754 Background: Pancreatic ductal adenocarcinoma (PDAC) is the third deadliest malignancy with most patients (pts) presenting with advanced disease. In those with localized disease at diagnosis who undergo curative intent surgery followed by adjuvant chemotherapy, the relapse rate remains high (70%). While there are known clinicopathological risk factors to predict relapse, little is known about genomic and transcriptomic features differentiating recurrent PDAC. Methods: We identified PDAC pts who had curative surgery at Princess Margaret Cancer Centre in Toronto, Canada between August 2008-2022 who had whole genome (WGS) and transcriptomic (RNA-Seq) data available. Patients were classified by timing of relapse after surgery: early (≤6 months), intermediate (6–12 months), late (>1 year), or no recurrence. Clinicopathologic and survival data were collected via chart review, and groups were analyzed using Fisher’s exact/Wilcoxon rank sum tests. Kaplan-Meier survival analyses were conducted. Results: Of 715 surgical resections, 170 had clinical and WGS/RNA-Seq data available. Ninety-three pts (55%) were female and 126 (74%) had at least N1 disease. Timing of relapse was classified as early (n=37, 22%), intermediate (n=46; 27%), late (n=61; 36%), and no recurrence (n=26; 15%) at time of data cut-off. There were no statistically significant differences in sex, Moffitt subtype, HRD/MMR status, or driver mutations such as KRAS , CDKN2A , and SMAD4 . Clinicopathological analysis revealed that vascular involvement (p=0.0231), nodal (N) status (p=0.0384), non-adenocarcinoma histology (p=0.0393) and G3 tumors (p<0.001) were significant predictors of early recurrence. Hepatic metastasis accounted for most early recurrences (51%), whereas late recurrences were often locoregional (36%) or pulmonary (20%). Genomic analysis revealed a higher incidence of ARID1A mutations in early vs. late recurrence (20% vs. 3.5%, p=0.0179), with 71% demonstrating monoallelic loss, while low ploidy (p=0.0371), PTEN mutation (p=0.0166) and TP53 wild-type status (p=0.0237) were significantly associated with no recurrence. Higher structural variant counts were also observed in the early recurrence group (p=0.0163). Differential expression analysis demonstrated significant enrichment in gene sets associated with MYC activation, glycolysis and hypoxia response (FDR q-value < 0.001) in early recurrence pts, while the late-recurrence pts showed enrichment in pancreatic beta cell-related gene sets (FDR q-value < 0.01). Conclusions: While clinicopathologic factors have been confirmed as prognostic for recurrence, we identified a higher incidence of ARID1A mutations in the early recurrence group and significant enrichment in gene sets associated with MYC/hypoxia. Multi-omics analysis could further improve recurrence prediction, guiding future therapeutic strategies.

774 Background: The optimal approach in r-PDAC remains unclear. Perioperative Modified FOLFIRINOX (mFFX) is increasingly used yet we lack biomarkers to guide this approach. NeoPancONE demonstrated GATA6 high expression at baseline and classical RNA subtype on resection tissue in pts receiving perioperative mFFX was associated with a trend toward improved EFS and OS. Low GATA6/basal-like predicts early disease progression and poor outcomes 1 . We now report on additional transcriptome and genomic subtypes identified in the NeoPancONE resection specimens. Methods: Patients with r-PDAC were enrolled following central review and underwent an EUS FNB for central analysis of baseline GATA6 by in-situ hybridization (ISH). Surgical resection specimens were processed in each centre and fresh frozen tumor tissue was transferred for central whole genome and transcriptome sequencing (WGTS) following laser capture microdissection. WGTS reports annotated mutations, copy number alterations, mutational signatures, and structural variant patterns. KRAS allelic states were reported with major imbalances in mutant KRAS (KRAS maj ) defined as mutant: Wildtype (WT) copy number ≥3. RNA transcriptomic subtypes (classical vs. basal-like) were classified by PurIST. We report a descriptive and survival analysis of the genomic subgroups. Results: Of the 84 enrolled baseline GATA6 ISH was analysed in 74 (88%); 81% high, 19% low and 73 (87%) proceeded to surgery. PurIST subtyping was available in 51 (70%) of resections; 86% classic, 14% basal. Spearman correlation coefficient between GATA6 ISH and RNA subtype = 0.51 (p<0.001). In those who recur/progress there was no association between GATA6 and RNA subtype and site of recurrence p=0.57 and p=0.18 respectively. WGS reports are available in 36 (49%) resections. KRASm were evident in 94% and KRAS G12D represented 50%. MTAP deletion was present in 22%, mostly correlated with CDKN2A deletions. Tumor suppressors TP53, CDKN2A and SMAD4 were lost in 86%, 69% and 50% respectively. KRAS maj was present in 8% of cases and polyploid tumors in 28%. Polyploid and KRAS major were enriched in the basal-like, and associated with significantly worse OS (p=0.046 and 0.0081 respectively). There was also enrichment of SMAD4 wt in basal-like tumors. ARID1A wt tumors demonstrated improved OS (P=0.032). Unstable genotypes were present in 6% with somatic-HRD evident in 3%. Conclusions: NeoPancONE represents a heterogeneous group of genotypes and molecular subgroups in r-PDAC. GATA6 low and basal-like subtype, associated with inferior outcomes, demonstrate less genomic stability. These results substantiate consideration of an alternative to mFFX based peri-operative therapy in GATA6 low/basal-like and reinforces the value of incorporating upfront genomic subtyping. Future r-PDAC trials should now incorporate RNA subtyping and KRAS directed therapies. Ref: 1. McLaughlin et al. ASCO 2025. Clinical trial information: NCT04472910 .

