Upregulation of interferon signaling predicts sustained complete remission in pediatric AML patients

Upregulation of interferon signaling predicts sustained complete remission in pediatric AML patients | 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 Article Upregulation of interferon signaling predicts sustained complete remission in pediatric AML patients Chiara Cugno, Shimaa Sherif, Aesha Ali, Darawan Rinchai, Mohammed Elanbari, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3990757/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 The immunological composition of the microenvironment has shown relevance for diagnosis, prognosis, and therapy in solid tumors, while it remains underexplored in acute leukemias. In this study, we investigated the significance of the acute myeloid leukemia (AML) bone marrow microenvironment in predicting chemosensitivity and long-term remission outcomes in pediatric patients. To this aim, we analyzed 32 non-promyelocytic pediatric AML patients at diagnosis using the NanoString PanCancer IO 360 assay and RNA-Sequencing and we validated our findings in the online available TARGET AML pediatric dataset. A short signature of 3 Interferon (IFN)-related genes (GBP1, PARP12, TRAT1) significantly distinguished chemosensitive diseases and stratified patients assigned to standard risk group, as per current treatment protocols, into 2 groups: patients with a high enrichment of the 3 genes at diagnosis had a significantly longer overall survival compared with patients with a low enrichment. The leukemia microenvironment associated with this signature showed a contextual enhancement of TH1/cytotoxic/NK-related pathways. Our results demonstrate the importance of immune response in the tumor microenvironment of pediatric AML and provide tools for a more refined stratification of pediatric patients otherwise categorized as “standard-risk” and as such, lacking adequate risk-oriented therapeutic strategies. Moreover, they offer a promising guide to tackle immune pathways and potentially exploit immune-targeted therapies. Figures Figure 1 Figure 2 Figure 3 Key Points The IFN genes GBP1, PARP12, and TRAT1 define a signature correlating with chemo-sensitive disease and better OS in pediatric AML patients. Standard risk pediatric AML with high enrichment of GBP1, PARP12, and TRAT1 have a 76% increased likelihood of a ≥ 6 months CR. Introduction Pediatric acute myeloid leukemia (AML) represents a complex and heterogeneous group of hematological malignancies that accounts for 20% of all pediatric leukemias. Despite the remarkable and extensive breakthroughs in therapeutic approaches in the last decades, AML is still characterized by suboptimal outcomes with an overall survival (OS) rate around 65% ( 1 , 2 ). Numerous studies emphasize the critical role of immune cell composition and behavior in the tumor microenvironment (TME) of solid cancers ( 3 – 5 ). The contexture, functional orientation, and intricate interactions of immune cell subsets and tumor cells within TME can directly influence patients’ treatment response and clinical outcomes ( 6 – 8 ). A deep understanding of these networks within TME led to the identification of novel biomarkers that improved the assessment of patients' responsiveness to treatment and the development of advanced therapeutic strategies ( 9 ). While it is tempting to speculate that the same model may be applicable to acute leukemias to exploit novel immunotherapies and extend patient survival ( 10 – 12 ), the role of the TME in leukemias (L-TME) is still underexplored ( 13 ). The architecture of the bone marrow sustains normal hematopoiesis through delicate interactions in microenvironment sub-compartments, including endosteal, reticular and vascular niches ( 14 ). At leukemia onset, the homeostasis of these finetuned networks is abruptly disrupted by the rapid invasion of leukemic cells, leaving a limited healthy counterpart available for assessment. To increase the likelihood of an immune-network analysis in this scenario, we chose to perform a gene expression study starting from an immune-directed platform, the NanoString PanCancer immune-oncology assay. We explored the importance of the leukemic BM (L-TME) in predicting chemosensitivity and ≥ 6 months remission in a multi-center group of 32 non-promyelocytic pediatric AML patients, and subsequently we validated the findings using data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) AML pediatric public database. Methods Ethical compliance The study was conducted in accordance with the ethical approvals granted in all collaborative sites: Qatar, Sidra Medicine Institutional Review Board #20110003636/2011; Pakistan, Aga Khan University Ethics Review Committee# 3825-Onc-ERC-15; Italy, IRCCS Fondazione Policlinico San Matteo Ethics Review Committee #1500786 and 1500787. All samples were collected after obtaining informed consent, as appropriate. Clinical samples and cohorts Thirty-two pediatric patients recruited in Qatar, Italy and Pakistan donated a BM sample at the time of AML diagnosis. The samples were preserved through live-frozen after Ficoll-gradient enrichment and channeled to Sidra Medicine for downstream analyses. Clinical and follow -up data for each patient were meticulously recorded and are detailed in Table 1 .The recruitment of pediatric patients involved the collection of data from 32 individuals diagnosed with AML at three distinct locations: Sidra Medicine in Doha, Qatar; IRCCS Fondazione Policlinico San Matteo in Pavia, Italy; and Aga Khan University in Karachi, Pakistan. Sustained complete remission (CR) refers to patients who achieved a 6-month CR after the first line of chemotherapy, while non-sustained CR includes patients who died or relapsed within 6 months and/or achieved CR after allo-HSCT. Of the 32 patients, twenty-five patients were analyzed by NanoString and formed a Discovery Cohort. A subset of 19 patients from the Discovery Cohort from whom sufficient RNA was available was subsequently studied using mRNA-Seq for technical validation (Cross-Validation Subset). Once the analysis of the discovery cohort was concluded, an independent small cohort of 7 patients was assessed by RNA-Seq and formed the Internal Validation Cohort. For pathway and gene enrichment analyses all RNA-Seq cohorts (RNA Cross-Validation Subset, n = 19; and Internal Validation Cohort, n = 7) were combined in the “whole-RNA-Seq” cohort. A detailed description of these cohorts is available in Table 1 . Patients achieving a 6-month complete remission (CR) after the first line of chemotherapy were assigned to “sustained-CR” group while patients who died or relapsed within 6 months and/or obtained CR after allogeneic hematopoietic stem cell transplantation (allo-HSCT) were assigned to “non-sustained CR” group. The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) pediatric AML RNA-Seq database ( https://portal.gdc.cancer.gov ) was used as External Validation Cohort. Patients with BM samples at diagnosis and complete clinical data available (n = 833) were selected and assigned to “sustained- “and “non-sustained CR” groups with the same criteria applied to the cohort of 32 patients. RNA analyses BM samples were processed for RNA extraction after thawing and analyzed with the nCounter NanoString PanCancer immune-oncology IO 360 assay, that consists in a 770-gene panel, highly enriched for immune-cells related genes and with mRNA sequencing (mRNA-seq), performed at a depth of 20-million-reads on an Illumina HiSeq 4000 sequencer. Data analysis from fastq to raw counts generation was performed using bcbio-nextgen (v1.2.0) rnaseq pipeline. The preliminary quality of sequencing reads was assessed using FASTQC (v.0.11.8). Raw reads were mapped to the human genome GRCh38.p13 (Genome Reference Consortium Human Build 38, INSDC Assembly GCA_000001405.28, Dec 