Pharmacogenomics to Predict Mucositis in Acute Myeloid Leukemia

Introduction Acute myeloid leukemia (AML) is a heterogenous hematologic malignancy that primarily affects older adults, with a median age of diagnosis at 68 years and five-year relative survival of 29.5%. The incidence of AML diagnosis is expected to increase with an aging population in the United States, encouraging the exploration for treatment risk stratification to inform care. One factor impacting survivorship is treatment-related toxicity. Cytarabine (active metabolite, Ara-C) continues as a mainstay agent for initial induction regimens in AML, but efforts to minimize its toxicity remain a challenge. Interpatient variability presents another intricacy, where response to cytarabine can manifest as a subset of patients who have an inadequate response or those who are overly sensitive to its effects. Pharmacogenes in the cytarabine pathway may be responsible for this inconsistent response and could influence toxicity susceptibility. We conducted a retrospective analysis of AML patients treated with cytarabine to evaluate whether single nucleotide polymorphisms (SNPs) in its pharmacogenes are associated with treatment-related mucositis. Specifically, mucositis can negatively impact quality of life with pain and difficulty eating and swallowing, Thus, identifying patients likely to develop mucositis can assist in timely supportive care. Methods Patients from the University of Florida Shand's Hospital who received cytarabine during their first induction therapy from 2009 to 2019 for de novo AML were screened for mucositis within the first thirty days. We obtained FFPE tissues of these patients and extracted genomic DNA. Using TaqMan real-time PCR assays (QuantStudio 3) in-house, we genotyped for ten SNPs from genes involved in the cytarabine metabolic pathway. Logistic regression models were used to test for association between these SNPs and the incidence of mucositis. All analyses were performed using Rstudio v4.1.0. SNPs with significant results were then tested for other SNPs that occur in linkage disequilibrium (LD) using HaploReg v4.1, in addition to testing for association with gene expression using the Genotype-Tissue Expression Portal (GTEx). Results In total, 184 patients were included in this study, with 92 in each group - mucositis and no mucositis (control). The median age was 64 years, 58% were male, 83% were white, 12% African American or black, and 0.2% were Asian. No difference in the incidence of mucositis was observed by sex or race. Logistic regression models identified two SNPs significantly associated (p<0.05) with incidence of mucositis within the first thirty days of cytarabine exposure. For rs5841, a synonymous coding SNP in NME4, presence of the variant T allele (CT/TT genotype) was associated with significantly increased incidence of mucositis (OR=1.51; 95% CI [1.09-2.11]) (Figure 1A and B). rs5841 occurs in LD with seven other SNPs impacting multiple regulatory motifs. Consistent with these results, presence of the T allele is associated with higher NME4 expression (Figure 1C), implying that higher ara-C activation in the variant T group is contributing to greater incidence of mucositis. For rs17103168, an intronic SNP in CMPK, patients homozygous for the variant allele (GG genotype) experienced significantly higher incidence of mucositis compared to those with AG or AA genotypes (OR=1.56; 95% CI [0.95-2.58]) (Figure 2A and B). This SNP occurs in LD with sixty other SNPs impacting numerous regulatory motifs as well. Presence of the GG genotype is associated with high gene expression (Figure 2C), suggesting that this increased gene expression in GG genotypes may result in higher ara-C levels, and subsequently increasing risk of toxicities. Conclusion Overall, we report a significant association between the risk of mucositis after initial cytarabine exposure and two SNPs in cytarabine metabolic pathway genes. Identifying those who may experience adverse toxicity using such SNP-based prediction models will allow for clinically meaningful interventions. Consideration of interpatient variability with cytarabine may lend valuable insight in shared-decision making between patient and clinician when weighing risks vs. benefit of treatment. Ongoing and future studies are focused on expanding this cohort to include evaluation of cytarabine pharmacogenomics with respect to disease progression and survival outcome in AML. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

Development of a “meta-model” to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support

