Aberrant expression of Human Endogenous Retrovirus K9-derived elements is associated with better clinical outcome of acute myelocytic leukemia

Aberrant expression of Human Endogenous Retrovirus K9-derived elements is associated with better clinical outcome of acute myelocytic leukemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Aberrant expression of Human Endogenous Retrovirus K9-derived elements is associated with better clinical outcome of acute myelocytic leukemia Ryo Yanagiya, So Nakagawa, Makoto Onizuka, Ai Kotani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4469567/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Acute myelocytic leukemia (AML) is a common hematological malignancy in adults. Although several risk stratifications based on cytogenetic and molecular abnormalities are available to decide the indication of allogeneic hematopoietic cell transplantation (allo-HCT), planning treatment strategies for AML without them remains challenging. Using transcriptome datasets, we investigated the association of event-free survival (EFS) of intensively treated AML cases and the aberrant expression status of endogenous retrovirus (ERV)-derived open reading frames (ORFs), which have been reported to be associated with the pathophysiology of various malignancies and have the potential to become neoantigens in specific cancers. Results : The expression values of human ERV family K9 (HERVK9) ORFs were found to be associated with EFS, independent of conventional risk stratifications. Furthermore, it was revealed that AML cells with higher expression of HERVK9 activated antigen processing and presentation, accompanied by excess expression of genes associated with responses to adaptive immune reaction and apoptosis, indicating that aberrant expression of HERVK9 may initiate an antineoplastic immune response against themselves via excess antigen presentation. Conclusions : In summary, quantitation of HERVK9 expression has the potential to provide prognostic prediction, which is crucial for determining the indications of upfront allo-HCT. acute myelocytic leukemia allogeneic hematopoietic cell transplantation endogenous retrovirus neoantigen antineoplastic immunity Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Acute myelocytic leukemia (AML) is a common hematological malignancy in adults. Although combinational chemotherapeutic regimens of cytarabine plus anthracyclines (so-called “3 + 7-based regimens”) have been established as the gold standard of care for AML for decades( 1 ), some cases are refractory to cytotoxic agents and allogeneic hematopoietic cell transplantation (allo-HCT) is required to achieve a cure( 2 ). The strong antineoplastic effect of allo-HCT mainly consists of two different mechanisms: cytotoxic effect derived from high-dose chemoradiotherapy before transplantation, and continuous disease control due to alloreactive immune response of donor-derived cells towards AMLs after transplantation (Graft-versus Leukemia effect)( 3 ). Especially, as the latter is crucial for prolonging disease-free survival, various experimental and clinical approaches have been considered to enhance alloreactive anti-neoplastic immune responses( 4 – 7 ). Although deeper remission and longer disease-free survival can be obtained by performing allo-HCT, its higher treatment-related mortality caused by chemotoxicities, severe infection, vaso-occlusive diseases, and alloreactive immune response towards recipient tissues (Graft-versus Host disease) should not be ignored when treatment strategies are decided( 8 – 12 ). Therefore, allo-HCT should be performed upfront (at the initial remission) in newly diagnosed AML cases where the risk of relapse after chemotherapy is of great concern( 13 ). With a better understanding of the cytological and genetic abnormalities of AML, it was revealed that disease outcomes of AML cases could be stratified into three outcome groups, which were adopted in the guidelines of the National Comprehensive Cancer Network 2017 (NCCN2017)( 14 ) and European Leukemia Network (ELN2017)( 15 ); the three groups are good (NCCN2017) or favorable (ELN2017), intermediate, and poor (NCCN2017) or adverse (ELN2017). AML cases classified as good/favorable-risk show a better response to chemotherapy and seldom require allo-HCT at initial remission, whereas those classified as poor/adverse-risk require allo-HCT for curing AML. Nevertheless, allo-HCT indications for AML cases classified as intermediate-risk, including normal karyotypes and/or the absence of detectable genetic abnormalities, are controversial. Furthermore, some good/favorable-risk AML cases relapse after completion of chemotherapies and eventually require allo-HCT. Thus, an additional classification of these cases is desired. Endogenous retroviruses (ERVs) occupy approximately 8% of the human genome( 16 ) and their aberrant expression has been observed in various malignancies( 17 , 18 ). Associations between the expression of specific ERV families and cancer pathophysiology have been reported in various studies. For instance, the aberrant expression of human endogenous retrovirus family K (HERVK) sequences initiates cancer cell proliferation in solid tumors( 19 , 20 ). It has also been reported that ERV expression initiates antineoplastic adaptive immune responses in cancers. Indeed, various ERVs contain certain length of open reading frames (ORFs) mainly derived from retroviral genes( 21 ), and several ERV ORFs are translated and targeted as neoantigens( 22 , 23 ). However, the clinical impact of aberrant ERV expression in hematological malignancies, including AML, remains unclear. In this study, we analyzed the aberrant expression of ERV ORFs using RNA-seq data obtained from The Cancer Genome Atlas (TCGA) and Sequence Read Archive (SRA) databases followed by statistical analyses of the association between their expression values and event-free survival (EFS) in AML. We found that the expression of HERV family K9 (HERVK9)-derived ORFs was associated with EFS independent of known prognostic factors, including NCCN2017 risk stratification. Furthermore, it was found that AML cells with higher expression of HERVK9-derived ORFs possibly initiated antigen processing and presentation. Therefore, genes associated with response to alloreactive immune responses and apoptosis were expressed, indicating that the host immune system could capture and attack those AML cells, avoiding the need to perform upfront allo-HCT. Based on these results, quantitation of HERVK9 expression may be helpful for decision-making regarding upfront allo-HCT indications for good/favorable- and intermediate-risk AML cases at diagnosis. Methods Study approval We obtained approval to access the RNA-seq data used in this study (see Supplemental Table 1 ) from the Institutional Review Board of Tokai University School of Medicine (19-R-323). Data Collection of publicity available RNA-seq data BAM-formatted RNA-seq data and associated clinical information were obtained for 151 AML cases from TCGA (TCGA-LAML). The BAM data files were converted into FASTQ files using bam2fastq version 1.1.0. Genetic mutation annotation and clinical status at allo-HCT of the 151 cases were obtained from the supplementary information of TCGA-LAML paper( 24 ). Furthermore, FASTQ-formatted RNA-seq data of 21 AML cases were obtained from the Gene Expression Omnibus (GEO; GSE49642). FASTQ-formatted RNA-seq data for normal hematopoietic stem cells (HSCs) were obtained from GEO (21 and 8 files from GSE111085 and GSE114922, respectively). Differentially-expressed ERV analysis All FASTQ files were mapped to the human genome (hg38; obtained from https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips ) using HISAT2 version 2.2.1( 25 ) with the default parameters. The reads were then counted and annotated using StringTie version 2.1.6( 26 ), with the GTF-formatted annotation file of the human ERV ORF definition obtained from the gEVE database version 1.1( 27 ). Note that gEVE entries overlapped with annotation of Repbase( 28 ) ERV family obtained from the gEVE database were considered as ERV-derived ORFs in this study. DESeq2 version 1.42.0 was used to calculate statistical differences in ERV expression between AML and normal HSCs, and those with an adjusted p value < 0.05 were extracted as differentially-expressed ERVs (DE-ERVs). We conducted gene-set enrichment analysis (GSEA) of DE-ERVs based on Repbase ERV family annotations using clusterProfiler version 3.18( 29 ). The chromosomal loci of the DE-ERVs were visualized using Chromomap version 4.1.1( 30 ). All the datasets were obtained on 10th June, 2021. Differentially-expressed human gene analysis All FASTQ files were mapped to the human genome using HISAT2 24 with the “--known-splicesite-infile” option and the following human gene annotation file: hg38.ncbiRefSeq.gtf obtained from https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes (accessed on 10th June, 2021). StringTie 25 and DESeq2 were used to calculate statistical differences in gene expression, and those with adjusted p value < 0.25 accompanied with absolute value of log2-fold change of ≥ 0.5 were extracted as differentially-expressed human genes. GSEAs of differentially expressed human genes were performed using clusterProfiler 28 with three following gene annotation datasets obtained from the molecular signatures database (MSigDB; https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp , accessed on 10th June, 2021): Hallmark gene sets, Kyoto Encyclopedia of Genes and Genomes (KEGG) legacy subset of canonical pathway, and Gene Ontology gene sets. Cell cultures THP1, KG1, HL60, HEL, and K562 were purchased from Japanese Collection of Research Bioresources. Cells were cultured at 37℃, CO2-free incubator in RPMI-1640 medium (FUJIFILM Wako, #189–02025) with 10% fatal bovine serum (Gibco, #26140079) and Penicillin-Streptomycin Solution (FUJIFILM Wako, #168-23191). Reverse transcription polymerase chain reaction Total RNA was extracted using Sepasol-RNA I Super G (Nakalai Tesque, #09379-55), following manufacturer’s protocol. Complementary DNA from RNA with polyadenylated tail was synthesized using ReverTra Ace qPCR RT Master Mix with gDNA Remover (TOYOBO, #FSQ-301), following manufacturer’s protocol. Polymerase chain reaction was performed using THUNDERBIRD SYBR qPCR Mix (TOYOBO, #QPS-201) and StepOnePlus Real-Time PCR System (Applied Biosystems). Primer sequences were as follows; ACTB-Forward:5’-CTCTTCCAGCCTTCCTTCCT-3’, ACTB-Reverse: 5’-AGCACTGTGTTGGCGTACAG-3’, ORF of chr19.21415760-21416326.--Forward:5’-AACCACTTCCAGCGGAAAAAC-3’, ORF of chr19.21415760-21416326.--Reverse:5’-AAATGTTGGAGCTATGTGCCC-3’, ORF of chr19.21417123-21417419.--Forward:5’-CTGCTAGCACAGGCAACGA-3’, ORF of chr19.21417123-21417419.