Cancer research最新文献

{"title":"Abstract B110: Effector CD4 T cells convert transient responses to KRAS targeted therapy into long-term durable remissions in pancreatic cancer","authors":"Li Qiang, Megan Hoffman, Jung-Ho Chun, Brendan Parent, Felix Hambitzer, Frank Peprah, Courtney Kureshi, Birkley Lim, Eugena Chang, Michael Walsh, Julissa Tello, Tavus Atajanova, Hojeong Shin, Corey Perkins, Rakeeb Kureshi, Yaniris Molina-Aponte, James Dougan, Chong Zuo, Lauren Brais, Thomas Clancy, James Cleary, Jason Hornick, Brandon Huffman, Joseph Mancias, George Molina, Mark Fairweather, Jonathan Nowak, Kimberly Perez, Douglas Rubinson, Sarah Slater, Ritchell van Dams, Jiping Wang, Brian Wolpin, Kwok-kin Wong, Harshabad Singh, Andrew Aguirre, David Baker, Michael Dougan, Stephanie Dougan","doi":"10.1158/1538-7445.pancreatic26-b110","DOIUrl":"https://doi.org/10.1158/1538-7445.pancreatic26-b110","url":null,"abstract":"Pancreatic ductal adenocarcinoma is refractory to most therapies, including immunotherapies, where reinvigoration of CD8 T cells with immune checkpoint blockade is insufficient to induce long-term durable remissions. Direct inhibitors of KRAS are clinically promising, although acquired resistance is common. To test if immunotherapy can synergize with KRAS inhibition to achieve long-term durable remission, we modeled KRAS inhibitor (KRASi) response and relapse in mice by using poorly immunogenetic orthotopically implanted PDAC mouse models. Unlike chemotherapy or checkpoint blockade combinations, a computationally designed IL-21 cytokine mimic (21h10) developed in collaboration with David Baker’s lab leads to long-term durable remissions. These durable remissions were observed across multiple mouse models , in combination with multiple KRAS inhibitors (MRTX1133, daraxonrasib, adagrasib), and in tumors with acquired resistance to KRAS inhibition via YAP amplification. Neither CD8 T cells nor MHC class I expression on tumor cells were important for efficacy of KRAS inhibition and 21h10 immunotherapy. Instead cDC2-primed CD4 T cells producing IFNg were crucial. Single cell analysis of the tumor microenvironment revealed that 21h10 along with KRAS inhibitors induced dramatic expansion of Th1-polarized CD4 T cells, and we used Tbx21-/- mice lacking Th1 cells and Ifng-/- mice to confirm a requirement for IFNg-producing CD4 T cells . CD4 T cells do not kill tumor cells directly, as cancer cell lacking MHCII expression preserved complete response to the treatment. Phagocytic macrophages were induced both in vivo and in vitro in the presence of 21h10 and Th1-polarized CD4 T cells, suggesting that macrophages are orchestrated to phagocytose cancer cells. To determine whether 21h10 might have a similar mechanism of action in humans, we analyzed ex vivo culture of 21h10 with human PDAC infiltrating T cells by single cell transcriptional profiling and showed a strong induction of IFNG production and other genes related to T cell activation and loss of Tregs. Thus IL-21-elicited CD4 T cells exert anti-tumor activity in mice, and potentially in humans, which can convert transient responses to KRAS inhibition into durable remissions. Citation Format: Li Qiang, Megan Hoffman, Jung-Ho Chun, Brendan Parent, Felix Hambitzer, Frank Peprah, Courtney Kureshi, Birkley Lim, Eugena Chang, Michael Walsh, Julissa Tello, Tavus Atajanova, Hojeong Shin, Corey Perkins, Rakeeb Kureshi, Yaniris Molina-Aponte, James Dougan, Chong Zuo, Lauren Brais, Thomas Clancy, James Cleary, Jason Hornick, Brandon Huffman, Joseph Mancias, George Molina, Mark Fairweather, Jonathan Nowak, Kimberly Perez, Douglas Rubinson, Sarah Slater, Ritchell van Dams, Jiping Wang, Brian Wolpin, Kwok-kin Wong, Harshabad Singh, Andrew Aguirre, David Baker, Michael Dougan, Stephanie Dougan. Effector CD4 T cells convert transient responses to KRAS targeted therapy into long-term durable remissions in pancreatic canc","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"6 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B122: The timing matters: schedule-dependent optimization of gemcitabine and RAS(ON) inhibition in pancreatic cancer, a call for a new approach to clinical trial design","authors":"Jonathan