Page 276 - Haematologica Vol. 109 - July 2024

LETTER TO THE EDITOR
Proteogenomic profiling uncovers differential therapeutic vulnerabilities between TCF3::PBX1 and TCF3::HLF translocated B-cell acute lymphoblastic leukemia
Although therapy escalation has led to improved 5-year overall survival rates for patients with B-cell acute lym- phoblastic leukemia (B-ALL), few effective treatment op- tions are available for relapsed and treatment-resistant disease. This applies particularly to specific subtypes of B-ALL, such as patients harboring TCF3 (formerly E2A) fusions. TCF3, encoding members of the E protein (class I) family of helix-loop-helix transcription factors, is a master regulator of B-cell development and is involved in several chromosomal translocations associated with lymphoid malignancies, such as the translocation t(1;19)(q23;p13.3), resulting in the TCF3::PBX1 fusion (5% of pediatric B-ALL) or the translocation t(17;19)(q22;p13) generating the TCF3::HLF fusion (~0.5% of pediatric B-ALL).2 Omics research for the discovery of novel treatment strategies in hematological cancer is still based largely on transcriptomics, although it is increasingly recognized that this does not translate well into the expression of proteins, which are the main targets of drugs and functional entities of biological pro- cesses. In this study, we comprehensively analyzed the proteomic landscapes of TCF3::HLF+ (N=6) and TCF3::PBX1+ (N=5) B-ALL employing primary patient-derived xenografts (PDX), liquid chromatography tandem mass spectrometry and data-dependent acquisition. Approval for the study reported here was granted by the Ethics Committee of the Medical Faculty of the Christian-Albrechts-University, Kiel, Germany (vote D508/13). We detected 6,863 proteins (6,123 without ≥2 missing values; Online Supplementary Table S1), which allowed a clear distinction between TCF3::HLF+ and TCF3::PBX1+ leukemia by unsupervised hierarchical clustering and principal component analysis (Figure 1A, B). Proteomic profiling proved a useful tool for prioritizing drug targets, as only 8.45% of the significantly differentially expressed genes (N=119/1,409; P<0.05 and minimal log2 fold change of ±1) previously detected by RNA sequencing2 showed differential expression on protein level confirmed by our proteomic analysis (Online Supplemenary Figure S1A). In contrast, 34.8% (N=119/342) of differentially regulated pro- teins detected by proteomics were also differentially ex- pressed on RNA level. As a proof-of-concept, we examined overlap of differentially expressed genes (cutoffs: P<0.05 and minimal log2 fold change of ±1) from RNA sequencing and proteomic analysis obtained from a previously pub- lished dataset of ETV6::RUNX1+ (N=9) and high hyperdiploid (N=18) primary ALL patient samples.3 While only 3.63% (N=82/2,262) of differentially expressed genes detected via RNA sequencing showed differential expression on protein
level, 92.13% (N=82/89) of differentially regulated proteins were also differentially expressed on RNA level (Online Supplementary Figure S1B).
In order to identify protein classes presenting specific ther- apeutic vulnerabilities, we performed gene set enrichment analysis (GSEA). We identified several gene sets enriched in either of the two subgroups (Figure 1C). RNA biology, mitochondrial translation and cellular respiration were the most prominent enriched gene sets for TCF3::HLF+ leuke- mia. In addition, strongly increased MYC expression and enrichment in MYC targets (Figure 1D, E) were detected, consistent with TCF3::HLF-driven activation of a MYC en- hancer cluster previously shown using extensive functional genomics.4 For TCF3::PBX1+ leukemia, immune response/ cell cycle, actin cytoskeleton, cell morphogenesis and RTK signaling were among the most prominent enriched gene sets (Figure 1C). We validated therapeutic vulnerabilities indicated by GSEA using high-throughput drug screening. To this end, we tested the sensitivity of leukemic cell lines (TCF3::HLF+: HAL-01; TCF3::PBX1+: 697 and RCH-ACV) and mononuclear cells from peripheral blood of three healthy donors against a drug library of over 600 Food and Drug Administration-approved or clinical trial phase I-IV an- ti-cancer drugs. TCF3::HLF+ and TCF3::PBX1+ leukemic cells showed a differential response towards 109 drugs based on the area under the curve (AUC) as response parameter (Figure 2A; Online Supplementary Table S2; AUC<0.8 and >1.2 as a cutoff). Compared to our previous screening of bioactive compounds (N=98) employing the PDX sam- ples,2 the cell lines showed similarly increased sensitivity towards compounds, such as BCL2 and mTOR inhibitors for TCF3::HLF+, and aurora kinase and polo-like kinase inhibitors for TCF3::PBX1+ B-ALL (Figure 2A). In addition, we identified novel potential drug targets. These included MDM2 and DNA/RNA synthesis for TCF3::HLF+ and micro- tubule/tubulin and cyclin-dependent kinases (CDK) for TCF3::PBX1+ leukemic cells (Figure 2A). In order to confirm these findings, we chose drugs from those groups, which did not affect normal peripheral blood cells (Figure 2B-E), and treated TCF3::PBX1+ (RCH-ACV) and TCF3::HLF+ (HAL-01) cells with half half-maximal inhibitory concentration (IC50), IC50 and double IC50 concentrations to investigate apoptosis induction. We demonstrated increased caspase 3/7 activity and apoptotic subG1 cells in TCF3::PBX1+ B-ALL in response to the microtubule/tubulin inhibitor ixabepilone and the CDK inhibitor SNS-032. For TCF3::HLF+ leukemic cells, we verified increased apoptotic cell death upon idasanutlin
Haematologica | 109 July 2024
2290