tbuckley/cabot-search huggingface dataset:
Abstract LB367: CT1113, a dual USP25/28 inhibitor, promotes antitumor immunity by preventing YTHDF2-mediated complement activation and potentiates anti-PD-1 therapy
Authors: Shujie Zhou, Jiarui Liu, Limin Chen, Jin Peng, Xinghua Zhen, Lingzhi Wu, Xupeng Bai, Pumin Zhang
Journal: Cancer Research (2025)

Abstract Immune checkpoint inhibitor (ICI) has reshaped the landscape of cancer therapy and improved the prognosis of a number of malignancies. However, a considerable proportion of cancer patients fail to benefit from ICI treatment. The main reason for the failure is the poor infiltration of lymphocytes due to the immunosuppressive milieu that dictates a “cold” tumor microenvironment. The ubiquitin-proteasome system plays crucial roles in many cellular processes including tumor progression and immune evasion. USP25 and USP28, two homologous deubiquitinases, maintain the stability of a wide range of oncoproteins by suppressing their proteasomal degradation. We previously found that CT1113, a novel dual USP25/28 inhibitor, could induce a potent and broad-spectrum anti-tumor effect. Here, we demonstrate that CT1113 triggers a robust cytotoxic CD8+ T cell-dependent anti-tumor immune response in several types of “cold” tumors. Dual inhibition of tumoral USP25/28 reverses the immunosuppressive microenvironment by increasing CD8+ T cell infiltration/activity and attenuates the priming of protumoral M2-like macrophages through flow cytometry and single-cell RNA-seq (scRNA-seq). Mechanistically, we identified YTH domain-containing family protein 2 (YTHDF2) as a substrate of USP25/28. The two deubiquitinases redundantly promote the stability of YTHDF2. Dual inhibition of the two increases the degradation of YTHDF2 and abolishes YTHDF2-mediated complement activation and immunosuppression. As a result, both pharmacologic and genetic inhibition of USP25/28 strongly enhance anti-PD-1 therapy against two murine “cold” tumor models without discernable systemic toxicity. Together, these results provide a rationale for future clinical trials evaluating the synergy between inhibiting USP25/28 and ICI to improve the efficacy of immunotherapy. Citation Format: Shujie Zhou, Jiarui Liu, Limin Chen, Jin Peng, Xinghua Zhen, Lingzhi Wu, Xupeng Bai, Pumin Zhang. CT1113, a dual USP25/28 inhibitor, promotes antitumor immunity by preventing YTHDF2-mediated complement activation and potentiates anti-PD-1 therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB367.
Abstract LB419: HDAC8 over-expression promotes drug resistance and alters chromatin architecture in melanoma cells
Authors: Kanchan Gupta, Richard L. Bennett, Michael F. Emmons, Yufeng Xiao, Guangrong Zheng, Keiran S. Smalley, Jonathan D. Licht
Journal: Cancer Research (2025)

Abstract Background: BRAF and MEK inhibitors are standard treatments for patients with BRAF V600E-mutated melanoma. However, resistance to these therapies poses a significant challenge. Understanding the molecular mechanisms underlying this resistance is critical for the development of more effective treatment strategies. Objective: To investigate the role of histone deacetylase 8 (HDAC8) in drug resistance in melanoma and evaluate the potential of HDAC8 degradation as a therapeutic strategy. Methods: We utilized overexpression vector to examine the effects of HDAC8 in melanoma cells. The proliferative, migratory, and drug sensitivity phenotypes were assessed in HDAC8-overexpressing melanoma cells. Additionally, we tested YX862, a selective HDAC8 degrader, to explore its effects on HDAC8 activity and downstream cellular processes. Acetylation of SMC3, a cohesin complex protein, was measured, and genome-wide HiC analysis was employed to assess chromatin structural changes. Results: HDAC8 overexpression promoted a more proliferative and migratory phenotype in melanoma cells. Furthermore, HDAC8-overexpressing cells exhibited reduced sensitivity to BRAF and MEK inhibitors. YX862 treatment specifically degraded HDAC8, leading to increased acetylation of SMC3. Genome-wide HiC analysis revealed that changes in SMC3 acetylation altered the three-dimensional structure of chromatin, potentially contributing to drug resistance. Conclusion: These findings highlight the critical role of HDAC8 in melanoma progression and drug resistance. HDAC8 degraders like YX862 may offer a promising therapeutic approach to overcome resistance and inhibit melanoma progression by modulating key epigenetic and cellular pathways. Citation Format: Kanchan Gupta, Richard L. Bennett, Michael F. Emmons, Yufeng Xiao, Guangrong Zheng, Keiran S. Smalley, Jonathan D. Licht. HDAC8 over-expression promotes drug resistance and alters chromatin architecture in melanoma cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB419.
