Biology subjects

Anderson, A.

Publications and source records attributed to Anderson, A..

10 recordsLinked to original sources

bioRxiv2026

Ex vivo human tumor slices more accurately predict patient responses to an oncolytic virus than in vivo mouse models

Immunotherapies, including oncolytic viruses (OV), are promising therapies that can enhance anti-tumor immune responses. However, preclinical success of immunotherapies in mouse models has not always translated to clinical benefit in cancer patients. This study compared preclinical efficacy and mechanism of action for ASP9801, a vaccinia virus expressing IL-7 and IL-12, using mouse models of colorectal cancer (CRC) in vivo and in human organotypic tumor slice models ex vivo. The murine surrogate for ASP9801 significantly reduced tumor volumes in treated and abscopal tumors in two different CRC models in vivo (MC38 and RO100). Treatment efficacy was accentuated when combined with anti-PD1 treatment, and single-cell RNA sequencing analysis revealed depletion of tumor cells and increased T cell infiltration and activation in both treated and abscopal tumors. However, human tissue analysis ex vivo (E-slices) using PDX models and patient samples showed that ASP9801 is not effective in CRC, consistent with clinical trial results. On the other hand, ASP9801 was highly effective in GBM, indicating indication-specific efficacy of ASP9801, and how E-slice assays can be used to identify treatment-sensitive indications. This study demonstrates the superiority of E-slices over mouse models for predicting clinical response and its utility in planning clinical trials.

cancer biology↗

bioRxiv2026

Cardiomyocyte-specific loss of Smyd5 leads to a robust activation of inflammatory signaling and heart failure in mice.

Background: Cardiomyocytes respond to stress by undergoing hypertrophic growth driven by dynamic changes in gene expression. Epigenetic mechanisms, including histone methylation, play critical roles in regulating these transcriptional programs, yet the enzymes controlling these modifications during cardiac disease remain largely unknown. The SMYD family of histone methyltransferases regulates gene expression in multiple biological contexts, but the function of SMYD5 in the mammalian heart has never been investigated. Methods: SMYD5 expression was assessed in human heart failure samples and in a mouse model of cardiac hypertrophy. To define its functional role in vivo, we generated inducible cardiomyocyte-specific Smyd5 knockout mice and characterized their cardiac phenotype using molecular, histological, and functional analyses. Chromatin immunoprecipitation-quantitative PCR (ChIP-qPCR) was performed to examine histone H4 lysine 20 trimethylation (H4K20me3) at the Il-6 promoter. Results: SMYD5 expression was altered in diseased human and mouse hearts. Under basal conditions, cardiomyocyte-specific deletion of Smyd5 resulted in baseline structural cardiac remodeling and transcriptional signatures characteristic of pathological stress. Smyd5-deficient hearts exhibited marked inflammatory activation resembling a cytokine storm with immune cell infiltration and heart failure. Notably, Smyd5 knockout mice displayed a 100-fold increase in Il-6 expression, accompanied by a global reduction in H4K20me3. ChIP-qPCR analysis of the Il-6 promoter, together with loss- and gain-of-function analysis of SMYD5, supports a direct epigenetic role of SMYD5 in regulating Il-6 expression through H4K20me3 in cardiomyocytes. Conclusions: SMYD5 is a previously unrecognized epigenetic regulator of cardiac homeostasis that restrains inflammatory signaling in cardiomyocytes under normal conditions. Loss of Smyd5 disrupts H4K20me3, leading to derepression of Il-6 in cardiomyocytes and a robust inflammatory response characterized by immune cell recruitment and fibrosis, accompanied by rapid progression of cardiac remodeling and heart failure. These findings identify SMYD5 as a critical regulator of intrinsic cardiomyocyte inflammatory signaling and reveal a novel chromatin-based mechanism contributing to inflammatory cardiomyopathies.

molecular biology↗

bioRxiv2018

A natural killer cell gene signature predicts melanoma patient survival

Animal models have demonstrated that natural killer (NK) cells can limit the metastatic dissemination of tumors, however their ability to combat established human tumors has been difficult to investigate.\n\nA number of computational methods have been developed for the deconvolution of immune cell types within solid tumors. We have taken the NK cell gene signatures from several tools, then curated and expanded this list using recent reports from the literature. Using a gene set scoring method to investigate RNA-seq data from The Cancer Genome Atlas (TCGA) we show that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of greater NK cell infiltration. Furthermore, these survival effects are enhanced in tumors which have a higher expression of NK cell stimuli such as IL-15, suggesting NK cells are part of a coordinated immune response within these patients. Using this signature we then examine transcriptomic data to identify tumor and stromal components which may influence the penetrance of NK cells into solid tumors.\n\nThese data support a role for NK cells in the regulation of human tumors and highlight potential survival effects associated with increased NK cell activity. Furthermore, our computational analysis identifies a number of potential targets which may help to unleash the anti-tumor potential of NK cells as we enter the age of immunotherapy.

