Intratumoural delivery of TRAIL mRNA induces colon cancer cell apoptosis.

Novel strategies in intratumoral injection and emerging immunotherapies have heralded a new era of precise cancer treatments. The affinity of SARS-CoV-2 to ACE2 receptors, a feature which facilitates virulent human infection, is leveraged in this research. Colon cancer cells, with their high ACE2 expression, provide a potentially strategic target for using this SARS-CoV-2 feature. While the highly expression of ACE2 is observed in several cancer types, the idea of using the viral spike protein for targeting colon cancer cells offers a novel approach in cancer treatment. Intratumoral delivery of nucleic acid-based drugs is a promising alternative to overcoming the limitations of existing therapies. The increasing importance of nucleic acids in this realm, and the use of Lipid Nanoparticles (LNPs) for local delivery of nucleic acid therapeutics, are important breakthroughs. LNPs protect nucleic acid drugs from degradation and enhance cellular uptake, making them a rapidly evolving nano delivery system with high precision and adaptability. Our study leveraged a tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) combined with a receptor-binding domain from the SARS-CoV-2 spike protein, encapsulated in LNPs, to target colon cancer cells. Our results indicated that the TRAIL fusion-mRNA induced apoptosis in vitro and in vivo. Collectively, our findings highlight LNP-encapsulated TRAIL fusion-mRNA as a potential colon cancer therapy.

Integrating External Controls by Regression Calibration for Genome-Wide Association Study.

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study.

A multidimensional platform of patient-derived tumors identifies drug susceptibilities for clinical lenvatinib resistance.

Lenvatinib, a second-generation multi-receptor tyrosine kinase inhibitor approved by the FDA for first-line treatment of advanced liver cancer, facing limitations due to drug resistance. Here, we applied a multidimensional, high-throughput screening platform comprising patient-derived resistant liver tumor cells (PDCs), organoids (PDOs), and xenografts (PDXs) to identify drug susceptibilities for conquering lenvatinib resistance in clinically relevant settings. Expansion and passaging of PDCs and PDOs from resistant patient liver tumors retained functional fidelity to lenvatinib treatment, expediting drug repurposing screens. Pharmacological screening identified romidepsin, YM155, apitolisib, NVP-TAE684 and dasatinib as potential antitumor agents in lenvatinib-resistant PDC and PDO models. Notably, romidepsin treatment enhanced antitumor response in syngeneic mouse models by triggering immunogenic tumor cell death and blocking the EGFR signaling pathway. A combination of romidepsin and immunotherapy achieved robust and synergistic antitumor effects against lenvatinib resistance in humanized immunocompetent PDX models. Collectively, our findings suggest that patient-derived liver cancer models effectively recapitulate lenvatinib resistance observed in clinical settings and expedite drug discovery for advanced liver cancer, providing a feasible multidimensional platform for personalized medicine.

Galectin-7 Induction by EHMT2 Inhibition Enhances Immunity in Microsatellite Stability Colorectal Cancer.

BACKGROUND & AIMS: Although immunotherapy shows substantial advancement in colorectal cancer (CRC) with microsatellite instability high, it has limited efficacy for CRC with microsatellite stability (MSS). Identifying combinations that reverse immune suppression and prime MSS tumors for current immunotherapy approaches remains an urgent need. METHODS: An in vitro CRISPR screen was performed using coculture models of primary tumor cells and autologous immune cells from MSS CRC patients to identify epigenetic targets that could enhance immunotherapy efficacy in MSS tumors. RESULTS: We revealed EHMT2, a histone methyltransferase, as a potential target for MSS CRC. EHMT2 inhibition transformed the immunosuppressive microenvironment of MSS tumors into an immunomodulatory one by altering cytokine expression, leading to T-cell-mediated cytotoxicity activation and improved responsiveness to anti-PD1 treatment. We observed galectin-7 up-regulation upon EHMT2 inhibition, which converted a "cold" MSS tumor environment into a T-cell-inflamed one. Mechanistically, CHD4 repressed galectin-7 expression by recruiting EHMT2 to form a cotranscriptional silencing complex. Galectin-7 administration enhanced anti-PD1 efficacy in MSS CRC, serving as a potent adjunct cytokine therapy. CONCLUSIONS: Our findings suggest that targeting the EHMT2/galectin-7 axis could provide a novel combination strategy for immunotherapy in MSS CRC.

N6-methyladenosine demethylase FTO enhances chemo-resistance in colorectal cancer through SIVA1-mediated apoptosis.

