Biology subjects

Hoyd, R.

Publications and source records attributed to Hoyd, R..

12 recordsLinked to original sources

bioRxiv2026

Mitochondrial Oxygen Consumption Drives Lung Tumor Hypoxia and Resistance to Therapy via Copy Number Alteration in Mitochondrial Electron Transport Subunit NDUFB5

Decades of research have shown that tumor hypoxia is associated with resistance to anti-cancer treatments. Analysis of TCGA gene expression profiles indicates that NSCLC is among the most hypoxic of cancers despite the high levels of oxygen in the surrounding lung tissue. Several groups have shown that extrinsic factors such as poorly formed tumor vascular contributes to tumor hypoxia. Here, we have investigated the possibility that genetic abnormalities within the tumor also contribute to the development of hypoxia. Our analysis of NSCLC patient datasets in the Cancer Genome Atlas (TCGA) PanCancer and ORIEN datasets revealed a strong correlation between tumor hypoxia and amplification of chromosome 3q which is found in up to 40% of NSCLC. Several oncogenic driver genes have been identified in 3q, and we identified a passenger gene encoding mitochondrial complex I subunit NDUFB5 at 3q26.33. To provide experimental evidence that NDUFB5 amplification can drive tumor hypoxia, we have used CRISPR activation technology to generate murine cells overexpressing the endogenous NDUFB5 gene. We found that cells overexpressing NDUFB5 have elevated rates of oxygen consumption, and tumors grown from these cells have increased amounts of hypoxia with associated treatment resistance. Here, we investigate the impact of manipulating NDUFB5 gene expression on mitochondrial complex I activity and experimentally validate the clinical observations that NDUFB5 overexpression leads to increased levels of intratumoral hypoxia and increased resistance to radiation therapy and immunotherapy.

cancer biology↗

bioRxiv2025

Candida albicans infiltrates colon and rectal cancers causing therapeutic resistance and decreased survival

The microbiome is increasingly recognized as a modifier of cancer progression and therapy response, yet the role of intratumoral fungi remains poorly defined. Here, we identify Candida albicans colonization within human colorectal tumors as a predictor of reduced survival and impaired radiation response. Leveraging the Oncology Research Information Exchange Network (ORIEN) cohort, we show that high intratumoral Candida burden is associated with decreased survival across multiple gastrointestinal cancers, with the strongest treatment-specific effect in rectal cancer patients receiving radiotherapy. This observation was validated in independent rectal cancer cohorts using RNA sequencing and quantitative PCR. In immune-competent murine colorectal cancer models, oral gavage of C. albicans resulted in intratumoral colonization, accelerated tumor growth, and radiation resistance, effects not observed with Saccharomyces cerevisiae or PBS controls. Colonized tumors exhibited increased hypoxia, altered metabolic and transcriptional programs, and distinct expression of genes linked to cytokine signaling and cell survival. Hypoxia conditioned C. albicans secreted metabolites that directly conferred radiation resistance to colorectal cancer cells in vitro, implicating a cancer cell intrinsic mechanism independent of immune signaling. Untargeted metabolomics revealed enrichment of nucleosides and lipid oxidation intermediates under hypoxia, suggesting that C. albicans metabolites may provide substrates facilitating tumor recovery after irradiation. These findings establish C. albicans as a causal modifier of tumor biology and radiation response, highlighting intratumoral fungi as future potential therapeutic targets. Modulating fungal colonization or metabolism may improve radiotherapy outcomes and broaden our understanding of interactions between microbes and tumors.

cancer biology↗

bioRxiv2025

Primary tumor microbiomes predict distant metastasis of colorectal cancer

Metastasis causes most cancer-related deaths in colorectal carcinoma (CRC), and microbiome markers may have prognostic value. We hypothesized that primary tumor microbiomes predict distant metastases. We analyzed 5-year metastasis-free survival (MFS) in a retrospective cohort of 900 ORIEN CRC tumor microbiomes (RNAseq). ORIEN findings were validated on an independent cohort using 16S rDNA sequencing and pathobiont-specific qPCR. Microbiome alpha diversity was higher in primary tumors than metastases and positively correlated with metastasis risk. Microbiome beta diversity distinguished primary vs. metastasis and predicted 5-year MFS. High primary tumor abundance of B. fragilis and low F. nucleatum were associated with short MFS. Enterobacteriaceae, including E. coli, were enriched in metastases. qPCR identified increased enterotoxigenic B. fragilis and pks+ E. coli detection in CRC metastasizers. Microbial co-occurrence analysis identified a 3-species clique that predicts metastasis (OR 1.9 [1.4-2.6]). Results suggest that primary tumor microbiomes and specific pathobionts are precision markers for metastasis risk.

cancer biology↗

bioRxiv2025

mt.surv: Multi-Threshold Survival Analysis for Associating Continuous Predictor Variables with Time-to-Event Outcomes

MotivationTime-to-event models are a useful and common approach to infer the importance of biological variables. Most often, the predictor variable is binarized and associated with censored time to death--a so-called Kaplan-Meier curve. However, the threshold to binarize the continuous variable is often arbitrary. We sought a rigorous way to define thresholds and evaluate the strength and consistency of association with the event of interest. ResultsWe present {mt.surv}, an R package for multi-threshold survival analyses. The primary function performs a time-to-event analysis, where a continuous predictor variable is stratified into two groups at an arbitrary number of places, defined by the percentile of the distribution. The result can be visualized with a function that creates a custom line plot with the -log of a log-likelihood p-value against the threshold percentile. A third function operates on this plot and calculates the area above a significance threshold, creating a scalar that can be used to rank biological variables for their association with the event of interest. This framework can be broadly applied to any continuous predictor variable including, but not limited to, gene expression and microbial abundances. Several helper functions are included for structuring input data and job submission in a cluster framework. We found that this method has value in discovery-type analyses where one lacks prior information about appropriate stratification thresholds. However, we found additional biological insight is possible-- indeed, quite common--as many variables show different associations at different stratification thresholds. AvailabilityOn CRAN as {mt.surv}. Contactdaniel.spakowicz@osumc.edu

bioinformatics↗

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↗

bioRxiv2024

Breast tumor microbiome regulates anti-tumor immunity and T cell-associated metabolites

