Toruner, Merih Deniz

Abstract Purpose: Pancreatic ductal adenocarcinoma (PDAC) frequently recurs after neoadjuvant therapy (NT) and curative-intent resection. Although major pathologic response predicts favorable outcomes, most patients achieve only minor response with a heterogeneous recurrence risk. We asked whether the spatial organization of residual PDAC encodes clinically relevant biology beyond residual tumor burden. Experimental Design: In a retrospective cohort of 203 patients with resected PDAC treated with NT and restricted to minor pathologic response, routine hematoxylin and eosin (H&E) whole-slide images were segmented into cancer and stroma using an artificial intelligence–enabled pipeline. Spatial composition (patch density, edge density) and configuration (compactness/complexity, intermixing) were quantified and tested for associations with disease-free survival (DFS) using multivariable models adjusted for standard clinicopathologic factors. Results: Shorter DFS was associated with a fragmented, interface-rich tumor–stroma ecology featuring higher edge density and diversity and reduced homotypic aggregation, independent of clinicopathologic variables. Two spatial risk models were independently prognostic: (i) cancer mean shape index plus stromal shape index variability [adjusted hazard ratio (HR), 1.71; P = 0.003] and (ii) mean stromal patch area plus edge density (adjusted HR, 2.19; P = 0.002). Both models stratified outcomes in which pathologic response grading and residual cancer area did not. High-risk configurations were further associated with reduced intratumoral tumor-infiltrating lymphocyte (TIL) density and infiltration ratio, with relative TIL accumulation at the cancer periphery and within the stroma, consistent with an immune-excluded phenotype. Conclusions: Residual cancer–stroma topology quantified from standard H&E slides yields independent prognostic signals after NT in PDAC, provides a cellular immune correlate for spatial risk, and motivates prospective validation and spatially informed adjuvant strategies.

Abstract Background: The spatial organization of the tumor microenvironment (TME) encodes the selective pressures that drive cancer evolution, but many tools focus on local cell contacts or single-modality readouts. We developed Tumor Landscape Analysis (TLA), a modular framework that borrows from landscape ecology and spatial statistics to quantify heterogeneity, fragmentation, and diversity across scales, from single cells to whole-slide ecosystems. Methods: TLA ingests whole-cell or nuclear centroids and region-level segmentations to compute (i) point statistics such as nearest-neighbor distance, Ripley’s H, and Morisita-Horn colocalization; (ii) kernel-smoothed local spatial profiles including Getis-Ord Gi* hot and cold spots; and (iii) fragmentation, shape, and diversity metrics such as patch density, contagion, shape index, and Shannon entropy. We also introduce Local Microenvironments (LMEs), unsupervised ecological niches defined by joint cell-type abundance and spatial mixing. Pilot analyses were performed in ductal carcinoma in situ (DCIS; n=353 samples, 175 patients) with cell-level maps of epithelial, lymphocyte, and fibroblast populations, and in pancreatic ductal adenocarcinoma (PDAC; n=203 resected cases after neoadjuvant therapy) using cancer and stroma segmentations. Results: In DCIS, spatial relationships rather than raw abundance associated with recurrence risk. Shorter lymphocyte-to-fibroblast minimum nearest-neighbor distance and lower Ripley’s H, indicating greater intermixing, correlated with earlier recurrence independent of cell counts. This highlights immune–stromal proximity as a candidate eco-evolutionary driver of persistence. In PDAC, tissue-level fragmentation captured prognostic architecture after therapy. Two shape-based signatures emerged: (1) stromal uniformity, where lower standard deviation of stromal patch shape index associated with improved disease-free survival, consistent with a more constrained, less reactive stroma; and (2) tumor geometric complexity, where higher mean cancer patch shape index, reflecting more irregular or dendritic nests, associated with lower recurrence risk, whereas compact nests predicted worse outcomes. Across datasets, LME maps localized niche phenotypes, such as mixed-immune or segmented tumor habitats, that align with hypothesized selection landscapes and provide inputs for fragmentation analysis. Conclusions: TLA operationalizes an eco-evolutionary view of cancer by unifying interpretable, multiscale spatial metrics in a single workflow that works with any rasterized data input. By quantifying how habitats, edges, and mixing patterns are organized, TLA reveals niche structures and architectural motifs linked to clinical outcomes and complements foundation models and modality-specific pipelines with theory-grounded, clinically translatable descriptors. Ongoing work integrates TLA features with molecular data and outcomes to derive robust biomarkers and to inform adaptive, niche-targeted strategies in precision oncology. Citation Format: Ryan M. Carr, Luis Cisneros, Merih Deniz. Toruner, Martin E. Fernandez-Zapico, Carlo Maley. Tumor landscape analysis: An ecologically informed framework to understand tumor microenvironments [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85(23_Suppl):Abstract nr A015.

