Chromosomal 3q amplicon encodes essential regulators of secretory vesicles that drive secretory addiction in cancer.

Cancer cells exhibit heightened secretory states that drive tumor progression. Here, we identify a chromosome 3q amplicon that serves as a platform for secretory regulation in cancer. The 3q amplicon encodes multiple Golgi-resident proteins, including the scaffold Golgi integral membrane protein 4 (GOLIM4) and the ion channel ATPase Secretory Pathway Ca2+ Transporting 1 (ATP2C1). We show that GOLIM4 recruits ATP2C1 and Golgi phosphoprotein 3 (GOLPH3) to coordinate calcium-dependent cargo loading and Golgi membrane bending and vesicle scission. GOLIM4 depletion disrupts the protein complex, resulting in a secretory blockade that inhibits the progression of 3q-amplified malignancies. In addition to its role as a scaffold, GOLIM4 maintains intracellular manganese (Mn) homeostasis by binding excess Mn in the Golgi lumen, which initiates the routing of Mn-bound GOLIM4 to lysosomes for degradation. We show that Mn treatment inhibits the progression of multiple types of 3q-amplified malignancies by degrading GOLIM4, resulting in a secretory blockade that interrupts pro-survival autocrine loops and attenuates pro-metastatic processes in the tumor microenvironment. Potentially underlying the selective activity of Mn against 3q-amplified malignancies, ATP2C1 co-amplification increases Mn influx into the Golgi lumen, resulting in a more rapid degradation of GOLIM4. These findings show that functional cooperativity between co-amplified genes underlies heightened secretion and a targetable secretory addiction in 3q-amplified malignancies.

Unleashing the Potential of 1,3-Diketone Analogues as Selective LH2 Inhibitors.

Lysyl hydroxylase 2 (LH2) catalyzes the formation of highly stable hydroxylysine aldehyde-derived collagen cross-links (HLCCs), thus promoting lung cancer metastasis through its capacity to modulate specific types of collagen cross-links within the tumor stroma. Using 1 and 2 from our previous high-throughput screening (HTS) as lead probes, we prepared a series of 1,3-diketone analogues, 1-18, and identified 12 and 13 that inhibit LH2 with IC50's of approximately 300 and 500 nM, respectively. Compounds 12 and 13 demonstrate selectivity for LH2 over LH1 and LH3. Quantum mechanics/molecular mechanics (QM/MM) modeling indicates that the selectivity of 12 and 13 may stem from noncovalent interactions like hydrogen bonding between the morpholine/piperazine rings with the LH2-specific Arg661. Treatment of 344SQ WT cells with 13 resulted in a dose-dependent reduction in their migration potential, whereas the compound did not impede the migration of the same cell line with an LH2 knockout (LH2KO).

EMT activates exocytotic Rabs to coordinate invasion and immunosuppression in lung cancer.

Epithelial-to-mesenchymal transition (EMT) underlies immunosuppression, drug resistance, and metastasis in epithelial malignancies. However, the way in which EMT orchestrates disparate biological processes remains unclear. Here, we identify an EMT-activated vesicular trafficking network that coordinates promigratory focal adhesion dynamics with an immunosuppressive secretory program in lung adenocarcinoma (LUAD). The EMT-activating transcription factor ZEB1 drives exocytotic vesicular trafficking by relieving Rab6A, Rab8A, and guanine nucleotide exchange factors from miR-148a-dependent silencing, thereby facilitating MMP14-dependent focal adhesion turnover in LUAD cells and autotaxin-mediated CD8+ T cell exhaustion, indicating that cell-intrinsic and extrinsic processes are linked through a microRNA that coordinates vesicular trafficking networks. Blockade of ZEB1-dependent secretion reactivates antitumor immunity and negates resistance to PD-L1 immune checkpoint blockade, an important clinical problem in LUAD. Thus, EMT activates exocytotic Rabs to drive a secretory program that promotes invasion and immunosuppression in LUAD.

ICUnet++: an Inception-CBAM network based on Unet++ for MR spine image segmentation.

