To interrogate the transcriptional programs associated with aberrant expression of TLX1 and TLX3, we analyzed gene expression data from 82 human T-ALLs ⁶. This analysis revealed that TLX1 and TLX3 tumors share a common expression signature including 319 up-regulated and 450 down-regulated gene transcripts respectively (Fold change >2, P < 0.005) (Fig. 1a; Supplementary Table 1). Moreover, non negative matrix factorization (NMF) and Principal Component Analysis showed that TLX1 and TLX3 leukemias are highly related and clustered together separate from the rest of T-ALL samples in our series (Supplementary Figure 1). These results support a broadly overlapping role of TLX1 and TLX3 in the induction of T-ALL, however, TLX1 and TLX3 leukemias have been associated with different prognosis in some series ^1,7, suggesting important biological differences between these two groups. Consistently, comparative marker analysis identified a broad gene expression signature in TLX1 T-ALLs compared with TLX3 tumors (Supplementary Figure 2).

Next, we analyzed TLX1 ChIP-chip data from ALL-SIL, a T-ALL cell line expressing high levels of TLX1 as result of the t(10;14)(q24;q11) translocation³ and performed ChIP-chip analysis for TLX3 in HPB-ALL, a t(5;14)(q35;q32) TLX3-activating translocation positive line⁸. These analyses identified 2,236 promoters bound by TLX1 and 3,148 promoters occupied by TLX3 with a significance cutoff of P < 10⁻⁹ (Supplementary Table 2). Strikingly, 75% of TLX1 direct targets were also bound by TLX3 (Chi-square P < 0.001) (Fig. 1b). Finally, Gene Set Enrichment Analysis (GSEA) demonstrated a highly significant enrichment of genes whose promoter was bound by TLX1 and TLX3 in the expression signature associated with TLX1 and TLX3 leukemias (P < 0.001) (Fig. 1c) (Supplementary Table 3). Most notably, genes bound by TLX1 and TLX3 were characteristically downregulated in this group (Fig. 1c), strongly suggesting that TLX1 and TLX3 primarily function as transcriptional repressors in the pathogenesis of T-ALL.

We then used the ARACNe reverse-engineering algorithm^9,10 to generate a genome-wide T-ALL transcriptional network or T-ALL interactome (T-ALLi) using gene expression data from 228 T-ALLs. This analysis yielded a T-ALLi including 19,689 genes (nodes) connected via 471,824 interactions (edges) (Supplementary Figure 3). Notably, MYC target genes inferred in the T-ALLi were markedly enriched in MYC ChIP-chip direct target genes (74/252, Chi-square P = 2.5×10⁻⁵) supporting the soundness of this approach (Supplementary Figure 4). Analysis of TLX1 and TLX3 connected genes in this setting identified 325 candidate TLX target genes (Fig. 2a), including 70 TLX1- and TLX3- highly significant (P < 0.0001) ChIP-chip target genes (Chi-square P = 0.02) (Fig. 2b) and 117 genes differentially expressed (P < 0.0001) in TLX1- and TLX3- T-ALLs (Chi-square P < 0.001) (Fig. 2c).

Next, we defined the TLX-subnetwork (TLXi) as the space of the T-ALLi encompassing the 445 TLX1- and TLX3- direct target genes (ChIP-chip P < 0.0001) that are also differentially expressed in TLX1- and TLX3-expressing T-ALLs (P < 0.0001) and their most direct interconnections (Fig. 3a). The TLXi subnetwork retains the topological features of the TALLi. Thus, 411/445 (92%) of the genes were involved in at least one interaction, but only 8/445 (< 2%) showed 50 or more direct interactions (Fig. 3b)(Supplementary Table 4). Notably, and consistent with the role of TLX1 as transcriptional repressor TLXi genes transcripts were also characteristically downregulated by GSEA in a transgenic mouse model of TLX1-induced T-ALL¹¹ (Supplementary Figure 5). Moreover, GSEA analysis of the expression signatures induced by shRNA knockdown of TLX1 in ALL-SIL cells and of TLX3 in the HPB-ALL cell line demonstrated a high level of enrichment of genes in the TLXi among the transcripts upregulated upon inactivation of TLX1 and TLX3 respectively (Supplementary Figures 6 and 7).

