DNA methylome and transcriptome of normal human CD4 + and CD8 + T-cells
To identify molecular changes associated with PTCL development, we first sought to determine methylation and gene expression patterns in normal T-cells. To achieve that we obtained CD4 + and CD8 + normal human T-cell samples isolated from peripheral blood (Precision Medicine) and subjected them to Whole Genome Bisulfite Sequencing (WGBS) and RNA-seq analysis. WGBS yielded more than 28M reads for each sample (Fig. S1). We next analyzed methylation data along with data obtained from purified CD4 + T-cells (sample CD4_1) for which both WGBS and RNA-seq were publicly available. The methylation analysis revealed that 21,936,712 individual CpG dinucleotides (CGs) were covered ≥ 5x in all three samples and were used further. A majority of CGs had a high level of methylation with more than 1.4 x 107 CGs methylated to at least 75% in all three samples, whereas only 1 x 106 CGs were methylated ≤ 25% (Fig. 1A). Further analysis revealed that more than 15,000 out of 34,858 promoters (defined as − 1500 bp to + 500 bp relative to the transcription start site -TSS) were heavily methylated (at least 75%) and only ~ 4,000 promoters were methylated at lower levels (less than 25%) in each sample (Figs. 1B, 1C and Supporting Information 1). The positional variability in promoter methylation was low with very similar patterns across samples as determined by Pearson’s correlation with R values close to 1 in all pairwise comparisons (Fig. 1D). We next utilized RNA-seq data to determine if methylation may impact gene expression. This analysis revealed that in general, the degree of promoter methylation is inversely correlated with gene expression (Figs. 1E, 1F and Supporting Information 2). Pairwise comparisons of gene expression and methylation revealed that FPKM values for genes with different percentages of promoter methylation were significantly different (p < 0.05, two-tailed Student’s t-test) in all comparisons except 0–25% vs 26–50% and 26–50% vs 51–75% in the first CD4 + T-cells sample (Fig. 1F). For example, genes with promoters that were less than 25% methylated were expressed at higher levels than genes with highly methylated promoters (> 75%; Fig. 1F). Three analyzed T-cell samples were more variable in gene expression patterns as R values in pairwise comparisons were lower than those seen based on promoter methylation (Fig. 1G). This result suggests that promoter methylome is more stable than transcriptome in normal T-cells across individuals and perhaps less prone to polymorphic changes in the human population.
Pathway enrichment analysis of 3,001 highly expressed genes (FPKM ≥ 5) by EnrichR revealed, not surprisingly, significant enrichment in genes related to T-cell development. The most significant in Biocarta 2016 were T-cell receptor, IL2 -, IL7- signaling, and T-cell apoptosis (Fig. 1H).
To further investigate methylation variability in human T-cells, we analyzed the similarity of methylation patterns among an additional three samples for which WGBS data are available. A pairwise comparison of the methylation status of 7,628,019 cytosines (covered 5x) that overlapped in all controls revealed that samples showed a high degree of similarity irrespective of their immunophenotypes (R = 0.73–0.85) for all pairwise comparisons (Fig. 1I).
Our analysis thus demonstrates that a majority of CpG dinucleotides and promoters are mostly methylated in normal T-cells and that methylation polymorphism is limited resulting in relatively infrequent differences in promoter methylation between CD4 + and CD8 + T-cells.