cancer biology↗

bioRxiv2018

Using neural networks to bridge scales in cancer: Mapping signaling pathways to phenotypes

Cancer is an evolving system subject to mutation and selection. Selection is driven by the microenvironment that the cancer cells are growing in and acts on the cell phenotype, which is in turn modulated by intracellular signaling pathways regulated by the cell genotype. Integrating all of these processes requires bridging different biological scales. We present a mathematical model that uses a neural network as a means to connecting these scales. In particular, we consider the mapping from intracellular pathway activity to phenotype under different microenvironmental conditions.

cancer biology↗

bioRxiv2018

Functional characterization of sensory neuron membrane proteins (SNMPs)

Sensory neuron membrane proteins (SNMPs) play a critical role in the insect olfactory system but there is a deficit of functional studies beyond Drosophila. Here, we provide functional characterisation of insect SNMPs through the use of bioinformatics, genome curation, transcriptome data analysis, phylogeny, expression profiling, and RNAi gene knockdown techniques. We curated 81 genes from 35 insect species and identified a novel lepidopteran SNMP gene family, SNMP3. Phylogenetic analysis shows that lepidopteran SNMP3, but not the previously annotated lepidopteran SNMP2, is the true homologue of the dipteran SNMP2. Digital expression, microarray and qPCR analyses show that the lepidopteran SNMP1 is specifically expressed in adult antennae. SNMP2 is widely expressed in multiple tissues while SNMP3 is specifically expressed in the larval midgut. Microarray analysis suggest SNMP3 may be involved in the silkworm immunity response to virus and bacterial infections. We functionally characterised SNMP1 in the silkworm using RNAi and behavioural assays. Our results suggested that Bombyx mori SNMP1 is a functional orthologue of the Drosophila melanogaster SNMP1 and plays a critical role in pheromone detection. Split-ubiquitin yeast hybridization study shows that BmorSNMP1 has a protein-protein interaction with the BmorOR1 pheromone receptor, and the BmorOrco co-receptor. Concluding, we propose a novel molecular model in which BmorOrco, BmorSNMP1 and BmorOR1 form a heteromer in the detection of the silkworm sex pheromone bombykol.

molecular biology↗

bioRxiv2018

EREFinder: Genome-wide detection of estrogen response elements

MotivationEstrogen response elements (EREs) are specific DNA sequences to which ligand-bound estrogen receptors (ERs) physically bind, allowing them to act as transcription factors for target genes. Locating EREs and ER responsive regions is therefore a potentially important component of the study of estrogen-regulated pathways.\n\nResultsWe tested and demonstrated the ability of EREFinder, a novel algorithm we developed, to locate regions of ER-binding across the human genome and show that these regions designated by the program occur more frequently near estrogen responsive genes. EREFinder can handle large input files, has settings to allow for broad and narrow searches, and provides the full output to allow for greater data manipulation. These features facilitate a wide range of hypothesis testing for researchers and make EREFinder an excellent tool to aid in estrogen-related research.\n\nAvailability and ImplementationSource code and binaries freely available for download at https://github.com/JonesLabIdaho/EREfinder, implemented in C++ and supported on Linux and MS Windows.\n\nContactaanderson@bio.tamu.edu\n\nSupplemental MaterialsR scripts can be found at https://github.com/JonesLabIdaho/EREfinder

bioinformatics↗

bioRxiv2017

Collateral sensitivity is contingent on the repeatability of evolution

Antibiotic resistance represents a growing health crisis that necessitates the immediate discovery of novel treatment strategies. One such strategy is the identification of collateral sensitivities, wherein evolution under a first drug induces susceptibility to a second. Here, we report that sequential drug regimens derived from in vitro evolution experiments may have overstated therapeutic benefit, predicting a collaterally sensitive response where cross resistance ultimately occurs. We quantify the likelihood of this phenomenon by use of a mathematical model parametrised with combinatorially complete fitness landscapes for Escherichia coli. Through experimental evolution we then verify that a second drug can indeed stochastically exhibit either increased susceptibility or increased resistance when following a first. Genetic divergence is confirmed as the driver of this differential response through targeted and whole genome sequencing. Taken together, these results highlight that the success of evolutionarily-informed therapies is predicated on a rigorous probabilistic understanding of the contingencies that arise during the evolution of drug resistance.

