Heterogeneous DNA methylation and gene expression patterns underly metabolic plasticity in canine astrocytoma-derived stem-like cells

Frontiers in Oncology · 2026-01-20

Introduction: Glioma stem cells (GSCs) have been implicated in radio- and chemotherapeutic resistance of glioblastoma (GBM). Therapeutic targeting of GSCs has shown promise in immunocompromised rodent models but have not been translated into effective therapies for human patients. These failures underscore the translational limitations of rodent models and highlight the need for complementary models that accurately and reliably predict therapeutic translation for human HGG. Spontaneous canine high-grade gliomas (HGGs) may provide a complementary translational model for human therapeutic development. While described in canine HGGs, little is known about canine glioma stem cell biology. Methods: Here, we evaluated cellular metabolism, cytosine modifications, gene expression, and functional tests of malignancy to interrogate differences between canine high-grade astrocytoma-derived glioma stem-cell like cells (GSLC) and a traditional non-stem cell glioma cell line following exposure to hypoxia. Results: Hypoxia increased oxygen consumption rates in GSLCs and augmented features of malignancy in GSLCs. We observed variable cytosine modifications and mRNA expression across cell lines, and our data did not correlate cytosine modification patterns with oxygen consumption capacity following hypoxia. However, we did demonstrate a positive correlation between up-regulated genes in human GBM GSCs and hypomethylation of orthologous canine genes following hypoxia. Discussion: Together, these data support that hypoxia enhances distinct stem-like traits in canine astrocytoma GSLCs, similar to human GSCs.

Characterizing Response to PARP Inhibitor Treatment Combinations in Advanced Prostate Cancer

Biomedicines · 2026-04-22

Background/Objectives: Combinations of PARP inhibitors (PARPi) and androgen receptor pathway inhibitors (ARPi) have led to clinical success in treating advanced prostate cancer. However, it is unclear where in the clinical paradigm these combinations will fare best, and their mechanism of action remains unclear. We sought to address open questions and explore alternative strategies to enhance PARPi efficacy. Methods: Viability and morphology were assessed in response to (1) abiraterone, olaparib, or combination and (2) enzalutamide, talazoparib, or combination in castration-resistant C4-2B cells and abiraterone- or enzalutamide-resistant derivative cell models (ARPi-resistant). The efficacy of the ATM inhibitor lartesertib with and without a PARPi was also determined. Western blots and RNA-sequencing were used to interrogate the mechanistic effects of treatment. Results: PARPi and ARPi combinations were effective in all models but provided the most benefit in ARPi-sensitive C4-2B cells. Mechanistically, ARPi was not found to affect homologous recombination repair gene expression but may increase PARP activity. Prolonged PARP inhibition was found to increase the expression of AR target genes, and PARPi pre-treatment increased sensitivity to enzalutamide. ATM inhibition significantly increases PARPi efficacy and appears to outperform ARPi-containing combinations in ARPi-resistant models. Conclusions: PARPi and ARPi combinations are effective in ARPi-resistant models, but efficacy appears stronger in ARPi-sensitive CRPC cells. Presented findings support a novel hypothesis that PARP inhibition may increase ARPi sensitivity with increasing AR activity. Additionally, ATM inhibition may provide more benefit than an ARPi in combination with a PARPi in ARPi-resistant settings. These findings support continued PARPi development for improving patient outcomes.

Abstract 1617: Targeting CDK11 in high-risk B cell acute lymphoblastic leukemia

Cancer Research · 2025-04-21

Abstract The outcome for children with high-risk B-cell acute lymphoblastic leukemia (B-ALL) is poor. Disease relapse is speculated to be due to leukemia cells escaping treatment. Cyclin-dependent kinases (CDKs) have been demonstrated to be therapeutic targets in many cancers, and inhibitors of some CDKs have already been developed in the clinic. For example, CDK4/6 inhibitors have been used for treating patients with breast cancer, and a CDK2 inhibitor is being evaluated in a clinical trial for solid cancers. CDK11, known as a direct splicing regulator in CDKs, is overexpressed in several types of tumors, such as osteosarcoma and multiple myeloma and is shown to be a therapeutic target in many cancers. In B-ALL, dysregulated RNA splicing is linked to drug resistance and disease relapse. In this study, we investigated the therapeutic potential of CDK11 inhibition in B-ALL. CDK11 protein expression was significantly higher in B-ALL cell lines (Reh and JM1) and 26 primary B-ALL samples (13 each for standard-risk and high-risk), regardless of the risk group, than in normal B-cells and hematopoietic stem cells. A CDK11 inhibitor, OTS964, showed significant dose-dependent cytotoxicity in the cell lines with IC50 of 22nM and 14nM. OTS964 also showed significant cytotoxicity in three harvested high-risk patient-derived xenograft (PDX) samples with high CDK11 expression. OTS964 did not show cytotoxicity in normal B-cells at the same tested concentrations. OTS964 is known to regulate RNA splicing by the deactivation of SF3B1, a core component of the spliceosome. We confirmed OTS964 rapidly inhibited the phosphorylation of SF3B1, which is highly activated in B-ALL. G2/M cell cycle arrest and apoptosis induction were observed 24 hours after treatment in B-ALL cell lines. OTS964 induced p53 expression, which regulates cell cycle and apoptosis, as early as 1 hour after treatment. Furthermore, OTS964 induced short pro-apoptotic spliced isoforms, instead of anti-apoptotic forms, in MCL-1, a gene within the BCL-2 family, as early as 3 hours after treatment. The combination of splicing modulators and BCL-2 inhibitors shows synergism by each drug inhibiting different BCL-2 family genes. Therefore, we tested the combination with OTS964 and navitoclax, a BCL-2 inhibitor inhibiting BCL-2, BCL-x, and BCL-w in BCL-2 family genes. As expected, the combination showed strong synergisms in B-ALL cell lines and in the three harvested high-risk PDX samples. It was also effective in the sample with low CDK11 expression in vitro. Our in vivo study of the combination therapy is ongoing. In conclusion, these data demonstrated the therapeutic potential of CDK11 inhibition in high-risk B-ALL. OTS964 rapidly deactivated SF3B1 and induced the pro-apoptotic forms of MCL-1, leading to cell apoptosis. Additionally, the new combination with OTS964 and navitoclax showed synergism. In future studies, we will investigate how CDK11 inhibition regulates splicing factors other than SF3B1. Citation Format: Yuki Murakami, Elizabeth Helmke, Kamhung Lam, John McPherson, Noriko Satake. Targeting CDK11 in high-risk B cell acute lymphoblastic leukemia [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 1617.