DNA methylome of Peripheral T-cell lymphomas
To analyze DNA methylation in human PTCL, we first obtained a set of seven primary human lymphoma samples from both the Cooperative Human Tissue Network (CHTN) and commercial sources. This resulted in a collection of four ALCL (T1-T4), one AITL sample (T5), and two PTCL-NOS (T6, T8) (Fig. S2). After DNA isolation, we subjected the obtained samples to global methylation profiling using WGBS. Analysis of the data revealed that we were able to obtain sequencing depths ranging from 16.5 to 24M CGs that were covered by at least five sequence reads in each sample (Fig. S3). For analysis of tumor-specific methylation, we used control sample consisting of methylation data obtained from five normal human T cells - CD3, CD4_1, CD4_2, CD4_3 and CD8 - averaged out by Metilene. A pairwise comparison of the methylation status of 7,628,019 cytosines (covered 5x, averaged out by Metilene) that overlapped in all samples revealed that tumors were severely hypomethylated relative to the controls (Figs. 2A, S1 and S3). This was further confirmed by the analysis of differentially methylated cytosines (DMCs) which were those that had at least 10% methylation change relative to controls (p < 0.05 (MWU)). Our pairwise analysis on CGs covered in all samples revealed that most of the DMCs were hypomethylated relative to normal T-cells in all tumors (Fig. 2B). Hypomethylation was most pronounced in T1 and T8 lymphomas and the least in T2 and T4 that had almost equal numbers of hypo- and hypermethylated DMCs (Fig. 2B). More stringent analysis of methylation changes using differentially methylated regions (DMRs; defined as ≥ 10% methylation change in the same direction in three consecutive cytosines in ≤ 100 bp, p < 0.05 (MWU)) confirmed the large-scale deregulation of methylation in all tumors. Notably, thousands of hyper- and especially hypomethylated DMRs were observed in all tumors in various genomic elements including promoters, enhancers, introns, exons, and repeats (Figs. 2C-G). Like in mouse hematologic malignancies 17–19, the presence of hypo- rather than hypermethylated DMRs in promoters was a more frequent event in all tumors ranging from ~ 6,000 in T2 to ~ 20,000 in T8 (Fig. 2C). However, promoter hypermethylation was seen in all tumors and ranged from ~ 2,000 (T1) to 8,000 (T3). In some tumors – T2 and T3 – promoter hypo and hypermethylation were almost equal in frequency (Fig. 2C). Patterns of hypo- and hypermethylation changes didn’t appear to depend on tumor type since the most hypomethylated tumors – T1 and T8 – belonged to different PTCL subtypes (ALCL and PTCL-NOS, respectively).
While tumors varied in the frequency of hypo and hypermethylated DMRs in genomic elements, the trend remained similar within individual tumors. For example, the most hypomethylated tumors were T1 and T8, and hypomethylation was manifested across all genomic elements. Similarly, the ratio between hypo and hypermethylated DMRs was the smallest in T2, and that trend was seen across all genomic elements (Figs. 2C-G and data not shown).
Not surprisingly, the biggest ratio favoring hypo- over hypermethylated DMRs was observed in repetitive elements that are known to be prone to loss of methylation in tumors (Fig. 2G). Interestingly, as many as 767 hypo- and 567 hypermethylated DMRs were recurrently observed in all tumors tested (Figs. 2H, 2I, S4 and Supporting Information 3). We consider these high-frequency DMRs as representing the ‘Core PTCL Methylation Signature’. These DMRs were distributed in various genomic elements but especially in repeats and introns (Figs. 2I and S5). For example, MPZL1 and UCK2 loci uniformly hypermethylated in normal T-cells were hypomethylated in lymphomas with high frequency (Figs. 2J and S6). Similarly, MIATNB and TXK loci hypomethylated in normal T-cells gained methylation in all tested lymphomas. (Figs. 2J and S6). Among other notable genes with promoter hypermethylation were LCK, CD101, PGGHG, RASSF3, and SLC39A2 genes, and with promoter hypomethylation PTAFR, MPZL1, PRDM11, CTNND1, FLI1, GIT2, EML5, RBFOX1, KSR1, and others (Supporting Information 3).
Further dissection of this signature may reveal methylation markers of PTCL associated with the disease initiation and progression, as well as genes driving the disease development.
Analysis of gene expression in ALCL and PTCL-NOS
To determine the extent to which methylation changes may contribute to deregulated transcriptome in PTCL, we next performed global gene expression profiling of PTCL samples using RNA-seq analysis. In addition to PTCL samples used for methylation analysis, we also included RNA isolated from three additional PTCL-NOS samples (T7, T9, T10) along with RNA from normal T-cells used for methylation analysis (Figs. 1, S1 and S2). These samples were subjected to RNA-seq analysis and the data were analyzed using SeqMonk software and DeSeq2 method to calculate differential expression. Our analysis revealed large-scale deregulation in transcriptomes of PTCL samples relative to normal T-cells manifested by findings that 12,240 genes were deregulated in at least one tumor relative to the averaged-out values of independent normal T-cell samples (Figs. 3A, S2 and Supporting Information 4).
