Philip Law

Immunomodulatory drugs (IMiDs) are associated with an increased risk of arterial thromboembolism (ATE) in multiple myeloma (MM), but determinants and mechanisms remain poorly defined. We evaluated incidence, timing, and clinical risk factors in 4287 patients with newly diagnosed MM from the Myeloma-XI-trial; all received IMiD-based induction and 1412 were randomised to Lenalidomide (Len) maintenance. Associations with tumour and inherited genetics, the latter through a genome-wide association study (GWAS), were interrogated in subsets of 1800 patients each. ATE occurred in 92 patients (2.1%), predominantly as ischaemic stroke (>80%). At MM diagnosis, higher leucocyte count, lower serum albumin, and Len-based-induction in elderly were independent risk factors. Older age was the predominant determinant for maintenance-related ATE. Among tumour genetics, del(17p) (HR 3.5, P = 0.001) was strongly associated with ATE risk. Finally, GWAS identified a germline locus at 6q15 independently linked to ATE risk (OR 4.8, P = 3.9 × 10 ⁻⁸ ). Functional annotation implicates vascular enhancer activity and regulation of MAP3K7 and BACH2 . Together, these findings demonstrate distinct temporal patterns of IMiD-associated ATE risk across treatment phases. In addition to clinical risk factors, inherited variation at 6q15 may contribute to individual susceptibility. These results are hypothesis-generating and require validation in independent cohorts but may ultimately inform individualised prevention strategies.

Background Establishing functional relationships between distal enhancers and their target genes is a central challenge in genome biology and cancer genetics. CRISPR/dCas9-mediated perturbations followed by single-cell RNA sequencing (Perturb-seq) has emerged as an efficient means of establishing enhancer-target gene relationships. Results We used genome-wide CRISPRi Perturb-seq with 35,139 guide RNAs targeting 12,117 enhancers to create a functional enhancer-gene map of colorectal cancer (CRC), identifying 238 significant regulatory associations (FDR < 0.1). Integration of chromatin accessibility (ATAC-seq), histone modification profiling (ChIP-seq), and ultra-high resolution chromatin conformation capture (Micro-C) data revealed that enhancer regulation in CRC is constrained by topological domains and often targets the nearest gene, findings corroborated by Activity-by-Contact modelling. Conclusions We present here a comprehensive, genome-scale functional atlas of enhancer-gene associations in CRC. This resource should provide a foundation for studying gene regulation in colorectal tumorigenesis and for prioritising candidate non-coding drivers in cancer sequencing studies.

Background Establishing functional relationships between distal enhancers and their target genes is a central challenge in genome biology and cancer genetics. CRISPR/dCas9-mediated perturbations followed by single-cell RNA sequencing (Perturb-seq) has emerged as an efficient means of establishing enhancer-target gene relationships. Results We used genome-wide CRISPRi Perturb-seq with 35,139 guide RNAs targeting 12,117 enhancers to create a functional enhancer-gene map of colorectal cancer (CRC), identifying 238 significant regulatory associations (FDR < 0.1). Integration of chromatin accessibility (ATAC-seq), histone modification profiling (ChIP-seq), and ultra-high resolution chromatin conformation capture (Micro-C) data revealed that enhancer regulation in CRC is constrained by topological domains and often targets the nearest gene, findings corroborated by Activity-by-Contact modelling. Conclusions We provide a comprehensive functional enhancer-gene interaction map for CRC. This resource should provide a foundation for studying gene regulation in colorectal tumorigenesis and for prioritising candidate non-coding drivers in cancer sequencing studies.

Genome wide association studies (GWAS), combined with fine-mapping have identified 196 independent signals associated with breast cancer risk. Deciphering the functional basis of these associations can inform our understanding of the biology and aetiology of breast cancer. Decoding GWAS risk associations is challenging due to linkage disequilibrium between variants and because most variants map to non-coding regions, influencing breast cancer risk via cis-regulatory mechanisms that modulate the expression of target genes. To identify the functional variants driving breast cancer risk associations, we carried out a lentivirus-based massively parallel reporter assay (lentiMPRA) to screen 5,116 credible causal variants across these signals. We identified 709 variants mapping to 140 risk regions, that are associated with significant variation between REF and ALT alleles. A follow-up investigation at 14q32.11 revealed rs7153397 may impact expression of CCDC88C to influence both breast cancer risk and prognosis. These findings provide a prioritised set of functional variants for downstream analyses, advancing our understanding of breast cancer risk mechanisms.