Based on these results we proposed that the hierarchical regulatory structure of the TLXi subnetwork could reflect, at least in part, the functional hierarchy of TLX1- and TLX3- target genes involved in T-cell transformation. In this context, RUNX1, a critical transcription factor in hematopietic development¹² frequently mutated in acute myeloid leukemias^13-15 stood up as the single most highly interconnected hub in the TLXi (Fig.3b,c). ChIP analysis of TLX1 and TLX3 confirmed the binding of these transcription factors binding to the RUNX1 promoter (Supplementary Fig. 8). In addition, RUNX1 was significantly more interconnected in the TLXi-subnetwork than in the T-ALLi as a whole (Chi-square P = 2.14×10⁻¹³³) and stood up as one of the most prominent TLXi genes downregulated in mouse TLX1-induced T-ALLs (Supplementary Figure 5). Consistently, Master Regulator Analysis ^16,17 identified RUNX1 as one of the top most prominent master regulators of the transcriptional program associated with human TLX1 and TLX3 induced leukemias (Supplementary Table 5). The model that emerges from this analysis is a regulatory feedforward loop in which downregulation of RUNX1 by TLX1 and TLX3 would subsequently affect the expression of numerous other TLX target genes (Supplementary Fig. 9). To test this possibility we performed ChIP-chip analysis of RUNX1 direct targets in HPB-ALL cells. In this analysis we identified 308 high confidence RUNX1 target genes (P < 0.0001) (Supplementary Table 6). Strikingly, and in concordance with our network analysis, 50% of RUNX1 occupied promoters were also bound by TLX1 and TLX3 (Chi-square P < 10⁻¹⁵). Moreover, GSEA analysis of RUNX1 direct target genes showed a high level of enrichment of RUNX1 targets among the top transcripts downregulated in T-ALL cells expressing high levels of TLX1 or TLX3 (P = 0.05) (Fig. 3d).

These results suggest that RUNX1 could mediate, at least in part, some of the oncogenic effects of TLX1 and TLX3 overexpression. Consistent with this hypothesis, retroviral expression of RUNX1 in TLX1-positive (ALL-SIL) and TLX3 positive (HPB-ALL) cells resulted in impaired cell growth (Supplementary Figure 10) indicating a possible tumor suppressor role for RUNX1 in T-ALL. Mutation analysis of RUNX1 in T-ALL revealed the presence of RUNX1 mutations in 4/12 (33.3%) T-ALL cell lines and 5/114 (4.4%) T-ALL primary samples (Fig. 4a, Supplementary Tables 7 and 8). Interestingly, all ALLs identified in kindreds with FPDMM (platelet disorder, familiar, with associated myeloid malignancy, MIM ID #601399), a leukemia predisposition syndrome caused by mutations in RUNX1, happen to be T-ALLs ^18-20.

RUNX1 mutations found in T-ALL were heterozygous frameshift truncating mutations (3/10) and missense single nucleotide changes (6/10) (Fig. 4a,b). Notably, DNA sequence analysis of samples obtained at the time of clinical remission demonstrated the somatic origin of RUNX1 mutations in each of the 2 cases with available material (Fig. 4b). Moreover, five of these RUNX1 mutant alleles (pL29S, pH58N, pH78Y, pS114fs and pG138fs) have been previously described as oncogenic mutations in myeloid tumors ^21-25. Interestingly, all four RUNX1-mutated samples with available immunophenotype data showed a CD4 and CD8 double negative immunophenotype indicative of a very early arrest in T-cell maturation (Supplementary Table 9). Mapping of T-ALL RUNX1 mutations on the structure of the RUNX1 runt domain (PDB 1H9D) showed clustering of these amino acid substitutions in the DNA recognition interface of RUNX1 (Fig 4.c). Most strikingly, the RUNX1 H78 residue resides within a highly structurally conserved 16.9 Å diameter cavity frequently targeted by RUNX1 AML mutant alleles, which is adjacent to the DNA binding interface and is predicted to be disrupted in the RUNX1 H78Y T-ALL mutant (Fig 4.c). Next we tested the functional significance of the RUNX1 mutants predicted to be most structurally disruptive in luciferase reporter assays. In these experiments RUNX1 H78Y, RUNX1 S114fs and RUNX1 G138fs showed marked (5 fold) reductions in their capacity to activate a RUNX1-responsive CSF promoter reporter construct compared with wild type RUNX1(Fig. 4d).

Next we analyzed the transcriptional programs and disease kinetics of leukemias occurring in Lck-TLX1 transgenic Runx1 wild type mice and in Lck-TLX1 Runx1 heterozygous knockout animals. This analysis revealed that TLX1 Runx1 +/+ and TLX1 Runx1+/− share a common gene expression program consisting of 215 commonly differentially expressed genes (fold change > 2, P <0.001). However, and consistent with the presence of 50% non overlapping target genes between RUNX1 and TLX1, loss of one copy of Runx1 partially changes the transcriptional signature of TLX1-induced leukemias resulting in 540 differentially expressed transcripts between TLX1 Runx1+/+ and TLX1 Runx1+/− tumors (fold change > 2, P <0.001) (Supplementary Figure 11). Notably, and despite these transcriptional differences, Lck-TLX1 transgenic Runx1 wild type and Lck-TLX1 Runx1 haploinsuficient mice developed T-ALL with identical kinetics (Supplementary Fig. 12), suggesting that, in agreement with the prediction of our network analysis, the oncogenic effects of TLX1 are overlapping with the tumor suppressor activity of Runx1.

Overall, the integrative analyses presented here (Supplementary Figure 13) show a high level of functional overlap between TLX1 and TLX3 in T-cell transformation and identify RUNX1 as a tumor suppressor gene in T-ALL. Notably, this work highlights the power network analysis to decipher the structure of complex oncogenic circuitries and to identify critical genes and pathways involved in the pathogenesis of human cancer. Moreover, reverse engineering of signaling and transcriptional networks controlling phenotypes associated with distinct gene expression signatures such as cell transformation, metastatic potential or drug resistance could be exploited to identify new therapeutic targets.

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