Abstract Background & Objectives Biliary tract cancer (BTC) groups a family of rare cancers with high recurrence rates after surgery and poor survival, especially in advanced disease. Several molecular classification systems based on transcriptomics demonstrate prognostic relevance, however clinical implementation has been limited by a lack of consensus or elucidation of their biological underpinnings. Methods We performed whole genome and transcriptome sequencing of 169 tumors enriched with laser capture microdissection and annotated with clinical records. Results Network integration and clustering on existing classifiers revealed that BTC transcriptomes can be summarized into two consensus molecular subtypes (BTC-CMS). BTC-CMS-A tumors almost exclusively carry the clinically actionable alterations characteristic of BTC (IDH1 mutations and FGFR2 fusion). These tumors are restricted to the liver and confer superior survival after surgery (P = 9.5e-05) and in advanced disease (P = 0.02) in cohorts without precision therapy treatments. BTC-CMS-B tumors span the biliary tree and are associated with chronic inflammation risk factors including primary sclerosing cholangitis (P = 0.016), fluke infection (P = 1.8e-10) and cholecystitis (P = 0.0029). These tumors exhibit mutations characteristic of pancreatic cancer (TP53, SMAD4, CDKN2A and KRAS), a connection further supported by similarities in their transcriptomic profiles. BTC-CMS can also be reliably differentiated by tumour proteomes and methylomes, offering multiple modalities for tumor classification. de novo expression signature extraction by non-negative matrix factorization and subsequent projection of these signatures onto single cell RNAseq data supports that individual cells from a given tumour are exclusively one subtype, with divergent copy number and expression profiles. Furthermore, we show patient tumors are restricted to one subtype across metastases and through progression or recurrence, suggesting these are immutable programmes. Conclusion The BTC-CMS robustly defines two distinct molecular lineages of BTC that improve clinical and biological stratification over anatomy atone.

PURPOSE:Predicting recurrence of pancreatic cancer after surgery could inform clinical decision making, including adjuvant therapies and follow-up. This study aimed to develop and validate a deep learning model using digitized whole-slide images (WSI) of histopathology. METHODS:Publicly available WSI of pancreatic ductal adenocarcinoma resections from three cohorts were used for training. The model consisted of a pan-cancer foundation model to generate embeddings, mean-pooling across tissue patches, and then a fully connected neural network. Model predictions were compared with human-labeled histopathologic features and genomic alterations. The model was externally validated in a meta-analysis of a single-center cohort from Princess Margaret Cancer Centre, a multicenter cohort from France, and the PRODIGE 24 trial of adjuvant chemotherapy. RESULTS:The deep learning model was trained on 12,594 tissue patches from 257 patients. High-risk classifications were associated with squamous morphology, reactive stroma, tumor cellularity, and necrosis, whereas low-risk classifications were associated with tubulopapillary and conventional morphologies, as well as deserted stroma. High-risk cancers were enriched for basal-like gene expression profiles and distinct oncogenic pathways. In a meta-analysis of the external cohorts, the hazard ratio (HR) for death comparing high-versus low-risk cancers was 1.49 (95% CI, 1.25 to 1.79, P < .001), whereas the HR for recurrence or death was 1.41 (95% CI, 1.19 to 1.68, P < .001). The classifications remained prognostic among moderately differentiated cancers. CONCLUSION:An open-source deep learning model using WSI from pancreatic cancer resections generated risk classifications that correlated with histopathologic and genomic features. Classifications were externally validated in a meta-analysis of three cohorts. This model could be applied to WSI to provide individualized prognostic information for patients.

