HOXA9 cooperates with activated JAK/STAT signaling to drive ......2018/03/01 · Ellen Geerdens1,2, Simon Bornschein1,2, Rik Gijsbers3, Jean Soulier4, Jules P. Meijerink5, Merja Heinäniemi

HOXA9 cooperates with activated JAK/STAT signaling to drive leukemia development

Charles E. de Bock1,2, Sofie Demeyer1,2, Sandrine Degryse1,2, Delphine Verbeke1,2, Bram Sweron1,2,

Olga Gielen1,2, Roel Vandepoel1,2, Carmen Vicente1,2, Marlies Vanden Bempt1,2, Antonis Dagklis1,2,

Ellen Geerdens1,2, Simon Bornschein1,2, Rik Gijsbers3, Jean Soulier4, Jules P. Meijerink5, Merja

Heinäniemi6, Susanna Teppo7, Maria Bouvy-Liivrand6, Olli Lohi7, Enrico Radaelli1,2 and Jan Cools*1,2

1 KU Leuven, Center for Human Genetics, Leuven, Belgium

2 VIB, Center for Cancer Biology, Leuven, Belgium

3 Laboratory for Viral Vector Technology and Gene Therapy, Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, 3000 Leuven, Belgium

4 U944 INSERM and Hematology laboratory, St-Louis Hospital, APHP, Hematology University Institute, University Paris-Diderot, Paris, France

5 Princess Máxima Center for pediatric oncology, Utrecht, The Netherlands.

6 Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland

7 Tampere Centre for Child Health Research, University of Tampere and Tampere University Hospital, Tampere, Finland

RUNNING TITLE: Mutant JAK3 and HOXA9 cooperation in leukemia

*Corresponding Author: Prof. Jan Cools Laboratory for Molecular Biology of Leukemia VIB-KU Leuven Center for Cancer Biology KU Leuven Campus Gasthuisberg O&N 4, Herestraat 49 - B 912, 3000 Leuven, Belgium ph:+ 32 16 33 00 82 [email protected] KEY WORDS: oncogenes, leukemia, mouse model, transcriptional regulation, signaling

Competing Financial Interest Statement: The authors declare no competing financial interests.

Research. on January 15, 2021. © 2018 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 1, 2018; DOI: 10.1158/2159-8290.CD-17-0583

http://cancerdiscovery.aacrjournals.org/

Abstract

Leukemia is caused by the accumulation of multiple genomic lesions in hematopoietic precursor

cells. However, how these events cooperate during oncogenic transformation remains poorly

understood. We studied the cooperation between activated JAK3/STAT5 signaling and HOXA9

overexpression, two events identified as significantly co-occurring in T-cell acute lymphoblastic

leukemia. Expression of mutant JAK3 and HOXA9 led to a rapid development of leukemia

originating from multipotent or lymphoid-committed progenitors, with a significant decrease in

disease latency compared to JAK3 or HOXA9 alone. Integrated RNA-seq, ChIP-seq and ATAC-seq

revealed that STAT5 and HOXA9 have co-occupancy across the genome resulting in enhanced

STAT5 transcriptional activity and ectopic activation of Fos/Jun (AP-1). Our data suggest that

oncogenic transcription factors such as HOXA9 provide a fertile ground for specific signaling

pathways to thrive, explaining why JAK/STAT pathway mutations accumulate in HOXA9 expressing

cells.

Significance

The mechanism of oncogene cooperation in cancer development remains poorly characterized. In

this study, we model the cooperation between activated JAK-STAT signaling and ectopic HOXA9

expression during T-cell leukemia development. We identify a direct cooperation between STAT5

and HOXA9 at the transcriptional level and identify PIM1 kinase as a possible drug target in mutant

JAK-STAT/HOXA9 positive leukemia cases.




Introduction

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological disease, which arises

from the malignant transformation of developing T-cell progenitors. The best characterized genetic

defects in T-ALL include inactivation of cell cycle regulators (p16/p15), overexpression of

transcription factors (TLX1, TLX3, TAL1, HOXA), mutations that activate the NOTCH1 and PI3K

signaling cascades, and hyperactivation of signaling pathways by mutant kinases such as JAK3

mutants or the NUP214-ABL1 fusion (1-3). In T-ALL there are an average of 10-20 damaging

genomic lesions per case (4-6). These mutations target critical cellular pathways including lymphoid

development, cell cycle regulation, tumor suppression, lymphoid signaling and drug responsiveness

(6,7). Recent studies using next-generation sequencing have identified mutations in the

IL7R/JAK3/STAT5 pathway in 28% of T-ALL cases with JAK3 tyrosine kinase mutations being the

most frequent mutations found in up to 16% of T-ALL cases (4,5,8,9).

In normal T-cell biology, the JAK3 and JAK1 kinases are essential components of the heterodimeric

interleukin-7 (IL7) receptor complex. JAK3 binds the common gamma chain (IL2RG) and JAK1

binds the IL7Ralpha chain (10). When the receptor complex is bound by its ligand IL7, there is

phosphorylation of both JAK1 and JAK3. This leads to the recruitment and phosphorylation of

STAT5 that then translocates to the cell nucleus to regulate gene expression. The binding of IL7 to

its receptor is essential for the survival and proliferation of double-negative thymocytes and is also

important in the homeostasis, differentiation and activity of mature T-cells (11,12). The JAK3

mutations found in T-ALL patients are able to circumvent this tightly controlled signaling process

and activate STAT5 in the absence of cytokine, transform cells in vitro and drive the development of

T-ALL in vivo using a murine bone marrow transplant leukemia model (13).

Whilst the validation of JAK3 mutations as a bona fide "driver" mutation using a one-oncogene

murine model is significant, from a clinical perspective, patients have additional mutations that may

cooperate for malignant cell transformation. For example, it has been shown that BCR-ABL and

NUP98-HOXA9 can cooperate synergistically in acute myeloid leukemia and modeling this

cooperation identified compounds that selectively target leukemic stem cells (14,15). More recently,




the existence of multiple "mini-driver" mutations has also been suggested that might substitute for a

major driver change (16) and therefore given the high number of genetic changes within T-ALL, it is

likely that leukemia development and progression requires synergistic modulation of downstream

signaling pathways by cooperating oncogenic lesions in a polygenic manner.

Some cooperating lesions in T-ALL have already been identified and include Arf inactivation in

combination with Lmo2 overexpression significantly accelerating the development of the T-cell

malignancies and acquisition of Notch1 mutations (17). Similarly work in our laboratory found that

loss of the phosphatase PTPN2 enhanced the kinase activity of the NUP214-ABL1 fusion or JAK1

mutations, thereby sensitizing cells to leukemogenic transformation (18,19). However, the vast

majority of co-occurring lesions and their ability to cooperate in driving tumourigenesis still require

functional validation.

In the current work, we set about identifying cooperating oncogenes in the context of a JAK3

mutant driven leukemia and functionally validating these in vivo using a two-oncogene model. We

found that T-ALL cases with JAK3 mutations are often HOXA+, specifically expressing high levels of

HOXA9 and together can drive an aggressive leukemia using mouse models.