In recent years, more attention paid to the spine caused by related diseases, spinal parsing (the multi-class segmentation of vertebrae and intervertebral disc) is an important part of the diagnosis and treatment of various spinal diseases. The more accurate the segmentation of medical images, the more convenient and quick the clinicians can evaluate and diagnose spinal diseases. Traditional medical image segmentation is often time consuming and energy consuming. In this paper, an efficient and novel automatic segmentation network model for MR spine images is designed. The proposed Inception-CBAM Unet++ (ICUnet++) model replaces the initial module with the Inception structure in the encoder-decoder stage base on Unet++ , which uses the parallel connection of multiple convolution kernels to obtain the features of different receptive fields during in the feature extraction. According to the characteristics of the attention mechanism, Attention Gate module and CBAM module are used in the network to make the attention coefficient highlight the characteristics of the local area. To evaluate the segmentation performance of network model, four evaluation metrics, namely intersection over union (IoU), dice similarity coefficient(DSC), true positive rate(TPR), positive predictive value(PPV) are used in the study. The published SpineSagT2Wdataset3 spinal MRI dataset is used during the experiments. In the experiment results, IoU reaches 83.16%, DSC is 90.32%, TPR is 90.40%, and PPV is 90.52%. It can be seen that the segmentation indicators have been significantly improved, which reflects the effectiveness of the model.

EMT-activated secretory and endocytic vesicular trafficking programs underlie a vulnerability to PI4K2A antagonism in lung cancer.

Hypersecretory malignant cells underlie therapeutic resistance, metastasis, and poor clinical outcomes. However, the molecular basis for malignant hypersecretion remains obscure. Here, we showed that epithelial-mesenchymal transition (EMT) initiates exocytic and endocytic vesicular trafficking programs in lung cancer. The EMT-activating transcription factor zinc finger E-box-binding homeobox 1 (ZEB1) executed a PI4KIIIß-to-PI4KIIα (PI4K2A) dependency switch that drove PI4P synthesis in the Golgi and endosomes. EMT enhanced the vulnerability of lung cancer cells to PI4K2A small-molecule antagonists. PI4K2A formed a MYOIIA-containing protein complex that facilitated secretory vesicle biogenesis in the Golgi, thereby establishing a hypersecretory state involving osteopontin (SPP1) and other prometastatic ligands. In the endosomal compartment, PI4K2A accelerated recycling of SPP1 receptors to complete an SPP1-dependent autocrine loop and interacted with HSP90 to prevent lysosomal degradation of AXL receptor tyrosine kinase, a driver of cell migration. These results show that EMT coordinates exocytic and endocytic vesicular trafficking to establish a therapeutically actionable hypersecretory state that drives lung cancer progression.

PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers.

MOTIVATION: Synthetic lethality (SL) is a type of genetic interaction in which the simultaneous inactivation of two genes leads to cell death, while the inactivation of a single gene does not affect the cell viability. It can effectively expand the range of anti-cancer therapeutic targets. SL interactions are identified mainly by experimental screening and computational prediction. Recent machine-learning methods mostly learn the representation of each gene individually, ignoring the representation of the pairwise interaction between two genes. In addition, the mechanisms of SL, the key to translating SL into cancer therapeutics, are often unclear. RESULTS: To fill the gaps, we propose a pairwise interaction learning-based graph neural network (GNN) named PiLSL to learn the representation of pairwise interaction between two genes for SL prediction. First, we construct an enclosing graph for each pair of genes from a knowledge graph. Secondly, we design an attentive embedding propagation layer in a GNN to discriminate the importance among the edges in the enclosing graph and to learn the latent features of the pairwise interaction from the weighted enclosing graph. Finally, we further fuse the latent features with explicit features extracted from multi-omics data to obtain powerful gene representations for SL prediction. Extensive experimental results demonstrate that PiLSL outperforms the best baseline by a large margin and generalizes well under three realistic scenarios. Besides, PiLSL provides an explanation of SL mechanisms via the weighted paths in the enclosing graphs by attention mechanism. AVAILABILITY AND IMPLEMENTATION: Our source code is available at https://github.com/JieZheng-ShanghaiTech/PiLSL.

NSF4SL: negative-sample-free contrastive learning for ranking synthetic lethal partner genes in human cancers.

