Abstract Targeting Porcupine (PORCN), a key regulator of the WNT-signalling pathway, has shown therapeutic potential in multiple cancers. Despite strong target engagement and acceptable safety profiles through human phase I clinical trials, low phase II efficacy has stalled further clinical development. Given that aberrant WNT signalling can drive tumorigenesis by inducing chromosomal instability (CIN), we hypothesised that genomic CIN signatures might serve as a predictive biomarker to help improve response rates. Using a controlled in vitro model and single-cell whole-genome sequencing, we demonstrate that acute WNT-activation directly induces three distinct types of CIN: whole genome duplication, replication stress, and impaired homologous recombination. We translated these observations into a composite CIN signature biomarker that significantly correlated with both genetic dependency and pharmacological inhibition of PORCN across 195 and 24 cell lines, respectively. Through a large-scale meta-analysis of patient-derived and cell line xenografts, we established that this composite CIN signature biomarker quantitatively predicts in vivo PORCN inhibitor sensitivity (R=-0.71, p<0.002). By applying an optimised biomarker threshold, refined through modelling of human patient data, to the The Cancer Genome Atlas dataset, we successfully retrospectively modelled previous trial results and identified gastroesophageal cancers as a high-prevalence (36.6%) indication for future development. We validated this strategy in a mouse clinical trial of gastric and esophageal xenografts, where biomarker-guided stratification achieved an objective response rate of 60% and significantly decreased risk of progression (HR=0.21, p=0.0345). These data establish an actionable, trail-ready framework for further PORCN inhibitor clinical development.

Cytotoxic chemotherapies are typically administered without the use of precision biomarkers. As such, many patients can experience severe toxic side effects without any benefit. Here, we present a set of chromosomal instability (CIN) signature biomarkers that can identify patients resistant to platinum-, taxane-, and anthracycline-based treatment using a single genomic test. We leveraged real-world cohorts to retrospectively emulate a series of biomarker clinical trials across a total of 740 patients. In emulations where patients were pseudo-randomized to a single chemotherapy treatment arm or alternative standard-of-care arm, predicted resistant patients had increased risk of treatment failure for taxane in ovarian (HR=8.751, 95% CI=3.072-24.924), taxane in metastatic breast (HR=5.24, 95% CI=1.41-19.43), taxane in metastatic prostate (HR=4.01, 95% CI=1.73-9.27), anthracycline in ovarian (HR=2.34, 95% CI=1.25-4.59), and anthracycline in metastatic breast (HR=3.53, 95% CI=1.85-6.75). Non-randomized emulations showed predictive capacity for platinum resistance in ovarian (HR=1.65, 95% CI=1.27-2.14) and anthracycline in sarcoma (HR=3.59, 95% CI=1.19-10.81). We also demonstrate that implementation using gene capture panel sequencing of tissue or shallow whole genome sequencing of cell free DNA from liquid biopsies may be feasible. Our findings highlight the clinical value of CIN signatures in predicting resistance to various chemotherapies across multiple different types of cancer. Ultimately, this has the potential to transform the current one-size-fits-all chemotherapy approach into a more precise and tailored treatment. Joe S. Thompson, Laura Madrid, Barbara Hernando, Carolin M. Sauer, Maria Vias, Maria Escobar-Rey, Wing-Kit Leung, Diego Garcia-Lopez, Jamie Huckstep, Magdalena Sekowska, Karen Hosking, Mercedes Jimenez-Linan, Marika A. Reinius, Abhipsa Roy, Omar Abdulle, Justina Pangonyte, Harry Dobson, Amy Cullen, Dilrini De Silva, David Gómez-Sánchez, Marina Torres, Ángel Fernández-Sanromán, Deborah Sanders, Filipe Correia Martins, Ionut-Gabriel Funingana, Giovanni Codacci-Pisanelli, Miguel Quintela-Fandino, Florian Markowetz, Jason Yip, James D. Brenton, Anna M. Piskorz, Geoff Macintyre. Predicting resistance to cytotoxic chemotherapy [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 6363.

