Manuscript bioRxiv preprint doi: https://doi.org/10.1101/124263; this version postedClick June here14, 2017. to download The copyright Manuscript holder for this preprint (which _ClinicEpigen_4- was not certified by peer review) is the author/funder, who has granted bioRxiv a3-17_CG1_FINAL.docx license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Click here to view linked References 1 2 3 4 5 1 A multicenter, randomized study of decitabine as 6 7 8 2 epigenetic priming with induction in 9 10 11 3 children with AML 12 13 14 4 15 5 Lia Gore,1 Timothy J. Triche, Jr.,2 Jason E. Farrar,3 Daniel Wai,4 Christophe Legendre,5 16 17 6 Gerald C Gooden,2,5 Winnie S. Liang,5 John Carpten,5 David Lee,6 Frank Alvaro,7 18 19 1 8 9 10 11 20 7 Margaret E. Macy, Carola Arndt, Philip Barnette, Todd Cooper, Laura Martin, Aru 21 22 8 Narendran,12 Jessica Pollard,13 Soheil Meshinchi,14 Jessica Boklan,15 Robert J 23 24 9 Arceci*,4,6,15 and Bodour Salhia*,2,5 25 26 10 27 11 28 12 Children’s Hospital Colorado and University of Colorado School of Medicine, Aurora CO1; Jane 29 13 Anne Nohl Division of Hematology, Keck Medicine of USC and Norris Comprehensive Cancer 30 14 Center, Los Angeles, CA2; Arkansas Children’s Research Institute and University of Arkansas for 31 15 Medical Sciences, Little Rock, AR3; Ron Matricaria Institute of Molecular Medicine, Phoenix, AZ4; 32 16 Translational Genomics Research Institute, Phoenix, AZ5; Sidney Kimmel Comprehensive 33 17 Cancer Center at Johns Hopkins’ University, Baltimore MD6; John Hunter Hospital, New South 34 18 Wales, Australia7; Mayo Clinic, Rochester MN8; Primary Children’s Medical Center and the 35 19 University of Utah, Salt Lake City UT9; Children’s Healthcare of Atlanta, Atlanta GA10; Nationwide 36 20 Children’s Hospital, Columbus OH11; Alberta Children’s Hospital and University of Calgary, 37 21 Calgary, AB, Canada12; Seattle Children’s Hospital13 Seattle, WA and Fred Hutchinson Cancer 38 22 Research Center14, Seattle WA; Phoenix Children’s Hospital, Phoenix, AZ15 39 23 40 24 41 [email protected], [email protected], 42 25 [email protected], [email protected], 43 26 [email protected], [email protected], [email protected], [email protected], 44 27 [email protected], [email protected], 45 28 [email protected], [email protected], [email protected], 46 29 [email protected], [email protected], [email protected], 47 30 [email protected], [email protected], [email protected], [email protected], 48 31 [email protected] 49 32 50 33 *These authors contributed equally to this work. 51 34 52 35 Corresponding Author: Lia Gore, MD 53 36 Center for Cancer and Blood Disorders 54 37 Children’s Hospital Colorado 55 38 13123 East 16th Av, Box B115 56 57 39 Aurora CO 80045 58 40 Tel: 720-777-6458 59 41 Fax: 720-777-7339 60 42 [email protected] 61 62 1 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 43 5 ABSTRACT 6 7 8 44 Background: Decitabine is a deoxycytidine nucleoside derivative inhibitor of DNA- 9 10 45 methyltransferases, which has been studied extensively and is approved for 11 12 13 46 myelodysplastic syndrome in adults but with less focus in children. Accordingly, we 14 15 47 conducted a phase 1 multicenter, randomized, open-label study to evaluate decitabine 16 17 48 pre-treatment before standard induction therapy in children with newly diagnosed AML to 18 19 49 assess safety and tolerability and explore a number of biologic endpoints. 20 21 22 23 50 Results: Twenty-four patients were fully assessable for all study objectives per protocol 24 25 51 (10 in Arm A, 14 in Arm B). All patients experienced neutropenia and thrombocytopenia. 26 27 52 The most common grade 3 and 4 non-hematologic adverse events observed were 28 29 53 gastrointestinal toxicities and hypophosphatemia. Plasma decitabine PK were similar to 30 31 32 54 previously reported adult data. Overall CR/CRi was similar for the two arms. MRD 33 34 55 negativity at end-induction was 85% in Arm A versus 67% in Arm B patients. DNA 35 36 56 methylation measured in peripheral blood over the course of treatment tracked with blast 37 38 57 clearance and matched marrow aspirates at day 0 and day 21. Unlike end-point marrow 39 40 58 analyses, promoter methylation in blood identified an apparent reversal of response in 41 42 43 59 the lone treatment failure, one week prior to the patient’s marrow aspirate confirming 44 45 60 non-response. Decitabine-induced effects of end-induction marrows in Arm A were 46 47 61 reflected by changes in DNA methylation and expression comparison with matched 48 49 62 paired marrow diagnostic aspirates. 50 51 52 53 63 Conclusions: This first-in-pediatrics trial demonstrates that decitabine prior to standard 54 55 64 combination chemotherapy is feasible and well tolerated in children with newly 56 57 65 diagnosed AML. Pre-treatment with decitabine may represent a newer therapeutic 58 59 66 option for pediatric AML, especially as it appears to induce important epigenetic 60 61 62 2 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 67 alterations. The novel biological correlates studied in this trial offer a clinically relevant 5 6 68 window into disease progression and remission. Additional studies are needed to 7 8 9 69 definitively assess whether decitabine can enhance durability responses in children with 10 11 70 AML. This trial was registered at www.clinicaltrials.gov as NCT01177540. 12 13 14 71 AML, , pediatrics, pharmacokinetics, pharmacodynamics, 15 Keywords: 16 17 72 promoter methylation, repeat element 18 19 20 73 21 22 23 24 74 BACKGROUND 25 26 27 28 75 Attaining complete response/remission (CR) is currently considered the essential 29 30 76 first step in the effective treatment of acute myelogenous leukemia (AML). Historically, 31 32 77 the most widely used induction therapy included seven days of plus three 33 34 78 days of anthracycline (known as “7+3”). With this approach, 75-80% of children with 35 36 79 37 AML achieve CR (1-3). Subsequently, the addition of a third agent such as etoposide to 38 39 80 7+3 (ADE) along with expanded supportive care measures, has led to higher remission 40 41 81 induction rates of approximately 85%. Of patients who do not attain remission, 42 43 82 approximately one-half have resistant leukemia and a substantial proportion will die from 44 45 83 complications of the disease or treatment. Thus, there is a need to develop new 46 47 48 84 treatment strategies to improve outcomes for these patients. 49 50 85 Pediatric tumors have been shown to have lower mutation burdens than adult 51 52 86 tumors and many of these mutations occur in the plethora of known epigenetic 53 54 87 complexes (4). In addition, significant aberrant DNA methylation is also observed in 55 56 57 88 pediatric cancers such as AML including in patients with the poorest risk sub-types (5). 58 59 89 These studies argue for the importance of identifying novel epigenetic therapies that 60 61 62 3 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 90 target both and/or DNA methylation modifications. Specifically, reversal of 5 6 91 7 promoter DNA hypermethylation and associated gene silencing is an attractive 8 9 92 therapeutic approach in adult cancers. The DNA methylation inhibitors decitabine and 10 11 93 are efficacious for hematological neoplasms at lower, less toxic, doses (6). 