tbuckley/cabot-search huggingface dataset:
Abstract 2940: Preclinical development of a next generation antibody drug conjugate (ADC) targeting CDH17 for treatment of solid tumors
Authors: Wei Lian, Qing Zong, Chun Deng, Peng Zou, Liang Xiao, Tongtong Xue, Jiaqiang Cai
Journal: Cancer Research (2025)

Abstract Cadherin-17 (CDH17) is a membranous cell adhesion protein predominantly expressed in intestinal epithelial cells. High CDH17 expression is associated with metastatic progression in different cancers that mainly originate from gastrointestinal tracts. YL217 is a novel CDH17 -targeting antibody-drug conjugate (ADC) and built on MediLink’s TMALIN® platform, which can release the payload both in the tumor cell and in the tumor microenvironment. This study evaluated efficacy, safety and pharmacokinetic profiles of YL217 in non-clinical studies. YL217 could specifically bind to CDH17 on tumor cell surfaces, followed with payload release both in the lysosome through internalization and in the tumor microenvironment. These translate in vivo where YL217 showed superior efficacy in causing tumor regressions and good tolerability in both cell lines and patient-derived xenograft (CDX and PDX) mouse models with low to high CDH17 expression levels. The in vivo pharmacokinetics in monkeys showed that YL217 is highly stable with less than 0.1% (molar ratio) payload released in circulation. These non-clinical data suggest the high stability of the linker in circulation as well as efficient payload release in tumors. Moreover, acceptable safety profiles were also noted in monkey GLP toxicity studies. Taken together, preclinical data suggest that YL217 could be further developed in the treatment of CDH17-positive cancer patients. Citation Format: Wei Lian, Qing Zong, Chun Deng, Peng Zou, Liang Xiao, Tongtong Xue, Jiaqiang Cai. Preclinical development of a next generation antibody drug conjugate (ADC) targeting CDH17 for treatment of solid tumors [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 2940.
Abstract 3369: Identifying biomarkers of resistance for immunotherapy-treated patients with non-small-cell lung cancer using real-world evidence and machine learning
Authors: Chao Fang, Natasha Markuzon, Sajan Khosla
Journal: Cancer Research (2025)

Abstract Immunotherapy (IO) has revolutionized the management of non-small-cell lung cancer (NSCLC), leading to groundbreaking improvements in patient survival. However, resistance to IO in some patients remains a challenge. Existing IO resistance biomarkers are based on either clinical or genomic factors. Combining genomic factors with patient health characteristics is pivotal in developing new resistance markers and improving the accuracy of predictions of treatment resistance. The objective of this study is to develop a data-driven model with no a priori assumptions using real-world evidence (RWE) data to identify predictors of treatment resistance in NSCLC patients treated with first-line IO. By integrating clinical and genomic features, we aim to assess the differences in the importance of genomic mutations under various clinical conditions and identify new biomarkers associated with IO resistance. Using clinical and genomic features from real-world evidence (RWE) Flatiron dataset for an NSCLC cohort treated with first-line IO, we developed a machine learning (ML) model to identify predictors of treatment resistance. Using progression-free survival (PFS) as an endpoint, the model identified key contributing factors, a combination of clinical and genomic features, where the importance of genomic mutations varies under different clinical conditions. Specifically, in patients with ECOG scores of 0 or 1 and no metastasis, the CDKN2A mutation was associated with reduced PFS; for those with ECOG scores of 0 or 1 and with metastasis, CDKN2A was associated with decreased PFS and KRAS with improved PFS; and for patients with ECOG scores of 2-4, KRAS was associated with improved PFS. Our results underscore the value of RWE in identifying resistance biomarkers, demonstrate the efficacy of a data-driven methodology able to extract combinations of important features without prior assumptions, and highlighted the importance of integrating clinical and molecular data to understand patient-level risk factors of treatment resistance. Using our methodology, we pinpointed statistically significant biomarkers linked to an increased risk of treatment resistance in NSCLC patients undergoing first-line IO treatment. Citation Format: Chao Fang, Natasha Markuzon, Sajan Khosla. Identifying biomarkers of resistance for immunotherapy-treated patients with non-small-cell lung cancer using real-world evidence and machine learning [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 3369.
Abstract 3398: Gene-level and global genomic insights into colorectal cancer ovarian metastasis: Molecular mechanisms and determinants
Authors: Ying Fang, Yan Sun, Xiaotian Zhao, Xiaoying Wu, Jiaohui Pang, Haimeng Tang, Qiuxiang Ou, Che Chen, Ziwei Xu
Journal: Cancer Research (2025)