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with most patients diagnosed at advanced stages and limited therapeutic options. The aggressive biology and substantial molecular heterogeneity of PDAC underscore the need for approaches that enable more personalized treatment selection. Patient-derived organoids (PDOs) have emerged as powerful in vitro tumor model that retains key genetic, transcriptional, and phenotypic features of the tumors from which they are derived. As such, PDOs provide an opportunity to bridge fundamental cancer biology with translational and clinical applications, including functional precision medicine. Our work focuses on integrating PDO models into prospective clinical studies of PDAC, most prominently through the PASS-01 clinical trial, a multi-institutional study of patients with stage IV disease. Within this trial, biopsies were collected for molecular correlatives including PDO establishment across six institutions in the United States and Canada. A total of 186 biopsies from 183 enrolled patients were shipped to Cold Spring Harbor Laboratory for PDO generation and characterization. Biopsy samples originated primarily from liver metastases but also included primary pancreatic tumors and other metastatic sites such as peritoneal, omental, lymph node, lung, and brain lesions. KRAS mutation analysis using droplet digital PCR was used to confirm neoplastic organoid cultures and identify pseudonormal epithelial outgrowth, which occurred in approximately 25–40% of samples.Overall, malignant PDOs were successfully established from approximately 48% of patient biopsies. Interestingly, successful organoid establishment was associated with patients who experienced more rapid clinical progression, and organoid morphological features also correlated with patient outcomes. Molecular characterization revealed that PDOs were more frequently generated from tumors belonging to the classical transcriptional subtype and from tumors harboring KRAS and TP53 mutations. Established PDO lines were expanded, biobanked, and subjected to high-throughput pharmacotyping against a library of more than 120 standard-of-care and investigational compounds. The median time from tissue receipt to drug screening data was 70 days, with several lines yielding results within two months, enabling pharmacotyping data to be presented during monthly molecular tumor boards. In several cases, PDO drug response profiles were considered alongside genomic and clinical data to help inform second-line therapy decisions following disease progression. Ongoing work integrates PDO pharmacologic response data with genomic and transcriptomic profiling to identify predictive biomarkers of therapy response and resistance. Our results thus far demonstrated some concordance with patient responses to first-line chemotherapy, particularly gemcitabine/nab-paclitaxel. In parallel, PDO models are being used to evaluate emerging targeted therapies, including RAS inhibitors and combination strategies aimed at PDAC vulnerabilities. Together, these studies highlight the potential for patient-derived cancer models to accelerate translational discovery and support clinically actionable precision medicine approaches in pancreatic cancer. Citation Format: Amber N Habowski, Fatim Kouassi, Hardik Patel, James Rouse, Caitlin Tsang, Luce Kelly, Dennis Plenker, Gun Ho Jang, Deepthi Budagavi, Grainne O'Kane, Raditya Utama, Julie Wilson, Anna Dodd, Stephanie Ramotar, Julien Hohenleitner, Kimberly Perez, Robert C Grant, Eileen M O’Reilly, Steven Gallinger, Dan A Laheru, Brian M Wolpin, Andrew J Aguirre, Kenneth H Yu, Elizabeth M Jaffee, Jennifer Knox, Daniel A King, Faiyaz Notta, David A Tuveson. Human cancer models: From patient-derived systems to clinical translation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(8_Suppl):Abstract nr SY36-02.