2013) using STAR_2.6.1d aligner and featureCounts v2.0.0 was used to generate the raw counts. Normalization of counts was performed utilizing the Variance Stabilizing Transformation (VST) of DESeq2 R package. Gene expression normalization for all the cohorts was performed within lanes, to correct for gene-specific effects (including GC content) and between lanes, to correct for sample-related differences (including sequencing depth) using EDASeq (v. 2.30.0). Statistical analyses All analyses, unless otherwise specified, were performed in “R”, version 4.2.1. DEGs analysis between the CR groups was performed using log2 normalized expression matrix using limma (v. 3.52.4) ( 15 ) with Benjamini-Hochberg (B-H) FDR correction. The visualization for the DEGs was performed by the ggplot2 (v. 3.4.2) ( 16 ) and the ComplexHeatmap (v. 2.12.1) ( 17 ). Survival analysis was conducted using survminer (v.0.4.9) ( 18 ) to generate Kaplan–Meier curves. Hazard ratios (HRs) and corresponding P values, along with 95% confidence intervals (95% CI), were calculated through Cox proportional hazard regression analysis using the R package survival (v.3.5.7) ( 19 ). Additionally, the overall P value for comparing survival among two or more groups was determined using the log-rank test. Multivariate Cox regression was performed using clinical variables, including age at diagnosis (ordinal), clinical risk groups (categorical) in addition to the 3-genes signature that was significant in the univariate Cox proportional hazard regression analysis. To perform the pathway enrichment analysis, the DEGs in CR groups ( p < 0.05) from the Whole-RNA-Seq combined cohort (19 + 7 samples) were uploaded to Ingenuity Pathways Analysis (IPA) to discover the enriched pathways. Raw data was then downloaded from IPA into R and plotted using the R package ggplot2 (v. 3.4.2). Single Sample Gene Set Enrichment Analysis (ssGSEA) was performed using normalized, log2 transformed expression data to calculate gene enrichment scores (ES) using the R package GSVA (v. 1.44.5) ( 20 ). Gene sets of immune cell-specific signatures were used as described in Bindea et al . ( 21 ) with slight modifications ( 22 ). To visualize the enrichment scores of different signatures we used the ComplexHeatmap R package (v. 2.12.1) ( 17 ). The correlation analysis between age at diagnosis and the 3-genes ES was performed using stat_cor function from the ggpubr R package (v. 0.6.0). Results Patients with sustained-CR present a TH1-enriched L-TME at AML diagnosis We analyzed the expression profiles of patient samples from the different cohorts. We performed a DEG analysis in the NanoString assay on the Discovery Cohort between patients with sustained-CR (n = 6) vs non-sustained CR (n = 19) and identified a clear distinction between myeloid suppression-related genes and a T-cell infiltration signature represented by 67 DEGs ( p ≤ 0.05) (Fig. 1 a). By mapping these genes in the 2 CR groups, the T-cell signature, including cytotoxic T-cell infiltration ( CD3D , CD8A , PRF1 , GNLY ), IFN signaling activation ( IFIT3 , IFITM1 , IFI27 , STAT1 , GBP1 , PARP12 , TRAT1 ), antigen presentation and B-cell functions ( TAP1 , CD19 ) characterized patients with sustained-CR, while early loss of CR and refractory disease were marked by the macrophage-myeloid suppression signature ( CD68 , TREM1 ) and an intrinsic oncogene signaling linked to T-cell exclusion and immune suppression ( WNT5A, WNT-β -catenin pathway, and EFGR ) (Fig. 1 b). To further qualify this signature on a broader gene set, we first validated it with mRNA-Seq in a subset of 19 patients from the Discovery Cohort, then we confirmed it in the Internal Validation Cohort (Supplementary Fig. 1a). Finally, we combined all mRNA-Seq-based cohorts (Whole mRNA-Seq Cohort, see Table 1 ) to perform functional pathway analyses. By applying t -distributed stochastic neighbor embedding ( t SNE) analysis on the transcriptome of the Whole-mRNA-Seq Cohort, we observed a distinct separation pattern between sustained and non-sustained CR (Fig. 1 c), indicating major biological differences between the compared groups. The IPA enrichment-pathway-analysis confirmed that patients with sustained-CR benefit from an immune-rich L-TME, with a prevalence of T helper 1 (TH1) activation pathways, IL-15 production (positive activation z-score), and inhibition of the PD1-PDL1 and myeloid TREM1 pathways (negative activation z-score), proving an association between a TH1-skewed/cytotoxic L-TME and long-term remission (Fig. 1 d). A TH1-enriched L-TME at AML diagnosis is represented by a 3-gene IFN signature To restrict this gene signature to a robust and clinically applicable signature, we shortlisted the 3 genes common to all cohorts and most significantly upregulated in the Differential-Expression-Gene (DEG) analysis: guanylate-binding protein ( GBP1 ); poly-ADP-ribose-polymerase-12 enzyme ( PARP12 ) and T-cell receptor-associated transmembrane adapter ( TRAT1 ) (Fig. 1 e). Of note, all 3 genes are related to IFN: GBP1 ( 23 , 24 ) and PARP12 ( 25 , 26 ) are type II and type I- IFN-induced genes innate immunity enhancers with strong antimicrobial properties, while TRAT1 is involved in cytotoxic/IFNg-polarized immune responses ( 27 ) and correlates with immune cell infiltration and favorable prognosis in some solid tumors, as non-small cell lung carcinomas and Diffuse Large B Cell Lymphoma ( 27 , 28 ). Finally, we validated the identified IFN-signature in the TARGET database to evaluate its potential clinical relevance. Also in the TARGET Cohort, the 3-genes-IFN signature successfully discriminated between children with sustained and non-sustained CR (Fig. 1 f) and performed better than all other genes in distinguishing patients with the lowest Hazard Ratio (HR) (Fig. 2 a). This proves that a cytotoxic and IFN-skewed BM microenvironment is relevant in controlling AML disease. A high 3-gene IFN signature enrichment at AML diagnosis confers a longer OS to AML standard-risk patients The intensity of the treatment received by AML patients is calibrated on the relapse risk categorized as low, standard (or “intermediate” in the AIEOP protocol, ( 29 ) ) and high based on cytogenetic abnormalities and early response to treatment ( 30 , 31 ). Standard-risk patients commonly lack prognostic factors that qualify the low- and high-risks categories and cannot benefit from a personalized therapeutic strategy. An allo-HSCT intensified treatment usually is offered if an HLA-matched related donor is available ( 29 , 32 ). Thus, standard-risk patients’ OS is significantly lower than low-risk patients and comparable with the high-risk group that benefits of an intensified treatment, as confirmed also in the analyzed TARGET Cohort (Fig. 2 b). We performed an OS analysis on the TARGET Cohort according to the 3-IFN genes signature and we discovered that a high and medium 3-genes enrichment scores (ES) in the L-TME of patients conferred a significant OS advantage per se (medium vs low p = 0.0008, high vs low p = 0.006) (Fig. 2 c). The significance for OS was maintained after a multivariate analysis including clinical risk and age at diagnosis ( p = 0.0399) (Fig. 2 d). We analyzed the distribution of the 3-IFN genes signature in the clinical risk groups. The signature was more represented in the low and standard clinical risk groups (Fig. 2 e). To investigate whether the 3-genes score could improve the stratification within the risk groups, we tested the OS in each group. A survival advantage was granted by a high 3-genes ES only in the standard-risk cohort (high vs low p = 0.0299) (Fig. 2 f). To assess whether the 3-genes signature represented the same TH1-enriched L-TME seen in the Discovery Cohort, we performed an enrichment analysis using a previously published immune subpopulations genes set (see Methods section) ( 33 ). The analysis