Abstract Background: Approximately 5% of female patients with colorectal cancer (CRC) develop ovarian metastasis (OM), predominantly in young women and leading to poorer survival. As the molecular mechanisms underlying OM remain unclear, this study aimed to identify the genetic determinants of OM and to investigate the mechanisms of synchronous and metachronous metastasis. Methods: We retrospectively enrolled 81 patients with CRC who had developed OM (28 with matched primary and metastatic lesion samples, 30 with primary lesion, 23 with metastatic samples). Treatment-naïve primary and metastatic lesion tissue samples underwent next-generation sequencing targeting 437 genes to identify gene-level and global genomic determinants of OM. Two external CRC cohorts (MSKCC cohort, N=643; TCGA cohort, N=448) were analyzed for further mechanism exploration. Results: Among 81 included patients, copy number variations (CNVs) of SMAD4 (p&amp;lt;0.01) and SMAD2 (p&amp;lt;0.01) were more frequently detected in metastatic lesions than primary lesions, with similar findings in 28 pairs of matched samples (SMAD4, p=0.05; SMAD4, p=0.02). Associations of SMAD4 (p&amp;lt;0.001) and SMAD2 (p=0.09) CNV loss with unfavorable overall survival were observed in the MSKCC cohort. RNA sequencing data from the TCGA cohort demonstrated up-regulated angiogenesis pathway related to SMAD4 (q=0.03) and SMAD2 (q&amp;lt;0.001) CNV loss. Higher chromosome instability was observed in metastatic lesions than primary lesions in the study cohort (p&amp;lt;0.01; matched samples, p=0.08), consistent with the findings in the MSKCC cohort (p&amp;lt;0.001). The APOBEC mutational signature was related to OM synchronous metastasis, with heavier burdens in both the primary (p=0.04) and metastatic (p=0.01) lesions of synchronous metastasis than those of metachronous metastasis; however, primary lesions of metachronous metastasis exhibited higher whole-genome doubling fraction (p=0.04) and ploidy (p=0.02). Moreover, in metastatic lesions, FLT1 (17.6%, p=0.05) and ZNF217 (17.6%, p=0.05) CNV gains were exclusively detected of synchronous metastasis when compared to metastatic lesions of metachronous metastasis. Conclusions: SMAD4/2 CNV loss and chromosome instability may drive OM in CRC. Synchronous metastasis displays distinct gene-level and global genome features different from metachronous metastasis. Citation Format: Ying Fang, Yan Sun, Xiaotian Zhao, Xiaoying Wu, Jiaohui Pang, Haimeng Tang, Qiuxiang Ou, Che Chen, Ziwei Xu. Gene-level and global genomic insights into colorectal cancer ovarian metastasis: Molecular mechanisms and determinants [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 3398.
Abstract 62: Tumor targeted contrast agents for pancreatic adenocarcinoma
Authors: Sylvana Knapp, Ryan Bynum, Molly McNally, William E. Grizzle, Lacey R. McNally
Journal: Cancer Research (2025)

