Aging induces changes in cancer formation and microbial content in a murine model of bladder cancer.

Bladder cancer (BCa) incidence is tightly linked to aging. Older patients with BCa present with higher grade tumors and have worse outcomes on Bacillus-Calmette-Guerin (BCG) immunotherapy. Aging is also known to result in changes in the gut microbiome over mammalian lifespan, with recent studies linking alterations in the gut microbiome to changes in tumor immunity. There is limited information on the microbiome in BCa models though, despite known links to aging and immunotherapy. The purpose of this study was to evaluate how aging impacts tumor formation, inflammation, and the microbiome of mice given the model BCa carcinogen N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN). We hypothesized old animals would have larger, more inflamed tumors and a shift in their fecal microbiome compared to their younger counterparts. Young (~8-week-old) or old (~78-week-old) C57Bl/6J animals were administered 0.05% BBN in drinking water for 16 weeks and then euthanized or allowed to progress for an additional 4 weeks. After 16 weeks of BBN, old mice had higher bladder to body weight ratio than young mice, and also muscle invasive tumors, which were not seen in their young counterparts. Old animals also had increased innate immune recruitment, but CD4+/CD8+ T cell recruitment did not appear different. BBN dramatically altered the microbiome in both sets of animals as measured by ß-diversity, including changes in multiple genera of bacteria. These data suggest old mice have a differential response to BBN-induced BCa. Given the median age of patients with BCa, understanding how the aged phenotype interacts with BCa is imperative.

Computer-aided, resistance gene-guided genome mining for proteasome and HMG-CoA reductase inhibitors.

Secondary metabolites (SMs) are biologically active small molecules, many of which are medically valuable. Fungal genomes contain vast numbers of SM biosynthetic gene clusters (BGCs) with unknown products, suggesting that huge numbers of valuable SMs remain to be discovered. It is challenging, however, to identify SM BGCs, among the millions present in fungi, that produce useful compounds. One solution is resistance gene-guided genome mining, which takes advantage of the fact that some BGCs contain a gene encoding a resistant version of the protein targeted by the compound produced by the BGC. The bioinformatic signature of such BGCs is that they contain an allele of an essential gene with no SM biosynthetic function, and there is a second allele elsewhere in the genome. We have developed a computer-assisted approach to resistance gene-guided genome mining that allows users to query large databases for BGCs that putatively make compounds that have targets of therapeutic interest. Working with the MycoCosm genome database, we have applied this approach to look for SM BGCs that target the proteasome ß6 subunit, the target of the proteasome inhibitor fellutamide B, or HMG-CoA reductase, the target of cholesterol reducing therapeutics such as lovastatin. Our approach proved effective, finding known fellutamide and lovastatin BGCs as well as fellutamide- and lovastatin-related BGCs with variations in the SM genes that suggest they may produce structural variants of fellutamides and lovastatin. Gratifyingly, we also found BGCs that are not closely related to lovastatin BGCs but putatively produce novel HMG-CoA reductase inhibitors. ONE-SENTENCE SUMMARY: A new computer-assisted approach to resistance gene-directed genome mining is reported along with its use to identify fungal biosynthetic gene clusters that putatively produce proteasome and HMG-CoA reductase inhibitors.

Aphid cluster recognition and detection in the wild using deep learning models.

Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community.

Innovative Family-Based Genetically Informed Series of Analyses of Whole-Exome Data Supports Likely Inheritance for Grammar in Children with Specific Language Impairment.

Individuals with specific language impairment (SLI) struggle with language acquisition despite average non-verbal intelligence and otherwise typical development. One SLI account focuses on grammar acquisition delay. The current study aimed to detect novel rare genetic variants associated with performance on a grammar assessment, the Test of Early Grammatical Impairment (TEGI), in English-speaking children. The TEGI was selected due to its sensitivity and specificity, consistently high heritability estimates, and its absence from all but one molecular genetic study. We performed whole exome sequencing (WES) in eight families with SLI (n = 74 total) and follow-up Sanger sequencing in additional unrelated probands (n = 146). We prioritized rare exonic variants shared by individuals with low TEGI performance (n = 34) from at least two families under two filtering workflows: (1) novel and (2) previously reported candidate genes. Candidate variants were observed on six new genes (PDHA2, PCDHB3, FURIN, NOL6, IQGAP3, and BAHCC1), and two genes previously reported for overall language ability (GLI3 and FLNB). We specifically suggest PCDHB3, a protocadherin gene, and NOL6 are critical for ribosome synthesis, as they are important targets of SLI investigation. The proposed SLI candidate genes associated with TEGI performance emphasize the utility of precise phenotyping and family-based genetic study.