confirmed a contextual cytotoxic/NK/Th1 rich-TME present at AML onset in the high 3-genes of standard-risk patients’ group (Fig. 3 a). Specifically, a logistic regression analysis revealed that children categorized as “standard-risk” and falling in the high 3-genes ES tertile have a 76% increased likelihood of a ≥ 6 months CR with respect to children in the low 3-genes ES tertile (Fig. 3 b). Of note, a high 3-genes ES at diagnosis correlated with higher OS, independent of FAB/WHO classifications, and surprisingly was more represented in infants (Fig. 3 c-d). A negative correlation between age and 3-genes ES was further confirmed by a correlation analysis (Supplementary Fig. 1b). This observation along with recent literature challenging the dogma of “impaired neonatal immunity”, suggest that infant T cells may have innate-like functions and are prompt to danger signals response ( 34 – 36 ). Indeed, the ability to stratify infant AML with a favorable prognostic factor is very promising, considering that this patient group is usually faces a poor clinical outcome with respect to older pediatric AML patients ( 37 ). Discussion The TME has been extensively described in solid tumor literature: “hot” T-cell infiltrated tumors have been associated with a favorable prognosis, in contrast to “cold” tumors ( 3 , 22 ). While the leukemic composition of the AML TME has been extensively investigated, ( 38 – 40 ) the immune L-TME is less studied. Recent literature describes how the TME in is deeply impacted by AML ( 14 ) but the correlation between a clear pattern of immune infiltration and prognosis is still controversial ( 41 ). Recently Lasry et al ( 42 ) used single cell RNA-sequencing to profile sorted BM immune cells from 22 pediatric and 20 adult AML patients and described a signature of “immune inflammation” correlating with worse prognosis. Our results, generated by whole mRNA-Seq, which is capturing the overall effect of the interaction between immune and cancer cells, show instead that an IFN-sustained proinflammatory L-TME facilitates AML clearance. Our signature points to a favorable composition of the L-TME inflammation enriched with cytotoxic/NK/Th1 cells and associated with a better prognosis, particularly in standard-risk pediatric AML. Despite the size limitation of our Discovery Cohort, nonetheless representative of 3 independent recruiting sites (Italy, Qatar and Pakistan), the analyses initially guided by the Nanostring panel and tailored on subsets of tumor infiltrating genes, likely favored a less sparse L-TME analysis in the discovery phase. In summary, we have identified a signature of 3-IFN-related genes that correlates with an increase of OS in pediatric AML patients. This correlation remains independent of the current cytogenetic and molecular leukemia classification, particularly favoring infant "standard-risk" patients. This discovery has the potential to enhance the stratification of "standard-risk" patients who currently lack appropriate risk-oriented treatment options. Furthermore, these findings may serve as a valuable roadmap for addressing immune pathways and exploring the potential efficacy of immune-targeted therapies. Declarations Acknowledgements The authors thank the patients and their families for participating in the study. Also, we acknowledge Sara Tomei and Lisa Sara Mathew for running the transcriptome analyses. Funding This work was supported by a grant from Qatar National Research Fund (QNRF grant NPRP8-2297-3-494 to CC) and partly by a grant from the Italian Ministry of Health (Ricerca Corrente 08069119 to PC). Authorship Authors’ contributions: S. S., A. A., D. R., F.V., and M. T. analyzed the data; M. E. performed statistical analysis; T. M., P.C., K. G.,Z. F., B. D., A. E., and A. S. coordinated patients recruitment, and provided clinical data; D. K., S. H., and C. L. banked samples, curated patients database; S. D. , and A. A. wrote the manuscript; S. D. guided analyses; D. B., and C. C. conceived the study, guided analyses. Consent for publication: All authors contributed to the article and approved the submitted version. All authors reviewed the manuscript. Competing interests: The authors declare no competing financial interests. The current affiliation for M.T. is the Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA. Correspondence: Chiara Cugno, Research Department and Pediatric Hematology and Oncology Department, Sidra Medicine, Qatar, Doha; [email protected] . Ethics approval and consent to participate. Informed consent to participate in the study was obtained from all participants and was also obtained from parent or legal guardian in the case of children under 16 years. This Registry study was conducted under the ethical approval of Sidra Medicine Institutional Review Board (IRB) (protocol # IRB #20110003636/2011). The AKU Ethics Review Committee approval number is 3825-Onc-ERC-15. The comitato etico Ethics Review Committee approval numbers are # 1500786 and # 1500787. Availability of data and materials Patients' clinical data may be found in a data supplement available in the online version of this article. RNA expression matrices and TARGET clinical data have been deposited to https://doi.org/10.6084/m9.figshare.24152742. 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An inflammatory state remodels the immune microenvironment and improves risk stratification in acute myeloid leukemia. Nature Cancer. 2023;4(1):27-42. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementalFigure1.pdf Supplemental Figure 1: Functional orientation of leukemic bone marrow tumor microenvironment (L-TME) and the 3-IFN-related genes enrichment score (ES) distinguish sustained complete remission (CR) in pediatric AML patients. (a) Heatmap showing the mRNA-Seq gene expression of the Nanostring differentially expressed genes (DEGs) in all patients from whom sufficient RNA was available, including: n=19 patients of the Discovery Cohort, called Cross-Validation subset, left panel; and n=7 patients of the Internal Validation Cohort, right panel. The analysis technically validates the Nanostring gene set with the RNA-Seq platform. Note: only 64 out of 67 DEGs detected in the Discovery Cohort using Nanostring were present in the final RNA-Seq matrix after QC filter. (b) Spearman’s correlation analysis between the age at diagnosis and 3-genes ES in the TARGET cohort. ‘r’ is the correlation coefficient, ‘p’ is the p -value. Table1.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-3990757","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":276957837,"identity":"45f733ab-c043-407d-aa38-30b71cb7aa15","order_by":0,"name":"Chiara Cugno","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYPACGwY2hgTStKSRruUwEBOrhb/9jPHrgorz9nzsuQ8/MFTcs2sgpEXiTI6Z9YwztxPbeJ4bSzCcKU4mqMVAgsfMmLftdgKbRBqDBGNbQjJBh0G1nLMHamH+QawW48e8bQcY2yTS2EC22BHUInEmrYyZ50wy0C/P2CwSziQkENTC335482eeCjt7+fY05hsfKhLsCWoBAjYJOBNoRWIDEVqYPyDziLJlFIyCUTAKRhYAAABmM9JCx6MkAAAAAElFTkSuQmCC","orcid":"","institution":"Sidra medicine","correspondingAuthor":true,"prefix":"","firstName":"Chiara","middleName":"","lastName":"Cugno","suffix":""},{"id":276957838,"identity":"d6592f5f-0786-43c9-a684-11fd2cdb7c38","order_by":1,"name":"Shimaa Sherif","email":"","orcid":"","institution":"Sidra Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shimaa","middleName":"","lastName":"Sherif","suffix":""},{"id":276957839,"identity":"941b89a0-78b7-4767-b19e-f56d15b323d4","order_by":2,"name":"Aesha Ali","email":"","orcid":"","institution":"Sidra 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Dhanya","middleName":"","lastName":"Kizhakayil","suffix":""},{"id":276957843,"identity":"b6b06359-2f68-49aa-a153-553001045a47","order_by":6,"name":"Mohammed Toufiq","email":"","orcid":"","institution":"The Jackson Laboratory for