Aurore Touzart

T-cell acute lymphoblastic leukemia (T-ALL) accounts for one of the most aggressive hematological tumors associated with poor prognosis, notably for relapsed and refractory patients. Hence, an unmet medical need persists for T-ALL patients who fail to durably respond to conventional treatments. Herein, we report the antileukemic efficacy of anti-thymocyte globulin (ATG), a polyclonal cocktail of IgG enriched in antibodies directed against human thymocyte antigens. The treatment of preclinical models of T-ALL reveals a cytotoxic mechanism of ATG mediated by TNFα signaling. We demonstrate that ATG resistance may occur via the induction of negative regulators of TNFα-induced cell death and can be circumvented by SMAC mimetic birinapant ex vivo and in vivo. These results are validated in a series of prospective primary relapsed/refractory T-ALL samples. Here, we provide a strong rationale for clinically relevant ATG-based immunotherapy in combination with birinapant to treat T-ALL patients.

Supplementary Figure 1. Summary of the somatic alterations in the 39 ckAML samples. Supplementary Figure 2. Proportion of samples expressing the top pyjacker hits, for several AML cohorts profiled with RNA-seq. Supplementary Figure 3. Example rearrangements leading to gene activation and TP53 inactivation. Supplementary Figure 4. Rearrangements leading to MECOM monoallelic expression in sample 15KM20146. Supplementary Figure 5. Validation of the translocation t(1;3)(p36;q21) in sample 16KM11270 and of the breakpoints for the CDK6 enhancer duplication next to MNX1 in sample 15PB8708 by genomic PCR. Supplementary Figure 6. Deletion between the TCR beta locus and MNX1 leading to MNX1 expression. Supplementary Figure 7. Alternative rearrangements leading to MNX1 expression. Supplementary Figure 8. Differential gene expression analysis of MNX1 status in adult and pediatric AML. Supplementary Figure 9. Gene expressions of 25 selected cancer and hematological development associated genes differentially expressed under MNX1 activation. Supplementary Figure 10. Copy number alterations on chromosome 7 for samples profiled with scRNA-seq. Supplementary Figure 11. Reciprocal 4C. Supplementary Figure 12: Knockdown of MNX1 reduces tumor load of AML PDX cells in vivo.

ST1 Methods for enhancer hijacking detection ST2 Ranked and scored hematopoietic enhancers (based on ChIP-seq data for H3K27ac and P300 from K562, MOLM1 and Kasumi1) ST3 Pyjacker results for the 10 AML cell lines ST4 Pyjacker results for the 120 medulloblastoma samples ST5 Cis-x results for the 120 medulloblastoma samples ST6 Summary information for the 39 ckAML patient samples ST7 SNVs detected in WGS data for the 39 ckAML samples ST8 SVs detected in WGS data for the 39 ckAML samples ST9 CNAs detected in WGS data for the 39 ckAML samples ST10 Fusion transcripts detected in RNAseq data for the 39 ckAML samples ST11 Pyjacker results for the 39 ckAML samples ST12 Primers for qRT-PCR and validation of breakpoints (hg19) ST13 Summary of the MNX1 expression screens ST14 Summary of the MNX1-expressing samples profiled with WGS ST15 Mutations detected in the MNX1-expressing samples profiled with WGS ST16 Results of the differential expression analysis (adult and pediatric AML) ST17 TADs derived from HiC data of HSPCs ST18 Samples IDs from the TARGET-AML cohort used for the RNA-seq analysis ST19 Clinical characteristics of the patient samples from which were derived the PDX ST20 Primers and conditions used for 4C ST21 Antibodies used