600 Background: Biliary tract cancers (BTC), including cholangiocarcinomas and gallbladder adenocarcinomas, remain poorly understood. Stratifying this heterogeneous group of diseases based on molecular features may lead to novel precision treatments and yield urgently needed improvements in outcomes. Methods: We performed whole-genome and transcriptome sequencing of 165 laser-capture microdissected BTC tumors with paired normal tissues, representing 165 patients. First, we assigned all samples to 28 gene expression-based BTC signatures from nine previously published classifiers to create a signature similarity network. Using Markov clustering on the optimized network, we identified broad consensus molecular subtypes. Next, we used non-negative matrix factorization to decompose the consensus molecular subtype signatures into underlying components to identify associated cell types and processes. Finally, we integrated subtypes with de novo derived mutational signatures and drivers as well as electronic health records to search for prognostic and outcome implications. Results: Published subtyping classifications reflected only two underlying consensus molecular subtypes, which we named BTC-CMS-A and BTC-CMS-B. BTC-CMS-A tumors represented two thirds of intrahepatic cholangiocarcinomas and all mixed hepatocellular carcinomas, but no extrahepatic BTCs or gallbladder cancers, while BTC-CMS-B tumors were found along the whole biliary tract. BTC-CMS-B components exhibited markers of common bile duct epithelium but also shared expression profiles with intestinal Goblet cells, suggestive of an intestinal metaplasia histopathology. In contrast, BTC-CMS-A expressed hepatoblast markers while also exhibiting upregulation of pathways associated with kidney cell lineage. These results were validated by projecting all CMS-associated expression components onto two public single-cell RNAseq datasets. Of 16 drivers identified de novo from WGS, BAP1 and TP53 mutations were most frequent, mutually exclusive, and each was restricted to BTC-CMS-A and BTC-CMS-B, respectively. Mutational signature extraction revealed BTC-CMS-B tumors had increased tumor mutational burden and APOBEC-associated mutational signatures, whereas BTC-CMS-A was associated with increased non-homologous end-joining. Clinically, hepatitis B was more frequent in BTC-CMS-A cases, while patients with primary sclerosing cholangitis exclusively had BTC-CMS-B tumors. BTC-CMS-A was associated with significantly longer overall survival compared to CMS-B, after adjusting for clinically relevant covariates. This finding was validated in three external cohorts. Conclusions: BTC-CMS unify previous BTC classifications and display distinct clinical and genomic profiles and therefore should guide the development of targeted therapies and biomarker-driven trials.

Open reaction data sets, particularly those gathered through high-throughput experimental (HTE) reaction screening campaigns, hold considerable value in downstream data analysis and reaction understanding. Data can be used to reveal important trends. Such open reaction data are increasingly being reported and deposited to appropriate databases, particularly for the most popular chemical reactions, e.g., Suzuki-Miyaura cross-coupling (SMCC) of organohalides and organoboron compounds, catalyzed by Pd and mediated by a suitable base. While there is considerable complexity associated with SMCC reactions, as informed by many independent mechanistic studies over the years, one can bring out essential trends through detailed data analysis. In this study, we have taken an open reaction data set reported by Pfizer and evaluated the properties of closely related substrates and the effect of the reaction solvent, base, and ligands. Through a detailed analysis of the reaction outcomes, we have focused on the occurrence of a common side-product in SMCCs, resulting from protodeborylation of the organoboron coupling component. There is considerable benefit in exploring the main cross-coupled product yield compared with the amount of protoborylated compound formed. In our analysis, we have delineated several key and interesting trends, which reveal value in evaluating side-product(s)/main product yields. A Shiny app has been developed for the rapid evaluation of the SMCC reaction data set.

Abstract In pancreatic cancer, 50% of the patients are diagnosed at the metastatic stage due to a lack of symptoms. The overall survival rate dramatically reduces from 44% in early-stage resectable patients to 3% in metastatic cases. However, even in early-stage patients, >75% of them still recur after resection and adjuvant therapies. Therefore, there is an urgent need to develop a sensitive strategy to detect this disease earlier before it spreads. Characterizing circulating tumor DNA (ctDNA) in plasma is an effective approach to detect and monitor cancer. Whole-genome sequencing (WGS) tracks all the tumor mutations simultaneously, and has a higher sensitivity than targeted approaches, especially in samples with extremely low tumor burden. In this study, to investigate the ctDNA dynamics and early dissemination in pancreatic cancer, we established a cohort of 1,013 samples from 277 donors, including plasma WGS at 20-60x, alongside germline DNA WGS, tissue WGS and transcriptomic sequencing. Using a tumor-guided approach, we found that the early-stage resectable cases shed very little ctDNA (<1% tumor fraction, TFx). Plasma TFx levels were elevated at the metastatic stage, but were also dependent on metastatic tissue site, where patients with liver metastases had higher TFx than the ones with only non-hepatic lesions. We further discovered the tumor-intrinsic features that were related to increasing ctDNA shedding, such as whole-genome duplication (WGD), high cell cycle activity and non-glandular morphology, as well as extrinsic features related to reduced shedding, including a reactive microenvironment and B cell immunity. Our analysis on tumor clonal architecture revealed that subclonal mutations were more frequently detected than the clonal ones in plasma samples with low ctDNA levels or from early-stage patients, which strongly suggests that dissemination from early-stage primary tumors mostly derived from subclones. In the longitudinal plasma, we observed that subclones seeded metastasis years before imaging diagnosis. This study with a unique large cohort of paired tumor and plasma sequencing data provided a comprehensive insight on ctDNA dynamics, disease monitoring and early detection in pancreatic cancer. Citation Format: Yuanchang Fang, Michelle Chan-Seng-Yue, Karen Ng, Amy Zhang, Tuan Hoang, Gun Ho Jang, Sabiq Chaudhary, Catia Gaspar, Eugenia Flores-Figueroa, Daniela Bevacqua, Stephanie Ramotar, Ayelet Borgida, Shawn Hutchinson, Anna Dodd, Barbara Grünwald, Julie Wilson, Robert Grant, Erica Tsang, George Zogopoulos, Masoom Haider, Jennifer Knox, Steven Gallinger, Faiyaz Notta. Pervasive early dissemination in pancreatic cancer uncovered by tissue-paired plasma whole-genomes [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 1126.