Abstract Purpose: Pancreatic cancer has a low relative survival rate of only 12.8%, with surgical resection being an option for just 20% of patients. Achieving R0 resection is vital, as it forms the cornerstone of curative treatment strategies. pH low insertion peptides (pHLIPs), a novel class of targeted imaging agents, are being developed for use with multispectral optoacoustic imaging (MSOT) for potential clinical or intraoperative applications. However, existing pHLIPs have limitations in dynamic range and solubility. To address these issues, a new pHLIP variant, V7Phe, was designed to enhance both solubility and dynamic range for identifying the acidic tumor microenvironment in pancreatic cancer. Methods: V7 and V7F peptides were synthesized using microwave chemistry, then removed from resin, lyophilized, and rehydrated. Conjugation with HiLyte 750 dye via maleimide chemistry was confirmed through spectroscopy. Pancreatic cancer cell lines S2VP10 and S2013 were treated with V7-750 and V7F-750 at pH 7.4 and pH 6.6. Probe uptake was assessed using near-infrared fluorescence imaging. Results: V7F-750 demonstrated a significantly higher signal in vitro than V7-750 at both pH 7.4 and pH 6.6 (p&amp;lt;0.01). V7F-750 also exhibited greater signal in more acidic conditions (p&amp;lt;0.01). Although solubility was moderately improved, V7F-750 did not show an enhanced dynamic range compared to V7-750, with the ratio of signal at pH 6.6 to 7.4 being 1.19 for V7F-750 versus 1.39 for V7-750 (p=0.21) across both cell lines. Conclusion: V7F-750 effectively targets the acidic tumor microenvironment, demonstrating improvements in peptide solubility Citation Format: Sylvana Knapp, Ryan Bynum, Molly McNally, William E. Grizzle, Lacey R. McNally. Tumor targeted contrast agents for pancreatic 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 62.
Abstract 1450: Differences in expression levels of putative breast cancer risk genes in normal breast tissues between Black and White Americans
Authors: Lili Liu, Guochong Jia, Jie Ping, Xingyi Guo, Ran Tao, Bingshan Li, Qiuyin Cai, Wei Zheng
Journal: Cancer Research (2025)

Abstract Background: Despite having a similar incidence of breast cancer, Black American women persistently endure higher mortality rates than their white counterparts. Reasons for this disparity are unclear. We evaluated possible racial differences in expression levels of putative breast cancer susceptibility genes identified in recent studies. Methods: Included in this analysis are 429 lincRNA or protein-coding risk genes identified in our recent transcriptome-wide association and fine-mapping studies using multi-ancestry data. In this study, The RNA sequencing data for these genes were derived from normal breast tissue samples obtained from 161 Black and 287 white participants recruited in the Susan G. Komen Biobank. To address potential batch effects, we used residuals after the probabilistic estimation of expression residuals correction, with standardized gene expression levels as input data after quantile and inverse rank normalization. Generalized linear regressions were used to investigate racial difference in gene expression levels. Models were adjusted for age, body mass index, and menopausal status. The significance threshold was set at 0.05 divided by the number of independent tests. Results: Out of the 429 genes evaluated in the study, 50 genes showed significant racial differences in expression levels at P &amp;lt; 4.54e-4 (0.05/110), the significant threshold after adjusting for multiple comparisons. Of them, 28 genes showed a higher expression level in Black than white participants. The top three genes with highest percent differences were GSTM1, PRSS45, and DIRC3. The GSTM1 gene showed a 65.86% higher expression level in Black samples (P=2.58e-9), followed by PRSS45 (percent difference = 37.75%, P = 9.84e-9) and DIRC3 (15.52%, 2.95e-11). On the other side, the top three genes with lowest percent difference were LRRC37A, ARL17A and LRRC37A2. Gene LRRC37A showed a 35.27% lower expression level in Black than white samples (P=2.22e-16), followed by gene ARL17A (-25.14%, 1.35e-15) and LRRC37A2 (-15.38%, 6.51e-13). Conclusion: We identified 50 genes showing racial differences in expression levels in breast normal tissues. Further research is needed to understand the reasons for these differences. Citation Format: Lili Liu, Guochong Jia, Jie Ping, Xingyi Guo, Ran Tao, Bingshan Li, Qiuyin Cai, Wei Zheng. Differences in expression levels of putative breast cancer risk genes in normal breast tissues between Black and White Americans [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 1450.
Abstract 3078: Targeting a ROS-induced oxidation and O-GlcNAcylation cascade to overcome drug resistance across multiple cancer types
Authors: Juanjuan Li, Huadong Pei, Feng Yao, Hemeng Zhang, Junfeng Ma
Journal: Cancer Research (2025)