Brody","doi":"10.1158/1538-7445.pancreatic26-b122","DOIUrl":"https://doi.org/10.1158/1538-7445.pancreatic26-b122","url":null,"abstract":"Recent data published from the RASolute 302 trial (NCT06625320), showing that RAS inhibition nearly doubles median survival to 13.2 months compared to 6.7 months with standard-of-care chemotherapy for patients with pancreatic ductal adenocarcinoma (PDAC), received an ovation when presented at the 2026 ASCO Oncology meeting. Despite these advances, most patients develop resistance, indicating that RAS inhibition alone is unlikely to provide durable control and that rational combination strategies will be required. The current phase 3 RASolute 303 clinical trial combines RAS(ON) inhibition with standard-of-care chemotherapeutic regimens using concurrent administration. This approach, based on coordination, has not been evaluated for efficacy and tolerability against alternative scheduling strategies. In fact, in PDAC models, the scheduling of gemcitabine (GEM) before nab-paclitaxel improves therapeutic efficacy compared to concurrent administration. These findings suggest that the timing of treatment is a biological variable to be exploited to enhance patient outcomes. Herein, we assessed whether the timed-sequencing of GEM and RAS(ON) inhibitor daraxonrasib (RMC-6236; hereafter RASi) administration alters therapeutic outcomes in an immunocompetent orthotopic PDAC model. In this study, using a clinically tractable sequencing paradigm, we compared concurrent therapy with sequential regimens in which GEM preceded RASi (GEM→RASi) or vice versa (RASi→GEM). Specifically, we tested six different arms to this pre-clinical trial to assess relevant translational endpoints including: 1) tumor growth kinetics; 2) survival outcomes; 3) tolerability and systemic effects. We show that GEM followed by RAS inhibitor (GEM→RASi) scheduling is associated with improved survival and altered tumor growth dynamics relative to reverse scheduling and monotherapies, and favorable survival trends compared to concurrent treatment, with reduced total agent exposure. These findings identify timed-treatment scheduling as a modifiable variable with direct implications for the clinical integration of RAS(ON) inhibitors. Mechanistically, a strong putative explanation for the observed sequencing effect may lie in gemcitabine-induced cell cycle redistribution. Ongoing studies are evaluating whether GEM arrests surviving cells at the G1/S transition, a phase associated with heightened RAS pathway dependency, during which oncogenic KRAS activity is essential for cell cycle re-entry. RAS inhibitors are currently under clinical evaluation in combination with standard-of-care chemotherapy in PDAC. Our data question the assumption that concurrent administration is optimal, instead suggesting that defined therapeutic sequencing may enhance efficacy, while reducing overall chemotherapy exposure and eventual therapeutic resistance. This is an important consideration given that adverse events to extended regimens are frequently dose-limiting, and the logistical burden of infusion-based therapy","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"19 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract A081: Prospective Evaluation of a Regulatory-Approved ApoA2-Isoform In Vitro Diagnostic Assay for Pancreatic Cancer Risk Stratification Toward Interception in a Community-Based Screening Cohort","authors":"Kazufumi Honda, Masaki Kuwatani, Shiroh Tanoue, Hiroshi Fujita, Hiroki Taguchi, Kaoru Oketani, Mototsugu Kato, Keiko Takeuchi, Hiroshi Konishi, Hirohito Tsubouchi, Naoya Sakamoto, Akio Ido, Ayumi Kashiro, Kengo Nagashima","doi":"10.1158/1538-7445.pancreatic26-a081","DOIUrl":"https://doi.org/10.1158/1538-7445.pancreatic26-a081","url":null,"abstract":"Background: Early detection and interception are essential for improving outcomes of pancreatic cancer (PC). Blood-based biomarkers capable of identifying individuals at increased risk before clinical diagnosis may enable targeted imaging surveillance and interception strategies. Since 2017, we have conducted a prospective pancreatic cancer screening program among community-dwelling individuals aged 50 years or older using a research-use-only (RUO) ApoA2-isoform assay. However, the original RUO assay differed from the subsequently