765 Background: In resected pancreatic cancer (PDA), morphology from routine histopathology slides provides a rapid inexpensive biomarker that predicts overall survival and correlates with transcriptomic subtypes. In this study, we evaluated clinical and genomic associations of morphological subtypes in resected and advanced disease and validated the consistency of subtypes in patient-derived organoids (PDO) and mouse xenografts (PDXs) during in vitro and in vivo modeling. Methods: Our cohort included PDA tumor tissues from 152 resectable (stage I/II) and 228 advanced cases (Stage III/IV). Hematoxylin and eosin-stained slides were blindly reviewed by two pathologists and classified into subtypes based on Kalimuthu (1). Morphological subtypes were correlated with clinical and genomic data from whole-genome and transcriptome sequencing. Histological preparations from PDOs and PDXs obtained from pancreatic resections and metastases were reviewed using the same criteria. Results: Morphological subtypes were significantly associated with clinical patterns. Locally advanced PDA exhibited the highest proportion of glandular tumors. Metastatic tumors were enriched for non-glandular morphologies. The non-glandular morphologies were significantly associated with lower survival rates in both resected and advanced tumors. Furthermore, morphological subtypes were significantly associated with unique genomic alterations. Compared to glandular tumors, non-glandular tumors were associated with increased KRAS copy number, KRAS imbalances, and polyploid genomes. In advanced settings, glandular and non-glandular tumors mostly exhibited classical and basal-like transcriptional subtypes, respectively. Squamous tumors had the highest mutation burden and the highest proportion of Basal A signature (2). Using differential gene expression, we identified transcriptional signatures of the morphological subtypes. PDOs and PDXs maintained the morphological subtypes, although non-glandular tumors had a lower success rate for PDO establishment. Conclusions: Morphological subtypes have distinct clinical and genomic associations across all stages of pancreatic cancer, which can be consistently modeled both in vitro and in vivo . These results demonstrate that morphological subtyping offers a rapid and biologically-relevant classification of PDA that could be used for drug development and stratification to predict therapy selection. 1. Gut; 69:317-328 (2020). 2. Nat Gen; 52:231-240 (2020).

Figure S1. hENT1 expression by modified Moffitt subtypes Figure S2. OS in GnP cohort stratified by hENT1 and modified Moffitt Classifier

We previously developed the CellPhe toolkit, an open-source R package for automated cell phenotyping from ptychography time-lapse videos. To align with the growing adoption of python-based image analysis tools and to enhance interoperability with widely used software for cell segmentation and tracking, we developed a python implementation of CellPhe, named CellPhePy. CellPhePy preserves all of the core functionality of the original toolkit, including single-cell phenotypic feature extraction, time-series analysis, feature selection and cell type classification. In addition, CellPhePy introduces significant enhancements, such as an improved method for identifying features that differentiate cell populations and extended support for multiclass classification, broadening its analytical capabilities. Notably, the CellPhePy package supports CellPose segmentation and TrackMate tracking, meaning that a set of microscopy images are the only required input with segmentation, tracking and feature extraction fully automated for downstream analysis, without reliance on external applications. The workflow's increased flexibility and modularity make it adaptable to different imaging modalities and fully customisable to address specific research questions. CellPhePy can be installed via PyPi or GitHub, and we also provide a CellPhePy GUI to aid user accessibility.