Abstract Introduction Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers. While total neoadjuvant therapy (TNT) has improved outcomes, recurrence rates remain high. Major pathologic response after TNT is prognostic of better survival, but is rarely achieved. Current pathologic systems primarily consider residual tumor percentage, nodal status, and perineural invasion but overlook spatial organization within the tumor microenvironment (TME). We aimed to evaluate spatial features of the TME to identify biologically and clinically relevant patterns of TME conformation and configuration after TNT and surgical resection of PDAC. Methods We retrospectively analyzed patients with PDAC treated with TNT followed by surgery at Mayo Clinic (2010-2022) with partial or no response per College of American Pathologists (CAP) criteria. Hematoxylin and eosin-stained whole slide images (WSIs) were processed using PathExplore™ PDAC (v1.0) to quantify spatial TME features. These were correlated with disease-free survival (DFS). Results A total of 203 patients were included: 47.3% female, median age 66.5 years. All received TNT; 73.4% had neoadjuvant chemotherapy followed by chemoradiation. Surgical procedures included pancreaticoduodenectomy (62.1%), distal pancreatectomy (22.2%), and total pancreatectomy (15.8%). CAP-defined response was partial in 82.1% and absent in 17.9%. As expected, pathologic T stage (p=0.001), N stage (p=0.002), lymphovascular invasion (p=0.048), and R2 resection (p=0.007) were significantly associated with DFS. However, CAP response category (p=0.053), residual cancer area (p=0.47), and cancer-to-stroma ratio (p=0.25) were not. We analyzed 297 WSIs from 203 patients. Of 31 spatial features evaluated, 5 were associated with DFS (p<0.20). Recursive partitioning using ln(mean cancer shape index [CSI]) <1.775 and ln(SD of stroma shape index [SSI]) >0.651 identified a high-risk group (n=63) with median DFS of 7.23 months (95% CI: 5.19–9.63), compared to 11.57 months in the low-risk group (95% CI: 9.24–17.92) (HR=1.82; 95% CI: 1.31–2.52; p=0.0003). This remained significant after adjusting for T and N stage (HR=1.75; 95% CI: 1.17–2.63; p=0.007). An alternate prognostic index derived via backwards elimination, calculated using 0.2701(mean stromal area) + 0.3905(edge density), also stratified patients. High-risk patients (n=51) had a significantly shorter (p<0.001) DFS (6.2 months) than those in the low-risk category (20.0 months). This significant difference holds in a multivariable analysis controlling for pathologic stage, N stage, and resection margins (HR=1.94, 95% CI: 1.21-3.10, p=0.006). Discussion Current assessment of residual PDAC after TNT is limited by subjective grading and lack of spatial context. Using Tumor Landscape Analysis, we identified high-risk spatial features of the TME that independently predict recurrence. These findings support a novel spatially-informed framework for risk stratification in resected PDAC with residual disease. Citation Format: Miranda Lin, Luis Cisneros, Merih D. Toruner, Andrea Maraone, Paul Dizona, Qian Shi, Martin E. Fernandez-Zapico, Ryan M. Carr. Spatial features of the tumor microenvironment predict recurrence risk in resected pancreatic ductal adenocarcinoma after total neoadjuvant therapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr A051.

For over a century, scientists reported the disruption of normal nuclear shape and size in cancer. These changes have long been used as tools for diagnosis and staging of malignancies. However, to date, the mechanisms underlying these aberrant nuclear phenotypes and their biological significance remain poorly understood. Using a model of pancreatic ductal adenocarcinoma (PDAC), the major histological subtypes of pancreatic cancer, we found that oncogenic mutant KRAS reduces nuclear size. Transcriptomic and protein expression analysis of mutant KRAS–expressing PDAC cells revealed differential levels of several nuclear envelope–associated genes. Further analysis demonstrated the nuclear lamina protein, Emerin (EMD), acted downstream of KRAS to mediate nuclear size reduction in PDAC. Analysis of human PDAC samples showed that increased EMD expression associates with reduced nuclear size. Finally, in vivo genetic depletion of EMD in a mutant KRAS–driven PDAC model resulted in increased nuclear size and a reduced incidence of poorly differentiated PDAC. Thus, our data provide evidence of a potentially novel mechanism underlying nuclear size regulation and its effect in PDAC carcinogenesis.