12 13 94 Experimentally, high doses induce rapid DNA damage and cytotoxicity, which do not 14 15 95 explain the prolonged response observed in adult patients (6). Studies have consistently 16 17 18 96 shown that transient low doses of DNA demethylating agents exert durable anti-tumor 19 20 97 effects on hematological and epithelial tumor cells (6). Moreover, studies have 21 22 98 demonstrated that DNA hypomethylating agents can sensitize resistant cancer cells to 23 24 99 cytotoxic agents in vitro and in vivo (7-15) and can enhance chemosensitivity of human 25 26 27 100 leukemia cells to cytarabine (16). Therefore, pretreatment with a DNA hypomethylating 28 29 101 agent may increase the efficacy of pediatric AML induction therapy (17). However, to 30 31 102 date there are no studies to demonstrate the safety, tolerability, or any signal of efficacy 32 33 103 of decitabine in combination with conventional multi-agent chemotherapy for AML in 34 35 104 36 children. We report here the first phase 1 clinical evaluation of decitabine in children 37 38 105 with newly diagnosed AML as a feasibility study to determine the safety, tolerability, and 39 40 106 preliminary efficacy when used as epigenetic pretreatment before induction 41 42 107 chemotherapy. In addition to assessing toxicity and morphologic remission, this study 43 44 108 examined decitabine pharmacokinetics and minimal residual disease (MRD) impact. We 45 46 47 109 also performed global DNA methylation and RNA-seq analyses to examine how 48 49 110 decitabine priming impacted the methylome and in end-induction 50 51 111 marrows when compared with matched diagnostic marrow baseline controls. We 52 53 112 believe that this feasibility study was essential prior to longer-term studies assessing 54 55 56 113 whether epigenetic-directed therapy in pediatric AML can lead to enhanced response 57 58 114 rates or more durable responses. 59 60 61 62 4 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 115 5 PATIENTS, MATERIALS, AND METHODS 6 7 116 Patient eligibility 8 9 117 Eligible patients were 1 to 16 years of age (inclusive), had histologically 10 11 12 118 confirmed de novo AML with >20% bone marrow blasts and adequate cardiac function 13 14 119 (defined as ejection fraction >50% or shortening fraction >26%). Patients with acute 15 16 120 promyelocytic leukemia (FAB M3 subtype), symptomatic CNS involvement, white blood 17 18 121 count over 100,000/l, significant renal or hepatic disease, any prior chemotherapy 19 20 21 122 or radiation therapy for AML, known HIV infection, history of CML, congenital syndromes 22 23 123 known to predispose to AML (for example, Down syndrome, Fanconi anemia, Kostmann 24 25 124 syndrome, or Diamond-Blackfan anemia) were excluded. 26 27 125 The study protocol was approved by the institutional review boards at 28 29 30 126 participating sites and was conducted in accordance with the Declaration of Helsinki, 31 32 127 Good Clinical Practice, and all local and federal regulatory guidelines. A parent or legal 33 34 128 guardian provided written informed consent, with patient assent as appropriate 35 36 129 according to institutional requirements. 37 38 130 39 Study design 40 41 131 This multicenter, open-label study randomized patients to one of two arms: either 42 43 132 five days of decitabine followed by standard induction chemotherapy with cytarabine, 44 45 133 , and etoposide (Arm A=DADE), or standard induction chemotherapy with 46 47 134 cytarabine, daunorubicin, and etoposide without decitabine (Arm B=ADE). The trial was 48 49 50 135 listed under ClinicalTrials.gov identifier: NCT00943553. Twenty-five children age 1-16 51 52 136 years with newly diagnosed de novo AML were randomized to receive either Arm A or 53 54 137 Arm B. Given the feasibility nature of the study, sample size was selected based on the 55 56 138 likelihood of how many patients might be accrued in a reasonable time frame so that 57 58 59 139 future studies could be planned. Patients were stratified by age group and then 60 61 62 5 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 140 randomized within each stratum in a 1:1 ratio by an Interactive Voice Response System 5 6 141 7 via a random number generator. Three age strata were used: 1 to <2 years, 2-11 years, 8 9 142 and 12-16 years, with efforts made to balance enrollment among the age groups. 10 11 143 All patients received one cycle of study treatment in the absence of clinically 12 13 144 significant disease progression, unacceptable toxicity, or patient/guardian choice to 14 15 145 discontinue participation. Patients were not pre-medicated prior to the first dose of 16 17 18 146 decitabine; however, all other supportive care measures were allowed according to 19 20 147 institutional standards. Following the completion of the study therapy, therapy continued 21 22 148 at the treating physician’s discretion. 23 24 149 Treatment was administered to patients in hospital, and hospitalization through 25 26 27 150 count recovery was mandated. The dose and schedule of decitabine used in this study 28 29 151 was known to be safe and tolerable in adults and was known to induce adequate 30 31 152 hypomethylation (18, 19), inhibit DNA methyltransferase, and induce tumor suppressor 32 33 153 gene activation as early as 3-5 days following initiation. Treatment included: a) 34 35 154 2 36 decitabine 20 mg/m IV infusion for 1 hour daily for 5 days (Arm A) on Days 1-5; b) age- 37 38 155 based dosing of intrathecal cytarabine (1 to <2 years: 30 mg; 2 to <3 years: 50 mg; >3 39 40 156 years: 70 mg) at the time of diagnostic lumbar puncture or on Day 1; c) cytarabine 100 41 42 157 mg/m2/dose (3.3 mg/kg/dose for BSA <0.6 m2) slow IV push over 15 minutes, every 12 43 44 158 hours for 10 days on Days 1-10 (Arm B) or Days 6 to 15 (Arm A); d) daunorubicin 50 45 46 2 2 47 159 mg/m (1.67 mg/kg/dose for BSA <0.6 m ) IV over 6 hours for 3 days on Days 1, 3, 5 48 49 160 (Arm B) or Days 6, 8 and 10 (Arm A); and e) etoposide 100 mg/m2/dose (3.3 50 51 161 mg/kg/dose for BSA <0.6 m2) IV over 4 hours for 5 days on Days 1-5 (Arm B) or Days 6- 52 53 162 10 (Arm A). 54 55 56 163 Toxicity was graded according to the National Cancer Institute Common 57 58 164 Terminology Criteria for Adverse Events (CTCAE), version 4.0 (http://ctep.cancer.gov; 59 60 165 National Cancer Institute, Bethesda, MD). Treatment-related toxicity was defined as 61 62 6 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 166 non-resolving grade 3 or grade 4 non-hematologic or hematologic toxicity or time to 5 6 7 167 platelet recovery to ≥100,000/l and neutrophil recovery to ≥1,000/l more than 55 days 8 9 168 from the last day of induction chemotherapy in the absence of leukemia. Events 10 11 169 considered by the investigator to be possibly, probably, or definitely related to decitabine 12 13 170 were considered treatment-related toxicity. 14 15 16 171 Safety assessments 17 18 172 Induction mortality was defined as death occurring within six weeks following 19 20 173 initial diagnosis of AML. An independent Data Safety and Monitoring Board assessed 21 22 174 the first 12 patients enrolled. This Board remained active for continuous analyses and 23 24 175 25 recommendations throughout the conduct of the study. Stopping rules were included in 26 27 176 the protocol to ensure appropriate safety of participants and that in the event of 28 29 177 unacceptable toxicity additional patients would not be placed at risk. All investigators 30 31 178 had access to the primary data. 32 33 179 On-study evaluations 34 35 36 180 Required assessments included physical examinations and recording of adverse 37 38 181 events at screening/baseline, on day 5, and at the completion of study therapy. 39 40 182 Required hematology and serum chemistry assessments were performed on days 1, 2, 41 42 183 6, 7, 14, 15, and weekly thereafter. Bone marrow evaluations for morphology, MRD and 43 44 45 184 molecular analyses were performed at screening/baseline, 3-4 weeks following the 46 47 185 completion of induction chemotherapy regardless of peripheral blood count recovery, 48 49 186 and then as clinically indicated until count recovery. Any clinically appropriate 50 51 187 assessment or test was allowed at the treating physician’s discretion to maintain 52 53 54 188 standards of care. 55 56 189 Efficacy assessments 57 58 190 The primary efficacy variable was Complete Response (CR), defined by the 59 60 191 International Working Group 2003 criteria (20), requiring patients to have a morphologic 61 62 7 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 192 leukemia-free state and an absolute neutrophil count >1000/L and platelets of 5 6 7 193 >100,000/L. Neither hemoglobin nor hematocrit was considered to have bearing on 8 9 194 response although patients were required to be red blood cell transfusion independent to 10 11 195 enroll. Secondary efficacy variables included Leukemia-Free Survival (LFS), Overall 12 13 196 14 Survival (OS), methylation of DNA following decitabine therapy, times to platelet and 15 16 197 neutrophil recovery, and level of minimal residual disease at the end of induction 17 18 198 therapy. LFS and OS were assessed on patients every three months until disease 19 20 199 progression, death, or loss to follow up. MRD analysis was performed at the post- 21 22 200 induction therapy assessment by multi-parameter flow immunophenotyping. Due to the 23 24 25 201 small sample size, statistical analyses were primarily descriptive. 