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Background: The presence of measurable residual disease (MRD) is strongly associated with treatment outcomes in acute myeloid leukemia (AML). However, > 20% of MRD negative cases (as assessed by flow cytometry) subsequently relapse. We sought to determine if ultrasensitive Duplex Sequencing (DS), which relies on double-stranded consensus-making to achieve an error rate below one-in-ten-million, yields better prognostic performance via molecular MRD detection. Methods: Retrospective targeted DNA sequencing of 29 genes recurrently mutated in adult AML was performed on paired diagnostic and remission bone marrow samples from patients enrolled on the SWOG trial S0106 (randomized 7+3 versus 7+3 + gemtuzumab ozogamicin (GO)). Patients were selected if they had remission samples with flow cytometry results (n=67). Non-error corrected sequencing was performed on diagnostic samples (average depth 279x) and DS was performed on remission samples (average duplex molecular depth 27,002x). For each patient, potential germline variants were identified and excluded from the analysis if the variant allele fraction (VAF) was ≥ 35% at both diagnosis and remission, or ≥ 40% at either time point and a gnomAD allele frequency ≥ 0.05. Somatic variants present at diagnosis were classified as potentially deleterious if computationally predicted as such and with a VAF ≥ 5% (≥ 1% for FLT3-ITD/NPM1 insertions). For analysis of residual disease in remission, we evaluated the following outcomes (events): overall survival (OS; death), relapse-free survival (RFS) and time to relapse (TTR; relapse with death a competing event). All outcomes were measured from date of morphologic remission to date of event, with patients without event censored at date of last contact. Associations between residual disease and outcomes were assessed using Cox regression models (cause-specific model for TTR). Results: The median age was 48 years (range 8-60). 32 patients were randomized to 7+3 and 30 to 7+3+GO. A total of 172 potentially deleterious variants were identified in the diagnostic samples. Variants had an average VAF of 31% (range 1.4-91.5%) at diagnosis and were detected in 23 of the 29 genes, with FLT3 being the most frequently mutated. Of the 67 patients analyzed, 93% (n=62) had at least one variant detected at diagnosis (median 2, range 0-9) and 68% (n=42) had at least one residual diagnostic variant also found in the remission sample. We defined the presence of DS MRD as non-DTA (DNMT3A, TET2, ASXL1) time-of-diagnosis mutations identified at the remission time point with a VAF > 0.1% and/or an NPM1 VAF > 0.01% (PMID:31860405). DS MRD was strongly associated with all outcomes, with hazard ratios (and 95% CI) for TTR: 7.1 (2.7-18.9); RFS: 4.9 (2.2-10.9) and OS: 5.1 (2.1-12.3). As a comparator, we correlated treatment outcomes with the results of a flow cytometry MRD assay previously carried out on the same samples during the S0106 trial. The prognostic association of flow MRD with TTR, RFS and OS was less strong (i.e., a smaller hazard ratio) than DS MRD, with TTR: 2.5 (0.9-6.7); RFS: 2.2 (0.9-5.4) and OS: 2.4 (1.0-6.1). RFS and TTR for DS MRD and flow cytometry are plotted below for the 62 patients with a variant detected at diagnosis. Comparing DS MRD with flow cytometry, discordance was found in 20 cases: 15 cases where DS was positive and flow negative, and 5 cases in the opposite direction. Among the 15 discordant cases with DS positive and flow negative, 9 relapsed and 2 died without relapse and 4 were alive without relapse at last contact. Among the 5 discordant cases with DS negative and flow positive, 1 relapsed, 1 died without relapse, and 3 were alive without relapse at last contact. Conclusions: Among the 67 patients evaluated in this prospectively collected study, the presence of MRD defined by DS was strongly associated with adverse disease outcomes. The vast majority of patients had at least one time-of-diagnosis mutation that could be tracked as a measure of MRD. When compared with flow cytometry, DS exhibited superior negative and positive predictive values for foretelling future relapse, although this could potentially reflect the historical version of the flow cytometry assay used. These findings suggest that DS is a powerful tool that could be used in patient management and for early treatment assessment in clinical trials. Figure 1 Figure 1. Disclosures Hourigan: Sellas: Research Funding. Radich: Amgen: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Genentech: Membership on an entity's Board of Directors or advisory committees; BMS: Membership on an entity's Board of Directors or advisory committees.