Results

JAK3 mutations are significantly associated with HOXA+ T-ALL cases

Based on results of large scale sequencing studies in T-ALL (4,5,8,9,20), it is becoming clear that

some mutations frequently co-occur, while others are mutually exclusive. We analyzed genetic data

for 155 T-ALL cases (Vicente dataset) (5) in which IL7R/JAK/STAT5 mutations occur in 28% of the

cases and found more frequently in HOXA+, immature and TLX1/3 positive cases. Focusing

specifically on JAK3 mutations alone, these account for the most frequent type of mutation found in

16% of the cases (5). Upon analyzing different T-ALL subgroups, these JAK3 mutations were found

to significantly associate with those T-ALL cases designated as HOXA+ (p value = 0.018) and




negatively associate with those designated as TAL1/LMO2 positive (p value = 0.002) (Fig. 1B). The

designation of HOXA+ included cases with chromosomal rearrangements known to either directly

or indirectly up-regulate genes within the HOXA cluster (i.e. CALM-AF10, SET-NUP214, NUP98-

RAP1GDS and TCRB-HOXA) (5,21,22). Analysis of a second independent cohort of T-ALL cases (Liu

dataset) (9) showed 22% of the patients had IL7R/JAK/STAT5 mutations (data included mutations

within JAK1, JAK3, IL7R, PTPN2, FLT3, STAT5B, and SH2B3) (Fig. 1A). These mutations were

enriched within the HOXA+, TLX1/3+ and LMO2/LYL1+ cases. Furthermore, focusing specifically

on JAK3 mutant cases, again revealed a significant positive association with HOXA+ and negative

association with TAL1/TAL2/LMO2 cases (Fig. 1B). Moreover, associated gene expression data

showed HOXA+ cases have overall higher JAK3 expression levels compared to the majority of other

T-ALL cases (Fig. 1C). Activation of the HOXA cluster in T-ALL typically results in upregulation of

both HOXA9 and HOXA10. Analysis of primary T-ALL samples (Supplementary Table S1) revealed

strong HOXA9 expression in 3 of 5 JAK3 mutant cases, that was up to 10-fold higher when

compared to HOXA10 and HOXA11 (Fig. 1D), suggesting that HOXA9 is the most important

upregulated gene of the HOXA cluster in T-ALL.

JAK3 mutations and HOXA9 co-expression transforms hematopoietic stem and progenitor cells and

leads to rapid leukemia development in vivo using a bone marrow transplant model

The most common JAK3 mutation found within T-ALL cases is the M511I mutation just upstream of

the pseudokinase domain (5,9,13). Therefore, we next sought to determine whether mutant JAK3

signaling (using the JAK3(M511I) mutant) can cooperate with HOXA9 in the transformation of

hematopoietic stem/progenitor cells (HSPCs) and leukemia development. Two separate retroviral

vectors containing JAK3(M511I) (also expressing GFP) or HOXA9 (also expressing mCHERRY) were

used for the co-transduction of murine HSPCs ex vivo and yielded a mixture of non-transduced,

single transduced or double transduced cells as assessed by GFP and mCHERRY fluorescence (Fig.

2A). HSPCs transduced with both JAK3(M511I) and HOXA9 rapidly transformed to cytokine




independent growth and expanded over 20 days in the absence all cytokines. All non-transduced

and single transduced cells were either out competed and/or did not grow (Fig. 2B). The various

JAK3 mutations activate downstream STAT5 signaling with the majority also requiring binding to a

functional cytokine receptor complex (13,23). Constitutively active STAT5B(N642H) mutations

have also been identified and functionally characterized in T-ALL (24,25). Therefore, the same

experimental set up was repeated using STAT5B(N642H) and HOXA9 retroviral expression

constructs. Again, only the double transduced STAT5B(N642H) and HOXA9 cells expanded in the

absence of all cytokines, phenocopying the JAK3(M511I) and HOXA9 result (Fig. 2C). Taken

together, these results show that oncogenic JAK3/STAT5 activation is necessary and sufficient for

cooperation with HOXA9 in ex vivo HSPC cell transformation.

Given this evident cooperation in HSPC transformation ex vivo, we next investigated the

cooperation between JAK3(M511I) and HOXA9 during in vivo leukemia development. Mouse

HSPCs were isolated and transduced with retroviral vectors containing JAK3(M511I) or HOXA9 or a

mixture of both and then injected into sub-lethally irradiated syngeneic recipient mice (BALB/c or

C57BL/6). JAK3(M511I)+HOXA9 recipient BALB/c mice rapidly developed a leukemia

characterized by a rapid increase in peripheral white blood cell (WBC) counts in excess of 10,000

cells/mm3 within 30 days compared to JAK3(M511I) only (Fig. 2D). For each recipient, the double

transduced JAK3(M511I) and HOXA9 leukemic clone outcompeted all single transduced clones

(Fig. 2E). This rapid leukemia development resulted in significantly decreased disease free survival

(DFS) (Median DFS = 25 days, p-value < 0.0001) when compared to JAK3 (M511I) alone (Median

DFS = 126.5 days) (Fig. 2F). In contrast, clones co-expressing TAL1 and JAK3(M511I) were

counter selected in vivo with mice succumbing to a JAK3(M511I) only disease (Fig. 2F,

Supplementary Fig. S1) and reconciled with JAK3 mutations negatively associating with TAL1 in

T-ALL (Fig. 1A). Notably, when the same experimental approach was used for HOXA10 together

with JAK3(M511), there was only a moderate and less profound cooperative effect compared to

HOXA9 (Median DFS = 100 days) (Supplementary Fig. S1).




To negate any mouse strain specific effects, the same experimental set up was repeated on a

C57BL/6 background. Similar to BALB/c, recipient C57BL/6 mice receiving co-transduced

JAK3(M511I)/HOXA9 had a rapid increase in WBC compared to JAK3(M511I) or HOXA9 alone

(Fig. 2G.) and in all cases the double transduced clone was the major clone at end stage (Fig. 2H).

This resulted in a significant decrease in DFS (Median DFS = 40 days) compared to JAK3(M511I)

(Median DFS = 150 days) or HOXA9 (Median DFS = 182 days) only recipient mice (Fig. 2I).

Moreover, when JAK3(M511I) was substituted for the constitutive active STAT5B(N642H) and co-

transduced with HOXA9, this further reduced the disease free survival (Median DFS = 12 days).

At end stage disease, the leukemia development in vivo was characterized by both myeloid and

lymphoid expansion. Phenotypic FACS staining and pathology analysis of the JAK3(M511I)/HOXA9

driven leukemia revealed that leukemic mice had polyclonal expansion of cells displaying either

myeloid (CD16/32+, CD11b+, Gr1+/-) or lymphoid (CD8+) lineage markers and occurred at

different frequencies (Fig 3A). At end stage disease, histopathologic and immunohistochemical

analysis revealed the development of a complex hematolymphoid neoplasm characterized by the co-

existence of different populations of atypical cells displaying both lymphoid and myeloid

differentiation. There was significant leukemic infiltration into spleen, lymph nodes and bone

marrow organs by atypical lymphocytes (CD3 positive, TdT negative) and myeloid/granulocytic

(Myeloperoxidase (MPO) positive) cells (Fig. 3B) (Supplementary Fig. S2). Western blot analysis

confirmed expression of JAK3 and HOXA9 in vivo with robust phosphorylation of STAT5. (Fig. 3C).

These cells proliferated for up to 30 days ex vivo in the absence of all cytokines with clonal selection

of those clones expressing high levels HOXA9 and JAK3(M511I) as assessed by GFP and mCHERRY

expression (Fig 3D,E). Moreover, sorted JAK3(M511I)/HOXA9 CD8+ T-ALL clones were able to

engraft secondary recipient mice with higher frequency compared to age matched JAK3(M511I)

CD8+ leukemic clones (Supplementary Fig. S3).

To determine whether the resulting leukemia remained dependent on activated JAK/STAT5

signaling directly, JAK3(M511I)/HOXA9 leukemic mice were treated for 20 days with the JAK3




inhibitor tofacitinib (40 mg/kg/day) (Fig. 3F). Within three days of treatment with tofacitinib,

there was a significant decrease in the levels of pSTAT5 compared to vehicle treated mice (Fig 3G).

After 20 days of tofacitinib treatment, there was a significant attenuation in leukemia white blood

cell expansion reduced splenomegaly (Fig 3. H-J). Staining of the spleen at end stage also showed a

significant reduction in the number of proliferative cells as assessed by Ki67 staining (Fig. 3K).

Tofacitinib is approved for rheumatoid arthritis and known to cause decreased numbers of

neutrophils, lymphocytes and NK cells but rarely to an extent that leads to serious adverse effects in

patients (26). Indeed, in our own studies, wild type mice treated at 40 mg/kg/day, showed a minor

decrease in NK cell frequency but no significant alterations in CD4+ or CD8+ T-cell frequencies

(Supplementary Fig. S4). Taken together, these results firstly provide strong validation that

ectopic expression of HOXA9 together with JAK3/STAT5 activation are bona fide cooperating

events in leukemia development. Secondly that there is a degree of transcription factor specificity

for cooperation with JAK3 mutations, since JAK3 mutations do not cooperate with the oncogenic

transcription factor TAL1 and thirdly, that the resulting leukemia remains dependent on activated

JAK3 signaling.