26 27 202 Pharmacokinetic evaluations 28 29 203 Serial blood samples (2 mL each) were drawn from all patients randomized to 30 31 204 Arm A at pre-decitabine, 30, 60 (just prior to end of infusion), 65, 90, 120, and 180 32 33 34 205 minutes after the start of decitabine infusion. A separate line was used to draw PK 35 36 206 samples not in proximity (i.e., not the contralateral lumen of a double lumen line) to the 37 38 207 decitabine infusion. Samples were collected in EDTA tubes containing 39 40 208 tetrahydrouridine, a cytidine deaminase inhibitor, to prevent decitabine degradation, and 41 42 209 43 were centrifuged at 4C within 30 minutes of collection. Plasma was harvested and 44 45 210 stored frozen at -70 to -80C and shipped on dry ice for central analysis. 46 47 211 Pharmacokinetic parameters were calculated from plasma decitabine 48 49 212 concentration-time data by non-compartmental methods using Phoenix WinNonlin 50 51 213 version 6.2 (Pharsight Corporation, Mountain View, CA). The maximum plasma 52 53 54 214 concentration (Cmax) and the time at which Cmax occurred (Tmax) were determined by 55 56 215 inspection of the individual data. AUC from time 0 until the last quantifiable 57 58 216 concentration (AUC0–tau) was determined by the linear up-log down trapezoidal rule. The 59 60 61 62 8 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 217 terminal phase elimination rate constant (Kel) was estimated from the slope of the 5 6 218 7 concentration-time data during the log-linear terminal phase using least square 8 9 219 regression analysis. The terminal phase elimination half-life (t1/2) was calculated using 10 11 220 the formula 0.693/Kel. The AUC-time curve from 0 to infinity (AUC0-infinity) was computed 12

13 221 as AUC0-t plus the extrapolation from the last quantifiable concentration, Ct, to infinity 14 15 222 using the formula Ct/Kel. Total body clearance (CLp) was calculated by the formula 16 17 18 223 Dose/AUC0-infinity. The volume of distribution at steady state (Vdss) was calculated using 19 20 224 the formula CLp x MRT. Area under the first moment curve (AUMC) was determined 21 22 225 using the linear trapezoidal rule to calculate AUMC0-tau and extrapolated to infinity as 23 24 2 226 AUMC0-tau + t * Ct/(Kel) . The formula used to determine mean residence time (MRT) 25 26 27 227 was [AUMC/AUC0-infinity –tau/2], where tau is the duration of infusion. 28 29 228 DNA methylation analysis 30 31 229 Bone marrow and blood samples were obtained from all patients at baseline and 32 33 230 at completion of induction therapy. In addition, blood samples were also collected on 34 35 231 36 days 7 and 14. DNA was extracted from marrow or peripheral blood lymphocytes (buffy 37 38 232 coat) using Qiagen’s AllPrep kit from samples enriched for leukemic blasts by standard 39 40 233 Ficoll separation. Global DNA methylation was evaluated using the Infinium® Human 41 42 234 Methylation450® BeadChip Array according to the manufacturer’s protocol (Illumina, 43 44 235 San Diego, CA) and as previously described (21). A total of 18 paired patient samples 45 46 47 236 with both diagnostic and remission bone marrows (9 pairs from Arm A and 9 pairs from 48 49 237 Arm B, totaling 36 samples) were used for DNA methylation analyses. In addition, 50 51 238 peripheral blood DNA from all time points was also analyzed. DNA Methylation levels 52 53 239 for each CpG residue are presented as β values, estimating the ratio of the methylated 54 55 56 240 signal intensity over the sum of the methylated and unmethylated intensities at each 57 58 241 locus. The average β value reports a methylation signal ranging from 0 to 1 59 60 242 representing completely unmethylated to completely methylated values, respectively. 61 62 9 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 243 DNA methylation data were preprocessed using the Illumina Methylation Analyzer (IMA; 5 6 244 7 doi: 10·1093/bioinformatics/bts013), including background and probe design corrections, 8 9 245 quantile normalization and logit transformation. Loci with detection p-values >0.05 in 10 11 246 25% of samples, on sex chromosomes, or within 10 bp of putative SNPs were removed 12 13 247 from analysis. Differential methylation analysis was performed by IMA. A paired 14 15 248 Wilcoxon rank test was conducted to compare end-induction marrows with diagnostic 16 17 18 249 marrows within each arm. Probes with p < 0.05 having group-wise differences in β 19 20 250 values of at least 0.15 were considered statistically significant and differentially 21 22 251 methylated. Differentially methylated loci were visualized on a heat map and separation 23 24 252 of groups was assessed by hierarchical cluster analysis using Manhattan distance and 25 26 27 253 Ward’s method. Unsupervised clustering was also performed on the top 0.1% most 28 29 254 variable probes by standard deviation. The DNA methylation data discussed here were 30 31 255 deposited in NCBI Gene Expression Omnibus Database and are accessible through 32 33 256 GEO Series accession number GSE78963. 34 35 257 36 RNA sequencing analysis 37 38 258 Total RNA was isolated using Qiagen’s AllPrep kit and used to generate whole 39 40 259 transcriptome libraries using Nugen’s Ovation RNA-Seq System v2 and KAPA 41 42 260 Biosystems’ Library Preparation Kit with Illumina adapters. Equimolar libraries were 43 44 261 pooled and used to generate clusters on Illumina HiSeq Paired End v3 flowcells on the 45 46 47 262 Illumina cBot using Illumina’s TruSeq PE Cluster Kit v3. Clustered flowcells were 48 49 263 sequenced by synthesis on the Illumina HiSeq 2000 using paired-end technology and 50 51 264 Illumina’s TruSeq SBS Kit, extending to 83 bp for each of two reads and a 7bp index 52 53 265 read. Approximately 75-100M reads per library were targeted for generation. Following 54 55 56 266 sequencing, BCL files were converted to FASTQs using Illumina BCLConverter tool. 57 58 267 FASTQ alignments were performed using Star to generate individual BAM files. 59 60 268 Cufflinks/Cuffdiff and DEseq2 were used to identify differentially expressed transcripts. 61 62 10 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 269 Annotations were based on Gencode version 3 (ENSEMBL) build 37.1. The RNA-seq 5 6 270 data discussed in this publication have been deposited in NCBI’s Gene Expression 7 8 9 271 Omnibus, and are also accessible through the GEO series accession GSE78963 10 11 272 Analysis of repeat element transcription 12 13 273 Kallisto (22) was used to quantify abundance of transcripts using ENSEMBL 14 15 274 build 80 and human repeat exemplars from RepBase 20_07 (23). Transcripts were 16 17 18 275 quantified in units of transcripts per million (TPM). The resulting estimates were 19 20 276 imported using the Artemis package (24) and annotated for presentation using TxDbLite 21 22 277 (25), the latter providing a “best fit” alignment of RepeatMasker and RepBase element- 23 24 278 wise repeat phylogeny. 25 26 27 279 Pathway analysis 28 29 280 Gene lists of interest were uploaded into IPA (Ingenuity® Systems, Redwood 30 31 281 City, CA) and the Core Analysis workflow was run with default parameters. Core 32 33 282 Analysis provides an assessment of significantly altered pathways, molecular networks 34 35 283 36 and biological processes represented in the samples' gene list. 37 38 284 39 40 41 285 RESULTS 42 43 286 Patients 44 45 46 287 Twenty-five patients, aged 1-16 years (median 8.0 years) with WBC at diagnosis 47 48 288 ranging from 1.19-58.09 x 103/L were randomized between March and November 49 50 289 2011. Two patients did not receive the full induction regimen due to toxicity. As shown 51 52 290 53 in Table 1, 24 were fully assessable for all study objectives per protocol (10 in Arm A, 14 54 55 291 in Arm B). Three patients had confirmed FLT3 internal tandem duplications, all with an 56 57 292 allelic ratio of >0.5 and one had a FLT3 D835 point mutation. Two patients had NPM1 58 59 293 mutations and two patients had CEBPA mutations. No patients had mutations of TET2, 60 61 62 11 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 294 IDH1, IDH2, or C-CBL exons 8 or 9. One patient each had a KIT exon 8 (N822K) and 5 6 295 7 17 (D816H) mutation. Three patients had WT1 exon 7 mutations and one patient had a 8 9 296 WT1 exon 9 mutation. On relative dose intensity analysis, patients received 99-100% of 10 11 297 the intended doses of decitabine, daunorubicin, and etoposide and 84% of the intended 12 13 298 doses of cytarabine. 