Abstract LB189: Prospective AI/ML-driven computational approaches for predicting T cell-mediated immunity and beyond
Authors: Chongming Jiang, Yulun Chiu, Xiling Shen, Cheng-chi Chao
Journal: Cancer Research (2025)

Abstract Background: T cells play a double-edged sword role in various physiopathological states. They have the ability to eliminate tumors and infected cells, and play an essential role in host defense. Instead, T cells can play pathogenic roles, activated by autoantigens and causing damage to self-tissues. Therefore, triggering and fine-tuning the control of T cell-peptide/major histocompatibility complex (MHC) (TCR-pMHC) recognition is crucial for maintaining the balance of many aspects of disease and pathological states for the treatment of cancer, infectious, and autoimmune disease. Methods: Despite the previous interest and attempt, generalizable predictive models of TCR-pMHC specificity remain nascent. Inspired by recent breakthroughs in protein structure prediction achieved by deep neural networks, we evaluated structural modeling using AlphaFold 3 (AF3)-based AI-enabled computation as a potential avenue for predicting TCR epitope specificity. Results: We show that a specialized version of the neural network predictor AlphaFold can generate models of TCR-pMHC interactions that can distinguish valid from invalid peptide epitopes with increasing accuracy. Accurate prediction of T-cell immunogenicity holds significant therapeutic value. It helps identify peptide epitopes linked to tumors, infectious agents, and autoimmune diseases. Higher-affinity T cells can be computationally designed with a higher success rate for T cell therapy. Strong immunogenicity of antigenic epitopes can be selected for vaccine development. Conclusions: An accurate TCR-pMHC model will significantly benefit T-cell-mediated immunotherapy and facilitate advanced drug design. Although much work remains before these predictions achieve widespread practical utility, we are optimistic that deep learning-based structural modeling represents a path to the generalizable prediction of TCR-pMHC interaction and beyond. Citation Format: Chongming Jiang, Yulun Chiu, Xiling Shen, Cheng-chi Chao. Prospective AI/ML-driven computational approaches for predicting T cell-mediated immunity and beyond [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB189.
