Biology subjects

Tan, A. C.

Publications and source records attributed to Tan, A. C..

11 recordsLinked to original sources

bioRxiv2025

Ephrin inhibition disrupts stromal-cancer crosstalk and reduces metastasis in pancreatic cancer

Cancer associated fibroblasts (CAFs) are critical drivers of disease progression and metastasis within the pancreatic tumour microenvironment. Using a 3D spheroid model of CAF-led invasion, we identified complementary expression of ephrin family receptors (EPHB2) and ligands (EPHRINB2) between cancer cells and CAFs, implicating this bidirectional signalling family in tumour progression. Through pancreatic stellate cell-derived CAFs isolated from genetically modified mouse models, where EphrinB was either lacking or modified by a gain-of-function mutation, we identified both forward and reverse signalling to be required for invasion. In a syngeneic murine orthotopic model of pancreatic cancer, we show that first-in-class tetramerisation inhibitors of Eph/Ephrin interactions can reduce local invasion in primary tumours, and significantly decrease metastatic tumour spread across multiple organ sites. Our data highlight Ephrin signalling as a critical nexus for CAF-cancer crosstalk and establish a foundation for the clinical development of targeted EPH-EPHRIN inhibitors to counter metastatic invasion.

cancer biology↗

bioRxiv2025

RNA splicing is required for timely completion of abscission and is modulated by the abscission checkpoint

The abscission checkpoint pauses the progression of cytokinetic abscission at the intercellular bridge when various checkpoint-sustaining errors are sensed. A unique cytoplasmic organelle, the abscission checkpoint body (ACB), forms when the abscission checkpoint is active. This organelle houses proteins involved in abscission and RNA splicing factors. Here, we investigate the role of RNA splicing factors in abscission checkpoint maintenance. A specific subset of RNA splicing factors localizes to ACBs, while others move back into the nucleus. The key RNA splicing factor SF3b1 is aberrantly localized in the cytoplasm and its catalytic activity limited when ACBs are present. Efficient abscission was found to rely on RNA splicing, and we have identified patterns in gene expression and RNA splicing that occur at this time in cell division. Finally, we found a unique RNA landscape associated with the formation of ACBs due to a specific set of changes in RNA expression and alternative splicing, revealing a previously unrecognized level of regulation imparted by abscission checkpoint activity.

cell biology↗

bioRxiv2025

Obesity promotes conserved inflammatory and metabolic transcriptional programs in mouse and human colon tumors

BackgroundThe global prevalence of obesity, an established risk and progression factor for colon cancer, is high and rising. Unfortunately, the mechanisms underlying the obesity-colon cancer association are incompletely understood, and new molecular targets enabling more effective intervention strategies to break the obesity-colon cancer link are urgently needed. ObjectiveThis study integrated RNA sequencing data from mouse and human colon tumor samples, as well as human adipose samples, to rigorously establish obesity-associated transcriptomic signatures conserved between the two species. MethodsWe employed a mouse colon cancer model with colonoscopy-guided orthotopic transplantation of syngeneic Apc-null;KrasG12D/+;Trp53-null;Smad4-null;tdTomato colon tumor organoids. Epithelial cell adhesion molecule (EpCAM)-positive cells from murine tumors, and 193 human colon tumors and 188 human mesenteric adipose tissue samples from the ColoCare cohort underwent transcriptomic analyses. ResultsDiet-induced obesity reduced survival in the mouse model of colon cancer. Integrated transcriptomic analyses of EpCAM-positive murine tumor cells and bulk human tumors revealed obesity-driven enrichment of inflammation and metabolic pathways, including upregulation of genes involved in innate immune sensing (TLR2, MYD88, IRF4) and tumor microenvironment remodeling (MMP9, TGFB1, SERPINE1). Analysis of paired mesenteric visceral adipose tissue and tumor samples from the ColoCare cohort indicated that obesity amplifies inflammatory signaling pathways through unique adipose ligand-tumor receptor interactions. ConclusionsThese results establish obesity-associated adipose tissue dysregulation as a key inter-tissue modulator of biology, with concordant cross-species effects on tumor cell-intrinsic inflammatory and metabolic programs.