14 15 299 16 17 18 300 Table 1. Patient Characteristics (by arm and overall) 19 20 Characteristic Patients …in Arm A …in Arm B 21 22 Consented 25 (100%) 11 (44%) 14 (56%) 23 Randomized and treated 25 (100%) 11 (44%) 14 (56%) 24 Evaluable for all study endpoints 24 (96%) 10 (40%) 14 (56%) 25 26 Sex: 27 Male 12 (48%) 7 (58%) 5 (42%) 28 Female 13 (52%) 4 (31%) 9 (69%) 29 30 Age, years 31 32 Median (range) 8.0 (1-16) 8 (2-16) 7.5 (1-16) 33 34 Race: 35 White 20 (80%) 8 (32%) 12 (48%) 36 African-American 1 (4%) 1 (4%) 0 (0%) 37 Asian 1 (4%) 0 (0%) 1 (4%) 38 American Indian or Alaska native 39 1 (4%) 1 (4%) 0 (0%) 40 Other 2 (8%) 1 (4%) 1 (4%) 41 42 Ethnicity, Hispanic 6 (24.0) 43 Ethnicity, not Hispanic or Latino 19 (76.0) 44 Cytogenetic Risk Groups: 45 Favorable 46 4 (8%) 1 (4%) 3 (12%) 47 Intermediate 17 (68%) 6 (24%) 11 (44%) 48 Unfavorable 4 (16%) 4 (16%) 0 (0%) 49 Cytogenetics: 50 Normal 51 7 (28%) 3 (12%) 4 (16%) t(8;21) 52 4 (16%) 1 (4%) 3 (12%) 53 11q23 9 (36%) 5 (20%) 4 (16%) 54 Other 5 (20%) 2 (8%) 3 (12%) 55 56 57 58 59 60 61 62 12 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 Location: 5 Alberta (Canada) 6 1 (4%) 1 (4%) 0 (0%) 7 Children’s Hospital Central California 1 (4%) 0 (0%) 1 (4%) 8 Denver 8 (32%) 5 (20%) 3 (12%) 9 Emory 1 (4%) 0 (0%) 1 (4%) 10 Johns Hopkins 5 (20%) 5 (20%) 0 (0%) 11 John Hunter Hospital (Australia) 2 (8%) 0 (0%) 2 (8%) 12 Mayo Clinic 13 1 (4%) 1 (4%) 0 (0%) 14 Nationwide Children’s Hospital 1 (4%) 0 (0%) 1 (4%) 15 Phoenix Children’s Hospital 1 (4%) 0 (0%) 1 (4%) 16 Primary Children’s Hospital 1 (4%) 0 (0%) 1 (4%) 17 Royal Children’s Hospital (Australia) 3 (12%) 1 (4%) 2 (8%) 18 19 20 301 21 22 302 Toxicity 23 24 303 Treatment-emergent AEs are summarized in Table 2. The most common grade 3 25 26 304 and grade 4 AEs were hematologic, including WBC decreased, anemia, platelet count 27 28 29 305 and neutrophil count decreased. Colitis (n=2), anorexia (n=3), hypophosphatemia (n=2), 30 31 306 and hypokalemia (n=3) were also noted. One patient in Arm A experienced colonic 32 33 307 perforation on Day 6 due to leukemic infiltration of the bowel wall that led to study 34 35 308 discontinuation. Two patients in Arm A died 6 months after completion of induction 36 37 38 309 therapy; one of necrotic bowel and Pseudomonas sepsis, and one of multisystem organ 39 40 310 failure. The latter patient died 5 months after study treatment as a complication of stem 41 42 311 cell transplantation. Neither death was attributed to decitabine nor to the chemotherapy 43 44 312 regimen received during study participation. 45 46 47 313 48 49 314 Table 2. Grade 3 and Grade 4 treatment-emergent adverse events (TEAEs) 50 315 reported in treated patients fully assessable for all study endpoints, as assessed 51 316 by the Common Terminology Criteria for Adverse Events, version 4.0 52 53 54 Decitabine + Chemotherapy Chemotherapy only 55 56

57 Grade 3 Grade 4 Grade 3 Grade 4 58 59 Adverse event n (%) n (%) n (%) n (%) 60 61 62 13 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 Any Grade 3 or Grade 4 TEAEs 7 (87.5) 7 (87.5) 7 (77.8) 7 (77.8) 5 6 Blood and lymphatic system 7 8 disorders 9 10 Anemia 6 (75.0) 2 (25.0) 4 (44.4) 1 (11.1) 11 12 Febrile neutropenia 1 (12.5) 0 4 (44.4) 0 13 14 15 Other: Neutropenia 1 (12.3) 3 (37.5) 1 (11.1) 1 (11.1) 16 17 Other: Thrombocytopenia 4 (50.0) 4 (50.0) 0 2 (22.2) 18 19 Gastrointestinal disorders 20 21 Colitis 2 (25.0) 0 2 (25.0) 0 22 23 Investigations 24 25 Neutrophil count decreased 0 0 1 (11.1) 3 (33.3) 26 27 Platelet count decreased 0 1 (12.5) 2 (22.2) 3 (33.3) 28 29 White blood cell count 30 6 (75.0) 6 (75.0) 3 (33.3) 5 (55.6) 31 decreased 32 33 Metabolism and nutrition 34 35 disorders 36 37 Decreased appetite 3 (37.5) 0 0 0 38 39 Hypokalemia 3 (37.5) 0 1 (11.1) 0 40 41 Hypophosphatemia 2 (25.0) 0 0 0 42 43 44 317 45 46 318 Pharmacokinetics 47 48 49 319 Plasma concentrations of decitabine were quantifiable in all patients up to the 50 51 320 last time point of 180 minutes. Post-infusion, plasma concentrations declined in a bi- 52 53 321 exponential manner (Figure 1). Selected PK parameters of decitabine in subjects 54 55 322 overall are shown in Table 3. The overall mean (standard deviation) PK parameters for 56 57 323 the decitabine treated patients were: Cmax, 294(104) ng/mL; AUC0 ∞, 214(72.4) ng•h/mL; 58 - - 59 60 324 CL, 128(92.3) L/h; Vdss, 45.5(41.1) L; t½, 0.453(0.0804) h; tmax, 0.831h(0.253). Estimated 61 62 14 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 325 PK values for a 70-kg adult male receiving a 1 hour decitabine 20 mg/m2 infusion are 5 6 326 shown for reference. The mean exposure to decitabine, as measured by Cmax and AUC 7 8 9 327 was similar in patients aged 12-16 years compared with those aged 2-11 years as 10 11 328 shown, and similar to those in adults. However, inter-patient variability in this study was 12 13 329 high. 14 15 16 330 17 18 19 331 Table 3. Mean Pharmacokinetic Parameters of Decitabine on Day 5 of 20 332 Treatment—Overall and by Age Group, Pharmacokinetic 21 333 Analysis Population 22 23 Age Group (y) Arm A Comparator 24 2 - 11 12 - 16 Total 70-kg Adult 25 Pharmacokinetic Parameter (N=7) (N=4) (N=11) Malea 26 Cmax (ng/mL) 286 (131) 307 (36·9) 294 (104) 107 27 28 tmax (h) 0·803 (0·272) 0·88 (0·243) 0·831 (0·253) NR 29 0·458 0·446 30 t½ (h) 0·453 (0·0804) 1·14 31 (0·0777) (0·0967) 32 AUC0-t (ng•h/mL) 211 (90·0) 218 (35·1) 214 (72·4) NR 33 b 34 AUC0-∞ (ng•h/mL) 212 (90·0) 219 (35·7) 215 (72·5) 580 35 CL (L/h) 110 (113) 161 (23·9) 128 (92·3) 298 36 37 Vdss (L) 40·7 (52·0) 54·1 (9·67) 45·5 (41·1) 116 38 39 40 AUC = area under the concentration-time curve; CL = total body clearance; Cmax = maximum 41 concentration; NR = not reported; t½ = half-life; tmax = time to Cmax; Vdss = volume of distribution at 42 steady-state concentrations. 43 a: Data based on population pharmacokinetic analysis 44 b: cumulative AUC value over entire 5-day dosing period. Single-day AUC value = 116 ng•h/mL. 45 All values presented as mean ± SD. standard deviation cannot be calculated where n=2 46 47 334 48 49 50 335 Anti-leukemic response 51 52 336 Morphologic CRs and CRs with incomplete count recovery (CRi) rates were 53 54 55 337 similar in both treatment arms: 100% CR/CRi in Arm A (decitabine) and 92% CR/CRi in 56 57 338 Arm B (control). The patient who discontinued study participation on day 6 after 58 59 339 receiving all decitabine doses and only one dose of cytarabine remained in a complete 60 61 62 15 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 340 remission for two months without any further leukemia-directed treatment. She 5 6 341 7 eventually resumed standard therapy two months later and remains in CR 56+ months 8 9 342 later. MRD by multi-parameter flow cytometry showed that 85% of patients in Arm A 10 11 343 were MRD negative at day 30 of induction compared to 67% in Arm B. In a follow-up of 12 13 344 patients as of August 2015, 8 of 14 (57%) in Arm B and 5 of 10 (50%) of evaluable 14 15 345 patients in Arm A relapsed following additional treatment. 