Jingjing Liu

Relapse remains a major barrier to survival in B-cell acute lymphoblastic leukemia (B-ALL). Both activation of B-cell signaling pathways and increased glucose consumption have been linked to chemoresistance and relapse risk. In this study, we connect these observations by showing that B-ALL cells with active mTOR signaling, marked by high phosphorylated ribosomal protein S6 (pS6+), are glucose dependent. Isotope tracing confirms that pS6+ cells are highly glycolytic and rely on glucose for de novo nucleotide synthesis. Uridine, but not other purines or pyrimidines, rescue pS6+ cells from glucose deprivation, highlighting uridine as essential for survival. Active mammalian target of rapamycin (mTOR) signaling in pS6+ cells drives de novo pyrimidine synthesis by activating carbamoyl phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase (CAD), which catalyzes the initial steps of de novo pyrimidine synthesis. Inhibiting signaling abolishes glucose dependency and CAD phosphorylation. Primary pS6+ cells express high levels of pyrimidine synthesis proteins, including dihydroorotate dehydrogenase (DHODH), the rate-limiting enzyme in pyrimidine synthesis. Increased DHODH expression correlates with relapse and poor event-free survival. Most B-ALL molecular subtypes exhibit DHODH activity. BAY-2402234, a DHODH inhibitor, effectively kills pS6+ cells in vitro, with 50% inhibitory concentration values correlating with pS6 signaling strength across 14 xenografts derived from patients with B-ALL. In vivo, DHODH inhibition prolongs survival and reduces leukemia burden in pS6+ B-ALL models. Together, these findings link active signaling to pyrimidine dependency and relapse risk, highlighting DHODH inhibition as a promising therapeutic strategy for chemotherapy-resistant B-ALL.

Effective therapies for high-risk medulloblastoma (MB), particularly MYC-driven Group 3 (G3) MB, remain elusive due to limited druggable mutations, poor blood–brain barrier (BBB) penetration, and rapid resistance. We developed SINBA (Synergy Inference by Data-driven Network-Based Bayesian Analysis), a systems biology framework that predicts synergistic, BBB-permeable drug combinations by identifying hidden drivers that sustain oncogenic programs without detectable genetic or transcriptional alterations. Integrating MB-specific gene networks, transcriptomic data, and drug–gene interactions, SINBA prioritized 32 candidate combinations, of which 19 were experimentally validated as synergistic. The MEK inhibitor mirdametinib and p38 inhibitor regorafenib emerged as the top brain-penetrant pair, suppressing G3 MB progression and extending survival in human xenograft and immunocompetent mouse models, with efficacy enhanced by low-dose radiation. Single-cell analysis revealed selective targeting of the developmental origins of G3 MB, accompanied by immune microenvironment reprogramming. These findings establish hidden-driver inference as a generalizable framework for rational drug combination discovery.

Group 3 medulloblastoma (G3MB) is a devastating disease of the central nervous system (CNS) that primarily affects infants and children. Chimeric antigen receptor (CAR) T cell therapy holds the promise to improve outcomes for CNS malignancies, but few studies have focused specifically on G3MB. We used publicly available datasets to demonstrate EphA2 and B7-H3 expression in primary G3MB and validated expression in patient-derived cell lines. EphA2-CAR T cells had greater cytolytic activity, persistence, and TH1 cytokine production than B7-H3-CAR T cells in coculture assays with MYC-driven G3MB cell lines in vitro . In vivo , EphA2-CAR T cells demonstrated superior tumor control and improved survival compared to B7-H3-CAR T cells in 2 of 3 orthotopic G3MB models. B7-H3-CAR T cells outperformed EphA2-CAR T cells in one model in which the antigen density of EphA2 was 5-fold lower than for B7-H3. The limited antitumor activity of EphA2-CAR T cells could be overcome with second genetic modifications that increase T cell functionality including deletion of DNMT3A or the expression of a constitutively active IL-18 chimeric cytokine receptor. Thus, our study nominates EphA2-CAR T cells as a promising alternative to B7-H3-CAR T cells, which are actively being explored in clinical studies for medulloblastoma.