Sensitive and specific detection of measurable residual disease (MRD) after treatment in pediatric acute myeloid leukemia (AML) is prognostic of relapse and is important for clinical decision making. Mutation-based methods are increasingly being used, but are hampered by the limited number of common driver gene mutations to target as clone markers. Additional targets would greatly increase MRD detection power. However, even in cases with many AML-defining mutations, it is the limited accuracy of current molecular methods which establishes the lower bounds of sensitivity. Here we describe an ultrasensitive approach for disease monitoring with personalized hybrid capture panels targeting hundreds of somatic mutations identified by whole genome sequencing (WGS), and using extremely accurate Duplex Sequencing (DS) in longitudinal samples. In a pilot cohort of 13 patients we demonstrate detection sensitivities several orders of magnitude beyond currently available single locus testing or less accurate sequencing. With multi-target panels, overall power for MRD detection is cumulative across sites. For example, if a patient has MRD at a true frequency of 1/30,000, sequencing a single mutant site to 10,000x molecular depth would be unlikely to detect MRD. However, sequencing 10 sites each to 10,000x would effectively total 100,000x informative site depth, increasing power to >95%. However, standard sequencing assays are insufficiently accurate to achieve this theoretical limit of detection (LOD). DS enables accurate detection of individual variants to Marrow aspirates were collected from 13 uniformly treated pediatric AML patients at time of diagnosis (TOD), during treatment (end of induction, EOI), in remission (end of therapy, EOT), and at relapse. 9/13 patients relapsed. DNA from TOD was analyzed by WGS. Germline variants were excluded and somatic single nucleotide variants (SNVs) were targeted by a custom probe panel designed for each patient. An average of 170 SNVs were targeted per patient (range 53-200). More than 90% of the SNVs were noncoding. Longitudinal samples were then analyzed with DS, which compares sequences from both strands of each DNA molecule to eliminate technical noise and reveal biological mutation signal with extreme accuracy and sensitivity. A median of 82% of WGS SNVs were validated by DS in the TOD DNA, and the vast majority of those were also present at relapse. Relapsers had more SNVs at diagnosis than non-relapsers. EOT samples were sequenced to an average Duplex molecular depth of 29,400x, with a maximum of 61,283x. The figure below shows time course plots tracking SNVs at diagnosis, EOT and relapse for 2 patients. Among mutations validated in TOD samples, a median of only 8 (5%) were detected per EOT sample (range 0-66 mutations). MRD was detected in 8/9 relapsers. Targeting 1 or even 10 SNVs would therefore have missed MRD in the majority of these patients. Among relapsers, median EOT SNV VAF was 0.069%. The lowest single VAF detected per EOT sample ranged from 0.036% to 0.002%. The presence of an SNV at diagnosis and relapse implies that it must truly be present at EOT, whether or not it is detected. Therefore, if a small minority of leukemic mutations are detected at EOT, the true overall MRD frequency is much lower than the LOD at any single site. In the only relapser where MRD was not detected, targeted SNVs were present at diagnosis and relapse, so additional sequencing depth at EOT would eventually reveal ultra-low frequency mutations. Among patients that did not relapse by the end of the study, median VAF at EOI (the latest time point DNA available) was 0.0258%. Therefore, non-relapsers have a lower median VAF at EOI than relapsers do even later at EOT, potentially indicating very early on that treatment is more successful. This study shows excellent performance of DS-based assays for detecting MRD with patient-specific panels. We have demonstrated that among large panels of validated somatic SNVs present at time of diagnosis, a median of 5% are identified at EOT in eventual relapsers. DS detected MRD in 8/9 patients, and at a median VAF well below the limit of detection of any other sequencing technology. Comprehensive personalized hybrid selection panels coupled with DS represents a powerful option for MRD monitoring in pediatric AML and potentially other cancers. Figure Disclosures Higgins: TwinStrand Biosciences: Current Employment. Lo:TwinStrand Biosciences: Current Employment. Hipp:TwinStrand Biosciences: Current Employment. Valentine:TwinStrand Biosciences: Current Employment. Williams:TwinStrand Biosciences: Current Employment. Radich:TwinStrand Biosciences: Research Funding. Salk:TwinStrand Biosciences: Current Employment.

