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T. Braun et al.
treatment of choice, the CD52-antibody alemtuzumab, is efficient in inducing initial responses, but nearly all patients relapse within 12-24 months thereafter.7,8 Second-line options are even less efficient and the medi- an overall survival (OS) of T-PLL patients is <2 years.1,3,9,10
Activating translocation of the T-cell leukemia/lym- phoma 1 (TCL1) proto-oncogene is the most prevalent genetic aberration in T-PLL.11 In addition, loss-of-func- tion perturbations of the tumor suppressor ataxia telang- iectasia mutated (ATM) are reported for >80% of T-PLL patients.1 Both alterations contribute towards a pheno- type of enhanced T-cell receptor signaling (TCR) and an aberrant DNA damage response, including resistance to p53-mediated cell death, deregulated cell cycle control, and deficient DNA repair mechanisms.1,11 Activating lesions in janus kinase (JAK) and signal transducer and activator of transcription (STAT) molecules as well as epi- genetic aberrations have emerged as further hallmarks of T-PLL pathology, resulting in a sustained survival signal- ing and pro-oncogenic cell cycle deregulation.1,12–15 Despite these recent advances, a better understanding of T-PLL's pathobiology is of importance in order to identi- fy novel treatment options.
MicroRNA (miR) have increasingly been recognized as relevant in the pathogenesis of hematopoetic and solid tumors. They are small non-coding RNA with an average length of 22 nucleotides. By targeting specific mRNA, miR function as posttranscriptional repressors.16 Importantly, most miR regulate a large set of genes, often resulting in a cooperative effect on a given cellular path- way, rather than a specific effect on a single gene. Both onco miR and tumor-suppressive miR have been causally implicated in mature B- and T-lymphoid malignancies.17– 19 As a prominent example, chronic lymphocytic leukemia (CLL) harbors an unique miR expression signa- ture with miR-181b downregulation as the best investi- gated miR deregulation.20 When overexpressing miR- 181b in the Em-TCL1A CLL mouse model, leukemic expansion is decelerated.21 Moreover, miR-181b as well as the miR-29 and miR-34b/c were shown to target the proto-oncogene TCL1A, reflected by an association of their downregulation with oncogenic TCL1A overex- pression in CLL.22 Likewise, specific miR have been iden- tified to be involved in the pathogenesis of mature T-cell tumors such as cutaneous T-cell lymphoma (CTCL) (e.g., deregulation of miR-29 and miR-200)17,23 or NK/T-cell lymphoma (downregulated miR-150).24 In T-PLL, fre- quent genomic aberrations of argonaute RISC catalytic component 2 (AGO2), a master regulator of miR process- ing,25 provide first hints for altered miR activity and miR expression signatures (miR-omes).1 However, global miR deregulations, likely involved in T-PLL’s pathophysiolo- gy, have not been reported.
In the presented study, we performed small-RNA sequencing to investigate the spectrum of differential miR expression in T-PLL. We identify global, T-PLL-spe- cific miR alterations associated with gene signatures affiliated to functional categories of survival signaling and DNA damage response pathways. In addition, we show that the miR-omes of T-PLL cells and of activated T cells are remarkably similar. Finally, we identify asso- ciations of miR alterations with cellular activation, clini- cal tumor burden, and patient outcome, all underlining the impact of miR deregulations in T-PLL.
Methods
Patient cohort
Primary isolates of 48 well-annotated T-PLL patients and of T cells from six age-matched healthy donors were studied (banked 2009-2019; patient characteristics in Table 1). The diagnosis of T- PLL was confirmed according to World Health Organization crite- ria26 and consensus guidelines.2 All patients (median age 68 years) provided informed consent according to the Declaration of Helsinki. Collection and use of the samples have been approved for research purposes by the ethics committee of the University Hospital of Cologne (#11–319). Most samples (82.6%) were col- lected prior to any first-line treatment (n=38 of 46).
Sequencing and data processing
RNA from peripheral blood mononuclear cells (PBMC) of T-PLL patients (median purity 95.4%) and CD3+ pan-T cells of six age- matched healthy controls (median purity 90.2%) was subjected to library preparation and sequenced on the NovaSeq 6000 (n=48 T- PLL) and the HiSeq4000 platform (n=46 T-PLL, Illumina, San Diego, USA) according to the manufacturer’s instructions for polyA-RNA and small-RNA sequencing, respectively. Details on cell isolation, stimulation, RNA isolation, library preparation, sequencing, and data processing are given in the Online Supplementary Methods.
Gene set enrichment analysis
Gene set enrichment analysis (GSEA) were performed on pre- ranked lists using the GSEA-software (v3.0)27 and MSigDB (v7.0)28 HALLMARK gene sets. For each considered miR, Spearman corre- lation coefficients for this miR and all protein-coding genes were determined by comparing the respective gene's count per million (CPM) and fragments per kilobase of million (FPKM) mapped reads. Sorted lists of correlation coefficients were then used as input for the GSEA.
MiR target prediction
In order to obtain putative mRNA targets for each miR, predict- ed miR bindings were first determined using the R-package multiMiR (v1.6.0, Database Version 2.3.0),29 all of eight prediction databases (diana_microt, elmmo, microcosm, miranda, mirdb, pic- tar, pita, targetscan), and a 20% default prediction cutoff. All bind- ings predicted by <2 different databases were removed. From the remaining predicted genes, we chose those as putative miR targets that showed a negative Spearman correlation (rho<0; P<0.05; false discovery rate [FDR] <0.25) of their expression values with the expression of the respective miR.
Correlations with clinical data
In order to test for associations of miR-223-3p, miR-21, miR-29, and miR-200c/141 with clinical characteristics, cytogenetics, immunophenotypes, and outcome data, cases were divided into groups by the mean or tertiles as cutoffs according to the distribu- tion of expression values within the patient cohort. Further details on statistics are provided in the Online Supplementary Methods.
Survival score
In order to develop a survival score, we (i) randomly divided our cohort into a training set (n=22) and a validation set (n=22). We then (ii) identified miR that were expressed in at least 80% of T- PLL samples and (iii) that were highly associated with OS in the training set (upper tertile of patients with highest vs. tertile with the lowest expression) using log-rank tests. Several other parame- ters that had been described to be prognostically relevant in T-PLL (e.g., leukocyte counts, TCL1 mRNA expression) were added to
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