tbuckley/cabot-search huggingface dataset:
Abstract LB042: An automated workflow for optical pooled screens with massively multiplexed phenotypic readouts on AVITI24
Authors: Avtar Singh, Daniel Honigfort, Mariam Dawood, Connor Thompson, Amy Chuong, Orit Rozenblatt-Rosen, Mike Previte
Journal: Cancer Research (2025)

Abstract Optical pooled screening (OPS) is a powerful tool for linking image-based measurements of cellular phenotypes with in situ readouts of genetic perturbations, and has been used to identify the regulators of diverse biological processes, such as immune signaling, mitosis, antiviral response, and cell morphology. Although the conventional OPS approach requires fairly simple hardware - imaging is most commonly performed on a fluorescence microscope equipped with custom filters - adoption of OPS has been restricted to a small number of laboratories due to the sophisticated expertise required for data generation and computational analysis. Furthermore, screens have been limited to measuring &amp;lt; 20 phenotypic markers, making it difficult to capture mechanistic information across a wide range of biological pathways in the same experiment. Here, we establish a fully automated end-to-end workflow for OPS data generation and computational analysis on the AVITI24 sequencing platform. First, we demonstrate highly accurate in situ sequencing-by-synthesis of genetic perturbations with an avidity-based chemistry, and streamline the process of template generation. Next, we combine this workflow with a highly multiplexed AVITI24 phenotyping panel (up to 50 proteins and 350 mRNAs) and apply the combined protocol to perform a 500-gene CRISPR screen for regulators of complex cellular processes. This work lays the foundation for automated OPS data generation, supporting rich profiling of perturbation effects at unprecedented scale. Citation Format: Avtar Singh, Daniel Honigfort, Mariam Dawood, Connor Thompson, Amy Chuong, Orit Rozenblatt-Rosen, Mike Previte. An automated workflow for optical pooled screens with massively multiplexed phenotypic readouts on AVITI24 [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 LB042.
Abstract LB135: BCG014: A CDH3-targeting ADC with a novel TOP1 inhibitor demonstrates robust anti-tumor efficacy in PDX models
Authors: Yi Yang, Kaiqing Zhang, Kunying Hao, Chengzhang Shang
Journal: Cancer Research (2025)

Abstract The CDH3 protein is a member of the cell adhesion molecule family and functions as a calcium-dependent glycoprotein that facilitates cell-cell adhesion. It is frequently overexpressed in several malignant solid tumors, including pancreatic, head and neck, esophageal, breast, gastric, lung, and colorectal cancers. Consequently, CDH3 represents a promising target for antibody-drug conjugates (ADCs), with significant potential for therapeutic applications across various solid tumors. Utilizing the fully human RenLite CDH3 knockout mouse platform, we screened the Ab.20 antibody, which specifically targets CDH3 without cross-reacting with other members of the CDH3 family. Ab.20 effectively binds to various tumor cell lines and demonstrates robust internalization activity, exhibiting high affinity for both human and cynomolgus CDH3 antigens. Moreover, Ab.20 shows promising developability under stress conditions. In proof-of-concept studies, the Ab.20-vcMMAE conjugate demonstrated superior efficacy compared to the benchmark Ab-vcMMAE in patient-derived xenograft (PDX) models. Subsequently, Ab.20 was conjugated with BLD1102, a novel topoisomerase I inhibitor payload, leading to the development of BCG014, which exhibited potent efficacy across multiple PDX models. Ongoing studies are evaluating the efficacy, pharmacokinetics, and toxicity of BCG014. These findings suggest that BCG014 has the potential to be a novel therapeutic alternative for tumors expressing CDH3. Citation Format: Yi Yang, Kaiqing Zhang, Kunying Hao, Chengzhang Shang. BCG014: A CDH3-targeting ADC with a novel TOP1 inhibitor demonstrates robust anti-tumor efficacy in PDX models [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 LB135.
Abstract LB118: Machine learning-based tumor grading in pancreatic ductal adenocarcinoma: Exploring texture features for automated classification and clinical decision support
Authors: Miracle Thomas
Journal: Cancer Research (2025)