Genomic Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Toufiq","suffix":""},{"id":276957844,"identity":"c0b8c2b0-550c-40b7-a0f9-f1a0af45f1d4","order_by":7,"name":"Fazulur Vempalli","email":"","orcid":"","institution":"Sidra Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fazulur","middleName":"","lastName":"Vempalli","suffix":""},{"id":276957845,"identity":"f50ff441-3f15-4fb2-9bca-da195fb81813","order_by":8,"name":"Tommaso Mina","email":"","orcid":"","institution":"Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.","correspondingAuthor":false,"prefix":"","firstName":"Tommaso","middleName":"","lastName":"Mina","suffix":""},{"id":276957846,"identity":"cc2c529a-8724-4e3c-9f75-940a2fdff804","order_by":9,"name":"Patrizia Comoli","email":"","orcid":"https://orcid.org/0000-0001-5964-0553","institution":"Fondazione IRCCS Policlinico San Matteo","correspondingAuthor":false,"prefix":"","firstName":"Patrizia","middleName":"","lastName":"Comoli","suffix":""},{"id":276957847,"identity":"7eb6215c-c2dd-4175-a579-eb8e269e6658","order_by":10,"name":"Kulsoom Ghias","email":"","orcid":"","institution":"Aga Khan University","correspondingAuthor":false,"prefix":"","firstName":"Kulsoom","middleName":"","lastName":"Ghias","suffix":""},{"id":276957848,"identity":"8abf72eb-054d-4fd2-8618-09ab4c4e4206","order_by":11,"name":"Zehra Fadoo","email":"","orcid":"","institution":"Aga Khan University","correspondingAuthor":false,"prefix":"","firstName":"Zehra","middleName":"","lastName":"Fadoo","suffix":""},{"id":276957849,"identity":"afbc3d3d-9230-495f-8f00-dff3bfe2bc8c","order_by":12,"name":"Sheanna Herrera","email":"","orcid":"","institution":"Sidra Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sheanna","middleName":"","lastName":"Herrera","suffix":""},{"id":276957850,"identity":"25ac8313-04fd-4fe3-a28e-14612c62837a","order_by":13,"name":"Che-Ann Lachica","email":"","orcid":"","institution":"Sidra Medicine","correspondingAuthor":false,"prefix":"","firstName":"Che-Ann","middleName":"","lastName":"Lachica","suffix":""},{"id":276957851,"identity":"094e457c-9a08-412b-993b-4a4b044c84f0","order_by":14,"name":"Blessing Dason","email":"","orcid":"","institution":"Sidra Medicine","correspondingAuthor":false,"prefix":"","firstName":"Blessing","middleName":"","lastName":"Dason","suffix":""},{"id":276957852,"identity":"1ca2f7dd-13da-4215-b372-f9e6aa0dbabe","order_by":15,"name":"Anila Ejaz","email":"","orcid":"","institution":"Sidra Medicine","correspondingAuthor":false,"prefix":"","firstName":"Anila","middleName":"","lastName":"Ejaz","suffix":""},{"id":276957853,"identity":"80ee22e0-40fe-4019-bf41-02dbf050c4d8","order_by":16,"name":"Ayman Saleh","email":"","orcid":"","institution":"Sidra Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ayman","middleName":"","lastName":"Saleh","suffix":""},{"id":276957854,"identity":"87ccbaca-fb2a-4829-b3bb-b5146c7c50d5","order_by":17,"name":"Sara Deola","email":"","orcid":"","institution":"Sidra Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Deola","suffix":""},{"id":276957855,"identity":"02933eea-6f33-43ce-9f9d-1c9e3f96f960","order_by":18,"name":"Davide Bedognetti","email":"","orcid":"","institution":"Sidra Medicine","correspondingAuthor":false,"prefix":"","firstName":"Davide","middleName":"","lastName":"Bedognetti","suffix":""}],"badges":[],"createdAt":"2024-02-26 11:15:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3990757/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3990757/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52451606,"identity":"d914abea-0123-4f00-a22e-1924b5769bb6","added_by":"auto","created_at":"2024-03-11 19:14:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1769820,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of prognostic signature from the gene expression data of the Discovery Cohort, Cross Validation Subset, and Internal Validation Cohort.\u003c/p\u003e\n\u003cp\u003e(a) Volcano plot of differentially expressed genes in sustained-CR (complete remission)\u003cem\u003e vs\u003c/em\u003enon-sustained CR from the Discovery Cohort (\u003cem\u003en=25\u003c/em\u003e). Red: up-regulated genes in the CR sustained groups with \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 and blue: down-regulated genes in the CR sustained groups with \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 and Fold Change of 1.\u003c/p\u003e\n\u003cp\u003e(b) Heatmap showing the expression of 67 DEGs between the sustained CR and non-sustained CR from the Discovery Cohort (\u003cem\u003en=25\u003c/em\u003e). FAB classification and clinical risk stratification are annotated on top of the heatmap.\u003c/p\u003e\n\u003cp\u003e(c) \u003cem\u003et\u003c/em\u003eSNE plot of the normalized expression values of 26 patients from the whole mRNA-Seq cohort annotated by CR outcome.\u003c/p\u003e\n\u003cp\u003e(d) IPA pathways analysis using IPA software of DEGs (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) between the sustained CR and non-sustained CR from Whole mRNA-Seq cohort (\u003cem\u003en=26\u003c/em\u003e). Red: upregulated in sustained-CR, blue, downregulated in sustained-CR. The orange line indicates the –log (\u003cem\u003ep\u003c/em\u003e-value). The dots display the predicted activation state of the implicated biological functions reflected by the activation z score. The bases of this inferred activation state are literature-derived relationships between genes and the corresponding biological function. Pathways that are activated in sustained-CR are marked with orange dots, indicating a positive activation score, whereas pathways inhibited sustained-CR patients are marked with a blue dot, indicating a negative activation score. A gray circle indicates that no literature-derived information exists to estimate the activation state.\u003c/p\u003e\n\u003cp\u003e(e) Venn diagram showing the overlap between the number of upregulated genes from the Discovery Cohort (\u003cem\u003en=25\u003c/em\u003e), the Cross-Validation subset (\u003cem\u003en= 19\u003c/em\u003e) and the Internal Validation Cohort (\u003cem\u003en=7\u003c/em\u003e). The intersection of these cohorts resulted in three genes (\u003cem\u003eGBP1\u003c/em\u003e, \u003cem\u003ePARP12\u003c/em\u003e, \u003cem\u003eTRAT1\u003c/em\u003e). The selection was performed by shortlisting significant DEGs (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) overexpressed in the sustained CR group in all the cohorts.\u003c/p\u003e\n\u003cp\u003e(f) Boxplots displaying the enrichment scores (ES) for the 3-genes signature between the sustained CR and non-sustained CR in the different cohorts. \u003cem\u003eP\u003c/em\u003e-value is calculated by the Student \u003cem\u003et\u003c/em\u003e-test, and the middle black line represents the median.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3990757/v1/b32767a33f38ff7d5a21e295.png"},{"id":52451604,"identity":"e7ae8487-d3b9-4d71-93ce-d77632c19745","added_by":"auto","created_at":"2024-03-11 19:14:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":720558,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic value of the 3-genes ES signature in the TARGET Cohort.\u003c/p\u003e\n\u003cp\u003e(a) Boxplot showing the Cox proportional hazard ratio (HR) for the 3 genes signature and genes with \u003cem\u003ep\u003c/em\u003e-value \u0026lt; 0.05 (2766 genes) out of the 18000 genes of all RNA-Seq matrix in the standard-risk group of the TARGET Cohort. The 3-genes signature measured the lowest HR among all significant genes.\u003c/p\u003e\n\u003cp\u003e(b) Kaplan-Meier overall survival curve (OS) for clinical risk groups in TARGET Cohort: low-, standard- and high-risk groups.\u003c/p\u003e\n\u003cp\u003e(c) Kaplan-Meier OS curve for the 3-genes enrichment score (ES) groups: high, medium, and low score groups within the whole TARGET Cohort.\u003c/p\u003e\n\u003cp\u003e(d) Multivariate analysis and univariate analysis of OS using the 3-genes ES signature in TARGET Cohort.