Biology subjects

Toruner, M. D.

Publications and source records attributed to Toruner, M. D..

3 recordsLinked to original sources

bioRxiv2025

Spatial Conformation of Pancreatic Cancer Is Associated with Disease Recurrence after Curative-Intent Total Neoadjuvant Therapy

Pancreatic ductal adenocarcinoma (PDAC) frequently recurs after total neoadjuvant therapy (TNT) and curative-intent resection. Traditional histopathologic response assessments demonstrate that major pathologic response (<5-10% viable residual cancer) is associated with favorable outcomes. However, most TNT cases achieve only a minor response. We investigated whether the spatial organization of residual PDAC encodes clinically meaningful biology beyond residual tumor burden. In a retrospective cohort of 203 resected PDAC patients, all with minor pathologic response, H&E whole-slide images were segmented into cancer and stroma to subsequently quantify spatial composition (e.g., patch size/density, edge density) and configuration (e.g., patch complexity/compactness, spatial intermixing) to model treatment-resistant tumor architecture. Non-response was associated with a more fragmented interface-rich ecology, higher edge density and diversity, and reduced homotypic aggregation, independent of conventional clinicopathologic features. This demonstrates a link between emergent tissue architecture of treated PDAC and therapeutic resistance. Further, two multivariable, spatial risk models were independently associated with disease-free survival (DFS): (1) cancer mean shape index and stromal shape-index variability (high-risk median DFS 7.23 vs 11.57 months; adjusted HR 1.75, p=0.007) and (2) mean stromal area with edge density (high- vs low-risk adjusted HR 1.94, p=0.006), outperforming traditional treatment response assessments. Quantifying residual cancer-stroma topology thus yields independent, prognostic signals in post-TNT PDAC and motivates prospective, spatially informed adjuvant strategies and mechanistic studies of edge habitats and mixing as therapeutic vulnerabilities.

cancer biology↗

bioRxiv2025

Tumor Landscape Analysis: An Ecologically Informed Framework to Understand Tumor Microenvironments

Tumor microenvironments (TMEs) are spatially complex and dynamic systems shaped by evolutionary pressures, tissue architecture, and cellular interactions. To capture this complexity, we developed the Tumor Landscape Analysis (TLA) pipeline, a computational framework applying principles from landscape ecology and spatial statistics to quantitatively characterize tumor spatial heterogeneity. TLA integrates spatially resolved pathology data, including whole-cell, point-based, and region-level formats, and computes multiscale metrics to assess cell distributions, neighborhood relationships, and tissue-level organization. The framework leverages ecological indices such as the Morisita-Horn index, Ripleys H function, and Shannon diversity to quantify intercellular proximity, spatial clustering, and cellular diversity. It also uses the concept of local microenvironments (LMEs), data-driven ecological niches defined by local cell-type abundance and spatial uniformity, enabling unsupervised and reproducible classification of tumor regions. The use of fragmentation metrics, including patch density, shape complexity, and interspersion, provide further insight into spatial disorganization and emergent tissue architecture. TLA is agnostic to imaging modality and biological context, supporting broad applicability across tumor types and sample formats. By translating complex tissue architectures into interpretable spatial metrics, the pipeline enables integrative analyses that link spatial ecology to clinical and molecular phenotypes. This approach facilitates a deeper understanding of how spatial features contribute to tumor progression, therapeutic resistance, and clinical outcomes, offering new opportunities for precision oncology rooted in spatial systems biology.

cancer biology↗

bioRxiv2024

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma.

Cancer cells show remarkable plasticity and can switch lineages in response to the tumor microenvironment. Cellular plasticity drives invasiveness and metastasis and helps cancer cells to evade therapy by developing resistance to radiation and cytotoxic chemotherapy. Increased understanding of cell fate determination through epigenetic reprogramming is critical to discover how cancer cells achieve transcriptomic and phenotypic plasticity. Glioblastoma is a perfect example of cancer evolution where cells retain an inherent level of plasticity through activation or maintenance of progenitor developmental programs. However, the principles governing epigenetic drivers of cellular plasticity in glioblastoma remain poorly understood. Here, using machine learning (ML) we employ cross-patient prediction of transcript expression using a combination of epigenetic features (ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq) of glioblastoma stem cells (GSCs). We investigate different ML and deep learning (DL) models for this task and build our final pipeline using XGBoost. The model trained on one patient generalizes to another one suggesting that the epigenetic signals governing gene transcription are consistent across patients even if GSCs can be very different. We demonstrate that H3K27Ac is the epigenetic feature providing the most significant contribution to cross-patient prediction of gene expression. In addition, using H3K27Ac signals from patients-derived GSCs, we can predict gene expression of human neural crest stem cells suggesting a shared developmental epigenetic trajectory between subpopulations of these malignant and benign stem cells. Our cross-patient ML/DL models determine weighted patterns of influence of epigenetic marks on gene expression across patients with glioblastoma and between GSCs and neural crest stem cells. We propose that broader application of this analysis could reshape our view of glioblastoma tumor evolution and inform the design of new epigenetic targeting therapies.

cancer biology↗