Abstract Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy and a leading cause of cancer-related death in the U.S. Due to late-onset symptoms, it often remains undiagnosed until advanced stages, resulting in poor prognosis. This study presents a machine learning approach to classify tumor grades based on texture features extracted from histological images, offering insights into prognosis and treatment decisions. A 2019 study by Qiu et al. demonstrated the effectiveness of machine learning-based CT texture analysis in predicting PDAC histopathological grades, achieving 86% accuracy, 78% sensitivity, and 95% specificity. Similarly, this work utilizes images from four tumor grades—Normal, Grade I, Grade II, and Grade III—obtained from Hematoxylin and Eosin and May-Grunwald-Giemsa staining. Texture features, including Gray-Level Co-occurrence Matrix (GLCM) properties, Local Binary Pattern (LBP) features, and Histogram of Oriented Gradients (HOG), were extracted to create a feature vector for each image. These vectors were used to train a Support Vector Machine (SVM) model with Error-Correcting Output Codes (ECOC) for multiclass classification, with hyperparameter optimization to improve model performance. Cross-validation was used to evaluate the model, yielding an accuracy of 53%. Although this accuracy is suboptimal, it represents a foundational step in automated PDAC tumor classification. The model could still serve as a useful tool in clinical practice, especially when used alongside other diagnostic methods or as a baseline for further improvements. Principal Component Analysis (PCA) was applied to visualize the feature distribution, and a confusion matrix was generated to assess classification performance. Results indicate that, despite the modest accuracy, the extracted texture features have potential for distinguishing between tumor grades, providing a starting point for automated classification and supporting clinical decision-making. The study introduces an innovative approach to PDAC tumor grading, addressing the urgent need for improved diagnostic tools. While further work is needed to optimize performance, this research sets the stage for future advancements that could impact clinical decision-making and patient outcomes. Citation Format: Miracle Thomas. Machine learning-based tumor grading in pancreatic ductal adenocarcinoma: Exploring texture features for automated classification and clinical decision support [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 LB118.
Abstract LB187: YF087 is a potent and selective inhibitor of WRN, specifically targeting MSI-H cancer cells
Authors: Yaolin Wang, Rongfeng Liu, Yanxin Yu, Hong Yang, Jifang Weng, Yan Weng, Hong Mei, David Dai
Journal: Cancer Research (2025)

Abstract The impairment of DNA mismatch repair (MMR) function occurs in various cancers, including colorectal (15%), gastric (22%), endometrial (20-30%) and ovarian (10-15%) cancers. MMR-deficient (dMMR) cancers are characterized by a high mutational load and frequent insertion and/or deletion events in repetitive DNA sequences, a condition known as microsatellite instability-high (MSI-H). The Werner syndrome protein (WRN) is essential for the survival of cancer cells exhibiting microsatellite instability (MSI) and represents a synthetic lethal target for MSI-H/dMMR cancers. We have identified YF087, a highly selective, reversible and potent WRN inhibitor that has demonstrated robust activity in both in vitro anti-proliferation assays and in vivo cancer xenograft models. YF087 exhibited an IC50 of 8.1 nM in the WRN helicase enzymatic assay and an IC50 of 42 nM in the anti-proliferation assay in SW48 (MSI-H) colorectal cancer cell. YF087 exhibits excellent selectivity against another helicase BLM at the enzymatic level and is highly selective for MSI-H cells compared to microsatellite stable (MSS) cell in the anti-proliferation assay. YF087 is more effective than both WRN reversible inhibitor HRO761 and irreversible inhibitor RG6457 in the SW48 cellular anti-proliferation assay in the presence of 50% human serum. In the HCT116 colorectal cancer xenograft model, 5 mg/kg, 15 mg/kg and 45 mg/kg YF087 oral and daily treatment for 22 days induced regression rates of 32%, 43% and 57%, respectively. Tumors continue to regress after the treatment ends and remained tumor-free status for more than two months after dose termination at the 45 mg/kg dose. YF087 also exhibits superior in vivo anti-tumor efficacy in other MSI-H/dMMR cancer xenograft models upon oral and daily treatment and maintained longer tumor regression after termination of treatment vs HRO761 and RG6457. In patient derived MSI-H gastrointestinal cancer models resistant to anti-PD-1 therapy, treatment with YF087 resulted in tumor inhibition or regression. YF087 also possesses favorable preclinical ADME profiles suitable for clinical development. Based on these findings, YF087 is currently in IND-enabling studies to support phase 1 clinical trial in MSI-H/dMMR cancer patients. Citation Format: Yaolin Wang, Rongfeng Liu, Yanxin Yu, Hong Yang, Jifang Weng, Yan Weng, Hong Mei, David Dai. YF087 is a potent and selective inhibitor of WRN, specifically targeting MSI-H cancer 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 LB187.
Abstract LB127: An unconventional antiestrogen reveals the novel therapeutic target, DKK1, in aggressive ER+ breast cancer
Authors: Kristen Young, Govinda R. Hancock, Steven Kregel, Sean W. Fanning
Journal: Cancer Research (2025)