Objective Like most real-world data, electronic health record (EHR)–derived data from oncology patients typically exhibits wide interpatient variability in terms of available data elements. This interpatient variability leads to missing data and can present critical challenges in developing and implementing predictive models to underlie clinical decision support for patient-specific oncology care. Here, we sought to develop a novel ensemble approach to addressing missing data that we term the “meta-model” and apply the meta-model to patient-specific cancer prognosis. Materials and Methods Using real-world data, we developed a suite of individual random survival forest models to predict survival in patients with advanced lung cancer, colorectal cancer, and breast cancer. Individual models varied by the predictor data used. We combined models for each cancer type into a meta-model that predicted survival for each patient using a weighted mean of the individual models for which the patient had all requisite predictors. Results The meta-model significantly outperformed many of the individual models and performed similarly to the best performing individual models. Comparisons of the meta-model to a more traditional imputation-based method of addressing missing data supported the meta-model’s utility. Conclusions We developed a novel machine learning–based strategy to underlie clinical decision support and predict survival in cancer patients, despite missing data. The meta-model may more generally provide a tool for addressing missing data across a variety of clinical prediction problems. Moreover, the meta-model may address other challenges in clinical predictive modeling including model extensibility and integration of predictive algorithms trained across different institutions and datasets.

Analysis of Multiplexed Single Cell RNA Sequencing Clinical Correlative Data in AML Reveals Biomarkers of Resistance

Single cell RNA sequencing (scRNA-seq) is a powerful method to understand gene expression changes in specific subsets of heterogeneous cell populations and to provide novel insight into mechanisms of drug resistance. However, substantial cost and interpatient variability have limited its application as a correlate for prospective clinical trials. To overcome these challenges, we employed a genetic polymorphism-based multiplexing approach to assay peripheral blood samples prospectively collected from patients with relapsed/refractory AML treated on a Phase 1b study using the anti-hepatocyte growth factor monoclonal antibody, Ficlatuzumab (NCT02109627). Ficlatuzumab was administered on study days 1, 15, 29 and 42-44 in combination with high-dose cytarabine. Peripheral blood was collected from 11 subjects on study days 0, 1, 2, 3 and 42-44. N = 7 subjects achieved a complete response to therapy and N = 4 did not respond to therapy. A peripheral blood sample from a healthy volunteer was collected and processed in parallel. 57 samples were multiplexed into 5 pools of 11 to 12 samples using a Latin square design to maximize genetic diversity within each pool and minimize batch effects. Each pool was assayed in duplicate on a 10X Chromium single cell isolation system using 3'-capture technology, targeting 40,000 cells per replicate pool. After initial bioinformatic processing, 175,667 unique cell barcodes were recovered. Genotype-free demultiplexing was performed using Freemuxlet (https://github.com/yelabucsf/popscle). Single nucleotide variants were annotated within the aligned reads from each pool using the 1000 Genomes variant database. A Bayesian clustering algorithm was applied to assign each cell to a group of cells with like genotype. Reference sample genotypes were determined using the Infinium OmniExpressExome-8 BeadChip (Illumina) and these were used to map each cluster to known subject identity. This approach unambiguously assigned 105,363 cells (60.0%) to single subjects and 60,787 (34.6%) to inter-sample doublets. 103,415 cells were retained after removal of intra-sample doublets and low-quality cells. Dimensionality reduction and clustering identified 3 groups of cells that expressed AML blast markers including CD33, CD34, KIT and HLA-DR. We hypothesized that induction of HGF expression by AML blasts might be a mechanism of resistance to anti-HGF therapy. AML blasts were stratified by treatment day and multivariable regression was performed using cluster assignment as a potential confounder. This showed that non-response to study therapy was independently correlated with higher HGF levels on study D0 (q = 0.008), D1 (q = 0.02), and D42-44 (q = 5x10-7). To identify additional biomarkers of resistance to anti-HGF therapy, we performed unsupervised gene module analysis and found 20 groups of genes that were co-regulated within the dataset. One module was strongly induced in non-responders compared to responders at D0 (q = 0), D1 (q = 1x10-278), and D42-44 (q = 0). This module was highly enriched for genes related to type-1 interferon signaling and antiviral response including IL6, IRF7, IRF2, STAT1, STAT2 and APOBEC3A. Unsupervised hierarchical clustering based on gene module scores segregated non-responders from complete responders and showed that the normal control cells were most similar to complete responders at D42-44. These data suggest that multiplexing using genetic polymorphisms increases the number of scRNA-seq samples it is feasible to analyze in the context of a prospective clinical trial. Gene module analysis is able to identify pathways as potential biomarkers of drug resistance in an unsupervised way. These approaches will aid studies in which interpatient variability is a challenge to interpretation of scRNA-seq data. Disclosures Andreadis: Merck: Research Funding; Gilead/Kite: Consultancy; Karyopharm: Honoraria; Jazz Pharmaceuticals: Honoraria; Novartis: Research Funding; Genentech: Consultancy, Current equity holder in publicly-traded company; BMS/Celgene/Juno: Honoraria, Research Funding; Incyte: Consultancy.