Functional inhibition of the RNA-binding protein HuR sensitizes triple-negative breast cancer to chemotherapy.

Chemotherapy remains the standard treatment for triple-negative breast cancer (TNBC); however, chemoresistance compromises its efficacy. The RNA-binding protein Hu antigen R (HuR) could be a potential therapeutic target to enhance the chemotherapy efficacy. HuR is known to mainly stabilize its target mRNAs, and/or promote the translation of encoded proteins, which are implicated in multiple cancer hallmarks, including chemoresistance. In this study, a docetaxel-resistant cell subline (231-TR) was established from the human TNBC cell line MDA-MB-231. Both the parental and resistant cell lines exhibited similar sensitivity to the small molecule functional inhibitor of HuR, KH-3. Docetaxel and KH-3 combination therapy synergistically inhibited cell proliferation in TNBC cells and tumor growth in three animal models. KH-3 downregulated the expression levels of HuR targets (e.g., ß-Catenin and BCL2) in a time- and dose-dependent manner. Moreover, KH-3 restored docetaxel's effects on activating Caspase-3 and cleaving PARP in 231-TR cells, induced apoptotic cell death, and caused S-phase cell cycle arrest. Together, our findings suggest that HuR is a critical mediator of docetaxel resistance and provide a rationale for combining HuR inhibitors and chemotherapeutic agents to enhance chemotherapy efficacy.

Integrated de novo gene prediction and peptide assembly of metagenomic sequencing data.

Metagenomics is the study of all genomic content contained in given microbial communities. Metagenomic functional analysis aims to quantify protein families and reconstruct metabolic pathways from the metagenome. It plays a central role in understanding the interaction between the microbial community and its host or environment. De novo functional analysis, which allows the discovery of novel protein families, remains challenging for high-complexity communities. There are currently three main approaches for recovering novel genes or proteins: de novo nucleotide assembly, gene calling and peptide assembly. Unfortunately, their information dependency has been overlooked, and each has been formulated as an independent problem. In this work, we develop a sophisticated workflow called integrated Metagenomic Protein Predictor (iMPP), which leverages the information dependencies for better de novo functional analysis. iMPP contains three novel modules: a hybrid assembly graph generation module, a graph-based gene calling module, and a peptide assembly-based refinement module. iMPP significantly improved the existing gene calling sensitivity on unassembled metagenomic reads, achieving a 92-97% recall rate at a high precision level (>85%). iMPP further allowed for more sensitive and accurate peptide assembly, recovering more reference proteins and delivering more hypothetical protein sequences. The high performance of iMPP can provide a more comprehensive and unbiased view of the microbial communities under investigation. iMPP is freely available from https://github.com/Sirisha-t/iMPP.

Exploring Gut Microbiome in Predicting the Efficacy of Immunotherapy in Non-Small Cell Lung Cancer.

We performed various analyses on the taxonomic and functional features of the gut microbiome from NSCLC patients treated with immunotherapy to establish a model that may predict whether a patient will benefit from immunotherapy. We collected 65 published whole metagenome shotgun sequencing samples along with 14 samples from our previous study. We systematically studied the taxonomical characteristics of the dataset and used both the random forest (RF) and the multilayer perceptron (MLP) neural network models to predict patients with progression-free survival (PFS) above 6 months versus those below 3 months. Our results showed that the RF classifier achieved the highest F-score (85.2%) and the area under the receiver operating characteristic curve (AUC) (95%) using the protein families (Pfam) profile, and the MLP neural network classifier achieved a 99.9% F-score and 100% AUC using the same Pfam profile. When applying the model trained in the Pfam profile directly to predict the treatment response, we found that both trained RF and MLP classifiers significantly outperformed the stochastic predictor in F-score. Our results suggested that such a predictive model based on functional (e.g., Pfam) rather than taxonomic profile might be clinically useful to predict whether an NSCLC patient will benefit from immunotherapy, as both the F-score and AUC of functional profile outperform that of taxonomic profile. In addition, our model suggested that interactive biological processes such as methanogenesis, one-carbon, and amino acid metabolism might be important in regulating the immunotherapy response that warrants further investigation.

Nano pom-poms prepared exosomes enable highly specific cancer biomarker detection.