evolutionary biology↗

bioRxiv2017

Hybrid approach for parameter estimation in agent-based models

Agent-based models are valuable in cancer research to show how different behaviors emerge from individual interactions between cells and their environment. However, calibrating such models can be difficult, especially if the parameters that govern the underlying interactions are hard to measure experimentally. Herein, we detail a new method to converge on parameter sets that fit an agent-based model to multiscale data using a model of glioblastoma as an example.

systems biology↗

bioRxiv2017

Dissecting targeted therapy resistance: Integrating models to quantify environment mediated drug resistance

Drug resistance is the single most important driver of cancer treatment failure for modern targeted therapies. This resistance may be due to the presence of dormant or aggressive tumor cell phenotypes or to context-driven protection. Non-malignant cells and other factors, constituting the microenvironment in which the tumor grows (the stroma), are now thought to play a crucial role in both therapeutic response and resistance. Specifically, the dialogue between the tumor and stroma has been shown to modulate the response to molecularly targeted therapies, through proliferative and survival signaling. The goal of this work is to investigate interactions between a growing tumor and its surrounding stroma in facilitating the emergence of drug resistance. We use mathematical modeling as a theoretical framework to bridge between experimental models and scales, with the aim of separating the intrinsic and extrinsic components of resistance in BRAF mutated melanoma. The model describes tumor-stroma dynamics both with and without treatment. Calibration of our model, through the integration of experimental data, revealed significant variation across animal replicates in either the intensity of stromal promotion or intrinsic tissue carrying capacity. Furthermore our study highlights the need to account for this variation in the design of treatment strategies. Major Findings. Through the integration of a simple mathematical model with in vitro and in vivo experimental growth dynamics of melanoma cell lines (both with and without drug), we were able to dissect the relative contributions of intrinsic versus environmental resistance. Our study revealed significant heterogeneity in vivo, indicating that there is a diversity of either stromal promotion or tumor carrying capacity under targeted therapy. We believe this variation may be one possible explanation for the heterogeneity observed across patients and within individual patients with multiple metastases. Therefore, quantifying this variation both within in vivo model systems and in individual patients could have a significant impact on the design of future treatment strategies that target both the tumor and stroma. Further, we present guidelines for building more effective and longer lasting therapeutic strategies utilizing our experimentally calibrated model. These strategies explicitly consider the protective nature of the stroma and utilize inhibitors that modulate it.\n\nPrecisQuantification of the environmental contribution to drug resistance reveals heterogeneity that significantly alters treatment dynamics that can be exploited for therapeutic gain.\n\nFinancial SupportPicco and Anderson: US National Cancer Institute grant U01CA151924.\n\nPicco: UK Engineering and Physical Sciences Research Council (EPSRC grant number EP/G037280/1).\n\nConflict of Interest DisclosureThe authors declare no potential conflicts of interest.

cancer biology↗

bioRxiv2016

Homeostasis Back and Forth: An Eco-Evolutionary Perspective of Cancer

The role of genetic mutations in cancer is indisputable: they are a key source of tumor heterogeneity and drive its evolution to malignancy. But the success of these new mutant cells relies on their ability to disrupt the homeostasis that characterizes healthy tissues. Mutated clones unable to break free from intrinsic and extrinsic homeostatic controls will fail to establish a tumor. Here we will discuss, through the lens of mathematical and computational modeling, why an evolutionary view of cancer needs to be complemented by an ecological perspective in order to understand why cancer cells invade and subsequently transform their environment during progression. Importantly, this ecological perspective needs to account for tissue homeostasis in the organs that tumors invade, since they perturb the normal regulatory dynamics of these tissues, often co-opting them for its own gain. Furthermore, given our current lack of success in treating advanced metastatic cancers through tumor centric therapeutic strategies, we propose that treatments that aim to restore homeostasis could become a promising venue of clinical research. This eco-evolutionary view of cancer requires mechanistic mathematical models in order to both integrate clinical with biological data from different scales but also to detangle the dynamic feedback between the tumor and its environment. Importantly, for these models to be useful, they need to embrace a higher degree of complexity than many mathematical modelers are traditionally comfortable with.

cancer biology↗