tbuckley/cabot-search huggingface dataset:
Abstract 5631: ZSTK3744, a novel AhR agonist, demonstrates antitumor efficacy in TNBC cell lines <i>in vitro</i> and <i>in vivo</i>
Authors: Kayoko Kawai-Asami, Takahiro Ohashi, Sayuri Terada, Kotomi Akatsuka, Yuko Nagata, Shinsuke Hiramoto, Tsubasa Nagasaki, Eika Higashi, Ryuichiro Ohshita, Keiko Fukushima
Journal: Cancer Research (2025)

Background: Breast cancer is the most commonly diagnosed cancer among women worldwide. Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer and has limited treatment options. TNBC lacks key molecular targets, making it challenging to treat with conventional therapies and limiting the availability of effective treatment options. Herein, we report a novel small molecule, ZSTK3744, which exhibits potent inhibitory effects on the proliferation of TNBC-derived cell lines both in vitro and in vivo. Method: We found a compound, ZSTK3744, that exhibits potent antitumor effects against the TNBC-derived cell line MDA-MB-468. We employed DNA microarray analysis and CRISPR-Cas9 technology to determine the molecular targets of ZSTK3744. Mechanistic studies were performed to evaluate ROS generation, DNA damage, and intracellular signaling pathways in ZSTK3744-treated cells. In vivo, ZSTK3744 was administered at a dose of 2.5 mg/kg every four days (days 0, 4, and 8) via tail vein injection in an MDA-MB-468 xenograft model. Tumor size and body wight were measured until day 28. In addition, safety was evaluated in dogs with weekly dose escalation to assess tolerability. Results: ZSTK3744 exhibited potent antitumor activity (GI50: 0.69 nM) against MDA-MB-468 cells. Target analysis identified ZSTK3744 as an aryl hydrocarbon receptor (AhR) agonist and demonstrated that AhR is essential for its growth-inhibitory effects. ZSTK3744 induced ROS generation, DNA damage, and apoptosis in MDA-MB-468 cells in a dose-dependent manner. In vivo, ZSTK3744 demonstrated significant anti-tumor activity, reducing tumor size from baseline and maintaining this reduction throughout the 3-week post-administration observation period. Importantly, ZSTK3744 treatment did not adversely affect the body weight of treated mice. In contrast to aminoflavone, a previously developed AhR agonist associated with severe lung toxicity, ZSTK3744 showed no evidence of lung toxicity in dog studies. Conclusion: ZSTK3744 demonstrated potent antitumor effects on TNBC-derived cells in vitro and in vivo, as well as having a favorable safety profile. These results suggest that ZSTK3744 is suitable for development as a novel therapeutic agent for TNBC. Ongoing studies are needed to identify biomarkers that predict response to ZSTK3744. Citation Format: Kayoko Kawai-Asami, Takahiro Ohashi, Sayuri Terada, Kotomi Akatsuka, Yuko Nagata, Shinsuke Hiramoto, Tsubasa Nagasaki, Eika Higashi, Ryuichiro Ohshita, Keiko Fukushima. ZSTK3744, a novel AhR agonist, demonstrates antitumor efficacy in TNBC cell lines in vitro and in vivo [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 5631.
