Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare nonsynonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs). We use independent test sets to demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. Importantly, prediction scores from both models are comparable, enabling easy adoption of integrated genotype-phenotype association analysis methods. All pre-computed nsSNV scores are available at http://www.liulab.science/MetaRNN . The stand-alone program is also available at https://github.com/Chang-Li2019/MetaRNN .

BACKGROUND: Polygenic risk scores (PRS), which offer information about genomic risk for common diseases, have been proposed for clinical implementation. The ways in which PRS information may influence a patient's health trajectory depend on how both the patient and their primary care provider (PCP) interpret and act on PRS information. We aimed to probe patient and PCP responses to PRS clinical reporting choices METHODS: Qualitative semi-structured interviews of both patients (N=25) and PCPs (N=21) exploring responses to mock PRS clinical reports of two different designs: binary and continuous representations of PRS. RESULTS: Many patients did not understand the numbers representing risk, with high numeracy patients being the exception. However, all the patients still understood a key takeaway that they should ask their PCP about actions to lower their disease risk. PCPs described a diverse range of heuristics they would use to interpret and act on PRS information. Three separate use cases for PRS emerged: to aid in gray-area clinical decision-making, to encourage patients to do what PCPs think patients should be doing anyway (such as exercising regularly), and to identify previously unrecognized high-risk patients. PCPs indicated that receiving "below average risk" information could be both beneficial and potentially harmful, depending on the use case. For "increased risk" patients, PCPs were favorable towards integrating PRS information into their practice, though some would only act in the presence of evidence-based guidelines. PCPs describe the report as more than a way to convey information, viewing it as something to structure the whole interaction with the patient. Both patients and PCPs preferred the continuous over the binary representation of PRS (23/25 and 17/21, respectively). We offer recommendations for the developers of PRS to consider for PRS clinical report design in the light of these patient and PCP viewpoints. CONCLUSIONS: PCPs saw PRS information as a natural extension of their current practice. The most pressing gap for PRS implementation is evidence for clinical utility. Careful clinical report design can help ensure that benefits are realized and harms are minimized.

BACKGROUND: Ganciclovir (GCV) is widely used in solid organ and haematopoietic stem cell transplant patients for prophylaxis and treatment of cytomegalovirus. It has long been considered a mutagen and carcinogen. However, the contribution of GCV to cancer incidence and other factors that influence its mutagenicity remains unknown. METHODS: This retrospective cohort study analysed genomics data for 121,771 patients who had undergone targeted sequencing compiled by the Genomics Evidence Neoplasia Information Exchange (GENIE) or Foundation Medicine (FM). A statistical approach was developed to identify patients with GCV-associated mutational signature (GCVsig) from targeted sequenced data of tumour samples. Cell line exposure models were further used to quantify mutation burden and DNA damage caused by GCV and other antiviral and immunosuppressive drugs. RESULTS: Mutational profiles from 22 of 121,771 patient samples in the GENIE and FM cohorts showed evidence of GCVsig. A diverse range of cancers was represented. All patients with detailed clinical history available had previously undergone solid organ transplantation and received GCV and mycophenolate treatment. RAS hotspot mutations associated with GCVsig were present in 9 of the 22 samples, with all samples harbouring multiple GCV-associated protein-altering mutations in cancer driver genes. In vitro testing in cell lines showed that elevated DNA damage response and GCVsig are uniquely associated with GCV but not acyclovir, a structurally similar antiviral. Combination treatment of GCV with the immunosuppressant, mycophenolate mofetil (MMF), increased the misincorporation of GCV in genomic DNA and mutations attributed to GCVsig in cell lines and organoids. CONCLUSIONS: In summary, GCV can cause a diverse range of cancers. Its mutagenicity may be potentiated by other therapies, such as mycophenolate, commonly co-prescribed with GCV for post-transplant patients. Further investigation of the optimal use of these drugs could help reduce GCV-associated mutagenesis in post-transplant patients.