Extracellular vesicles (EVs), particularly nano-sized small EV exosomes, are emerging biomarker sources. However, due to heterogeneous populations secreted from diverse cell types, mapping exosome multi-omic molecular information specifically to their pathogenesis origin for cancer biomarker identification is still extraordinarily challenging. Herein, we introduced a novel 3D-structured nanographene immunomagnetic particles (NanoPoms) with unique flower pom-poms morphology and photo-click chemistry for specific marker-defined capture and release of intact exosome. This specific exosome isolation approach leads to the expanded identification of targetable cancer biomarkers with enhanced specificity and sensitivity, as demonstrated by multi-omic exosome analysis of bladder cancer patient tissue fluids using the next generation sequencing of somatic DNA mutations, miRNAs, and the global proteome (Data are available via ProteomeXchange with identifier PXD034454). The NanoPoms prepared exosomes also exhibit distinctive in vivo biodistribution patterns, highlighting the highly viable and integral quality. The developed method is simple and straightforward, which is applicable to nearly all types of biological fluids and amenable for enrichment, scale up, and high-throughput exosome isolation.

RPfam: A refiner towards curated-like multiple sequence alignments of the Pfam protein families.

High-quality multiple sequence alignments can provide insights into the architecture and function of protein families. The existing MSA tools often generate results inconsistent with biological distribution of conserved regions because of positioning amino acid residues and gaps only by symbols. We propose RPfam, a refiner towards curated-like MSAs for modeling the protein families in the Pfam database. RPfam refines the automatic alignments via scoring alignments based on the PFASUM matrix, restricting realignments within badly aligned blocks, optimizing the block scores by dynamic programming, and running refinements iteratively using the Simulated Annealing algorithm. Experiments show RPfam effectively refined the alignments produced by the MSA tools ClustalO and Muscle with reference to the curated seed alignments of the Pfam protein families. Especially RPfam improved the quality of the ClustalO alignments by 4.4% and the Muscle alignments by 2.8% on the gp32 DNA binding protein-like family. Supplementary Table is available at http://www.worldscinet.com/jbcb/.

Evaluation of the Durability of the Immune Humoral Response to COVID-19 Vaccines in Patients With Cancer Undergoing Treatment or Who Received a Stem Cell Transplant.

Importance: The durability of the antibody response to COVID-19 vaccines in patients with cancer undergoing treatment or who received a stem cell transplant is unknown and may be associated with infection outcomes. Objective: To evaluate anti-SARS-CoV-2 spike protein receptor binding domain (anti-RBD) and neutralizing antibody (nAb) responses to COVID-19 vaccines longitudinally over 6 months in patients with cancer undergoing treatment or who received a stem cell transplant (SCT). Design, Setting, and Participants: In this prospective, observational, longitudinal cross-sectional study of 453 patients with cancer undergoing treatment or who received an SCT at the University of Kansas Cancer Center in Kansas City, blood samples were obtained before 433 patients received a messenger RNA (mRNA) vaccine (BNT162b2 or mRNA-1273), after the first dose of the mRNA vaccine, and 1 month, 3 months, and 6 months after the second dose. Blood samples were also obtained 2, 4, and 7 months after 17 patients received the JNJ-78436735 vaccine. For patients receiving a third dose of an mRNA vaccine, blood samples were obtained 30 days after the third dose. Interventions: Blood samples and BNT162b2, mRNA-1273, or JNJ-78436735 vaccines. Main Outcomes and Measures: Geometric mean titers (GMTs) of the anti-RBD; the ratio of GMTs for analysis of demographic, disease, and treatment variables; the percentage of neutralization of anti-RBD antibodies; and the correlation between anti-RBD and nAb responses to the COVID-19 vaccines. Results: This study enrolled 453 patients (mean [SD] age, 60.4 [13,1] years; 253 [56%] were female). Of 450 patients, 273 (61%) received the BNT162b2 vaccine (Pfizer), 160 (36%) received the mRNA-1273 vaccine (Moderna), and 17 (4%) received the JNJ-7846735 vaccine (Johnson & Johnson). The GMTs of the anti-RBD for all patients were 1.70 (95% CI, 1.04-2.85) before vaccination, 18.65 (95% CI, 10.19-34.11) after the first dose, 470.38 (95% CI, 322.07-686.99) at 1 month after the second dose, 425.80 (95% CI, 322.24-562.64) at 3 months after the second dose, 447.23 (95% CI, 258.53-773.66) at 6 months after the second dose, and 9224.85 (95% CI, 2423.92-35107.55) after the third dose. The rate of threshold neutralization (≥30%) was observed in 203 of 252 patients (80%) 1 month after the second dose and in 135 of 166 patients (81%) 3 months after the second dose. Anti-RBD and nAb were highly correlated (Spearman correlation coefficient, 0.93 [0.92-0.94]; P < .001). Three months after the second dose, anti-RBD titers were lower in male vs female patients (ratio of GMTs, 0.52 [95% CI, 0.34-0.81]), patients older than 65 years vs patients 50 years or younger (ratio of GMTs, 0.38 [95% CI, 0.25-0.57]), and patients with hematologic malignant tumors vs solid tumors (ratio of GMTs, 0.40 [95% CI, 0.20-0.81]). Conclusions and Relevance: In this cross-sectional study, after 2 doses of an mRNA vaccine, anti-RBD titers peaked at 1 month and remained stable over the next 6 months. Patients older than 65 years of age, male patients, and patients with a hematologic malignant tumor had low antibody titers. Compared with the primary vaccine course, a 20-fold increase in titers from a third dose suggests a brisk B-cell anamnestic response in patients with cancer.

