Biology subjects

Boytim, E.

Publications and source records attributed to Boytim, E..

6 recordsLinked to original sources

bioRxiv2026

Discrete Transcriptional States Define Biphasic Immune Response and Dynamic CMS Transitions in Colorectal Cancer

BackgroundSequential alterations in APC, KRAS, TP53, and SMAD4 have been proposed as a framework for colorectal cancer progression. Human colorectal cancer datasets have not revealed the biological transitions associated with these mutations. When examining a cohort of TCGA-colorectal tumors grouped as AK (APC/KRAS), AKP (APC/KRAS/TP53), and AKPS (APC/KRAS/TP53/SMAD4), we observed no significant differences in immune-cell composition, four previously defined Consensus Molecular Subtypes (CMS1/2/3/4), or transcriptomic clustering between these genomic groups. Therefore, these canonical alterations do not sufficiently characterize the known properties of metastatic progression in human colorectal cancer. MethodsTo overcome these limitations, we developed a genetically defined, organoid-based, orthotopic mouse model whereby mouse colon organoids modeling sequential APC, KRAS, TP53, and SMAD4 alterations were orthotopically injected into the colon. This was followed by RNA-sequence processing, normalization with DESeq2, differential expression, pathway enrichment, and immune/stromal inference. Gene co-expression modules were identified from variance-stabilized mouse expression data, mapped to 1:1 human orthologs, and summarized as eigengenes. A multinomial logistic regression model trained on mouse eigengenes was applied to TCGA-COAD human tumors to assign them to mouse-informed transcriptomic states (AK-like, AKP-like, AKPS-like), which were then used for downstream visualization and comparative analyses. ResultsWhole-transcriptome analysis revealed discrete transcriptional states and immune-cell differences between the organoid AK/AKP/AKPS groups. Early TP53 loss led to strong activation of immune pathways, accompanied by increased infiltration of NK and T cells. As tumors progressed with SMAD4 loss and metastasis, this immune activity collapsed, giving rise to broad immune suppression. CMS classifications also shifted, with AK tumors resembling epithelial CMS2, AKP tumors displaying immune-rich CMS1 features, and AKPS and metastatic lesions adopting mesenchymal CMS4 characteristics. We then applied a progression-based transcriptomic classifier to 460 human colorectal tumors. This reclassification revealed conserved immune remodeling, CMS transitions, pathway-level differences, and significant differences in patient survival. ConclusionWe show that organoid-derived progression profiles reveal hidden evolutionary structure in human colorectal cancer and provide a transcriptional framework for interpreting metastatic potential and clinical outcomes.

cancer biology↗

bioRxiv2025

Viral evolution of T1L mammalian orthoreovirus enhances breast cancer stem-like cell killing

BackgroundBreast cancer is the second leading cause of death in women in the US. Among the different subtypes of breast cancer, estrogen receptor positive (ER+) has a more favorable prognosis; however, patients can experience cancer recurrence 10 years or more after initial diagnosis. Breast cancer stem-like cells (BCSCs) are slowly proliferating cells that drive metastasis and resistance to therapies that target rapidly proliferating tumor cells. BCSC self-renewal and survival pathways make them difficult to eliminate; no selective therapeutics currently exist to target them. Our research aims to use mammalian orthoreovirus (MRV) as an oncolytic agent to eliminate BCSCs. MethodsWe evaluated the effects of three different MRV strains (T3D, T1L and R2) under both 2D and 3D culture conditions (which enrich for BCSCs) in parental and paclitaxel resistant (TaxR) MCF7 cells using cell viability assays, flow cytometry, western blotting, and tumorsphere assays. We identified differentially expressed genes in response to T1L by RNA sequencing. Novel MRV strains were developed by serial passaging of the parental T1L strain in TaxR BCSCs, leading to the isolation and sequencing of three distinct MRV clones. The novel MRV clones were assessed for their oncolytic effects using the aforementioned techniques alongside inhibitor-based studies targeting cell death pathways. ResultsNotably, at least two clones (T1L SP B and T1L SP C) demonstrated enhanced oncolytic potency compared to the parent T1L strain against MCF7 TaxR BCSCs, even though their infectivity and viral protein synthesis was reduced. Whole genome sequencing of the viral clones identified changes in viral protein sequences that correlate with enhanced potency against BCSCs. T1L, T1L SP B and T1L SP C induced apoptosis; however, T1L SP B may induce a more immunogenic form of cell death in the TaxR cells, as indicated by the increased release of extracellular ATP (eATP), a damage-associated molecular pattern (DAMP), which is associated with immunogenic cell death. ConclusionsIn summary, we developed novel oncolytic viruses promote oncolysis in therapy-resistant BCSCs. We found that T1L SP B induces apoptosis in a more immunogenic manner, highlighting the potential of these viruses to eliminate BCSCs and stimulate the immune system.

cancer biology↗

bioRxiv2025

SOX2 utilizes FOXA1 as a heteromeric transcriptional partner to drive proliferation in therapy-resistant prostate cancer

