipient has few potential donors from which to choose. However, this type of analysis does not yield a continuous donor score or allow direct quantification of the significance of a variable in the model. To quantify the utility of the six classifiers, we estimated performance using repeated 10-fold cross-validation [2]. The key outcome was overall survival (OS) of recipients in the preferred donor group. Specifically, we estimated the proportion of recipients alive at 60 months in the preferred donor group compared with the proportion alive for all recipients in the training set (reflecting standard of care). The results are shown in Fig. 2, with the average survival of the 10 cross-validation samples plotted. Classifier A, the most stringent, identified about 13% in the preferred donor group. 66.7% of patients in the preferred donor group per Classifier A were alive at 60 months, compared with the standard of care average of 44.9%. Classifier F, the least stringent, identified about 80% of patients in the preferred donor group. 46.3% of patients who received preferred donor stem cells were alive at 60 months. In our analysis, preferred donors were associated with lower relapse rates at 12 and 24 months, higher rates of remission at the time of death, and lower transplant-related mortality. Next steps include validation of classifier performance using an independent set of 320 donor samples from the CIBMTR. References: 1. Gadalla, S.M., et al., JAMA, 2015. 313(6): p. 594-602. 2. Krstajic, D., et al., Journal of Cheminformatics, 2014. 6(10). Figure 2. Figure 2. Disclosures Lee: Bristol-Myers Squibb: Consultancy; Kadmon: Consultancy.

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