Population Pharmacokinetic Modeling of von Willebrand Factor Activity in von Willebrand Disease Patients after Desmopressin Administration

Objective Most von Willebrand disease (VWD) patients can be treated with desmopressin during bleeding or surgery. Large interpatient variability is observed in von Willebrand factor (VWF) activity levels after desmopressin administration. The aim of this study was to develop a pharmacokinetic (PK) model to describe, quantify, and explain this variability. Methods Patients with either VWD or low VWF, receiving an intravenous desmopressin test dose of 0.3 µg kg−1, were included. A PK model was derived on the basis of the individual time profiles of VWF activity. Since no VWF was administered, the VWF dose was arbitrarily set to unity. Interpatient variability in bioavailability (F), volume of distribution (V), and clearance (Cl) was estimated. Results The PK model was developed using 951 VWF activity level measurements from 207 patients diagnosed with a VWD type. Median age was 28 years (range: 5–76), median predose VWF activity was 0.37 IU/mL (range: 0.06–1.13), and median VWF activity response at peak level was 0.64 IU/mL (range: 0.04–4.04). The observed PK profiles were best described using a one-compartment model with allometric scaling. While F increased with age, Cl was dependent on VWD type and sex. Inclusion resulted in a drop in interpatient variability in F and Cl of 81.7 to 60.5% and 92.8 to 76.5%, respectively. Conclusion A PK model was developed, describing VWF activity versus time profile after desmopressin administration in patients with VWD or low VWF. Interpatient variability in response was quantified and partially explained. This model is a starting point toward more accurate prediction of desmopressin dosing effects in VWD.

Tacrolimus dose modification in patients receiving concomitant isavuconazole after hematopoietic stem cell transplantation

Introduction Isavuconazole is increasingly being used for antifungal prophylaxis during stem cell transplantation. Isavuconazole is a moderate inhibitor of Cytochrome P4503A4, and tacrolimus levels are anticipated to be elevated when given concomitantly with isavuconazole. We developed and validated a dose-modified tacrolimus regimen to better achieve and maintain target tacrolimus levels. Methods: Allogeneic stem cell transplantation recipients who received concomitant tacrolimus and isavuconazole from September 2017 to September 2018 were included. Tacrolimus was adjusted to achieve a target range 8–12 ng/ml. Intravenous tacrolimus was first initiated at 0.02 mg/kg/day on day 1, and transitioned to oral therapy using a 2:1 conversion ratio ( n = 48). Clinical observations showed high interpatient variability. The intravenous dose was then reduced to 0.017 mg/kg/day, and oral:intravenous conversion changed to 3.1:1 ( n = 24). Results Interpatient variability was high (lower in the 0.017 mg/kg/day group; P < 0.0217). Patients in the 0.017 mg/kg/day group required fewer dose changes ( P < 0.023) and had fewer levels >15 ng/ml ( P < 0.021). Median tacrolimus dose declined over time; 0.016, 0.012 and 0.011 on days 1, 7 and 10 for patients receiving 0.02 mg/kg/day and 0.017, 0.014 and 0.013 in the 0.017 mg/kg group. Day 10 tacrolimus accumulation factor was 1.42 Rac(Cmax) in the 0.02 mg/kg/day cohort compared to 1.23 Rac(Cmax) in the 0.017 mg/kg/day cohort ( P < 0.015). When transitioned to oral therapy, a oral:intravenous conversion ratio >3.1:1 was shown to improve chances for achieving target levels ( P > 0.0744). Conclusion We recommend initiating intravenous tacrolimus dose at 0.017 mg/kg/day and using a 3.1:1 oral:intravenous conversion to reduce interpatient variability, drug accumulation and the number of suboptimal tacrolimus levels. Tacrolimus requires frequent drug level monitoring.