approved in vitro diagnostic (IVD) assay in assay design and quality-control procedures. Therefore, all archived plasma samples were remeasured using the PMDA-approved ApoA2-i IVD assay to evaluate its ability to identify individuals at elevated future risk of pancreatic cancer and support risk-stratified interception approaches. Methods: Archived plasma samples from a prospective screening cohort were remeasured de novo using the PMDA-approved ApoA2-i IVD assay. Results from the original RUO assay were not used. A total of 13,276 individuals aged 50 years or older were followed through regional cancer registries for up to four years. Participants were classified using the predefined clinical cut-off value of 60 μg/mL. Results: During follow-up, 20 pancreatic cancers were diagnosed. ApoA2-i positivity was observed in 452 individuals (3.4%), among whom six pancreatic cancers were identified (PPV 1.33%). Compared with ApoA2-i–negative individuals, participants with ApoA2-i levels below the clinical cut-off exhibited substantially increased risk of pancreatic cancer diagnosis. Risk ratios were 56.8 (95% CI, 14.5–222.6) for cancers diagnosed within 1 year, 21.3 (95% CI, 7.6–59.6) within 2 years, 15.5 (95% CI, 5.9–40.9) within 3 years, and 12.2 (95% CI, 4.7–31.5) during overall follow-up. Sensitivity was 66.7% for cancers diagnosed within 1 year and 30.0% during overall follow-up. Individuals with ApoA2-i levels below 60 μg/mL demonstrated significantly higher cumulative pancreatic cancer diagnosis rates than those above the cut-off (log-rank P < 0.001). Importantly, 50% of pancreatic cancers diagnosed during follow-up were detected at potentially curable R0-resectable stages. Conclusions: This study represents the first prospective evaluation of a regulatory-approved ApoA2-i IVD assay in a community-based screening cohort. ApoA2-i identified a small subgroup comprising only 3.4% of the screened population but exhibiting markedly elevated risk of future pancreatic cancer diagnosis. These findings support the clinical utility of ApoA2-i as a practical blood-based tool for pancreatic cancer risk stratification and for identifying individuals who may benefit from risk-adapted imaging surveillance and interception strategies. Integration of ApoA2-i–based risk stratification with targeted imaging may provide a scalable approach for pancreatic cancer interception in the general population. Citation Format: Kazufumi Honda, M","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"156 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract A082: IFOCOM Pancreas AI: A deep learning–based artificial intelligence system for early detection and clinical decision support in pancreatic cancer","authors":"MOUBARAK IBRAHIMA MADOUGOU","doi":"10.1158/1538-7445.pancreatic26-a082","DOIUrl":"https://doi.org/10.1158/1538-7445.pancreatic26-a082","url":null,"abstract":"Background: Pancreatic cancer remains one of the deadliest malignancies worldwide because of delayed diagnosis and limited opportunities for curative treatment. Artificial intelligence (AI) has emerged as a promising approach to improve early detection and support clinical decision-making through advanced medical image analysis. This study describes the development and preliminary technical evaluation of IFOCOM Pancreas AI, a deep learning–based artificial intelligence system designed to support early detection of pancreatic cancer. Methods: IFOCOM Pancreas AI was developed using a deep learning–based architecture for automated analysis of annotated pancreatic imaging datasets. The prototype integrates image quality assessment, preprocessing, feature extraction, lesion localisation, and AI-assisted classification to identify imaging patterns associated with pancreatic malignancy. Preliminary technical evaluation was performed to assess feasibility, robustness, and reproducibility. Prospective clinical validation is currently underway. Results: Preliminary technical evaluation demonstrated the feasibility of IFOCOM Pancreas AI for automated pancreatic image analysis. The prototype performed image quality assessment, lesion localisation, feature extraction, and AI-assisted lesion classification on annotated imaging datasets, supporting its potential as a computer-aided diagnostic support system for pancreatic cancer. Ongoing clinical validation aims to evaluate diagnostic performance, reproducibility, generalisability, and clinical utility. Conclusions: IFOCOM Pancreas AI represents a promising deep learning–based clinical decision support system for pancreatic cancer. Further prospective multicentre clinical validation will determine its diagnostic performance and potential integration into pancreatic cancer diagnostic pathways. AI Disclosure: During the preparation of this work, the authors used ChatGPT (OpenAI) to assist with language refinement and drafting support. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Citation Format: MOUBARAK IBRAHIMA MADOUGOU. IFOCOM Pancreas AI: A deep learning–based artificial intelligence system for early detection and clinical decision support in pancreatic cancer [abstract]. In: Proceedings of the AACR Conference on Pancreatic Cancer: New Frontiers in Biology and Therapeutic Development; 2026 Sep 25-28; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(18_Suppl_2): nr A082.","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"23 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B095: Discovery of treatment-associated peptide antigens for targeted immunotherapies in pancreatic ductal adenocarcinoma","authors":"Weixi Kang, Allison Glynn, Harshabad Singh, Ronald T. Raines, Andrew Liss, Forest M. White","doi":"10.1158/1538-7445.pancreatic26-b095","DOIUrl":"https://doi.org/10.1158/1538-7445.pancreatic26-b095","url":null,"abstract":"Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies and is largely refractory to immunotherapies due to its immune-cold tumor microenvironment and reduced antigen presentation. Mutated KRAS, present in most PDAC cases, drives tumor growth and contributes to immune evasion. Though RAS inhibitors have emerged as an extremely promising strategy to treat PDAC, tumor remission due to cancer resistance is virtually inevitable, indicating the need for combination therapies. As several studies have shown that RAS inhibition can resensitize PDAC tumors to anti-tumoral immunity, we hypothesized that RAS inhibition could reshape the PDAC MHC-I immunopeptidome (the cohort of all MHC peptides expressed on the cell surface) and thereby reveal treatment-induced tumor-associated peptide antigens that can be exploited for targeted immunotherapy. To test this hypothesis, we treated human PDAC cell lines with the pan-RAS inhibitor RMC-6236 and profiled the MHC-restricted peptides using an LC-MS/MS-based immunopeptidomics analysis platform. In parallel, we conducted paired global proteomics analysis, and differential peptide expression and pathway enrichment analyses were performed. We found that RAS inhibition increased MHC-I presentation on the PDAC cell surface and induced broad remodeling of the PDAC immunopeptidome. Of the 15706 peptide antigens identified, many that are downregulated upon 72 hours of treatment were sourced from cell cycle and DNA replication and repair pathways, including cyclin A2 and GDF15, suggesting that the cells were responding to the inhibitor and slowing cell proliferation. In contrast, many peptides that were upregulated are derived from pathways known to be involved in responses to or resistance to RAS inhibition, including altered signaling (Ral, PI3K/AKT, YAP-TAZ/Hippo pathways), apical junction, EMT, and ECM remodeling. Many of these pathway changes were corroborated by the global proteomics analysis, but the correlation between the immunopeptidome and proteome was weak, suggesting that the protein expression profile is a poor representative proxy for that of the immunopeptidome. Candidate peptide antigens were further prioritized based on treatment induction fold-change, source-protein expression, PDAC relevance, and tissue-expression patterns, and selected peptides were further validated using a targeted MS-based method, SureQuant MHC, for accurate identification and quantification (copies/cell). Several peptides we have validated are confirmed to be the correct sequence, and they have shown a 2- to 4-fold increase following treatment, consistent with the results from our discovery analysis. Together, these data suggest that RAS inhibition can expose new antigenic vulnerabilities