A novel retroviral vector to direct transgene expression within developing lymphoid cells

demonstrates cooperation of JAK3(M511I)/HOXA9 in T-ALL development

The previous data illustrate the potential of combined expression of JAK3(M511I) and HOXA9 to

generate mixed myeloid/lymphoid leukemia originating from multipotent hematopoietic precursor

cells. However, HOXA9 expression is often activated in T-ALL by T-cell specific enhancers that only

become active in lymphoid committed progenitors (e.g. TCRB-HOXA9) (21,22). To convincingly

demonstrate cooperation between JAK3(M511I) and HOXA9 during T-cell leukemia development,

and to specifically model lymphoid-specific activation of HOXA9 expression as observed in human

T-ALL, we developed a strategy to limit HOXA9 expression to developing lymphoid cells. We tested

a previously published strategy with 3’ UTR mir223 sites (27), but in the context of




JAK3(M511I)/HOXA9, this strategy was unable to prevent overt myeloid expansion and eventual

AML expansion that we speculate to indicate endogenous miR-223 saturation in the presence of

high levels of transcript driven by the strong MSCV promoter (Supplementary Fig. S5).

We therefore developed a novel retroviral vector in which one or two oncogenes are initially cloned

in an antisense orientation, and thus remain completely inactive. The expression of the oncogenes is

only activated upon Cre-mediated inversion of the lox66/71 flanked construct (Fig. 4A). By use of

hematopoietic cells isolated from cell type specific Cre mice, we could then direct expression of the

oncogenes to specific hematopoietic cell types. The novel vector with JAK3(M511I)+HOXA9 was

first validated in Ba/F3 cells and found to transform the cells to cytokine independent growth only

when Cre-recombinase was present and did not display ectopic "leaky" expression or cell

transformation in Ba/F3 wild type cells over an 8 day period (Fig. 4B). Western blot analysis

confirmed the expression of JAK3 and HOXA9 in the Cre positive Ba/F3 cells and highlighted that

although there was less JAK3(M511I) expression compared to constitutive expression of JAK3, both

had equivalent levels of phosphor-STAT5 (Fig. 4C). The novel retroviral vector strategy was also

validated in vivo using a well established NOTCH1-ICN bone marrow transplant model, but now

with ICN cloned in the antisense direction between the lox66/71 sites. When the viral vector was

transduced on cells derived from CD4-Cre mice the recipient mice succumbed to the anticipated

CD4+/CD8+ T-ALL leukemia (Median overall survival = 43 days) (Supplementary Fig. S6).

Using this new strategy, we next tested whether combined expression of JAK3(M511I) and HOXA9

via CD2-Cre mediated activation in developing lymphoid cells (28) (Fig. 4d) could generate T-cell

leukemia. Mice that received transplantation with inducible JAK3(M511I)-P2A-HOXA9 HSPCs

developed T-ALL like leukemia with decreased latency when compared to JAK3(M511I)-P2A-BFP

only (Median DFS = 135 days vs. 244 days). Notably, both JAK3(M511I) and JAK3(M511I)-P2A-

HOXA9 mice now developed a CD8+ or CD4-CD8- T-ALL, without myeloid expansion (Fig. 4E). For

all mice, CD2-mediated recombination was confirmed by PCR (Fig. 4F). These data illustrate not

only that JAK3 mutants cooperate with HOXA9 to induce leukemia when expressed in common




lymphoid progenitors, but also demonstrate the utility and efficacy of this novel Cre-inducible

retroviral vector for cell-type specific expression in the hematopoietic system.

STAT5 and HOXA9 co-occupy similar genetic loci resulting in increased JAK-STAT signaling in

leukemia cells

To determine the underlying molecular mechanism driving the enhanced oncogenic potential of

JAK3(M511I) in the presence of HOXA9, RNA-seq analysis was carried out on age matched sorted

leukemic CD8+ JAK3(M511I), CD8+ JAK3(M511I)/HOXA9 as well as sorted normal thymic CD8+ T

cells (Supplementary Table S2). The addition of HOXA9 led to skewed upregulation of genes

when compared to JAK3(M511I) only T-ALL (Fig. 5A). Notably, principal component analysis

revealed distinct clustering of myeloid and T-cell leukemias (Supplementary Fig. S7). The normal

CD8+ T-cells formed a tight cluster away from the JAK3(M511I)/HOXA9 CD8+ T-ALL cells and in

between the JAK3(M511I) only cells. Furthermore, within the myeloid cell cluster, the

JAK3(M511I)/HOXA9 driven AML cells clustered separately from Meis1/Hoxa9 and MLL-AF9 AML

(29) suggesting different mechanisms driving these leukemia and indeed there was no increased

Meis1 expression in our leukemia suggesting the co-operation is independent of Meis1. Flt3 ITD

driven leukemia (30) clustered closer to the JAK3(M511I)/HOXA9 and could in part reflect the

downstream activation of STAT5 in both leukemias (Supplementary Fig. S7).

ChIP-seq analysis of CD8+ JAK3(M511I)/HOXA9 leukemia showed significant co-occupancy for

both STAT5 and HOXA9 across the genome. Regions co-bound by STAT5 and HOXA9 were

associated with H3K27Ac and H3K4me3 marks indicating co-localization at proximal promoters

rather than distal enhancers (Fig. 5B,C). Comparison of the STAT5 signal in JAK3(M511I) vs.

JAK3(M511I)/HOXA9 showed strong overlap suggesting that HOXA9 does not significantly

reposition STAT5 to de novo sites (Supplementary Fig. S8). Specific analysis of canonical STAT5

target genes illustrated the strong co-occupancy of both STAT5 and HOXA9. For example, Pim1

kinase a known target gene for both STAT5 (31) and HOXA9(32) showed co-occupancy of the same




DNA region for both STAT5 and HOXA9. Similarly, Bcl2, Osm, Cish and Il7r also showed co-

occupancy by STAT5 and HOXA9 (Fig. 5D, Supplementary Fig. S8). Analysis of relevant human T-

ALL samples also showed strong co-occupancy of HOXA9 with STAT5 peaks at known STAT5

regulated genes including BCL6, MYC, SOCS and PIM1 (Supplementary Fig. S9)

This strong STAT5/HOXA9 co-localisation prompted the question whether a direct protein-protein

interaction occurs between STAT5 and HOXA9. Indeed, a direct interaction between HOXA9 and

STAT5 is suggested based on available mass spectrometry data (33). However, we were unable to

confirm a robust protein-protein interaction between HOXA9 and STAT5 using co-

immunoprecipitation, nor in an independent assay using a recombinant strepII-tagged HOXA9. Only

a weak interaction with the constitutively active STAT5B(N642H) mutant was observed but not

with wild-type STAT5 (Supplementary Fig. S10).

Global analysis of the regions with co-binding of HOXA9 and STAT5 found an association with

diminished levels of repressive H3K27me3 marks at upregulated genes and concomitantly

associated with increased levels of active H3K27Ac marks (Fig. 5E). Integrating the ChIP-seq

analysis with the RNA-seq differentially expressed genes also revealed STAT5 and HOXA9 loading

was higher in upregulated genes compared to down-regulated or unchanged genes (Fig. 5F)

illustrating that the presence of HOXA9 leads to increased gene activation. Indeed, GSEA pathway

analysis comparing CD8+ JAK3(M511I)-T-ALL vs. CD8+ T-cells and CD8+ JAK3(M511I)/HOXA9 vs.

CD8+ JAK3(M511I) found a significant and increasingly positive enrichment for JAK/STAT

signaling (Fig. 5G, Supplementary Table S3). These data show that ectopic expression of HOXA9

enhances STAT5 transcriptional activity in CD8+ leukemic cells rather than inducing a de novo

gene expression program complimentary to STAT5.