Abstract Breast cancer is the second leading cause of cancer-related deaths in women in the United States. Over 75% of breast cancer cases overexpress the hormone receptor estrogen receptor alpha (ERα), making it an attractive target for therapeutics. Although these therapies boast initial success, about 50% of women will experience disease recurrence in their lives. This disease returns aggressively, with many women having metastatic lesions with somatic mutations to ERα. One of the most clinically relevant mutation is a missense tyrosine to serine at position 537 of the receptor (Y537S). Developing treatments with higher efficacy that uniquely target pathways harnessed by Y537S ERα is urgently needed. To this end, our laboratory seeks to uncover hidden transcriptional programs specifically harnessed by Y537S ERα an unconventional antiestrogen termed T6I-29. We find that T6I-29 shows anti-tumoral activities in Y537S mutant xenograft murine models. Transcriptionally, T6I-29 uniquely downregulates DKK1, a secreted glycoprotein. The role of DKK1 is poorly understood in the context of ER+ breast cancer but has shown immunosuppressive and oncogenic properties in other cancer types. To first assess the relevance of DKK1 in ER+ breast cancer, we procured 108 ER+ breast cancer patient plasma samples and compared them to 100 matched healthy donors. Importantly, we find that DKK1 is significantly elevated in patient plasma. It has been previously reported in other cancer types that DKK1 specifically blunts the cytotoxicity of natural killer (NK) cells, an innate immune cell population critical for host defense. To explore the immunomodulatory effects of DKK1 in ERα+ breast cancers, we co-cultured NK cells with ERα+ breast cancer cells with and without the Y537S mutation and low and high levels of DKK1, respectively. We also cultured NK cells with recombinant DKK1. Importantly, we find that high levels of DKK1 and breast cancers with high DKK1 blunt the cytotoxicity of NK cells, which, when treated with T6I-29, are reinvigorated. Assessing other critical immune cell populations is important for further immunosuppressive characterization of DKK1 in ERα+ breast cancer. These novel findings potentiate DKK1 as a target in ERα+ breast cancers. Citation Format: Kristen Young, Govinda R. Hancock, Steven Kregel, Sean W. Fanning. An unconventional antiestrogen reveals the novel therapeutic target, DKK1, in aggressive ER+ 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 LB127.
Abstract CT247: FORTE: A phase 2 master protocol assessing plixorafenib for BRAF-altered cancers
Authors: Karisa C. Schreck, Macarena I. de la Fuente, Jessica Rine, Irina Kline, Stacie Peacock Shepherd, Jordi Rodon, Rona Yaeger, Eric Sherman
Journal: Cancer Research (2025)