Colonoscopy polyp detection and classification: Dataset creation and comparative evaluations.

Colorectal cancer (CRC) is one of the most common types of cancer with a high mortality rate. Colonoscopy is the preferred procedure for CRC screening and has proven to be effective in reducing CRC mortality. Thus, a reliable computer-aided polyp detection and classification system can significantly increase the effectiveness of colonoscopy. In this paper, we create an endoscopic dataset collected from various sources and annotate the ground truth of polyp location and classification results with the help of experienced gastroenterologists. The dataset can serve as a benchmark platform to train and evaluate the machine learning models for polyp classification. We have also compared the performance of eight state-of-the-art deep learning-based object detection models. The results demonstrate that deep CNN models are promising in CRC screening. This work can serve as a baseline for future research in polyp detection and classification.

Prospective correlation between the patient microbiome with response to and development of immune-mediated adverse effects to immunotherapy in lung cancer.

BACKGROUND: Though the gut microbiome has been associated with efficacy of immunotherapy (ICI) in certain cancers, similar findings have not been identified for microbiomes from other body sites and their correlation to treatment response and immune related adverse events (irAEs) in lung cancer (LC) patients receiving ICIs. METHODS: We designed a prospective cohort study conducted from 2018 to 2020 at a single-center academic institution to assess for correlations between the microbiome in various body sites with treatment response and development of irAEs in LC patients treated with ICIs. Patients must have had measurable disease, ECOG 0-2, and good organ function to be included. Data was collected for analysis from January 2019 to October 2020. Patients with histopathologically confirmed, advanced/metastatic LC planned to undergo immunotherapy-based treatment were enrolled between September 2018 and June 2019. Nasal, buccal and gut microbiome samples were obtained prior to initiation of immunotherapy +/- chemotherapy, at development of adverse events (irAEs), and at improvement of irAEs to grade 1 or less. RESULTS: Thirty-seven patients were enrolled, and 34 patients were evaluable for this report. 32 healthy controls (HC) from the same geographic region were included to compare baseline gut microbiota. Compared to HC, LC gut microbiota exhibited significantly lower α-diversity. The gut microbiome of patients who did not suffer irAEs were found to have relative enrichment of Bifidobacterium (p = 0.001) and Desulfovibrio (p = 0.0002). Responders to combined chemoimmunotherapy exhibited increased Clostridiales (p = 0.018) but reduced Rikenellaceae (p = 0.016). In responders to chemoimmunotherapy we also observed enrichment of Finegoldia in nasal microbiome, and increased Megasphaera but reduced Actinobacillus in buccal samples. Longitudinal samples exhibited a trend of α-diversity and certain microbial changes during the development and resolution of irAEs. CONCLUSIONS: This pilot study identifies significant differences in the gut microbiome between HC and LC patients, and their correlation to treatment response and irAEs in LC. In addition, it suggests potential predictive utility in nasal and buccal microbiomes, warranting further validation with a larger cohort and mechanistic dissection using preclinical models. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03688347 . Retrospectively registered 09/28/2018.

DRAGoM: Classification and Quantification of Noncoding RNA in Metagenomic Data.

