Biology subjects

Riedlinger, G.

Publications and source records attributed to Riedlinger, G..

8 recordsLinked to original sources

bioRxiv2026

Distinct clonal dynamics and interactions within the microenvironment near tumor stroma interfaces in rare histologic variants of bladder cancer

Rare histologic variants of bladder carcinoma, such as squamous cell and neuroendocrine carcinoma, generally have a worse prognosis compared to pure urothelial carcinoma (PUC), but the underlying molecular determinants are not well understood. We developed a novel computational genomics framework to characterize the dynamics of tumor-stroma-immune interactions at tumor borders and interiors from spatial transcriptomics data. We profiled bladder carcinoma samples of different histological variants on the Visium platform. Differences in clonal phylogeny and spatial heterogeneity of major subclones between the samples suggested disparate clonal spatiotemporal dynamics and interaction with stromal and immune compartments - which was notably prominent at the tumor-stroma interface. Our framework captured immune heterogeneity in the tumor microenvironment, including variations in the presence and architecture of tertiary lymphoid structures. Our analyses further indicated that there are substantial histology-specific differences in cell type composition, clonal spatial heterogeneity, inter-cellular signaling, and cellular processes. These variations collectively suggest divergent mechanisms of microenvironment remodeling across bladder cancer histologies. Cell-free DNA profiling from liquid biopsy captured tumor and microenvironment signatures from tumor boundaries and interiors, potentially allowing for tracking clonal dominance non-invasively. Our method tracks the trajectory of neoplastic disease in bladder cancer samples while identifying aggressive features.

genomics↗

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

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

Disruptive changes in tissue microenvironment prime oncogenic processes at different stages of carcinogenesis in lung

Carcinogenesis is characterized not only by the uncontrolled growth of malignant cells but also by the disruption of the normal balance of cellular processes and intercellular interactions in the microenvironment that overcome the constraints of tissue homeostasis and support malignant growth. We profiled benign pulmonary dysplasia, carcinoma in situ, and invasive lung carcinomas at single-cell resolution to identify composite changes in cellular processes, signaling, and interactions among tumor-immune-stromal cells in the microenvironment in progressively advanced disease stages. We developed OncoTerrain, a hyperparameter-tuned model that captured synergistic multimodal signatures of an increasingly perturbed microenvironment in malignant disease stages and identified composite microenvironmental changes that supported cancer hallmarks. Key cancer-related changes in transcriptional states, cellular processes, and intercellular interactions involving immune, fibroblast, and stromal cell types preceded tumor initiation and were often synergistic. The microenvironment of increasingly malignant tissues was characterized by immune avoidance, ECM remodeling, and altered cell mobility. There were changes in cell states in fibroblasts, macrophages, and their inter-cellular interactions with other cell types, whereas T-cell activation occurred late. The in-situ carcinomas showed variations in the composite microenvironmental states that corroborated their pathology, which was not apparent at the genome level. A subset of those harbored populations of tumor and non-tumor cells with aggressive characteristics in some but not all aspects of hallmarks of carcinogenesis in the lung. We suspect that the variation in the coordination of microenvironmental cues may influence why some but not all in-situ tumors progress to the advanced stages.

genomics↗

bioRxiv2024

Deconstructing evolutionary histories of complex genomic rearrangements in lung malignancies

Somatic genomic rearrangements are hallmarks of cancer. Complex genomic rearrangements (CGRs) involving multiple intertwined structural alterations are often present in tumor genomes. CGRs frequently harbor oncogenic drivers, but their genomic architectures and etiologies are poorly understood. We used deep-coverage optical mapping technology to profile the genomic landscapes of normal lung tissues, benign pulmonary lesions, carcinoma in situ, and advanced carcinomas to examine the patterns of genome disorganization and instability in different stages of carcinogenesis in lung. Large rearrangements and CGRs were prevalent in the carcinomas. We developed omcplR to resolve the architecture of CGRs and predict their genesis from optical mapping data using genome-graph concept. We found that CGRs often arose from hierarchical combinations of multiple, localized simple structural variations, and harbored allelic heterogeneity at the affected loci. Rearrangement patterns and associated genomic signatures suggested that chromoanasynthesis was a likely prevalent mechanism driving complex genomic rearrangements. The early rearrangement junctions in intra-chromosomal CGRs were usually localized within the same chromatin domains, but the late junctions in advanced tumors had more heterogeneous contexts suggesting progressive organizational heterogeneity. The CGRs, especially the late events therein were under positive selection. A composite signature of genomic alterations including the CGRs captured the trajectory of progressive genomic disorganization and instability with carcinogenesis in lung and underscored the extent of genomic structural heterogeneity among the in-situ tumors.

genomics↗

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

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↗

bioRxiv2019

All-FIT: Allele-Frequency-based Imputation of Tumor Purity from High-Depth Sequencing Data

MotivationClinical sequencing aims to identify somatic mutations in cancer cells for accurate diagnosis and treatment. However, most widely used clinical assays lack patient-matched control DNA and additional analysis is needed to distinguish somatic and unfiltered germline variants. Such computational analyses require accurate assessment of tumor cell content in individual specimens. Histological estimates often do not corroborate with results from computational methods that are primarily designed for normal-tumor matched data and can be confounded by genomic heterogeneity and presence of sub-clonal mutations.\n\nMethodsAll-FIT is an iterative weighted least square method to estimate specimen tumor purity based on the allele frequencies of variants detected in high-depth, targeted, clinical sequencing data.\n\nResultsUsing simulated and clinical data, we demonstrate All-FITs accuracy and improved performance against leading computational approaches, highlighting the importance of interpreting purity estimates based on expected biology of tumors.\n\nAvailability and ImplementationFreely available at http://software.khiabanian-lab.org.

bioinformatics↗