Abstract Reactive oxygen species (ROS) are critical in lipid peroxidation and the initiation of ferroptosis, which significantly impacts chemotherapeutic drug resistance. However, the mechanisms by which ROS function and are sensed remain poorly understood. In this study, we identified that O-GlcNAc transferase (OGT), a key enzyme for protein O-GlcNAcylation, acts as a sensor for ROS induced by ferroptosis. The ROS-induced oxidation of OGT at C845 in its catalytic domain activates OGT. Once activated, OGT O-GlcNAcylates FOXK2, which enhances its interaction with importin α, promoting its nuclear translocation and subsequent transcription of SLC7A11, thereby inhibiting ferroptosis. Moreover, ferroptosis-induced activation of the ATM-CHK2 pathway and subsequent phosphorylation of FOXK2 further boost FOXK2 O-GlcNAcylation and SLC7A11 transcription. Clinically, the elevated OGT-FOXK2-SLC7A11 axis drives tumorigenesis and promotes resistance to chemoradiotherapy across various cancer types, including hepatocellular carcinoma (HCC). Our study uncovers a ROS-induced oxidation-O-GlcNAcylation cascade specific to ferroptosis, which integrates ROS signaling, O-GlcNAcylation, FOXK2-mediated SLC7A11 transcription, and chemoradiotherapy resistance. Targeting this cascade represents a promising approach to overcoming drug resistance in multiple cancer types. Citation Format: Juanjuan Li, Huadong Pei, Feng Yao, Hemeng Zhang, Junfeng Ma. Targeting a ROS-induced oxidation and O-GlcNAcylation cascade to overcome drug resistance across multiple cancer types [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 3078.
Abstract 1096: Estimating risk factors and predicting various cancer types using integrated real-world data: An AI-based approach
Authors: Md Ashad Alam, Nick Duesbery, Daniel Fort
Journal: Cancer Research (2025)

Abstract Cancer remains a critical global health issue with numerous risk factors. However, comprehensive large-scale studies in the United States, particularly those examining cancer-related medical conditions, are limited. This study aims to develop AI-driven precision medicine algorithms by integrating data from electronic health records (EHR) to identify patients at elevated risk for various cancers, using age-, gender-, race-, and area deprivation index (ADI)-stratified data. By employing disease, demographic, and ADI data as covariates, we apply interpretable machine learning models to uncover insights into cancer risk factors. We explored eight machine and deep learning algorithms including logistic regression (LR) and random forest (RF) using 1, 229 truncated unique ICD-10-CM codes (selected through related risk analysis) as binary features across a total pool of 1.6 million patients with 5-fold cross-validation. First, we applied both LR and RF methods for all patients. Using LR, we observed that conditions such as nicotine dependence, irregular menstruation, and urinary disorders were highly associated with subsequent cancer diagnoses, while acute pharyngitis and cardiac murmurs were associated with a decreased risk, achieving an overall accuracy of 86%. RF further identified conditions, including acute pharyngitis, nicotine dependence, and other soft tissue disorders with high predictive value, achieving 96% training accuracy and 86% test accuracy. Finally, eight machine and deep learning algorithms were evaluated on various sample sizes, ranging from 200 to 20, 000, assessing accuracy, precision, recall, F1 score, ROC AUC, and MCC. Notably, RF accuracy increased from 69% to 78%, and ROC AUC from 79% to 86% as sample size grew, underscoring the model's robustness with larger datasets. Our findings contribute to early cancer detection and provide insights into disease progression. This comprehensive analysis has the potential to improve early detection, diagnostic pathways, and understanding of the pathological processes underlying cancer worldwide, as well as highlight variable performance across methodologies on this kind of data. Citation Format: Md Ashad Alam, Nick Duesbery, Daniel Fort. Estimating risk factors and predicting various cancer types using integrated real-world data: An AI-based approach [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 1096.
Abstract 1234: Mechanical control of cancer-associated adipocyte plasticity orchestrates pancreatic cancer progression
Authors: Bo Han, Zhi Yang, Shuqing Zhao, Thomas Schmittgen, Jamel Ali, Jonathan Barajas, Carolyn Ruiz-Rivera, Ba Xuan Hoang
Journal: Cancer Research (2025)