N6-methyladenosine (m6A) is the most pervasive RNA modification and is recognized as a novel epigenetic regulation in RNA metabolism. Although the m6A modification involves various physiological processes, its roles in drug resistance in colorectal cancer (CRC) still remain unknown. We analyzed the RNA expression profile of m6A/A (%) with MRM mass spectrometry in human 5-fluorouracil (5-FU)-resistant CRC tissues, and used the m6A RNA immunoprecipitation assay to validate the m6A-regulated target. Our results have shown that the m6A demethylase FTO was up-regulated in human primary and 5-FU-resistant CRC. Depletion of FTO decreased cell growth, colony formation and metastasis in 5-FU-resistant CRC cells in vitro and in vivo. Mechanistically, we identified SIVA1, a critical apoptotic gene, as a key downstream target of the FTO-mediated m6A demethylation. The m6A demethylation of SIVA1 at the CDS region induced its mRNA degradation via a YTHDF2-dependent mechanism. The SIVA1 levels were negatively correlated with the FTO levels in clinical CRC tissues. Notably, inhibition of FTO significantly reduced the tolerance of 5-FU in 5-FU-resistant CRC cells via the FTO-SIVA1 axis, whereas SIVA1-depletion could restore the m6A-dependent 5-FU sensitivity in CRC cells. In summary, our findings demonstrate a critical role of FTO as an m6A demethylase enhancing chemo-resistance in CRC cells, and suggest that FTO inhibition may restore the sensitivity of chemo-resistant CRC cells to 5-FU.

Gene-Based Association Tests Using New Polygenic Risk Scores and Incorporating Gene Expression Data.

Recently, gene-based association studies have shown that integrating genome-wide association studies (GWAS) with expression quantitative trait locus (eQTL) data can boost statistical power and that the genetic liability of traits can be captured by polygenic risk scores (PRSs). In this paper, we propose a new gene-based statistical method that leverages gene-expression measurements and new PRSs to identify genes that are associated with phenotypes of interest. We used a generalized linear model to associate phenotypes with gene expression and PRSs and used a score-test statistic to test the association between phenotypes and genes. Our simulation studies show that the newly developed method has correct type I error rates and can boost statistical power compared with other methods that use either gene expression or PRS in association tests. A real data analysis figure based on UK Biobank data for asthma shows that the proposed method is applicable to GWAS.

Control for population stratification in genetic association studies based on GWAS summary statistics.

Over the past years, genome-wide association studies (GWAS) have generated a wealth of new information. Summary data from many GWAS are now publicly available, promoting the development of many statistical methods for association studies based on GWAS summary statistics, which avoids the increasing challenges associated with individual-level genotype and phenotype data sharing. However, for population-based association studies such as GWAS, it has been long recognized that population stratification can seriously confound association results. For large GWAS, it is very likely that there exist population stratification and cryptic relatedness, which will result in inflated Type I error in association testing. Although many methods have been developed to control for population stratification, only two of these approaches can be used to control population stratification without individual-level data: one is based on genomic control (GC) and the other one is based on linkage disequilibrium score regression (LDSC). However, the performance of these two approaches is currently unknown. In this study, we use extensive simulation studies including populations with subpopulations, spatially structured populations, and populations with cryptic relatedness to compare the performance of these two approaches to control for population stratification using only GWAS summary statistics without individual-level data. Data sets from the genetic analysis workshop 19 and UK Biobank are also used to evaluate these two approaches. We demonstrate that the intercept of LDSC can be used as a more accurate correction factor than GC. The results from this study will provide very useful information for researchers using GWAS summary statistics while trying to control for population stratification.

Loss-of-Function Genetic Screening Identifies Aldolase A as an Essential Driver for Liver Cancer Cell Growth Under Hypoxia.

BACKGROUND AND AIMS: Hypoxia is a common feature of the tumor microenvironment (TME), which promotes tumor progression, metastasis, and therapeutic drug resistance through a myriad of cell activities in tumor and stroma cells. While targeting hypoxic TME is emerging as a promising strategy for treating solid tumors, preclinical development of this approach is lacking in the study of HCC. APPROACH AND RESULTS: From a genome-wide CRISPR/CRISPR-associated 9 gene knockout screening, we identified aldolase A (ALDOA), a key enzyme in glycolysis and gluconeogenesis, as an essential driver for HCC cell growth under hypoxia. Knockdown of ALDOA in HCC cells leads to lactate depletion and consequently inhibits tumor growth. Supplementation with lactate partly rescues the inhibitory effects mediated by ALDOA knockdown. Upon hypoxia, ALDOA is induced by hypoxia-inducible factor-1α and fat mass and obesity-associated protein-mediated N6 -methyladenosine modification through transcriptional and posttranscriptional regulation, respectively. Analysis of The Cancer Genome Atlas shows that elevated levels of ALDOA are significantly correlated with poor prognosis of patients with HCC. In a screen of Food and Drug Administration-approved drugs based on structured hierarchical virtual platforms, we identified the sulfamonomethoxine derivative compound 5 (cpd-5) as a potential inhibitor to target ALDOA, evidenced by the antitumor activity of cpd-5 in preclinical patient-derived xenograft models of HCC. CONCLUSIONS: Our work identifies ALDOA as an essential driver for HCC cell growth under hypoxia, and we demonstrate that inhibition of ALDOA in the hypoxic TME is a promising therapeutic strategy for treating HCC.