MOTIVATION: Detecting synthetic lethality (SL) is a promising strategy for identifying anti-cancer drug targets. Targeting SL partners of a primary gene mutated in cancer is selectively lethal to cancer cells. Due to high cost of wet-lab experiments and availability of gold standard SL data, supervised machine learning for SL prediction has been popular. However, most of the methods are based on binary classification and thus limited by the lack of reliable negative data. Contrastive learning can train models without any negative sample and is thus promising for finding novel SLs. RESULTS: We propose NSF4SL, a negative-sample-free SL prediction model based on a contrastive learning framework. It captures the characteristics of positive SL samples by using two branches of neural networks that interact with each other to learn SL-related gene representations. Moreover, a feature-wise data augmentation strategy is used to mitigate the sparsity of SL data. NSF4SL significantly outperforms all baselines which require negative samples, even in challenging experimental settings. To the best of our knowledge, this is the first time that SL prediction is formulated as a gene ranking problem, which is more practical than the current formulation as binary classification. NSF4SL is the first contrastive learning method for SL prediction and its success points to a new direction of machine-learning methods for identifying novel SLs. AVAILABILITY AND IMPLEMENTATION: Our source code is available at https://github.com/JieZheng-ShanghaiTech/NSF4SL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers.

MOTIVATION: Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. Wet-lab screening of SL pairs is afflicted with high cost, batch-effect, and off-target problems. Current computational methods for SL prediction include gene knock-out simulation, knowledge-based data mining and machine learning methods. Most of the existing methods tend to assume that SL pairs are independent of each other, without taking into account the shared biological mechanisms underlying the SL pairs. Although several methods have incorporated genomic and proteomic data to aid SL prediction, these methods involve manual feature engineering that heavily relies on domain knowledge. RESULTS: Here, we propose a novel graph neural network (GNN)-based model, named KG4SL, by incorporating knowledge graph (KG) message-passing into SL prediction. The KG was constructed using 11 kinds of entities including genes, compounds, diseases, biological processes and 24 kinds of relationships that could be pertinent to SL. The integration of KG can help harness the independence issue and circumvent manual feature engineering by conducting message-passing on the KG. Our model outperformed all the state-of-the-art baselines in area under the curve, area under precision-recall curve and F1. Extensive experiments, including the comparison of our model with an unsupervised TransE model, a vanilla graph convolutional network model, and their combination, demonstrated the significant impact of incorporating KG into GNN for SL prediction. AVAILABILITY AND IMPLEMENTATION: : KG4SL is freely available at https://github.com/JieZheng-ShanghaiTech/KG4SL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PIKE-R2P: Protein-protein interaction network-based knowledge embedding with graph neural network for single-cell RNA to protein prediction.

BACKGROUND: Recent advances in simultaneous measurement of RNA and protein abundances at single-cell level provide a unique opportunity to predict protein abundance from scRNA-seq data using machine learning models. However, existing machine learning methods have not considered relationship among the proteins sufficiently. RESULTS: We formulate this task in a multi-label prediction framework where multiple proteins are linked to each other at the single-cell level. Then, we propose a novel method for single-cell RNA to protein prediction named PIKE-R2P, which incorporates protein-protein interactions (PPI) and prior knowledge embedding into a graph neural network. Compared with existing methods, PIKE-R2P could significantly improve prediction performance in terms of smaller errors and higher correlations with the gold standard measurements. CONCLUSION: The superior performance of PIKE-R2P indicates that adding the prior knowledge of PPI to graph neural networks can be a powerful strategy for cross-modality prediction of protein abundances at the single-cell level.

Tumor organoid models in precision medicine and investigating cancer-stromal interactions.

Tumor development and progression require chemical and mechanical cues derived from cellular and non-cellular components in the tumor microenvironment, including the extracellular matrix (ECM), cancer-associated fibroblasts (CAFs), endothelial cells, and immune cells. Therefore, it is crucial to develop tissue culture models that can mimic in vivo cancer cell-ECM and cancer-stromal cell interactions. Three-dimensional (3D) tumor culture models have been widely utilized to study cancer development and progression. A recent advance in 3D culture is the development of patient-derived tumor organoid (PDO) models from primary human cancer tissue. PDOs maintain the heterogeneity of the primary tumor, which makes them more relevant for identifying therapeutic targets and verifying drug response. Other significant advances include development of 3D co-culture assays to investigate cell-cell interactions and tissue/organ morphogenesis, and microfluidic technology that can be integrated into 3D culture to mimic vasculature and blood flow. These advances offer spatial and temporal insights into cancer cell-stromal interactions and represent novel techniques to study tumor progression and drug response. Here, we summarize the recent progress in 3D culture and tumor organoid models, and discuss future directions and the potential of utilizing these models to study cancer-stromal interactions and direct personalized treatment.