Effects of pharmacogenetic variants on vemurafenib-related toxicities in patients with melanoma

Aim: The pharmacokinetics and pharmacodynamics of vemurafenib are characterized by a wide interpatient variability. Since multiple polymorphic enzymes and drug transporters are involved in vemurafenib pharmacokinetics, we studied associations of polymorphisms on vemurafenib-associated toxicities. Patients & methods: Prospectively collected samples of 97 melanoma patients treated with vemurafenib alone (n = 62) or in combination with cobimetinib (n = 35) were genotyped for ABCB1 (3435C>T), ABCG2 (421C>A, 34G>A) and CYP3A4 ( *22, 15389C>T) polymorphisms. Associations between these variants and the incidence of toxicities were studied. Results: CYP3A4*22 was significantly associated with increased risk for grade ≥3 nausea, grade 1–4 hyperbilirubinemia, and cutaneous squamous cell carcinoma. ABCB1 3435C>T was a predictor for grade ≥3 toxicity. Conclusion: Genetic variants in CYP3A4 and ABCB1 are associated with vemurafenib-associated toxicities.

High Interpatient Variability in Molecular MRD Response to Consolidation Chemotherapy in Acute Myeloid Leukemia

Introduction: Although induction chemotherapy results in a complete remission (CR) in ~70% of newly diagnosed AML patients, post-remission therapies are needed to eliminate minimal residual disease (MRD) and prevent relapse. Consolidation chemotherapy, either as definitive therapy or bridge to bone marrow transplantation (BMT), is currently the most common form of post-remission therapy. Yet, our understanding of its impact on MRD remains limited. In this study, we investigated the effects of consolidation chemotherapy on molecular MRD (mMRD) burden using ultra-deep next generation sequencing (NGS) and correlated treatment response with disease characteristics and survival outcomes in AML patients. Patients and Methods: 91 newly diagnosed AML patients who achieved CR following standard induction chemotherapy were evaluated. Targeted conventional NGS using a 54-gene panel was performed on whole blood (PB) or bone marrow samples collected at diagnosis. PB samples were collected during remission at two consecutive time points (T1 and T2), before and after 1 (n=79) or 2 (n=12) cycles of consolidation chemotherapy, for each patient. To detect mMRD, we used a custom 37-gene hybrid-capture panel and error-corrected NGS based on the duplex sequencing approach with a variant allele frequency (VAF) detection limit of ~1x10-4. For 10 patients, we also performed duplex sequencing analysis on their relapsed samples. Results: NGS of the diagnostic samples identified a total of 298 putative oncogenic mutations in 92% (n=84) of the 91 patients. Ninety percent of these mutations (n=267) were trackable by the custom hybrid-capture panel. Duplex sequencing detected persistence of 56% (n=149) of the trackable mutations in T1 samples; 34% (n=50) of which were clonal hematopoiesis-associated DTA mutations (those involving DNMT3A, TET2, or ASXL1), and the remaining 66% (n=99) were non-DTA mutations. Analysis of T2 samples showed that consolidation chemotherapy reduced the VAF of non-DTA mutations by a median of 73% and cleared 27% (n=27) of them at T2. In contrast, the burden of DTA mutations increased by 0.5% (P &lt; 0.0001 by Mann-Whitney test), and only 2% (n=1) of the mutations was cleared (P = 0.0001 by Fisher's exact test). These findings are consistent with prior studies demonstrating that non-DTA mutations are more reliable markers of leukemic burden than DTA mutations. To study the impact of consolidation chemotherapy at the level of individual patients, the