--Reverse:5’-TGGCCTGACTTGCTGATTTT-3’. Statistical analyses All statistical analyses were computed using R version 4.2.2 for Windows. Principal component analysis (PCA) was performed using the prcomp function in the default R package. Uniform manifold approximation and projection (UMAP) was conducted using Umap version 0.2.10.0. The best cut-off value of grouping for survival analysis was calculated using Maxstat version 0.7.25, with a Hosmer-Lemeshow test adjusted p value < 0.1 considered significant. Differences in the EFS periods between two groups were analyzed using the log-rank test. Multivariate analysis of the factors contributing to EFS was performed using the Cox proportional hazards model. The Fisher’s exact test was used to compare subgroups based on binary factors. Results Detection of AML-characterizing DE-ERV ORFs We first examined DE-ERV ORFs in AML by analyzing the RNA-seq data of patient-derived AML cells (151 and 21 samples from TCGA-LAML and GSE49642, respectively) and healthy volunteer-derived CD34 positive HSCs (23 and 8 samples from GSE111085 and GSE114922, respectively). The sequence reads were mapped to the human genome and counted using the gEVE database( 27 ). Access three independent DE analyses (TCGA-LAML vs. GSE111085, TCGA-LAML vs. GSE114922, and GSE49642 vs. GSE111085), a total of 698 DE-ERV ORFs were commonly extracted (Fig. 1 a and Supplemental Table S2 -4 ). After removal of 141 of them because of different directional fold changes among the three analyses, a total of 557 DE-ERV ORFs were annotated as AML-characterizing DE-ERV ORFs (Fig. 1 a-b and Supplemental Table S5 ). PCA of the expression profiles of these 557 DE-ERV ORFs in TCGA-LAML samples suggested that the cumulative proportion exceeded 80% for 51 PCs (80.4%; Supplemental Figure S1 a ). UMAP of these 51 PCs suggested that the expression pattern of DE-ERV ORFs in AML was independent of their common cytogenetic abnormalities and morphological features (referring to the French-American-British [FAB] classification), which are well-known prognostic factors for AML( 31 , 32 ) ( Supplemental Figure S1 b-c ). The genomic loci of the 557 DE-ERV ORFs were then mapped onto the human chromosomes, and genome-wide hot spots of DE-ERV ORF expression were visualized (Fig. 1 c). Notably, chromosome 19 contained the highest density of DE-ERV ORF expression sites among all the chromosomes, which is a well-known enriched site for HERV LTR elements( 33 – 36 ). Enriched DE-ERV families To identify ERV families that were differentially expressed in AML cells, we conducted GSEAs on two paired datasets (TCGA-LAML AML cells vs. GSE111085 HSCs, and GSE49642 AML cells vs GSE111085 HSCs). As a result, 10 ERV families were commonly annotated as DE-ERV families, with total of 155 core-enrichment DE-ERV ORFs (Fig. 2 a-b and Supplemental Tables S6-8 ). Among the 10 highly expressed ERV families, their expression patterns in AML cells were almost independent of each other, except for the correlation between HERVK and LTR5, which are known to be related (Fig. 2 c). The HERVK and HERVK9 families exhibited wider expression distributions than the other families, suggesting high heterogeneity among the expression of those families in AML patients (Fig. 2 d). HERVK9 expression correlated with EFS of AML cases with 3 + 7-based intensive chemotherapy As previously shown, the expression profiles of DE-ERVs were independent of known cytogenetic risk factors. Therefore, we investigated the correlation between the expression values of each DE-ERV family and the prognosis of AML cases. Among the 151 AML cases with clinical information from TCGA-LAML, we excluded elderly cases (> 65 years old), cases of acute promyelocytic leukemia, and cases performed non-intensive treatments ( i.e ., other than 3 + 7-based regimens) as an initial treatment (Fig. 3 a). Finally, a total of 90 cases were included in the survival analyses ( Table 1 and Supplemental Table S9 ). Among the 10 highly expressed DE-ERV families, only the HERVK9 family expression value was correlated with EFS using maxillary selected ranked analysis, with a cutoff total HERVK9-transcripts per million (TPM) value of 8,509.083 (Fig. 3 b and Supplemental Figure S2 ). The Kaplan-Meier curve of the 90 AML cases grouped by HERVK9 expression value with the measured cutoff showed that higher HERVK9 expression was associated with a longer EFS period (Fig. 3 c). This result was validated in the cases not performed upfront allo-HCT ( Supplemental Figure S3a ). Notably, this tendency was also observed in AML cases in the NCCN2017 good- and intermediate-risk groups (Fig. 3 d and Supplemental Figure S3b ). The Cox proportional hazards model was applied to the 90 AML cases with previously known prognostic factors (age, FAB classification, NCCN2017 molecular risk stratification, and gene mutations specifically described in ELN2017) and HERVK9 expression status to assess the impact of HERVK9 expression on prognosis. The results suggested that HERVK9 expression status was a risk factor independent of previously known factors ( Table 2 ). We further analyzed the associations between HERVK9 expression status and cytogenetic and the molecular abnormalities described in NCCN2017 and/or ELN2017 using the 151 AML cases from TCGA-LAML ( Supplemental Table S9 ). While chromosomal translocations associated with core-binding factors ( i.e. , RUNX1 :: RUNX1T1 and CBFB :: MYH11 ) were associated with higher HERVK9 expression, other cytogenetic and molecular abnormalities showed no significant relationship with HERVK9 expression value ( Supplemental Table S10 ). We further investigated the gene loci of HERVK9 associated with AML prognosis. Among the 21 commonly enriched HERVK9-derived ORFs ( Supplemental Table S6-8 ), two located on chromosome 19 showed a correlation of higher expression with longer EFS among all 151 analyzed AML cases ( Supplemental Tables S11-12 ). The two HERVK9 ORFs were derived from the same HERVK9 element (Fig. 3 e). The expression of these two HERVK9 ORFs was validated by reverse-transcription quantitative polymerase chain reaction of AML-derived cell lines (Fig. 3 f and Supplemental Figure S3c ). Taken together, the two HERVK9 ORFs located on chromosome 19 were annotated as independent prognostic factor for AML. Higher HERVK9 expression associated with allogeneic immune reactions towards AML cells and apoptotic signaling We analyzed the molecular phenotype of AML cells with higher expression of the HERVK9 family via GSEAs of differentially expressed human genes using Hallmark gene sets ( Supplemental Table S13 ), KEGG legacy subset of canonical pathway ( Supplemental Table S14 ), and Gene Ontology gene sets obtained from MSigDB ( Supplemental Table S15 ). Gene sets associated with the immune response were upregulated in the AML group with higher expression of HERVK9 elements ( Supplemental Tables S13-15 ). Furthermore, gene sets associated with antigen processing and presentation (Fig. 4 a), allograft rejection (Fig. 4 b), p53 pathway (Fig. 4 c), and apoptosis (Fig. 4 d-e) were upregulated in the high HERVK9 expression group. These results were validated using the RNA-seq data of AML cells from the GSE49642 dataset ( Supplemental Figure S4 ). As a hallmark gene set of allograft rejection includes the upregulated genes of allogeneic transplanted cells attacked by host immune cells, these results indicated that AML cells with higher expression of HERVK9 elements underwent apoptosis by an alloreactive immune response via aberrant antigen processing and presentation on major histocompatibility complexes. Taken together, the aberrant expression of HERVK9 elements in AML could initiate an antineoplastic immune response against themselves via increased antigen presentation, resulting in better disease control without alloreactive immune response-mediated disease control due to allo-HCT. Discussion In this study, we investigated the correlation between ERV expression profiles in AML cells and patient outcomes via pan-transcriptomic investigation, and successfully found HERVK9-derived ORFs, especially those on chromosome 19, that were associated with prolonged EFS in intensively-treated AML cases. Although many previous investigations on the aberrant expression of ERVs have been reported( 37 – 41 ), little is known about their impact in patient outcomes. Under these circumstances, this study strongly suggests that ERV-derived ORFs are associated with prolonged EFS. Furthermore, our data suggest that AML cells with a higher expression of HERVK9-derived ORFs upregulate genes associated with antigen presentation and responses to adoptive immune reactions. Since most ERV-derived ORFs are not expressed in normal tissues or organs, our results indicate the possibility that ERV-derived peptides are synthesized, processed and presented on major histocompatibility complexes as cancer neoantigens. We previously found that ERV3-1 protein was highly expressed in AML patients, particularly for monocytic lineage, although their expression and clinical profile are unclear( 40 ). A recent study revealed that expression of two HERV-derived genes, Suppressyn and Syncytin-2, affect prognosis of AML via activation of immune cell infiltration( 41 ). In this study, we investigated expression of all ERV-derived ORFs, including such known ERV-derived genes, and found that HERVK9-derived ORFs were the strongest contributor to AML prognosis. As neoantigens are ideal targets for immunotherapies, including allo-HCT and chimeric antigen receptor T-cell therapy, further investigations are planned to detect peptides translated from annotated DE-ERVs, including HERVK9, in AML cells. Clinically, one of the most important points in the treatment of AML is the precise determination of the necessity of upfront allo-HCT at diagnosis. Although both NCCN2017 and ELN2017 risk stratifications are reliable indexes to determine the validity of allo-HCT for better disease control, additional stratification criteria are required to identify poor responders to chemotherapies, especially in cases classified as good/favorable- and intermediate-risk. As our current study annotated HERVK9 expression value as a novel prognostic predictor independent of clinically available ones, quantification of HERVK9-derived ORFs might help physicians determine the necessity of upfront allo-HCT. However, there were limitations to this study. Due to a lack of information about the therapeutic response to initial induction chemotherapy in TCGA-LAML original article, our research failed to provide novel evidence to discuss whether upfront allo-HCT improves the EFS of AML cases classified as good or intermediate risk. We also need to consider the existence of unexpected confounding factors that may have affected the prognosis of the assessed cases. Nevertheless, we extracted cases with relatively poor EFS by indexing HERVK9 expression status, which provides helpful information for therapeutic strategies and molecular function of ERVs in humans. Conclusion While ERVs are aberrantly expressed in AML cells, HERVK9 expression could induce an anti-neoplastic immune reaction via excess antigen presentation and is associated with better EFS in cases treated with intensive chemotherapies, independent of known risk classifications, including the FAB classification