Ensemble learning models that predict surface protein abundance from single-cell multimodal omics data.

Single-cell protein abundance is a fundamental type of information to characterize cell states. Due to high cost and technical barriers, however, direct quantification of proteins is difficult. Single-cell RNA sequencing (scRNA-seq) data, serving as a cost-effective substitute of single-cell proteomics, may not accurately reflect protein expression levels due to measurement error, noise, post-transcriptional and translational regulation, etc. The recently emerging single-cell multimodal omics data, e.g. CITE-seq and REAP-seq, can simultaneously profile RNA and protein abundances in single cells, providing labeled data for predictive modeling in a supervised learning framework. Deep neural network-based transfer learning method has been applied to imputation of surface protein abundances from single-cell transcriptomic data. However, it is unclear if the artificial neural network is the best model, and it is desirable to improve the prediction performance (e.g. accuracy, interpretability) of machine learning models. In this paper, we compared several tree-based ensemble learning methods with neural network models, and found that ensemble learning often performed better than neural network, and Random Forest (RF) performed the best overall. Moreover, we used the feature importance scores from RF to interpret biological mechanisms underlying the prediction. Our study demonstrates the effectiveness of ensemble learning for reliable protein abundances prediction using single-cell multimodal omics data, and paves the way for knowledge discovery by mining single-cell multi-omics data in large scale.

Develop a High-Throughput Screening Method to Identify C-P4H1 (Collagen Prolyl 4-Hydroxylase 1) Inhibitors from FDA-Approved Chemicals.

Collagen prolyl 4-hydroxylase 1 (C-P4H1) is an α-ketoglutarate (α-KG)-dependent dioxygenase that catalyzes 4-hydroxylation of proline on collagen. C-P4H1-induced prolyl hydroxylation is required for proper collagen deposition and cancer metastasis. Therefore, targeting C-P4H1 is considered a potential therapeutic strategy for collagen-related cancer progression and metastasis. However, no C-P4H1 inhibitors are available for clinical testing, and the high content assay is currently not available for C-P4H1 inhibitor screening. In the present study, we developed a high-throughput screening assay by quantifying succinate, a byproduct of C-P4H-catalyzed hydroxylation. C-P4H1 is the major isoform of collagen prolyl 4-hydroxylases (CP4Hs) that contributes the majority prolyl 4-hydroxylase activity. Using C-P4H1 tetramer purified from the eukaryotic expression system, we showed that the Succinate-GloTM Hydroxylase assay was more sensitive for measuring C-P4H1 activity compared with the hydroxyproline colorimetric assay. Next, we performed high-throughput screening with the FDA-approved drug library and identified several new C-P4H1 inhibitors, including Silodosin and Ticlopidine. Silodosin and Ticlopidine inhibited C-P4H1 activity in a dose-dependent manner and suppressed collagen secretion and tumor invasion in 3D tissue culture. These C-P4H1 inhibitors provide new agents to test clinical potential of targeting C-P4H1 in suppressing cancer progression and metastasis.

Carnitine palmitoyltransferase 1C contributes to progressive cellular senescence.

Stable transfection manipulation with antibiotic selection and passaging induces progressive cellular senescence phenotypes. However, the underlying mechanisms remain poorly understood. This study demonstrated that stable transfection of the empty vector induced PANC-1 cells into cellular senescence. Metabolomics revealed several acylcarnitines and their upstream regulatory gene, carnitine palmitoyltransferase 1C (CPT1C) involved in fatty acid ß-oxidation in mitochondria, were strikingly decreased in senescent PANC-1 cells. Low CPT1C expression triggered mitochondrial dysfunction, inhibited telomere elongation, impaired cell survival under metabolic stress, and hindered the malignance and tumorigenesis of senescent cells. On the contrary, mitochondrial activity was restored by CPT1C gain-of-function in senescent vector PANC-1 cells. PPARα and TP53/CDKN1A, crucial signaling components in cellular senescence, were downregulated in senescent PANC-1 cells. This study identifies CPT1C as a key regulator of stable transfection-induced progressive PANC-1 cell senescence that inhibits mitochondrial function-associated metabolic reprogramming. These findings confirm the need to identify cell culture alterations after stable transfection, particularly when cells are used for metabolomics and mitochondria-associated studies, and suggest inhibition of CPT1C could be a promising target to intervene pancreatic tumorigenesis.

