Search

Amyotrophic lateral sclerosis (ALS) lacks a specific biomarker, but is defined by relatively selective toxicity to motor neurons (MN). As others have highlighted, this offers an opportunity to develop a sensitive and specific biomarker based on detection of DNA released from dying MN within accessible biofluids. Here we have performed whole genome bisulfite sequencing (WGBS) of iPSC-derived MN from neurologically normal individuals. By comparing MN methylation with an atlas of tissue methylation we have derived a MN-specific signature of hypomethylated genomic regions, which accords with genes important for MN function. Through simulation we have optimised the selection of regions for biomarker detection in plasma and CSF cell-free DNA (cfDNA). However, we show that MN-derived DNA is not detectable via WGBS in plasma cfDNA. In support of our experimental finding, we show theoretically that the relative sparsity of lower MN sets a limit on the proportion of plasma cfDNA derived from MN which is below the threshold for detection via WGBS. Our findings are important for the ongoing development of ALS biomarkers. The MN-specific hypomethylated genomic regions we have derived could be usefully combined with more sensitive detection methods and perhaps with study of CSF instead of plasma. Indeed we demonstrate that neuronal-derived DNA is detectable in CSF. Our work is relevant for all diseases featuring death of rare cell-types., Competing Interests: Declarations. Ethics approval and consent to participate: The study was approved by the South Sheffield Research Ethics Committee. Also, this study followed study protocols approved by Medical Ethical Committees for each of the participating institutions. Written informed consent was obtained from all participating individuals. All methods were performed in accordance with relevant national and international guidelines and regulations. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Background: Advanced prostate cancer (PC) is characterized by insensitivity to androgen deprivation therapy and chemotherapy, resulting in poor outcome for most patients. Thus, advanced PC urgently needs novel therapeutic strategies. Mounting evidence points to splicing dysregulation as a hallmark of advanced PC. Moreover, pharmacologic inhibition of the splicing process is emerging as a promising option for this disease., Method: By using a representative androgen-insensitive PC cell line (22Rv1), we have investigated the genome-wide transcriptomic effects underlying the cytotoxic effects exerted by three splicing-targeting drugs: Pladienolide B, indisulam and THZ531. Bioinformatic analyses were performed to uncover the gene structural features underlying sensitivity to transcriptional and splicing regulation by these treatments. Biological pathways altered by these treatments were annotated by gene ontology analyses and validated by functional experiments in cell models., Results: Although eliciting similar cytotoxic effects on advanced PC cells, Pladienolide B, indisulam and THZ531 modulate specific transcriptional and splicing signatures. Drug sensitivity is associated with distinct gene structural features, expression levels and cis-acting sequence elements in the regulated exons and introns. Importantly, we identified PC-relevant genes (i.e. EZH2, MDM4) whose drug-induced splicing alteration exerts an impact on cell survival. Moreover, computational analyses uncovered a widespread impact of splicing-targeting drugs on intron retention, with enrichment in genes implicated in pre-mRNA 3'-end processing (i.e. CSTF3, PCF11). Coherently, advanced PC cells displayed high sensitivity to a specific inhibitor of the cleavage and polyadenylation complex, which enhances the effects of chemotherapeutic drugs that are already in use for this cancer., Conclusions: Our study uncovers intron retention as an actionable vulnerability for advanced PC, which may be exploited to improve therapeutic management of this currently incurable disease., (© 2024. The Author(s).)

Background: High grade serous ovarian cancer (HGSOC) is highly lethal, partly due to chemotherapy resistance and limited availability of targeted approaches. Cyclin dependent kinases 12 and 13 (CDK12/13) are promising therapeutic targets in human cancers, including HGSOC. Nevertheless, the effects of their inhibition in HGSOC and the potential synergy with other drugs are poorly known., Methods: We analyzed the effects of the CDK12/13 inhibitor THZ531 in HGSOC cells and patient-derived organoids (PDOs). RNA sequencing and quantitative PCR analyses were performed to identify the genome-wide effects of short-term CDK12/13 inhibition on the transcriptome of HGSOC cells. Viability assays with HGSOC cells and PDOs were performed to assess the efficacy of THZ531 as single agent or in combination with clinically relevant drugs., Results: The CDK12 and CDK13 genes are deregulated in HGSOC and their concomitant up-regulation with the oncogene MYC predicts poor prognosis. HGSOC cells and PDOs display high sensitivity to CDK12/13 inhibition, which synergizes with drugs in clinical use for HGSOC. Transcriptome analyses revealed cancer-relevant genes whose expression is repressed by dual CDK12/13 inhibition through impaired splicing. Combined treatment with THZ531 and inhibitors of pathways regulated by these cancer relevant genes (EGFR, RPTOR, ATRIP) exerted synergic effects on HGSOC PDO viability., Conclusions: CDK12 and CDK13 represent valuable therapeutic targets for HGSOC. We uncovered a wide spectrum of CDK12/13 targets as potential therapeutic vulnerabilities for HGSOC. Moreover, our study indicates that CDK12/13 inhibition enhances the efficacy of approved drugs that are already in use for HGSOC or other human cancers., (© 2023. The Author(s).)

Background: Large medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative-an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients (N=36,736)., Methods: We quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes., Results: We identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals' SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p-value=2.32×10 -16 , EAA p-value=6.73×10 -11 ). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group., Conclusions: Overall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping., (© 2022. The Author(s).)