and cytological or genetic abnormalities. Abbreviations AML , acute myelocytic leukemia; allo-HCT , allogeneic hematopoietic cell transplantation; DE , differentially-expressed; EFS , event-free survival; ELN2017 , European Leukemia Network 2017; ERV , endogenous retrovirus; FAB , French-American-British; GEO , Gene Expression Omnibus; GSEA , gene-set enrichment analysis; HERVK , human endogenous retrovirus; HERVK9 , human endogenous retrovirus family K9; HSC , hematopoietic stem cells; KEGG , Kyoto Encyclopedia of Genes and Genomes; MSigDB, the Molecular Signatures Database; NCCN2017 , National Comprehensive Cancer Network 2017; ORF , open reading frame; PCA , principal component analysis; SRA , Sequence Read Archive; TCGA , the Cancer Genome Atlas; TPM, transcripts per million; UMAP , Uniform manifold approximation and projection; Declarations Ethics approval and consent to participate We obtained approval to access the RNA-seq data used in this study (see Supplemental Table 1 ) from the Institutional Review Board of Tokai University School of Medicine (19-R-323). Consent for publication This manuscript does not contain any identifiable individual person’s data. Availability of data and materials All raw data was available in TCGA database (controlled BAM data in TCGA-LAML) or GEO (GSE111085, GSE114922, and GSE49642). All processed data (read count data) was available in Supplemental Tables . Competing interests Authors have no conflict of interest to be disclosed. Funding This work was funded by Tokai University Tokuda Memorial Cancer/Genome Basic Research Grant for Young Investigators and 2024 Core research fund of the Institute of Medical Sciences, Tokai University. Authors' contributions R.Y. and S.N. conceptualized the study; R.Y., A.K. and S.N. designed the methodology; R.Y., A.K., M.O., and S.N. validated the study; R.Y. and S.N. conducted formal analysis; R.Y. and S.N. curated the data; R.Y., and S.N. wrote the original draft; all authors reviewed and edited the manuscript; R.Y. and S.N. performed visualization; M.O., A.K., and S.N. supervised the study; R.Y. and S.N. administrated the project; S.N. was responsible for funding acquisition; and all authors checked and agreed the final version of the manuscript. Acknowledgements The results here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. We thank all the members of Department of Innovative Medical Science and Department of Molecular Life Science at Tokai University for their support. References Murphy T, Yee KWL. Cytarabine and daunorubicin for the treatment of acute myeloid leukemia. Expert Opin Pharmacother. 2017 18(16):1765–80. Takami A. Hematopoietic stem cell transplantation for acute myeloid leukemia. Int J Hematol. 2018 107(5):513–8. O’Neill AT, Chakraverty R. Graft Versus Leukemia: Current Status and Future Perspectives. J Clin Oncol. 2021 39(5):361–72. Parmar S, Fernandez-Vina M, de Lima M. Novel transplant strategies for generating graft-versus-leukemia effect in acute myeloid leukemia. Curr Opin Hematol. 2011 18(2):98–104. Alyea EP. Modulating graft-versus-host disease to enhance the graft-versus-leukemia effect. Best Pract Res Clin Haematol. 2008 21(2):239–50. Negrin RS. Graft-versus-host disease versus graft-versus-leukemia. Hematology Am Soc Hematol Educ Program. 2015 2015:225–30. Locatelli F, Pende D, Falco M, Della Chiesa M, Moretta A, Moretta L. NK Cells Mediate a Crucial Graft-versus-Leukemia Effect in Haploidentical-HSCT to Cure High-Risk Acute Leukemia. Trends Immunol. 2018 Jul;39(7):577–90. Forlanini F, Zinter MS, Dvorak CC, Bailey-Olson M, Winestone LE, Shimano KA, et al. Hematopoietic Cell Transplantation-Comorbidity Index Score Is Correlated with Treatment-Related Mortality and Overall Survival following Second Allogeneic Hematopoietic Cell Transplantation in Children. Transplant Cell Ther. 2022 28(3):155.e1-155.e8. Majhail NS. Long-term complications after hematopoietic cell transplantation. Hematol Oncol Stem Cell Ther. 2017 10(4):220–7. Sahin U, Toprak SK, Atilla PA, Atilla E, Demirer T. An overview of infectious complications after allogeneic hematopoietic stem cell transplantation. J Infect Chemother. 2016 22(8):505–14. Zeiser R, Blazar BR. Acute Graft-versus-Host Disease - Biologic Process, Prevention, and Therapy. N Engl J Med. 2017 377(22):2167–79. Hamilton BK. Updates in chronic graft-versus-host disease. Hematology Am Soc Hematol Educ Program. 2021 Dec 10;2021(1):648–54. Pollyea DA, Bixby D, Perl A, Bhatt VR, Altman JK, Appelbaum FR, et al. NCCN Guidelines Insights: Acute Myeloid Leukemia, Version 2.2021. J Natl Compr Canc Netw. 2021 19(1):16–27. O’Donnell MR, Tallman MS, Abboud CN, Altman JK, Appelbaum FR, Arber DA, et al. Acute Myeloid Leukemia, Version 3.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2017 15(7):926–57. Döhner H, Estey E, Grimwade D, Amadori S, Appelbaum FR, Büchner T, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017 Jan 129(4):424–47. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al. Initial sequencing and analysis of the human genome. Nature. 2001 409(6822):860–921. Stricker E, Peckham-Gregory EC, Scheurer ME. CancerHERVdb: Human Endogenous Retrovirus (HERV) Expression Database for Human Cancer Accelerates Studies of the Retrovirome and Predictions for HERV-Based Therapies. J Virol. 2023 97(6):e0005923. Vergara Bermejo A, Ragonnaud E, Daradoumis J, Holst P. Cancer Associated Endogenous Retroviruses: Ideal Immune Targets for Adenovirus-Based Immunotherapy. Int J Mol Sci. 2020 21(14). Ko EJ, Kim ET, Kim H, Lee CM, Koh SB, Eo WK, et al. Effect of human endogenous retrovirus-K env gene knockout on proliferation of ovarian cancer cells. Genes Genomics. 2022 44(9):1091–7. Li M, Radvanyi L, Yin B, Rycaj K, Li J, Chivukula R, et al. Downregulation of Human Endogenous Retrovirus Type K (HERV-K) Viral env RNA in Pancreatic Cancer Cells Decreases Cell Proliferation and Tumor Growth. Clin Cancer Res. 2017 23(19):5892–911. Ueda MT, Kryukov K, Mitsuhashi S, Mitsuhashi H, Imanishi T, Nakagawa S. Comprehensive genomic analysis reveals dynamic evolution of endogenous retroviruses that code for retroviral-like protein domains. Mob DNA. 2020 11:29. Ng KW, Boumelha J, Enfield KSS, Almagro J, Cha H, Pich O, et al. Antibodies against endogenous retroviruses promote lung cancer immunotherapy. Nature. 2023 616(7957):563–73. Wang-Johanning F, Radvanyi L, Rycaj K, Plummer JB, Yan P, Sastry KJ, et al. Human endogenous retrovirus K triggers an antigen-specific immune response in breast cancer patients. Cancer Res. 2008 68(14):5869–77. Cancer Genome Atlas Research Network, Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013 368(22):2059–74. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019 37(8):907–15. Kovaka S, Zimin A V, Pertea GM, Razaghi R, Salzberg SL, Pertea M. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol. 2019 20(1):278. Nakagawa S, Takahashi MU. gEVE: a genome-based endogenous viral element database provides comprehensive viral protein-coding sequences in mammalian genomes. Database (Oxford). 2016;2016. Kojima KK. Human transposable elements in Repbase: genomic footprints from fish to humans. Mob DNA. 2018 9:2. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Cambridge (Mass)). 2021 2(3):100141. Anand L, Rodriguez Lopez CM. ChromoMap: an R package for interactive visualization of multi-omics data and annotation of chromosomes. BMC Bioinformatics. 2022 23(1):33. Canaani J, Beohou E, Labopin M, Socié G, Huynh A, Volin L, et al. Impact of FAB classification on predicting outcome in acute myeloid leukemia, not otherwise specified, patients undergoing allogeneic stem cell transplantation in CR1: An analysis of 1690 patients from the acute leukemia working party of EBMT. Am J Hematol. 2017 92(4):344–50. Moarii M, Papaemmanuil E. Classification and risk assessment in AML: integrating cytogenetics and molecular profiling. Hematology Am Soc Hematol Educ Program. 2017 2017(1):37–44. Vinogradova T, Volik S, Lebedev Y u, Shevchenko Y u, Lavrentyeva I, Khil P, et al. Positioning of 72 potentially full size LTRs of human endogenous retroviruses HERV-K on the human chromosome 19 map. Occurrences of the LTRs in human gene sites. Gene. 1997 199(1–2):255–64. Lapuk A V, Khil PP, Lavrentieva I V, Lebedev YB, Sverdlov ED. A human endogenous retrovirus-like (HERV) LTR formed more than 10 million years ago due to an insertion of HERV-H LTR into the 5’ LTR of HERV-K is situated on human chromosomes 10, 19 and Y. J Gen Virol. 1999 80 (Pt 4):835–9. Lebedev YB, Volik S V, Obradovic D, Ermolaeva OD, Ashworth LK, Lennon GG, et al. Physical mapping of sequences homologous to an endogenous retrovirus LTR on human chromosome 19. Mol Gen Genet. 1995 247(6):742–8. Lavrentieva I, Khil P, Vinogradova T, Akhmedov A, Lapuk A, Shakhova O, et al. Subfamilies and nearest-neighbour dendrogram for the LTRs of human endogenous retroviruses HERV-K mapped on human chromosome 19: physical neighbourhood does not correlate with identity level. Hum Genet. 1998 102(1):107–16. Januszkiewicz-Lewandowska D, Nowicka K, Rembowska J, Fichna M, Żurawek M, Derwich K, et al. Env gene expression of human endogenous retrovirus-k and human endogenous retrovirus-w in childhood acute leukemia cells. Acta Haematol. 2013 129(4):232–7. Engel K, Wieland L, Krüger A, Volkmer I, Cynis H, Emmer A, et al. Identification of Differentially Expressed Human Endogenous Retrovirus Families in Human Leukemia and Lymphoma Cell Lines and Stem Cells. Front Oncol. 2021 11:637981. Depil S, Roche C, Dussart P, Prin L. Expression of a human endogenous retrovirus, HERV-K, in the blood cells of leukemia patients. Leukemia. 2002 Feb;16(2):254–9. Nakagawa S, Kawashima M, Miyatake Y, Kudo K, Kotaki R, Ando K, et al. Expression of ERV3-1 in leukocytes of acute myelogenous leukemia patients. Gene. 2021 773:145363. Shen J, Wen X, Xing X, Fozza C, Sechi LA. Endogenous retroviruses Suppressyn and Syncytin-2 as innovative prognostic biomarkers in Acute Myeloid Leukemia. Front Cell Infect Microbiol. 2024 13: 1339673. Tables Table 1. Patient characteristics of 90 AML cases from TCGA-LAML for survival analyses Factor Age (range) 48.5 (21-65) Sex, n Male 49 (54.4%) Female 41 (45.6%) FAB classification, n M0 8 (8.9%) M1 27 (30.0%) M2 25 (27.8%) M4 19 (21.1%) M5 10 (11.1%) M6 1 (1.1%) Cytogenetic abnormality, n Normal karyotype 43 (47.8%) RUNX1 :: RUNX1T1 6 (6.7%) CBFB :: MYH11 8 (8.9%) MLL translocation, t(9;11) 1 (1.1%) Other intermediate risk cytogenetic abnormality 9 (10.0%) MLL translocation, otherwise 4 (4.4%) BCR :: ABL1 2 (2.2%) Other poor risk cytogenetic abnormality 7 (7.8%) Complex cytogenetics 8 (8.9%) Data not available 2 (2.2%) NCCN2017 risk stratification, n Good 14 (15.6%) Intermediate 49 (54.4%) Poor 25 (27.8%) Data not available 2 (2.2%) Upfront allo-HCT, n Performed 33 (36.7%) Not performed 57 (63.3%) total n=90 Abbreviations: FAB, French-American-British; NCCN, National Comprehensive Cancer Network; allo-HCT, allogeneic hematopoietic stem cell transplantation. Table 2. Cox proportional hazards model for EFS of 90 AML cases Factor Hazard ratio 95%CI p value Age 1.010 0.990-1.031 0.32440 FAB (vs. M0) M1 2.077 0.732-5.892 0.16940 M2 1.471 0.512-4.226 0.47390 M4 2.642 0.929-7.519 0.06858 M5 2.227 0.572-8.674 0.24840 M6 2.251 0.158-32.04 0.54940 NCCN2017 risk stratification (vs. Good) Intermediate 1.493 0.608-3.667 0.38230 Poor 2.045 0.762-5.490 0.15560 Gene mutations contributing to risk stratifications ASXL1 0.717 0.6633-7.760 0.78460 TP53 2.142 0.4532-10.120 0.33650 RUNX1 2.161 0.8089-5.775 0.12430 HERVK9 expression (vs. High expression) Low expression 2.615 1.166-5.866 0.01971 Abbreviations: EFS, Event-Free Survival; CI, confidence interval; HERVK9, Human Endogenous Retrovirus K9. Additional Declarations No competing interests reported. Supplementary Files SupplementalTables20240524.xlsx Supplementaldata20240524.