Carr, Ryan M

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).

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.

4006 Background: Elraglusib (9-ING-41) is a first-in-class inhibitor of GSK-3ß, which may mediate drug resistance, EMT, and damaged DNA and tumor immune response in advanced cancer. In pancreatic cancer models in mice, elraglusib combined with chemotherapy enhanced anti-tumor effects and survival. In a single-arm clinical study, elraglusib/GnP showed antitumor activity and prolonged survival in pts with mPDAC. Methods: Pts with previously untreated mPDAC were randomized 2:1 to GnP plus elraglusib 9.3 mg/kg IV once weekly or GnP in an open-label phase 2 study. The primary endpoint was 1-yr OS in the primary analysis set. Upon study completion, mOS will be the primary endpoint once survival distributions are compared after the 12-month follow-up using log-rank analysis. Secondary endpoints included DCR, ORR, mPFS, and TEAEs/TRAEs. The planned sample size was 207 evaluable pts (130 for elraglusib/GnP and 77 for GnP), assuming 1-yr OS of 55% with elraglusib/GnP and 35% with GnP to achieve 80% power with a chi-square test at a 2-sided 5% α. For OS, nonparametric log-rank test was used with statistical significance at p-value < 0.048. Cytokine/chemokine correlative biomarker assays were performed. The study completed enrollment in February 2024. Results: As of November 15, 2024 (preliminary data cut-off date), the primary analysis set included 155 pts in the elraglusib/GnP arm and 78 pts in the GnP arm, with 52.8% males and 57.5% ECOG PS 1. Median (range) CA 19-9 levels were 1568 U/mL (1 to 381,904 U/mL) in the elraglusib/GnP arm and 1590 U/mL (2 to 501,000 IU/mL) in the GnP arm. The 1-yr OS rate was 43.6% with elraglusib/GnP vs 22.5% with GnP (z-test p = 0.002); the mOS was 9.3 mo with elraglusib/GnP vs 7.2 mo with GnP (HR, 0.63; log rank p = 0.016; see Table). 38.1% of pts on elraglusib/GnP and 19.2% on GnP are censored, with the majority at > 10 months OS. Several biomarkers appear to be predictive for OS including IFNβ and PD-L1. The most common TRAE with elraglusib/GnP was grade 1-2 transient visual impairment in > 60% of patients (vs 9% with GnP). Grade ≥3 TEAEs occurred in 89.7% of pts on elraglusib/GnP and 80.8% on GnP. Most common grade ≥3 TEAEs with elraglusib/GnP (vs GnP) were neutropenia 51.6% (vs 29.5%), anemia 24.5% (vs 29.5%), and fatigue 16.1% (vs 5.1%). Conclusions: The preliminary results showed a statistically significant benefit for 1-yr OS and mOS and favorable trends for ORR and DCR with elraglusib/GnP over GnP, with manageable safety profile. The mOS for GnP is lower relative to MPACT and NAPOLI-3 but comparable to recent real-world meta-analyses, explained by advanced disease burden and higher mortality rate in the first 4 months in our study. Topline analysis (April 2025) and correlative biomarker analysis predictive for OS will be presented. Clinical trial information: NCT03678883 . Preliminary efficacy results. Elraglusib/GnPn=155 GnPn=78 1-year OS, %z-test p=0.002 43.6 22.5 mOS, moHR=0.63; log-rank p=0.016 9.3 7.2 Events, n (%) 96 (61.9) 63 (80.8) mPFS, moHR=0.91; p=NS 5.6 4.9 Events, n (%) 128 (82.6) 70 (89.7) DCR, % 42.6 33.3 ORR, n (%) 43 (27.7) 16 (20.5)

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 Pancreatic neuroendocrine neoplasm (panNEN) is a rare malignancy and the second most common type of pancreatic cancer after pancreatic ductal adenocarcinoma (PDAC), but its etiology is poorly understood. We investigated whether the risk factors of panNEN are concordant with those known for PDAC. We performed the largest case-control study to date on panNENs, comprising 927 sporadic nonfunctional panNEN cases and 1807 frequency-matched controls, using data from the Mayo Clinic Biospecimen Resource for Pancreas Research. We assessed associations for obesity, first-degree family history of pancreatic cancer, cigarette smoking, overall type II diabetes mellitus (T2DM), new-onset T2DM (&lt;1 year before panNEN diagnosis), longstanding T2DM (≥5 years), alcohol intake and aspirin use. Multivariable logistic regression was used to calculate odds ratios and 95% confidence intervals (CIs). Our results show that overall T2DM (OR = 1.71, 95% CI: 1.37–2.14) and new-onset T2DM (OR = 2.65, 95% CI: 1.92–3.69) are associated with higher odds of panNEN, but not longstanding T2DM (OR = 1.29, 95% CI: 0.94–1.75). A non-significant elevated odds of panNEN was observed among participants with a positive family history of pancreatic cancer (OR = 1.44, 95% CI: 0.96–2.14). Alcohol use was inversely related to panNEN (OR = 0.52, 95% CI: 0.42–0.66, ever-vs-never). No association was observed for smoking, obesity or aspirin use. These findings indicate that overall T2DM and new-onset T2DM are associated with higher odds of panNEN. Unlike PDAC, alcohol use was inversely related to panNEN, and we found no associations for cigarette smoking, obesity or aspirin use. These results indicate differences in the risk factor profiles of panNEN and PDAC.