\u003c/p\u003e\n\u003cp\u003e(e) Boxplots showing the 3-genes ES across the clinical risk groups. \u003cem\u003eP\u003c/em\u003e-value is calculated by the Student \u003cem\u003et\u003c/em\u003e-test, and the middle black line represents the median.\u003c/p\u003e\n\u003cp\u003e(f) Kaplan-Meier OS curve for the 3-genes ES groups: high, medium, and low score groups within the clinical standard-risk group.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3990757/v1/f4aad1e4c9f4d9dc7457e4af.png"},{"id":52451608,"identity":"a647a6cb-7b81-42b8-9fc5-f172d453c6c0","added_by":"auto","created_at":"2024-03-11 19:14:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1556348,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune contexture of the 3-genes ES groups and the association with clinical parameters in the TARGET Cohort.\u003c/p\u003e\n\u003cp\u003e(a) Heatmap displaying immune cells subpopulation enrichment scores across 3-genes ES groups within the standard group. These immune signatures were previously described in Sherif \u003cem\u003eet al\u003c/em\u003e (22). For each ES group CR outcome, FAB+WHO AML classification and age at diagnosis are annotated.\u003c/p\u003e\n\u003cp\u003e(b) Logistic regression analysis showing the odds ratios with 95% confidence interval (CI) of the 3-genes ES tertiles as an explanatory variable on CR in AML pediatric patients of TARGET Cohort.\u003c/p\u003e\n\u003cp\u003e(c) Distribution of the age groups (left) and AML phenotypes (right) within the different 3-genes ES signature groups in the TARGET Cohort. The pairwise proportion test showed a lower proportion of the age group \u0026gt;5 years in patients with high 3-genes ES compared to other ES groups (\u003cem\u003ep\u003c/em\u003e = 0.028); and a higher proportion of age group ≤ 1 year in patients with high 3-genes ES compared to other ES groups (\u003cem\u003ep\u003c/em\u003e= 0.005). For the FAB classification; a lower proportion of the patients with AML \u003cem\u003et\u003c/em\u003e(6;9) (p23;q34); DEK-NUP214 in patients with high 3-genes ES compared to other ES groups (\u003cem\u003ep\u003c/em\u003e = 0.004).\u003c/p\u003e\n\u003cp\u003e(d) Boxplots showing the 3-genes ES across the age at diagnosis groups. \u003cem\u003eP\u003c/em\u003e-value is calculated by the Student \u003cem\u003et\u003c/em\u003e-test, and the middle black line represents the median.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3990757/v1/41eac90175780d30aea5af3e.png"},{"id":56384279,"identity":"382d4c73-cd0c-4ebe-83ec-a92d2b863c5c","added_by":"auto","created_at":"2024-05-13 13:20:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1939619,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3990757/v1/47f31cba-6e6b-4c43-bf29-9388dffda01e.pdf"},{"id":52451609,"identity":"e6aa84a9-bd9b-4d88-a403-eca9faa4b081","added_by":"auto","created_at":"2024-03-11 19:14:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":481879,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 1:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional orientation of leukemic bone marrow tumor microenvironment (L-TME) and the 3-IFN-related genes enrichment score (ES) distinguish sustained complete remission (CR) in pediatric AML patients.\u003c/p\u003e\n\u003cp\u003e(a) Heatmap showing the mRNA-Seq gene expression of the Nanostring differentially expressed genes (DEGs) in all patients from whom sufficient RNA was available, including: n=19 patients of the Discovery Cohort, called Cross-Validation subset, left panel; and n=7 patients of the Internal Validation Cohort, right panel. The analysis technically validates the Nanostring gene set with the RNA-Seq platform. Note: only 64 out of 67 DEGs detected in the Discovery Cohort using Nanostring were present in the final RNA-Seq matrix after QC filter.\u003c/p\u003e\n\u003cp\u003e(b) Spearman’s correlation analysis between the age at diagnosis and 3-genes ES in the TARGET cohort. ‘r’ is the correlation coefficient, ‘p’ is the \u003cem\u003ep\u003c/em\u003e-value.\u003c/p\u003e","description":"","filename":"SupplementalFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3990757/v1/d1e735b358b9468ae598c073.pdf"},{"id":52451607,"identity":"3424a78f-c977-46ef-8e24-b876dda9d182","added_by":"auto","created_at":"2024-03-11 19:14:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":39669,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3990757/v1/7c0fa0b902d105f62034019d.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"\u003cp\u003eUpregulation of interferon signaling predicts sustained complete remission in pediatric AML patients\u003c/p\u003e","fulltext":[{"header":"Key Points","content":"\u003cul\u003e\n \u003cli\u003eThe IFN genes GBP1, PARP12, and TRAT1 define a signature correlating with chemo-sensitive disease and better OS in pediatric AML patients.\u003c/li\u003e\n \u003cli\u003eStandard risk pediatric AML with high enrichment of GBP1, PARP12, and TRAT1 have a 76% increased likelihood of a \u0026ge; 6 months CR.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003ePediatric acute myeloid leukemia (AML) represents a complex and heterogeneous group of hematological malignancies that accounts for 20% of all pediatric leukemias. Despite the remarkable and extensive breakthroughs in therapeutic approaches in the last decades, AML is still characterized by suboptimal outcomes with an overall survival (OS) rate around 65% (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies emphasize the critical role of immune cell composition and behavior in the tumor microenvironment (TME) of solid cancers (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The contexture, functional orientation, and intricate interactions of immune cell subsets and tumor cells within TME can directly influence patients\u0026rsquo; treatment response and clinical outcomes (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A deep understanding of these networks within TME led to the identification of novel biomarkers that improved the assessment of patients' responsiveness to treatment and the development of advanced therapeutic strategies (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile it is tempting to speculate that the same model may be applicable to acute leukemias to exploit novel immunotherapies and extend patient survival (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), the role of the TME in leukemias (L-TME) is still underexplored (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The architecture of the bone marrow sustains normal hematopoiesis through delicate interactions in microenvironment sub-compartments, including endosteal, reticular and vascular niches (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). At leukemia onset, the homeostasis of these finetuned networks is abruptly disrupted by the rapid invasion of leukemic cells, leaving a limited healthy counterpart available for assessment.\u003c/p\u003e \u003cp\u003eTo increase the likelihood of an immune-network analysis in this scenario, we chose to perform a gene expression study starting from an immune-directed platform, the NanoString PanCancer immune-oncology assay.\u003c/p\u003e \u003cp\u003eWe explored the importance of the leukemic BM (L-TME) in predicting chemosensitivity and \u0026ge;\u0026thinsp;6 months remission in a multi-center group of 32 non-promyelocytic pediatric AML patients, and subsequently we validated the findings using data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) AML pediatric public database.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eEthical compliance\u003c/h2\u003e\n \u003cp\u003eThe study was conducted in accordance with the ethical approvals granted in all collaborative sites: Qatar, Sidra Medicine Institutional Review Board #20110003636/2011; Pakistan, Aga Khan University Ethics Review Committee# 3825-Onc-ERC-15; Italy, IRCCS Fondazione Policlinico San Matteo Ethics Review Committee #1500786 and 1500787. All samples were collected after obtaining informed consent, as appropriate.