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4469567","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309092804,"identity":"bc3e8975-d5cf-4114-a8b6-bd3c04a40ae0","order_by":0,"name":"Ryo Yanagiya","email":"","orcid":"","institution":"Tokai University","correspondingAuthor":false,"prefix":"","firstName":"Ryo","middleName":"","lastName":"Yanagiya","suffix":""},{"id":309092805,"identity":"6693040b-cf27-487e-b40f-83208822e8f7","order_by":1,"name":"So Nakagawa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFAC5gMMDAcYeMBsCeK0sCU2kKqFxxCkhQQg397z/cGHM3YyDOyHHzBY7iBCi8GZsxsbZ9xI5mHgSTNgkDxDjBaJ3I3NPB+YgX7JYWCQbCPGYTNyHgK11PMw8L8hUgvDjRzGZp4bh3kYJIi1xeDMMcOZM84c52GTeGZwgCi/yLc3P/jw4Vi1PT9/8sPHksSEGBywAfFhyQZStIAA40eStYyCUTAKRsFIAABATTQ/K/jEFgAAAABJRU5ErkJggg==","orcid":"","institution":"Tokai University","correspondingAuthor":true,"prefix":"","firstName":"So","middleName":"","lastName":"Nakagawa","suffix":""},{"id":309092806,"identity":"3dfe418e-baa4-403f-9d3d-b470fecab5d1","order_by":2,"name":"Makoto Onizuka","email":"","orcid":"","institution":"Tokai University","correspondingAuthor":false,"prefix":"","firstName":"Makoto","middleName":"","lastName":"Onizuka","suffix":""},{"id":309092807,"identity":"9cf31f95-b8fa-48d5-8c8e-b9b419c4103b","order_by":3,"name":"Ai Kotani","email":"","orcid":"","institution":"Osaka University","correspondingAuthor":false,"prefix":"","firstName":"Ai","middleName":"","lastName":"Kotani","suffix":""}],"badges":[],"createdAt":"2024-05-24 02:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4469567/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4469567/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58152517,"identity":"bfda3c18-547b-4927-9974-ca4aa7f3ea1c","added_by":"auto","created_at":"2024-06-11 20:22:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1167591,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCommon differentially expressed endogenous retroviral open reading frames (DE-ERV ORFs) were detected in AML cell-derived transcripts using multiple transcriptome data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003eScheme of DE-ERV ORF detection using transcriptome data derived from AML (TCGA-LAML and GSE49642) and normal HSCs (GSE111085 and GSE114922).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb.\u003c/strong\u003eVolcano plot of 557 DE-ERV ORFs in AML cells from TCGA-LAML compared with those in HSCs from GSE111085.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec.\u003c/strong\u003eVisualization of chromosomal loci of 557 DE-ERV ORFs. Color scale indicates log2-fold change of each DE-ERV ORFs in AML cells from TCGA-LAML compared to those in HSCs from GSE111085.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469567/v1/bf99314832f8266cfa71fe14.jpg"},{"id":58152514,"identity":"c068777a-a3cb-4ba3-8c2e-6b9422648f73","added_by":"auto","created_at":"2024-06-11 20:22:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1735684,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDE-ERV families were extracted by GSEA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003eGSEA bar plot of 557 DE-ERV ORFs using GMT formatted annotation file of Repbase-ERV families.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb.\u003c/strong\u003eCommon DE-ERV families and common core enriched DE-ERV ORFs in two analyzed datasets described in \u003cstrong\u003eFigure 2a\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec.\u003c/strong\u003e Expression profile heatmap of 10 extracted DE-ERV families in AML cells from TCGA-LAML.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed.\u003c/strong\u003eExpression value (transcripts per million; TPM) of DE-ERV families in AML cells from TCGA-LAML (red) and HSCs in GSE111085 (blue). Bars indicate median.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469567/v1/cecd55845e71f6951c5d061e.jpg"},{"id":58152515,"identity":"c50d6424-1ff8-42d4-9440-88c87ebf98e1","added_by":"auto","created_at":"2024-06-11 20:22:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1629960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHERVK9 expression value is associated with prognosis of AML cases for which 3+7-based intensive chemotherapies were performed as initial treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eExtraction flow of AML cases applied for survival analyses. Detailed patient information of AML cases from TCGA-LAML is available in \u003cstrong\u003eSupplemental Table S9\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. \u003c/strong\u003eCalculation of best cut-off HERVK9-derived ORF expression value for log-rank analysis of 90 AML cases classified into two groups by Maxillary selected rank statistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec. \u003c/strong\u003eEFS curves of HERVK9 high- and low-expressed AML cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed. \u003c/strong\u003eEFS curves of AML cases classified according to HERVK9-derived ORF expression value and NCCN2017 risk stratification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ee. \u003c/strong\u003eGenomic loci of HERVK9-derived ORFs in chromosome 19 (red square).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ef. \u003c/strong\u003eReverse transcription polymerase chain reactions of two annotated DE-ERVs described in \u003cstrong\u003eFigure 3e\u003c/strong\u003e. \u003cem\u003eACTB\u003c/em\u003e was selected as an internal control.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469567/v1/54afc71ad961de2c7df2d501.jpg"},{"id":58153678,"identity":"9f720c7c-69f5-4161-bc4b-d372a26b1f26","added_by":"auto","created_at":"2024-06-11 20:30:26","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":769506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSEA analyses of differentially-expressed human genes in AML cells with higher expression of HERVK9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviation: NES, Normalized enrichment score. Detailed information is available in \u003cstrong\u003eSupplemental Tables S13-15\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4469567/v1/fe54abf27238b75e37b88c5c.jpg"},{"id":59519327,"identity":"14032ea9-01d9-457d-ac1f-4d8c98a1d85f","added_by":"auto","created_at":"2024-07-02 18:44:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6238749,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4469567/v1/4cadf658-2295-42e7-b26f-2b4d434be2b1.pdf"},{"id":58152519,"identity":"559d5997-3009-4a29-a13b-0e262c0a2836","added_by":"auto","created_at":"2024-06-11 20:22:26","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2753772,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTables20240524.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4469567/v1/49d942cc212d00dda72a4db3.xlsx"},{"id":58152520,"identity":"93b2eeef-522e-42c2-b92d-f2a983ad3a03","added_by":"auto","created_at":"2024-06-11 20:22:26","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":594688,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaldata20240524.docx","url":"https://assets-eu.researchsquare.com/files/rs-4469567/v1/33d71e27acb1bb271e9c1f11.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Aberrant expression of Human Endogenous Retrovirus K9-derived elements is associated with better clinical outcome of acute myelocytic leukemia","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute myelocytic leukemia (AML) is a common hematological malignancy in adults. Although combinational chemotherapeutic regimens of cytarabine plus anthracyclines (so-called \u0026ldquo;3\u0026thinsp;+\u0026thinsp;7-based regimens\u0026rdquo;) have been established as the gold standard of care for AML for decades(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), some cases are refractory to cytotoxic agents and allogeneic hematopoietic cell transplantation (allo-HCT) is required to achieve a cure(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The strong antineoplastic effect of allo-HCT mainly consists of two different mechanisms: cytotoxic effect derived from high-dose chemoradiotherapy before transplantation, and continuous disease control due to alloreactive immune response of donor-derived cells towards AMLs after transplantation (Graft-versus Leukemia effect)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Especially, as the latter is crucial for prolonging disease-free survival, various experimental and clinical approaches have been considered to enhance alloreactive anti-neoplastic immune responses(\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Although deeper remission and longer disease-free survival can be obtained by performing allo-HCT, its higher treatment-related mortality caused by chemotoxicities, severe infection, vaso-occlusive diseases, and alloreactive immune response towards recipient tissues (Graft-versus Host disease) should not be ignored when treatment strategies are decided(\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, allo-HCT should be performed upfront (at the initial remission) in newly diagnosed AML cases where the risk of relapse after chemotherapy is of great concern(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith a better understanding of the cytological and genetic abnormalities of AML, it was revealed that disease outcomes of AML cases could be stratified into three outcome groups, which were adopted in the guidelines of the National Comprehensive Cancer Network 2017 (NCCN2017)(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) and European Leukemia Network (ELN2017)(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e); the three groups are good (NCCN2017) or favorable (ELN2017), intermediate, and poor (NCCN2017) or adverse (ELN2017). AML cases classified as good/favorable-risk show a better response to chemotherapy and seldom require allo-HCT at initial remission, whereas those classified as poor/adverse-risk require allo-HCT for curing AML. Nevertheless, allo-HCT indications for AML cases classified as intermediate-risk, including normal karyotypes and/or the absence of detectable genetic abnormalities, are controversial. Furthermore, some good/favorable-risk AML cases relapse after completion of chemotherapies and eventually require allo-HCT. Thus, an additional classification of these cases is desired.