Publications

PURPOSE: In 2020, ASCO recommended that all women with epithelial ovarian cancer have germline testing for BRCA1/2 mutations, and those without a germline pathogenic variant (PV) should have somatic tumor testing to determine eligibility for a poly (ADP-ribose) polymerase inhibitor. Consequently, the majority of patients with ovarian cancer will have both germline testing and somatic testing. An alternate strategy is tumor testing first and then germline testing if there is a PV in the tumor and/or significant family history. The objective was to conduct a cost-effectiveness analysis comparing the two testing strategies. METHODS: The Markov model compared the costs (US dollars) and benefits of two testing strategies for newly diagnosed ovarian cancer: (1) ASCO strategy and (2) tumor testing triage for germline testing. Data were applied from SOLO-1, and costs were from wholesale acquisition prices, Medicare, and published sources. Sensitivity analyses accounted for uncertainty around various parameters. Monte Carlo simulation estimated the number tested and identified with germline and somatic BRCA PV for olaparib maintenance treatment annually in the US population. RESULTS: The ASCO strategy was more effective but more costly than tumor testing triage in identifying patients for olaparib, with an incremental cost-effectiveness ratio of $281,296 US dollars per progression-free life year gained. Assuming 10,000 eligible patients with ovarian cancer annually, Monte Carlo simulation yielded comparable numbers of patients with BRCA PV in the germline and tumor with the ASCO and tumor testing triage strategies (2,080 v 2,062, respectively), but substantially higher number of patients tested using the ASCO strategy (8,052 v 3,076). CONCLUSION: The ASCO strategy may identify more BRCA PVs but is not cost-effective. Tumor testing in epithelial ovarian cancer as triage for germline testing is the favored strategy in this health care system.

Antibiotic resistance is a global health crisis increasing in prevalence every day. To combat this crisis, alternative antimicrobial therapeutics are urgently needed. Antimicrobial peptides (AMPs), a family of short defense proteins, are produced naturally by all organisms and hold great potential as effective alternatives to small molecule antibiotics. Here, we present rAMPage, a scalable bioinformatics discovery platform for identifying AMP sequences from RNA sequencing (RNA-seq) datasets. In our study, we demonstrate the utility and scalability of rAMPage, running it on 84 publicly available RNA-seq datasets from 75 amphibian and insect species-species known to have rich AMP repertoires. Across these datasets, we identified 1137 putative AMPs, 1024 of which were deemed novel by a homology search in cataloged AMPs in public databases. We selected 21 peptide sequences from this set for antimicrobial susceptibility testing against Escherichia coli and Staphylococcus aureus and observed that seven of them have high antimicrobial activity. Our study illustrates how in silico methods such as rAMPage can enable the fast and efficient discovery of novel antimicrobial peptides as an effective first step in the strenuous process of antimicrobial drug development.

Circulating tumour DNA (ctDNA) in blood plasma is an emerging tool for clinical cancer genotyping and longitudinal disease monitoring¹. However, owing to past emphasis on targeted and low-resolution profiling approaches, our understanding of the distinct populations that comprise bulk ctDNA is incomplete^2-12. Here we perform deep whole-genome sequencing of serial plasma and synchronous metastases in patients with aggressive prostate cancer. We comprehensively assess all classes of genomic alterations and show that ctDNA contains multiple dominant populations, the evolutionary histories of which frequently indicate whole-genome doubling and shifts in mutational processes. Although tissue and ctDNA showed concordant clonally expanded cancer driver alterations, most individual metastases contributed only a minor share of total ctDNA. By comparing serial ctDNA before and after clinical progression on potent inhibitors of the androgen receptor (AR) pathway, we reveal population restructuring converging solely on AR augmentation as the dominant genomic driver of acquired treatment resistance. Finally, we leverage nucleosome footprints in ctDNA to infer mRNA expression in synchronously biopsied metastases, including treatment-induced changes in AR transcription factor signalling activity. Our results provide insights into cancer biology and show that liquid biopsy can be used as a tool for comprehensive multi-omic discovery.