16 17 18 346 Biological marker analysis 19 20 347 Quantitative DNA methylation analyses revealed global changes in methylation 21 22 348 following decitabine priming. Diagnostic and end-induction marrows were analyzed in 9 23 24 349 patients in each Arm (Figure S1). Paired differential methylation analysis of end- 25 26 27 350 induction marrows to patient matched screening marrows revealed 6990 differentially 28 29 351 methylated CpG loci (DML) encompassing 2518 in Arm A compared to only 1090 30 31 352 DML (539 genes) in Arm B (Tables S1A-B). Only DML in Arm A (n=4597) survived false 32 33 353 discovery p-value correction. Of all DML in Arm A, 4134 were hypomethylated and 2856 34 35 354 36 were hypermethylated. In Arm B, 785 DML were hypomethylated and 305 were 37 38 355 hypermethylated. There were 795 DML (438 genes) common to both arms. Although 39 40 356 about 80% of genes altered by DNA methylation in Arm B were common to Arm A, there 41 42 357 were significantly more probes altered for a given gene in Arm A. Moreover, 78% of 43 44 358 hypomethylated probes in Arm B were common with Arm A, compared with 56% of 45 46 47 359 hypermethylated probes common between treatment arms. The median delta-beta 48 49 360 values for Arms A and B were -0.27 and -0.28, respectively, indicating modest overall 50 51 361 hypomethylation induced by either treatment regimen at the specified delta-beta cut-off. 52 53 362 Forty-one percent of DML were hypermethylated after decitabine therapy compared with 54 55 56 363 28% after chemotherapy only. Regional and functional CpG distribution of DML after 57 58 364 therapy in both treatment arms was also examined. Functional distribution relates CpG 59 60 365 position to transcription start sites (TSS) −200 to −1500 bp, 5′ untranslated region (UTR), 61 62 16 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 366 and exon 1 for coding genes as well as gene bodies. In both treatment arms, gene body 5 6 367 7 hypermethylation was the most frequent change, followed by gene body 8 9 368 hypomethylation and TSS 200 hypomethylation (Figure S2). Regional distribution of 10 11 369 DML was assessed based on proximity to the closest CpG island. In addition to CpG 12 13 370 islands, shores are 0-2 kb from CpG islands, shelves are 2-4 kb away, and open sea 14 15 371 regions are isolated loci without a designation. CpG island hypomethylation occurred in 16 17 18 372 greater than 68% of DML in both groups. Hypermethylation occurred most prominently 19 20 373 in open sea regions and to a greater degree in Arm A patients compared with those in 21 22 374 Arm B receiving chemotherapy alone (Figure S3). 23 24 375 Unsupervised clustering analysis of DML for both treatment arms demonstrated 25 26 27 376 strong separation of screening and end-induction marrows except for one sample pair in 28 29 377 Arm A and three sample pairs in Arm B (Figure 2, top panels). In two of these cases, 30 31 378 pre- and post-treatment samples co-clustered with its matching sample. One case in 32 33 379 Arm A and Arm B clustered with diagnostic marrows, suggesting the marrow was 34 35 380 36 possibly unaffected by therapy and indeed the sample in Arm A (1006_1004) was from a 37 38 381 patient with stable disease. Overall, these data indicate that decitabine therapy has a 39 40 382 homogenizing effect on the recovering end-induction marrow in AML. This was evident 41 42 383 when compared with Arm B samples, where DNA methylation was more heterogeneous 43 44 384 after standard chemotherapy treatment. 45 46 47 385 To further assess the changes in recovering marrows in both arms, we 48 49 386 performed an unsupervised clustering analysis of the top 0.1% most variable CpG 50 51 387 probes (~430 probes) in the post-processed data. These data confirmed that the end- 52 53 388 induction recovering marrows were distinctive from screening marrows and more 54 55 56 389 homogenized in the decitabine-treated arm compared with those in the control arm 57 58 390 (Figure 2, lower panels). To ensure that samples were not molecularly different at 59 60 391 screening between the arms, we performed the above analyses comparing screening 61 62 17 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 392 marrows in Arm A with Arm B and observed only 492 DML. Of these, 291 were common 5 6 393 7 to the Arm A DML list, while 3 DML were common to both Arm A and B comparisons and 8 9 394 0 DML were common to Arm B DML (Figure 3). These intrinsically different loci were 10 11 395 excluded from downstream gene-level analysis. Among the genes most prevalently 12 13 396 hypomethylated in Arm A were FOXG1, VSTM2A, WT1, ZNF135, ZIC1, and ZIC4, 14 15 397 (Figure 4), which may potentially be used to measure decitabine activity. In addition, 16 17 18 398 time-dependent promoter hypomethylation of these genes also occurred in peripheral 19 20 399 blood lymphocytes (Figure 5), confirming their significance as potential biomarkers of 21 22 400 decitabine response. Most notably, recovery of promoter methylation in peripheral blood 23 24 401 was seen in a patient with stable disease and whose recovering marrow co-clustered 25 26 27 402 with diagnostic marrow, hinting at signs of preliminary efficacy. The data point to the 28 29 403 potential utility of these genes as biomarkers of minimal residual disease in patients 30 31 404 treated with decitabine. 32 33 405 Table 4 shows DNA methylation changes in several key biological pathways 34 35 406 36 potentially important to response to decitabine and chemotherapy. The top canonical 37 38 407 pathways in IPA for Arm A DML included gene alterations affecting mostly neuronal 39 40 408 signaling such as neuropathic pain signaling and glutamate receptor signaling (Table 4). 41 42 409 In Arm B, the top IPA canonical pathways included DML affecting embryonic stem cell 43 44 410 signaling and Rho GTPase signaling ( ). 45 Table 4 46 47 411 48 49 412 Table 4. Ingenuity Pathway Analysis of Differentially Methylated Genes. 50 51 p-value Overlap n (%) 52 Arm A Top Canonical Pathways 53 Neuropathic Pain Signaling in Doral Horn 7.5e-12 35/100 (35.0%) 54 55 Glutamate Receptor Signaling 2.45e-11 25/57 (43.9% 56 Amyotrophic Lateral Sclerosis Signaling 2.05e-09 31/98 (31.6%) 57 Synaptic Long Term Potentiation 1.00e-06 30/119 (25.2% 58 Hepatic Fibrosis/Hepatic Stellate Cell Activation 1.01e-06 40/183 (21.9%) 59 60 61 62 18 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 Arm B Top Canonical Pathways 5 Transcriptional Regulatory Network in Embryonic Stem 2.68e-04 4/40 (10.0%) 6 7 Cells 8 Thrombin Signaling 7.94e-04 7/191 (3.7%) 9 GPCR-Mediated Integration of Enteroendocrine Signaling 2.36e-03 4/71 (5.6%) 10 Exemplified by an L Cell 11 12 Signaling by Rho Family GTPases 2.54e-03 7/234 (3.0%) 13 CXCR4 Signaling 7.03e-03 5/152 (3.3%) 14 413 Differentially methylated genes for Arm A (decitabine + chemotherapy; n=2518) and Arm 15 414 B (chemotherapy alone; n=539) were entered into the core pathway analysis option. Top 16 415 5 canonical pathways are shown along with a significant p-value and the number of 17 416 genes in each list belonging to the pathway. 18 417 19 20 21 418 This study is unique in comparing the epigenetic and RNA expression 22 23 419 consequences of induction chemotherapy following pre-treatment with decitabine vs 24 25 420 chemotherapy alone in end-induction recovering bone marrow. This comparison 26 27 421 revealed 104 differentially expressed genes in Arm A (Table S2A) and 74 genes in Arm 28 29 30 422 B (Table S2B). There are 5 genes common to both treatment arms. Twelve genes that 31 32 423 were differentially expressed in Arm A had DNA methylation changes affecting 72 DML 33 34 424 (Table S3A,). Among these were 57 probes impacting PCDGHGB2, the potential 35 36 425 calcium-dependent cell-adhesion protein; the gene was hypomethylated but 37 38 426 39 downregulated. In Arm B there were only 23 DML (Table S3B) corresponding to eight 40 41 427 differentially expressed genes. Only SIX3 overlapped between both arms at a single 42 43 428 DML. Additionally, we saw no evidence of sustained repeat element reactivation in 44 45 429 recovering week three marrows with or without decitabine pre-treatment (Figure 6). 