\u003c/p\u003e \u003cp\u003eEndogenous retroviruses (ERVs) occupy approximately 8% of the human genome(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) and their aberrant expression has been observed in various malignancies(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Associations between the expression of specific ERV families and cancer pathophysiology have been reported in various studies. For instance, the aberrant expression of human endogenous retrovirus family K (HERVK) sequences initiates cancer cell proliferation in solid tumors(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). It has also been reported that ERV expression initiates antineoplastic adaptive immune responses in cancers. Indeed, various ERVs contain certain length of open reading frames (ORFs) mainly derived from retroviral genes(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), and several ERV ORFs are translated and targeted as neoantigens(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). However, the clinical impact of aberrant ERV expression in hematological malignancies, including AML, remains unclear.\u003c/p\u003e \u003cp\u003eIn this study, we analyzed the aberrant expression of ERV ORFs using RNA-seq data obtained from The Cancer Genome Atlas (TCGA) and Sequence Read Archive (SRA) databases followed by statistical analyses of the association between their expression values and event-free survival (EFS) in AML. We found that the expression of HERV family K9 (HERVK9)-derived ORFs was associated with EFS independent of known prognostic factors, including NCCN2017 risk stratification. Furthermore, it was found that AML cells with higher expression of HERVK9-derived ORFs possibly initiated antigen processing and presentation. Therefore, genes associated with response to alloreactive immune responses and apoptosis were expressed, indicating that the host immune system could capture and attack those AML cells, avoiding the need to perform upfront allo-HCT. Based on these results, quantitation of HERVK9 expression may be helpful for decision-making regarding upfront allo-HCT indications for good/favorable- and intermediate-risk AML cases at diagnosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy approval\u003c/h2\u003e \u003cp\u003eWe obtained approval to access the RNA-seq data used in this study (see \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e) from the Institutional Review Board of Tokai University School of Medicine (19-R-323).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData Collection of publicity available RNA-seq data\u003c/h2\u003e \u003cp\u003eBAM-formatted RNA-seq data and associated clinical information were obtained for 151 AML cases from TCGA (TCGA-LAML). The BAM data files were converted into FASTQ files using bam2fastq version 1.1.0. Genetic mutation annotation and clinical status at allo-HCT of the 151 cases were obtained from the supplementary information of TCGA-LAML paper(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Furthermore, FASTQ-formatted RNA-seq data of 21 AML cases were obtained from the Gene Expression Omnibus (GEO; GSE49642). FASTQ-formatted RNA-seq data for normal hematopoietic stem cells (HSCs) were obtained from GEO (21 and 8 files from GSE111085 and GSE114922, respectively).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially-expressed ERV analysis\u003c/h2\u003e \u003cp\u003eAll FASTQ files were mapped to the human genome (hg38; obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips\u003c/span\u003e\u003cspan address=\"https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using HISAT2 version 2.2.1(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) with the default parameters. The reads were then counted and annotated using StringTie version 2.1.6(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), with the GTF-formatted annotation file of the human ERV ORF definition obtained from the gEVE database version 1.1(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Note that gEVE entries overlapped with annotation of Repbase(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) ERV family obtained from the gEVE database were considered as ERV-derived ORFs in this study. DESeq2 version 1.42.0 was used to calculate statistical differences in ERV expression between AML and normal HSCs, and those with an adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were extracted as differentially-expressed ERVs (DE-ERVs). We conducted gene-set enrichment analysis (GSEA) of DE-ERVs based on Repbase ERV family annotations using clusterProfiler version 3.18(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). The chromosomal loci of the DE-ERVs were visualized using Chromomap version 4.1.1(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). All the datasets were obtained on 10th June, 2021.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially-expressed human gene analysis\u003c/h2\u003e \u003cp\u003eAll FASTQ files were mapped to the human genome using HISAT2\u003csup\u003e24\u003c/sup\u003e with the \u0026ldquo;--known-splicesite-infile\u0026rdquo; option and the following human gene annotation file: hg38.ncbiRefSeq.gtf obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes\u003c/span\u003e\u003cspan address=\"https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed on 10th June, 2021). StringTie\u003csup\u003e25\u003c/sup\u003e and DESeq2 were used to calculate statistical differences in gene expression, and those with adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 accompanied with absolute value of log2-fold change of \u0026ge;\u0026thinsp;0.5 were extracted as differentially-expressed human genes. GSEAs of differentially expressed human genes were performed using clusterProfiler\u003csup\u003e28\u003c/sup\u003e with three following gene annotation datasets obtained from the molecular signatures database (MSigDB; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/human/collections.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 10th June, 2021): Hallmark gene sets, Kyoto Encyclopedia of Genes and Genomes (KEGG) legacy subset of canonical pathway, and Gene Ontology gene sets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCell cultures\u003c/h2\u003e \u003cp\u003eTHP1, KG1, HL60, HEL, and K562 were purchased from Japanese Collection of Research Bioresources. Cells were cultured at 37℃, CO2-free incubator in RPMI-1640 medium (FUJIFILM Wako, #189\u0026ndash;02025) with 10% fatal bovine serum (Gibco, #26140079) and Penicillin-Streptomycin Solution (FUJIFILM Wako, #168-23191).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReverse transcription polymerase chain reaction\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using Sepasol-RNA I Super G (Nakalai Tesque, #09379-55), following manufacturer\u0026rsquo;s protocol. Complementary DNA from RNA with polyadenylated tail was synthesized using ReverTra Ace qPCR RT Master Mix with gDNA Remover (TOYOBO, #FSQ-301), following manufacturer\u0026rsquo;s protocol. Polymerase chain reaction was performed using THUNDERBIRD SYBR qPCR Mix (TOYOBO, #QPS-201) and StepOnePlus Real-Time PCR System (Applied Biosystems). Primer sequences were as follows; ACTB-Forward:5\u0026rsquo;-CTCTTCCAGCCTTCCTTCCT-3\u0026rsquo;, ACTB-Reverse: 5\u0026rsquo;-AGCACTGTGTTGGCGTACAG-3\u0026rsquo;, ORF of chr19.21415760-21416326.--Forward:5\u0026rsquo;-AACCACTTCCAGCGGAAAAAC-3\u0026rsquo;, ORF of chr19.21415760-21416326.--Reverse:5\u0026rsquo;-AAATGTTGGAGCTATGTGCCC-3\u0026rsquo;, ORF of chr19.21417123-21417419.--Forward:5\u0026rsquo;-CTGCTAGCACAGGCAACGA-3\u0026rsquo;, ORF of chr19.21417123-21417419.--Reverse:5\u0026rsquo;-TGGCCTGACTTGCTGATTTT-3\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses were computed using R version 4.2.2 for Windows. Principal component analysis (PCA) was performed using the prcomp function in the default R package. Uniform manifold approximation and projection (UMAP) was conducted using Umap version 0.2.10.0. The best cut-off value of grouping for survival analysis was calculated using Maxstat version 0.7.25, with a Hosmer-Lemeshow test adjusted \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 considered significant. Differences in the EFS periods between two groups were analyzed using the log-rank test. Multivariate analysis of the factors contributing to EFS was performed using the Cox proportional hazards model. The Fisher\u0026rsquo;s exact test was used to compare subgroups based on binary factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDetection of AML-characterizing DE-ERV ORFs\u003c/h2\u003e \u003cp\u003eWe first examined DE-ERV ORFs in AML by analyzing the RNA-seq data of patient-derived AML cells (151 and 21 samples from TCGA-LAML and GSE49642, respectively) and healthy volunteer-derived CD34 positive HSCs (23 and 8 samples from GSE111085 and GSE114922, respectively). The sequence reads were mapped to the human genome and counted using the gEVE database(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Access three independent DE analyses (TCGA-LAML vs. GSE111085, TCGA-LAML vs. GSE114922, and GSE49642 vs. GSE111085), a total of 698 DE-ERV ORFs were commonly extracted (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cb\u003eSupplemental Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e-4\u003c/b\u003e). After removal of 141 of them because of different directional fold changes among the three analyses, a total of 557 DE-ERV ORFs were annotated as AML-characterizing DE-ERV ORFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-b and \u003cb\u003eSupplemental Table S5\u003c/b\u003e). PCA of the expression profiles of these 557 DE-ERV ORFs in TCGA-LAML samples suggested that the cumulative proportion exceeded 80% for 51 PCs (80.4%; \u003cb\u003eSupplemental Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea\u003c/b\u003e). UMAP of these 51 PCs suggested that the expression pattern of DE-ERV ORFs in AML was independent of their common cytogenetic abnormalities and morphological features (referring to the French-American-British [FAB] classification), which are well-known prognostic factors for AML(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) (\u003cb\u003eSupplemental Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb-c\u003c/b\u003e). The genomic loci of the 557 DE-ERV ORFs were then mapped onto the human chromosomes, and genome-wide hot spots of DE-ERV ORF expression were visualized (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). Notably, chromosome 19 contained the highest density of DE-ERV ORF expression sites among all the chromosomes, which is a well-known enriched site for HERV LTR elements(\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEnriched DE-ERV families\u003c/h2\u003e \u003cp\u003eTo identify ERV families that were differentially expressed in AML cells, we conducted GSEAs on two paired datasets (TCGA-LAML AML cells vs. GSE111085 HSCs, and GSE49642 AML cells vs GSE111085 HSCs). As a result, 10 ERV families were commonly annotated as DE-ERV families, with total of 155 