BackgroundBreast cancer, the most common cancer type among women, was recently found to contain a specific tumor microbiome, but its impact on host biology remains unclear. CD8+ tumor-infiltrating lymphocytes (TILs) are pivotal effectors of anti-tumor immunity that influence cancer prognosis and response to therapy. This study aims to elucidate interactions between CD8+ TILs and the breast tumor microbiome and metabolites, as well as how the breast tumor microbiome may affect the tumor metabolome. MethodsWe investigated the interplay among CD8+ TILs, the tumor microbiome, and the metabolome in a cohort of 46 breast cancer patients with mixed subtypes (Cohort A). We characterized the tumor metabolome by mass spectrometry and CD8+ TILs by immunohistochemistry. Microbiome composition and T cell gene transcript levels were obtained from data from our previous study, which utilized 16S rRNA gene sequencing and a targeted mRNA expression panel. To examine interactions between intratumoral Staphylococcus and specific breast cancer subtypes, we analyzed RNA sequencing data from an independent cohort of 370 breast cancer patients (Cohort B). We explored the functions of the tumor microbiome using mouse models of triple-negative breast cancer (TNBC). ResultsIn tumors from Cohort A, the relative abundance of Staphylococcus positively correlated with the expression of T cell activation genes. The abundances of multiple metabolites exhibited significant correlations with CD8+ TILs, of which NADH, {gamma}-glutamyltryptophan, and {gamma}-glutamylglutamate displayed differential abundance in Staphylococcus-positive versus Staphylococcus-negative breast tumors. In a larger breast cancer cohort (Cohort B), we observed positive correlations between tumoral Staphylococcus and CD8+ TIL activity exclusively in TNBC. Preclinical experiments demonstrated that intratumoral administration of S. aureus, the predominant species of Staphylococcus in human breast tumors, resulted in a depletion of total NAD metabolites, and reduced the growth of TNBC tumors by activating CD8+ TILs. ConclusionsWe identified specific metabolites and microbial taxa associated with CD8+ TILs, delineated interactions between the breast tumor microbiome and metabolome, and demonstrated that intratumoral Staphylococcus influences anti-tumor immunity and TIL-associated metabolites. These findings highlight the role of low-biomass microbes in tumor tissues and provide potential biomarkers and therapeutic agents for breast cancer immunotherapy that merit further investigation.

cancer biology↗

bioRxiv2024

Dynamic Human Gut Microbiome and Immune Shifts During an Immersive Psychosocial Therapeutic Program

BackgroundDepression is a leading cause of disability worldwide yet its underlying factors, particularly microbial associations, are poorly understood. MethodsWe examined the longitudinal interplay between the microbiome and immune system in the context of depression during an immersive psychosocial intervention. 142 multi-omics samples were collected from 52 well-characterized participants before, during, and three months after a nine-day inquiry-based stress reduction program. ResultsWe found that depression was associated with both an increased presence of putatively pathogenic bacteria and reduced microbial beta-diversity. Following the intervention, we observed reductions in neuroinflammatory cytokines and improvements in several mental health indicators. Interestingly, participants with a Prevotella-dominant microbiome showed milder symptoms when depressed, along with a more resilient microbiome and more favorable inflammatory cytokine profile, including reduced levels of CXCL-1. ConclusionsOur findings reveal a protective link between the Prevotella-dominant microbiome and depression, associated with a less inflammatory environment and moderated symptoms. These insights, coupled with observed improvements in neuroinflammatory markers and mental health from the intervention, highlight potential avenues for microbiome-targeted therapies in depression management.

microbiology↗

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

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

Exogenous sequences in tumors and immune cells (exotic): a tool for estimating the microbe abundances in tumor RNAseq data

The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is unclear. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool to carefully identify the tumor microbiome within RNAseq datasets and then applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas (TCGA). We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non-high-throughput sequencing-based approaches. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We identify associations with survival and clinical variables that are highly cancer-specific and relatively few associations with immune composition. Finally, we explore potential mechanisms by which microbes and tumors may interact using a network approach. Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The exotic tool can support the discovery of microbes in tumors in a way that leverages the many existing and growing RNAseq datasets. Statement of SignificanceThe intrinsic tumor microbiome holds great potential for its ability to predict various aspects of cancer biology and as a target for rational manipulation. Here, we describe a tool to quantify microbes from within tumor RNAseq and apply it to two independent datasets. We show new associations with clinical variables that justify biomarker uses and more experimentation into the mechanisms by which tumor microbiomes affect cancer outcomes.

cancer biology↗

bioRxiv2020

Bayesian Structural Time Series for Mobile Health and Sensor Data: A Flexible Modeling Framework for Evaluating Interventions

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures as well as knowledge of the temporal and spatial properties of the data. Thus, biomedical sensor data need to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. We further show how this framework corrects for covariates to provide accurate assessments of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool around a specific Google implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the resulting software implementation to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity. In particular, we show how the framework is able to evaluate an exercise interventions effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

bioinformatics↗