46 47 430 48 49 50 431 DISCUSSION 51 52 432 53 This first-in-pediatric randomized trial of epigenetic priming in children with newly 54 55 433 diagnosed AML demonstrated safety and tolerability and establishes the feasibility 56 57 434 required to develop future trials for assessing durability of response. We did not assess 58 59 435 durable responses in this study because its main purpose was to first assess safety and 60 61 62 19 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 436 tolerability of decitabine in children. The results trended towards non-inferiority of 5 6 437 7 morphologic, immunophenotypic, and MRD response, although the small sample size 8 9 438 limited statistical analyses. There was also evidence of decitabine-induced effects in 10 11 439 end-induction bone marrow aspirates compared with those obtained at diagnosis. 12 13 440 Children treated with decitabine as a single agent for five days prior to conventional 14 15 441 cytotoxic therapy did not have rapid progression of their leukemic burden during the pre- 16 17 18 442 phase, further supporting the feasibility and safety of this approach. Based on these 19 20 443 results, a dose of 20 mg/m2 daily for 5 days prior to standard conventional chemotherapy 21 22 444 could be considered for testing for durable responses. 23 24 445 Most non-hematologic AEs reported in this study were mild to moderate in 25 26 27 446 severity (grade 1 or 2), reversible, and easily managed. In general, the safety profile of 28 29 447 decitabine in children with AML was consistent with that seen in adults. No previously 30 31 448 unreported decitabine toxicities were observed. -related hematologic toxicity, 32 33 449 anorexia, and asymptomatic grade 3 hypokalemia and hypophosphatemia were slightly 34 35 450 36 more common in decitabine treated patients. 37 38 451 This trial was not powered to detect a difference in response between the two 39 40 452 arms and treatment with decitabine prior to standard induction therapy resulted in a 41 42 453 similar morphological response compared to standard induction therapy. Of note, there 43 44 454 were more high-risk cytogenetic patients in the decitabine arm (4 versus 0), which may 45 46 47 455 indicate a benefit for decitabine priming prior to treatment in these patients. Patients in 48 49 456 this study were generally representative of children with de novo childhood AML in age, 50 51 457 gender, and biologic features, however there were slightly more patients with WT1 and 52 53 458 CEBPA mutations than previously reported. In addition, the impact on MRD suggests 54 55 56 459 that decitabine may induce a higher percentage of MRD negative remissions; however, 57 58 460 again the numbers in this study are too small to calculate statistics in a meaningful way. 59 60 461 There was a non-significant trend toward a longer time to recover neutrophil and platelet 61 62 20 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 462 counts in the decitabine-treated patients (data on file), and no AEs or SAEs were noted 5 6 463 7 as a result of delays in count recovery. These results suggest that exposure to 8 9 464 decitabine may have important implications for sensitizing potentially resistant leukemic 10 11 465 clones to cytotoxic chemotherapy, resulting in deeper remissions predictive of more 12 13 466 favorable outcomes. Larger randomized studies are needed to confirm these findings. 14 15 467 Alterations in DNA methylation and RNA expression patterns in end-induction 16 17 18 468 bone marrow between the two groups of patients suggest important consequences of 19 20 469 exposure to decitabine pretreatment on hematopoietic recovery after exposure to 21 22 470 intensive chemotherapy, where decitabine appears to have a homogenizing effect on 23 24 471 end-induction marrows that may ultimately be important in priming for chemotherapy 25 26 27 472 sensitization. Our data suggest that while chemotherapy alone may have an effect on 28 29 473 DNA methylation, the effect is clearly augmented by the addition of decitabine via 30 31 474 epigenetic changes that may impact both leukemic, normal hematopoietic progenitors, 32 33 475 as well as bone-marrow stromal cells. Furthermore, the data suggest that decitabine 34 35 476 36 therapy can be used to measure MRD/or patient response to therapy by assessing DNA 37 38 477 methylation status of specific promoter regions non-invasively in blood. The pathway 39 40 478 analysis conducted to compare differentially methylated loci in the decitabine treated 41 42 479 arm to the chemotherapy only arm revealed a number of pathways implicated to 43 44 480 neuronal signaling but the results were quite distinct from the ADE only arm. While the 45 46 47 481 implications of neuronal signaling are not currently clear, we postulate it must be related 48 49 482 to the bone marrow niche post-treatment since no dorsal root ganglia or other neuronal 50 51 483 tissue exist in the marrow. Presumably this could be due to ion channel currents that 52 53 484 play an important role in bone-marrow derived mesenchymal stem cells and 54 55 56 485 hematopoietic progenitors (26). Our observations further suggest that monitoring 57 58 486 changes in normal progenitors may be important in understanding the short and longer- 59 60 61 62 21 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 487 term consequences of exposure to methyltransferase inhibitors on malignant and normal 5 6 488 7 bone marrow progenitors. 8 9 489 An increased percentage of DMLs were hypermethylated in patients receiving 10 11 490 decitabine compared to those receiving chemotherapy alone, suggesting that decitabine 12 13 491 has an effect on the methylome of the recovering marrow beyond DNA demethylation. 14 15 492 This appears to result in changes that differ both qualitatively and quantitatively from 16 17 18 493 chemotherapy alone. Importantly, chemotherapy molecularly alters the recovering 19 20 494 marrow , but the number of changes is significantly fewer than with 21 22 495 decitabine and the pathways affected differ significantly. 23 24 496 25 26 27 497 CONCLUSIONS 28 29 498 The toxicity and PK results observed in the patients in this study suggest that decitabine 30 31 32 499 can be safely combined with standard doses and schedules of anticancer agents in 33 34 500 children with newly diagnosed AML. Furthermore, our data suggest that this regimen 35 36 501 alters DNA methylation and RNA expression compared to ADE chemotherapy alone, 37 38 502 and patients treated with decitabine could have minimal residual disease measured by 39 40 41 503 assessing DNA methylation status of specific promoter regions. Preclinical studies have 42 43 504 shown additive or synergistic activity when decitabine is combined with a variety of other 44 45 505 anticancer therapies (27-30) and results from trials such as this provide further evidence 46 47 506 of feasibility, safety and possible strategies for larger randomized trials in patients with 48 49 50 507 newly diagnosed or recurrent/refractory leukemia as well as in the minimal disease state 51 52 508 during post-remission follow-up. No excess or unexpected toxicities were seen. The 53 54 509 most common drug-related grade 3 or grade 4 AEs were hematologic and PK/PD were 55 56 510 as expected. Complete remission rates were similar. Patients treated with decitabine 57 58 511 prior to conventional chemotherapy had distinct changes in DNA methylation and 59 60 61 62 22 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 512 transcriptional regulation. Repeat element transcription may be of interest for further 5 6 513 7 mechanistic study. In conclusion, epigenetic therapy with decitabine is safe for use in 8 9 514 children and the clinical findings together with molecular correlative studies suggest that 10 11 515 there may be early signs of enhanced efficacy. However, further studies are needed to 12 13 516 definitively determine the long-term patient outcomes of decitabine priming in children 14 15 517 with AML. 16 17 18 518 19 20 21 519 LIST OF ABBREVIATIONS: 22 23 520 ADE = cytarabine/daunorubicin/etoposide chemotherapy regimen 24 25 26 521 AML = acute myelogenous leukemia 27 28 522 AUC = area under the curve 29 30 523 BSA = body surface area 31 32 524 C max = maximum plasma concentration 33 34 525 CML = chronic myelogenous leukemia 35 36 526 CNS = central nervous system 37 38 39 527 CR = complete remission 40 41 528 CR = complete remission with incomplete count recovery 42 43 529 DADE = decitabine plus daunorubicin/cytarabine/etoposide chemotherapy regimen 44 45 530 DML = differentially methylated loci 46 47 531 DNA = deoxyribonucleic acid 48 49 50 532 FAB = French-American-British classification 51 52 533 HIV = human immunodeficiency virus 53 54 534 LFS = leukemia-free survival 55 56 535 MRD = minimal residual disease 57 58 536 OS = overall survival 59 60 61 62 23 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 537 PD = pharmacodynamics 5 6 538 PK = pharmacokinetics 7 8 9 539 RNA = ribonucleic acid 10 11 540 Tmax = time to maximum plasma concentration 12 13 541 14 15 542 16 DECLARATIONS 17 18 543 Ethics Approval and Consent to Participate: 19 20 544 The study protocol was approved by each institutional review boards at every 21 22 545 participating site and was conducted in accordance with the Declaration of Helsinki, 23 24 25 546 Good Clinical Practice, and all local and federal regulatory guidelines. A parent or legal 26 27 547 guardian provided written informed consent, with patient assent as appropriate 28 29 548 according to institutional requirements. 