mean VAF of all persistent non-DTA mutations was calculated for each sample and used as a composite measure of mMRD burden (henceforth referred to as "cmMRD"). Analysis of the 10 patients with relapsed samples showed that cmMRD levels tracked well with achievement of remission and disease progression (Fig. 1). In the subset of patients with persistent non-DTA mutations at T1 (n=61), consolidation chemotherapy decreased cmMRD levels by a median of 36% at T2. However, we observed high interpatient variability (Fig. 2); 36% (n=22) of the patients experienced an increase in cmMRD burden after consolidation chemotherapy, and 36% (n=22) had less than a 1 log reduction. Only 28% (n=17) of the patients achieved a log reduction of greater than 1. The likelihood and magnitude of cmMRD response were significantly associated with cytogenetic risk (P = 0.026 by 3x3 Chi-square test; Fig. 3). The proportion of patients with favorable, intermediate, and poor-risk cytogenetics who experienced cmMRD expansion was 17%, 27%, and 71%, respectively. Consistent with these findings, a suboptimal response (defined as cmMRD ratio [T2/T1] &gt; 0.4) was associated with inferior overall survival (HR = 3.29, P = 0.007 by log-rank test; Fig. 4). Conclusions: Our analysis showed that mMRD response to consolidation chemotherapy was highly variable among patients. Although consolidation chemotherapy was effective in deepening the remission for a subset of patients, it failed to lower MRD levels for a substantial proportion of patients, especially those with poor risk cytogenetics. These findings challenge the practice of using consolidation chemotherapy to achieve a deeper remission prior to BMT for high-risk patients and indicate that the opposite outcome may occur instead. NGS-based monitoring of mMRD can potentially be used to distinguish between patients who can remain on consolidation chemotherapy as definitive therapy and those who require a switch in post-remission therapy. Disclosures Gupta: Sierra Oncology: 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; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Honoraria, Research Funding. Maze:Pfizer Inc: Consultancy; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees. McNamara:Novartis Pharmaceutical Canada Inc.: Consultancy. Minden:Trillium Therapetuics: Other: licensing agreement. Schimmer:Medivir Pharmaceuticals: Research Funding; Jazz Pharmaceuticals: Consultancy; Novartis Pharmaceuticals: Consultancy; Otsuka Pharmaceuticals: Consultancy. Schuh:Pfizer: 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; Jazz: Honoraria, Membership on an entity's Board of Directors or advisory committees; Agios: Honoraria; Astellas: Honoraria, Membership on an entity's Board of Directors or advisory committees; Teva Canada Innovation: Honoraria, Membership on an entity's Board of Directors or advisory committees; AbbVie: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees. Yee:Novartis, Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees; Astellas, Celgene, Otsuka, Shire, Takeda: Membership on an entity's Board of Directors or advisory committees; Agensys, Astex, Hoffman La Roche, MedImmune, Merck, Millenium, Roche/Genentech: Research Funding. Bratman:SVB: Other: is co-inventor of a patent relating to circulating tumor DNA detection technology, which has been licensed to Roche Molecular Diagnostics.. Chan:Agios: Honoraria; AbbVie Pharmaceuticals: Research Funding; Celgene: Honoraria, Research Funding.

Development of a Real Time Pharmacokinetic Testing Method to Allow for Targeted Melphalan Dosing in Multiple Myeloma Patients Undergoing Autologous Transplant

