ON-TARGETplus siRNA pools for gene expression knockdown.

List of the 44 3D structures of HSC70 (HSPA8) in complex with the BAG domain of BAG-1 used in comparative structural analyses.

Proportion of patients with genetic aberrations.

Primary vs. relapse copy number profiles.

Top relapse-specific copy number events.

Antibodies used for western blot analyses.

Relapse Vs. primary copy number variants.

Many of the >130 World Health Organization-defined sarcoma subtypes are chemoresistant. To identify drug candidates with potential for repurposing in sarcomas with unmet needs, we screened structurally diverse small molecules across 20 sarcoma cell lines of various histologies. The screen included 1,387 compounds from the Custom Clinical Collection, NCI-Approved Oncology Set IV, and Selleck Bioactive Collection. The most effective agents were microtubule inhibitors, aurora kinase inhibitors, and heat shock protein inhibitors. Differential drug sensitivity was observed by sarcoma subtype. Ewing sarcoma, for example, was sensitive to aurora kinases and to TORIN-2 (an mTOR inhibitor). In contrast, myxoid liposarcoma and synovial sarcomas exhibited marked sensitivity to SNS-032, a CDK2/7/9 inhibitor identified by our screen. Interestingly, SNS-032 abrogates the downstream YAP pathway, a known driver pathway in the growth of these histotypes. Therefore, similar drug responses across histologies may indicate shared driver pathways and vulnerabilities. SIGNIFICANCE:Sarcoma cells are resistant to most compounds that are currently approved for treating various diseases. Ewing sarcoma cells have distinct sensitivities to aurora kinases that can be explored, whereas myxoid liposarcoma and synovial sarcoma cells share similar drug sensitivity fingerprints that could be exploited in future basket trials.

Development and characterization of BAG-1 knockout mice.

Synthego BAG-1 sgRNA for BAG-1 CRISPR knockout clones.

Development of castration resistant prostate cancer patient derived xenograft organoids.

Thio-2 reduces genome-wide androgen receptor binding in LNCaP prostate cancer cells.

Mouse organ hematoxylin and eosin, and BAG-1 immunohistochemistry, in BAG-1 knockout mice.

Abstract Introduction: A major challenge in oncology drug development is confirming target engagement of therapeutics in patient tumors. Pharmacodynamic (PD) biomarkers provide this link, enabling early assessment of target validity, drug activity and rational dose selection. However, systematic resources connecting drug targets to validated PD biomarkers remain limited. To address this, we developed a comprehensive dataset and analytic framework to identify and prioritize target-specific PD biomarkers across nine major target classes implicated in cancer biology. Materials and Methods: We curated biomarker candidates from multiple genomic and pharmacologic resources for nine target classes: transcription factors/cofactors, kinases, phosphatases, ubiquitin ligases, deubiquitinases, acetyltransferases, deacetylases, methyltransferases, and demethylases. Source databases often apply broad definitions to functional classes such as transcription factors. To improve accuracy and reduce annotation artifacts, we cross-referenced PFAM, Enzyme Classification, and PDB databases to refine protein classifications. Using the canSAR interactome, we identified direct target-biomarker interactions supported by experimental evidence. To capture context-specific transcriptional biomarkers, we computed cohort-specific correlations using TCGA, TARGET, and GTEx datasets. Finally, we employ LLM-based fact-checking agent to extract and harmonize antibody annotations from the Antibody Registry, focusing on enzyme targets with measurable substrate modifications. Results: From 2,900 targets and 100,000 interactions, we propose 73,000 high-confidence target-biomarker relationships involving over 2,100 potential drug targets. Of these, 67% represent transcription factor-gene interactions and 33% enzyme-substrate interactions. Commercial antibodies were identified for over 2,800 biomarker candidates, supporting experimental validation. The resulting dataset covers more than 60% of the top 20 predicted targets across 19 cancer types. We provide all the data in our canSAR platform and as a download from canSAR-PD. Discussion: This resource provides a systematic framework for PD biomarker discovery in oncology. By integrating curated molecular interactions with LLM-derived antibody annotations, it enables robust evaluation of target engagement and drug activity. The dataset establishes a foundation for developing PD biomarkers to guide dose selection, monitor response, and accelerate cancer drug development. Citation Format: Yuntao Yang, Li Zhao, Seyedmehdi Orouji, Ying Zhu, Rebecca Johnson, David Maxwell, Kaitlyn Brickey, Bissan Al-Lazikani, . A comprehensive LLM-enabled pharmacodynamic biomarker resource to accelerate cancer drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2720.

Figure S2. Heatmap of the CDK2, CDK7, and CDK9 expression in sarcoma cells from the Cancer Cell Line Encyclopedia (CCLE) database.

Oestrogen receptor (ER) activation leads to the formation of DNA double strand breaks (DSB), promoting genomic instability and tumour heterogeneity. The single-stranded DNA cytosine deaminase APOBEC3B (A3B) serves as a co-activator of ER and is implicated in inducing DSBs at transcriptional enhancers regulated by ER. Using whole-genome sequencing in an engineered cell model lacking base excision repair (BER) function, we demonstrate that A3B preferentially targets transcriptionally active regulatory regions in an R-loop-dependent manner. Strand-specific DNA:RNA immunoprecipitation sequencing (ssDRIP-seq) and ssDNA-associated protein immunoprecipitation sequencing (SPI-seq) confirm that A3B binds to and deaminates ssDNA within R-loops, a process facilitated by ER transactivation. Furthermore, BER-mediated processing of A3B-induced uracil bases contributes to the formation of R-loop-associated DSBs, which are essential for ER-regulated gene activation. These findings establish a role for A3B in R-loop homeostasis and transcriptional regulation, with implications for understanding ER-driven genomic instability and potential therapeutic targeting of A3B.

Paediatric cancer specific cfDNA panel (ctPC Panel).

Fraction of genome altered by disease group.