Hematopoietic cell transplantation (HCT) is used in the treatment of hematologic malignancies (HM). Clonal hematopoiesis of indeterminate potential (CHIP) is the age-associated process whereby healthy individuals accumulate low-frequency clones driven by mutations in genes recurrently mutated in myeloid cancers; CHIP is associated with an increased risk of HM and cardiovascular disease. It has been hypothesized that CHIP clones present in an HCT donor might disproportionately proliferate in the recipient due to a relative growth advantage during immune reconstitution. Here we use Duplex Sequencing (DS) to investigate, years after HCT, whether the clonal makeup of CHIP in prior donors differs from that of the donor-derived hematopoietic system long after allogeneic transfer to a recipient. DS is an ultra-sensitive NGS error-correction technology that relies on sequencing of the individual paired strands of original source DNA molecules to dramatically reduce technical background compared to other NGS methodologies. Here we performed DS on 10 pairs of donor/recipient peripheral blood DNA samples collected 7-46 years post-transplant, as well as DNA from a healthy young control. We used a sequencing panel comprising 29 genes recurrently mutated in acute myeloid leukemia (AML) and generated mean DS molecular depth of 27,580x per sample, with maximum DS depth reaching 45,402x. We detected an average of 356 and 283 total variants per donor and recipient, respectively, and 462 total variants in the control. These included 291 single-count exonic variants (SEV) in the control and an average of 215 SEVs in donors (Fig. A). To enrich for CHIP (clonal) variants, we filtered out single-count (non-clonal) events, as well as intronic (non-splice site) variants and germline SNPs. Whereas the young control had only 5 clonal exonic variants (CEV), meaning nearly all of the variants in the control were SEVs, SNPs or intronic (Fig. A,B), HCT donors and recipients respectively averaged 44 and 38 CEVs. 91% of CEVs were non-synonymous (Fig. B), which is higher than expected by chance. The average age of the donors was 36 years old at the time of HCT (range 12-84), and the oldest donor (M1) had the most CEVs. 24/29 genes in the panel exhibited CEVs in the control or HCT donors (Fig. C). DNMT3A, TET2, BRINP3 (FAM5C) and ASXL1 had the greatest number of CEVs and this imbalance was not explained purely by gene size. The young control sample had an overall somatic mutation frequency (MF) of 4.7x10-7. HCT donors and recipients had average MFs of 1.0x10-6 and 1.7x10-6, respectively. 154 CEVs were shared in at least 1 donor-recipient pair, with the majority present at variant allele frequencies (VAFs) 10-fold, all in DNMT3A, TET2 and RAD21. Only 1 CEV decreased >10-fold: TP53 R273H (4.9x10-4 to 4.2x10-5). Analysis of the same 10 donor-recipient pairs using a commercial sequencing service found that, with a VAF cutoff of 2% and filtering for SNPs, only 7 shared CEVs could be detected that increased or decreased in any recipient. Our ultra-high accuracy DS data indicates that the structure of CHIP is more polyclonal than has been previously reported. In a handful of older healthy donors we detected dozens of concurrent CHIP mutations, with a relative gene distribution nearly identical to that previously reported in studies on thousands of patients using less sensitive method. We observed a similar age-dependent effect. Furthermore, our findings indicate that the clonal dynamics of CHIP clones between HCT donors and recipients are substantially more complex than previously known. Nearly all somatic variants shared between HCT donors and recipients were observed at Figure Disclosures Higgins: TwinStrand Biosciences: Employment. Pratt:TwinStrand Biosciences: Employment. Valentine:TwinStrand Biosciences: Employment. Williams:TwinStrand Biosciences: Employment. Radich:Novartis: Other: RNA Sequencing; TwinStrand Biosciences: Research Funding. Salk:TwinStrand Biosciences: Employment.