TAL1 is one of the most frequently dysregulated oncogenes in T-cell Acute Lymphoblastic Leukaemia (T-ALL). However, the precise frequency and prognostic impact associated with its dysregulation remains unclear and is confounded by TAL1's diverse dysregulation mechanisms. TAL1 dysregulation is detected by TAL1 transcript quantification, though this technique may be subject to interference by TAL1 transcripts deriving from residual haematological cells that physiologically express high levels of the gene. We hypothesised TAL1 DNA methylation could provide a more reliable biomarker than TAL1 transcript quantification alone. We extensively studied TAL1 dysregulation in a large adult and paediatric T-ALL cohort (n = 401) and designed a TAL1 specific MS-MLPA assay to determine methylation levels. Whereas monoallelic TAL1 + T-ALL had homogeneous gene expression profiles, never expressed other driver oncogenes and were TAL1 hypomethylated (methylation ratio <0.4), biallelic TAL1 + T-ALL were enriched in expression of other driver oncogenes (TLX1, TLX3, HOXA), and had heterogeneous transcriptomes and TAL1 methylation levels. In PDX analysis, monoallelic TAL1 expression was stable, contrary to biallelic expression which mostly derived from residual non-malignant haematopoietic cells. Importantly, we report 5 novel TAL1 dysregulation mechanisms using long-read nanopore and OGM analysis, and show that TAL1 hypomethylation identifies TAL1 dysregulation, and is associated with worse prognosis.

Aberrant EPO expression might cooperate with EPOR amplification in AEL. A,EPO expression in all samples in TPM, with sample 15KM18875 with EPO overexpression highlighted in green. B, Proportion of samples with nonzero EPO expression in three AEL cohorts profiled with RNA-seq (47–49). C, Copy numbers (CN) and SVs on chromosome 7 (containing EPO) and chromosome 11 in sample 15KM18875. Copy number losses are indicated in blue, and gains in red. SVs are shown at the top, with arcs connecting breakpoints or lines indicating the chromosome of the other side of the breakpoint (for C, D, and F the colors of SVs indicate the orientation: blue for deletion, red for duplication, purple for inversion, and green for interchromosomal SV). D, A 300 kb circular piece of DNA containing EPO and a putative enhancer (highlighted in orange), with P300 and H3K27ac peaks in the erythroid cell line K562. E,EPOR expression in TPM in all samples, with sample 15KM18875 highlighted in green. F, Copy numbers and SVs on chromosome 19 for sample 15KM18875. chr, chromosome.

The homeobox genes GSX2 and MNX1 can be activated by atypical mechanisms. A,GSX2 expression in all samples in TPM, with sample 16PB5693 with GSX2 expression highlighted in green. B,MNX1 expression in all samples in TPM, with sample 15PB8708 with MNX1 overexpression highlighted in green. C, Variant allele frequencies in WGS and RNA-seq for an SNP in MNX1 in sample 15PB8708 (major allele frequency in blue and minor allele frequency in red). D, Circos plot showing CNAs and SVs in sample 16PB5693 for the chromosomes involved in a chromothripsis event. Copy number (CN) losses are indicated in blue, and gains in red. SVs are shown as arcs at the center, with interchromosomal breakpoints in green, duplications in red, deletions in blue, and inversion in purple. E, HiC data from HSPCs (19) and ChIP-seq data from myeloid cell lines in the region around GSX2. The putative enhancer is highlighted in orange, and the region in gray is deleted in sample 16PB5693. F, CNs and breakpoints on chromosome 7 for sample 15PB8708. In the chromosome ideogram, regions highlighted in red and teal correspond to the regions shown in (G), with matching colors. G, ChIP-seq tracks for P300 and H3K27ac in the myeloid cell lines MOLM-1 and Kasumi-1 in the region around MNX1 on the rearranged chromosome of sample 15PB8708. Enhancers of the CDK6 region are highlighted in orange. chr, chromosome.

ST1 Methods for enhancer hijacking detection ST2 Ranked and scored hematopoietic enhancers (based on ChIP-seq data for H3K27ac and P300 from K562, MOLM1 and Kasumi1) ST3 Pyjacker results for the 10 AML cell lines ST4 Pyjacker results for the 120 medulloblastoma samples ST5 Cis-x results for the 120 medulloblastoma samples ST6 Summary information for the 39 ckAML patient samples ST7 SNVs detected in WGS data for the 39 ckAML samples ST8 SVs detected in WGS data for the 39 ckAML samples ST9 CNAs detected in WGS data for the 39 ckAML samples ST10 Fusion transcripts detected in RNAseq data for the 39 ckAML samples ST11 Pyjacker results for the 39 ckAML samples ST12 Primers for qRT-PCR and validation of breakpoints (hg19) ST13 Summary of the MNX1 expression screens ST14 Summary of the MNX1-expressing samples profiled with WGS ST15 Mutations detected in the MNX1-expressing samples profiled with WGS ST16 Results of the differential expression analysis (adult and pediatric AML) ST17 TADs derived from HiC data of HSPCs ST18 Samples IDs from the TARGET-AML cohort used for the RNA-seq analysis ST19 Clinical characteristics of the patient samples from which were derived the PDX ST20 Primers and conditions used for 4C ST21 Antibodies used