Supplementary Table S4 from Combination of Ribociclib with BET-Bromodomain and PI3K/mTOR Inhibitors for Medulloblastoma Treatment In Vitro and In Vivo

Supplementary Table S2 from Combination of Ribociclib with BET-Bromodomain and PI3K/mTOR Inhibitors for Medulloblastoma Treatment In Vitro and In Vivo

Supplementary Methods, Figures S1-S9 and Tables S3, S5-S7

Supplementary Table S1 from Combination of Ribociclib with BET-Bromodomain and PI3K/mTOR Inhibitors for Medulloblastoma Treatment In Vitro and In Vivo

Despite improvement in the treatment of medulloblastoma over the last years, numerous patients with MYC- and MYCN-driven tumors still fail current therapies. Medulloblastomas have an intact retinoblastoma protein RB, suggesting that CDK4/6 inhibition might represent a therapeutic strategy for which drug combination remains understudied. We conducted high-throughput drug combination screens in a Group3 (G3) medulloblastoma line using the CDK4/6 inhibitor (CDK4/6i) ribociclib at IC20, referred to as an anchor, and 87 oncology drugs approved by FDA or in clinical trials. Bromodomain and extra terminal (BET) and PI3K/mTOR inhibitors potentiated ribociclib inhibition of proliferation in an established cell line and freshly dissociated tumor cells from intracranial xenografts of G3 and Sonic hedgehog (SHH) medulloblastomas in vitro. A reverse combination screen using the BET inhibitor JQ1 as anchor, revealed CDK4/6i as the most potentiating drugs. In vivo, ribociclib showed single-agent activity in medulloblastoma models whereas JQ1 failed to show efficacy due to high clearance and insufficient free brain concentration. Despite in vitro synergy, combination of ribociclib with the PI3K/mTOR inhibitor paxalisib did not significantly improve the survival of G3 and SHH medulloblastoma-bearing mice compared with ribociclib alone. Molecular analysis of ribociclib and paxalisib-treated tumors revealed that E2F targets and PI3K/AKT/MTORC1 signaling genes were depleted, as expected. Importantly, in one untreated G3MB model HD-MB03, the PI3K/AKT/MTORC1 gene set was enriched in vitro compared with in vivo suggesting that the pathway displayed increased activity in vitro. Our data illustrate the difficulty in translating in vitro findings in vivo.See related article in Mol Cancer Ther (2022) 21(8):1306–1317.

Effective therapies for high-risk medulloblastoma (MB), particularly MYC-driven Group 3 (G3) MB, remain elusive due to limited druggable mutations, poor blood–brain barrier (BBB) penetration, and rapid resistance. We developed SINBA (Synergy Inference by Data-driven Network-Based Bayesian Analysis) , a systems biology framework that predicts synergistic, BBB-permeable drug combinations by identifying hidden drivers that sustain oncogenic programs without detectable genetic or transcriptional alterations. Integrating MB-specific gene networks, transcriptomic data, and drug–gene interactions, SINBA prioritized 32 candidate combinations, of which 19 were experimentally validated as synergistic. The MEK inhibitor mirdametinib and p38 inhibitor regorafenib emerged as the top brain-penetrant pair, suppressing G3 MB progression and extending survival in human xenograft and immunocompetent mouse models, with efficacy enhanced by low-dose radiation. Single-cell analysis revealed selective targeting of the developmental origins of G3 MB , accompanied by immune microenvironment reprogramming. These findings establish hidden-driver inference as a generalizable framework for rational drug combination discovery. SIGNIFICANCE: The SINBA platform enables rapid identification of brain-penetrant, synergistic drug combinations by targeting hidden bottleneck drivers in high-risk tumors. SINBA-guided screening revealed that MEK inhibitors combined with regorafenib selectively eliminate the developmental cell-of-origin in G3 medulloblastoma and reprogram the tumor microenvironment, providing a clinically actionable precision oncology strategy.

The variation of transcriptome size across cell types significantly impacts single-cell RNA sequencing (scRNA-seq) data normalization and bulk RNA-seq cellular deconvolution, yet this intrinsic feature is often overlooked. Here we introduce ReDeconv, a computational algorithm that incorporates transcriptome size into scRNA-seq normalization and bulk deconvolution. ReDeconv introduces a scRNA-seq normalization approach, Count based on Linearized Transcriptome Size (CLTS), which corrects differential expressed genes typically misidentified by standard count per 10 K normalization, as confirmed by orthogonal validations. By maintaining transcriptome size variation, CLTS-normalized scRNA-seq enhances the accuracy of bulk deconvolution. Additionally, ReDeconv mitigates gene length effects and models expression variances, thereby improving deconvolution outcomes, particularly for rare cell types. Evaluated with both synthetic and real datasets, ReDeconv surpasses existing methods in precision. ReDeconv alters the practice and provides a new standard for scRNA-seq analyses and bulk deconvolution. The software packages and a user-friendly web portal are available.