Noncoding RNAs (ncRNAs) play important regulatory and functional roles in microorganisms, such as regulation of gene expression, signaling, protein synthesis, and RNA processing. Hence, their classification and quantification are central tasks toward the understanding of the function of the microbial community. However, the majority of the current metagenomic sequencing technologies generate short reads, which may contain only a partial secondary structure that complicates ncRNA homology detection. Meanwhile, de novo assembly of the metagenomic sequencing data remains challenging for complex communities. To tackle these challenges, we developed a novel algorithm called DRAGoM (Detection of RNA using Assembly Graph from Metagenomic data). DRAGoM first constructs a hybrid graph by merging an assembly string graph and an assembly de Bruijn graph. Then, it classifies paths in the hybrid graph and their constituent readsinto differentncRNA families based on both sequence and structural homology. Our benchmark experiments show that DRAGoMcan improve the performance and robustness over traditional approaches on the classification and quantification of a wide class of ncRNA families.

Relating Gut Microbiome and Its Modulating Factors to Immunotherapy in Solid Tumors: A Systematic Review.

Background: Gut microbiome is proved to affect the activity of immunotherapy in certain tumors. However, little is known if there is universal impact on both the treatment response and adverse effects (AEs) of immune checkpoint inhibitors (ICIs) across multiple solid tumors, and whether such impact can be modulated by common gut microbiome modifiers, such as antibiotics and diet. Methods: A systematic search in PubMed followed by stringent manual review were performed to identify clinical cohort studies that evaluated the relevance of gut microbiome to ICIs (response and/or AEs, 12 studies), or association of antibiotics with ICIs (17 studies), or impact of diet on gut microbiome (16 studies). Only original studies published in English before April 1st, 2020 were used. Qualified studies identified in the reference were also included. Results: At the phylum level, patients who had enriched abundance in Firmicutes and Verrucomicrobia almost universally had better response from ICIs, whereas those who were enriched in Proteobacteria universally presented with unfavorable outcome. Mixed correlations were observed for Bacteroidetes in relating to treatment response. Regarding the AEs, Firmicutes correlated to higher incidence whereas Bacteroidetes were clearly associated with less occurrence. Interestingly, across various solid tumors, majority of the studies suggested a negative association of antibiotic use with clinical response from ICIs, especially within 1-2 month prior to the initiation of ICIs. Finally, we observed a significant correlation of plant-based diet in relating to the enrichment of "ICI-favoring" gut microbiome (P = 0.0476). Conclusions: Gut microbiome may serve as a novel modifiable biomarker for both the treatment response and AEs of ICIs across various solid tumors. Further study is needed to understand the underlying mechanism, minimize the negative impact of antibiotics on ICIs, and gain insight regarding the role of diet so that this important lifestyle factor can be harnessed to improve the therapeutic outcomes of cancer immunotherapy partly through its impact on gut microbiome.

The long noncoding RNA lnc-HLX-2-7 is oncogenic in Group 3 medulloblastomas.

BACKGROUND: Medulloblastoma (MB) is an aggressive brain tumor that predominantly affects children. Recent high-throughput sequencing studies suggest that the noncoding RNA genome, in particular long noncoding RNAs (lncRNAs), contributes to MB subgrouping. Here we report the identification of a novel lncRNA, lnc-HLX-2-7, as a potential molecular marker and therapeutic target in Group 3 MBs. METHODS: Publicly available RNA sequencing (RNA-seq) data from 175 MB patients were interrogated to identify lncRNAs that differentiate between MB subgroups. After characterizing a subset of differentially expressed lncRNAs in vitro and in vivo, lnc-HLX-2-7 was deleted by CRISPR/Cas9 in the MB cell line. Intracranial injected tumors were further characterized by bulk and single-cell RNA-seq. RESULTS: Lnc-HLX-2-7 is highly upregulated in Group 3 MB cell lines, patient-derived xenografts, and primary MBs compared with other MB subgroups as assessed by quantitative real-time, RNA-seq, and RNA fluorescence in situ hybridization. Depletion of lnc-HLX-2-7 significantly reduced cell proliferation and 3D colony formation and induced apoptosis. Lnc-HLX-2-7-deleted cells injected into mouse cerebellums produced smaller tumors than those derived from parental cells. Pathway analysis revealed that lnc-HLX-2-7 modulated oxidative phosphorylation, mitochondrial dysfunction, and sirtuin signaling pathways. The MYC oncogene regulated lnc-HLX-2-7, and the small-molecule bromodomain and extraterminal domain family‒bromodomain 4 inhibitor Jun Qi 1 (JQ1) reduced lnc-HLX-2-7 expression. CONCLUSIONS: Lnc-HLX-2-7 is oncogenic in MB and represents a promising novel molecular marker and a potential therapeutic target in Group 3 MBs.

