Citations

Abstract The TopBP1-ATR axis is critical for maintaining genomic stability during DNA replication stress, yet the precise regulation of TopBP1 in replication stress responses remains poorly understood. In this study, we identified PHD and Ring Finger Domains 1 (PHRF1) as an important ATR activator through its interaction with TopBP1. Our analysis revealed a correlation between PHRF1 and genomic stability in cancer patients. Mechanistically, PHRF1 is recruited to DNA lesions in a manner dependent on its PHD domain and histone methylation. Subsequently, PHRF1 mono-ubiquitinates TopBP1 at lysine 73, which enhances the TopBP1-ATR interaction and activates ATR. Depletion of PHRF1 disrupts ATR activation and sensitizes cells to replication stress-inducing agents. Furthermore, conditional knockout of Phrf1 in mice leads to early lethality and impaired ATR-Chk1 axis signaling. Collectively, our findings establish PHRF1 as a novel E3 ligase for TopBP1, coordinating the replication stress response by enhancing TopBP1-ATR signaling.

Most patients with metastatic prostate cancer eventually develop resistance to primary androgen deprivation therapy. To identify predictive biomarker for Abiraterone acetate/prednisone resistance, we screened alternative splice variants between responders and non-responders from the PROMOTE clinical study and pinned down the most significant variant, CENPK–delta8. Through preclinical patient-derived mouse xenograft (PDX) and 3D organoids obtained from responders and non-responders, as well as in vitro models, aberrant CENPK–delta8 expression was determined to link to drug resistance via enhanced migration and proliferation. The FLNA and FLOT1 were observed to specifically bind to CENK–delta8 rather than wild-type CENPK, underscoring the role of CENPK–delta8 in cytoskeleton organization and cell migration. Our study, leveraging data from the PROMOTE study, TCGA, and TCGA SpliceReq databases, highlights the important function of alternative splice variants in drug response and their potential to be prognostic biomarkers for improving individual therapeutic outcomes in precision medicine.

Abstract Organs that face external environments, such as skin and gut, are lined by epithelia, which have two functions – to provide a semi-permeable barrier and to sense stimuli. The intestinal lumen is filled with diverse chemical and physical stimuli. Intestinal epithelial cells sense these stimuli and signal to enteric neurons which coordinate a range of physiologic processes required for normal digestive tract function. Yet, the neuro-epithelial connections between intestinal epithelial cells and enteric neurons remain poorly resolved, which leaves us with limited mechanistic understanding of their function. We describe the development of a two-compartment microfluidic device for modeling neuro-epithelial interactions, and apply it to form the gut’s neuro-epithelial connections. The device contains epithelial and neuronal compartments connected by microgrooves. The epithelial compartment was designed for cell seeding via injection and confinement of intestinal epithelial cells derived from human intestinal organoids. We demonstrated that organoids planarized effectively and retained epithelial phenotype for over a week. In the second chamber we dissociated and cultured intestinal myenteric neurons including intrinsic primary afferent neurons (IPANs) from transgenic mice that expressed the fluorescent protein tdTomato. IPANs extended projections into microgrooves, surrounded and frequently made contacts with epithelial cells. The density and directionality of neuronal projections were enhanced by the presence of epithelial cells in the adjacent compartment. Our microfluidic device represents a platform for dissecting structure and function of neuro-epithelial connections in the gut and other organs (skin, lung, bladder, and others) in health and disease.

Abstract We identified resistance mechanisms to abiraterone acetate/prednisone (AA/P) in patients with metastatic castration-resistant prostate cancer (mCRPC) in the Prostate Cancer Medically Optimized Genome-Enhanced Therapy (PROMOTE) study. We analyzed whole-exome sequencing (WES) and RNA-sequencing data from 83 patients with metastatic biopsies before (V1) and after 12 weeks of AA/P treatment (V2). Resistance was determined by time to treatment change (TTTC). At V2, 18 and 11 of 58 patients had either short-term (median 3.6 months; range 1.4–4.5) or long-term (median 29 months; range 23.5–41.7) responses, respectively. Nonresponders had low expression of TGFBR3 and increased activation of the Wnt pathway, cell cycle, upregulation of AR variants, both pre- and posttreatment, with further deletion of AR inhibitor CDK11B posttreatment. Deletion of androgen processing genes, HSD17B11, CYP19A1 were observed in nonresponders posttreatment. Genes involved in cell cycle, DNA repair, Wnt-signaling, and Aurora kinase pathways were differentially expressed between the responder and non-responder at V2. Activation of Wnt signaling in nonresponder and deactivation of MYC or its target genes in responders was detected via SCN loss, somatic mutations, and transcriptomics. Upregulation of genes in the AURKA pathway are consistent with the activation of MYC regulated genes in nonresponders. Several genes in the AKT1 axis had increased mutation rate in nonresponders. We also found evidence of resistance via PDCD1 overexpression in responders. Implications: Finally, we identified candidates drugs to reverse AA/P resistance: topoisomerase inhibitors and drugs targeting the cell cycle via the MYC/AURKA/AURKB/TOP2A and/or PI3K_AKT_MTOR pathways.