30 31 549 32 33 34 550 Consent for Publication: Not applicable 35 36 551 Availability of Data and Materials: 37 38 552 The datasets generated and analyzed in this study are included in this article and are 39 40 553 available from the corresponding author on reasonable request. In addition, the DNA 41 42 43 554 methylation and RNA-seq data discussed in this publication have been deposited in 44 45 555 NCBI’s Gene Expression Omnibus, and are accessible through the GEO series 46 47 556 accession number GSE78963. 48 49 557 50 51 558 52 Competing Interests: 53 54 559 Mark Jones and Peter Tarassoff are former employees of Eisai Pharmaceuticals. Robert 55 56 560 Arceci was a consultant for Pfizer. Partial support from Eisai was obtained for 57 58 59 60 61 62 24 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 561 correlative laboratory studies (R.J.A., S.M.). There are no other potential conflicts of 5 6 562 7 interest to declare. 8 9 563 10 11 564 Funding and Support: 12 13 565 This work was partially supported by Eisai Pharmaceuticals, Inc. In addition, the studies 14 15 566 were partially supported by the Ron Matricaria Institute of Molecular Medicine at Phoenix 16 17 18 567 Children’s Hospital (R.J.A.), the University of Arizona (R.J.A.), and TGen, Inc (R.J.A., 19 20 568 B.S.). During part of the study period, R.J.A. was supported by the King Fahd Chair in 21 22 569 Pediatric Oncology at Johns Hopkins University. Additional support was from The 23 24 570 McCormick Tribune Foundation (L.G.), Alex’s Lemonade Stand (L.G.), the Morgan 25 26 27 571 Adams Foundation (L.G. and M.E.M.), the St. Baldrick’s Foundation (R.J.A.), The Lund 28 29 572 Foundation (R.J.A.), and the Najafi Fund (R.J.A.). M.E.M. was partially supported by the 30 31 573 National Institutes of Health K12 CA086913-08. J.E.F. is partially supported by the 32 33 574 Arkansas Biosciences Institute. L.G. is partially supported by the Clark Family and Ergen 34 35 575 Chairs in Pediatric Cancer Therapeutics at Children’s Hospital Colorado. 36 37 38 576 39 40 577 Authorship Contributions: 41 42 578 L.G. and R.J.A. designed and wrote the study concept and scientific hypotheses, derived 43 44 579 the full protocol, provided study oversight during the conduct of the study, enrolled 45 46 47 580 patients on the study, reviewed data and toxicity of patients, and interpreted the data. 48 49 581 R.J.A. performed many of the scientific experiments and contributed to a draft of the 50 51 582 manuscript. L.G. performed the literature search, wrote the first, all drafts and the final 52 53 583 manuscript, helped prepare and reviewed the Figures and Tables, and collated and 54 55 56 584 incorporated comments from all co-authors. B.S. conducted experiments and provided 57 58 585 oversight of the scientific experiments not completed by R.J.A., prepared and reviewed 59 60 586 the Figures and Tables, and contributed substantially to the data analysis and writing 61 62 25 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 587 and editing of the manuscript. T.T. helped analyze and review the experiments and 5 6 588 7 Figures and Tables, and assisted substantially in the writing and reviewing of the final 8 9 589 manuscript. J.E.F., D.W., C.L., G.C.G., W.S.L., J.C., D.L., and S.M. contributed to the 10 11 590 scientific experiments and data analyses including Figure preparation and review. J.E.F. 12 13 591 and T.T. also contributed to writing the Methods sections, reviewing, and editing the 14 15 592 manuscript. S.M. contributed to the data analysis and commentary. F.A., M.E.M., C.A., 16 17 18 593 P.B., T.C., L.M., A.N., J.P., J. B. enrolled patients on the clinical trial and reviewed and 19 20 594 commented on the manuscript prior to submission. M.J., L.C., S.S., and P.T. reviewed 21 22 595 the study and manuscript. M.J. representing the Eisai Inc., provided study enrollment 23 24 596 materials, oversight and analysis within the context of the protocol operations and data 25 26 27 597 analysis. Eisai, Inc., is the manufacturer of decitabine and the company providing partial 28 29 598 support for the conduct of this trial. 30 31 599 32 33 600 Acknowledgements: 34 35 601 n’s Hospital, Australia) and Dr. Wendy Tcheng 36 Dr. Francoise Mechinaud (Royal Childre 37 38 602 (Children’s Hospital of Central California) also treated patients on this trial. Dr. Peter 39 40 603 Tarrasoff had oversight and analysis of the protocol operations on behalf of Eisai, Inc., 41 42 604 the manufacturer of decitabine and the company providing partial support for the 43 44 605 conduct of this trial. 45 46 47 606 48 49 607 Lori Cuyugan and Shobana Sekar (Translational Genomics Research Institute, Phoenix, 50 51 608 AZ) for the generation and analysis of RNA-seq data. 52 53 609 54 55 56 610 Dedication: 57 58 611 The authors of this paper would like to recognize the fundamental contributions that Dr. 59 60 612 Robert Arceci made not only in the conception, design, and conduct of this study and the 61 62 26 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 613 associated biology work incorporated within, but to the field of pediatric oncology as a 5 6 614 7 whole. During the laboratory correlate analysis and preparation of this manuscript, Dr. 8 9 615 Arceci’s life was tragically cut short in a fatal traffic accident. His collaborators on this 10 11 616 study wish to acknowledge his myriad contributions to this work and the field, and 12 13 617 remain dedicated to pursuing the highest degree of scientific collaboration and integrity 14 15 618 in his honor. 16 17 18 619 19 20 620 REFERENCES 21 22 23 621 1. Buckley JD, Lampkin BC, Nesbit ME, Bernstein ID, Feig SA, Kersey JH, et al. 24 622 Remission induction in children with acute non-lymphocytic leukemia using cytosine 25 623 arabinoside and doxorubicin or daunorubicin: a report from the Childrens Cancer Study 26 624 Group. Med Pediatr Oncol. 1989;17(5):382-90. 27 28 625 2. Hurwitz CA, Mounce KG, Grier HE. 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The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 650 10. Eramo A, Pallini R, Lotti F, Sette G, Patti M, Bartucci M, et al. Inhibition of 5 6 651 DNA methylation sensitizes glioblastoma for tumor necrosis factor-related apoptosis- 7 652 inducing ligand-mediated destruction. Cancer Res. 2005;65(24):11469-77. 8 653 11. Fulda S, Kufer MU, Meyer E, van Valen F, Dockhorn-Dworniczak B, Debatin 9 654 KM. Sensitization for death receptor- or drug-induced apoptosis by re-expression of 10 655 caspase-8 through demethylation or gene transfer. Oncogene. 2001;20(41):5865-77. 11 12 656 12. Kanda T, Tada M, Imazeki F, Yokosuka O, Nagao K, Saisho H. 5-aza-2'- 13 657 deoxycytidine sensitizes hepatoma and pancreatic cancer cell lines. Oncol Rep. 14 658 2005;14(4):975-9. 15 659 13. Niitsu N, Hayashi Y, Sugita K, Honma Y. Sensitization by 5-aza-2'-deoxycytidine 16 660 of leukaemia cells with MLL abnormalities to induction of differentiation by all-trans 17 18 661 retinoic acid and 1alpha,25-dihydroxyvitamin D3. Br J Haematol. 2001;112(2):315-26. 