Background: Some previous studies in Ph+ ALL suggested that T315I mutations of the ABL1 KD are present in many pts prior to tyrosine kinase inhibitor (TKI) treatment. However, these reports used RT-PCR to generate complementary DNA (cDNA) prior to analysis, which may introduce random errors and lead to false positive results. We hypothesized that ultrasensitive DS of genomic DNA (gDNA) would more accurately detect low-level pretreatment ABL1 mutations in Ph+ ALL because it does not rely upon error prone RT-PCR. Methods: DS compares the nucleotide sequences of each strand of double-stranded molecules, improving the accuracy of conventional next-generation sequencing by more than 10,000-fold. DS of exons 4-10 of ABL1 was performed to an average molecular depth of >10,000x. A mixture of known ABL1 variants at varying low variant allelic frequencies (VAFs) versus a negative control were used to determine sensitivity and specificity of DS. DS of ABL1 was performed on 64 pts with newly diagnosed Ph+ ALL prior to receiving frontline hyper-CVAD plus a TKI (imatinib, n=5; dasatinib, n=38; ponatinib, n=21). DS was also performed on cDNA samples from RT-PCR to assess the RT-induced error rate. On RNA from relapse samples, the KD (codons 221 through 500) of BCR-ABL1 was sequenced by the Sanger method, using a nested PCR approach, with a detection limit of 10-20%. Results: A total of 115 pretreatment ABL1 mutations were detected by DS, with ≥1 mutation detected in 47/64 pts (73%). These mutations were generally present at very low levels (median VAF 0.008% [range, 0.004%-0.649%]). Eleven TKI resistance (TKI-R) mutations (10% of all detected mutations) were identified by DS in 7 pts (Table 1). Eighteen pts (28%) relapsed; however, none of the pts with TKI-R mutations relapsed, despite 5 pts receiving a TKI at least intermediately resistant to the detected mutation. Using Sanger sequencing, TKI-R mutations were detected at relapse in 9 pts (T315I, n=6; F317I, V229L and V338G, n=1 each). None of these TKI-R mutations was detected by DS in pretreatment samples. Together, these results suggest that low-level pretreatment ABL1 KD mutations are not clinically meaningful in Ph+ ALL. To validate the DS methodology, we examined its sensitivity and specificity using 6 gDNA samples containing ABL1 KD mutations (9 different SNVs, including 6 TKI-R) mixed with control gDNA from a healthy young individual for predicted VAFs of 1/250 to 1/25,000. Mutations were identified by DS with 100% sensitivity down to VAF In the control samples, the background mutation frequency of ABL1 was ~5x10-7. Using a null model of random distribution of mutations in normal samples at this frequency, there was no significant difference in the proportion of TKI-R mutations detected with DS versus the expected background frequency, suggesting that there is no biased detection of TKI-R mutations by DS. To assess whether cDNA may be susceptible to artefactual mutations induced by RT steps, DS was performed on cDNA from 6 pts in whom no pretreatment ABL1 KD mutation was detected by DS. cDNA generated by RT had a 50-100x increased mutation frequency relative to the normal gDNA samples (Figure 2). False TKI-R mutations were observed in all cDNA samples (ranging from 11-16 TKI-R variants per sample). Overall, the fraction of TKI-R variants relative to total variants detected was significantly higher in all 6 cDNA samples compared to