Citations

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains among the most aggressive cancers, due to its limited therapeutic options and poor prognosis. Even with the adoption of total neoadjuvant therapy (TNT) and potentially curative surgical resection, PDAC relapse rates remain unacceptably high. Current systems for evaluating tumor response are valuable but limited, as they fail to capture the ecological complexity and broader architecture of the tumor microenvironment (TME). This results in uncertainty regarding the prognosis and recurrence risk of patients. To address this and to capture the complexity of the PDAC TME, we developed the Tumor Landscape Analysis (TLA). Using digitized H&E slides from 203 PDAC patients who received TNT and surgical resection at our institution, we applied a machine-learning pipeline to segment tumor and stromal compartments. We then quantified the TME architecture using ecological frameworks to assess the disease in multiple scales: individual patches, whole-slide landscapes, and aggregated patient-level summaries. Our analysis revealed that higher-order spatial features are independently prognostic of disease-free survival (DFS). Two statistical models showed different but complementary spatial characteristics that predict disease risk. In the first model, patients with more irregular cancer patch shapes and lower variability in stromal shape had significantly better DFS, with high-risk patients relapsing in a median of 7.2 months compared to 11.6 months in the low-risk group (HR=1.82, p=0.0003). In the second model, smaller stromal patch areas combined with greater tumor-stroma edge density identified patients at elevated risk, with high-risk individuals showing a median DFS of 8.0 months versus 15.7 months in low-risk patients (HR=1.73, p=0.0007). Importantly, these associations persisted even after adjustment for the T and N stage, showing that spatial features provide unique information beyond traditional pathology measures. These models help visualize PDAC as a comprehensive ecosystem that carries clinical significance. By quantifying tumor conformation and configuration, our models reveal prognostic information not available through CAP grading, percent tumor burden, or routine cell counts. This study suggests that tumor-stroma interactions and their spatial organizations are potentially biologically important for recurrence. We present a new, ecologically inspired approach to digital pathology for PDAC that not only improves our ability to predict recurrence and survival in PDAC but also sheds light on cancer as a complex, evolving ecosystem. Citation Format: Merih D. Toruner, Luis H. Cisneros, Miranda Lin, Andrea Maraone, Carlo Maley, Paul Dizona, Qian Shi, Martin E. Fernandez-Zapico, Ryan M. Carr. Ecological landscape analysis of the tumor microenvironment predicts recurrence risk in pancreatic ductal adenocarcinoma [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 B027.

Abstract Background Studies show that only 20–30% of untreated ductal carcinoma in situ (DCIS) cases will evolve into invasive breast cancer. Thus, many patients are subject to overtreatment and its associated morbidities. The ecological context of the tumor microenvironment may impact how DCIS evolves. We applied landscape ecology statistics to hematoxylin and eosin (H&E) images from DCIS to test whether they predict recurrence of DCIS or progression to invasive disease. The ability to make such predictions is key for optimizing treatment decisions, reducing associated risks, and improving the efficiency of healthcare resource allocation. Methods We conducted a retrospective cohort study comprising 158 patients with a DCIS diagnosis, which included 57 cases of DCIS recurrence (2–228 months) and 45 cases of progression to invasive disease (12–180 months). Of these, 21 patients received lumpectomy, 92 had lumpectomy followed by radiation therapy, and 45 had total mastectomy. We generated a digital pathology library of whole-slide images (WSIs) from H&E tumor sections and performed machine learning guided nuclear segmentation and classification. A comprehensive set of spatial statistics was calculated for each WSI to characterize the spatial architecture and cellular interactions within the microenvironment. Cross-validation using a linear spline-based hazard regression model (HARE) was applied to a training subset of the data. The resulting survival model was validated on the test subset to predict disease-free survival (DFS) of recurrence and progression. Results We found that the type of DCIS treatment significantly stratified patients based on DFS (log-rank test p<0.0001). Further analysis of patients who received lumpectomy + radiation or mastectomy revealed that decreased mean nearest neighbor distance between tumor cells and lymphocytes, and between fibroblasts and lymphocytes, was significantly associated with improved recurrence-free survival (concordance=0.68). Stratification of samples based on the median risk score derived from our model was discriminatory with a p-value <0.015. Additionally, a decreased nearest neighbor distance between lymphocytes and fibroblasts showed a marginal association with an increased time to invasive disease (p<0.06). Conclusions Integrating spatial ecological statistics derived from digital pathology provides enhanced risk stratification for DCIS, beyond the established effects of initial treatment. Our findings underscore the importance of the tumor microenvironment's ecological dynamics in DCIS recurrence and progression. The predictive risk associated with specific spatial interactions, particularly involving lymphocytes and fibroblasts, suggests that previously unappreciated ecological mechanisms may play a role in the development of early micro-metastases, influencing outcomes even after standard therapies such as mastectomy or lumpectomy with radiation. These insights highlight novel targets for improved prognostic tools and more personalized therapeutic strategies. Citation Format: Luis H. Cisneros. Microenvironmental drivers of DCIS recurrence and progression to breast cancer [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 A016.