Ingenuity Pathways Analysis (IPA) of differentially expressed genes [FC ≥ 1.5, p < 0.05 by DESeq2) in human T-cell lymphomas (n = 10) relative to control T-cells (n = 4)] revealed that all PTCLs had Inhibited Pathways related to Regulation of IL-2 Expression in Activated and Anergic T Lymphocytes, T Cell Receptor Signaling, and HIPPO signaling (Fig. 3B). Additional frequently suppressed pathways included RHOGDI Signaling, Cell Cycle G1/S Checkpoint Regulation, tumor suppressive PTEN Signaling, and others (Fig. 3B). In contrast, pathways frequently activated in all tested samples included Estrogen-mediated S-phase Entry, Cell Cycle Control of Chromosomal Replication, Signaling by Rho Family GTPases, and the STAT3 pathway. Additionally, there was a high frequency of activation in Interferon and RHOA Signaling pathways, along with other cancer-related pathways (Fig. 3B).
The expression of 231 genes was increased and the expression of 91 genes was decreased at least 2-fold in all ten PTCLs. We term these molecular changes as ‘Core Gene Expression Signature’ (Figs. 3C and Supporting Information 5). The TOP five genes overexpressed in all PTCL samples encode the following: phospholipase PLA2G2D, organic anion transporting polypeptide SLCO2B1, metalloprotease ADAMDEC1, complement protein C1QB, and receptor activity modifying protein RAMP3, with the average increase in expression over 500-fold. (Supplementary Figs. S7 and Supporting Information 5). None of these genes seem to have an established role in T-cell transformation and their deregulation can be a consequence of changes in signaling and epigenetic alterations in PTCL. GO enrichment analysis identified pathways linked to cell cycle regulation and in particular to Cell Cycle G2/M Phase Transition, Mitotic Nuclear Division, and Sister Chromatid Segregation (Fig. 3D). Analysis of genes not directly linked to the cycle revealed pathways related to Complement Activation, Angiogenesis, Blood Coagulation, and Notch Pathway Signaling (Fig. S8). ChIP enrichment analysis revealed the possible involvement of FOXM1, E2F1 and E2F4, SOX2, KLF4, and MYBL2 transcription factors in the pathogenesis of PTCL as they may play a role in the deregulation of the ‘Core PTCL Expression Signature’ (Fig. S9).
In all 10 PTCL samples, 91 genes exhibited a consistent decrease in expression, with at least a 2-fold reduction (Fig. 3C). Notably, the most frequently downregulated genes included FGFBP2 (also known as KSP37), teneurin TENM1, and a group of Y-linked genes such as USP9Y, TTTY15, and UTY (Figs. 3C and S7). Their role in lymphomagenesis remains unclear.
The GO enrichment analysis identified pathways linked to the Cell cycle, Histone demethylation, epigenetic dysregulation, and B cell differentiation likely occurring with the contribution of transcription factors FOXO1, FOXOM1, MYB, and others (Figs. 3E, S10 and S11).
Up-regulation of TBX21 or GATA3 and their target genes (EOMES, CXCR3, IL2RB, CCL3, IFNγ, and CCR4, IL18RA, CXCR7, IK respectively) was previously shown to distinguish two subclasses of PTCL-NOS with distinct clinical outcomes 6,27. To determine if any of the five samples included in our analysis belongs to a specific PTCL-NOS subtype, we analyzed RNA-seq expression patterns further. However, we did not see up-regulation of TBX21 or GATA3 when compared to normal controls in any of the five cases of PTCL-NOS perhaps because our analysis was limited by the small sample size (Fig. S12).