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003eClinical samples and cohorts\u003c/h2\u003e\n \u003cp\u003eThirty-two pediatric patients recruited in Qatar, Italy and Pakistan donated a BM sample at the time of AML diagnosis. The samples were preserved through live-frozen after Ficoll-gradient enrichment and channeled to Sidra Medicine for downstream analyses. Clinical and follow -up data for each patient were meticulously recorded and are detailed in Table \u003cspan\u003e1\u003c/span\u003e.The recruitment of pediatric patients involved the collection of data from 32 individuals diagnosed with AML at three distinct locations: Sidra Medicine in Doha, Qatar; IRCCS Fondazione Policlinico San Matteo in Pavia, Italy; and Aga Khan University in Karachi, Pakistan. Sustained complete remission (CR) refers to patients who achieved a 6-month CR after the first line of chemotherapy, while non-sustained CR includes patients who died or relapsed within 6 months and/or achieved CR after allo-HSCT.\u003c/p\u003e\n \u003cp\u003eOf the 32 patients, twenty-five patients were analyzed by NanoString and formed a Discovery Cohort. A subset of 19 patients from the Discovery Cohort from whom sufficient RNA was available was subsequently studied using mRNA-Seq for technical validation (Cross-Validation Subset). Once the analysis of the discovery cohort was concluded, an independent small cohort of 7 patients was assessed by RNA-Seq and formed the Internal Validation Cohort. For pathway and gene enrichment analyses all RNA-Seq cohorts (RNA Cross-Validation Subset, n\u0026thinsp;=\u0026thinsp;19; and Internal Validation Cohort, n\u0026thinsp;=\u0026thinsp;7) were combined in the \u0026ldquo;whole-RNA-Seq\u0026rdquo; cohort. A detailed description of these cohorts is available in Table \u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003ePatients achieving a 6-month complete remission (CR) after the first line of chemotherapy were assigned to \u0026ldquo;sustained-CR\u0026rdquo; group while patients who died or relapsed within 6 months and/or obtained CR after allogeneic hematopoietic stem cell transplantation (allo-HSCT) were assigned to \u0026ldquo;non-sustained CR\u0026rdquo; group.\u003c/p\u003e\n \u003cp\u003eThe Therapeutically Applicable Research to Generate Effective Treatments (TARGET) pediatric AML RNA-Seq database (\u003cspan\u003e\u003cspan\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003c/span\u003e) was used as External Validation Cohort. Patients with BM samples at diagnosis and complete clinical data available (n\u0026thinsp;=\u0026thinsp;833) were selected and assigned to \u0026ldquo;sustained- \u0026ldquo;and \u0026ldquo;non-sustained CR\u0026rdquo; groups with the same criteria applied to the cohort of 32 patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003eRNA analyses\u003c/h2\u003e\n \u003cp\u003eBM samples were processed for RNA extraction after thawing and analyzed with the nCounter NanoString PanCancer immune-oncology IO 360 assay, that consists in a 770-gene panel, highly enriched for immune-cells related genes and with mRNA sequencing (mRNA-seq), performed at a depth of 20-million-reads on an Illumina HiSeq 4000 sequencer. Data analysis from fastq to raw counts generation was performed using bcbio-nextgen (v1.2.0) rnaseq pipeline. The preliminary quality of sequencing reads was assessed using FASTQC (v.0.11.8). Raw reads were mapped to the human genome GRCh38.p13 (Genome Reference Consortium Human Build 38, INSDC Assembly GCA_000001405.28, Dec 2013) using STAR_2.6.1d aligner and featureCounts v2.0.0 was used to generate the raw counts.\u003c/p\u003e\n \u003cp\u003eNormalization of counts was performed utilizing the Variance Stabilizing Transformation (VST) of DESeq2 R package.\u003c/p\u003e\n \u003cp\u003eGene expression normalization for all the cohorts was performed within lanes, to correct for gene-specific effects (including GC content) and between lanes, to correct for sample-related differences (including sequencing depth) using EDASeq (v. 2.30.0).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eStatistical analyses\u003c/h2\u003e\n \u003cp\u003eAll analyses, unless otherwise specified, were performed in \u0026ldquo;R\u0026rdquo;, version 4.2.1.\u003c/p\u003e\n \u003cp\u003eDEGs analysis between the CR groups was performed using log2 normalized expression matrix using limma (v. 3.52.4) (\u003cspan\u003e15\u003c/span\u003e) with Benjamini-Hochberg (B-H) FDR correction. The visualization for the DEGs was performed by the ggplot2 (v. 3.4.2) (\u003cspan\u003e16\u003c/span\u003e) and the ComplexHeatmap (v. 2.12.1) (\u003cspan\u003e17\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSurvival analysis was conducted using survminer (v.0.4.9) (\u003cspan\u003e18\u003c/span\u003e) to generate Kaplan\u0026ndash;Meier curves. Hazard ratios (HRs) and corresponding \u003cem\u003eP\u003c/em\u003e values, along with 95% confidence intervals (95% CI), were calculated through Cox proportional hazard regression analysis using the R package survival (v.3.5.7) (\u003cspan\u003e19\u003c/span\u003e). Additionally, the overall \u003cem\u003eP\u003c/em\u003e value for comparing survival among two or more groups was determined using the log-rank test. Multivariate Cox regression was performed using clinical variables, including age at diagnosis (ordinal), clinical risk groups (categorical) in addition to the 3-genes signature that was significant in the univariate Cox proportional hazard regression analysis.\u003c/p\u003e\n \u003cp\u003eTo perform the pathway enrichment analysis, the DEGs in CR groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) from the Whole-RNA-Seq combined cohort (19\u0026thinsp;+\u0026thinsp;7 samples) were uploaded to Ingenuity Pathways Analysis (IPA) to discover the enriched pathways. Raw data was then downloaded from IPA into R and plotted using the R package ggplot2 (v. 3.4.2).\u003c/p\u003e\n \u003cp\u003eSingle Sample Gene Set Enrichment Analysis (ssGSEA) was performed using normalized, log2 transformed expression data to calculate gene enrichment scores (ES) using the R package GSVA (v. 1.44.5) (\u003cspan\u003e20\u003c/span\u003e). Gene sets of immune cell-specific signatures were used as described in Bindea \u003cem\u003eet al\u003c/em\u003e. (\u003cspan\u003e21\u003c/span\u003e) with slight modifications (\u003cspan\u003e22\u003c/span\u003e). To visualize the enrichment scores of different signatures we used the ComplexHeatmap R package (v. 2.12.1) (\u003cspan\u003e17\u003c/span\u003e). The correlation analysis between age at diagnosis and the 3-genes ES was performed using stat_cor function from the ggpubr R package (v. 0.6.0).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePatients with sustained-CR present a TH1-enriched L-TME at AML diagnosis\u003c/h2\u003e \u003cp\u003eWe analyzed the expression profiles of patient samples from the different cohorts. We performed a DEG analysis in the NanoString assay on the Discovery Cohort between patients with sustained-CR (n\u0026thinsp;=\u0026thinsp;6) \u003cem\u003evs\u003c/em\u003e non-sustained CR (n\u0026thinsp;=\u0026thinsp;19) and identified a clear distinction between myeloid suppression-related genes and a T-cell infiltration signature represented by 67 DEGs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). By mapping these genes in the 2 CR groups, the T-cell signature, including cytotoxic T-cell infiltration (\u003cem\u003eCD3D\u003c/em\u003e, \u003cem\u003eCD8A\u003c/em\u003e, \u003cem\u003ePRF1\u003c/em\u003e, \u003cem\u003eGNLY\u003c/em\u003e), IFN signaling activation (\u003cem\u003eIFIT3\u003c/em\u003e, \u003cem\u003eIFITM1\u003c/em\u003e, \u003cem\u003eIFI27\u003c/em\u003e, \u003cem\u003eSTAT1\u003c/em\u003e, \u003cem\u003eGBP1\u003c/em\u003e, \u003cem\u003ePARP12\u003c/em\u003e, \u003cem\u003eTRAT1\u003c/em\u003e), antigen presentation and B-cell functions (\u003cem\u003eTAP1\u003c/em\u003e, \u003cem\u003eCD19\u003c/em\u003e) characterized patients with sustained-CR, while early loss of CR and refractory disease were marked by the macrophage-myeloid suppression signature (\u003cem\u003eCD68\u003c/em\u003e, \u003cem\u003eTREM1\u003c/em\u003e) and an intrinsic oncogene signaling linked to T-cell exclusion and immune suppression (\u003cem\u003eWNT5A, WNT-β\u003c/em\u003e-catenin pathway, and \u003cem\u003eEFGR\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further qualify this signature on a broader gene set, we first validated it with mRNA-Seq in a subset of 19 patients from the Discovery Cohort, then we confirmed it in the Internal Validation Cohort (Supplementary Fig.