Hsp47 promotes cancer metastasis by enhancing collagen-dependent cancer cell-platelet interaction.

Increased expression of extracellular matrix (ECM) proteins in circulating tumor cells (CTCs) suggests potential function of cancer cell-produced ECM in initiation of cancer cell colonization. Here, we showed that collagen and heat shock protein 47 (Hsp47), a chaperone facilitating collagen secretion and deposition, were highly expressed during the epithelial-mesenchymal transition (EMT) and in CTCs. Hsp47 expression induced mesenchymal phenotypes in mammary epithelial cells (MECs), enhanced platelet recruitment, and promoted lung retention and colonization of cancer cells. Platelet depletion in vivo abolished Hsp47-induced cancer cell retention in the lung, suggesting that Hsp47 promotes cancer cell colonization by enhancing cancer cell-platelet interaction. Using rescue experiments and functional blocking antibodies, we identified type I collagen as the key mediator of Hsp47-induced cancer cell-platelet interaction. We also found that Hsp47-dependent collagen deposition and platelet recruitment facilitated cancer cell clustering and extravasation in vitro. By analyzing DNA/RNA sequencing data generated from human breast cancer tissues, we showed that gene amplification and increased expression of Hsp47 were associated with cancer metastasis. These results suggest that targeting the Hsp47/collagen axis is a promising strategy to block cancer cell-platelet interaction and cancer colonization in secondary organs.

Polydopamine-modified dual-ligand nanoparticles as highly effective and targeted magnetic resonance/photoacoustic dual-modality thrombus imaging agents.

BACKGROUND: Platelet activation and subsequent aggregation are the initial stages of thrombosis. A molecular probe that specifically targets activated platelets and remains retained under high shear stress in vivo can enhance the imaging effect to achieve early and accurate diagnosis. METHODS AND MATERIALS: In this study, we constructed nanoparticles (NPs) using polydopamine to carry two peptides that simultaneously bind integrin αIIbß3 and P-selectin on activated platelets to enhance the targeting of NPs to thrombus. RESULTS: The targeting specificity and binding stability of the NPs on red and white thrombi were demonstrated in vitro using a simulated circulatory device and the targeting effect of the NPs on mixed thrombus was studied by  magnetic resonance (MR)/photoacoustic (PA) dual-modality imaging in vivo. NPs that were surface modified with both peptides have higher selectivity and retention to red and white thrombi in vitro than NPs with a single or no peptide, and the targeting effect was closely related to the number and distribution of activated platelets as well as the structure and type of thrombus. The NPs also have MR/PA dual-modality imaging functionality, significantly enhancing the imaging of mixed thrombus in vivo. CONCLUSION: These dual-targeted NPs have improved targeting specificity and binding stability to different thrombi under high shear stress and are beneficial for the early diagnosis of thrombosis.

Heat shock protein 47 (HSP47) binds to discoidin domain-containing receptor 2 (DDR2) and regulates its protein stability.

Cell-collagen interactions are crucial for cell migration and invasion during cancer development and progression. Heat shock protein 47 (HSP47) is an endoplasmic reticulum-resident molecular chaperone that facilitates collagen maturation and deposition. It has been previously shown that HSP47 expression in cancer cells is crucial for cancer invasiveness. However, exogenous collagen cannot rescue cell invasion in HSP47-silenced cancer cells, suggesting that other HSP47 targets contribute to cancer cell invasion. Here, we show that HSP47 expression is required for the stability and cell-surface expression of discoidin domain-containing receptor 2 (DDR2) in breast cancer tissues. HSP47 silencing reduced DDR2 protein stability, accompanied by suppressed cell migration and invasion. Co-immunoprecipitation results revealed that HSP47 binds to the DDR2 ectodomain. Using a photoconvertible technique and total internal reflection fluorescence microscopy, we further demonstrate that HSP47 expression significantly sustains the membrane localization of the DDR2 protein. These results suggest that binding of HSP47 to DDR2 increases DDR2 stability and regulates its membrane dynamics and thereby enhances cancer cell migration and invasion. Given that DDR2 has a crucial role in the epithelial-to-mesenchymal transition and cancer progression, targeting the HSP47-DDR2 interaction might be a potential strategy for inhibiting DDR2-dependent cancer progression.