Abstract 2513: Factors influencing participant attrition in Phase 0 hybrid clinical trials for patients with high-grade glioma
Authors: Jocelyn L. Harmon, Amy Hong, Artak Tovmasyan, An-Chi Tien, Kristin Hendrickson, Jennifer Eschbacher, Wonsuk Yoo, Igor Barani, William Kennedy, Zaman Mirzadeh, Yoshie Umemura, Shwetal Mehta, Nader Sanai
Journal: Cancer Research (2025)

Abstract Background: Phase 0 hybrid trial designs administer a subtherapeutic experimental agent pre-surgically and then use predetermined pharmacokinetic (PK) and/or pharmacodynamic (PD) thresholds to qualify patients for follow-on therapeutic dosing. While such protocols are rare, they are increasingly prevalent within neuro-oncology, necessitating a better understanding of factors influencing participation. Here, we investigate participant enrollment in Phase 0 hybrid trials for high-grade glioma patients at a single center. Methods: This retrospective study examined characteristics of high-grade glioma patients enrolled in Phase 0 hybrid clinical trials at the Ivy Brain Tumor Center. Clinical, pathological and demographic data, as well as PK/PD profiles, were analyzed and related to participant enrollment and attrition. Results: A total of 284 newly-diagnosed and recurrent high-grade glioma patients were analyzed and 276 (97.2%) participants completed the Phase 0 component of their study. Of these, 176 (63.8%) Phase 0 participant tumors did not meet criteria for therapeutic dosing, including 63 (35.8%) due to histological diagnosis, 62 (35.2%) due to PK/PD results, 39 (22.2%) due to clinical status decline, 7 (4.0%) due to patients’ decision to not proceed, and 5 (2.8%) for unknown reasons. Six (2.1%) participants did not complete Phase 0 and 2 (0.7%) participants were screen failures. Conclusions: In the upfront and recurrent settings, tumor histopathology and drug PK/PD performance are the principal determinants of participant suitability for the therapeutic dosing component of a Phase 0 hybrid study. In this single-center analysis, the graduation rate from Phase 0 to therapeutic dosing was 36.2%. This retrospective study, the largest to date in high-grade glioma patients, highlights participant- and protocol design-related features that will help refine future Phase 0 hybrid protocols in neuro-oncology. Citation Format: Jocelyn L. Harmon, Amy Hong, Artak Tovmasyan, An-Chi Tien, Kristin Hendrickson, Jennifer Eschbacher, Wonsuk Yoo, Igor Barani, William Kennedy, Zaman Mirzadeh, Yoshie Umemura, Shwetal Mehta, Nader Sanai. Factors influencing participant attrition in Phase 0 hybrid clinical trials for patients with high-grade glioma [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 2513.
Abstract 5612: Taletrectinib, a next generation selective ROS1 inhibitor, inhibits growth of ROS1 wild-type and ROS1-G2032R xenografts
Authors: Hitisha K. Patel, Laura Heller, Kevin Bowman, Zheyi Hu, Yan Zhang, Irma M. Grossi, Max Pan, Gary Hattersley
Journal: Cancer Research (2025)

Abstract The ROS1 gene is altered in approximately 2% of non-small cell lung cancer (NSCLC) patients. ROS1 fusions demonstrate ligand independent activity driving cell growth and survival. While several inhibitors for ROS1 have been FDA approved, including crizotinib, entrectinib and, more recently, repotrectinib, each present unique challenges. Resistance mechanisms such as secondary kinase-domain mutations including ROS1-G2032R can limit the efficacy of crizotinib and entrectinib, while repotrectinib can cause central nervous system (CNS) adverse reactions including dizziness, ataxia and cognitive impairment that have been attributed to inhibition of the tropomyosin receptor kinase B, TRKB. Therefore, a need remains for more tolerable and effective treatment options for ROS1 positive cancers.Taletrectinib is an oral, potent, selective and CNS-active next-generation ROS1 inhibitor specifically designed for the treatment of patients with ROS1-positive NSCLC. Herein, we describe taletrectinib and its activity in multiple in vitro and in vivo ROS1 fusion tumor models. Additionally, the antitumor activity of taletrectinib in comparison to other ROS1 inhibitors is examined. Taletrectinib exhibits selectivity for ROS1 over other kinases, including TRKB. Taletrectinib exhibits in vivo activity in models harboring ROS1 wild-type (ROS1-CD74, ROS1-SLC34A2) and ROS1-G2032R resistance mutation fusions (ROS1 G2032R-CD74, ROS1 G2032R-SLC34A2). Moreover, taletrectinib prolonged survival (&amp;gt;70 days) of mice implanted intracranially with a ROS1 fusion xenograft model. Our nonclinical data illustrates that taletrectinib has a profile which exhibits efficacy in ROS1 wild-type-, and mutant-driven cancers while maintaining selectivity against TRKB, resulting in an overall favorable profile. Taletrectinib is being evaluated in patients with locally advanced or metastatic ROS1-positive NSCLC in two Phase 2 single-arm pivotal studies: TRUST-I (NCT04395677) in China, and TRUST-II (NCT04919811), a global study. Based on the integrated analysis of the TRUST-I and TRUST-II clinical studies, an NDA has been submitted for taletrectinib to the U.S. FDA for the treatment of patients with locally advanced or metastatic ROS1-positive NSCLC. Citation Format: Hitisha K. Patel, Laura Heller, Kevin Bowman, Zheyi Hu, Yan Zhang, Irma M. Grossi, Max Pan, Gary Hattersley. Taletrectinib, a next generation selective ROS1 inhibitor, inhibits growth of ROS1 wild-type and ROS1-G2032R xenografts [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 5612.