Activation of MECOM and its homolog PRDM16 by a GATA2 enhancer. A, Expression of MECOM in all samples in TPM, ranked by expression of MECOM, in which samples 15PB19457 and 15KM20146 with breakpoints near MECOM are highlighted in green. B, Variant allele frequencies in WGS (DNA) and RNA-seq for SNPs in MECOM for sample 15PB19457 (major allele frequencies in blue and minor allele frequencies in red). C, Copy numbers (CN) and SVs on chromosome 3 for sample 15PB19457. Copy number losses are indicated in blue, and gains in red. SVs are shown as arcs at the top, in which the color indicates the orientation of the breakpoint: blue for deletion, red for duplication, and purple for inversion. In the chromosome ideogram, the three regions that are displayed with a zoom-in in (D) are highlighted in colors, with colors matching the arrows in (D). D, ChIP-seq tracks for P300 and H3K27ac in the myeloid cell lines MOLM-1 and Kasumi-1 in the region around MECOM for the rearranged chromosome of sample 15PB19457. The putative enhancer is highlighted in orange. E, Expression of PRDM16 in all samples, ranked by PRDM16 expression, in which sample 16KM11270 with a breakpoint near PRDM16 is highlighted in green. F, Variant allele frequencies in WGS (DNA) and RNA-seq for SNPs in PRDM16 in TPM for sample 16KM11270 (major allele frequency in blue and minor allele frequency in red). G, ChIP-seq tracks for P300 and H3K27ac in the myeloid cell lines MOLM-1 and Kasumi-1 in the region around PRDM16 on the rearranged chromosome of sample 16KM11270. The putative enhancer is highlighted in orange. chr, chromosome.

Supplementary Figure 1. Summary of the somatic alterations in the 39 ckAML samples. Supplementary Figure 2. Proportion of samples expressing the top pyjacker hits, for several AML cohorts profiled with RNA-seq. Supplementary Figure 3. Example rearrangements leading to gene activation and TP53 inactivation. Supplementary Figure 4. Rearrangements leading to MECOM monoallelic expression in sample 15KM20146. Supplementary Figure 5. Validation of the translocation t(1;3)(p36;q21) in sample 16KM11270 and of the breakpoints for the CDK6 enhancer duplication next to MNX1 in sample 15PB8708 by genomic PCR. Supplementary Figure 6. Deletion between the TCR beta locus and MNX1 leading to MNX1 expression. Supplementary Figure 7. Alternative rearrangements leading to MNX1 expression. Supplementary Figure 8. Differential gene expression analysis of MNX1 status in adult and pediatric AML. Supplementary Figure 9. Gene expressions of 25 selected cancer and hematological development associated genes differentially expressed under MNX1 activation. Supplementary Figure 10. Copy number alterations on chromosome 7 for samples profiled with scRNA-seq. Supplementary Figure 11. Reciprocal 4C. Supplementary Figure 12: Knockdown of MNX1 reduces tumor load of AML PDX cells in vivo.

Detection of enhancer hijacking in 39 ckAML samples. A, Schematic representation of the main sources of information used by Pyjacker: breakpoints, overexpression, monoallelic expression, and enhancers. B, Scatter plot of genes identified by Pyjacker in 39 ckAML samples as being potentially activated by genomic rearrangements in one or more samples, in which the x-axis shows the genomic location of the genes, and the y-axis shows the FDR. Gene names for the enhancer hijacking candidates are written in bold, and if a fusion transcript is detected, the fusion partner is named. chr, chromosome.