PIM1 kinase is over expressed in a number of different cancers and is an emerging cancer drug

target (34,35). Consistent with our RNA-seq and ChIP-seq data, we confirmed increased PIM1

expression in JAK3(M511I)/HOXA9 leukemic mouse samples by qPCR (Fig. 6A). Analysis of

primary human T-ALL patient samples showed the highest levels of PIM1 expression in HOXA+




cases and those cases with IL7R/JAK3/JAK1 mutations (Fig. 6B). Furthermore, PIM1 RNA and

protein levels were rapidly downregulated in a HOXA9+ PDX sample with active JAK-STAT

signaling after treatment with the JAK1 inhibitor ruxolitinib (Fig. 6C). This provided further

evidence for a functional signaling network of PIM1 regulation downstream of JAK-STAT signaling.

Exploiting this observation, we next sought to determine whether dual inhibition of PIM1 and JAK1

would be beneficial in JAK/STAT mutant T-ALL samples. Here we observed a synergistic response in

JAK3 mutant T-ALL samples when treated with a JAK kinase inhibitor (ruxolitinib) in combination

with a PIM1 inhibitor (AZD1208) within ex vivo cell culture and a significant attenuation in

leukemia burden in vivo using a patient derived xenograft sample (Fig. 6D,E). Taken together,

these data suggest that the ectopic expression of HOXA9 co-binds with STAT5 to enhance the JAK-

STAT and targeting of both PIM1 and JAK1 provides a strong therapeutic benefit.

Chromatin accessibility by ATAC-seq analysis reveals a role for AP-1 activation

To further investigate and identify additional factors and mechanisms underlying the JAK3/HOXA9

cooperation, chromatin accessibility was assessed at a global level using ATAC-seq comparing

JAK3(M511I) to JAK3(M511I)/HOXA9 leukemia. This revealed significant changes of chromatin

architecture with appearing peaks associated with upregulated gene expression and loading of

STAT5 and HOXA9 (Fig. 7A,B). Surprisingly, many upregulated sites were neither bound by STAT5

or HOXA9. We interpreted this finding as activation by secondary events downstream of activated

STAT5/HOXA9 signaling. Motif scanning using iCis-target revealed these peaks were highly

enriched for Fos and Jun motifs (Fig. 7C). RNA-seq data further confirmed increased levels of

members of the AP-1 complex including Fos and Jun (Fig. 7D) and increased expression of AP-1

target genes (Fig. 7E). Whilst some of these target genes such as FasL were bound by STAT5 and

HOXA9, many others such as App, Klf4 and Csf1 had very weak or no binding (Fig. 7F) and

represent examples of genes activated by AP-1 independent of STAT5.




Discussion

The development of T-cell acute lymphoblastic leukemia (T-ALL) is a step-wise process via the

accumulation of somatic mutations (36). Whilst we and others have identified and confirmed

various important driver mutations in T-ALL, this has often been in the context of a single

oncogene, either within transgenic (e.g. TLX1 (37)) or bone marrow transplant mouse models (e.g.

JAK3(M511I) (13)). However, patients present with multiple mutations at diagnosis and we

therefore hypothesized that mutations that significantly associate with one another are potentially

cooperating events in driving T-ALL. In a recent analysis, significant associations were found

between mutations in the IL7R-JAK signaling pathway and epigenetic regulators (5). Here we

extend this analysis and find that those cases with JAK3 mutations are significantly associated with

cases designated HOXA+. In particular, we show that HOXA9 expression levels are significantly

elevated compared to either HOXA10 or HOXA11 in HOXA+ cases and co-expression of HOXA9 and

JAK3(M511I) leads to the rapid development of leukemia in vivo.

In our initial mouse model, the developing leukemia in vivo was characterized by overt myeloid and

lymphoid expansion due to the constitutive expression of both JAK3(M511I) and HOXA9 within the

HSPCs. Traditionally, the modeling of T-cell leukemia in mice and the role on oncogenes has often

used transgenic mice using tissue specific promotors to limit the expression within developing

lymphoid cells (e.g. CD2-Lmo2 (38), Lck-TLX1 (37)). However, generating transgenic knock-in

mice remains technically laborious and time consuming. To this end, we developed a novel

retroviral expression strategy for lineage specific expression using the bone marrow transplant

system that is compatible with the numerous well characterized Cre-mice now available, and we

demonstrated the efficacy of this novel retroviral vector to rapidly generate mouse T-ALL models.

Moreover, this system allows a more physiological comparison of cellular competition between wild

type and transgene expressing cells during differentiation and potential leukemia development.

From a mechanistic perspective, the integrated ChIP-seq and RNA-seq of the JAK3(M511I)/HOXA9

driven leukemias revealed that HOXA9 expression leads to a significant amplification of the STAT5




transcriptional output, and to addiction of the leukemia cells to STAT5 activation. This reconfirms

STAT5 as a major central player in T-ALL pathogenesis and identifies STAT5 target genes (such as

PIM1) as potential targets for therapy. PIM1 is a constitutive serine threonine kinase over expressed

in a number of cancers and regulates a number of different oncogenic processes including cell

survival and apoptosis making it an attractive target for inhibition (39). Indeed, PIM1 inhibition has

been shown to be effective in a triple negative breast cancer model in vivo (40), and more recently

also in T-ALL cell lines and human T-cell leukemia virus type 1 (HTLV-1)–associated adult T-cell

leukemia and T-cell lymphoma (ATL) (41,42). Secondly, HOXA9 through co-binding with STAT5

also leads to the upregulation of Fos and Jun and AP-1 target genes. Members of the AP-1 complex

are over expressed in numerous lymphoid malignancies including T-cell leukemia and has been

recently reviewed (43) and therefore could further contribute to the enhanced leukemogenesis. The

addition of HOXA9 with JAK3 mutations thereby upregulates the AP-1 complex at the

transcriptional levels directly rather than the canonical AP-1 activation by the upstream MAPK-

signaling pathway.

HOXA9 has a well established role in both hematopoiesis and leukemia. Hoxa9 loss of function

studies in mice show deficits in committed myeloid, erythroid and B-cell progenitors and

perturbations in T-cell development (44,45). Conversely, over expression of HOXA9 in transgenic

mice using lymphoid specific regulatory elements results in mild CD8 expansion (46). Early

evidence for HOXA9 in the development of leukemia was the identification of recurrent

translocation t(7;11) producing the NUP98-HOXA9 fusion (47) with subsequent studies showing

MLL-fusions and NPM1 also resulting in HOXA9 over expression (48). Early studies of ectopic

Hoxa9 expression in mice showed weak leukemogenic potential with either AML developing after a

long latency using retroviral transduction/transplantation (49,50) or a T-cell malignancy (preT

LBL) using a transgenic approach (51). Our bone marrow transplant studies presented here showed

high leukemia penetrance for MSCV-based HOXA9 expression and reconciles with a recent study

using a similar retroviral strategy (52) highlighting the role of promotor strength and viral titers in

dictating leukemia latency.




Nevertheless, what is now well understood is that the ability for HOXA9 to drive rapid leukemia

development and cellular transformation is dependent on the expression of different cofactors. Most

of these co-factors belong to the “three loop amino-acid-loop extension” (TALE) homeobox family

(48). Early work over a decade ago showed an essential role for Meis1 as a co-factor of Hoxa9

driven AML in mice (49,53) and since this time many additional HOXA9 interacting factors have

been identified including STAT5 in AML (33,48). Indeed, STAT5B activation is important in AML

and loss of STAT5B decreases the leukemia-initiating cell frequency in Hoxa9/MN1-induced

leukemias (54). In our current work, we find a striking growth advantage and cellular

transformation of HSPC cells transduced with JAK3(M511I) and HOXA9 that is phenocopied by

constitutive active STAT5B(N642H) co-expressed with HOXA9. Moreover, in our BMT model we see

the rapid development of both AML and T-ALL clones in vivo showing that sustained STAT5

activation and HOXA9 expression are bona-fide cooperative events in driving leukemia development

in both lineages. Significantly, the levels of Meis1 expression are very low in our STAT5/HOXA9

driven leukemias and together with the PCA analysis segregating Hoxa9/Meis1 AML away from

both JAK3(M511I)/HOXA9 AML and T-ALL suggest that Meis1 is not important in our

JAK3(M511I)/HOXA9 leukemia models. Furthermore, we showed that the resulting

JAK3(M511I)/HOXA9 driven leukemia were sensitive to tofacitinib suggesting these leukemias

remain dependent on the STAT5 signaling and is not a purely HOXA9 driven leukemia. Taken

together, our data identify STAT5 as a novel co-factor of HOXA9 in T-ALL.