Abstract Capture-based targeted sequencing is routinely used for small variant detection in cancer clinical care. Alongside targeted DNA, off-target DNA is also sequenced. These off-target reads are distributed across the genome, allowing for a whole genome copy number profile to be derived without the need for a SNP backbone. Here we present CopyRight, a method that generates robust genome-wide copy number profiles, which can be used for downstream chromosomal instability (CIN) quantification, overcoming various sources of technical noise with a novel approach that only requires a single tumor sample. We analyzed a variety of capture-based NGS protocols using our novel computational method, including the TruSight Oncology 500 (TSO500) panel, and compared them to the current gold standard for genome-wide copy number profiling from FFPE tissues: shallow whole genome sequencing (sWGS). Comparable results were obtained from targeted sequencing depending on sample purity, preservation method, and read depth. Additionally, benchmarks of CopyRight against other computational algorithms show an improvement in performance without the need for a matched normal tissue, the usual drawback for other methods in the cancer field. CIN signatures are a new set of emerging biomarkers that reflect the diversity of defective pathways that have operated in a tumor, which require robust genome-wide copy number profiles. These CIN signatures can be used to predict response to cytotoxic agents and targeted therapies and could ultimately help guide therapy selection in patients. As most clinical sequencing workflows rely on targeted sequencing, CopyRight can be included in current clinical assays, enabling CIN biomarker quantification with no extra technical or experimental requirements. Citation Format: David Gómez-Sánchez, Joe Sneath Thompson, Barbara Hernando, Diego García-López, Hector de Galard, Abhipsa Roy, Amy Cullen, Laura Madrid, José Teles, Ania Piskorz, Jason Yip, Alice Cádiz, Maria Escobar-Rey, Roberto Moreno-Vellisca, Nuria Carrizo, Eva Álvarez, Miguel Quintela-Fandino, Mariano Barbacid, Javier Ramos-Paradas, Juan Manuel Coya, Irene Ferrer, Jon Zugazagoitia, Luis Paz-Ares, Geoff Macintyre. Enabling biomarkers of chromosomal instability for tumor only targeted gene panel sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7422.

The estrogen receptor-α (ER) drives 75% of breast cancers. On activation, the ER recruits and assembles a 1-2 MDa transcriptionally active complex. These complexes can modulate tumour growth, and understanding the roles of individual proteins within these complexes can help identify new therapeutic targets. Here, we present the discovery of ER and ZMIZ1 within the same multi-protein assembly by quantitative proteomics, and validated by proximity ligation assay. We characterise ZMIZ1 function by demonstrating a significant decrease in the proliferation of ER-positive cancer cell lines. To establish a role for the ER-ZMIZ1 interaction, we measured the transcriptional changes in the estrogen response post-ZMIZ1 knockdown using an RNA-seq time-course over 24 h. Gene set enrichment analysis of the ZMIZ1-knockdown data identified a specific delay in the response of estradiol-induced cell cycle genes. Integration of ENCODE data with our RNA-seq results identified that ER and ZMIZ1 both bind the promoter of E2F2. We therefore propose that ER and ZMIZ1 interact to enable the efficient estrogenic response at subset of cell cycle genes via a novel ZMIZ1-ER-E2F2 signalling axis. Finally, we show that high ZMIZ1 expression is predictive of worse patient outcome, ER and ZMIZ1 are co-expressed in breast cancer patients in TCGA and METABRIC, and the proteins are co-localised within the nuclei of tumour cell in patient biopsies. In conclusion, we establish that ZMIZ1 is a regulator of the estrogenic cell cycle response and provide evidence of the biological importance of the ER-ZMIZ1 interaction in ER-positive patient tumours, supporting potential clinical relevance.

Cancer cells often exhibit DNA copy number aberrations and can vary widely in their ploidy. Correct estimation of the ploidy of single cell genomes is paramount for downstream analysis. Based only on single-cell DNA sequencing information, scAbsolute achieves accurate and unbiased measurement of single-cell ploidy and replication status, including whole-genome duplications. We demonstrate scAbsolute’s capabilities using experimental cell multiplets, a FUCCI cell cycle expression system, and a benchmark against state-of-the-art methods. scAbsolute provides a robust foundation for single-cell DNA sequencing analysis across different technologies and has the potential to enable improvements in a number of downstream analyses.