e14626 Background: Despite advancements in neoadjuvant therapy and surgical techniques, patients with pancreatic ductal adenocarcinoma (PDAC) often experience high recurrence rates after total neoadjuvant therapy (TNT) and resection. Existing tumor response scoring systems, such as the College of American Pathologists (CAP) guidelines, fail to stratify recurrence risk effectively, emphasizing the need for novel, objective approaches. Methods: This study evaluated spatial characteristics of residual cancer and stroma in a multi-site cohort of 203 patients with resected PDAC following TNT, not having achieved a major pathologic response (CAP 2 or 3). Whole slide images (WSI) of hematoxylin and eosin-stained sections were digitized and analyzed using artificial intelligence to perform tissue segmentation. Metrics quantifying spatial morphology and configuration, inspired by principles of landscape ecology, were computed to explore spatial features of the tumor microenvironment (TME). Statistical models (recursive partitioning and regression tree [RPRT] model and Cox model with backwards eliminations) incorporating these features were developed to stratify recurrence risk. Results: Five metrics were used for risk classification model build. Three models successfully classified patients into high- and low-risk groups based on spatial metrics. Models demonstrated moderate discriminatory power (C-statistics: 0.560–0.566) and showed that increased tumor fragmentation, like stromal patch density and cancer patch shape index, were strongly associated with improved outcomes. Conclusions: Integrating spatial metrics of cancer-stroma configuration enhances risk stratification beyond conventional scoring systems. Given increased tumor fragmentation is associated with improved outcomes, these data indirectly suggest ecological mechanisms of treatment resistance. This approach offers a pathway toward personalized post-operative management strategies to improve outcomes in PDAC. Risk classification models. Model Description Event/n Median DFS (95% CI) HR(95% CI) p-value C-statistics 1(RPRT) High risk: log standard deviation of the stroma shape index > 0.651 87 / 101 8.22(5.72 – 10.1) 1.64(1.20 – 2.25) 0.002 0.562 Low risk: log standard deviation of the stroma shape index ≤ 0.651 71 / 102 11.90(9.24 – 25.1) Ref 2(RPRT) High risk: log standard deviation of the stroma shape index > 0.651 & mean cancer shape index ≤ 1.775 58 / 63 7.23(5.19 – 9.63) 1.82(1.31 – 2.52) 0.0003 0.560 Low risk: (log standard deviation of the stroma shape index > 0.651 & mean cancer shape index > 1.775) or log standard deviation of the stroma shape index ≤ 0.651 100 / 140 11.57(9.24 – 17.92) Ref 3(Backwards elimination) High risk: PI * > median 86 / 101 8.02(6.08 – 9.86) 1.73(1.26 – 2.37) 0.0007 0.566 Low risk:PI * ≤ median 72 / 102 15.72(9.97 – 25.05) Ref *PI: (0.2701 * log mean stroma area) + (0.3905 * log edge density).

4155 Background: The tumor microenvironment (TME) that surrounds pancreatic ductal adenocarcinoma (PDAC) is a multi-faceted and dynamic ecosystem in which stromal fibroblasts communicate with cancer cells to mediate tumor growth, metastasis, and chemotherapy resistance. It is well recognized that both cancer-associated fibroblasts (CAFs) and its non-cellular, fibrotic components within the TME can foster a protumorigenic environment for PDAC. However, we still lack a comprehensive understanding of the precise mechanisms in which this dense, fibrotic matrix can help drive malignant behaviors. Here, we reveal a novel mechanism in which the zinc-finger transcription factor GLI2 regulates type 1 collagen expression within CAFs and how the soluble variant of this collagen promotes irinotecan chemoresistance. Methods: We leveraged transcriptomic data from The Cancer Genome Atlas, International Cancer Genome Consortium, and Clinical Proteomic Tumor Analysis Consortium to evaluate GLI2 expression and stromal content of human PDAC tumors through bulk RNA-sequencing deconvolution. Using single-nucleus RNA sequencing of human PDAC tumors, we validated the association of GLI2 expression and stromal matrix constituents in CAFs. Chromatin immunoprecipitation assays in human CAFs confirmed GLI2 binding at the COL1A1 promoter. Through RNAi-based inactivation of GLI2, we determined how loss of GLI2 impacts regulation of type 1 collagen. Additionally, we conducted MTT assays to assess tumor viability in response to irinotecan treatment. Results: Transcriptomic analysis revealed that GLI2 is highly enriched in CAFs and strongly correlated with stromal fibrosis compared to other non-tumor cell constituents within the TME. We have shown that GLI2 directly binds to the promoter of COL1A1, a key component of type 1 collagen, in CAFs and regulates its transcription in a manner dependent on TGFβ1 signaling. Interestingly, PDAC tumors exposed to type 1 collagen show increased expression of pro-tumorigenic pathways involved in inflammation, EGR signaling, cytokine-receptor interactions, and irinotecan resistance. We further validate that human PDAC cells pre-treated with collagen can confer chemoresistance to irinotecan with viability assays. Conclusions: Taken together, our study demonstrates a novel mechanism in which GLI2 regulates the secretion of collagen within CAFs, which in turn can enable PDAC to acquire resistance to standard-of-care treatments. These findings highlight not only the oncogenic functions for CAFs and their fibrotic secretome, but also open new avenues in which therapeutic targeting of the TME may provide clinical benefit for PDAC patients.