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.

BACKGROUND Implementing Mohs case complexity grading systems is critical due to inherent complexities of Mohs workflow coordination. Precise scheduling can improve wait times and resource allocation. OBJECTIVE To predict Mohs number of stages and reconstruction type as surrogates of case complexity and to use information derived from model training and practice to develop scheduling recommendations. METHODS Machine learning models were trained to predict number of Mohs stages and complex reconstruction across multiple practices and surgeons in an institutional enterprise, using information available on pathology reports. A workflow analysis by health systems engineering was conducted to identify improvement areas for efficiency. Information from modeling and workflow analysis was combined to develop fixed rules for scheduling recommendations. RESULTS In total, 18,473 Mohs cases were used for training/validation and 2,053 for testing. Models achieved an AUC-ROC of 0.83 (95% CI: 0.81–0.85) for prediction of complex reconstruction and 0.62 (0.60–0.66) for number of stages (1 vs 2 vs 3 or more). Anatomical location was the most valuable feature, with midface locations predicting higher case complexity and longer surgical time. CONCLUSION We demonstrated feasibility for prediction of Mohs surgery case complexity. Combining machine and human-derived knowledge provides complementary insights that can assist with improving scheduling and workflow.

Key Points Primary hyperoxaluria type 1 is a genetic disorder associated with hepatic oxalate overproduction, kidney failure, and systemic oxalosis. Lumasiran treatment for up to 60 months was associated with sustained reductions in urinary and plasma oxalate and encouraging clinical outcomes. Lumasiran's safety profile was acceptable with mild injection site reactions being the most common adverse events. Background Primary hyperoxaluria type 1 (PH1) is characterized by hepatic oxalate overproduction, kidney stones, and progressive kidney disease. Lumasiran is the first approved treatment for PH1. Methods ILLUMINATE-A (NCT03681184) was a 60-month pivotal, phase 3, multinational clinical trial of lumasiran which consisted of a 6-month double-blind, placebo-controlled period (6M DB), followed by a treatment extension period of up to 54 months in which all patients received lumasiran. Eligible patients were aged ≥6 years with genetically confirmed PH1, eGFR ≥30 ml/min per 1.73 m 2 , and mean 24-hour urinary oxalate (UOx) excretion ≥0.70 mmol/24 h per 1.73 m 2 . Results Of 39 patients enrolled, 24 of 26 randomized to lumasiran in the 6M DB (lumasiran/lumasiran group) and 13 of 13 randomized to placebo in the 6M DB (placebo/lumasiran group) completed the study. Sustained reductions in 24-hour UOx were observed in both treatment groups. At month 60 relative to study baseline, the mean (SEM) 24-hour UOx percentage reductions were 54 (6) and 54% (8%) in the lumasiran/lumasiran and placebo/lumasiran groups, respectively, and the mean (SEM) plasma oxalate concentrations had decreased by 35 (5) and 38% (7%). eGFR remained stable through the end of the study in both treatment groups. Kidney stone event rates during the study were 0.47 (95% confidence interval, 0.36 to 0.62) and 0.54 (0.37 to 0.78) per patient-year in the lumasiran/lumasiran group and placebo/lumasiran group, respectively. Medullary nephrocalcinosis grade improved at the end of the study in 21 of 28 (75%) patients with nephrocalcinosis at baseline and an end of study assessment. The safety profile of lumasiran was acceptable. Injection site reactions were the most common adverse events during lumasiran treatment. Most adverse events were mild or moderate in severity. Conclusions Lumasiran treatment for up to 60 months in ILLUMINATE-A was associated with sustained reductions in UOx excretion and plasma oxalate concentration, encouraging clinical outcomes including stable eGFR in a population that would be expected to show eGFR decline, reduced kidney stone event rates, improved medullary nephrocalcinosis, and indications of improved health-related quality of life. Clinical Trial registry name and registration number: ClinicalTrials.gov NCT03681184. Podcast This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2026_03_18_CJASNMar.21.3.31820.mp3