in PDAC by reshaping the antigen presentation landscape. This work provides a framework for identifying actionable treatment-induced peptide antigens and supports rational combination strategies pairing KRAS/RAS inhibitor","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"103 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract A087: Distinct age-specific prediagnostic metabolic trajectories in early- and late-onset pancreatic cancer: a nationwide longitudinal cohort study","authors":"Yeo Gyeong Ko, Hee Seung Lee","doi":"10.1158/1538-7445.pancreatic26-a087","DOIUrl":"https://doi.org/10.1158/1538-7445.pancreatic26-a087","url":null,"abstract":"Background: Prediagnostic changes in body weight and glycemic status are well-recognized metabolic features preceding pancreatic cancer diagnosis. However, previous studies have largely described average metabolic changes, potentially masking distinct prediagnostic metabolic phenotypes. Whether these phenotypes differ according to age at disease onset remains unknown. We therefore investigated age-specific and within-age metabolic heterogeneity using longitudinal body mass index (BMI) and fasting blood sugar (FBS) trajectories. Methods: We conducted a retrospective nationwide cohort study using Korean National Health Insurance Service health screening data. Repeated BMI and FBS measurements obtained up to 10 years before pancreatic cancer diagnosis were analyzed. Patients were classified as early-onset (<50 years) or late-onset (>=50 years) pancreatic cancer (EOPC and LOPC), with age- and sex-matched controls. Group-based trajectory modeling (GBTM) was applied separately to BMI and FBS trajectories in the combined cohort and within each age group to evaluate age-specific and within-group metabolic heterogeneity. Case-control z-score differences (delta z) were tracked throughout the prediagnostic period to assess the timing and pattern of metabolic changes. Results: A total of 42,603 pancreatic cancer patients (3,199 EOPC; 39,404 LOPC) and 42,603 matched controls were included. In the combined cohort, GBTM identified four BMI and three FBS trajectory classes. A persistently increasing BMI class showed the weakest association with pancreatic cancer (OR 1.47) and was more frequent in EOPC than LOPC (6.8% vs 2.4%), whereas the gradually declining BMI class showed a stronger association (OR 2.86) and was more frequent in LOPC (22.3% vs 12.8%). Likewise, a rapidly increasing FBS trajectory was more frequent in LOPC than EOPC (9.5% vs 5.2%) and showed a strong association with pancreatic cancer (OR 3.61). Within the EOPC and LOPC groups, the numerically dominant phenotype was metabolically quiet, whereas overt metabolic derangement was confined to clinically distinct minority subgroups whose profiles differed by age. On delta z analysis, temporal patterns diverged: EOPC patients remained heavier than matched controls throughout follow-up, converging toward control levels only near diagnosis, whereas LOPC declined below controls with progressively worsening glycemia. Conclusions: Pancreatic cancer does not follow a single preclinical metabolic pathway but exhibits distinct age-specific metabolic phenotypes with within-group heterogeneity. Most patients were metabolically quiet, indicating that classical metabolic warning signs characterize only a minority of patients. Divergent delta z trajectories further support distinct prediagnostic metabolic evolution between early- and late-onset pancreatic cancer, providing a rationale for phenotype-based risk stratification and age-tailored early detection strategies. Citation Format: Yeo Gyeong Ko,","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"2 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract IA009: Profiling and targeting pancreatic cancer innervating neurons","authors":"Andreas Trumpp","doi":"10.1158/1538-7445.pancreatic26-ia009","DOIUrl":"https://doi.org/10.1158/1538-7445.pancreatic26-ia009","url":null,"abstract":"The peripheral nervous system (PNS) orchestrates organ function in health and disease. Many cancers, including pancreatic ductal adenocarcinoma (PDAC), are infiltrated by PNS neurons that contribute to the complexity of the tumor microenvironment (TME). However, the cell bodies of these