core-enrichment DE-ERV ORFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-b \u003cb\u003eand Supplemental Tables S6-8\u003c/b\u003e). Among the 10 highly expressed ERV families, their expression patterns in AML cells were almost independent of each other, except for the correlation between HERVK and LTR5, which are known to be related (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The HERVK and HERVK9 families exhibited wider expression distributions than the other families, suggesting high heterogeneity among the expression of those families in AML patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHERVK9 expression correlated with EFS of AML cases with 3\u0026thinsp;+\u0026thinsp;7-based intensive chemotherapy\u003c/h2\u003e \u003cp\u003eAs previously shown, the expression profiles of DE-ERVs were independent of known cytogenetic risk factors. Therefore, we investigated the correlation between the expression values of each DE-ERV family and the prognosis of AML cases. Among the 151 AML cases with clinical information from TCGA-LAML, we excluded elderly cases (\u0026gt;\u0026thinsp;65 years old), cases of acute promyelocytic leukemia, and cases performed non-intensive treatments (\u003cem\u003ei.e\u003c/em\u003e., other than 3\u0026thinsp;+\u0026thinsp;7-based regimens) as an initial treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Finally, a total of 90 cases were included in the survival analyses (\u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e and \u003cb\u003eSupplemental Table S9\u003c/b\u003e). Among the 10 highly expressed DE-ERV families, only the HERVK9 family expression value was correlated with EFS using maxillary selected ranked analysis, with a cutoff total HERVK9-transcripts per million (TPM) value of 8,509.083 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and \u003cb\u003eSupplemental Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). The Kaplan-Meier curve of the 90 AML cases grouped by HERVK9 expression value with the measured cutoff showed that higher HERVK9 expression was associated with a longer EFS period (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). This result was validated in the cases not performed upfront allo-HCT (\u003cb\u003eSupplemental Figure S3a\u003c/b\u003e). Notably, this tendency was also observed in AML cases in the NCCN2017 good- and intermediate-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed and \u003cb\u003eSupplemental Figure S3b\u003c/b\u003e). The Cox proportional hazards model was applied to the 90 AML cases with previously known prognostic factors (age, FAB classification, NCCN2017 molecular risk stratification, and gene mutations specifically described in ELN2017) and HERVK9 expression status to assess the impact of HERVK9 expression on prognosis. The results suggested that HERVK9 expression status was a risk factor independent of previously known factors (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e). We further analyzed the associations between HERVK9 expression status and cytogenetic and the molecular abnormalities described in NCCN2017 and/or ELN2017 using the 151 AML cases from TCGA-LAML (\u003cb\u003eSupplemental Table S9\u003c/b\u003e). While chromosomal translocations associated with core-binding factors (\u003cem\u003ei.e.\u003c/em\u003e, \u003cem\u003eRUNX1\u003c/em\u003e::\u003cem\u003eRUNX1T1\u003c/em\u003e and \u003cem\u003eCBFB\u003c/em\u003e::\u003cem\u003eMYH11\u003c/em\u003e) were associated with higher HERVK9 expression, other cytogenetic and molecular abnormalities showed no significant relationship with HERVK9 expression value (\u003cb\u003eSupplemental Table S10\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further investigated the gene loci of HERVK9 associated with AML prognosis. Among the 21 commonly enriched HERVK9-derived ORFs (\u003cb\u003eSupplemental Table S6-8\u003c/b\u003e), two located on chromosome 19 showed a correlation of higher expression with longer EFS among all 151 analyzed AML cases (\u003cb\u003eSupplemental Tables S11-12\u003c/b\u003e). The two HERVK9 ORFs were derived from the same HERVK9 element (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). The expression of these two HERVK9 ORFs was validated by reverse-transcription quantitative polymerase chain reaction of AML-derived cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef and \u003cb\u003eSupplemental Figure S3c\u003c/b\u003e). Taken together, the two HERVK9 ORFs located on chromosome 19 were annotated as independent prognostic factor for AML.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHigher HERVK9 expression associated with allogeneic immune reactions towards AML cells and apoptotic signaling\u003c/h2\u003e \u003cp\u003eWe analyzed the molecular phenotype of AML cells with higher expression of the HERVK9 family via GSEAs of differentially expressed human genes using Hallmark gene sets (\u003cb\u003eSupplemental Table S13\u003c/b\u003e), KEGG legacy subset of canonical pathway (\u003cb\u003eSupplemental Table S14\u003c/b\u003e), and Gene Ontology gene sets obtained from MSigDB (\u003cb\u003eSupplemental Table S15\u003c/b\u003e). Gene sets associated with the immune response were upregulated in the AML group with higher expression of HERVK9 elements (\u003cb\u003eSupplemental Tables S13-15\u003c/b\u003e). Furthermore, gene sets associated with antigen processing and presentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), allograft rejection (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), p53 pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), and apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-e) were upregulated in the high HERVK9 expression group. These results were validated using the RNA-seq data of AML cells from the GSE49642 dataset (\u003cb\u003eSupplemental Figure S4\u003c/b\u003e). As a hallmark gene set of allograft rejection includes the upregulated genes of allogeneic transplanted cells attacked by host immune cells, these results indicated that AML cells with higher expression of HERVK9 elements underwent apoptosis by an alloreactive immune response via aberrant antigen processing and presentation on major histocompatibility complexes. Taken together, the aberrant expression of HERVK9 elements in AML could initiate an antineoplastic immune response against themselves via increased antigen presentation, resulting in better disease control without alloreactive immune response-mediated disease control due to allo-HCT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the correlation between ERV expression profiles in AML cells and patient outcomes via pan-transcriptomic investigation, and successfully found HERVK9-derived ORFs, especially those on chromosome 19, that were associated with prolonged EFS in intensively-treated AML cases. Although many previous investigations on the aberrant expression of ERVs have been reported(\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), little is known about their impact in patient outcomes. Under these circumstances, this study strongly suggests that ERV-derived ORFs are associated with prolonged EFS. Furthermore, our data suggest that AML cells with a higher expression of HERVK9-derived ORFs upregulate genes associated with antigen presentation and responses to adoptive immune reactions. Since most ERV-derived ORFs are not expressed in normal tissues or organs, our results indicate the possibility that ERV-derived peptides are synthesized, processed and presented on major histocompatibility complexes as cancer neoantigens. We previously found that ERV3-1 protein was highly expressed in AML patients, particularly for monocytic lineage, although their expression and clinical profile are unclear(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). A recent study revealed that expression of two HERV-derived genes, Suppressyn and Syncytin-2, affect prognosis of AML via activation of immune cell infiltration(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). In this study, we investigated expression of all ERV-derived ORFs, including such known ERV-derived genes, and found that HERVK9-derived ORFs were the strongest contributor to AML prognosis. As neoantigens are ideal targets for immunotherapies, including allo-HCT and chimeric antigen receptor T-cell therapy, further investigations are planned to detect peptides translated from annotated DE-ERVs, including HERVK9, in AML cells.\u003c/p\u003e \u003cp\u003eClinically, one of the most important points in the treatment of AML is the precise determination of the necessity of upfront allo-HCT at diagnosis. Although both NCCN2017 and ELN2017 risk stratifications are reliable indexes to determine the validity of allo-HCT for better disease control, additional stratification criteria are required to identify poor responders to chemotherapies, especially in cases classified as good/favorable- and intermediate-risk. As our current study annotated HERVK9 expression value as a novel prognostic predictor independent of clinically available ones, quantification of HERVK9-derived ORFs might help physicians determine the necessity of upfront allo-HCT.\u003c/p\u003e \u003cp\u003eHowever, there were limitations to this study. Due to a lack of information about the therapeutic response to initial induction chemotherapy in TCGA-LAML original article, our research failed to provide novel evidence to discuss whether upfront allo-HCT improves the EFS of AML cases classified as good or intermediate risk. We also need to consider the existence of unexpected confounding factors that may have affected the prognosis of the assessed cases. Nevertheless, we extracted cases with relatively poor EFS by indexing HERVK9 expression status, which provides helpful information for therapeutic strategies and molecular function of ERVs in humans.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWhile ERVs are aberrantly expressed in AML cells, HERVK9 expression could induce an anti-neoplastic immune reaction via excess antigen presentation and is associated with better EFS in cases treated with intensive chemotherapies, independent of known risk classifications, including the FAB classification and cytological or genetic abnormalities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eAML\u003c/strong\u003e, acute myelocytic leukemia; \u003cstrong\u003eallo-HCT\u003c/strong\u003e, allogeneic hematopoietic cell transplantation; \u003cstrong\u003eDE\u003c/strong\u003e, differentially-expressed; \u003cstrong\u003eEFS\u003c/strong\u003e, event-free survival; \u003cstrong\u003eELN2017\u003c/strong\u003e, European Leukemia Network 2017; \u003cstrong\u003eERV\u003c/strong\u003e, endogenous retrovirus; \u003cstrong\u003eFAB\u003c/strong\u003e, French-American-British; \u003cstrong\u003eGEO\u003c/strong\u003e, Gene Expression Omnibus; \u003cstrong\u003eGSEA\u003c/strong\u003e, gene-set enrichment analysis; \u003cstrong\u003eHERVK\u003c/strong\u003e, human endogenous retrovirus; \u003cstrong\u003eHERVK9\u003c/strong\u003e, human endogenous retrovirus family K9; \u003cstrong\u003eHSC\u003c/strong\u003e, hematopoietic stem cells; \u003cstrong\u003eKEGG\u003c/strong\u003e, Kyoto Encyclopedia of Genes and Genomes; MSigDB, the Molecular Signatures Database; \u003cstrong\u003eNCCN2017\u003c/strong\u003e, National Comprehensive Cancer Network 2017; \u003cstrong\u003eORF\u003c/strong\u003e, open reading frame; \u003cstrong\u003ePCA\u003c/strong\u003e, principal component analysis; \u003cstrong\u003eSRA\u003c/strong\u003e, Sequence Read Archive; \u003cstrong\u003eTCGA\u003c/strong\u003e, the Cancer Genome Atlas; TPM, transcripts per million; \u003cstrong\u003eUMAP\u003c/strong\u003e, Uniform manifold approximation and projection;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained approval to access the RNA-seq data used in this study (see \u003cstrong\u003eSupplemental Table 1\u003c/strong\u003e) from the Institutional Review Board of Tokai University School of Medicine (19-R-323).