cancer biology↗

bioRxiv2025

Epigenetic Modulation, Intra-tumoral Microbiome and Immunity in Early Onset Colorectal Cancer

BackgroundThe incidence of colorectal cancer (CRC) in young adults (age of diagnosis < 50 years old) has been rapidly increasing. Although [~]20% of early-onset (EO) CRC cases are due to germline mutations, the etiology of the majority of EOCRC cases remains poorly understood. Non-genetic factors such as environmental exposure and lifestyle changes are likely to have a direct link to the increased incidence of sporadic EOCRC. We hypothesize that such factors may be observable as differences in the EOCRC epigenome, microbiome and immunome. We sought to address this by comparing differences in DNA methylation from the cohort of colorectal cancer patients in The Cancer Genome Atlas (TCGA). Further, we carefully identified intra-tumoral microbes from TCGA and two other datasets and then related the microbes to EOCRC status and deconvolved immune cell abundances. We found that DNA methylation (DNAm) age acceleration by12 years when compared with average-onset CRC (AOCRC) patients. Differentially methylated sites associated with genes are related to CREB signaling in neurons, G protein coupled receptor signaling, phagosome formation and S100 family signaling. These differences were validated in the gene expression from TCGA and a second, larger real-world dataset from the Oncology Research Information Exchange Network (ORIEN). However, no consistent differences were observed in the intra-tumor microbes between EOCRC and AOCRC. Interestingly, the most abundant microbes interacted with the immune systems differently between the EOCRC and AOCRC tumors, characterized by more, larger, positive correlations in EOCRC. These data suggest epigenetic modulation and accelerated aging may play a key role in the development of EOCRC. SIGNIFICANCEWe investigated whether environmentally driven factors contribute to early-onset colorectal cancer (EOCRC). We observed accelerated epigenetic aging in EOCRC and epigenetic changes associated with chronic inflammation. Tumor immune cell abundances correlated more strongly with microbes in EOCRC than average-onset CRC. These data suggest a dysregulation of immune response in EOCRC, driving chronic inflammation and tissue aging.

cancer biology↗

bioRxiv2023

The tumor microbiome reacts to hypoxia and can influence response to radiation treatment in colorectal cancer

Tumor hypoxia has been shown to predict poor patient outcomes in several cancer types, partially because it reduces radiations ability to kill cells. We investigated whether some of the clinical effects of hypoxia could also be due to its impact on the tumor microbiome. We examined the RNA-seq data from the Oncology Research Information Exchange Network (ORIEN) database of colorectal cancer (CRC) patients treated with radiotherapy. For each tumor, we identified microbial RNAs and related them to the hypoxic gene expression scores calculated from host mRNA. Our analysis showed that the hypoxia expression score predicted poor patient outcomes and identified tumors enriched with certain microbes such as Fusobacterium nucleatum. The presence of other microbes, such as Fusobacterium canifelinum, predicted poor patient outcomes, suggesting a potential interaction between hypoxia, the microbiome, and radiation response. To investigate this concept experimentally, we implanted CT26 CRC cells into both immune-competent BALB/c and immune-deficient athymic nude mice. After growth, where tumors passively acquired microbes from the gastrointestinal tract, we harvested tumors, extracted nucleic acids, and sequenced host and microbial RNAs. We stratified tumors based on their hypoxia score and performed metatranscriptomic analysis of microbial gene expression. In addition to hypoxia-trophic and -phobic microbial populations, analysis of microbial gene expression at the strain level showed expression differences based on the hypoxia score. Hypoxia appears to not only associate with different microbial populations but also elicit an adaptive transcriptional response in intratumoral microbes. SIGNIFICANCETumor hypoxia reduces radiations ability to kill cells. We explored whether some of the clinical effects of hypoxia could also be due to interaction with the tumor microbiome. Hypoxic expression scores associated with certain microbes and elicited an adaptive transcriptional response in others.