\u0026nbsp;1a). Finally, we combined all mRNA-Seq-based cohorts (Whole mRNA-Seq Cohort, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to perform functional pathway analyses.\u003c/p\u003e \u003cp\u003eBy applying \u003cem\u003et\u003c/em\u003e-distributed stochastic neighbor embedding (\u003cem\u003et\u003c/em\u003eSNE) analysis on the transcriptome of the Whole-mRNA-Seq Cohort, we observed a distinct separation pattern between sustained and non-sustained CR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), indicating major biological differences between the compared groups. The IPA enrichment-pathway-analysis confirmed that patients with sustained-CR benefit from an immune-rich L-TME, with a prevalence of T helper 1 (TH1) activation pathways, IL-15 production (positive activation z-score), and inhibition of the PD1-PDL1 and myeloid TREM1 pathways (negative activation z-score), proving an association between a TH1-skewed/cytotoxic L-TME and long-term remission (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eA TH1-enriched L-TME at AML diagnosis is represented by a 3-gene IFN signature\u003c/h2\u003e \u003cp\u003eTo restrict this gene signature to a robust and clinically applicable signature, we shortlisted the 3 genes common to all cohorts and most significantly upregulated in the Differential-Expression-Gene (DEG) analysis: guanylate-binding protein (\u003cem\u003eGBP1\u003c/em\u003e); poly-ADP-ribose-polymerase-12 enzyme (\u003cem\u003ePARP12\u003c/em\u003e) and T-cell receptor-associated transmembrane adapter (\u003cem\u003eTRAT1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). Of note, all 3 genes are related to IFN: \u003cem\u003eGBP1\u003c/em\u003e (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and \u003cem\u003ePARP12\u003c/em\u003e (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) are type II and type I- IFN-induced genes innate immunity enhancers with strong antimicrobial properties, while \u003cem\u003eTRAT1\u003c/em\u003e is involved in cytotoxic/IFNg-polarized immune responses (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and correlates with immune cell infiltration and favorable prognosis in some solid tumors, as non-small cell lung carcinomas and Diffuse Large B Cell Lymphoma (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, we validated the identified IFN-signature in the TARGET database to evaluate its potential clinical relevance.\u003c/p\u003e \u003cp\u003eAlso in the TARGET Cohort, the 3-genes-IFN signature successfully discriminated between children with sustained and non-sustained CR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef) and performed better than all other genes in distinguishing patients with the lowest Hazard Ratio (HR) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). This proves that a cytotoxic and IFN-skewed BM microenvironment is relevant in controlling AML disease.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eA high 3-gene IFN signature enrichment at AML diagnosis confers a longer OS to AML standard-risk patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe intensity of the treatment received by AML patients is calibrated on the relapse risk categorized as low, standard (or \u0026ldquo;intermediate\u0026rdquo; in the AIEOP protocol, (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) ) and high based on cytogenetic abnormalities and early response to treatment (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStandard-risk patients commonly lack prognostic factors that qualify the low- and high-risks categories and cannot benefit from a personalized therapeutic strategy. An allo-HSCT intensified treatment usually is offered if an HLA-matched related donor is available (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThus, standard-risk patients\u0026rsquo; OS is significantly lower than low-risk patients and comparable with the high-risk group that benefits of an intensified treatment, as confirmed also in the analyzed TARGET Cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eWe performed an OS analysis on the TARGET Cohort according to the 3-IFN genes signature and we discovered that a high and medium 3-genes enrichment scores (ES) in the L-TME of patients conferred a significant OS advantage \u003cem\u003eper se\u003c/em\u003e (medium \u003cem\u003evs\u003c/em\u003e low \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0008, high \u003cem\u003evs\u003c/em\u003e low \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The significance for OS was maintained after a multivariate analysis including clinical risk and age at diagnosis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0399) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003eWe analyzed the distribution of the 3-IFN genes signature in the clinical risk groups. The signature was more represented in the low and standard clinical risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003eTo investigate whether the 3-genes score could improve the stratification within the risk groups, we tested the OS in each group. A survival advantage was granted by a high 3-genes ES only in the standard-risk cohort (high \u003cem\u003evs\u003c/em\u003e low \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0299) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003eTo assess whether the 3-genes signature represented the same TH1-enriched L-TME seen in the Discovery Cohort, we performed an enrichment analysis using a previously published immune subpopulations genes set (see \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMethods\u003c/span\u003e section) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The analysis confirmed a contextual cytotoxic/NK/Th1 rich-TME present at AML onset in the high 3-genes of standard-risk patients\u0026rsquo; group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSpecifically, a logistic regression analysis revealed that children categorized as \u0026ldquo;standard-risk\u0026rdquo; and falling in the high 3-genes ES tertile have a 76% increased likelihood of a\u0026thinsp;\u0026ge;\u0026thinsp;6 months CR with respect to children in the low 3-genes ES tertile (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eOf note, a high 3-genes ES at diagnosis correlated with higher OS, independent of FAB/WHO classifications, and surprisingly was more represented in infants (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d). A negative correlation between age and 3-genes ES was further confirmed by a correlation analysis (Supplementary Fig.