neurons reside in distant PNS ganglia, causing cancer- or organ-innervating neurons to be largely absent from current tumor sequencing datasets. To molecularly characterize PDAC-innervating neurons at single-cell resolution, we developed Trace-n-seq, a strategy combining Fast Blue–mediated retrograde axonal tracing from tumors or healthy pancreas to their respective ganglia. Individual FB+ neurons are isolated from the ganglia by FACS and their transcriptomes established by single-cell RNA sequencing. Using this approach, we established transcriptomic profiles of >5,000 sympathetic and sensory neurons and >4,000 neurons innervating PDAC or healthy pancreas. Our analyses reveal previously unrecognized neuronal cell types and molecular networks that distinguish PDAC-innervating neurons from those associated with healthy pancreas or pancreatitis. Integration of neuronal and TME single-cell datasets delineates cancer-induced reprogramming of neuronal states, identifies a pancreatic cancer–nerve signature, and defines a neuro–cancer–microenvironment interactome. Functionally, pharmacological denervation induces a pro-inflammatory TME and enhances immune-checkpoint inhibitor efficacy. In addition, nab-paclitaxel induces intratumoral neuropathy by blocking sensory neuron activity, attenuating PDAC growth; when combined with sympathetic denervation, this approach results in synergistic tumor regression (Thiel et al., Nature, 2025). Together, our multidimensional analyses provide new insights into the networks and functions of pancreas- and PDAC-innervating neurons with direct clinical implications. These findings are being translated into SYNERGY, a multicenter phase I/II clinical trial combining targeting of celiac ganglion–derived innervating nerves, checkpoint-inhibitors and nab-paclitaxel. Citation Format: Andreas Trumpp. Profiling and targeting pancreatic cancer innervating neurons [abstract]. In: Proceedings of the AACR Conference on Pancreatic Cancer: New Frontiers in Biology and Therapeutic Development; 2026 Sep 25-28; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(18_Suppl_2): nr IA009.","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"59 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract B025: A spatial proximity-weighted algorithm (SPAN) defines reproducible tumor niches and a persister ecosystem underlying acquired resistance to pan-RAS inhibition in PDAC","authors":"Alvaro Curiel-Garcia, Melani Franchini, Lorenzo Tomassoni, Urszula Wasko-Kornberg, Andrea Califano, Kenneth P. Olive","doi":"10.1158/1538-7445.pancreatic26-b025","DOIUrl":"https://doi.org/10.1158/1538-7445.pancreatic26-b025","url":null,"abstract":"Spatial transcriptomics extends single-cell analysis of the tumor microenvironment into a mappable ecosystem by resolving gene expression in situ. Within it, cells assemble into recurrent multicellular niches whose spatial organization shapes tumor progression, immune evasion, and therapy response and resistance. Yet these niches are typically defined by arbitrary fixed-radius neighborhoods and unstable clustering, limiting reproducibility and obscuring how ecosystems remodel under therapy. To address this, we developed SPAN (Spatial Proximity-weighted Analysis of Niches), a generalizable framework that defines robust, reproducible niches from any spatial dataset. We focus on pancreatic ductal adenocarcinoma (PDAC), a stroma-rich, treatment-refractory tumor where niche architecture is thought to shape therapeutic failure. This is now especially pressing for pan-RAS/KRAS inhibitors, which are changing how PDAC is treated but face acquired resistance; how the ecosystem remodels under them remains unclear, with consequences for rational combinations and second-line therapy. We therefore applied SPAN to the autochthonous KPC model of PDAC, profiled by 10x Xenium across three states: untreated, one week of RMC-7977, and end-stage tumors that regrew despite continued therapy. For each cell, SPAN performs a multi-radius neighborhood search and builds a distance-weighted composition vector across radii, so proximal cells receive more weight. Composition matrices are partitioned by silhouette-optimized k-means, and spatial co-localization of the resulting niches is validated by