the negative control. Conclusion: DS had 100% sensitivity and specificity for detection of TKI-R mutations in ABL1. In contrast, RT-generated cDNA increased mutation frequency by 50- to 100-fold, suggesting that RT-based methods may falsely assess the presence of TKI-R mutations. Prior to TKI treatment, DS detected ABL1 mutations at a rate similar to healthy controls, and even in cases when a low-level TKI-R mutation was detected by DS, there was no association with relapse. Together, these findings suggest that pretreatment ABL1 mutations in Ph+ ALL are due to random age-related mutagenesis and do not represent clinically meaningful subclones that contribute to relapse. The superior accuracy of DS compared to RT-PCR has broad implications in the detection of low-level mutations across cancers, which may allow for early interventions to target emerging resistant subclones. Disclosures Short: Takeda Oncology: Consultancy, Research Funding; AstraZeneca: Consultancy; Amgen: Honoraria. Kantarjian:Ariad: Research Funding; AbbVie: Honoraria, Research Funding; Takeda: Honoraria; Agios: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Cyclacel: Research Funding; Daiichi-Sankyo: Research Funding; Astex: Research Funding; Jazz Pharma: Research Funding; Pfizer: Honoraria, Research Funding; Immunogen: Research Funding; BMS: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Research Funding. Sasaki:Otsuka: Honoraria; Pfizer: Consultancy. Ravandi:Macrogenix: Consultancy, Research Funding; Cyclacel LTD: Research Funding; Menarini Ricerche: Research Funding; Selvita: Research Funding; Xencor: Consultancy, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Cortes:Astellas Pharma: Consultancy, Honoraria, Research Funding; Immunogen: Consultancy, Honoraria, Research Funding; Sun Pharma: Research Funding; Merus: Consultancy, Honoraria, Research Funding; BiolineRx: Consultancy; Bristol-Myers Squibb: Consultancy, Research Funding; Biopath Holdings: Consultancy, Honoraria; Takeda: Consultancy, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; Forma Therapeutics: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Daiichi Sankyo: Consultancy, Honoraria, Research Funding. Konopleva:Reata Pharmaceuticals: Equity Ownership, Patents & Royalties; Astra Zeneca: Research Funding; Agios: Research Funding; Genentech: Honoraria, Research Funding; Ascentage: Research Funding; Kisoji: Consultancy, Honoraria; Calithera: Research Funding; Stemline Therapeutics: Consultancy, Honoraria, Research Funding; Forty-Seven: Consultancy, Honoraria; Eli Lilly: Research Funding; AbbVie: Consultancy, Honoraria, Research Funding; Cellectis: Research Funding; Amgen: Consultancy, Honoraria; Ablynx: Research Funding; F. Hoffman La-Roche: Consultancy, Honoraria, Research Funding. Garcia-Manero:Novartis: Research Funding; Amphivena: Consultancy, Research Funding; Helsinn: Research Funding; AbbVie: Research Funding; Celgene: Consultancy, Research Funding; Astex: Consultancy, Research Funding; Onconova: Research Funding; H3 Biomedicine: Research Funding; Merck: Research Funding. Pratt:TwinStrand Biosciences: Employment. Williams:TwinStrand Biosciences: Employment. Valentine:TwinStrand Biosciences: Employment. Salk:TwinStrand Biosciences: Employment. Radich:TwinStrand Biosciences: Research Funding; Novartis: Other: RNA Sequencing. Jabbour:Pfizer: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Cyclacel LTD: Research Funding.