Tumor-associated macrophages in immunotherapy.

Tumor-associated macrophages (TAMs) are essential components of the tumor microenvironment involved in the progression and metastasis of cancer. They are intimately involved in angiogenesis and immunosuppression in normal and malignant tissues, as well as pro-fibrotic activities. With the development of immunotherapy, eradication of cancer cells through activation of the innate immune system has achieved inspiring results, whereas only a handful of patients show a durable response. The tumor-suppressive environment has been investigated with respect to playing a vital role in cancer relapse. In this review, we uncover the heterogeneity of the origin of TAMs, as well as the functions of TAMs in tumor progression associated with intricate regulatory networks in the tumor microenvironment, aiming to inspire therapeutic insight for tumor immunotherapy.

RNA-binding protein RALY reprogrammes mitochondrial metabolism via mediating miRNA processing in colorectal cancer.

OBJECTIVE: Dysregulated cellular metabolism is a distinct hallmark of human colorectal cancer (CRC). However, metabolic programme rewiring during tumour progression has yet to be fully understood. DESIGN: We analysed altered gene signatures during colorectal tumour progression, and used a complex of molecular and metabolic assays to study the regulation of metabolism in CRC cell lines, human patient-derived xenograft mouse models and tumour organoid models. RESULTS: We identified a novel RNA-binding protein, RALY (also known as hnRNPCL2), that is highly associated with colorectal tumour aggressiveness. RALY acts as a key regulatory component in the Drosha complex, and promotes the post-transcriptional processing of a specific subset of miRNAs (miR-483, miR-676 and miR-877). These miRNAs systematically downregulate the expression of the metabolism-associated genes (ATP5I, ATP5G1, ATP5G3 and CYC1) and thereby reprogramme mitochondrial metabolism in the cancer cell. Analysis of The Cancer Genome Atlas (TCGA) reveals that increased levels of RALY are associated with poor prognosis in the patients with CRC expressing low levels of mitochondrion-associated genes. Mechanistically, induced processing of these miRNAs is facilitated by their N6-methyladenosine switch under reactive oxygen species (ROS) stress. Inhibition of the m6A methylation abolishes the RALY recognition of the terminal loop of the pri-miRNAs. Knockdown of RALY inhibits colorectal tumour growth and progression in vivo and in organoid models. CONCLUSIONS: Collectively, our results reveal a critical metabolism-centric role of RALY in tumour progression, which may lead to cancer therapeutics targeting RALY for treating CRC.

Children's Non-symbolic and Symbolic Numerical Representations and Their Associations With Mathematical Ability.

Most empirical evidence supports the view that non-symbolic and symbolic representations are foundations for advanced mathematical ability. However, the detailed development trajectories of these two types of representations in childhood are not very clear, nor are the different effects of non-symbolic and symbolic representations on the development of mathematical ability. We assessed 253 4- to 8-year-old children's non-symbolic and symbolic numerical representations, mapping skills, and mathematical ability, aiming to investigate the developmental trajectories and associations between these skills. Our results showed non-symbolic numerical representation emerged earlier than the symbolic one. Four-year-olds were capable of non-symbolic comparisons but not symbolic comparisons; five-year-olds performed better at non-symbolic comparisons than symbolic comparisons. This performance difference disappeared at age 6. Children at age 6 or older were able to map between symbolic and non-symbolic quantities. However, as children learn more about the symbolic representation system, their advantage in non-symbolic representation disappeared. Path analyses revealed that a direct effect of children's symbolic numerical skills on their math performance, and an indirect effect of non-symbolic numerical skills on math performance via symbolic skills. These results suggest that symbolic numerical skills are a predominant factor affecting math performance in early childhood. However, the influences of symbolic and non-symbolic numerical skills on mathematical performance both declines with age.

Children's Non-symbolic, Symbolic Addition and Their Mapping Capacity at 4-7 Years Old.

The study aimed to examine the developmental trajectories of non-symbolic and symbolic addition capacities in children and the mapping ability between these two. We assessed 106 4- to 7-year-old children and found that 4-year-olds were able to do non-symbolic addition but not symbolic addition. Five-year-olds and older were able to do symbolic addition and their performance in symbolic addition exceeded non-symbolic addition in grade 1 (approximate age 7). These results suggested non-symbolic addition ability emerges earlier and is less affected by formal mathematical education than symbolic addition. Meanwhile, we tested children's bi-directional mapping ability using a novel task and found that children were able to map between symbolic and non-symbolic representations of number at age 5. Their ability in mapping non-symbolic to symbolic number became more proficient in grade 1 (approximate age 7). This suggests children at age 7 have developed a relatively mature symbolic representation system.