High dose melphalan (HDM) followed by autologous stem cell transplantation (ASCT) is standard of care after induction therapy for eligible patients with multiple myeloma (MM). However, ~40% of patients fail to achieve complete remission (CR), largely due to interpatient variability (~10-fold) in melphalan exposure (AUC). Thus, many patients experience subtherapeutic exposure and suboptimal response when using conventional body-surface-area (BSA)-based dosing. HDM, administered at a dose of 100mg/m2 on day -2 and -1, allows for potential modification of the day -1 dose if pharmacokinetic (PK) testing with rapid turnaround can be performed on day -2. Here we describe a clinically feasible, reproducible method of measuring melphalan PK that allows for real-time dose adjustments. We developed and validated a robust melphalan assay with quality control for intra-day and inter-day precision, sample processing, and final LC-MS/MS analysis. MM patients aged ≥ 18 years undergoing HDM at a dose of 140 or 200 mg/m2 were eligible. HDM was administered days -2 and -1 prior to ASCT as a 30-minute infusion followed by saline flush. Blood for PK sampling (5ml aliquots), drawn at 10 specific time points post-infusion, were placed on ice and delivered to the PK lab for centrifugation and storage within 5 minutes of being drawn. PK parameters were determined from plasma concentration versus time data by a noncompartmental analysis using WinNonlin 8.1. Toxicities were assessed up to 30 days after ASCT using NCI-CTAE, version 5.0 grading. Patients were also followed for day 90 disease response rates. Twenty MM patients were enrolled with 18 receiving 200mg/m2 and 2 receiving 140mg/m2 due to end stage renal disease and age over 70 years. Using the first 5 patients, we confirmed that the AUC from day -2 correlated with day -1 (r=0.8), establishing that day -2 PK could be used to adjust the day -1 dose using a linear, dose-proportional calculation for future PK-directed studies. Notably there was considerable variation in day -2 AUC parameters, further confirming melphalan interpatient variability among patients. The median single-day AUC on day -2 was 7.49 (4.95-11.28) mg*h/L with an interquartile range of 2.66 mg*h/L. When comparing the upper and lower quartiles, there was a 1.85 fold higher single-day AUC (10.2 vs. 5.5 mg*h/L). Based on each patient's PK profile, we then calculated the theoretical dose for day -1 in order to target the total median melphalan AUC and compared it to the BSA-based dose received. The change in absolute melphalan dose on day -1 to achieve the total median AUC ranged from -124 mg to +206 mg (Figure 1A). When expressed as a BSA-based mg/m2 dose, the total 2-day dose would range from 133-308 mg/m2 (Figure 1B). Using D-optimality approaches we determined that reducing blood sampling from 8 to 4 time points did not affect AUC prediction, as confirmed by paired comparison tests comparing the mean AUCs between the new (4 point) sampling time schedule and the original (7.76 vs. 7.80 mg*h/L; p=0.8193). Lastly, we examined clinical outcomes and whether AUC correlated with toxicity. One patient with a median single-day AUC of 10.58 mg*h/L died from sepsis. No patients developed grade 3/4 mucositis or GI-related toxicity. In modified Poisson regression, higher AUC correlated with increased risk of febrile neutropenia (RR 3.02, CI 1.00-9.26, p=0.05) and a trend towards increased use of antiemetics (RR 1.86, CI 0.95-3.65, p=0.07). Collection of disease response data is ongoing. Here we describe the potential impact of performing rapid melphalan PK testing in a clinical setting. We report minimal intrapatient variability in melphalan exposure from day-2 to day-1 allowing a linear, dose-proportional method of dose-adjustment without need for a pre-transplant test dose or modification of the initial dosing regimen. Significant interpatient variability is observed, and dose adjustment simulations based on patients' PK data demonstrate that large, bi-directional dose adjustments are required to target a median AUC, emphasizing the importance of PK-directed HDM dosing. Our study posits that individualized melphalan dosing will both decrease interpatient variability and improve myeloma outcomes, and through allowing development of PK-directed HDM studies, suggests a novel approach to dosing in MM patients undergoing ASCT. Figure 1 Disclosures Galvin: Incyte: Consultancy. Quigley:Alexion: Membership on an entity's Board of Directors or advisory committees; Alnylam: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Dova Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Janssen: Membership on an entity's Board of Directors or advisory committees; TEVA: Research Funding. Patel:Amgen: Consultancy, Speakers Bureau; Janssen: Speakers Bureau; Celgene: Speakers Bureau.