Interestingly, transformed progenitor cells expressing Hoxa9 and Meis1a but not hoxa9 induced the

expression of Flt3 and Il7R (50), both of which activate STAT5 downstream suggesting that our

leukemia model in part bypasses the requirement of Meis1 via direct activation of STAT5. The link

between HOX cluster activation and activated STAT signaling is also observed in acute

megakaryoblastic leukemia with HOXr cells also carrying an activating mutation of the MPL

receptor driving the phosphorylation of STAT5 (55). Similarly, MLL intragenic abnormalities in

AML upregulate HOXA9 and also correlated with FLT3 mutations that activate STAT5 (56).




In conclusion, we show that HOXA9 and JAK3/STAT5 are bona fide cooperating factors in driving

leukemia development and that the cooperation is situated at the transcriptional level where STAT5

and HOXA9 co-occupy similar genomic regions. Furthermore, our results have not only significance

for T-ALL but also for other leukemias where activated STAT5 would potentially cooperate with

HOXA9.

Methods

Expression plasmids and Retrovirus production

The MSCV-JAK3 (M511I)-IRES-GFP retroviral vector is as previously described (13). The human

HOXA9 cDNA was synthesized by Genscript and cloned into the MSCV-IRES-mCHERRY vector. The

lox66/71-IRES GFP vector was generated by cloning in the lox66/71 sites directly into the multiple

cloning site of MSCV-GFP retroviral vector. Retrovirus production using 293T cells and retroviral

transduction of Ba/F3 cells and hematopoietic lineage negative cells were performed as previously

described (13,57).

RNA extractions and qPCR

RNA was extracted from tissue and cells using either the illustra RNAspin Mini Kit (GE Healthcare

Life Sciences) or the Maxwell 16 LEV Simply RNA purification kit (Promega) as per manufacturers

instructions. cDNA synthesis was carried out using GoScript (Promega) and real time quantitative

performed using the GoTaq qPCR master mix (Promega) with the ViiA7 Real Time PCR system

(Applied Biosystem). Quality control, primer efficiency and data analysis was carried out using

qbase+ software (Biogazelle). All gene expression was normalized using two housekeeping

reference genes. Primers used for qPCR are listed in Supplementary Table S4.

RNA-Sequencing

The single-end RNA-sequencing data was first cleaned (i.e. removal of adapters and low quality

parts) with the fastq-mcf software after which a quality control was performed with FastQC. The




reads were then mapped to the Mus Musculus (mm10) genome with Tophat2. To identify the gene

expression HTSeq-count was used to count the number of reads per gene. These read count

numbers were then normalized to the sample size. Differential gene expression analysis was

performed with the R-package DESeq2 (Supplementary Table S2). Pathway enrichment and other

geneset enrichment analyses were done with GSEA. GSEA datasets were downloaded from:

http://download.baderlab.org/EM_Genesets/ The data is accessible through GEO Series accession

number GSE109653 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE109653).

Western blotting

Cells were lysed in cold lysis buffer containing 5 mM NA3VO4 and protease inhibitors (Complete

EDTA -free, Roche). The proteins were separated on NuPAGE NOVEX Bis-Tris 4%-12% gels (Life

Technologies) and transferred to PVDF membranes. Subsequent Western blot analysis was

performed using antibodies directed against phospho-JAK1/JAK3 (SC-16773), STAT5 (SC-1081),

JAK1 (Millipore 05-1154), phospho-STAT5, (Cell Signaling #9359), HOXA9 (Millipore 07-178) and

beta-actin (Sigma A1978). Anti-phospho-JAK1 antibody was used to detect both phosphorylated

JAK1 and JAK3. Western blot detection was performed with secondary antibodies conjugated with

horseradish peroxidase (GE Healthcare). Bands were visualized using a cooled charge-coupled

device camera system (ImageQuant LAS-4000, GE Health Care).

Murine bone marrow transplantation

Male C57BL/6 or BALB/c mice were purchased from Charles River Laboratories. Male mice were

sacrificed and bone marrow cells were harvested from femur and tibia. Lineage negative cells were

enriched (STEMCELL Technologies) and cultured overnight in RPMI with 20% FCS with IL3

(10ng/mL, Peprotech), IL6 (10ng/mL, Peprotech), SCF (50ng/mL, Peprotech), and penicillin-

streptomycin. The following day, 1x106 cells were transduced by spinoculation (90 min at

2500rpm) with viral supernatant and 8 μg/mL polybrene. The following day, the cells were washed

in PBS and injected (1×106 cells/0.3 mL) into the lateral tail vein of sub-lethally irradiated (5 Gy)

syngeneic female recipient mice. Mice were housed in IVC cages and monitored daily. End point




analysis for disease free survival was defined at WBC >30,000 cells/mm3. In vivo treatment of mice

with tofacitinib was carried out as previously described (13).

Ex vivo cell culture transformation assay

The Lin- cells were spin infected with two retroviral constructs leading to lineage negative cells with

all four combinations – non-transduced, single transduced JAK3(M511I) only, HOXA9 only, and

double transduced JAK3(M511I)/HOXA9. The day after transduction, the cells were split into

media containing only 20% FBS/RPMI, removing the SCF, IL6 and IL3 that was present. The cells

were then left to grow in this cytokine free media and split when appropriate. There was no sorting

of cells prior to cytokine withdrawal.

In vivo and ex vivo treatment of patient derived xenograft (PDX) samples

PDX samples were transplanted in 8 week old NSG mice through tail vain injection. Human

leukemic cell expansion was monitored through human CD45 staining on blood samples. Single

cells were isolated from the spleen, which at time of sacrifice contained > 85% human CD45+

cells. Spleen cells were seeded in 96-well plate (5 x 105 cells/well) and incubated with vehicle

(DMSO) or inhibitor. Cell viability was assessed 48 hours using ATP-Lite. CompuSyn was used to

calculate the combination index (CI). For in vivo treatment studies, the XC65 was transduced

overnight with lentivirus pCH-SFFV-eGFP-P2A-fLuc. The GFP positive cells were then sorted using

the S3 Sorter (Bio-Rad) and re-transplanted back into recipient NSG mice. Upon confirmation that

XC65 was greater than >95% GFP positive, leukemic cells were isolated from the spleen and

reinjected into a larger cohort of NSG mice for acute 7-day in vivo treatment. Ruxolitinib (HY-

50858, MedChem Express) was dissolved in 0.5% methylcellulose and AZD1208 dissolved in 50%

PEG400/0.5% methylcellulose and both were administered by oral gavage.

Flow cytometry analyses

Single-cell suspensions were prepared from peripheral blood, bone marrow, spleen, thymus and

lymph nodes. Cells were analyzed on either a FACS Canto flow cytometer (BD Bio-sciences) or




MACSQuant Vyb (Miltenyi) with the following antibodies: CD3e, CD8a, CD45, CD4, CD45R, CD44,

CD25, CD11b, Gr1/Ly6G, CD127, CD117 and TCRbeta. These antibodies were conjugated to

phycoerythrin (PE), allophycocyanin (APC) or peridinin chlorophyll protein Cyanine5.5 (PerCP-

Cy5), VioBlue, PE-Vio770, or APC-Vio770. For intracellular phospho-STAT5 staining, cells were first

fixed in a 1:1 ration with IC fix buffer (Invitrogen #00-8222-49) and then permeabilized in ice cold

100% methanol. Staining was then carried out in PBS/2% FCS with 1:20 dilution pSTAT5-APC

(eBioscience #4334964) or IgG1 kappa isotype (eBioscience #17-4714-41) control. Data were

analyzed with the FlowJo software (Tree Star).