Importance Observational studies have associated use of aspirin and selective cyclooxygenase inhibitors with decreased recurrence and improved survival in patients with colon cancer. While randomized clinical trials have not shown benefit across all patients, these findings suggest that select subgroups may benefit from their use. Despite the well-established prognostic value of circulating tumor DNA (ctDNA), its role in guiding treatment remains unclear. Objective To investigate the predictive value of postoperative ctDNA for survival outcomes with adjuvant celecoxib alongside conventional chemotherapy in patients with stage III colon cancer. Design, Setting, and Participants This was a post hoc analysis of the phase 3 Cancer and Leukemia Group B (now Alliance)/Southwest Oncology Group 80702 randomized clinical trial (2010-2015) assessing adjuvant celecoxib vs placebo and 3 vs 6 months of adjuvant 5-fluorouracil, leucovorin, and oxaliplatin for stage III colon cancer. Patients consented to biospecimen collection and had ctDNA analysis performed. Data analysis was performed from September 2024 to June 2025. Exposures Postoperative ctDNA positivity was determined using a clinically validated, tumor-informed 16-plex-polymerase chain reaction-next-generation sequencing assay (Signatera; Natera Inc) performed between surgery and initiation of adjuvant therapy. Main Outcomes and Measures Disease-free survival (DFS) and overall survival (OS). Survival by ctDNA status and adjuvant celecoxib use were assessed as part of a post hoc companion study with prespecified statistical analysis plan. Results Among 940 patients (mean [SD] age, 60.9 [10.8] years; 426 female [45.3%] and 515 male [54.7%] individuals; 222 [23.6%] with prior low-dose aspirin use; and median follow-up of 6.0 [95% CI, 6.0-6.0] years), 767 (81.6%) were ctDNA negative and 173 (18.4%) were ctDNA positive. ctDNA positivity was highly prognostic of worse DFS (reference, ctDNA negativity; adjusted hazard ratio [aHR], 6.12; 95% CI, 4.66-8.03) and OS (aHR, 5.86; 95% CI, 4.19-8.19). In patients with ctDNA positivity, celecoxib was associated with improved DFS (aHR, 0.61; 95% CI, 0.42-0.89) and OS (aHR, 0.62; 95% CI, 0.40-0.96) compared to placebo. Among patients with ctDNA negativity, celecoxib did not provide survival benefit (DFS: aHR, 0.76; 95% CI, 0.53-1.09; OS: aHR, 0.85; 95% CI, 0.54-1.36), although the interaction was not significant ( P for interaction, .41 and .33 for DFS and OS, respectively). These findings persisted when stratifying patients by microsatellite instability status and PIK3CA mutational status. Conclusion and Relevance The findings of this post hoc analysis suggest that ctDNA status has the potential to inform clinical decision-making among patients with stage III colon cancer who should consider adjuvant celecoxib in addition to conventional chemotherapy. Trial Registration ClinicalTrials.gov Identifier: NCT01150045

This study leverages an experiment of nature framework to examine sex-based differences in ultraendurance swimming using English Channel performance data. Males were faster than females, with the greatest differences observed among older swimmers and top performers. Performance was optimal in moderately cool water temperatures (17–18°C). These findings highlight how naturally occurring endurance events provide unique opportunities to study physiological limits and the combined influences of sex, age, and environment on human performance.

Objectives Damaged articular cartilage cannot regenerate spontaneously. Chondrocyte therapy, the current treatment of choice, requires laboratory expansion, necessitating two surgical procedures. Adding a second cell type to intraoperatively isolated chondrocytes enables single-stage chondrocyte-based repair strategies and has shown promise in vitro . The benefit of this strategy in vivo and in the clinic has not yet been comprehensively assessed. This systematic review assesses the efficacy of cartilage repair by co-implantations of chondrocytes with other cell types over all available in vivo studies. Design Medline, Embase and Cochrane databases were searched for studies on co-implantations of chondrocytes with other cell types for hyaline cartilage repair. For each study, extracted data were tabulated and reporting quality and risk of bias were assessed. Studies were categorized based on their control groups for qualitative synthesis. Results The search yielded 48 studies across 46 publications: 44 animal studies (26 ectopic and 18 orthotopic) and four clinical trials. Ectopic studies scored poorly on reporting quality and bias, while orthotopic studies were only moderately better. Twenty-seven of 27 experiments with chondrocyte-only controls demonstrated synergistic cartilage formation through co-implantation. Cartilage formation by co-implantations was also found in 15 of 17 experiments without such control and in four clinical trials. Conclusion Cartilage repair by articular chondrocytes is improved by adding a second cell type, like mesenchymal stromal cells. This is likely caused by the second cell types’ stimulatory support. Other second cell types and chondrocyte sources have shown promising results too. This is encouraging for further clinical exploration of single-stage co-implantation strategies.