immunology↗

bioRxiv2023

Dearth of smoking-induced mutations in oncogene-driven non-small-cell lung cancer despite smoking exposure

Non-small cell lung cancers (NSCLCs) in non-smokers are mostly driven by mutations in the oncogenes EGFR, ERBB2, and MET, and fusions involving ALK and RET. We term these "non-smoking-related oncogenes" (NSROs). In addition to occurring in non-smokers, NSRO-driven tumors also occur in smokers, and the clonal architecture and genomic landscape of these tumors remain unknown. We investigated genomic and transcriptomic alterations in 173 tumor sectors from 48 patients with NSRO-driven or typical-smoking NSCLCs. NSRO-driven NSCLCs in smokers and non-smokers have similar genomic landscapes. Surprisingly, even in patients with prominent smoking histories, the mutational signature caused by tobacco smoking was essentially absent in NSRO-driven NSCLCs. However, NSRO-driven NSCLCs in smokers had higher transcriptomic activities related to regulation of the cell cycle, suggesting that smoking still affects tumor phenotype independently of genomic alterations. Statement of significanceThis study highlights the lack of genomic scars caused by smoking in NSCLCs driven by non-smoking-related oncogenes regardless of smoking history. The impact of smoking on these tumors is mainly non-genomic. The transcriptomic features of NSCLCs associated with smoking may help in the development of therapeutic approaches.

cancer biology↗

bioRxiv2023

The tumor microbiome as a predictor of outcomes in patients with metastatic melanoma treated with immune checkpoint inhibitors

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICIs). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA-seq was conducted on the formalin-fixed paraffin-embedded (FFPE) tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival [≥]24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic}tool. The 71 patients with metastatic melanoma ranged in age from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy responsive versus non-responsive tumors. Responders showed significant enrichment of several microbes including Fusobacterium nucleatum, and non-responders showed enrichment of fungi, as well as several bacteria. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs. SignificanceWe analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy, and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.

immunology↗

bioRxiv2023

A bioinformatics tool for identifying intratumoral microbes from the ORIEN dataset

Evidence supports significant interactions among microbes, immune cells, and tumor cells in at least 10-20% of human cancers, emphasizing the importance of further investigating these complex relationships. However, the implications and significance of tumor-related microbes remain largely unknown. Studies have demonstrated the critical roles of host microbes in cancer prevention and treatment responses. Understanding interactions between host microbes and cancer can drive cancer diagnosis and microbial therapeutics (bugs as drugs). Computational identification of cancer-specific microbes and their associations is still challenging due to the high dimensionality and high sparsity of intratumoral microbiome data, which requires large datasets containing sufficient event observations to identify relationships, and the interactions within microbial communities, the heterogeneity in microbial composition, and other confounding effects that can lead to spurious associations. To solve these issues, we present a bioinformatics tool, MEGA, to identify the microbes most strongly associated with 12 cancer types. We demonstrate its utility on a dataset from a consortium of 9 cancer centers in the Oncology Research Information Exchange Network (ORIEN). This package has 3 unique features: species-sample relations are represented in a heterogeneous graph and learned by a graph attention network; it incorporates metabolic and phylogenetic information to reflect intricate relationships within microbial communities; and it provides multiple functionalities for association interpretations and visualizations. We analyzed 2704 tumor RNA-seq samples and MEGA interpreted the tissue-resident microbial signatures of each of 12 cancer types. MEGA can effectively identify cancer-associated microbial signatures and refine their interactions with tumors. SIGNIFICANCEStudying the tumor microbiome in high-throughput sequencing data is challenging because of the extremely sparse data matrices, heterogeneity, and high likelihood of contamination. We present a new deep-learning tool, microbial graph attention (MEGA), to refine the organisms that interact with tumors.

