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Health See other Health Articles Title: Korean researchers developed a new technology to treat cancer cells by reverting them to normal cells without killing them. Massimo @Rainmaker1973 Korean researchers developed a new technology to treat cancer cells by reverting them to normal cells without killing them. [Gong, J., et al. (2024). Control of Cellular Differentiation Trajectories for Cancer Reversion. Advanced Science. doi. org/10.1002/advs.202402132] https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202402132 Control of Cellular Differentiation Trajectories for Cancer Reversion Abstract Cellular differentiation is controlled by intricate layers of gene regulation, involving the modulation of gene expression by various transcriptional regulators. Due to the complexity of gene regulation, identifying master regulators across the differentiation trajectory has been a longstanding challenge. To tackle this problem, a computational framework, single-cell Boolean network inference and control (BENEIN), is presented. Applying BENEIN to human large intestinal single-cell transcriptome data, MYB, HDAC2, and FOXA2 are identified as the master regulators whose inhibition induces enterocyte differentiation. It is found that simultaneous knockdown of these master regulators can revert colorectal cancer cells into normal-like enterocytes by synergistically inducing differentiation and suppressing malignancy, which is validated by in vitro and in vivo experiments. 1 Introduction Cancer reversion has been proposed as a new therapeutic approach that aims to revert cancer cells into their differentiated and non-malignant state,[1-5] by inducing re-expression of differentiation associated genes.[6-8] Interestingly, in acute myeloid leukemia, breast cancer, and hepatocellular carcinoma, it was found that differentiation or trans- differentiation of cancer cells can induce such reversion.[6-8] However, systematic identification of master regulators that induce differentiation/trans-differentiation remains elusive. If master regulators across normal differentiation processes can be identified and utilized to regulate cancer cells, they may constitute an alternative approach to overcome the limitations of current anti-cancer therapies. Despite the importance of identifying master regulators of cellular differentiation, it remains a challenging problem due to the complex and strongly nonlinear nature of gene regulation.[9] Hence, there is a pressing need to develop a computational framework to identify master regulators that encompass dynamic processes in cellular differentiation. Although the dynamics of Boolean networks may appear overly simplistic in contrast to the intricate nature of biological systems, they still represent the essential features of biological mechanisms, making Boolean network modeling an appropriate approach.[10, 11] In previous studies, Boolean network modeling of cellular differentiation was proposed by constructing the structure of gene regulatory networks (GRNs) based on correlation coefficients, performing pseudotime analysis, and using Boolean satisfiability (SAT) solvers to infer Boolean logics.[12] However, such studies have shown problems including limited scalability, incompleteness in elucidating specific structural information, and the assumption of irregular time point intervals in inferring the regulation logics of Boolean network models (Table S1, Supporting Information). To overcome these limitations, we develop a computational framework for single-cell Boolean network inference and control (BENEIN). BENEIN can reconstruct Boolean models of GRNs and identify a set of master regulators, whose regulation leads to the desired cellular differentiation.[13, 14] In particular, BENEIN splits the transcriptional status of each single-cell into pre- and post-transition states using the exonic and intronic information of transcripts and infers the regulation logic of the underlying GRNs by assuming that the two states before and after the state transition correspond to exonic and intronic expression levels, which also remains unbiased with respect to uneven cell clusters upon the pseudotime trajectory. BENEIN further employs complex network control to identify the master regulators that can induce the desired cellular differentiation. BENEIN reveals insight into hidden gene regulation dynamics and offers a systemic way of controlling them. Applying BENEIN to single-cell transcriptome data of adult human intestine,[15] we identified a combination of master regulators, consisting of MYB, HDAC2, and FOXA2, which play a critical role in blocking enterocyte differentiation. We examined the regulation effects of these control targets by in silico analysis of the reconstructed GRN model, as well as comparative analysis with various public transcriptome data. To further confirm the effect of cancer reversion, we simultaneously inhibited these targets in three colorectal cancer cell lines and xenograft mouse models and found that their combinatorial inhibition strongly induces differentiation into normal-like cells. This demonstrates the potential for BENEIN to reveal novel control targets for differentiation trajectories in cancer reversion. In addition to applying BENEIN to adult human intestine single-cell transcriptome data, we explored its utility in a different organism and cellular context. We applied BENEIN to the granule neuron differentiation in the developing mouse hippocampus. Through this process, we identified a combination of control targets: Tcf4[16] (overexpression), Klf9[17] (overexpression), and Etv4[18] (inhibition). These targets are known to play pivotal roles in granular cell differentiation, as validated by literature. This application of BENEIN highlights the capability of BENEIN not only in reconstructing Boolean GRN models but also in identifying control targets for control of the cellular differentiation trajectories in diverse contexts. These findings suggest that BENEIN is a powerful tool for identifying control targets that are potentially pivotal in cancer reversion and other biological processes. 2 Result 2.1 Overview of the BENEIN Workflow To regulate cellular differentiation trajectories for cancer reversion, we developed a computational framework, BENEIN. BENEIN utilizes single- cell transcriptome data across a differentiation process and quantifies the abundance of pre-mature and mature mRNA reads. This quantification allows the transcriptional status of each single-cell to be separated into two dynamical states: pre- and post-transition states (Figure 1A). To reconstruct an accurate Boolean model of the GRN based on these pre- and post-transition states, BENEIN first infers potential regulatory structures between transcription factors (TFs) and their target genes (TGs). To uncover temporal gene regulatory interactions during the differentiation process, BENEIN groups cells into several clusters along with the differentiation trajectory and infers a structure within each cluster by computing conditional mutual information (CMI)[19] and eliminating indirect interactions between TFs and their TGs using the cisTarget database.[20] BENEIN integrates these structures across the first half clusters and establishes the regulatory network structure. Since the dynamics of the GRN are dominantly driven by TFs with complex feedback structures,[21-23] BENEIN extracts the largest strongly connected component (SCC) from the regulatory network structure (Figure 1B). Poster Comment: Cats & Dogs Universe @CatsandDogsmem Japanese scientists have created a hydrogel that reverts cancer cells back to cancer stem cells in 24 hours. This could lead to multiple treatments that could remove cancer permanently https://x.com/i/status/1891232614134001665 Post Comment Private Reply Ignore Thread
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