Abstract LB169: Establishment of patient-derived organoid (PDO) from single cell-originated core-needle biopsied (CNB) samples of breast cancer
Authors: Wen-Hung Kuo, Jia-Yang Chen, Chia-Chun Liu, Mark D. Pegram, Yu-Min Lin, Chia-Yuan Chang, Ying-Chih Chang
Journal: Cancer Research (2025)

Abstract Breast cancer (BC) exhibits differential subtypes with morphological and genetic heterogeneity. Lack of adequate ex vivo model and relevant PDO bank limits following clinical and applications and anti-cancer developments. Three-dimensional cell cultures, such as tumor spheroids, offered a promising alternative tool to reduce animal testing and act as patient avatar for cancer research. However, current platforms, such as scaffold-free low-adherence attachment plates, face challenges with low efficiency of spheroid formation and suffocate in handling rare cell samples. To address this issue, we employed the R3CE platform (Rapid, Reproducible, Rare Cell 3D Expansion; AcroCyte Therapeutics) to facilitate PDO cultivation and establishment including CNB samples based on single cell-derived 3D culture. Paired total 23 normal, 22 tumor, and 14 lymph node CNB tissue samples from various subtypes of cancer patients were collected for PDO culture. Observations revealed varied morphologies in paired normal versus tumor PDOs after culture. The overall successful rate was 84% (93% exclude lymph node) with normal PDO achieving 100% (23/23) success and tumor PDO culture reaches 86% (19/22) successful rate. Lymph node cultures on PDO had a success rate of 57% (8/14) due to potential sentinel node evaluation during routine examination. The established PDOs were confirmed by IHC staining for ER, PR, or Her2 expression. The tumorigenicity of the tumor PDOs was also validated through in vivo xenograft formation with pathology verified histological appearance. Our results demonstrate a streamlined procedures on amplification and establishment, enhancing PDOs model development and further clinical applications. Citation Format: Wen-Hung Kuo, Jia-Yang Chen, Chia-Chun Liu, Mark D. Pegram, Yu-Min Lin, Chia-Yuan Chang, Ying-Chih Chang. Establishment of patient-derived organoid (PDO) from single cell-originated core-needle biopsied (CNB) samples of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB169.
Abstract LB170: Establishing 3D organoid models from patient-derived conditionally reprogrammed cells to bridge preclinical and clinical insights in pancreatic cancer
Authors: Galam Leem, Jin Su Kim, Seungmin Bang
Journal: Cancer Research (2025)

Abstract Background: Pancreatic cancer is a highly lethal malignancy with limited treatment response. Despite advancements in treatment, systemic chemotherapy remains the primary therapeutic approach for over 80% of patients, with no established biomarkers to guide drug selection. Traditional two-dimensional (2D) culture models fail to replicate the tumor microenvironment, necessitating the development of more advanced models, such as three-dimensional (3D) organoid models. Methods: We established 3D organoid cultures using patient-derived conditionally reprogrammed cell (CRC) lines, originally cultured under 2D conditions. These CRC organoids were developed using a Matrigel-based platform without organoid-specific medium components to preserve the intrinsic molecular subtypes of the cells. Morphological, molecular, and drug sensitivity analyses were performed to compare the clinical responses of 3D CRC organoids with those of their 2D counterparts and clinical responses. Results: The 3D CRC organoids retained the molecular characteristics and mutational profiles of the original CRCs and displayed distinct morphologies corresponding to cancer stages and differentiation. Drug response profiling of gemcitabine plus nab-paclitaxel (Abraxane) and FOLFIRINOX demonstrated that the 3D organoids more accurately mirrored patient clinical responses than the 2D cultures. Notably, the IC50 values for the 3D organoids were higher, reflecting the structural complexity and drug penetration barriers observed in vivo. Conclusion: Matrigel-based 3D organoid culture models provide a robust platform for pre-clinical drug evaluation, overcoming the limitations of 2D models. Although time- and resource-intensive, integrating both 2D and 3D platforms enables efficient initial screening and validation. This approach holds promise for identifying predictive biomarkers and advancing precision medicine in pancreatic cancer treatment. Citation Format: Galam Leem, Jin Su Kim, Seungmin Bang. Establishing 3D organoid models from patient-derived conditionally reprogrammed cells to bridge preclinical and clinical insights in pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB170.