Preliminary Lasso Regression Analysis Identifies DNA-Damage Gene Expression Signature Predictive of Clinical Outcomes in Patients Using Gemtuzumab Ozogamicin

Background: In September 2017, the FDA announced re-approval of gemtuzumab ozogamicin (GO), a CD33-directed antibody-drug conjugate, for treatment of newly diagnosed and relapsed/refractory AML setting the pace for a new era of personalized therapeutic options for AML. While the future of GO as a therapy in AML is bright, studies have shown that clinical response to GO is subject to interpatient variability (Gbadamosi 2018). Hitherto, efforts to understand the interpatient variation have focused on profiling CD33 expression levels (Pollard et al. 2016) and more recently on genetic variation in CD33 and ABCB1 predicative of GO response (Chauhan et al. 2019, Rafiee et al. 2019). However, efforts to understand interpatient variability in regards to calicheamicin, the DNA-damaging cytoxin linked to the antibody portion of GO, have been limited. Thus, we hypothesized that interpatient differences in expression levels of genes involved in pharmacodynamic effects of calicheamicin may impact GO response/resistance. Methods: Herein, our group has used the least absolute shrinkage and selection operator (LASSO) regression analysis method to identify critical genes with expression levels predictive of clinical outcomes in response to GO. Using data from the TARGET database, the expression levels of 18 genes, selected for their role in calicheamicin induced DNA-damage response, were extracted for AML patients treated with GO in the AAML03P1 and AAML0531 clinical trials (N=128; Table 1). Using a penalized LASSO regression algorithm (glmnet R-package), a Cox regression model was fit on to the expression levels of the selected gene. A thousand bootstraps of LASSO regression were performed and genes included in greater than 95% of the models were further selected. Average coefficients from the LASSO model were used to generate a gene signature equation designated as the DNA-damage response score (DDRS) given the nature of the genes included. Patients were classified into high (DDRS High) or low (DDRS Low) values for DDRS according to the median value and evaluated using Cox-proportional hazard model for survival data analysis, while the Chi-square test was used to examine the differences between categories, and the Wilcoxon rank-sum test was used to assess the differences in averages where appropriate. Results: The DDRS equation was defined as DDRS = (AKT1*-0.070) + (CASP9*-0.091) + (H2AFX*-0.160) + (XRCC4*0.373) where gene expression levels are multiplied by the coefficients obtained from the LASSO regression (Figure 1). Patients in the DDRS High group had significantly worse event free survival (EFS; HR = 2.22, P &lt; 0.001; Figure 2A), lower complete remission rates (67.7% vs 94.6%, P &lt; 0.001; Figure 3A), and a trend towards worse overall survival (OS; HR = 1.70, P = 0.058) as compared to patients within DDRS Low group. DDRS score was also significantly different amongst patients that were MRD+ve vs. MRD-ve after induction 1 therapy (21% difference, P = 0.038; Figure 3B). Consistent results were seen within standard risk group patients where patients in the DDRS High group had significantly worse EFS (HR = 2.29, P = 0.01; Figure 2B) as compared to those in the DDRS Low group. In preliminary multivariate Cox regression analysis, the DDRS remained a significant predictor of EFS amongst age, risk group, FLT3 status, and WBC (HR 2.42, P &lt; 0.001; Table 2). Conclusion: Our preliminary investigation using LASSO regression model defined DDRS, a gene signature predictive of clinical response in patients treated with GO. The model included four genes well known for their involvement in DNA-damage response: AKT1, a kinase that regulates cell growth and division; CASP9, The initiator caspase involved in apoptosis; H2AFX, a DNA-damage marker that recruits other DNA-damage response proteins to damaged loci; and XRCC4, a ligase for DNA damage repair. Our future work focuses on expanding this investigation in bigger cohorts of patients representing different risk groups of AML as well as in vitro mechanistic studies. Once validated for its sensitivity and specificity to calicheamicin response, our results hold promise towards developing strategies for understanding interpatient variability in GO response and personalizing GO therapy based on diagnostic gene expression signatures. Disclosures No relevant conflicts of interest to declare.