Supplementary Table 5 from The Germline and Somatic Origins of Prostate Cancer Heterogeneity

2025-05-02

<p>Supplementary Table 5 displays summary statistics from PRS and HOXB13 associated with somatic drivers. β and P-value from logistic regression correcting for five genetic principal components, age and somatic mutation burden. FDR = false discovery rate.</p>

Supplementary Table 4 from The Germline and Somatic Origins of Prostate Cancer Heterogeneity

2025-05-02

<p>Supplementary Table 4 shows driver selection for dQTL nomination and prevalence of drivers in cohorts</p>

Supplementary Table 9 from The Germline and Somatic Origins of Prostate Cancer Heterogeneity

2025-05-02

<p>Supplementary Table 9 summarizes the characterization of 16 SNPs associated with 23 concordant dQTLs.</p>

Supplementary Table 8 from The Germline and Somatic Origins of Prostate Cancer Heterogeneity

2025-05-02

<p>Supplementary Table 8 shows summary statistics of 16 SNPs associated with 23 concordant dQTLs across cohorts</p>

Genotype Prediction from Retinal Fundus Images Using Deep Learning in Eyes with Age-Related Macular Degeneration

Ophthalmology Science · 2025-05-27

Purpose: To employ deep learning models to predict high-risk genetic variants associated with age-related macular degeneration (AMD) from retinal fundus photographs of patients with this condition. Design: Deep learning algorithm development to classify single-nucleotide polymorphism in the complement factor H (CFH) and age-related maculopathy susceptibility 2 (ARMS2) genes using retinal fundus images. Participants: Thirty-one thousand two hundred seventy-one retinal color fundus photographs of 1754 participants from the Age-Related Eye Disease Study. Methods: We trained deep learning models including convolution neural networks and vision transformers (ViTs) to classify patients into high-risk (homozygous high-risk alleles) or low-risk (heterozygous or homozygous low-risk alleles) genotypes for CFH or ARMS2, then evaluated algorithm performance on an independent test set. The complexity of genotype predictions was compared with AMD severity or gender classification tasks using V-usable information. Attribution mapping was performed to identify fundus regions used to predict genotype from phenotype. Main Outcome Measures: Area under the receiver operating characteristic curve (AUROC), balanced accuracy, and average precision for predicting high-risk genotypes. Results: Our trained ViT models predicted high-risk genotypes in CFH and ARMS2 with an AUROC of 0.719 and 0.741 across all eyes, respectively. For genotype predictions for ARMS2, model performance is improved in eyes with advanced AMD (AUROC 0.867), choroidal neovascularization (AUROC 0.833), and geographic atrophy (AUROC 0.957). Genotype predictions from fundus images appear more difficult than AMD severity or gender classification tasks, although saliency mapping supports biological plausibility by demonstrating attention to the central macula for genotype predictions. Conclusions: Deep learning can predict high-risk genotypes in CFH and ARMS2 from retinal fundus images of patients with AMD. Our findings provide proof of principle for inferring genotype from noninvasive eye imaging and reveal insights into genotype-phenotype relationships in AMD. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Data from The Germline and Somatic Origins of Prostate Cancer Heterogeneity

2025-05-02

<div>Abstract<p>Newly diagnosed prostate cancers differ dramatically in mutational composition and lethality. The most accurate clinical predictor of lethality is tumor tissue architecture, quantified as tumor grade. To interrogate the evolutionary origins of prostate cancer heterogeneity, we analyzed 666 prostate tumor whole genomes. We identified a compendium of 223 recurrently mutated driver regions, most influencing downstream mutational processes and gene expression. We identified and validated individual germline variants that predispose tumors to acquire specific somatic driver mutations: these explain heterogeneity in disease presentation and ancestry differences. High-grade tumors have a superset of the drivers in lower-grade tumors, including increased frequency of <i>BRCA2</i> and <i>MYC</i> mutations. Grade-associated driver mutations occur early in tumor evolution, and their earlier occurrence strongly predicts cancer relapse and metastasis. Our data suggest high- and low-grade prostate tumors both emerge from a common premalignant field, influenced by germline genomic context and stochastic mutation timing.</p>Significance:<p>This study uncovered 223 recurrently mutated driver regions using the largest cohort of prostate tumors to date. It reveals associations between germline SNPs, somatic drivers, and tumor aggression, offering significant insights into how prostate tumor evolution is shaped by germline factors and the timing of somatic mutations.</p></div>

Supplementary Table 3 from The Germline and Somatic Origins of Prostate Cancer Heterogeneity

2025-05-02

<p>Supplementary Table 3 illustrates driver co-occurrence analysis, driver clusters, and associations of drivers with clinical features</p>