TPS4232 Background: PDAC is a devastating malignancy. High-throughput genomic technologies have yielded insights regarding the molecular underpinnings and heterogeneity of PDAC. Systemic treatment options are limited to cytotoxic chemotherapies, except for approx. 10%, who receive targeted treatment based on genomic profiling. PDO’s are three-dimensional ex vivo experimental models grown directly from tumor tissue and can provide a direct assessment of drug response. By directly exposing cancer cells to potential drug therapies, functional profiling provides a dynamic measurement of response that is more informative than static gene panels. PDOs can theoretically be used to direct therapeutic decisions, offering an opportunity to expand the reach of precision therapies for PDAC beyond genomics. To date, PDO testing has been limited by small sample sizes, few drugs included in the screens, and retrospective studies. To expand the impact of precision therapy, we developed a rapid high-throughput screening (HTS) platform where over 3,000 drugs can be tested in PDOs within 8-10 weeks of diagnosis. In ADOPT, we aim to formally investigate the efficacy of PDO-directed therapy in a prospective phase II study, leveraging our existing platforms using real-time HTS of PDOs . This study represents one of the first formal trials of PDO-directed therapy in solid tumors. Our novel approach will enroll pts with advanced PDAC who do not have alternative treatment options. Methods: This is an actively recruiting prospective, single-arm phase II trial. Patients (pts) with advanced epithelial PDAC are eligible if they either: 1) progressed on, were intolerant to, or refused first-line or subsequent therapies (Cohort A), or 2) have stable disease after ≥8 cycles of FOLFIRINOX (“Maintenance” Cohort B) and have a PDO showing sensitivity to an approved HC drug. Pts will be recruited, from multiple ongoing studies including PROSPER-PANC where we have successfully generated and tested a PDO. PDO-directed treatment will be selected based on drug sensitivity as tested through our validated HTS platform. Each case will be discussed at our PDO dedicated tumor board. All pts must meet the inclusion/exclusion and drug-specific eligibility criteria. The primary endpoint is disease control rate. A Simon’s two-stage optimal design will be used to test the hypothesis: H0: P ≤ 0.05 versus H1: P ≥ 0.25. In the first stage, 9 pts will be evaluated. The trial will be discontinued if no disease control response is observed in this stage. If at least one response is observed, then the trial will continue to the second stage and an additional 17 pts will be evaluated for a total of 26 evaluable. This design has a one-sided alpha of 0.05 and power of 80%. We will reject the null hypothesis after 26 if 3 or more responses are observed. Clinical trial information: awaited .

Understanding how cancer-stromal interactions shape cancer progression requires tools that can capture dynamic phenotypic changes in physiologically relevant conditions. Traditional approaches for studying co-culture interactions, such as transcriptomics and flow cytometry, provide valuable insights but are limited by their static nature and reliance on fixed or dissociated cells. In contrast, label-free time-lapse microscopy preserves temporal and spatial context, enabling observation of live-cell behaviours over time. A major challenge, however, lies in the analysis of the resulting high-dimensional datasets. Using co-cultures of breast cancer cells and cancer-associated fibroblasts (CAFs) as a model system, we show that the CellPhe toolkit enables label-free identification and phenotypic characterisation of different cell types within complex live-cell imaging datasets. Our analysis shows that exposure to CAFs drives marked phenotypic shifts in breast cancer cells, including elongation, loss of cell–cell adhesion, and redistribution of intracellular components - hallmarks of epithelial–mesenchymal transition (EMT). To probe the underlying mechanisms, we performed a Luminex immunoassay on CAF-conditioned media and identified secreted analytes strongly associated with EMT induction. Together, these results highlight how automated phenotyping can be integrated with molecular profiling to identify and characterise cellular processes shaped by stromal interactions and reveal the signalling mediators that drive them. ### Competing Interest Statement The authors have declared no competing interest. Medical Research Council, https://ror.org/03x94j517, MR/X018067/1 Biotechnology and Biological Sciences Research Council, BB/Y513970/1 Wellcome Trust, 310891/Z/24/Z

Comprehensive molecular profiling can identify alterations in biliary tract cancer (BTC) potentially treatable with targeted therapies. However, the impact of whole-genome and transcriptome sequencing (WGTS) on therapeutic decision-making in a public healthcare system is unknown. Here, BTC patients prospectively received WGTS to inform clinical care at a large Canadian academic cancer center. We characterized the proportion of targetable alterations, the treatment recommendations generated by a molecular tumor board, targeted therapies received, patient outcomes, and the financing of these treatments. A total of 55 patients with BTC prospectively underwent WGTS to inform clinical care. Of those 55, 28 (51%, 95% CI 38–64%) harbored targetable alterations. Molecular tumor boards recommended consideration of targeted therapies for 43 (78% CI: 66–87%) of 55 cases. Among the 15 patients who progressed to second-line therapy and harbored targetable alterations, 8 received nine targeted therapies. No targeted therapies were funded through the public system, and most therapies were funded through compassionate access programs from companies. These results highlight the challenges and potential for inequities when implementing precision oncology in a publicly funded healthcare system.