Abstract BACKGROUND: Type I RAF inhibitors in combination with MEK inhibitors demonstrate durable responses but high toxicity rates in BRAF V600-mutated tumors including pyrexia and cardiovascular, hepatic, gastrointestinal, and ocular toxicities. A type II RAF inhibitor shows benefit in low-grade glioma with BRAF fusions or V600 mutations but confers the risk of major hemorrhage, skin toxicities, reductions in growth velocity, and viral and upper respiratory infections. There remains an unmet need for tolerable and efficacious targeted therapies for the treatment of BRAF-altered tumors. Plixorafenib (FORE8394; PLX8394) is a novel, oral, small-molecule BRAF inhibitor highly selective for BRAF V600 monomers and BRAF-containing dimers. Plixorafenib binding disrupts RAF dimerization and prevents paradoxical activation, targeting both Class I V600 mutations and Class II fusions and avoiding the need for combination with a MEK inhibitor. In a phase 1/2a study, plixorafenib demonstrated promising safety and clinical activity across a range of doses tested in tumors with BRAF V600 mutations and BRAF fusions. The most common adverse events included predominantly grade 1-2 liver function test changes and grade 1 fatigue, nausea, diarrhea, and vomiting. METHODS: The FORTE Phase 2 basket study is currently enrolling patients ≥10 years of age into 4 sub-protocols. Study details are shown in the Table. Eligible patients have received prior therapy for advanced disease, have measurable disease, and have a Karnofsky (≥16 years) or Lansky (&amp;lt;16 years) Performance Score of ≥60 at study entry. All patients receive plixorafenib continuous dosing, in some cohorts coadministered with cobicistat, a pharmacokinetic booster. Prior MAPK inhibitor therapy is excluded unless otherwise specified below. As of January 2025, the trial is recruiting participants in 9 countries globally, with 54 sites currently activated. Clinical trial registry number: NCT05503797. Citation Format: Karisa C. Schreck, Macarena I. de la Fuente, Jessica Rine, Irina Kline, Stacie Peacock Shepherd, Jordi Rodon, Rona Yaeger, Eric Sherman. FORTE: A phase 2 master protocol assessing plixorafenib for BRAF-altered cancers [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 CT247.
Abstract LB108: CarD-T: Interpreting Carcinomic Lexicon via Transformers
Authors: James (Jamey) ONeill, Parag A. Katira
Journal: Cancer Research (2025)

Abstract The identification and classification of carcinogens is critical in cancer epidemiology. We introduce the Carcinogen Detection via Transformers (CarD-T) framework, combining transformer-based machine learning with probabilistic analysis to efficiently nominate potential carcinogens from scientific texts. Trained on 60% of established carcinogens, CarD-T correctly identifies all remaining known carcinogens and nominates ∼1,600 potential new carcinogens. Comparative assessment against GPT-4 reveals CarD-T's comparable precision (0.894 vs 0.903), and superior recall (0.857 vs 0.705), implying improved ability to classify carcinogens not in major databases. Additionally, CarD-T highlights 554 entities with disputing evidence, analyzed using Bayesian Probabilistic Carcinogenic Denomination (PCarD). The framework reveals significant shifts in research focus from chemical carcinogens to broader categories including environmental factors (18%), biological agents (10%), and emerging threats like COVID-19, supported by 577 publications since 2020. This framework enhances the agility of public health responses to carcinogen identification, setting a new benchmark for automated, scalable toxicological investigations. Citation Format: James (Jamey) ONeill, Parag A. Katira. CarD-T: Interpreting Carcinomic Lexicon via Transformers [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 LB108.
Abstract LB100: Automated feature reduction approaches for lung cancer detection through radiomic analysis
Authors: Mostafa Zahed
Journal: Cancer Research (2025)