Abstract 5610: BAY 2927088: a novel treatment option for patients with NSCLC with <i>HER2</i> mutations
Authors: Franziska Siegel, Kristýna Kotýnková, Gizem Karsli-Uzunbas, Thibaud Jourdan, Melanie Berthold, Stephan Siegel, Daniel Korr, Markus Berger, Haruna Tomono, Sawyer Andersen, Daniel Denney, Jérémie Mortier, Andrew Cherniack, Matthew Meyerson, Heidi Greulich
Journal: Cancer Research (2025)

Abstract Lung cancer is the most prevalent tumor type worldwide, and despite advancements in providing patients with improved therapies, the 5-year overall survival rate is still only 5.8%. About 2-4% of patients with non-small cell lung cancer (NSCLC) carry either a HER2 exon20 insertion or a HER2 point mutation, which are potent oncogenic drivers. The antibody-drug conjugate fam-trastuzumab deruxtecan-nxki was the first targeted treatment option to be approved for patients with HER2-mutant NSCLC. However, there is still a significant unmet need for more effective and better tolerated therapies with the potential to improve response rates and response durability. Here we present preclinical data for BAY 2927088, a highly potent and selective tyrosine kinase inhibitor targeting HER2. BAY 2927088 shows potent in vitro antiproliferative activity in tumor cell lines carrying HER2 mutations, and strongly blocks HER2 phosphorylation, thereby inhibiting HER2-mediated downstream MAPK signaling. We also confirmed the strong anti-tumor activity of BAY 2927088 in in vivo models with HER2 exon20 insertions. To further explore the therapeutic potential of BAY 2927088, we evaluated combination treatment with current standard-of-care therapies in NSCLC in vitro and in vivo. In addition, we profiled BAY 2927088 vs. other HER2 tyrosine kinase inhibitors by pooled screening of 866 barcoded human cancer cell lines to identify additional oncogenic dependencies and patient populations that might benefit from treatment with BAY 2927088. We found that BAY 2927088 was also active in a subset of HER2-amplified breast and gastroesophageal cancer cell lines. Single-agent BAY 2927088 is currently being evaluated in clinical trials in patients with NSCLC with HER2 mutations. Preliminary results demonstrate efficacy and safety in pretreated patients (NCT05099172, NCT06452277). Citation Format: Franziska Siegel, Kristýna Kotýnková, Gizem Karsli-Uzunbas, Thibaud Jourdan, Melanie Berthold, Stephan Siegel, Daniel Korr, Markus Berger, Haruna Tomono, Sawyer Andersen, Daniel Denney, Jérémie Mortier, Andrew Cherniack, Matthew Meyerson, Heidi Greulich. BAY 2927088: a novel treatment option for patients with NSCLC with HER2 mutations [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 5610.