MALDI-TOF MS (matrix-assisted laser desorption/ionization time-of-flight mass spectrometry) of ethoxylate products produces spectra with distributions of regularly spaced peaks resulting from the addition of monomer units of ethylene oxide to the oligomer. We show that overlapping peak distributions from the different ethoxylated constituents of natural raw materials can be resolved, so that features of the individual distributions (m/z at distribution maximum, intensity at the distribution maximum, width of the distribution at half height, and ratio of the distribution to the major peak distribution) can be extracted and used with statistical pattern recognition techniques to study ethoxylated products. Crucially, we weight the extracted features, so that features from a distribution with a high ratio to the main distribution are given more importance ('ratio-scaled'). We exemplify the method by characterizing the structural variation between types of compositionally diverse Polysorbate 80, PEG castor oil and Oleth-20, and compare the chemometric analysis using our extracted features with analysis of the full spectra. We demonstrate that using ratio-scaled extracted features gives superior results to the full spectrum, both in terms of identifying subtle compositional differences that would otherwise be missed, and in interpretability. Importantly, the integrated auto-assignment of peak distributions to possible compounds allows the results to be reported in terms of the most abundant oligomers of the raw material constituents. This simplification facilitates interpretation of the results and allows the comparison of closely related products.

4181 Background: Pancreatic cancer is an aggressive malignancy with limited therapeutic options and a poor prognosis. Current approaches to prognostication are limited, especially in advanced disease. We explored whether machine learning integrating multi-modal data could predict outcomes in advanced pancreatic cancer. Methods: We developed and evaluated machine learning models predicting disease control rate and one-year survival from the COMPASS trial (NCT02750657). Data modalities included clinical features, histopathology, radiology, RNAseq, and whole-genome sequencing (WGS). After pre-processing, we applied LASSO and XGBoost to each modality and early and late fusion techniques. Hyperparameter tuning and performance assessment were performed using repeated nested cross-validation. The PurIST RNAseq classifier served as a baseline. Area under the curve (AUC) was the primary metric. Results: The cohort included 260 patients (105 female; median age 64 [IQR 58–70]; 141 treated with FOLFIRINOX, 97 with gemcitabine and nab-paclitaxel). 170 (65%) achieved disease control and 168 (65%) survived at least one year. The performance of the machine learning models is shown in the Table. Predictions from the unimodal models had limited correlation with each other (the maximum pairwise correlation averaged across folds was between clinical and histopathology models, 0.21). The late fusion models up-weighted data modalities with stronger unimodal performance. Conclusions: Multiple individual data modalities can predict outcomes in advanced pancreatic cancer, with PurIST serving as a strong baseline. Despite differing predictions across data modalities, multimodal integration did not improve prognostic performance in this cohort. AUC for the PurIST baseline, the top 2 unimodal models, and the best fusion model for each outcome. Outcome Data Modality AUC (95% confidence interval) Disease control PurIST 0.69 (0.69, 0.70) Radiomics 0.75 (0.72, 0.79) RNAseq 0.71 (0.70, 0.72) Fusion (late) 0.71 (0.69, 0.73) One-year survival PurIST 0.63 (0.62, 0.63) DNA mutations 0.64 (0.61, 0.66) RNAseq 0.57 (0.55, 0.60) Fusion (early) 0.61 (0.56, 0.66)