Abstract LB195: Activity based protein profiling on kinase PROTACs leads to the discovery of selective APT1/2 degrader
Authors: Tian Qiu, Samuel Maiwald, Brendan G. Dwyer, Leyna Duong, Katherine A. Donovan, Woong Sub Byun, Hannah M. Jones, Eric S. Fischer, Stephen M. Hinshaw, Tinghu Zhang, Brenda A. Schulman, Nathanael S. Gray
Journal: Cancer Research (2025)

Abstract Targeted protein degradation (TPD) refers to the use of small molecules to induce ubiquitin-dependent protein degradation. Most success has been achieved by designing selective degraders starting from known binders of a protein of interest (POI). Chemo-proteomics analysis of these degraders can be used to confirm their selectivity for target proteins but often results in the serendipitous discovery of additional degradation targets. While this has provided a means to the discovery of unanticipated degraders, the relatively low throughput of proteomics combined with the idiosyncrasy of discovery has prevented this approach being systematically applied. To improve the throughput of this approach, we explored using activity-based protein profiling (ABPP) to prescreen a degrader library to select for compounds that could potentially inhibit or degrade targets of interest before performing chemo-proteomics profiling. Here, we demonstrate the application of this combined ABPP-chemo-proteomics workflow using a library of heterobifunctional kinase degraders which allowed us to efficiently discover acyl-protein thioesterase APT1/2 as off-targets of a compound originally developed as a FGFR1/2 hetero-bivalent degrader. APT1/2 is responsible for removing S-palmitoylation, a post-translational modification that regulates oncoprotein function and localization in cancer cells. Further optimization and molecular engineering results in the development of a potent APT1/2 degrader TWQ-02-083 that no longer induces FGFR1/2 degradation. We show that TWQ-02-083 induces a strong ternary complex between VHL and APT1/2 and has high cooperativity comparable to classical molecular glues. Our work highlights the potential utility of ABPP strategies for rapid discovery of heterobifunctional degraders on a subset of proteins and delivers a robust and scalable workflow for identifying novel unanticipated degraders that could be valuable in cancer research. Citation Format: Tian Qiu, Samuel Maiwald, Brendan G. Dwyer, Leyna Duong, Katherine A. Donovan, Woong Sub Byun, Hannah M. Jones, Eric S. Fischer, Stephen M. Hinshaw, Tinghu Zhang, Brenda A. Schulman, Nathanael S. Gray. Activity based protein profiling on kinase PROTACs leads to the discovery of selective APT1/2 degrader [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB195.
Abstract LB081: Carboxyamidotriazole orotate (CTO) with temozolomide (TMZ) for recurrent high-grade gliomas (rHGG) and in combination with TMZ/RT-&amp;gt;TMZ in newly diagnosed glioblastoma (ndGBM): Long term follow-up of phase IB trial
Authors: Thomas Kaley, Elena Pentsova, Cameron Brennan, Debyani Chakravarty, Lindsey Myers, Nancy Keith, Adam Newton, Kathryn Beal, Robert Aiken, Antonio Omuro, Priya Kumthekar, Rashida A. Karmali
Journal: Cancer Research (2025)

Abstract Background: CTO, an oral brain penetrant inhibitor of calcium signaling pathways was evaluated in rHGG and ndGBM. Intracellular Ca2+ homeostasis is altered in cancer cells and results in abnormal gene expression and immune evasion.Primary objectives of safety, dose-limiting toxicity (DLT) and recommended phase 2 dose were reported [Omuro et al. JCO 2018]. This analysis presents secondary outcome of overall survival (OS) and comparison with historical controls. Methods: In a 3+3 design, CTO was given daily p.o. in: i) rHGG: CTO (219-812.5mg/m2/d and in 6 pts 600mg/day) and TMZ (150 mg/m2 x 5 every 28 d). OS was compared to phase III in rHGG treated with lomustine . (N=259)ii) ndGBM: CTO (219-481 mg/m2) and standard of care TMZ/RT [Stupp et al. NEJM 2005] OS was compared to phase III in ndGBM treated with SOC (N=1,339). Results: i) rHGG (N=26 pts): CTO was well tolerated (no DLT) and median OS (mOS) was 10.5 mo with 95% CI (8.0, 14.8) and 1-year OS of 47.4%, compared to lomustine treated pts with mOS 7.0 months (6.1, 7.7) and 1-year OS of 25.4%, p=0.0126.ii) ndGBM: All pts (N=15) had GBM (methylated MGMT gene in 33%; EGFR in 27%). In SOC+CTO pts, treatment was well tolerated, mOS was 37.8 mo (95% CI 20.9, 51.9) and a 2-yr survival of 69%, compared to historical control mOS of 15.8 mo 95% CI (15.1, 16.5) and 2-yr OS of 28.4%, p=0.006. Conclusion: Addition of CTO to TMZ or TMZ/RT respectively was safe with no DLT. Survival in both recurrent HGG and newly diagnosed GBM patients compares favorably with historical controls. Phase 2 randomized trials are planned. Citation Format: Thomas Kaley, Elena Pentsova, Cameron Brennan, Debyani Chakravarty, Lindsey Myers, Nancy Keith, Adam Newton, Kathryn Beal, Robert Aiken, Antonio Omuro, Priya Kumthekar, Rashida A. Karmali. Carboxyamidotriazole orotate (CTO) with temozolomide (TMZ) for recurrent high-grade gliomas (rHGG) and in combination with TMZ/RT-&amp;gt;TMZ in newly diagnosed glioblastoma (ndGBM): Long term follow-up of phase IB trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB081.