Abstract 5012: Multi-omics signature for estimating survival time in breast cancer patients
Authors: Srinivasulu Yerukala Sathipati, Manya Reddy Elugoti, Rachel Tillman, Scott Hebbring, Rohit Sharma
Journal: Cancer Research (2025)

Abstract Introduction: Predicting survival outcomes for breast cancer patients is critical for tailoring personalized treatments. However, effectively integrating multi-omics datasets for survival estimation is a persistent challenge due to the high dimensionality and complexity of the data. To address this, we developed the Multi-Omics Survival Time Estimator (MOST), a novel machine learning framework designed to accurately predict survival time by leveraging multi-omics data. Methods: MOST employs a multi-step pipeline combining stringent feature selection, evolutionary learning, and survival estimation. Using datasets encompassing copy number variations (CNV), microRNA (mRNA), mRNA, and reverse-phase protein array (RPPA) data, the platform identifies a concise yet informative set of biomarkers that contribute to survival time prediction. Feature selection was optimized to ensure relevance and robustness, while evolutionary algorithms enhanced predictive accuracy. The model was rigorously validated to ensure reliability and generalizability. Results: MOST identified a 38-feature multi-omics signature capable of predicting survival time with a correlation coefficient (R) of 0.90 and mean absolute error (MAE) of 1.06 year between predicted and actual survival times. The top 10 features contributing to the prediction included ENSG00000145826.7 and ENSG00000101624.9 from CNVs; hsa-miR-3928 and hsa-miR-326 from miRNAs; KIAA1199, SYT10, PSG3, ACTG1, MYPN from mRNAs; and GUSP4 from RPPA data. These results emphasize the role of integrated genetic, epigenetic, and proteomic markers in defining patient survival. MOST demonstrated superior predictive performance compared to existing methodologies. Conclusions: MOST offers a machine learning framework for integrating multi-omics data to predict survival outcomes in breast cancer patients. Its ability to identify a robust multi-omics signature establishes a foundation for future exploration of multi-omics data across other cancers, reinforcing its clinical relevance and potential translational impact. Citation Format: Srinivasulu Yerukala Sathipati, Manya Reddy Elugoti, Rachel Tillman, Scott Hebbring, Rohit Sharma. Multi-omics signature for estimating survival time in breast cancer patients [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 5012.
Abstract 6776: Biological characterization of small molecule eIF4E inhibitors to assess their therapeutic potential in cancer
Authors: Rachel L. O'Rourke, Emilio L. Cárdenas, Jesse Duque, Amanda Falzetta, Jim Clauwaert, Jason Rech, John R. Prensner, Amanda L. Garner
Journal: Cancer Research (2025)

Abstract The dysregulation of translation is a hallmark of cancer that enables rapid changes in the cell proteome to shape oncogenic phenotypes that promote tumor survival. The predominant signaling pathways leading to dysregulation of translational control in cancer are the PI3K-AKT-mTORC1, RAS-RAF-MAPK, and MYC pathways, which all converge on eukaryotic translation initiation factor 4E (eIF4E), an RNA-binding protein which binds to the m7GpppX cap structure at the 5' end of mRNAs to initiate cap-dependent translation. eIF4E is the rate-limiting factor of translation initiation, and its overexpression is known to drive oncogenic transformation, progression, and chemoresistance across many cancers, establishing it as an attractive therapeutic target. Traditionally, target transcripts of eIF4E have been characterized as mRNAs with long 5'-UTR motifs, but recent studies point to context-dependent translation of eIF4E-sensitive transcripts that is contingent on the regulation status of eIF4E. Therefore, we hypothesize that mechanistically distinct eIF4E inhibitors will elicit different phenotypic responses in tumors based on the dysregulation of select pools of eIF4E-sensitive transcripts. Research in our laboratory focuses on developing mechanistically distinct eIF4E inhibitors, including m7GpppX prodrug derivatives which bind directly to the cap-binding pocket of eIF4E. Efforts to characterize the effects of these molecules on cap-dependent translation regulation through cell-based phenotypic assays and ribosome profiling will be discussed. Citation Format: Rachel L. O'Rourke, Emilio L. Cárdenas, Jesse Duque, Amanda Falzetta, Jim Clauwaert, Jason Rech, John R. Prensner, Amanda L. Garner. Biological characterization of small molecule eIF4E inhibitors to assess their therapeutic potential in cancer [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 6776.