Abstract Cancer-associated adipocytes (CAAs) are key components of the pancreatic tumor microenvironment, yet how their phenotype and function are regulated by environmental conditions remains poorly understood. Here, we demonstrate that oxygen tension and matrix stiffness dramatically alter CAA behavior and their subsequent effects on pancreatic ductal adenocarcinoma (PDAC) progression. Under normoxic conditions and soft matrix, CAAs exhibit an activated phenotype characterized by extended podia formation and enhanced secretion of matrix-degrading enzymes, particularly MMP-2 and MMP-9, leading to dissolution of organoid extracellular matrix (ECM) and restriction of PDAC cell growth through direct cell-cell contact. In contrast, when cultured within stiff ECM environments (∼30 kPa), CAAs maintain a rounded, stem cell-like morphology without matrix-degrading activity, but notably promote PDAC cell migration. Gene expression confirmed that ECM-primed CAAs revealed upregulation of stemness markers including OCT4, SOX2, and SOX9, along with enhanced expression of pro-inflammatory cytokines. Using ectopic pancreatic tumor models, we found that CAAs primed in stiff ECM significantly accelerate tumor progression and invasion compared to controls, with increased tumor volume and invasion into the muscle layer 4 weeks post-implantation. Mechanistically, stiff ECM-primed CAAs showed distinct stem cell-like phenotype and altered secretome profiles. When cocultured with PDAC cells, their crosstalk promoted increased secretion of pro-inflammatory cytokines IL-6 and TNF-α while downregulating anti-inflammatory cytokine IL-10, creating a more tumor-supportive microenvironment. IHC reveals macrophage F4/80 showed no significant difference between groups, however, CD163+ macrophages were more prevalent at the early stage in the inactivated CAA-PDAC group. These findings reveal previously unrecognized plasticity in CAA function regulated by mechanical and oxygen tension cues, suggesting that the spatial distribution of these environmental factors within pancreatic tumors may create distinct zones of tumor promotion versus restriction. Our results highlight the complex role of CAAs in PDAC progression and suggest that targeting ECM stiffness could be a strategy to modify CAA behavior for therapeutic benefit. Understanding this mechanical control of CAA function could lead to novel therapeutic strategies for PDAC treatment. Citation Format: Bo Han, Zhi Yang, Shuqing Zhao, Thomas Schmittgen, Jamel Ali, Jonathan Barajas, Carolyn Ruiz-Rivera, Ba Xuan Hoang. Mechanical control of cancer-associated adipocyte plasticity orchestrates pancreatic cancer progression [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 1234.
Abstract 2638: Study of the effect of ribosomal protein L23 on the metastasis related protein of human osteosarcoma cell lines
Authors: Chia-Hsuan Chou, Shun-Fa Yang
Journal: Cancer Research (2025)

Abstract Introduction: Lung metastases are the main cause of death and poor prognosis in patients with osteosarcoma. Its high propensity to metastasize is the leading cause for treatment failure and poor prognosis. Human ribosomal protein L23 (RPL23), a novel RBP, has been reported that involved in various physiological and pathological processes, including cell proliferation, cell apoptosis and cycle arrest. The higher expression of RPL23 is associated with the aggressiveness of the disease. However, the underlying functions and mechanisms of RPL23 in osteosarcoma metastasis has not been explored. Material and methods: The GSE33382, GSE19276, and GSE 21257 database from Gene Expression Omnibus (GEO) were analyzed. Boyden chamber assay was used to evaluate the effects of cell migration and invasion. The FAM111B mRNA level and transcription activity were assessed by PCR, real-time PCR and luciferase reporter assay in osteosarcoma cells (U-2OS and HOS) after transfected with RPL23 siRNA. Results: We found that RPL23 was upregulated in osteosarcoma tissues and its high expression was associated with poor prognosis in osteosarcoma patients. Using RNA-seq, microRNA-seq, and bioinformatic analysis, we revealed FAM111B and miR-30b-5p as downstream targets of RPL23. Knockdown of RPL23 significantly reduced the migration, invasion, and metastatic potential of osteosarcoma cell lines in vitro. However, these effects were reversed by the miR-30b-5p inhibitor. Conclusions: RPL23 may be a novel osteosarcoma treatment strategy and a useful biomarker for predicting osteosarcoma progression. Citation Format: Chia-Hsuan Chou, Shun-Fa Yang. Study of the effect of ribosomal protein L23 on the metastasis related protein of human osteosarcoma cell lines [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 2638.