DNA methylation modifiers are deregulated in human PTCL
To determine if levels of DNA methylation modifiers are deregulated in human PTCL, we further analyzed RNA-seq data and found that DNMT3A was significantly reduced in all tumors when compared to either CD4 + or CD8 + normal T-cells from peripheral blood (Fig. 4A). In contrast, DNMT1 and DNMT3B were unchanged with only one ALCL tumor, T3, showing a significant increase in transcript levels (Fig. 4A). Analysis of DNA demethylases showed that TET1 transcripts were low in all samples (FPKM < 1) but still significantly downregulated in the majority of tested tumors, whereas levels of TET2 and TET3 were unchanged relative to normal thymocytes (Figs. 4A and S13). Another protein that can affect DNA methylation is the TCL1A protein which was shown to inhibit DNA methyltransferases biochemically and affect global methylation in a mouse model of hematological malignancies 15. We, therefore, analyzed its expression and found six out of ten analyzed tumors had significantly increased levels of TCL1A RNA relative to normal T-cells (Fig. 4A). In contrast, the expression of another member of the TCL family - TCL1B – that does not inhibit Dnmts 15 was unchanged in PTCL (Fig. S13).
To analyze protein levels, we next performed immunoblot analysis of DNMTs in a subset of PTCLs for which we had frozen tissues available (Tumors 1–7). Consistent with RNA-seq data analysis, DNMT3A was downregulated in most tumors relative to normal peripheral blood lymphocytes (Fig. 4B). Interestingly, we detected low protein levels of DNMT3B in T2, T4, T5, and T6 suggesting that despite unchanged mRNA levels, protein down-regulation may occur in primary PTCL (Fig. 4C). Whether DNMT3B levels are affected by TCL1A overexpression or the protein is down-regulated by other mechanisms, remains unclear. Unlike DNMT3A/B, DNMT1 levels did not seem to be changed in lymphomas (data not shown).
To determine whether deregulated expression of methylation modifiers is more broadly observed in PTCL subtypes, we next utilized publicly available data generated by RNA-seq analysis of 15 primary Natural killer/T-cell lymphomas (NKTCL), 21 ALCL (ALCL-2), eight Adult T-cell leukemia/lymphoma (ATLL), and eight T-lymphoblastic lymphomas (TLBL), and our five NOS and four ALCL (ALCL-1). While the expression of DNMT1 was mostly unchanged, DNMT3A levels were significantly reduced across all PTCL subtypes except TLBL (Fig. 4D). Like DNMT1, DNMT3B expression was unchanged across most PTCL except for a significant increase in TLBL (Fig. 4D). The levels of TET1 RNA showed a tendency for downregulation in a majority of analyzed PTCLs although it did not reach statistical significance, while TET3 was mostly unchanged irrespective of tumor type (Fig. S14).
TCL1A was upregulated in a majority of PTCLs, whereas TCL1B was not significantly increased in any of the tested tumor subtypes (Figs. 4D, S14).
Altogether, this analysis revealed that several DNA methylation modifiers are deregulated in PTCL, with the downregulation of DNMT3A and TET1 and up-regulation of TCL1A in most tested samples. These molecular changes, along with the downregulation of DNMT3B protein and genetic alterations found in these modifiers in a subset of PTCL as reported by others 28–30, are the most likely reasons for the large-scale deregulation of PTCL methylomes we observed.
Deregulated promoter methylation is associated with changes in gene expression
To determine the extent to which methylation changes in PTCL may affect transcription we further compared the data sets generated by WGBS and RNA-seq. First, we found that methylation changes also affected the expression of various repetitive elements, including DNA transposons, LTR and Non-LTR retrotransposons, and satellite repeats (Fig. S15). In a subset of repeat elements, such as UCON80, LTR39, and LTR180, hypomethylation mostly correlated with an increased expression. In contrast, hypomethylation of UCON34 correlated with decreased, rather than increased expression (Fig. S15). However, we didn’t detect any consistent pattern of methylation changes, gene expression, or their correlation in examined tumors suggesting that repeats may not serve as good markers of disease development or progression.