neighborhood-enrichment testing. SPAN recovered reproducible niches whose composition tracked treatment state, revealing discrete, interconvertible states, not a static map. Vehicle tumors comprised epithelial and mixed epithelial/fibroblast/myeloid niches. Response abolished five baseline niches that never re-formed, including the epithelial-dominant ones, and the residual bed became fibroblast-dominated. One niche, epithelial-enriched fibroblasts, persisted across all three states, marking a treatment-persistent reservoir at the epithelial-stromal interface. Resistance was not a reversion to baseline but rather was associated with the development of a distinct ecosystem: a fibroblast/endothelial niche shared with treated tumors plus three resistance-restricted niches, including a fibroblast/myeloid/B-cell niche unique to regrowing tumors, implicating stromal-immune reorganization in relapse. A TGF-β/Notch persistence signature from the persistent niche stratified overall survival in human PDAC, linking the mouse reservoir to clinical outcome in untreated patients. Together, SPAN converts spatial data into reproducible, permutation-validated niches and reframes pan-RAS-inhibitor response and resistance as ecosystem-level state transitions. It nominates a treatment-persistent epithelial-fibroblast niche as a candidate residual-disease reservoir and a resistance-specific stromal-imm","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"15 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract A046: Mechanotransduction in pancreatic ductal adenocarcinoma: the role of the hERG1/β1integrin complex","authors":"Elisa Boccolucci, Claudia Duranti, Jessica iorio, Lucia Gardini, Chiara Caldini, Giacomo Bagni, Patrizia Nardini, Marco Capitanio, Thomas Schmidt, Annarosa Arcangeli","doi":"10.1158/1538-7445.pancreatic26-a046","DOIUrl":"https://doi.org/10.1158/1538-7445.pancreatic26-a046","url":null,"abstract":"Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy causing ∼7% of cancer-related deaths. Its poor treatment response is driven by a desmoplastic tumor microenvironment (TME) that renders the cancer tissue rigid, hypoxic and almost avascular. The unraveling of novel mechanisms sustaining tumor progression and therapy resistance in PDAC is urgently needed. Ion channels are novel, crucial drivers in tumor progression. Among them, the potassium channel hERG1, through its binding to β1 integrins, triggers a signaling cascade that reorganizes the cytoskeleton and activates cell migration. This project was aimed to elucidate the role of the hERG1/β1 integrin complex in mechanotransduction in PDAC malignancy. The mechanical profiles of different pancreatic cell lines HPDE (Immortal Human Pancreatic Duct Epithelial), serving as the healthy epithelial cells, PANC-1, SUIT-028, and SUIT-007, all PDAC cell lines were characterized through traction force measurements using fibronectin (FN)-coated elastic micropillar arrays at three stiffnesses: 10, 29, and 47 kPa. PDAC cells developed higher traction forces on stiffer micropillars compared to HPDE. Force generation inversely correlated with the abundance of the hERG1/β1 integrin complex, quantified through either Western blot (WB) or immunofluorescence (IF) as in Duranti, C. & Iorio, J et al., Life Science Alliance, 2024. Treating cells with a single-chain bispecific antibody in the format of a diabody (scDb) which targets and harnesses the hERG1/β1 integrin complex consistently increased traction forces in PDAC cells but had no effect on HPDE. The effects of the treatment were particularly evident in PANC-1 cells seeded onto FN-coated micropillar with a stiffness of 28 kPa, i.e. very close to the stiffness of pancreatic cancers in vivo. Key mechanotransduction effectors Yes-Associated Protein 1 (YAP1) and Activating Transcription Factor 3 (ATF3) were analyzed through IF, WB and RT-qPCR. ATF3 expression decreased with increasing substrate stiffness, in agreement with literature, while YAP1 was unaffected. Notably, ATF3 downregulation was reversed by treatment with scDb. Finally, Stochastic Optical Reconstruction Microscopy (STORM) (LENS, University of Florence) and custom code analysis was applied to PANC-1 and PANC-1 KO cells. These analyses identified distinct morphological and