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript does not contain any identifiable individual person’s data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw data was available in TCGA database (controlled BAM data in TCGA-LAML) or GEO (GSE111085, GSE114922, and GSE49642). All processed data (read count data) was available in \u003cstrong\u003eSupplemental Tables\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have no conflict of interest to be disclosed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by Tokai University Tokuda Memorial Cancer/Genome Basic Research Grant for Young Investigators and 2024 Core research fund of the Institute of Medical Sciences, Tokai University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.Y. and S.N. conceptualized the study; R.Y., A.K. and S.N. designed the methodology; R.Y., A.K., M.O., and S.N. validated the study; R.Y. and S.N. conducted formal analysis; R.Y. and S.N. curated the data; R.Y., and S.N. wrote the original draft; all authors reviewed and edited the manuscript; R.Y. and S.N. performed visualization; M.O., A.K., and S.N. supervised the study; R.Y. and S.N. administrated the project; S.N. was responsible for funding acquisition; and all authors checked and agreed the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. We thank all the members of Department of Innovative Medical Science and Department of Molecular Life Science at Tokai University for their support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMurphy T, Yee KWL. Cytarabine and daunorubicin for the treatment of acute myeloid leukemia. Expert Opin Pharmacother. 2017 18(16):1765\u0026ndash;80.\u003c/li\u003e\n\u003cli\u003eTakami A. Hematopoietic stem cell transplantation for acute myeloid leukemia. Int J Hematol. 2018 107(5):513\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Neill AT, Chakraverty R. Graft Versus Leukemia: Current Status and Future Perspectives. J Clin Oncol. 2021 39(5):361\u0026ndash;72.\u003c/li\u003e\n\u003cli\u003eParmar S, Fernandez-Vina M, de Lima M. Novel transplant strategies for generating graft-versus-leukemia effect in acute myeloid leukemia. Curr Opin Hematol. 2011 18(2):98\u0026ndash;104.\u003c/li\u003e\n\u003cli\u003eAlyea EP. Modulating graft-versus-host disease to enhance the graft-versus-leukemia effect. Best Pract Res Clin Haematol. 2008 21(2):239\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eNegrin RS. Graft-versus-host disease versus graft-versus-leukemia. Hematology Am Soc Hematol Educ Program. 2015 2015:225\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eLocatelli F, Pende D, Falco M, Della Chiesa M, Moretta A, Moretta L. NK Cells Mediate a Crucial Graft-versus-Leukemia Effect in Haploidentical-HSCT to Cure High-Risk Acute Leukemia. Trends Immunol. 2018 Jul;39(7):577\u0026ndash;90.\u003c/li\u003e\n\u003cli\u003eForlanini F, Zinter MS, Dvorak CC, Bailey-Olson M, Winestone LE, Shimano KA, et al. Hematopoietic Cell Transplantation-Comorbidity Index Score Is Correlated with Treatment-Related Mortality and Overall Survival following Second Allogeneic Hematopoietic Cell Transplantation in Children. Transplant Cell Ther. 2022 28(3):155.e1-155.e8.\u003c/li\u003e\n\u003cli\u003eMajhail NS. Long-term complications after hematopoietic cell transplantation. Hematol Oncol Stem Cell Ther. 2017 10(4):220\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eSahin U, Toprak SK, Atilla PA, Atilla E, Demirer T. An overview of infectious complications after allogeneic hematopoietic stem cell transplantation. J Infect Chemother. 2016 22(8):505\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eZeiser R, Blazar BR. Acute Graft-versus-Host Disease - Biologic Process, Prevention, and Therapy. N Engl J Med. 2017 377(22):2167\u0026ndash;79.\u003c/li\u003e\n\u003cli\u003eHamilton BK. Updates in chronic graft-versus-host disease. Hematology Am Soc Hematol Educ Program. 2021 Dec 10;2021(1):648\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003ePollyea DA, Bixby D, Perl A, Bhatt VR, Altman JK, Appelbaum FR, et al. NCCN Guidelines Insights: Acute Myeloid Leukemia, Version 2.2021. J Natl Compr Canc Netw. 2021 19(1):16\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Donnell MR, Tallman MS, Abboud CN, Altman JK, Appelbaum FR, Arber DA, et al. Acute Myeloid Leukemia, Version 3.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2017 15(7):926\u0026ndash;57.\u003c/li\u003e\n\u003cli\u003eD\u0026ouml;hner H, Estey E, Grimwade D, Amadori S, Appelbaum FR, B\u0026uuml;chner T, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017 Jan 129(4):424\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eLander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al. Initial sequencing and analysis of the human genome. Nature. 2001 409(6822):860\u0026ndash;921.\u003c/li\u003e\n\u003cli\u003eStricker E, Peckham-Gregory EC, Scheurer ME. CancerHERVdb: Human Endogenous Retrovirus (HERV) Expression Database for Human Cancer Accelerates Studies of the Retrovirome and Predictions for HERV-Based Therapies. J Virol. 2023 97(6):e0005923.\u003c/li\u003e\n\u003cli\u003eVergara Bermejo A, Ragonnaud E, Daradoumis J, Holst P. Cancer Associated Endogenous Retroviruses: Ideal Immune Targets for Adenovirus-Based Immunotherapy. Int J Mol Sci. 2020 21(14).\u003c/li\u003e\n\u003cli\u003eKo EJ, Kim ET, Kim H, Lee CM, Koh SB, Eo WK, et al. Effect of human endogenous retrovirus-K env gene knockout on proliferation of ovarian cancer cells. Genes Genomics. 2022 44(9):1091\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eLi M, Radvanyi L, Yin B, Rycaj K, Li J, Chivukula R, et al. Downregulation of Human Endogenous Retrovirus Type K (HERV-K) Viral env RNA in Pancreatic Cancer Cells Decreases Cell Proliferation and Tumor Growth. Clin Cancer Res. 2017 23(19):5892\u0026ndash;911.\u003c/li\u003e\n\u003cli\u003eUeda MT, Kryukov K, Mitsuhashi S, Mitsuhashi H, Imanishi T, Nakagawa S. Comprehensive genomic analysis reveals dynamic evolution of endogenous retroviruses that code for retroviral-like protein domains. Mob DNA. 2020 11:29.\u003c/li\u003e\n\u003cli\u003eNg KW, Boumelha J, Enfield KSS, Almagro J, Cha H, Pich O, et al. Antibodies against endogenous retroviruses promote lung cancer immunotherapy. Nature. 2023 616(7957):563\u0026ndash;73.\u003c/li\u003e\n\u003cli\u003eWang-Johanning F, Radvanyi L, Rycaj K, Plummer JB, Yan P, Sastry KJ, et al. Human endogenous retrovirus K triggers an antigen-specific immune response in breast cancer patients. Cancer Res. 2008 68(14):5869\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eCancer Genome Atlas Research Network, Ley TJ, Miller C, Ding L, Raphael BJ, Mungall AJ, et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N Engl J Med. 2013 368(22):2059\u0026ndash;74.\u003c/li\u003e\n\u003cli\u003eKim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019 37(8):907\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eKovaka S, Zimin A V, Pertea GM, Razaghi R, Salzberg SL, Pertea M. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol. 2019 20(1):278.\u003c/li\u003e\n\u003cli\u003eNakagawa S, Takahashi MU. gEVE: a genome-based endogenous viral element database provides comprehensive viral protein-coding sequences in mammalian genomes. Database (Oxford). 2016;2016.\u003c/li\u003e\n\u003cli\u003eKojima KK. Human transposable elements in Repbase: genomic footprints from fish to humans. Mob DNA. 2018 9:2.\u003c/li\u003e\n\u003cli\u003eWu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Cambridge (Mass)). 2021 2(3):100141.\u003c/li\u003e\n\u003cli\u003eAnand L, Rodriguez Lopez CM. ChromoMap: an R package for interactive visualization of multi-omics data and annotation of chromosomes. BMC Bioinformatics. 2022 23(1):33.\u003c/li\u003e\n\u003cli\u003eCanaani J, Beohou E, Labopin M, Soci\u0026eacute; G, Huynh A, Volin L, et al. Impact of FAB classification on predicting outcome in acute myeloid leukemia, not otherwise specified, patients undergoing allogeneic stem cell transplantation in CR1: An analysis of 1690 patients from the acute leukemia working party of EBMT. Am J Hematol. 2017 92(4):344\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eMoarii M, Papaemmanuil E. Classification and risk assessment in AML: integrating cytogenetics and molecular profiling. Hematology Am Soc Hematol Educ Program. 2017 2017(1):37\u0026ndash;44.\u003c/li\u003e\n\u003cli\u003eVinogradova T, Volik S, Lebedev Y u, Shevchenko Y u, Lavrentyeva I, Khil P, et al. Positioning of 72 potentially full size LTRs of human endogenous retroviruses HERV-K on the human chromosome 19 map. Occurrences of the LTRs in human gene sites. Gene. 1997 199(1\u0026ndash;2):255\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eLapuk A V, Khil PP, Lavrentieva I V, Lebedev YB, Sverdlov ED. A human endogenous retrovirus-like (HERV) LTR formed more than 10 million years ago due to an insertion of HERV-H LTR into the 5\u0026rsquo; LTR of HERV-K is situated on human chromosomes 10, 19 and Y. J Gen Virol. 1999 80 (Pt 4):835\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eLebedev YB, Volik S V, Obradovic D, Ermolaeva OD, Ashworth LK, Lennon GG, et al. Physical mapping of sequences homologous to an endogenous retrovirus LTR on human chromosome 19. Mol Gen Genet. 1995 247(6):742\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eLavrentieva I, Khil P, Vinogradova T, Akhmedov A, Lapuk A, Shakhova O, et al. Subfamilies and nearest-neighbour dendrogram for the LTRs of human endogenous retroviruses HERV-K mapped on human chromosome 19: physical neighbourhood does not correlate with identity level. Hum Genet. 1998 102(1):107\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eJanuszkiewicz-Lewandowska D, Nowicka K, Rembowska J, Fichna M, Żurawek M, Derwich K, et al. Env gene expression of human endogenous retrovirus-k and human endogenous retrovirus-w in childhood acute leukemia cells. Acta Haematol. 2013 129(4):232\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eEngel K, Wieland L, Kr\u0026uuml;ger A, Volkmer I, Cynis H, Emmer A, et al. Identification of Differentially Expressed Human Endogenous Retrovirus Families in Human Leukemia and Lymphoma Cell Lines and Stem Cells. Front Oncol. 