Abstract 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, Ripley’s 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.

Abstract Despite advances in surgery and chemotherapy, patients with resectable pancreatic ductal adenocarcinoma (PDAC) face high recurrence rates (40-60%). Total neoadjuvant therapy (TNT) seeks to enhance margin-negative resections (R0) and address micrometastatic disease, with potential survival benefits. Yet, TNT still results in high recurrence rates, with 1-, 2-, and 3-year disease-free survival (DFS) at 65%, 48%, and 32%, respectively. Residual cancer extent is a key prognostic factor in PDAC after TNT. Tumor response scores show favorable outcomes only for patients achieving near-complete or complete pathologic response. In a cohort of 511 PDAC patients treated with TNT and surgery (2015-2022), improved DFS was only found in those with a complete pathologic response (pCR, 7.8%). This highlights the limited use of current response scoring in stratifying post-TNT patient risk, underscoring the need to better understand disease persistence and relapse. Here we developed a quantitative method to assess residual cancer. Using a digital pathology H&E library from 2180 PDAC cases (2000-2022), an AI platform segmented tissue into cancer epithelium, stroma, and normal regions. For 202 TNT-treated cases without pCR, we calculated a ratio of cancer area to stromal area from representative slides. Modeled after the histologic tumor regression grade (HTRG) system, cases were classified into <5%, 5-50%, or >50% cancer-to-stroma ratios. However, this measure alone showed no difference in DFS, suggesting tumor burden cannot fully explain differences in clinical outcomes. We hypothesized that the spatial organization of the tumor microenvironment (TME) is a determinant of patient outcomes. Drawing from landscape ecology, which studies spatial properties of ecosystems, we applied similar principles to analyze tissue architecture. Using an AI-based two-class segmentation of whole-slide images, we assessed spatial features like patch area, perimeter, density, and Shannon diversity. Python’s ‘pylandstats’ package calculated 21 spatial metrics, reduced via stepwise Cox regression using the Akaike information criterion (AIC). The final model included mean Shannon diversity, mean and standard deviation of stromal patch areas, and stromal patch density. Kaplan-Meier analysis of 202 samples stratified model predictions into quartiles, correlating with high (mean DFS 10.85 months), medium (19.32 months), and low risk (25.92 months) groups. These findings aligned with model expectations, underscoring the potential of spatial TME analysis to predict outcomes. Citation Format: Andrea V. Maraone, Zafar Siddiqui, Luis H. Cisneros, Merih D. Toruner, Mark Truty, Cornelius Thiels, Robert R. McWilliams, Steven R. Alberts, Rondell P. Graham, Carlo Maley, Qian Shi, Martin E. Fernandez-Zapico, Ryan M. Carr. Tumor spatial configuration predicts pancreatic cancer recurrence risk after total neoadjuvant therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5070.

Purpose: To investigate the molecular characteristics of and potential for precision medicine in KRAS wildtype pancreatic ductal adenocarcinoma (PDAC). Patients and Methods: We investigated 27 patients with KRASWT PDAC at our institution. Clinical data were obtained via chart review. Tumor specimens for each subject were interrogated for somatic single nucleotide variants, insertion and deletions, and copy number variants by DNA sequencing. Gene fusions were detected from RNA-seq. A patient-derived organoid (PDO) was developed from a patient with a MET translocation and expanded ex vivo to predict therapeutic sensitivity prior to enrollment in a phase 2 clinical trial. Results: Transcriptomic analysis showed our cohort may be stratified by the relative gene expression of the KRAS signaling cascade. The PDO derived from our patient harboring a TFG-MET rearrangement was found to have in vitro sensitivity to the multi-tyrosine kinase inhibitor crizotinib. The patient was enrolled in the phase 2 SPARTA clinical trial and received monotherapy with vebrelitinib, a c-MET inhibitor, and achieved a partial and durable response. Conclusions: KRASWT PDAC is molecularly distinct from KRASMUT and enriched with potentially actionable genetic variants. In our study, transcriptomic profiling revealed that the KRAS signaling cascade may play a key role in KRASWT PDAC. Our report of a KRASWT PDAC patient with TFG-MET rearrangement who responded to a cMET inhibitor further supports the pursuit of precision oncology in this sub-population. Identification of targetable mutations, perhaps through approaches like RNA-seq, can help enable precision-driven approaches to select optimal treatment based on tumor characteristics.