770 Background: Pancreatic ductal adenocarcinoma (PDAC) is associated with poor prognosis and limited treatment options. A 584 bp deletion in CTRB2 , which impairs chymotrypsin B2 function, has been linked to increased PDAC risk. This study investigates the impact of this deletion on progression and survival outcomes in patients with PDAC. Methods: There were 633 patients in whom CTRB2ex6 deletion was genotyped; 263 patients received chemotherapy and had time to progression (TTP) information. TTP was calculated from diagnosis date until earliest of 1) progression date (event), 2) surgery, death, or next chemotherapy date (competing risk), or 3) date last known to be alive (censor). Overall survival (OS) was calculated for the cohort from diagnosis until date of death (event) or date last known to be alive (censor). TTP was analyzed using cumulative incidence methods accounting for competing risks; OS was analyzed using Kaplan-Meier methods. Cox proportional hazard models adjusted for age and sex were used to test associations and compute hazard ratios (HR) and 95% confidence intervals (CI). Results: Median TTP in those with CTRB2ex6 deletion negative carrier status (n=209) was 1.3 years vs 5.9 years for those with positive carrier status (n=54). Estimated 3-year progression rates were 54.2% for patients with no CTRB2ex6 deletions, 43.7% for patients with one deletion (n=48), and 33.3% for patients with two CTRB2ex6 deletions (n=6). TTP was not associated with CTRB2 deletion carrier status (HR: 0.8, 95% CI: 0.6-1.3). Median OS for CTRB2ex6 deletion negative carriers (n=514) was 1.0 year vs 0.9 years for positive carriers (n=119); within deletion carriers, heterozygous carriers (n=110) had a median OS of 0.9 years, and homozygous carriers (n=9) had a median OS of 2.6 years. OS was not associated with CTRB2 deletion carrier status (HR: 1.0, 95% CI: 0.9-1.3). Conclusions: Although we did not detect statistically significant differences in TTP and OS by CTRB2ex6 deletion carrier status, there was an interesting trend toward longer TTP and OS in the small number of patients who carried homozygous CTRB2ex6 deletions. Larger studies are warranted to validate these findings.

727 Background: PARP inhibition (PARPi) is a standard maintenance therapy for patients (pts) with germline BRCA1/2 mutations in pancreatic cancer. Combination of PARPi therapy and immunotherapy has potential to increase efficacy and may offer benefit beyond germline BRCA1/2 . Methods: We performed a multisite single-arm phase 2 study using the highly potent PARPi niraparib with anti-PD1 drug dostarlimab in pts with germline or somatic HRD mutations in BRCA1/2, PALB2, RAD51C/D, or BARD1 . Pts were required to have metastasis, progressed on ≥1 regimen, and prior platinum-exposure unless refused/intolerant. Pts were treated with niraparib 200 mg orally once daily continuously. Dostarlimab 500 mg was administered IV every 3 weeks for 4 cycles, then 1000 mg IV every 6 weeks. The primary endpoint was disease control rate (DCR) at 12 weeks (DCR12) using iRECIST criteria. At least 8 pts with DCR12 in the first 19 eligible pts (41%) would be considered promising for further trials. Results: 22 pts were enrolled from Dec 2020 to Oct 2023. Two pts withdrew prior to receiving therapy, and 2 were later found to be ineligible, leaving 18 eligible pts for final analyses. Median age was 63.0 yrs, 39% male, 94% non-Hispanic white, 1 black (6%). 17 pts were platinum exposed, 7 were platinum-refractory, and 5 had previously progressed on PARPi maintenance. Mutation distribution was: BRCA2 13/18 (72%), BRCA1 4/18 (22%), BARD1 2/18 (11%). One pt had both BRCA1 (somatic) and BRCA2 (germline) mutations. Germline mutations were identified in 14/18 (78%). DCR12 was achieved in 5/18 (27.8%), so the primary endpoint (>41%) was not met. Pts received a median 2 cycles (range 1-8) of niraparib + dostarlimab. No clinical factors were significantly associated with improved DCR12 (all p > 0.05) in subgroup analyses, but we did observe the following statistically nonsignificant associations. Pts who were platinum-refractory had a lower DCR12 compared to those not (14.3% vs. 36.4%). Counterintuitively, PARPi exposed/refractory pts had a higher DCR12 than non-exposed/non-refractory (50 vs 17% exposed/nonexposed and 40% vs 23% refractory/nonrefractory). Pts with germline mutations had a higher DCR12 compared to those without (38.5% vs. 0%). Non- BRCA2 mutations had a higher DCR12 than those with BRCA2 mutations (50% vs.17%), with highest DCR12 rates among BRCA1 patients (67%). Tolerability was in line with similar combinations, with 60% non-hematologic Gr3 or higher, including fatigue (15%), AST increase (10%), nausea/vomiting (10%), sepsis (10%). Two grade 5 events were not attributed to treatment. Conclusions: PARPi plus anti-PD1 checkpoint inhibition was not sufficiently active as later line therapy in metastatic pancreatic cancer for the majority of patients with HRD mutations. Additional molecular analyses to elucidate predictive markers of sensitivity/resistance are ongoing. Clinical trial information: NCT04493060 .