2021 11:637981.\u003c/li\u003e\n\u003cli\u003eDepil S, Roche C, Dussart P, Prin L. Expression of a human endogenous retrovirus, HERV-K, in the blood cells of leukemia patients. Leukemia. 2002 Feb;16(2):254\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eNakagawa S, Kawashima M, Miyatake Y, Kudo K, Kotaki R, Ando K, et al. Expression of ERV3-1 in leukocytes of acute myelogenous leukemia patients. Gene. 2021 773:145363.\u003c/li\u003e\n\u003cli\u003eShen J, Wen X, Xing X, Fozza C, Sechi LA. Endogenous retroviruses Suppressyn and Syncytin-2 as innovative prognostic biomarkers in Acute Myeloid Leukemia. Front Cell Infect Microbiol. 2024 13: 1339673.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"648\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"97.68518518518519%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. Patient characteristics of 90 AML cases from TCGA-LAML for survival analyses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eAge (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e48.5 (21-65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"11\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eSex, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e49 (54.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e41 (45.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"11\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eFAB classification, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eM0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;8 (8.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e27 (30.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e25 (27.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e19 (21.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e10 (11.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;1 (1.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"11\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eCytogenetic abnormality, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eNormal karyotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e43 (47.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003e\u003cem\u003eRUNX1\u003c/em\u003e::\u003cem\u003eRUNX1T1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;6 (6.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003e\u003cem\u003eCBFB\u003c/em\u003e::\u003cem\u003eMYH11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;8 (8.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003e\u003cem\u003eMLL\u003c/em\u003e translocation, t(9;11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;1 (1.1%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eOther intermediate risk cytogenetic abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;9 (10.0%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003e\u003cem\u003eMLL\u003c/em\u003e translocation, otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;4 (4.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003e\u003cem\u003eBCR\u003c/em\u003e::\u003cem\u003eABL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;2 (2.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eOther poor risk cytogenetic abnormality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;7 (7.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eComplex cytogenetics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;8 (8.9%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eData not available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;2 (2.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"11\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eNCCN2017 risk stratification, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e14 (15.6%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e49 (54.4%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e25 (27.8%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eData not available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e\u0026nbsp;2 (2.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"11\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eUpfront allo-HCT, n\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003ePerformed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e33 (36.7%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\n \u003cp\u003eNot performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003e57 (63.3%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55.401234567901234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"42.28395061728395%\"\u003e\n \u003cp\u003etotal n=90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"97.68518518518519%\" colspan=\"2\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAbbreviations: FAB, French-American-British; NCCN, National Comprehensive Cancer Network; allo-HCT, allogeneic hematopoietic stem cell transplantation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.314814814814815%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"72\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"38\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"84.69055374592834%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Cox proportional hazards model for EFS of 90 AML cases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.309446254071661%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e1.010\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.990-1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.32440\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eFAB (vs. M0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e2.077\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.732-5.892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.16940\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e1.471\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.512-4.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.47390\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eM4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e2.642\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.929-7.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.06858\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e2.227\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.572-8.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.24840\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eM6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e2.251\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.158-32.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.54940\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eNCCN2017 risk stratification (vs. Good)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eIntermediate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e1.493\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.608-3.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.38230\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e2.045\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.762-5.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.15560\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eGene mutations contributing to risk stratifications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003e\u003cem\u003eASXL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e0.717\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.6633-7.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.78460\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e2.142\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.4532-10.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.33650\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003e\u003cem\u003eRUNX1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e2.161\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e0.8089-5.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.12430\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eHERVK9 expression (vs. High expression)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.71288743882545%\"\u003e\n \u003cp\u003eLow expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.986949429037521%\"\u003e\n \u003cp\u003e2.615\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.965742251223492%\"\u003e\n \u003cp\u003e1.166-5.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.33442088091354%\"\u003e\n \u003cp\u003e0.01971\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003eAbbreviations: EFS, Event-Free Survival; CI, confidence interval; HERVK9, Human Endogenous Retrovirus K9.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"acute myelocytic leukemia, allogeneic hematopoietic cell transplantation, endogenous retrovirus, neoantigen, antineoplastic immunity","lastPublishedDoi":"10.21203/rs.3.rs-4469567/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4469567/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Acute myelocytic leukemia (AML) is a common hematological malignancy in adults. Although several risk stratifications based on cytogenetic and molecular abnormalities are available to decide the indication of allogeneic hematopoietic cell transplantation (allo-HCT), planning treatment strategies for AML without them remains challenging. Using transcriptome datasets, we investigated the association of event-free survival (EFS) of intensively treated AML cases and the aberrant expression status of endogenous retrovirus (ERV)-derived open reading frames (ORFs), which have been reported to be associated with the pathophysiology of various malignancies and have the potential to become neoantigens in specific cancers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The expression values of human ERV family K9 (HERVK9) ORFs were found to be associated with EFS, independent of conventional risk stratifications. Furthermore, it was revealed that AML cells with higher expression of HERVK9 activated antigen processing and presentation, accompanied by excess expression of genes associated with responses to adaptive immune reaction and apoptosis, indicating that aberrant expression of HERVK9 may initiate an antineoplastic immune response against themselves via excess antigen presentation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: In summary, quantitation of HERVK9 expression has the potential to provide prognostic prediction, which is crucial for determining the indications of upfront allo-HCT.\u003c/p\u003e","manuscriptTitle":"Aberrant expression of Human Endogenous Retrovirus K9-derived elements is associated with better clinical outcome of acute myelocytic leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 20:22:21","doi":"10.21203/rs.3.rs-4469567/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5188972c-c327-4fc4-b034-d5e900584b1e","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T18:36:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 20:22:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4469567","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4469567","identity":"rs-4469567","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}