Background: Kinase domain (KD) mutations in ABL1 are the dominant mechanism of relapse in patients (pts) with Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL). Previous studies in Ph+ ALL have suggested that some pts harbor subclonal T315I mutations prior to tyrosine kinase inhibitor (TKI) treatment. However, nearly all reports have relied upon inherently error prone RT-PCR to generate template cDNA prior to mutation analysis. We hypothesized that conventional assays might over-estimate the incidence of pre-existing resistance mutations, and that improved sequencing accuracy might yield important information for risk stratification and TKI selection. Methods: Duplex sequencing (DS) is a molecular tagging method that improves the accuracy of conventional next-generation sequencing by more than 10,000-fold, by comparing the nucleotide sequences of each strand of double-stranded molecules. ABL1 DS was performed on genomic DNA from 64 pts with newly diagnosed Ph+ ALL treated with hyper-CVAD plus a TKI. DS of exons 4-10 of ABL1 was performed to an average molecular depth of >10,000x. Among pts who relapsed, using RNA extracted from relapse samples, the KD (codons 221 through 500) of the BCR-ABL1 fusion transcript was sequenced by the Sanger method using a nested PCR approach, with a detection limit of 10-20%. Results: The median age of the cohort was 54 years (range, 20-80 years). The TKI used was imatinib in 5 pts, dasatinib in 38 pts, and ponatinib in 21 pts. All pts achieved complete remission, and 12 pts (19%) underwent allogeneic stem cell transplantation in first remission. A total of 115 pretreatment ABL1 KD mutations were detected among 47 pts (73%). The median number of pretreatment ABL1 KD mutations was 2 (range, 0-6 mutations). The median variant allelic frequency (VAF) of the detected somatic mutations was 0.008% (range, 0.004%-0.649%). Five mutations (4%) and 40 mutations (35%) were present at a VAF ≥0.1% and ≥0.01%, respectively. Eleven mutations known to confer resistance to at least one TKI were detected in 7 pts (11%), and included: F317L in 4 pts, E225K in 2 pts, and E225V, L384M, M244V, Q252H and T315I in 1 pt each. Five mutations were detected in ≥1 pretreatment sample (F317L in 4 pts, M244V in 3 pts, and E255K, E459K and V355V in 2 pts each). Of these 7 pts with pretreatment resistance mutations, 5 pts received a TKI known not to be sensitive to the mutation(s); 2 pts who received ponatinib had mutations at least intermediately sensitive to ponatinib (1 pt with F317L and 1 pt with both E255K and M244V mutations). With a median duration of follow-up of 54 months (range, 1-124 months), 18 pts have relapsed. None of the 7 pts with known pretreatment resistance mutations relapsed. There was no difference in the number of pretreatment mutations between pts who relapsed and those who did not (median mutations: 1 [range, 0-4] and 2 [range, 0-6], respectively; P=0.26). Of the 18 pts who relapsed, 14 underwent Sanger sequencing for ABL1 KD mutations at the time of relapse. Clonal resistance mutations were detected at relapse in 9 pts (64% of sequenced samples): T315I in 6 pts, and F317I, V229L and V338G in 1 pt each (all in pts treated with dasatinib, except V338G in pt on imatinib). Relapse mutations were not observed in pretreatment samples in any of the pts. Conclusions: DS identified very low level pretreatment ABL1 KD mutations in a majority of pts with newly diagnosed Ph+ ALL but these appear inconsequential. Ninety percent of mutations identified have not been described as resistance mutations, suggesting that they may be either synonymous or functionally neutral amino acid changes resulting from normal aging. There was no apparent association of these mutations and risk of relapse, even in the minority of cases in which known resistance mutations were detected at baseline. These data suggest that pretreatment testing for ABL1 KD mutations in Ph+ ALL is unlikely to affect treatment decisions. Disclosures Short: Takeda Oncology: Consultancy. Sasaki:Otsuka Pharmaceutical: Honoraria. Ravandi:Jazz: Honoraria; Orsenix: Honoraria; Orsenix: Honoraria; Seattle Genetics: Research Funding; Sunesis: Honoraria; Xencor: Research Funding; Bristol-Myers Squibb: Research Funding; Amgen: Honoraria, Research Funding, Speakers Bureau; Abbvie: Research Funding; Astellas Pharmaceuticals: Consultancy, Honoraria; Seattle Genetics: Research Funding; Bristol-Myers Squibb: Research Funding; Astellas Pharmaceuticals: Consultancy, Honoraria; Amgen: Honoraria, Research Funding, Speakers Bureau; Macrogenix: Honoraria, Research Funding; Jazz: Honoraria; Abbvie: Research Funding; Xencor: Research Funding; Macrogenix: Honoraria, Research Funding; Sunesis: Honoraria. Cortes:Pfizer: Consultancy, Research Funding; Astellas Pharma: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Arog: Research Funding. Konopleva:Stemline Therapeutics: Research Funding. Radich:TwinStrand Biosciences: Research Funding. Jabbour:Takeda: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Novartis: Research Funding; Pfizer: Consultancy, Research Funding; Abbvie: Research Funding.