ChIPmentation, ChIP-seq and CUT&RUN ChIP-Seq

ChIPmentation ChIP-seq on mouse leukemic samples was carried out as described with

modifications (58). ALL-SIL cells were originally sourced from DSMZ, Germany in 2004 and

authenticated using STR analysis and PCR for the NUP214-ABL1 fusion in 2017. Briefly, 20-40

million cells were washed in PBS and cross-linked with 1% formaldehyde for 10 min at room

temperature and then quenched by addition of glycine (125 mM final concentration). For Nuclei

isolation, cells were resuspended in 1X RSB buffer (10 mM Tris pH7.4, 10 mM NaCl, 3 mM MgCl2)

and left on ice for 10 min to swell. Cells were collected by centrifugation and resuspended in

RSBG40 buffer (10 mM Tris pH7.4, 10 mM NaCl, 3 mM MgCl2, 10% glycerol, 0.5% NP40) with

1/10 v/v of 10% detergent (3.3% w/v sodium deoxycholate, 6.6% v/v Tween-40). Nuclei were

collected by centrifugation and resuspended in L3B+ buffer (10 mM Tris-Cl pH 8.0, 100 mM NaCl,

1 mM EDTA, 0.5 mM EGTA, 0.1% Na-Deoxycholate, 0.5% N-Lauroylsarcosine, 0.2% SDS).

Chromatin was fragmented to 200-400 bp using 20 cycles (30 sec on, 30 sec off, High Setting) using

the Bioruptor (Diagenode). Chromatin immunoprecipitation was carried out overnight in the

presence antibodies (H3K27me3 07-449 Millipore or H3K27me3 Cat#39155 Active Motif

Lot#06517018; H3K4me3 Cat#39159 Active Motif Lot#15617005, H3K4me1 Cat#39297 Active

Motif Lot#01714002 Active Motif, H3K27Ac Cat#4729 Lot#GR251958-1 or H3K27Ac Cat#39134

Active Motif Lot#20017009): STAT5 Cell Signaling Cat#8363 Lot9; HOXA9 Sigma Atlas Antibodies




Cat#HPA061982 Lot#86503) coupled to magnetic protein A/G beads (Millipore). Tagmentation

and library preparation was carried out as described (http://www.medical-

epigenomics.org/papers/schmidl2015/). DNA was purified using triple-sided SPRI bead cleanup

using 1.0X; 0.65X; 0.9X ratios (Agencourt AMPure Beads, Beckman Coulter) and analyzed by

Illumina Hiseq 2000 (Illumina, San Diego, CA, USA). Raw sequencing data were mapped to the

human reference genome (GRCh37/h19) using Bowtie. Peak calling was performed using MACS

1.4. A custom Python script was used to generate the centered heatmaps. To find the enriched

motifs in the peaks we made use of i-CisTarget (https://gbiomed.kuleuven.be/apps/lcb/i-

cisTarget/). For the patient sample XE89, CUT&RUN ChIP seq was carried out as described(59)

using HOXA9 Sigma Atlas Antibodies Cat#HPA061982 Lot#86503.

Ethical aspects

Animal experiments were approved by the Ethical committee on animal experimentation of KU

Leuven. Human leukemia samples were collected at the University Hospital Leuven (UZ Leuven)

after approval by the ethical committee. Informed consent was obtained from all patients or their

parents, according to the Declaration of Helsinki.

Acknowledgements

This work was supported by grants from KU Leuven (PF/10/016 SymBioSys), FWO-Vlaanderen,

Foundation against Cancer, Swiss Bridge Foundation and an ERC-consolidator grant (to J.C.).

MVDB holds a SB Fellowship of the Research Foundation-Flanders. D.V and S.B. were supported by

a fellowship from FWO-Vlaanderen, A.D. was supported by a fellowship from Kom op Tegen

Kanker.




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Figure Legends

Figure 1. JAK3 mutations and ectopic expression HOXA9 are significantly associated in

clinical T-ALL cases. (A) Frequency of IL7R-JAK-STAT mutations in 155 T-ALL cases (Vicente

Dataset) and of 264 T-ALL cases (Liu Dataset) within different defined transcription factor

subgroups. Dotted line denotes average % of IL7R-JAK-STAT mutations across entire data set. (B)

Association between JAK3 mutations only and defined transcription factor subgroups within the

Vicente and Liu datasets. (C) RNA-seq FPKM levels for JAK3 and HOXA9 in the Liu dataset. (D)

Real time qPCR of primary T-ALL samples for HOXA9, HOXA10 and HOXA11.

Figure 2. Co-expression of JAK3(M511I) and HOXA9 transforms hematopoietic progenitor

cells and leads to rapid leukemia development in vivo. (A) Schematic of the two retroviral

expression vectors used in the transduction of murine hematopoietic stem progenitor cells (HSPCs)

for the ex vivo transformation assay and bone marrow transplant model. Dual transduction results in

single, double and non-transduced cells for direct clonal competition analyses ex vivo and in vivo.

(B) Cell proliferation of HSPCs transduced with both JAK3(M511I) and HOXA9 over 20 days in the

absence all cytokines with matching FACS profiles. (C) Cell proliferation of HSPCs transduced with

both STAT5(N642H) and HOXA9 over 20 days in the absence all cytokines with matching FACS

profiles. (D) White Blood Cell counts (WBC) in recipient BALB/C mice that received lin- cells

transduced with JAK3(M511I) and HOXA9 retrovirus or JAK3(M511I) only retrovirus. (E) Clonal

analysis by FACS for GFP and mCHERRY reporters representing JAK3(M511I) and HOXA9

respectively at injection start and end-stage. (F) Kaplan-Meier disease free survival (DFS) for

JAK3(M511I)/HOXA9 mice (Median DFS = 25 days, p-value < 0.0001), JAK3 (M511I) alone

(Median DFS = 126.5 days); JAK3(M511I)/TAL1 (Median DFS = 144.5 days). DFS determined by

WBC <30,000 cells/mm3. (G,H) Reciprocal experiments in C57BL/6 mice co-expressing

JAK3(M511I) and HOXA9 for WBC and clonal analysis by FACS for GFP and mCHERRY reporters




representing JAK3(M511I) and HOXA9 respectively at injection start and end-stage. (I) Kaplan-

Meier disease free survival for STAT5B(N642H)/HOXA9 (Median DFS = 12 days);

JAK3(M511I)/HOXA9 (Median DFS = 40 days); JAK3(M511I) (Median DFS = 150 days) and

HOXA9 (Median DFS = 182 days) only recipient C57BL/6 mice.

Figure 3. Constitutive expression of JAK3(M511I) and HOXA9 leads to development of both

AML and T-ALL in vivo. (A) Representative peripheral blood FACS of expanding leukemic clones

expressing JAK3(M511I) and HOXA9. (B) Histopathology and for MPO and CD3 staining of bone

marrow, spleen and lymph nodes (Scale bar Bone marrow = 40 μM, Spleen and lymph nodes = 80

μM). (C) Western blot analysis spleen and bone marrow leukemic cells. (D,E) Ex vivo growth assay

of leukemia clones and matching FACS with JAK3(M511I) and HOXA9 levels measured by GFP and

mCHERRY expression respectively. (F) Schematic of in vivo treatment of primary bone marrow

transplant recipients. Mice were treated daily for 20 days from day 14 post-transplantation with

either 40 mg/kg/day tofacitinib (oral gavage) or vehicle control. (G) Phospho-flow cytometry of

pSTAT5 levels in JAK3(M511I)/HOXA9 leukemic cells in peripheral blood after three days

Tofacitinib treatment compared to vehicle control. (H) Change in white blood cell count before and

after the treatment. (I) Total white blood cell counts at day 35. (J) Spleen weights of tofacitinib vs.

vehicle treated mice at day 35. (K) Ki67 staining of spleens.

Figure 4. Novel retroviral strategy to restrict JAK3(M511I)/HOXA9 expression to developing

lymphoid cells results in significantly decreased leukemia latency. (A) Schematic of new

retroviral vector incorporating anti-parallel lox66/71 sites for unidirectional inversion in the

presence of Cre-recombinase. (B,C) Cell proliferation of Ba/F3 or Ba/F3_Cre recombinase cells

transduced with the retroviral constructs encoding JAK3(M511I) or JAK3(M511I)_P2A_HOXA9 in

between the lox66/71 sites. (C)Western blot analysis of the transduced Ba/F3. (D) Model of

improved BMT utilizing Cre-donor mice in combination with novel retroviral vector. (E) Kaplan-




Meier disease free survival cure for mice with hCD2-iCre driven inversion of JAK3(M511I) and

HOXA9 and the cell surface phenotype frequency for the resulting leukemia. (DFS = WBC <30,000

cells/mm3). (F) Genomic PCR detection of the inversion of the retroviral construct.