bioinformatics↗

bioRxiv2022

The translational role of SOS1 in colorectal cancer

BackgroundIt has been challenging to develop agents directly targeting KRAS driver mutations in colorectal cancer (CRC). Recent efforts have focused on developing inhibitors targeting SOS1 as an attractive approach for KRAS-mutant cancers. Here, we aimed to study the translational role of SOS1 in CRC using patient-derived organoids (PDOs). MethodIn this study, we used CRC PDOs as preclinical models to evaluate their sensitivity to SOS1 inhibitor BI3406 and its cellular effects. We utilized large CRC datasets including GENIE cohort, TCGA PanCancer Atlas, and CPTAC-2 cohort to study the significance of molecular alterations of SOS1 in CRC. To identify potential predictive markers, we performed immunohistochemistry (IHC) on CRC tissue for SOS1/2 protein expression and RNA sequencing to identify discriminative gene sets for sensitivity to SOS1 inhibition. The findings were validated by DepMap data for SOS1 dependency. ResultCRC PDOs instead of cell lines had differential sensitivities to BI3406. There was a significant correlation between SOS1 and SOS2 mRNA expressions (Spearmans {rho} 0.56, p<0.001). SOS1/2 protein expression by IHC were universal with heterogeneous patterns in cancer cells but only minimal to none in surrounding non-malignant cells. SOS1 protein expression was associated with worse overall survival in patients with RAS/RAF mutant CRC (p=0.04). We also found that SOS1/SOS2 protein expression ratio > 1 by IHC (p=0.03) instead of KRAS mutation (p=1) was a better predictive marker to BI3406 sensitivity of CRC PDOs. This was concordant with the significant correlation between SOS1/SOS2 protein expression ratio by mass spectrometry and SOS1 dependency score. RNA-seq and gene set enrichment analysis revealed differentially expressed genes and 7 enriched gene sets involving cholesterol homeostasis, epithelial mesenchymal transition, and TNF/NF{kappa}B signaling in BI3406-resistant CRC PDOs. We further discovered that GTP-bound RAS level underwent rebound at 48 hours upon treatment with BI3406 even in BI3406-sensitive PDOs with no change of KRAS effector genes downstream. Cellular adaptation mechanisms to SOS1 inhibition may involve upregulation of SOS1/2 mRNA and SOS1 protein expressions, which may be overcome by SOS1 knockdown/degradation or synergistic effect of BI3406 with trametinib. ConclusionIn summary, CRC PDOs could serve as better models for translational study of SOS1 in CRC. High SOS1 protein expression was a worse prognostic factor in CRC. High SOS1/SOS2 protein expression ratio predicted sensitivity to SOS1 inhibition and dependency. Our preclinical findings supported further clinical development of SOS1-targeting agents in CRC.

cancer biology↗

bioRxiv2021

Crowdsourced Identification of Multi-Target Kinase Inhibitors for RET- and TAU- Based Disease: the Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ( polypharmacology). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology. Author SummaryMany modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact, dirty; they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop cleaner therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease.

pharmacology and toxicology↗

bioRxiv2018

GSEA-InContext: Identifying novel and common patterns in expression experiments

MotivationGene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment.\n\nResultsWe compiled a compendium of 442 small molecule transcriptomic experiments and used GSEA to characterize common patterns of positively and negatively enriched gene sets. To identify experiment-specific gene set enrichment, we developed the GSEA-InContext method that accounts for gene expression patterns within a user-defined background set of experiments to identify statistically significantly enriched gene sets. We evaluated GSEA-InContext on experiments using small molecules with known targets and show that it successfully prioritizes gene sets that are specific to each experiment, thus providing valuable insights that complement standard GSEA analysis.\n\nAvailability and ImplementationGSEA-InContext is implemented in Python. Code, the background expression compendium, and results are available at: https://github.com/CostelloLab/GSEA-InContext

bioinformatics↗