Acute myeloid leukemia (AML) is a challenging disease to treat: most patients achieve remission after induction chemotherapy, but the majority eventually relapse. Minimal residual disease (MRD) after initial treatment is the best predictor of relapse and is thus a critical metric around which to develop new treatments. However, conventional MRD diagnostics, including cytology and flow cytometry, are of variable sensitivity and often only perform well in specialty centers, and there is no gold standard. Molecular tests developed to measure trace MRD in other hematological malignancies (i.e. CML) are high-resolution but assay a single, universally-present mutation, while many different genetic drivers exist in AML and these are spread among dozens of genes. Virtually every AML patient harbors a unique combination of mutations, making it difficult to design an effective universal assay. As such, most reported molecular AML MRD assays are either sensitive for mutations that are only found in a narrow subset of patients, or can screen many potential sites of mutation, but with low sensitivity. Here we present a broadly applicable Duplex Sequencing-based AML MRD assay that can readily detect mutant allele frequencies (MAF) below 1/10,000 across a large panel of genes, and below 1/100,000 in a focused panel. Conventional next generation sequencing (NGS) introduces errors during amplification and sequencing, creating a background of artifactual noise that obscures true mutations present below ~1%. Duplex Sequencing improves accuracy >100,000-fold through a molecular tagging approach whereby both strands of each original DNA duplex are ligated with a unique molecular barcode and amplified such that the reads generated from each strand can be related back to their unique original duplex. Reads can also be distinguished from those of their mate strand, thus the two strands of each DNA duplex can be compared and any discrepant nucleotide positions are discounted as errors. Our complete AML panel targets 151 exons or hotspot codons in 29 genes with a 59 kilobase (kb) hybrid-capture footprint. This region comprises loci containing single-nucleotide and short indel mutations found in approximately 90% of adult AMLs. A mean Duplex error-corrected sequence depth of 10,837 and a maximum Duplex depth of 14,967 was obtained across these targets from a single library preparation using 250 ng of sheared leukocyte DNA (Fig. 1). Duplex depth can be readily increased by preparing additional Duplex libraries from the same source DNA to achieve proportionally higher sensitivity for rarer variants. This stands in contrast to conventional NGS where, beyond a modest level, an increase in depth simply increases the number of background errors identified (Fig. 2A). We simulated low-level residual disease by mixing control DNA from a healthy young blood donor with DNA from 9 human cell lines harboring known AML mutations at dilutions from 1:100 to 1:100,000 (Table 2). The genomic loci of these 9 mutations in NRAS, KRAS and TP53 were captured with a small 1 kb probe panel. This mixture was sequenced to a mean Duplex depth of >1,000,000-fold, with the highest and lowest MAFs shown in Fig. 2B. All were close to expected frequencies (r2=0.96) with MAF as low as 6x10-6 (Fig. 3). As proof of specificity, we examined all coding nucleotide positions (excluding the 9 expected variants) and identified only 241 background variant counts out of 414,452,402 total Duplex BP, for an aggregate mutation frequency of 5.8x10-7, consistent with the estimated background of normal human aging. Our Duplex Sequencing-based AML MRD assay is flexible, broadly applicable and extremely sensitive. The assay is easily implemented using standard NGS equipment and automated cloud-based analysis software. The ~90% of AML patients served by this SNV-focused panel can be expanded to nearly 100% with complementary indel detection via targeted NGS RT-PCR. Optionally, when a patient's mutation profile from time-of-diagnosis is known, MRD testing can focus exclusively on those targets using a subset of pre-validated probes to reduce sequencing cost. Improved MRD testing will facilitate accurate prognostication, better selection among treatment options, and could serve as a surrogate endpoint in clinical trials to bring new treatments to patients faster. We are currently evaluating Duplex Sequencing MRD tests in both retrospective and prospective clinical trials. Disclosures Higgins: TwinStrand Biosciences: Employment. Williams:TwinStrand Biosciences: Employment. Buckley:CTI Biopharma: Employment; TwinStrand Biosciences: Consultancy. Radich:TwinStrand Biosciences: Research Funding. Salk:TwinStrand Biosciences: Employment, Equity Ownership.