Figure 5. HOXA9 and STAT5 co-occupy similar genomic regions and increases JAK/STAT

signaling. (A) Volcano plot of differential gene expression analysis of age matched CD8+ T-ALL

JAK3(M511I)/HOXA9 vs. JAK3(M511I) only leukemia cells. (B) JAK3(M511I)/HOXA9 leukemia

ChIP-seq density heat maps centered on STAT5 signal alongside HOXA9 and associated H3K27Ac,

H3K27me3, H3K4me3 and H3K4me1 histone marks. (C) Frequency of STAT5/HOXA9 co-occupied

DNA regions with promotors (H3K4me3+ve) and Enhancers (H3K27Ac+ve/H3K4me3-ve). (D)

Individual ChIP-seq tracks for canonical STAT5 target genes Pim1 and Bcl2. (E) GSEA analysis of

RNA-seq differentially expressed genes for JAK3(M511I)/HOXA9 vs. JAK3(M511I) and loss of the

repressive H3K27me3 chromatin marks or gain in H3K27ac activation mark. (F) Transcription

factor loading of STAT5 and HOXA9 in upregulated, downregulated or genes that do not change.

(G) Heat map and GSEA pathway of JAK/STAT signaling pathway genes in CD8+ control T-cell vs.

JAK3(M511I) vs. JAK3(M511I)/HOXA9 leukemia.

Figure 6. PIM1 expression in increased in JAK3(M511I)/HOXA9 leukemia (A) qPCR analysis of

Pim1 levels in normal CD8+ T-cells, CD8+ JAK3(M511I) and CD8+ JAK3(M511I)/HOXA9 cells

(B) Analysis of clinical T-ALL samples for PIM1 expression levels and those cases with

JAK3/IL7R/STAT mutations (Red dots). (C) Analysis of PIM1 RNA and protein expression in

HOXA9 positive PDX samples ex vivo after 1 μM Ruxolitinib over 6 hours. (D) Combinatorial

treatment with small molecular inhibitors against JAK kinases (Ruxolitinib) and PIM1 kinase

(AZD1208) in DND41(IL7R mutant) and JAK3 mutant PDX samples. (E) In vivo combination

treatment with Ruxolitinib and AZD1208 (7 days) of the JAK3 mutant XC65 PDX sample.




Figure 7. ATAC-seq reveals role for AP-1 complex in JAK3(M511I)/HOXA9 driven leukemia.

(A) GSEA results showing appearing and disappearing ATAC-seq peaks correlated to up-regulated

and down-regulated genes. (B) HOXA9 and STAT5 bound genomic regions relative to appearing

and disappearing ATAC-seq peaks. (C) iCis-Target results for DNA-binding motifs within the

appearing ATAC-seq peaks. (D) Relative expression of members of the AP-1 complex including Fos,

Jun, Jund and FosL2. (E) Relative expression of AP-1 target genes in normal CD8+ cells and

JAK3(M511I) or JAK3(M511I)/HOXA9 leukemias. (F) ChIP-seq analysis tracks for AP-1 targets such

as FasL (bound by STAT5 and HOXA9) and Csf1, App and Klf4 not bound by STAT5 or HOXA9 (all

track heights =0-100).




JAK3 wild type JAK3 mutant(n=130) (n=25)

HOXA+ p = 0.018TAL1/LMO2+ p = 0.002

HOXA+

TLX1/3+TAL1/LMO2+

12%15%

32%

24%36%

32%41%8%

XB37 X1

2XB

47 X15

X17

XB41 X1

3X1

0XB

34 X09

XA32

XC57 X1

61Q

9483

9U9H

6086

3IO

623

X11

7Q05

389E

382L

02468

10

50

100

150

CN

RQ

mut JAK3

HOXA10HOXA9

HOXA11

OTHER

A B

0 50 100

TAL1+(n=54)

TLX1/TLX3+ (n=41)

HOXA+(n=10)

Immature (n=15)

IL7R-JAK-STAT5 pathway mutation (Vicente dataset)

Percentage

FIGURE 1

28

0 22 50 100

NKX2_1(n=14)

TAL1/TAL2(n=95)

TLX1/TLX3+(n=72)

HOXA+(n=33)

LMO1/2(n=10)

LMO2_LYL1(n=18)

IL7R-JAK-STAT5 pathway mutation (Liu dataset)

Percentage

HOXA+ TLX1/3

LMO2LYL1

TAL1/TAL2/LMO2

Other

JAK3 wild type JAK3 mutant(n=244) (n=20)

HOXA+ p = 0.006TAL1/TAL2/LMO2+ p = 0.0047

C D

HO

XALM

O2_

LYL1

NKX

2_1

TLX1

TLX3

Unk

now

nTA

L1/2

LMO

1/2

0

20

40

60

0

20

40

60

80

RN

Aseq

FPK

M

RN

Aseq

FPK

M

HOXA9 JAK3

HO

XA

LMO

2_LY

L1N

KX2_

1

TLX1

TLX3

Unk

now

nTA

L1/2

LMO

1/2

27%11%

13%

7%

35% 25%

10%20%42%

10%

HOXA+OTHER

TLX1/3+

TAL1/LMO2+

HOXA+TLX1/3

LMO2LYL1

TAL1/TAL2/LMO2

Other










5’LTR 3’LTR5’LTR 3’LTRLox66 Lox71

GFP

JAK3(M511I)-P2A-HOXA9

CD2-Cre Donor

Retroviral spinfection of Lin- cells

Injection into sublethally irradiated wild type recipients

Leukemia Latency/Disease progression assessment

JAK3(M511I)-P2A-BFPJAK3(M5( 11I)-P) 2A-BFP

JJAK3(M5( 11I)-P) 2A-HOXA9

Days post-transplant

Dise

ase

Free

Sur

viva

l

0 100 200 3000

50

100 JAK3 (M511I)-P2A-BFP (n=12)

JAK3 (M511I)-P2A-HOXA9 (n = 13)

** p = 0.0014

23% Mixed CD8+/DN100% CD8+

77% CD8+

JAK3 (M511I)-P2A-BFP JAK3 (M511I)- P2A-HOXA9

0 2 4 6 80

2

4

6

8

10

Day (-IL3)

Cel

l Pro

lifera

tion

(Fol

d C

hang

e) JAK3(M511I)

0 2 4 6 80

2

4

6

8

10

Days (-IL3)

Cel

l Pro

lifera

tion

(Fol

d C

hang

e)

Ba/F3 Ba/F3_Cre

[JAK3(M511I-P2A-HOXA9]Lox66/71[JAK3(M5111I)-P2A-BFP]Lox66/71

phospho-JAK1phospho-JAK3

JAK3

phospho-STAT5

STAT5

HOXA9

GFP

b-actin

IL3:Cre:

- + + - - -

MSCV-JAK3(M

511I)

[JAK3(M

511I)-

P2A-BFP]L

ox66/71

[JAK3(M

511I)-

P2A-HOXA9]L

ox66/71

MSCV-JAK3(M

511I)

[JAK3(M

511I)-

P2A-BFP]L

ox66/71

[JAK3(M

511I)-

P2A-HOXA9]L

ox66/71

- - - + + +

JAK3(M511I)

[JAK3(M511I-P2A-HOXA9]Lox66/71[JAK3(M5111I)-P2A-BFP]Lox66/71

3’LTR3’LTRGFPDMLox LoxP

5’LTR JAK3 (M511I) P2A HOXA9HOXA9JAK3 (M( 511I) ) P2A

F1 F2R1 R2

M12

: JAK

3-P2

A-BF

P

M13

: JAK

3-P2

A-BF

P

M14

: JAK

3-P2

A-BF

P

M18

: JAK

3-P2

A-H

OXA

9

M20

: JAK

3-P2

A-H

OXA

9

M57

: JAK

3-P2

A-H

OXA

9

F1+R1

F2+R2

A B

C D

E F

FIGURE 4Research. on January 15, 2021. © 2018 American Association for Cancercancerdiscovery.aacrjournals.org Downloaded from