Next, we examined gene expression changes that correlated with methylation changes in gene promoters at high frequency and found that 2,810 genes contained hypomethylated DMRs in 5 out of 7 tumors (Supporting Information 6). Out of those, 153 genes had increased expression in 5 out of 7 tumors (Fig. S16). EnrichR pathway enrichment analysis coupled with Wiki Pathway datasets revealed pathways regulating multiple cellular processes that are commonly upregulated in cancers, such as VEGFA-VEGFR2 Signaling, PI3K-Akt signaling, and EGF/EGFR signaling (Fig. S17). Interestingly, several pathways related to Hippo signaling such as Hippo-Merlin Signaling Dysregulation, Overview of leukocyte-intrinsic Hippo pathway, Pathways Regulating Hippo and Hippo-Yap signaling pathway raising the possibility that the deregulation of this pathway in T-cells could play a functional role in transformation. Promoters of 1,281 genes contained hypomethylated DMRs in at least 6 out of 7 tumors. Out of these, 39 genes were associated with increased expression (≥ 1.5) in PTCL suggesting that loss of methylation may have contributed to their deregulation (Fig. 5A).
Several genes from this group, such as UHRF1, MPZL1, CDK14, RACGAP1, RAB13, and others have oncogenic functions in various solid tumors, leukemias, and lymphomas either predicting poor prognosis, involved in tumor migration, metastasis, or maintenance 31–35.
We identified 1,220 hypermethylated genes with the frequency of 5 out of 7 tumors out of which 74 are also downregulated in 5 out of 7 tumors (Supporting Information 6 and Fig. S18). Decreased expression of these genes was associated with the deregulation of various pathways including the Wnt Signaling Pathway and Pluripotency and Pathways Regulating Hippo Signaling. (Fig. S19). Promoters of 578 genes showed hypermethylated DMRs with a high frequency of 6 out of 7 PTCL. Out of these, the expression of 56 genes decreased by at least 1.5-fold. (Fig. 5A). Among these were genes with putative tumor suppressor function in different cancer types including FOXP1, STK4, ATM, and others 36–38.
Interestingly, several genes with potential oncogenic functions, such as SF3B1, FYN, and LCK are also frequently downregulated but the physiological relevance of these changes for PTCL development remains unclear.
Our data show a relatively high number of recurrent methylation events observed in PTCL correlate with changes in gene transcription. Given that changes in expression affect many genes involved in various aspects of tumor biology, DNA methylation changes likely contribute to PTCL development in a causative way.
Loss of DNA methylation correlates with up-regulation of genes critical for cancer cell proliferation
To further explore whether promoter hypomethylation may affect aspects of T-cell lymphomagenesis, we next focused on the analysis of 39 genes hypomethylated and overexpressed with high frequency in PTCL (Fig. 5A). To determine if any of these genes may play a role in PTCL maintenance, we utilized data from CRISPR knockout screens in the DepMap database (Broad Institute, (39, 40)) and investigated whether any of the genes affected the growth of hematologic cell lines. This search revealed that knockouts of most genes did not significantly impact the proliferation of hematological cell lines, as indicated by the gene effect scores ranging from − 0.75 to + 0.75 (Figs. 5B and S20). In contrast, the knockout of RACGAP1 and RCC1 genes was lethal to the majority of cell lines, and they were therefore classified as Common essential required for proliferation of cells in general. Interestingly, TRIP13 (thyroid hormone receptor interactor 13) characterized by DepMap as Strongly Selective was critical for the maintenance of several cancer cell lines including B-cell lymphoma OCILY19 and Human eosinophilic leukemia EOL1. Because TRIP13 is not a Common essential and therefore its targeting may be less toxic, we, therefore, focused on the analysis of this gene and sought to further explore its role in lymphomagenesis by determining its expression in primary TCL. Our analysis revealed that the gene was overexpressed in 9/10 PTCLs on average by ~ 10-fold relative to controls (Fig. 6A). Analysis of publicly available RNA-seq data revealed overexpression of TRIP13 in ALCL, ATLL, NKTCL, TLBL and NOS (Fig. 6B).
We next performed immunoblot analysis of various cell lines to determine if the TRIP13 protein is present in hematologic cell lines. This analysis revealed that TRIP13 was expressed in T-cell lymphoma (T8ML-1, MJ, HH), T-cell leukemia (JURKAT, MOLT4, MOT, SUPT1, LOUCY, CCRF-CEM, DND-41) B-cell lymphoma (RAJI), B-cell leukemia (MEC-1, MEC-2) and myeloid leukemia (K562, CTV-1) cell lines (Fig. 6C). Altogether, our data show that TRIP13 is overexpressed in primary TCL and expressed in various hematologic cell lines and may be involved in their maintenance.