Abstract 2444: A deep learning framework for the reconstruction of gene expression profiles derived from formalin-fixed paraffin-embedded samples
Authors: Lingli He, Kai Song, Ying Li, Celia Yujuan Dong, Cherry Y. Wong, Lin Qi, Xianrui Zhang, Yufeng Chen, Louis Vermeulen, Shentao Zhou, Simon Siu Ng, Xin Wang
Journal: Cancer Research (2025)

Abstract Formalin-fixed paraffin-embedded (FFPE) tissue samples often suffer from RNA degradation due to fixation, embedding, and storage processes, affecting the integrity of transcripts for RNA sequencing (RNA-seq). Herein, we developed a novel deep learning model to reconstruct gene expression profiles from FFPE-derived RNA-seq data. We first compared the transcript integrity numbers (TINs) of two in-house datasets derived from paired fresh frozen (FF) and FFPE tissues, with different fixation durations and library preparation strategies. We found that tissue samples fixed for 24 hours and subjected to the rRNA depletion-based library preparation strategy exhibited higher TINs. Using RNA-seq data for 832 FFPE samples across seven cancer types, we identified genes with consistently low TINs on the genome-wide scale. Interestingly, the signature genes of immune cells are more prone to degradation, compared to other cell types such as tumor epithelial cells. To reconstruct gene expression profiles, we developed a deep learning framework, based on 9568 FF tumor samples across 28 cancer types in the TCGA database. The framework demonstrated remarkable performance on both simulated data (672 transcriptomes) and in-house cancer cohorts with RNA-seq data, including one FFPE and three FF-FFPE-matched cohorts. On the simulated data with noise levels ranging from 10% to 60%, the reconstructed data achieved consistently high correlation coefficients (&amp;gt; 0.85) with the ‘clean’ data without noise. For the FF-FFPE-matched datasets, compared to the original FFPE-derived data, the reconstructed gene expression profiles significantly improved the correlation with matched FF profiles, even for immune cell signature genes (all P &amp;lt; 0.05). In our application to cancer subtyping, the gene expression profiles reconstructed improved the classification accuracy from 67% to 92%. Furthermore, the reconstructed profiles recapitulated the subtype-specific biological properties lost in the FFPE-derived data and showed more significant association with patient survival. As the first deep learning model to rectify FFPE-derived RNA-seq data, our framework enables more efficient use of archived FFPE tissue samples that are widely available in the clinic. Citation Format: Lingli He, Kai Song, Ying Li, Celia Yujuan Dong, Cherry Y. Wong, Lin Qi, Xianrui Zhang, Yufeng Chen, Louis Vermeulen, Shentao Zhou, Simon Siu Ng, Xin Wang. A deep learning framework for the reconstruction of gene expression profiles derived from formalin-fixed paraffin-embedded samples [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 2444.
Abstract 5821: Targeting adenosine receptor signaling enhances anti-PD-L1 therapy efficacy in MTAP-deficient urothelial cancer via reprogramming tumor-associated macrophages
Authors: Ying Mei, Jianfeng Chen, Yuxue Zhang, Yu-Yi Chu, Omar Alhalabi, Jianjun Gao
Journal: Cancer Research (2025)

Abstract Methylthioadenosine phosphorylase (MTAP) deficiency in urothelial cancer (UC) correlates with poor clinical outcomes and reduced response rates to immune checkpoint inhibitor (ICI) therapy. Understanding the mechanisms by which MTAP loss contributes to ICI resistance and identifying therapeutic strategies to enhance ICI efficacy in MTAP-deficient tumors is of critical importance. Using CRISPR-Cas9, we deleted MTAP in the MB49 murine UC cell line and established orthotopic bladder tumors with wild-type (WT) and MTAP knock-out MB49 cells. CyTOF analysis revealed that MTAP-deficient tumors showed a marked reduction in M1-like macrophages (∼50%) and a substantial increase in M2-like macrophages (∼3-fold). Mechanistically, MTAP-deficient tumors accumulated elevated levels of methylthioadenosine (MTA, &amp;gt;100-fold) in the tumor microenvironment (TME), which drove the polarization of tumor-associated macrophages (TAMs) towards a pro-tumorigenic M2-like phenotype via adenosine receptor (A2A/A2B) signaling. Notably, inhibition of A2A/A2B adenosine receptors significantly enhanced anti-PD-L1 therapy efficacy, achieving an overall tumor growth inhibition of 50% compared to anti-PD-L1 monotherapy. These findings suggest that targeting the adenosine pathway may provide a promising strategy to improve therapeutic outcomes in MTAP-deficient urothelial cancer. Citation Format: Ying Mei, Jianfeng Chen, Yuxue Zhang, Yu-Yi Chu, Omar Alhalabi, Jianjun Gao. Targeting adenosine receptor signaling enhances anti-PD-L1 therapy efficacy in MTAP-deficient urothelial cancer via reprogramming tumor-associated macrophages [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 5821.