Abstract Glioblastoma (GBM) is a rapidly fatal malignancy typically treated with radiation and temozolomide (TMZ), an alkylating chemotherapeutic. These cytotoxic therapies cause oxidative stress and DNA damage, yielding a senescent-like state of replicative arrest in surviving tumor cells. Unfortunately, recurrence is inevitable and may be driven by surviving tumor cells eventually escaping senescence. A growing number of so-called “senolytic” drugs have been recently identified that are defined by their ability to selectively eliminate senescent cells. A growing inventory of senolytic drugs is under consideration for several diseases associated with aging, inflammation, DNA damage, as well as cancer. Ablation of senescent tumor cells after radiation and chemotherapy could help mitigate recurrence by decreasing the burden of residual tumor cells at risk of recurrence. This strategy has not been previously explored for GBM. We evaluated a panel of 10 previously described senolytic drugs to determine whether any could exhibit selective activity against human GBM persisting after exposure to radiation or TMZ. Three of the 10 drugs have known activity against BCL-XL and preferentially induced apoptosis in radiated or TMZ-treated glioma. This senolytic activity was observed in 12 of 12 human GBM cell lines. Efficacy could not be replicated with BCL-2 inhibition or senolytic agents acting against other putative senolytic targets. Knockdown of BCL-XL decreased survival of radiated GBM cells, whereas knockdown of BCL-2 or BCL-W yielded no senolytic effect. Implications: These findings imply that molecularly heterogeneous GBM lines share selective senescence-induced BCL-XL dependency increase the significance and translational relevance of the senolytic therapy for latent glioma.

Approximately one‐third of patients with metastatic castration‐resistant prostate cancer (CRPC) exhibited primary abiraterone resistance. To identify alternative treatment for abiraterone nonresponders, we performed drug discovery analyses using the L1000 database using differentially expressed genes identified in tumor biopsies and patient‐derived xenograft (PDX) tumors between abiraterone responders and nonresponders enrolled in PROMOTE trial. This approach identified 3 drugs, including topoisomerase II (TOP2) inhibitor mitoxantrone, CDK4/6 inhibitor palbociclib, and pan‐CDK inhibitor PHA‐793887. These drugs significantly suppressed the growth of abiraterone‐resistant cell lines and PDX models. Moreover, we identified 11 genes targeted by all 3 drugs that were associated with worse outcomes in both the PROMOTE and Stand Up To Cancer cohorts. This 11‐gene panel might also function as biomarkers to select the 3 alternative therapies for this subgroup of patients with CRPC, warranting further clinical investigation.

Objectives We previously discovered that the single nucleotide polymorphisms (SNP) rs9940645 in the ZNF423 gene regulate ZNF423 expression and serve as a potential biomarker for response to selective estrogen receptor modulators (SERMs). Here we explored pathways involved in ZNF423-mediated SERMs response and drugs that potentially sensitize SERMs. Methods RNA sequencing and label-free quantitative proteomics were performed to identify genes and pathways that are regulated by ZNF423 and the ZNF423 SNP. Both cultured cells and mouse xenograft models with different ZNF423 SNP genotypes were used to study the cellular responses to metformin. Results We identified ribosome and AMP-activated protein kinase (AMPK) signaling as potential pathways regulated by ZNF423 or ZNF423 rs9940645 SNP. Moreover, using clustered regularly interspaced short palindromic repeats/Cas9-engineered ZR75-1 breast cancer cells with different ZNF423 SNP genotypes, striking differences in cellular responses to metformin, either alone or in the combination of tamoxifen, were observed in both cell culture and the mouse xenograft model. Conclusions We found that AMPK signaling is modulated by the ZNF423 rs9940645 SNP in estrogen and SERM-dependent fashion. The ZNF423 rs9940645 SNP affects metformin response in breast cancer and could be a potential biomarker for tailoring the metformin treatment.

Abstract Modern AI-based clinical decision support models owe their success in part to the very large number of predictors they use. Safe and robust decision support, especially for intervention planning, requires causal, not associative, relationships. Traditional methods of causal discovery, clinical trials and extracting biochemical pathways, are resource intensive and may not scale up to the number and complexity of relationships sufficient for precision treatment planning. Computational causal structure discovery (CSD) from electronic health records (EHR) data can represent a solution, however, current CSD methods fall short on EHR data. This paper presents a CSD method tailored to the EHR data. The application of the proposed methodology was demonstrated on type-2 diabetes mellitus. A large EHR dataset from Mayo Clinic was used as development cohort, and another large dataset from an independent health system, M Health Fairview, as external validation cohort. The proposed method achieved very high recall (.95) and substantially higher precision than the general-purpose methods (.84 versus .29, and .55). The causal relationships extracted from the development and external validation cohorts had a high (81%) overlap. Due to the adaptations to EHR data, the proposed method is more suitable for use in clinical decision support than the general-purpose methods.