Background: The mechanisms underlying obesity-related cancer risk are incompletely understood. We investigated whether the gut microbiome causally mediates this relationship. Methods: We performed two-sample Mendelian randomisation, with mediation analysis, to assess causal links between genetically predicted body mass index (BMI)/waist-to-hip ratio adjusted for BMI (WHRadjBMI), 211 gut microbial taxa, and eight cancers (384,738 cases) of European ancestry. Significant associations were replicated in the FinnGen cohort. Results: Genetically predicted BMI was associated with risk of colorectal (CRC; odds ratio per standard deviation (OR _SD ): 1.12; 95% confidence interval (CI): [1.06–1.17]; P = 4.95 × 10 ⁻⁶ ), kidney (RCC) (OR _SD : 1.48; 95% CI: [1.34–1.63]; P = 1.61 × 10 ⁻¹⁵ ), endometrial (OR _SD : 1.70; 95% CI: [1.55–1.87]; P = 2.09 × 10 ⁻²⁷ ), lung (OR _SD : 1.20; 95% CI: [1.12–1.29]; P = 1.40 × 10 ⁻⁷ ), and oesophageal cancer (OR _SD : 1.25; 95% CI: [1.13–1.39]; P = 3.09 × 10 ⁻⁵ ). Seven microbial taxa were associated with CRC risk. Phylum and class Actinobacteria showed the strongest effects (OR _SD : 1.48; 95% CI: [1.29–1.70]; P = 1.78 × 10 ⁻⁸ ) and (OR _SD : 1.36; 95% CI: [1.22–1.51]; P = 2.57 × 10 ⁻⁸ ), respectively, and replicated in FinnGen, mediating 29% (95% CI: [8-50]) and 21% (95% CI: [4–37]) of the BMI to CRC risk—collectively accounting for 50% of the relationship. No consistent microbiome mediation was observed for other cancers. Conclusions: Gut Actinobacteria may contribute to obesity-driven CRC risk, supporting the rationale of microbiome-targeted interventions to reduce CRC risk.

Susceptibility transcription factors (TF) whose DNA bindings are altered by genetic variants regulating colorectal cancer (CRC) risk genes remain poorly defined. Using generalized linear mixed models, we analyze 218 TF ChIP-Seq datasets alongside GWAS data from 100,204 CRC cases and 154,587 controls of East Asian and European ancestries. We identify 51 TFs and TF-cofactor interactions, including VDR-cofactors, as key regulators of CRC risk. Integrating these TF insights with transcriptome-wide association studies (TWAS), we further evaluate associations between genetically predicted gene expression, alternative splicing, and alternative polyadenylation with CRC risk, using RNA-seq data from 364 Asian-ancestry and 707 European-ancestry individuals. Multi-ancestry TWAS identify 222 risk genes, including 95 novel genes and 48 potentially druggable targets. Single-cell analysis provides additional functional evidence supporting ~45% of these genes, and experimental validation confirms oncogenic roles for RHPN2, IRS2, and TXN. Our findings elucidate key TF–gene regulatory networks and uncover novel CRC risk genes.

Genome-wide association studies (GWAS) have identified over 60 autosomal risk loci associated with clear cell renal cell carcinoma (ccRCC), yet the functional mechanisms underlying these associations remain largely unclear. To establish connections between risk variants and their target genes, we applied the activity-by-contact (ABC) model, which integrates epigenomic data and Micro-C interactions, complemented with renal-specific quantitative trait loci, to predict enhancer-gene relationships. Our analyses implicate variation in hypoxia sensing, cell cycle regulation, and telomerase maintenance pathways as central mediators of ccRCC risk. These findings provide new insights into the molecular basis of ccRCC susceptibility and highlight potential therapeutic avenues for prevention and treatment.

Supplementary Methods shows the formulas for calculating F-statistics.