TRIP13 downregulation inhibits the proliferation of malignant T-cells
The AAA ATPase TRIP13 (thyroid hormone receptor interactor 13) is known to participate in various regulatory steps related to the cell cycle, such as the mitotic spindle assembly checkpoint and meiotic recombination, as well as the DNA repair by immediate-early DNA damage sensing and ATM signaling activation 39. To determine if TRIP13 is required for the proliferation of PTCL, we transduced a PTCL-NOS cell line, T8ML-1, with lentiviruses expressing shRNAs either targeting scrambled or TRIP13 and co-expressing mCherry. Cells were cultured over twenty days and the percentage of mCherry positive cells was measured at different time points by FACS. The percentage of cells expressing scrambled shRNA remained relatively stable suggesting that expression of scrambled shRNA or mCherry did not affect the proliferation of cells (Figs. 7A and 7B). In contrast, the percentage of TRIP13 shRNA-1 expressing cells was gradually reduced over time suggesting that the proliferation of T8ML-1 cells is impaired by TRIP13 downregulation. (Figs. 7A and 7B). The proliferative defect was reproducible and was also seen using independent TRIP13 shRNA-2 (Figs. 7C and S21). We next asked whether TRIP13 knockdown can affect the proliferation of T-cell leukemia JURKAT cells. These cells could be routinely transduced to more than 99% efficiency thus allowing for direct cellular and molecular analysis (Fig. S22). As expected, both TRIP13-specific shRNAs efficiently decreased RNA and protein levels of TRIP13 (Figs. 7D and S23). Similar to T8ML-1 cells, TRIP13 knockdown resulted in impaired proliferation and reduced viability of JURKAT cells as determined by the decreased percentage of cells in “live gate” in FACS analysis and cell counts upon culturing over time (Figs. 7E and data not shown). TRIP13 reduction resulted in decreased BrdU incorporation, G2-M arrest, increased Annexin V expression, and apoptosis (Figs. 7F-I). In contrast to downregulation, lentivirally mediated overexpression of C-terminally FLAG-tagged TRIP13 CDS from lentiviral backbone co-expressing fluorescent protein in T8ML-1 did not affect cell growth of T8ML-1 or JURKAT cells as the percentage of EGFP + cells remained relatively stable over time in both TRIP13 and control cells (data not shown). Altogether, these data suggest that the downregulation of TRIP13 has antiproliferative effects by inducing G2-M arrest accompanied by apoptosis.
Treatment of T8ML-1 cells with TRIP13 inhibitor DCZ0415 impairs proliferation and induces cell death
To test if targeting TRIP13 may have therapeutic potential, we next treated lymphoma cell lines with DCZ0415, a TRIP13-specific inhibitor 40. The treatment of T8ML-1 cells with various concentrations of DCZ0415 (10–25 µM) showed dose-dependent cell death with EC50 values of 10.0 µM and 5.5 µM upon 2- and 3-day treatment, respectively (Figs. 8A and S24). The cell treatment was accompanied by the downregulation of endogenous TRIP13 protein suggesting not only that DCZ0415 inhibits this protein but also contributes to its downregulation accompanied by impaired proliferation and cell death (Fig. 8B). When T8ML-1 cells were treated longer, even lower DCZ0415 concentration effectively impaired cellular proliferation. For example, a 14-day treatment of T8ML-1 cells severely reduced viability at 5 µM and 10 µM DCZ0415 concentrations (Fig. 8C). Even at concentrations as low as 1 µM, DCZ0415 reduced cellular viability by 50%. Like with shRNA-mediated TRIP13 downregulation, the treatment with DCZ0415 inhibitor impaired cell proliferation by reducing BrdU incorporation, inducing G2-M arrest, and cell death (Figs. 8D and 8E). Consistently with G2-M arrest, the levels of G2-M proteins - Cdc25A and Cyclin B1 – were elevated upon the drug treatment, whereas G1-S Cyclin D1 was downregulated.
Altogether, our data show that targeting TRIP13 may be beneficial in treating PTCL.