Diffuse large B cell lymphoma (DLBCL) is the most common B cell non-Hodgkin lymphoma and remains incurable in around 40% of patients. Efforts to sequence the coding genome identified several genes and pathways that are altered in this disease, including potential therapeutic targets^1-5. However, the non-coding genome of DLBCL remains largely unexplored. Here we show that active super-enhancers are highly and specifically hypermutated in 92% of samples from individuals with DLBCL, display signatures of activation-induced cytidine deaminase activity, and are linked to genes that encode B cell developmental regulators and oncogenes. As evidence of oncogenic relevance, we show that the hypermutated super-enhancers linked to the BCL6, BCL2 and CXCR4 proto-oncogenes prevent the binding and transcriptional downregulation of the corresponding target gene by transcriptional repressors, including BLIMP1 (targeting BCL6) and the steroid receptor NR3C1 (targeting BCL2 and CXCR4). Genetic correction of selected mutations restored repressor DNA binding, downregulated target gene expression and led to the counter-selection of cells containing corrected alleles, indicating an oncogenic dependency on the super-enhancer mutations. This pervasive super-enhancer mutational mechanism reveals a major set of genetic lesions deregulating gene expression, which expands the involvement of known oncogenes in DLBCL pathogenesis and identifies new deregulated gene targets of therapeutic relevance.

Aim: This study examined circulating cell-free DNA (cfDNA) biomarkers associated with androgen treatment resistance in metastatic castration resistance prostate cancer (mCRPC). Materials & methods: We designed a panel of nine candidate cfDNA methylation markers using droplet digital PCR (Methyl-ddPCR) and assessed methylation levels in sequentially collected cfDNA samples from patients with mCRPC. Results: Increased cfDNA methylation in eight out of nine markers during androgen-targeted treatment correlated with a faster time to clinical progression. Cox proportional hazards modeling and logistic regression analysis further confirmed that higher cfDNA methylation during treatment was significantly associated with clinical progression. Conclusion: Overall, our findings have revealed a novel methylated cfDNA marker panel that could aid in the clinical management of metastatic prostate cancer.

Introduction: Increasingly, logistic regression methods for genetic association studies of binary phenotypes must be able to accommodate data sparsity, which arises from unbalanced case-control ratios and/or rare genetic variants. Sparseness leads to maximum likelihood estimators (MLEs) of log-OR parameters that are biased away from their null value of zero and tests with inflated type 1 errors. Different penalized-likelihood methods have been developed to mitigate sparse-data bias. We study penalized logistic regression using a class of log-F priors indexed by a shrinkage parameter m to shrink the biased MLE towards zero. For a given m, log-F-penalized logistic regression may be easily implemented using data augmentation and standard software.

Method: We propose a two-step approach to the analysis of a genetic association study: first, a set of variants that show evidence of association with the trait is used to estimate m; and second, the estimated m is used for log-F-penalized logistic regression analyses of all variants using data augmentation with standard software. Our estimate of m is the maximizer of a marginal likelihood obtained by integrating the latent log-ORs out of the joint distribution of the parameters and observed data. We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation.

Results: We evaluate the statistical properties of our proposed two-step method and compared its performance to other shrinkage methods by a simulation study. Our simulation studies suggest that the proposed log-F-penalized approach has lower bias and mean squared error than other methods considered. We also illustrate the approach on data from a study of genetic associations with "super senior" cases and middle aged controls.

Discussion/conclusion: We have proposed a method for single rare variant analysis with binary phenotypes by logistic regression penalized by log-F priors. Our method has the advantage of being easily extended to correct for confounding due to population structure and genetic relatedness through a data augmentation approach.

Imprinting is a critical part of normal embryonic development in mammals, controlled by defined parent-of-origin (PofO) differentially methylated regions (DMRs) known as imprinting control regions. Direct nanopore sequencing of DNA provides a means to detect allelic methylation and to overcome the drawbacks of methylation array and short-read technologies. Here, we used publicly available nanopore sequencing data for 12 standard B-lymphocyte cell lines to acquire the genome-wide mapping of imprinted intervals in humans. Using the sequencing data, we were able to phase 95% of the human methylome and detect 94% of the previously well-characterized, imprinted DMRs. In addition, we found 42 novel imprinted DMRs (16 germline and 26 somatic), which were confirmed using whole-genome bisulfite sequencing (WGBS) data. Analysis of WGBS data in mouse (Mus musculus), rhesus monkey (Macaca mulatta), and chimpanzee (Pan troglodytes) suggested that 17 of these imprinted DMRs are conserved. Some of the novel imprinted intervals are within or close to imprinted genes without a known DMR. We also detected subtle parental methylation bias, spanning several kilobases at seven known imprinted clusters. At these blocks, hypermethylation occurs at the gene body of expressed allele(s) with mutually exclusive H3K36me3 and H3K27me3 allelic histone marks. These results expand upon our current knowledge of imprinting and the potential of nanopore sequencing to identify imprinting regions using only parent-offspring trios, as opposed to the large multi-generational pedigrees that have previously been required.