Design, Synthesis, and Evaluation of Novel p-(Methylthio)styryl Substituted Quindoline Derivatives as Neuroblastoma RAS (NRAS) Repressors via Specific Stabilizing the RNA G-Quadruplex.

The human proto-oncogene neuroblastoma RAS ( NRAS) contains a guanine-rich sequence in the 5'-untranslated regions (5'-UTR) of the mRNA that could form an RNA G-quadruplex structure. This structure acts as a repressor for NRAS translation and could be a potential target for anticancer drugs. Our previous studies found an effective scaffold, the quindoline scaffold, for binding and stabilizing the DNA G-quadruplex structures. Here, on the basis of the previous studies and reported RNA-specific probes, a series of novel p-(methylthio)styryl substituted quindoline (MSQ) derivatives were designed, synthesized, and evaluated as NRAS RNA G-quadruplex ligands. Panels of experiments turned out that the introduction of p-(methylthio)styryl side chain could enhance the specific binding to the NRAS RNA G-quadruplex. One of the hits, 4a-10, showed strong stabilizing activity on the G-quadruplex and subsequently repressed NRAS's translation and inhibited tumor cells proliferation. Our finding provided a novel strategy to discover novel NRAS repressors by specifically binding to the RNA G-quadruplex in the 5'-UTR of mRNA.

Human Cancer and Platelet Interaction, a Potential Therapeutic Target.

Cancer patients experience a four-fold increase in thrombosis risk, indicating that cancer development and progression are associated with platelet activation. Xenograft experiments and transgenic mouse models further demonstrate that platelet activation and platelet-cancer cell interaction are crucial for cancer metastasis. Direct or indirect interaction of platelets induces cancer cell plasticity and enhances survival and extravasation of circulating cancer cells during dissemination. In vivo and in vitro experiments also demonstrate that cancer cells induce platelet aggregation, suggesting that platelet-cancer interaction is bidirectional. Therefore, understanding how platelets crosstalk with cancer cells may identify potential strategies to inhibit cancer metastasis and to reduce cancer-related thrombosis. Here, we discuss the potential function of platelets in regulating cancer progression and summarize the factors and signaling pathways that mediate the cancer cell-platelet interaction.

Iodine-Promoted Semmler-Wolff Reactions: Step-Economic Access to meta-Substituted Primary Anilines via Aromatization.

An atom- and step-economic access to an array of unprotected meta-substituted primary anilines was disclosed using the Semmler-Wolff reaction, promoted by molecular iodine. Therein, noble metal catalysts and inert atmosphere are unnecessary while the forcing reaction conditions and the lengthy synthesis can be avoided. The synthetic utility of this approach is evident in the de novo syntheses of three bioactive molecules with good total yields.

New Disubstituted Quindoline Derivatives Inhibiting Burkitt's Lymphoma Cell Proliferation by Impeding c-MYC Transcription.

The c-MYC oncogene is overactivated during Burkitt's lymphoma pathogenesis. Targeting c-MYC to inhibit its transcriptional activity has emerged as an effective anticancer strategy. We synthesized four series of disubstituted quindoline derivatives by introducing the second cationic amino side chain and 5-N-methyl group based on a previous study of SYUIQ-5 (1) as c-MYC promoter G-quadruplex ligands. The in vitro evaluations showed that all new compounds exhibited higher stabilities and binding affinities, and most of them had better selectivity (over duplex DNA) for the c-MYC G-quadruplex compared to 1. Moreover, the new ligands prevented NM23-H2, a transcription factor, from effectively binding to the c-MYC G-quadruplex. Further studies showed that the selected ligand, 7a4, down-regulated c-MYC transcription by targeting promoter G-quadruplex and disrupting the NM23-H2/c-MYC interaction in RAJI cells. 7a4 could inhibit Burkitt's lymphoma cell proliferation through cell cycle arrest and apoptosis and suppress tumor growth in a human Burkitt's lymphoma xenograft.