751 Background: Pancreatic ductal adenocarcinoma (PDAC) is associated with a hypercoagulable state leading to thrombosis. Risk models such as the Khorana score automatically classify PDAC as intermediate-high risk, and recent guidelines recommend consideration of thromboprophylaxis. However, little is known about the molecular correlates of PDAC for venous thromboembolism (VTE). Methods: We examined clinical and genomic data from the prospective multi-institution COMPASS trial (NCT02750657), which enrolled patients with treatment-naïve metastatic PDAC who underwent a fresh tumor biopsy for real-time whole genome and transcriptome sequencing. Laser capture microdissection was performed. Patients underwent restaging scans at 8-week intervals. Detailed chart review was conducted to focus on VTE risk factors, timing of VTE diagnosis, and anticoagulation. We also compared clinical and molecular factors based on timing of VTE diagnosis. Overall survival was defined as time from VTE to death. Results: Of 268 patients enrolled in the COMPASS trial, 166 patients had detailed clinical data available regarding VTE status. 82 patients (49%) developed a VTE, where 16 (20%) had breakthrough clots. Baseline epidemiological variables were similar for those with and without VTE, including no differences between age, sex, BMI, and baseline CA 19-9. No patients were on routine prophylactic anticoagulation. SMAD4 mutations were more frequently seen in the VTE subgroup (57% vs. 40%, p= 0.04), but no differences were seen in other driver genes (KRAS, TP53, CDKN2A), Moffitt subtype, or homologous recombination deficiency status. Patients who developed an early VTE (within 3 months) had a higher baseline CA 19-9 (median 4015 vs. 891, p= 0.02), and none were port-associated. A higher incidence of KRAS wildtype cases (10%) were observed in the early vs. later VTE groups (p= 0.03), but no differences in KRAS allelic status. There were no differences in burden of SNVs, indels, or SVs. Early VTE occurred in 46% of all basal cases, which are typically more aggressive, and only in 37% of classical subtypes. Overall survival was shorter with early VTE (HR 1.74, p= 0.02). Conclusions: A higher incidence of SMAD4 alterations were observed among patients with metastatic PDAC who developed VTE. VTE diagnosed earlier were associated with shorter survival, suggesting that early thromboprophylaxis should be considered at the time of diagnosis. Clinical trial information: NCT02750657 .

High-throughput drug screening enables rapid testing of numerous drugs on tumor samples and has achieved breakthroughs in treatment selection for fatal diseases such as acute myeloid leukemia. This study leverages high-throughput screening to identify molecular vulnerabilities in pancreatic ductal adenocarcinoma (PDAC), a highly lethal cancer with a 5-year survival rate of just 13%. Using PDAC patient-derived organoids (PDOs), we developed a platform to screen approximately 3, 000 compounds at a single dose and 600 compounds across six doses on 120 PDOs. Additionally, we designed a tailored focused screening of 22 drugs at 20 doses, prioritizing Health Canada-approved drugs identified as promising in earlier screenings. To ensure robust data analysis, we developed an optimized bioinformatics pipeline incorporating a three-tier evaluation: Area Under the Curve (AUC), Drug Sensitivity Score (DSS), and maximum efficacy (Emax). Results from each PDO were systematically compared against the entire cohort, ensuring that sensitivity profiles reflect reliable outcomes. Additionally, we integrated drug response data with genomics and transcriptomics obtained through whole-genome and whole-exome sequencing of PDOs. We identified highly potent compounds capable of suppressing tumor growth in over 55% of PDOs, prioritizing clinically approved drugs to facilitate rapid translation to patient care. Drug sensitivity patterns from single-dose screenings were corroborated through six-dose validation and a focused screening platform. By integrating drug response data with multi-omics analyses, we uncovered patient-specific drug response profiles linked to molecular vulnerabilities. Notably, we validated a gene-drug association involving anagrelide, a selective agent that induces cytotoxicity in cancer cells with elevated phosphodiesterase PDE3A levels, highlighting the robustness of our approach. Preliminary findings demonstrate a strong concordance between PDO-derived and patient drug responses, establishing a foundation for actionable clinical insights in the ADOPT trial to guide personalized treatment strategies. In conclusion, our high-throughput drug screening platform, integrated with multi-omics analysis and robust bioinformatics, enables the identification of patient-specific molecular vulnerabilities in PDAC. This approach advances the potential for precision oncology, providing a foundation for tailored therapeutic strategies in this highly lethal cancer. Nikta Feizi, Eugenia Flores-Figueroa, Karen Ng, Zhen-Mei Liu, Farnoosh Abbas Aghababazadeh, Gun Ho Jang, Daniela Bevacqua, Stephanie Ramotar, Shawn Hutchinson, Anna Dodd, Julie Wilson, Robert Grant, Ronan Mclaughlin, Erica Tsang, Jennifer Knox, Steven Gallinger, Benjamin Haibe-Kains, Faiyaz Notta. High throughput drug screening to uncover molecular vulnerabilities in pancreatic ductal adenocarcinoma [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 3161.

Clinical data of analyzed BRCA PDAC patients