Family-Based Whole-Exome Analysis of Specific Language Impairment (SLI) Identifies Rare Variants in BUD13, a Component of the Retention and Splicing (RES) Complex.

Specific language impairment (SLI) is a common neurodevelopmental disorder (NDD) that displays high heritability estimates. Genetic studies have identified several loci, but the molecular basis of SLI remains unclear. With the aim to better understand the genetic architecture of SLI, we performed whole-exome sequencing (WES) in a single family (ID: 489; n = 11). We identified co-segregating rare variants in three new genes: BUD13, APLP2, and NDRG2. To determine the significance of these genes in SLI, we Sanger sequenced all coding regions of each gene in unrelated individuals with SLI (n = 175). We observed 13 additional rare variants in 18 unrelated individuals. Variants in BUD13 reached genome-wide significance (p-value < 0.01) upon comparison with similar variants in the 1000 Genomes Project, providing gene level evidence that BUD13 is involved in SLI. Additionally, five BUD13 variants showed cohesive variant level evidence of likely pathogenicity. Bud13 is a component of the retention and splicing (RES) complex. Additional supportive evidence from studies of an animal model (loss-of-function mutations in BUD13 caused a profound neural phenotype) and individuals with an NDD phenotype (carrying a CNV spanning BUD13), indicates BUD13 could be a target for investigation of the neural basis of language.

Targeting the interaction between RNA-binding protein HuR and FOXQ1 suppresses breast cancer invasion and metastasis.

Patients diagnosed with metastatic breast cancer have a dismal 5-year survival rate of only 24%. The RNA-binding protein Hu antigen R (HuR) is upregulated in breast cancer, and elevated cytoplasmic HuR correlates with high-grade tumors and poor clinical outcome of breast cancer. HuR promotes tumorigenesis by regulating numerous proto-oncogenes, growth factors, and cytokines that support major tumor hallmarks including invasion and metastasis. Here, we report a HuR inhibitor KH-3, which potently suppresses breast cancer cell growth and invasion. Furthermore, KH-3 inhibits breast cancer experimental lung metastasis, improves mouse survival, and reduces orthotopic tumor growth. Mechanistically, we identify FOXQ1 as a direct target of HuR. KH-3 disrupts HuR-FOXQ1 mRNA interaction, leading to inhibition of breast cancer invasion. Our study suggests that inhibiting HuR is a promising therapeutic strategy for lethal metastatic breast cancer.

3D cell culture stimulates the secretion of in vivo like extracellular vesicles.

For studying cellular communications ex-vivo, a two-dimensional (2D) cell culture model is currently used as the "gold standard". 2D culture models are also widely used in the study of RNA expression profiles from tumor cells secreted extracellular vesicles (EVs) for tumor biomarker discovery. Although the 2D culture system is simple and easily accessible, the culture environment is unable to represent in vivo extracellular matrix (ECM) microenvironment. Our study observed that 2D- culture derived EVs showed significantly different profiles in terms of secretion dynamics and essential signaling molecular contents (RNAs and DNAs), when compared to the three-dimensional (3D) culture derived EVs. By performing small RNA next-generation sequencing (NGS) analysis of cervical cancer cells and their EVs compared with cervical cancer patient plasma EV-derived small RNAs, we observed that 3D- culture derived EV small RNAs differ from their parent cell small RNA profile which may indicate a specific sorting process. Most importantly, the 3D- culture derived EV small RNA profile exhibited a much higher similarity (~96%) to in vivo circulating EVs derived from cervical cancer patient plasma. However, 2D- culture derived EV small RNA profile correlated better with only their parent cells cultured in 2D. On the other hand, DNA sequencing analysis suggests that culture and growth conditions do not affect the genomic information carried by EV secretion. This work also suggests that tackling EV molecular alterations secreted into interstitial fluids can provide an alternative, non-invasive approach for investigating 3D tissue behaviors at the molecular precision. This work could serve as a foundation for building precise models employed in mimicking in vivo tissue system with EVs as the molecular indicators or transporters. Such models could be used for investigating tumor biomarkers, drug screening, and understanding tumor progression and metastasis.