ultrastructural actin cytoskeleton phenotypes in PANC-1 compared to PANC-1 KO cells. Overall, our results demonstrate that the hERG1/β1 integrin complex acts as a key mediator of mechanotransduction in PDAC cells, mainly through the modulation of ATF3 transcription factor. Targeting this pathway with scDb successfully alters these cascades, offering potential promising therapeutic strategies against PDAC progression. Finally, applying STORM microscopy to oncology research allows for detecting nanoscale actin changes, providing deeper insights into tumor mechanobiology. Citation Format: Elisa Boccolucci, Claudia Du","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"52 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract A100: A computational framework for integrating multiscale spatial data in intraductal papillary mucinous neoplasms","authors":"Yang Liu, Akiko Sagara, Jimin Min, Tian Chu, Benson Chellakkan Selvanesan, Yunhe Liu, Aatur Singh, Anirban Maitra, Linghua Wang","doi":"10.1158/1538-7445.pancreatic26-a100","DOIUrl":"https://doi.org/10.1158/1538-7445.pancreatic26-a100","url":null,"abstract":"Background Recent advances in spatial multi-omics have improved our ability to study tissue architecture, cellular heterogeneity, and cell-cell interactions within the tumor microenvironment. Xenium and Visium HD provide complementary strengths: Xenium offers true single-cell and subcellular spatial resolution but is limited by targeted gene panels, whereas Visium HD provides transcriptome-wide profiling at fixed spatial bins, making cell-level reconstruction challenging. Robust computational strategies are therefore needed to bridge these platforms and construct spatially resolved single-cell transcriptomic atlases of human tumors. Methods We developed a framework to integrate multiscale spatial transcriptomic data from Visium HD and Xenium in intraductal papillary mucinous neoplasms (IPMN). To convert Visium HD bin-level measurements into cell-level profiles, we benchmarked SMURF, Bin2cell, and the 10x Genomics bin-to-cell workflow. Methods were evaluated by reconstructed cell quality, transcript assignment, clustering structure, and downstream interpretability. SMURF was selected because it integrates nuclear segmentation with transcriptional similarity and generated the highest-quality cell-level profiles. We then tested cross-platform integration strategies, including Harmony, BBKNN, scANVI, and related approaches. Because conventional batch-correction methods showed limited ability to harmonize Visium HD and Xenium data due to differences in gene coverage, chemistry, and resolution, we adopted label transfer to leverage both platforms while preserving platform-specific spatial information. Results Our workflow reconstructed cell-level transcriptomic profiles from Visium HD data and enabled integration with Xenium datasets. Compared with alternative bin-to-cell methods, SMURF produced cells with improved transcriptomic quality, clearer clustering, and more reliable cell type annotation. Label transfer allowed Xenium-derived single-cell spatial annotations to be projected onto Visium HD-derived transcriptome-wide profiles, enabling spatially resolved characterization of tumor, stromal, and immune populations across IPMN tissues. This approach preserved the single-cell spatial precision of Xenium while expanding biological interpretation through whole-transcriptome coverage from Visium HD. The resulting atlas provided a more comprehensive view of cellular states and spatial organization within the IPMN microenvironment than either platform alone. Conclusions We present a computational framework for integrating Visium HD and Xenium data to construct a single-cell spatial transcriptomic atlas of IPMN. By benchmarking bin-to-cell reconstruction methods and evaluating cross-platform integration strategies, we show that SMURF-based cell reconstruction combined with label transfer bridges transcriptome-wide profiling and true single-cell spatial resolution. This framework provides a scalable strategy for studying tumor heterogeneity, microenvironmen","PeriodicalId":9441,"journal":{"name":"Cancer research","volume":"17 1","pages":""},"PeriodicalIF":11.2,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}