Abstract Over the years, cancer has increasingly become a global health issue, with early detection and diagnosis being critical for successful treatment. Radiomics, which utilizes imaging techniques such as CT, PET, MRI, or Ultrasound, involves extracting features from image-based data and applying ma- chine learning for quantitative analysis and disease prediction. These extracted features are divided into three categories namely first, second and higher order radiomics resulting in a massive number of features that necessitate the use of feature engineering to address the curse of dimensionality. This research focuses on exploring dimension reduction techniques to decrease the number of features and make the data more manageable. The radiomics workflow includes imaging, segmentation, feature extraction, and analysis. In this study, a large-scale CT dataset for lung cancer diagnosis, collected by scholars from a medical university in China, is utilized to demonstrate dimension reduction techniques using R, SAS, and Python software. The techniques employed include PCA, clustering, and manifold-based algorithms. These methods successfully reduced the number of features from 110 to 39 using PCA and to 21 with clustering. Overall, the analysis highlighted that texture-based features, particularly those in the second-order radiomics category, are the most significant in the lung cancer data. Citation Format: Mostafa Zahed. Automated feature reduction approaches for lung cancer detection through radiomic analysis [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 LB100.
Abstract LB278: KEAP1 loss-of-function modulates ROS and AKT-mTOR pathway leading to resistance to KRAS inhibitor
Authors: Weijia Lu, Mei Ning, Zhui Chen
Journal: Cancer Research (2025)

Abstract Resistance to KRAS G12C inhibitors remains a significant clinical challenge in the treatment of non-small cell lung cancer (NSCLC). Among KRAS-mutated NSCLC cases, which account for approximately 30% of all NSCLC, the G12C subtype is the most prevalent, representing about 40% of KRAS-positive mutations. Clinical evidence demonstrates that KEAP1 loss-of-function (LoF) mutations are associated with early disease progression and poor responses to KRAS G12C inhibitor monotherapy, underscoring the urgent need to decipher and counteract this resistance mechanism. This study aims to elucidate the molecular basis of KEAP1-mediated resistance to KRAS G12C inhibitors and explore potential therapeutic strategies to overcome it. We performed in vitro experiments using KRAS G12C-mutated NSCLC cell lines, both with and without KEAP1 LoF mutations, and in vivo mouse models bearing KEAP1 LoF xenografts. Experimental procedures included treating cells with KRAS G12C inhibitors, measuring ROS levels via flow cytometry, assessing GSH and GSSG levels with a colorimetric enzymatic assay, and analyzing AKT-mTOR pathway activity through western blotting. These analyses revealed that KEAP1 mutations suppress ROS accumulation, promote glutathione synthesis, and activate the AKT signaling pathway, thereby promoting resistance. Combinatorial therapeutic approaches targeting glutaminase (e.g., CB-839) and mTOR pathways (e.g., TAK-228) have shown potential for effectively inhibiting tumor cell growth in KEAP1-mutant contexts. Our findings demonstrate that KEAP1 mutations confer resistance to KRAS G12C inhibitors in NSCLC through a multifaceted mechanism involving oxidative stress regulation and compensatory pathway activation. This resistance extends beyond KRAS inhibitors, mirroring resistance mechanisms observed with other therapies, including epidermal growth factor receptor (EGFR) inhibitors, BRAF, MEK, and ALK inhibitors, as well as chemotherapeutic agents, radiotherapy, and anti-angiogenic therapies. These observations underscore the broader role of KEAP1 mutations in enhancing tumor adaptability and survival under diverse therapeutic pressures. In conclusion, our results suggest that combinatorial therapeutic approaches targeting glutaminase (e.g., CB-839) and mTOR pathways (e.g., TAK-228) hold promise for overcoming resistance in KEAP1-mutant tumors. These strategies may restore sensitivity to KRAS G12C inhibitors and improve clinical outcomes. Citation Format: Weijia Lu, Mei Ning, Zhui Chen. KEAP1 loss-of-function modulates ROS and AKT-mTOR pathway leading to resistance to KRAS inhibitor [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 LB278.