Rank in Ordered Dataset

Rank in Ordered Dataset

Dataset: CD8+ vs. JAK3(M511I)Geneset: JAK-STAT SIGNALING PATHWAY KEGG HSA04630

0 17,500

NES: 1.525p-value: 0.007FDR q-value:0.135

Dataset: JAK3(M511I) vs. JAK3(M511I)+HOXA9Geneset: JAK-STAT SIGNALING PATHWAY KEGG HSA04630

0 17,500

NES: 1.659p-value: 0.018FDR q-value:0.098

IL2RBIL7RPIK3R5IFNGR1JAK3IL4RPIM1STAT3IL21RPIK3R1SOCS3STAT4TYK2CBLBCREBBPIL10RBIL6RSTAT2PIK3CGBCL2L1SOS2SOCS1IL10RACISHIL12RB1CRLF2PIAS3OSMIFNGCSF2RAIL15RAIL3RAMYCIL2RASOCS2IL20RBCSF3RCSF2RBSPRY2SPRY4LIFLIFRIL13RA1CSF2IL5RAIL12AIL15EPORIL10IL22RA2IL6

CD8JA

K3(M51

1I)

JAK3(M

511I)

+HOXA9

FIGURE 5

A

-8 -6 -4 -2 0 2 4 6 80

5

10

15

203035404550

Log2 fold change

Log1

0(p-

adj)

HOXA9

B

summit+5

kb

-5 k

b

Scor

e-ra

nked

STA

T5 C

hIP-

seq

peak

s ce

nter

ed a

t the

sum

mits

STAT5 HOXA9 H3K27Ac H3K27me3 H3K4me3 H3K4me1

E

G

-2.0 2.00.0

Dataset: JAK3(M511I)/HOXA9 vs. JAK3(M511I)Geneset: Disappearing H3K27me3 peaks

Dataset: JAK3(M511I)/HOXA9 vs. JAK3(M511I)Geneset: Appearing H3K27Ac peaks

Rank in ordered dataset0 25,000

NES: 1.32p-value: 0.029FDR q-value: 0.032

Rank in ordered dataset0 25,000

D

NES: 1.43p-value: 0.018FDR q-value: 0.038

H3K27me3H3K4me3H3K4me1

STAT5

H3K27Ac


STAT5

H3K27Ac

HOXA9

[0-100][0-50][0-1000][0-200][0-100]

[0-100][0-50][0-1000][0-200][0-100][0-40]

JAK

3(M

511I

)JA

K3(

M51

1I)

+ H

OX

A9

5 kb

Pim1


STAT5

H3K27Ac


STAT5

H3K27Ac

HOXA9

[0-100][0-50][0-1000][0-200][0-100]

[0-100][0-50][0-1000][0-200][0-100][0-40]

JAK

3(M

511I

)JA

K3(

M51

1I)

+ H

OX

A9

Bcl2 Kdsr Vps4b

50 kb

UpDownNeutral

STAT5 Signal

-4kb -2kb +2kb +4kb

2

4

6

8

10

12

Sign

al (x

103 )

UpDownNeutral

HOXA9 signal

0.5

1

1.5

2

2.5

3

Sig

nal (

x104 )

-4kb -2kb +2kb +4kb

TSS

TSS

Promotors

Enhancers

STAT5/HOXA9 overlapping

peaks

80%

18%

summit

+5 k

b

-5 k

b summit

+5 k

b

-5 k

b summit

+5 k

b

-5 k

b summit

+5 k

b

-5 k

b summit

+5 k

b

-5 k

b

Other

C

F




Vehicle

Ruxo

AZDCom

bi0

1

2

3

4

BLI - absolute difference (Day 7 vs. Day1)XC65 - Day 7

VehicleRuxolitinib

(50 mg/kg/day)AZD1208

(30 mg/kg/day) CombinationR

OI (

x 10

9 ; p/s

)

6.0 x107

4.0x107

2.0x107

CORE ENRICHMENT

0

1

2

3

4Pi

m1

Expr

essi

on (C

NR

Q)

**

6

7

8

9

10

11

2091

93_a

t PIM

1 (R

MA,

Log

2) JAK3/JAK1/IL7R mutation

0

50

100

150 X10(CI = n/a)

0

50

100

150XC57

(CI = n/a)

0

50

100

150 XC65(CI = 0.53)

Viab

ility

0

50

100

150 X11(CI =- 0.29)

AZD1208Ruxolitinib

- + - +- - + +

- + - +- - + +

Viab

ility

Viab

ility

Viab

ility

AZD1208Ruxolitinib

- + - +- - + +

- + - +- - + +

JAK3 Mutant (PDX) JAK3 Wild Type (PDX)

0

50

100

150DND41

Prol

ifera

tion

AZD1208Ruxolitinib

- + - +- - + +

(CI = 0.16)

A B C

D

E

FIGURE 6

*

**

2h D

MSO

2h R

UXO

4h D

MSO

4h D

MSO

6h D

MSO

6h R

UXO0

0.5

1.0

1.5

PIM

mR

NA

(CN

RQ

)

DM

SO

RU

XO

DM

SO

RU

XO

DM

SO

RU

XO

2h 4h 6h

PDX (XE89) : HOXA9 positive

pSTAT5

PIM1

b-actin

CD8+ T-ce

ll

CD8+ JA

K3(M51

1I)

CD8+ JA

K3(M51

1I)/H

OXA9HOXA

TAL1

IMMATURE

TLX1

TLX3




FIGURE 7

0 25,000Rank in Ordered Dataset

Dataset: JAK3(M511I)/HOXA9 vs. JAK3(M511I)Geneset: Appearing ATAC-seq peaks

NES: 1.83p-value <0.001FDR q-value <0.001

0 25,000Rank in Ordered Dataset

Dataset: JAK3(M511I)/HOXA9 vs. JAK3(M511I)Geneset: Disappearing ATAC-seq peaks

NES: -1.69p-value <0.001FDR q-value <0.001

Jun (NES = 6.29)Fosl2 (NES = 6.34)

CD8+

JAK3(M

511I)

JAK3(M

511I)

/HOXA9

81012141618

Nor

mal

ized

Cou

nts Fosl2

81012141618

Nor

mal

ized

Cou

nts Fos

CD8+

JAK3(M

511I)

JAK3(M

511I)

/HOXA9

10

12

14

16

18

Nor

mal

ized

Cou

nts

Jun

10

12

14

16

18

Nor

mal

ized

Cou

nts Jund

CD8+

JAK3(M

511I)

JAK3(M

511I)

/HOXA9

CD8+

JAK3(M

511I)

JAK3(M

511I)

/HOXA9

STAT5HOXA9STAT5ATACATAC

JAK3(M511I)

JAK3(M511I)/HOXA9

CD8+

JAK3(M511I)

JAK3(M511I)/HOXA9

JAK3(M511I)

ATAC-seq

JAK3(M511I)/HOXA9

ATAC-seq

JAK3(M511I)

STAT5 ChIP

JAK3(M511I)/HOXA9

STAT5 ChIP HOXA9 ChIP

Disappearing ATAC-seq Peaks

Appearing ATAC-seq Peaks

Cebpb

Cebpa

Csf1 Fgl2Fas

LGzm

bApp

Klf40

5

10

15

Norm

aliz

ed C

ount

sAP1 target genes

Fasl

5 kb 50 kb

JAK3(M511I)JAK3(M511I)/HOXA9


JAK3(M511I)

JAK3(M511I)/HOXA9


Klf4

5 kb


JAK3(M511I)

JAK3(M511I)/HOXA9


App


JAK3(M511I)

JAK3(M511I)/HOXA9


5 kb

Csf1

A B

C

D E

F




Published OnlineFirst March 1, 2018.Cancer Discov Charles E. de Bock, Sofie Demeyer, Sandrine Degryse, et al. leukemia development.HOXA9 cooperates with activated JAK/STAT signaling to drive

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