The Estrogen Receptor (ER) drives 75% of breast cancers. On activation, the ER recruits co-factors to form a transcriptionally active complex. These co-factors can modulate tumour growth, and understanding their roles can help to identify new therapeutic targets. Here, we present the discovery of an ER-ZMIZ1 interaction by quantitative proteomics, and validated by proximity ligation assay. We characterise ZMIZ1 function by demonstrating that targeting ZMIZ1 results in the reduction of ER transcriptional activity at estrogen response elements and a significant decrease in the proliferation of ER-positive cancer cell lines. To establish a role for the ER-ZMIZ1 interaction, we measured the transcriptional changes in the estrogen response post-ZMIZ1 knockdown using an RNA-seq time-course over 24 hours. GSEA analysis of the ZMIZ1-knockdown data identified a specific delay in the response of estradiol-induced cell-cycle genes. Integration of ENCODE data with our RNA-seq results identified ER and ZMIZ1 binding at the promoter of E2F2. We therefore propose that ER and ZMIZ1 co-regulate an important subset of cell cycle genes via a novel ER-ZMIZ1-E2F2 signalling axis. Finally, we show that high ZMIZ1 expression is predictive of worse patient outcome, ER and ZMIZ1 are co-expressed in breast cancer patients in TCGA, METABRIC, and the proteins are co-localised within the nuclei of tumours cell in patient biopsies. In conclusion, we establish that ZMIZ1 is a regulator of the estrogenic cell cycle response and provide evidence of the biological importance of the ER-ZMIZ1 interaction ER+ patient tumours, supporting potential clinical relevance.

Following publication of the original article [1], the authors reported that Figs. 4 and 5 had mistakenly been transposed. Please find the correct Figs. 4 and 5 below. The original article [1] has been corrected.

Estrogen Receptor-alpha (ER) drives 75% of breast cancers. Stimulation of the ER by estra-2-diol forms a transcriptionally-active chromatin-bound complex. Previous studies reported that ER binding follows a cyclical pattern. However, most studies have been limited to individual ER target genes and without replicates. Thus, the robustness and generality of ER cycling are not well understood. We present a comprehensive genome-wide analysis of the ER after activation, based on 6 replicates at 10 time-points, using our method for precise quantification of binding, Parallel-Factor ChIP-seq. In contrast to previous studies, we identified a sustained increase in affinity, alongside a class of estra-2-diol independent binding sites. Our results are corroborated by quantitative re-analysis of multiple independent studies. Our new model reconciles the conflicting studies into the ER at the TFF1 promoter and provides a detailed understanding in the context of the ER's role as both the driver and therapeutic target of breast cancer.

A key challenge in quantitative ChIP combined with high-throughput sequencing (ChIP-seq) is the normalization of data in the presence of genome-wide changes in occupancy. Analysis-based normalization methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor (TF) binding, these assumptions do not hold true. The challenges in normalization are confounded by experimental variability during sample preparation, processing and recovery. We present a novel normalization strategy utilizing an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome-wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalization. We compare our approach to normalization by total read depth and two alternative methods that utilize external experimental controls to study TF binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in patient-derived xenographs. This is supported by an adaptable pipeline to normalize and quantify differential TF binding genome-wide and generate metrics for differential binding at individual sites.

AbstractVULCAN infers regulatory interactions of transcription factors by overlaying networks generated from tumor expression data onto ChIP-seq data. VULCAN analysis of estrogen receptor (ER) activation in breast cancer highlighted key components of the ER complex alongside a novel interaction with GRHL2. We demonstrate that GRHL2 is recruited to a subset of ER binding sites and regulates the transcriptional output of ER, as evidenced by: changes in ER-associated eRNA expression; and stronger ER binding at active enhancers (H3K27ac sites) after GRHL2 knockdown. Our findings provide new insight into ER signaling and demonstrate VULCAN, available from Bioconductor, as a powerful predictive tool.

A key challenge in quantitative ChIP-seq is the normalisation of data in the presence of genome-wide changes in occupancy. Analysis-based normalisation methods were developed for transcriptomic data and these are dependent on the underlying assumption that total transcription does not change between conditions. For genome-wide changes in transcription factor binding, these assumptions do not hold true. The challenges in normalisation are confounded by experimental variability during sample preparation, processing, and recovery. We present a novel normalisation strategy utilising an internal standard of unchanged peaks for reference. Our method can be readily applied to monitor genome- wide changes by ChIP-seq that are otherwise lost or misrepresented through analytical normalisation. We compare our approach to normalisation by total read depth and two alternative methods that utilise external experimental controls to study transcription factor binding. We successfully resolve the key challenges in quantitative ChIP-seq analysis and demonstrate its application by monitoring the loss of Estrogen Receptor-alpha (ER) binding upon fulvestrant treatment, ER binding in response to estrodiol, ER mediated change in H4K12 acetylation and profiling ER binding in Patient-Derived Xenographs. This is supported by an adaptable pipeline to normalise and quantify differential transcription factor binding genome- wide and generate metrics for differential binding at individual sites.