Abstract LB111: Convolutional neural network-based artificial intelligence for immune subtype classification in pan-cancer
Authors: Minh Huu Nhat Le, Ha-Hieu Pham, Huy Quoc Nguyen, Hong Xuan Ong, Hien Quang Kha, Phat Ky Nguyen, Thanh-Huy Nguyen, Han Hong Huynh, Dang Nguyen, Thanh-Minh Nguyen, An Thuy Vo, Thuy Vu Minh Nguyen, Trung Minh Tu Tran, Lam Huu Phuc Nguyen, Nguyen Quoc Khanh Le
Journal: Cancer Research (2025)

Abstract Background: Pan-cancer analysis offers a holistic perspective on cancer, spanning 33 types and over 11,000 tumor samples from The Cancer Genome Atlas (TCGA). This analysis provides key insights into molecular features shared across cancers and aids in identifying immune subtypes, which are critical for developing personalized immunotherapies. Six distinct immune subtypes were identified in the immune landscape of cancer, offering a framework to explore tumor-immune interactions and their impact on prognosis. Methods: RNA sequencing (RNA-Seq) data were extracted from the TCGA Pan-Cancer Atlas, normalized, and processed for classification. Convolutional neural networks (CNNs), a subset of artificial intelligence, were employed to classify immune subtypes. The CNN model utilized convolutional layers with ReLU activation, dropout regularization, and batch normalization to enhance robustness and prevent overfitting. The dataset was split into training, validation, and testing subsets to evaluate model performance. Performance metrics, including accuracy and the area under the curve (AUC), were used to benchmark the CNN against traditional machine learning methods such as XGBoost, Random Forest, and TabNet. Results: The CNN model achieved an AUC of 0.993 and an overall accuracy of 98.85%, outperforming traditional classification methods. It demonstrated resilience to class imbalance and accurately classified underrepresented immune subtypes. The approach was validated against independent datasets, demonstrating consistent performance across diverse processing pipelines and normalization methods. Conclusions: This study highlights the utility of artificial intelligence, particularly CNNs, in accurately classifying immune subtypes across pan-cancer datasets. By leveraging RNA-Seq data, this approach offers a scalable and robust framework for pan-cancer analysis, paving the way for advancements in precision oncology and personalized immunotherapy. Future efforts will incorporate multi-omics data and validate these findings in clinical settings to enhance their translational potential. Citation Format: Minh Huu Nhat Le, Ha-Hieu Pham, Huy Quoc Nguyen, Hong Xuan Ong, Hien Quang Kha, Phat Ky Nguyen, Thanh-Huy Nguyen, Han Hong Huynh, Dang Nguyen, Thanh-Minh Nguyen, An Thuy Vo, Thuy Vu Minh Nguyen, Trung Minh Tu Tran, Lam Huu Phuc Nguyen, Nguyen Quoc Khanh Le. Convolutional neural network-based artificial intelligence for immune subtype classification in pan-cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB111.