\u0026nbsp;1b). This observation along with recent literature challenging the dogma of \u0026ldquo;impaired neonatal immunity\u0026rdquo;, suggest that infant T cells may have innate-like functions and are prompt to danger signals response (\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Indeed, the ability to stratify infant AML with a favorable prognostic factor is very promising, considering that this patient group is usually faces a poor clinical outcome with respect to older pediatric AML patients (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe TME has been extensively described in solid tumor literature: \u0026ldquo;hot\u0026rdquo; T-cell infiltrated tumors have been associated with a favorable prognosis, in contrast to \u0026ldquo;cold\u0026rdquo; tumors (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile the leukemic composition of the AML TME has been extensively investigated, (\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) the immune L-TME is less studied. Recent literature describes how the TME in is deeply impacted by AML (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) but the correlation between a clear pattern of immune infiltration and prognosis is still controversial (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Recently Lasry \u003cem\u003eet al\u003c/em\u003e (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) used single cell RNA-sequencing to profile sorted BM immune cells from 22 pediatric and 20 adult AML patients and described a signature of \u0026ldquo;immune inflammation\u0026rdquo; correlating with worse prognosis.\u003c/p\u003e \u003cp\u003eOur results, generated by whole mRNA-Seq, which is capturing the overall effect of the interaction between immune and cancer cells, show instead that an IFN-sustained proinflammatory L-TME facilitates AML clearance.\u003c/p\u003e \u003cp\u003eOur signature points to a favorable composition of the L-TME inflammation enriched with cytotoxic/NK/Th1 cells and associated with a better prognosis, particularly in standard-risk pediatric AML.\u003c/p\u003e \u003cp\u003eDespite the size limitation of our Discovery Cohort, nonetheless representative of 3 independent recruiting sites (Italy, Qatar and Pakistan), the analyses initially guided by the Nanostring panel and tailored on subsets of tumor infiltrating genes, likely favored a less sparse L-TME analysis in the discovery phase.\u003c/p\u003e \u003cp\u003eIn summary, we have identified a signature of 3-IFN-related genes that correlates with an increase of OS in pediatric AML patients. This correlation remains independent of the current cytogenetic and molecular leukemia classification, particularly favoring infant \"standard-risk\" patients. This discovery has the potential to enhance the stratification of \"standard-risk\" patients who currently lack appropriate risk-oriented treatment options. Furthermore, these findings may serve as a valuable roadmap for addressing immune pathways and exploring the potential efficacy of immune-targeted therapies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors thank the patients and their families for participating in the study. Also, we acknowledge Sara Tomei and Lisa Sara Mathew for running the transcriptome analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from Qatar National Research Fund (QNRF grant NPRP8-2297-3-494 to CC) and partly by a grant from the Italian Ministry of Health (Ricerca Corrente 08069119 to PC).\u003c/p\u003e\n\u003cp\u003eAuthorship\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e S. S., A. A., D. R., F.V., and M. T. analyzed the data; M. E. performed statistical analysis; T. M., P.C., K. G.,Z. F., B. D., A. E., and A. S. coordinated patients recruitment, and provided clinical data; D. K., S. H., and C. L. banked samples, curated patients database; S. D. , and A. A. wrote the manuscript; S. D. guided analyses; D. B., and C. C. conceived the study, guided analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the article and approved the submitted version. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003eThe current affiliation for M.T. is the Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCorrespondence:\u003c/strong\u003e Chiara Cugno, Research Department and Pediatric Hematology and Oncology Department, Sidra Medicine, Qatar, Doha; [email protected].\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate.\u003c/p\u003e\n\u003cp\u003eInformed consent to participate in the study was obtained from all participants and was also obtained from parent or legal guardian in the case of children under 16 years.\u003c/p\u003e\n\u003cp\u003eThis Registry study was conducted under the ethical approval of Sidra Medicine Institutional Review Board (IRB) (protocol # IRB #20110003636/2011). The AKU Ethics Review Committee approval number is 3825-Onc-ERC-15.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe comitato etico Ethics Review Committee approval numbers are # 1500786 and # 1500787.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003ePatients\u0026apos; clinical data may be found in a data supplement available in the online version of this article. RNA expression matrices and TARGET clinical data have been deposited to https://doi.org/10.6084/m9.figshare.24152742.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe R code used for this analysis can be found in this GitHub repository (https://github.com/Sidra-TBI-FCO/IFN-CR-AML) or in a zenodo snapshot of this repository created at the time of manuscript acceptance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAplenc R, Meshinchi S, Sung L, Alonzo T, Choi J, Fisher B, et al. 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Nature Cancer. 2023;4(1):27-42.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\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":"","lastPublishedDoi":"10.21203/rs.3.rs-3990757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3990757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe immunological composition of the microenvironment has shown relevance for diagnosis, prognosis, and therapy in solid tumors, while it remains underexplored in acute leukemias. In this study, we investigated the significance of the acute myeloid leukemia (AML) bone marrow microenvironment in predicting chemosensitivity and long-term remission outcomes in pediatric patients. To this aim, we analyzed 32 non-promyelocytic pediatric AML patients at diagnosis using the NanoString PanCancer IO 360 assay and RNA-Sequencing and we validated our findings in the online available TARGET AML pediatric dataset. A short signature of 3 Interferon (IFN)-related genes (GBP1, PARP12, TRAT1) significantly distinguished chemosensitive diseases and stratified patients assigned to standard risk group, as per current treatment protocols, into 2 groups: patients with a high enrichment of the 3 genes at diagnosis had a significantly longer overall survival compared with patients with a low enrichment.\u003c/p\u003e\n\u003cp\u003eThe leukemia microenvironment associated with this signature showed a contextual enhancement of TH1/cytotoxic/NK-related pathways. Our results demonstrate the importance of immune response in the tumor microenvironment of pediatric AML and provide tools for a more refined stratification of pediatric patients otherwise categorized as “standard-risk” and as such, lacking adequate risk-oriented therapeutic strategies. Moreover, they offer a promising guide to tackle immune pathways and potentially exploit immune-targeted therapies.\u003c/p\u003e","manuscriptTitle":"Upregulation of interferon signaling predicts sustained complete remission in pediatric AML patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-11 19:14:00","doi":"10.21203/rs.3.rs-3990757/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":"759dceaf-0cad-4013-88cb-3255ba8aff2a","owner":[],"postedDate":"March 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-13T13:12:04+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-11 19:14:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3990757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3990757","identity":"rs-3990757","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}