Maley, Carlo C.

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 Therapeutic resistance to chemotherapy is frequently the proximate cause of increased tumor burden, cancer treatment failure, and ultimately, patient mortality. The most successful therapy strategy derived from evolutionary practices to date, Adaptive Therapy (AT), leverages the fitness cost of developing therapeutic resistance. AT avoids treating cancer cells with the Maximum Tolerated Dose (MTD) of therapeutic drugs and instead incorporates a series of adaptive dosing schedules. Treatment-sensitive cells are bolstered by modulating or pausing treatment to out-compete and suppress treatment-resistant cell growth. We are evaluating AT for the first time in colorectal cancer, using a syngeneic orthotopic xenograft model. This work represents several key advances: it is the first to longitudinally and non-invasively track sensitive and resistant clones in vivo, to evaluate adaptive therapy in an immunocompetent mouse model, to incorporate multi-drug treatment regimens, and to integrate a targeted therapeutic agent. The dual-bioluminescent sensitive and resistant CT26 cell lines developed for this study allow real-time visualization and quantification of each population throughout tumor progression and treatment. Together, these advances establish a robust and clinically relevant platform for characterizing resistance dynamics and refining adaptive therapy strategies aimed at improving treatment outcomes in colorectal cancer. Citation Format: Harley I. Richker, Bailey Sierra Kane, Gissel Marquez Alcaraz, Ellie Pahl, Ryan Carr, Nagarajan Kannan, Carlo C. Maley. Harnessing clonal dynamics to overcome resistance: An adaptive therapy framework in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 702.

Abstract Motivation Intratumor heterogeneity arises from ongoing somatic evolution and complicates cancer diagnosis, prognosis, and treatment. Reconstructing evolutionary dynamics typically requires spatiotemporal samples, which are often unavailable in clinical settings. Computational approaches that can infer tumor evolutionary history from single-timepoint bulk sequencing data remain limited. Results We present estimating evolutionary events through single-timepoint sequencing (TEATIME), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric—fitness diversity—which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types. Availability and implementation R implementation of TEATIME is available on GitHub (https://github.com/liliulab/TEATIME) and Zenodo (https://zenodo.org/records/17422174).

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 Intratumor heterogeneity arises from ongoing somatic evolution complicating cancer diagnosis, prognosis, and treatment. Here we present TEATIME (es t imating e volution a ry events t hrough s i ngle-ti m epoint s e quencing), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric—fitness diversity—which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types.

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).

e12568 Background: Modern preventive screening have made it possible to identify ductal carcinoma in situ (DCIS). Progression of DCIS to invasive disease is known to be driven by somatic evolution and affected by clinical interventions. Yet studies indicate that only 20–30% of the cases of DCIS will progress further if not treated, implying that a large portion of patients are over treated. Recognizing which pre-cancerous tumors are likely to progress to cancer or recur as DCIS after initial treatment is of great importance to optimize treatment options, lower associated risks and allocate healthcare efforts to patients that most benefit from early treatment. Methods: This study consists of a retrospective cohort of 158 patients, 57 of which had a DCIS recurrence over 2 to 228 months after treatment, and 45 progressed to invasive disease over 12 to 180 months. We generated a digital pathology library consisting of hematoxylin and eosin (H&E) stained whole slide images (WSI), and applied machine learning methods to perform duct and nuclear segmentation followed by cell classification. We then computed a set of spatial statistics from landscape ecology to characterize each sample. We used LASSO on half of the data (the training set) to select a Cox proportional hazards model that predicted recurrence of DCIS or cancer and used the other half of the data (the test set) to validate the predictors in the Cox model. Results: When considering the time to recurrence as disease-free survival (DFS), the type of DCIS treatment stratifies patients between Lumpectomy Only and More than Lumpectomy groups (log-rank test p-value < 0.0001), recapitulating previous results. When the subset of patients that had more than a lumpectomy was analyzed using spatial landscape ecology metrics, a decrease in the interaction between tumor cells and fibroblasts and between tumor cells and lymphocytes, measured by the mean nearest neighbor distance, were associated with improved DFS (concordance = 0.717) in the model validation set. Stratification of samples by the median risk is discriminatory with a p-value < 0.001. Alternatively, decreased nearest neighbor distance between lymphocytes and fibroblasts was associated with the time to invasive disease (p-value < 0.05). Conclusions: Integrating spatial ecological statistics of cell distributions enhances risk stratification of DCIS samples beyond the known effects of early treatment. Our results highlight the importance of ecological mechanisms in recurrence, progression and treatment resistance. The risk associated with spatial interactions of lymphocytes and fibroblasts suggests previously unknown ecological interactions may be associated with early micro-metastases that result in breast cancer even after mastectomy or lumpectomy with radiation in patients with DCIS.

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 &lt;5%, 5-50%, or &gt;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.

Abstract One of the main reasons we have not been able to cure cancers is that treatments select for drug-resistant cells. Pest managers face similar challenges with pesticides selecting for pesticide-resistant insects, resulting in similar mechanisms of resistance. Pest managers have developed 10 principles that could be translated to controlling cancers: (i) prevent onset, (ii) monitor continuously, (iii) identify thresholds below which there will be no intervention, (iv) change interventions in response to burden, (v) preferentially select nonchemical control methods, (vi) use target-specific drugs, (vii) use the lowest effective dose, (viii) reduce cross-resistance, (ix) evaluate success based on long-term management, and (x) forecast growth and response. These principles are general to all cancers and cancer drugs and so could be employed broadly to improve oncology. Here, we review the parallel difficulties in controlling drug resistance in pests and cancer cells. We show how the principles of resistance management in pests might be applied to cancer. Integrated pest management inspired the development of adaptive therapy in oncology to increase progression-free survival and quality of life in patients with cancers where cures are unlikely. These pest management principles have the potential to inform clinical trial design.