Treatment options and diagnostic outlook for men with advanced, therapy resistant prostate cancer (PCa) are extremely poor; this is primarily due to the common lack of durable response to androgen receptor (AR) targeted therapies and phenotypic transdifferentiation into a particularly lethal subtype known as neuroendocrine prostate cancer (NEPC). In this study, we mechanistically determine that SOX2 (a transcription factor originally repressed by AR) physically binds and acts in a concerted manner with FOXA1 (a key AR pioneering cofactor) to regulate a subset of genes which promote cell cycle progression, and lineage plasticity in AR-refractory prostate cancers. Our findings assert the SOX2/FOXA1 interaction as an important mediator of resistance to AR-targeted therapy and a driver of NEPC and lineage plasticity; their coordinated action and downstream signaling offers a potential novel therapeutic opportunity in late-stage PCa.

cancer biology↗

bioRxiv2024

Deciphering Colorectal Cancer-Hepatocyte Interactions: A Multiomic Platform for Interrogation of Metabolic Crosstalk in the Liver-Tumor Microenvironment

Metabolic reprogramming is a hallmark of cancer, enabling tumor cells to adapt to and exploit their microenvironment for sustained growth. The liver is a common site of metastasis, but the interactions between tumor cells and hepatocytes remain poorly understood. In the context of liver metastasis, these interactions play a crucial role in promoting tumor survival and progression. This study leverages multiomics coverage of the microenvironment via liquid chromatography and high-resolution, high-mass accuracy mass spectrometry-based untargeted metabolomics, 13C-stable isotope tracing, and RNA sequencing to uncover the metabolic impact of co-localized primary hepatocytes and a colon adenocarcinoma cell line, SW480, using a 2D co-culture model. Metabolic profiling revealed disrupted Warburg metabolism with an 80% decrease in glucose consumption and 94% decrease in lactate production by hepatocyte-SW480 co-cultures relative to SW480 control cultures. Decreased glucose consumption was coupled with alterations in glutamine and ketone body metabolism, suggesting a possible fuel switch upon co-culturing. Further, integrated multiomic analysis indicates that disruptions in metabolic pathways, including nucleoside biosynthesis, amino acids, and TCA cycle, correlate with altered SW480 transcriptional profiles and highlight the importance of redox homeostasis in tumor adaptation. Finally, these findings were replicated in 3-dimensional microtissue organoids. Taken together, these studies support a bioinformatic approach to study metabolic crosstalk and discovery of potential therapeutic targets in preclinical models of the tumor microenvironment.

bioinformatics↗

bioRxiv2023

A Simple Bias Reduction Algorithm for RNA Sequencing Datasets

RNA sequencing (RNA-seq) is the conventional genome-scale approach used to capture the expression levels of all detectable genes in a biological sample. This is now regularly used in the clinical diagnostic space for cancer patients. While the information gained is intended to impact treatment decisions, numerous technical and quality issues remain. This includes inaccuracies in the dissemination of gene-gene relationships. For such reasons, clinical decisions are still mostly driven by DNA biomarkers, such as gene mutations or fusions. In this study, we aimed to correct for systemic bias based on RNA-sequencing platforms in order to improve our understanding of the gene-gene relationships. To do so, we examined standard pre-processed RNA-seq datasets obtained from three studies conducted by two consortium efforts including The Cancer Genome Atlas (TCGA) and Stand Up 2 Cancer (SU2C). We particularly examined the TCGA Bladder Cancer (n = 408) and Prostate Cancer (n = 498) studies as well as the SU2C Prostate Cancer study (n = 208). Using various statistical tests, we detected expression-level dependent, per-sample biases in all datasets. Using simulations, we show that these biases corrupt the results of t-tests designed to identify expression level differences between subpopulations. Importantly, these biases introduce large errors into estimates of gene-gene correlations. To mitigate these biases, we introduce Local Leveling as a novel mathematical approach that transforms count level data and corrects these observed biases. Local Leveling specifically corrects for the bias due to the inherent differential detection of transcripts that is driven by differential expression levels. Based on standard forms of count data (Raw counts, transcripts per million, fragments per kilobase of exon per million), we demonstrate that local leveling effectively removes the observed per-sample biases, and improves the accuracy in simulated statistical tests. Importantly, this led to systemic changes of gene-gene relationships when examining the correlation of key oncogenes, such as the Androgen Receptor, with all other detectable genes. Altogether, Local Leveling improves our capacity towards understanding gene-gene relationships, which may lead to novel ways to utilize the information derived from clinical tests.

genomics↗

bioRxiv2023

Unraveling the Global Proteome and Phosphoproteome of Prostate Cancer Patient-Derived Xenografts

Resistance to androgen deprivation therapies leads to metastatic castration-resistant prostate cancer (mCRPC) of adenocarcinoma (AdCa) origin that can transform to emergent aggressive variant prostate cancer (AVPC) which has neuroendocrine (NE)-like features. To this end, we used LuCaP patient-derived xenograft (PDX) tumors, clinically relevant models that reflects and retains key features of the tumor from advanced prostate cancer patients. Here we performed proteome and phosphoproteome characterization of 48 LuCaP PDX tumors and identified over 94,000 peptides and 9,700 phosphopeptides corresponding to 7,738 proteins. When we compared 15 NE versus 33 AdCa PDX samples, we identified 309 unique proteins and 476 unique phosphopeptides that were significantly altered and corresponded to proteins that are known to distinguish these two phenotypes. Assessment of protein and RNA concordance from these tumors revealed increased dissonance in transcriptionally regulated proteins in NE and metabolite interconversion enzymes in AdCa.

cancer biology↗