19 662 14. Plumb JA, Strathdee G, Sludden J, Kaye SB, Brown R. Reversal of drug 20 663 resistance in human tumor xenografts by 2'-deoxy-5-azacytidine-induced demethylation 21 664 of the hMLH1 gene promoter. Cancer Res. 2000;60(21):6039-44. 22 23 665 15. Qiu YY, Mirkin BL, Dwivedi RS. Inhibition of DNA methyltransferase reverses 24 666 induced drug resistance in murine neuroblastoma cells. Cancer Detect Prev. 25 667 2005;29(5):456-63. 26 668 16. Qin T, Youssef EM, Jelinek J, Chen R, Yang AS, Garcia-Manero G, et al. Effect 27 669 of cytarabine and decitabine in combination in human leukemic cell lines. Clin Cancer 28 29 670 Res. 2007;13(14):4225-32. 30 671 17. 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Cheson BD, Bennett JM, Kopecky KJ, Buchner T, Willman CL, Estey EH, et al. 43 682 Revised recommendations of the International Working Group for Diagnosis, 44 683 Standardization of Response Criteria, Treatment Outcomes, and Reporting Standards for 45 46 684 Therapeutic Trials in . J Clin Oncol. 2003;21(24):4642-9. 47 685 21. Legendre CR, Demeure MJ, Whitsett TG, Gooden GC, Bussey KJ, Jung S, et al. 48 686 Pathway Implications of Aberrant Global Methylation in Adrenocortical Cancer. PLoS 49 687 One. 2016;11(3):e0150629. 50 51 688 22. Wang D, Yan L, Hu Q, Sucheston LE, Higgins MJ, Ambrosone CB, et al. IMA: 52 689 an R package for high-throughput analysis of Illumina's 450K Infinium methylation data. 53 690 Bioinformatics. 2012;28(5):729-30. 54 691 23. Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J. 55 692 Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 56 57 693 2005;110(1-4):462-7. 58 59 60 61 62 28 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 694 24. Triche T CA. artemis: Stable version used in EISAI repeat analyses. 5 6 695 http://zenodo.org/record/33157/export/hm - .V0e1RMvmpiwAccessed November 3, 7 696 2015. 8 697 25. Triche T CA. TxDbLite: Stable version used in EISAI repeat analyses. . 9 698 http://zenodo.org/record/33170/export/hm - .V0e17cvmpiwAccessed November 3, 2015. 10 699 26. Li GR, Deng XL. Functional ion channels in stem cells. World J Stem Cells. 11 12 700 2011;3(3):19-24. 13 701 27. Kuendgen A, Lubbert M. Current status of epigenetic treatment in 14 702 myelodysplastic syndromes. Ann Hematol. 2008;87(8):601-11. 15 703 28. Kumagai T, Wakimoto N, Yin D, Gery S, Kawamata N, Takai N, et al. Histone 16 704 deacetylase inhibitor, suberoylanilide hydroxamic acid (Vorinostat, SAHA) profoundly 17 18 705 inhibits the growth of human pancreatic cancer cells. Int J Cancer. 2007;121(3):656-65. 19 706 29. Leshchenko VV, Kuo PY, Shaknovich R, Yang DT, Gellen T, Petrich A, et al. 20 707 Genomewide DNA methylation analysis reveals novel targets for drug development in 21 708 mantle cell lymphoma. Blood. 2010;116(7):1025-34. 22 23 709 30. Yang H, Hoshino K, Sanchez-Gonzalez B, Kantarjian H, Garcia-Manero G. 24 710 Antileukemia activity of the combination of 5-aza-2'-deoxycytidine with valproic acid. 25 711 Leuk Res. 2005;29(7):739-48. 26 712 27 28 29 713 FIGURE LEGENDS 30 31 714 32 33 34 715 Figure 1. Mean decitabine blood concentration-time profile measured in whole 35 36 716 blood by age group. 37 38 717 39 40 41 718 Figure 2. Hierarchical clustering of differentially methylated loci (DML) in Arm A 42 43 719 (decitabine + chemotherapy) and Arm B (chemotherapy alone). Top Panel: 44 45 46 720 unsupervised clustering analysis of 6990 DML in Arm A and 1090 DML in Arm B 47 48 721 revealed separation of end of induction recovering marrows at week 3 from 49 50 51 722 screening marrows (X0BM). Lower Panel: Unsupervised hierarchical clustering of 52 53 723 the top 0.1% most variable loci (SD) also separated screening marrows (X0BM) 54 55 724 56 from end of induction recovery marrows at week 3. In both analyses, Arm A 57 58 725 shows better separation of the two time points than in Arm B, indicating that 59 60 61 62 29 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 726 decitabine therapy appears to homogenize marrows to a larger degree than in 5 6 7 727 chemotherapy alone. 8 9 728 10 11 12 729 Figure 3. Overlap of differentially methylated loci between arms and time points 13 14 730 in Arm A (DADE), Arm B (ADE), and screening vs. recovery marrow aspirates. 15 16 731 17 Screening marrows for samples in Arm A and Arm B are also compared and 18 19 732 demonstrate little intrinsic bias between groups. 20 21 733 22 23 24 734 Figure 4. Integrated Genomic Viewer snapshot of differentially methylated genes 25 26 735 affected by hypomethylation in response to decitabine therapy. Vertical heatmaps 27 28 736 represent significantly differentially methylated (p value <0.05) probes in the 6 genes 29 30 31 737 illustrated. Each row on the heatmap represent a unique sample. Many more probes 32 33 738 were differentially methylated in Arm A (decitabine + chemotherapy) compared with Arm 34 35 739 B (chemotherapy alone) for the probes shown. Hypomethylation (green) in response to 36 37 740 decitabine (Arm A) is evident in end of induction recovery marrows (wk 3) compared with 38 39 40 741 diagnostic marrows (screen). Array avg  values are represented in the heatmap. Scale 41 42 742 ranges from 0-1, where 0 is unmethylated (green) and 1 is fully methylated (red). Tracks 43 44 743 shown: gene, CpG 450K probe, and CpG island. 45 46 744 47 48 49 745 Figure 5. Time-course collection of peripheral blood samples in Arm A reveal 50 51 746 consistent decreases in promoter methylation at relevant transcription start sites 52 53 54 747 over treatment in all responders, as well as a reversal of this decrease in the sole 55 56 748 non-responsive patient (1006-1004). A distinct uptick in the patient’s promoter 57 58 59 60 61 62 30 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 2 3 4 749 methylation from day 14 to 21 is noted, which corresponded clinically to the 5 6 7 750 patient’s disease progression. 8 9 751 10 11 12 752 Figure 6. Unsupervised hierarchical clustering of repeat element expression in 13 14 753 screening and week 3 bone marrows, with (Arm A) or without (Arm B) decitabine 15 16 754 17 priming, in transcripts per million (TPM). No clear separation by trial arm is 18 19 755 visible, and a maximum of 4 transcripts per million is observed, suggesting that 20 21 756 mobilization of repeats by demethylating agents does not persist beyond the 22 23 24 757 course of treatment and is not significantly different between the treatment arms. 25 26 758 27 28 29 759 SUPPLEMENTARY FIGURE LEGENDS 30 31 760 32 33 761 34 Figure S1. Schema of sample analysis workflow. 35 36 762 37 38 763 Figure S2. Distribution of differentially methylated loci (DML) according to functional 39 40 764 CpG contextual distribution in Arms A (decitabine + chemotherapy) and B 41 42 765 (chemotherapy alone). Pie charts demonstrate the frequency by which hyper or 43 44 45 766 hypomethylated loci are distributed according to their functional position. 46 47 767 48 49 768 Figure S3. Distribution of differentially methylated loci (DML) according to CpG Island 50 51 769 contextual distribution in Arms A (decitabine + chemotherapy) and B (chemotherapy 52 53 54 770 alone). Pie charts demonstrate the frequency by which hyper or hypomethylated loci are 55 56 771 distributed according to their proximity of CpG islands. 57 58 772 59 60 61 62 31 63 64 65 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

Figure 1 Click here to download Figure Figure_1_final_nl.tif Figure 2 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. TheClick copyright here toholder download for this preprint Figure (which Figure_2_final_nl.tif was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Figure 3 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. TheClick copyright here toholder download for this preprint Figure (which Figure_3_final_nl.tif was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Figure 4 bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. TheClick copyright here toholder download for this preprint Figure (which Figure_4_final_nl.tif was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. bioRxiv preprint doi: https://doi.org/10.1101/124263; this version posted June 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

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