BACKGROUND: Exome sequencing (ES) is becoming more widely available in prenatal diagnosis. However, data on its clinical utility and integration into clinical management remain limited in practice. Herein, we report our experience implementing prenatal ES (pES) in a large cohort of fetuses with anomalies detected by ultrasonography using a hospital-based in-house multidisciplinary team (MDT) facilitated by a three-step genotype-driven followed by phenotype-driven analysis framework. METHODS: We performed pES in 1618 fetal cases with positive ultrasound findings but negative for karyotyping and chromosome microarray analysis between January 2014 and October 2021, including both retrospective (n=565) and prospective (n=1053) cohorts. The diagnostic efficiency and its correlation to organ systems involved, phenotypic spectrum, and the clinical impacts of pES results on pregnancy outcomes were analyzed. RESULTS: A genotype-driven followed by phenotype-driven three-step approach was carried out in all trio pES. Step 1, a genotype-driven analysis resulted in a diagnostic rate of 11.6% (187/1618). Step 2, a phenotype-driven comprehensive analysis yielded additional diagnostic findings for another 28 cases (1.7%; 28/1618). In the final step 3, data reanalyses based on new phenotypes and/or clinical requests found molecular diagnosis in 14 additional cases (0.9%; 14/1618). Altogether, 229 fetal cases (14.2%) received a molecular diagnosis, with a higher positive rate in the retrospective than the prospective cohort (17.3% vs. 12.4%, p<0.01). The diagnostic rates were highest in fetuses with skeletal anomalies (30.4%) and multiple organ involvements (25.9%), and lowest in fetuses with chest anomalies (0%). In addition, incidental and secondary findings with childhood-onset disorders were detected in 11 (0.7%) cases. Furthermore, we described the prenatal phenotypes for the first time for 27 gene-associated conditions (20.0%, 27/135) upon a systematic analysis of the diagnosed cases and expanded the phenotype spectrum for 26 (19.3%) genes where limited fetal phenotypic information was available. In the prospective cohort, the combined prenatal ultrasound and pES results had significantly impacted the clinical decisions (61.5%, 648/1053). CONCLUSIONS: The genotype-driven approach could identify about 81.7% positive cases (11.6% of the total cohort) with the initial limited fetal phenotype information considered. The following two steps of phenotype-driven analysis and data reanalyses helped us find the causative variants in an additional 2.6% of the entire cohort (18.3% of all positive findings). Our extensive phenotype analysis on a large number of molecularly confirmed prenatal cases had greatly enriched our current knowledge on fetal phenotype-genotype correlation, which may guide more focused prenatal ultrasound in the future. This is by far the largest pES cohort study that combines a robust trio sequence data analysis, systematic phenotype-genotype correlation, and well-established MDT in a single prenatal clinical setting. This work underlines the value of pES as an essential component in prenatal diagnosis in guiding medical management and parental decision making.

BACKGROUND: Cancer recurrence after tumor resection in early-stage non-small cell lung cancer (NSCLC) is common, yet difficult to predict. The lung microbiota and systemic immunity may be important modulators of risk for lung cancer recurrence, yet biomarkers from the lung microbiome and peripheral immune environment are understudied. Such markers may hold promise for prediction as well as improved etiologic understanding of lung cancer recurrence. METHODS: In tumor and distant normal lung samples from 46 stage II NSCLC patients with curative resection (39 tumor samples, 41 normal lung samples), we conducted 16S rRNA gene sequencing. We also measured peripheral blood immune gene expression with nanoString®. We examined associations of lung microbiota and peripheral gene expression with recurrence-free survival (RFS) and disease-free survival (DFS) using 500 × 10-fold cross-validated elastic-net penalized Cox regression, and examined predictive accuracy using time-dependent receiver operating characteristic (ROC) curves. RESULTS: Over a median of 4.8 years of follow-up (range 0.2-12.2 years), 43% of patients experienced a recurrence, and 50% died. In normal lung tissue, a higher abundance of classes Bacteroidia and Clostridia, and orders Bacteroidales and Clostridiales, were associated with worse RFS, while a higher abundance of classes Alphaproteobacteria and Betaproteobacteria, and orders Burkholderiales and Neisseriales, were associated with better RFS. In tumor tissue, a higher abundance of orders Actinomycetales and Pseudomonadales were associated with worse DFS. Among these taxa, normal lung Clostridiales and Bacteroidales were also related to worse survival in a previous small pilot study and an additional independent validation cohort. In peripheral blood, higher expression of genes TAP1, TAPBP, CSF2RB, and IFITM2 were associated with better DFS. Analysis of ROC curves revealed that lung microbiome and peripheral gene expression biomarkers provided significant additional recurrence risk discrimination over standard demographic and clinical covariates, with microbiome biomarkers contributing more to short-term (1-year) prediction and gene biomarkers contributing to longer-term (2-5-year) prediction. CONCLUSIONS: We identified compelling biomarkers in under-explored data types, the lung microbiome, and peripheral blood gene expression, which may improve risk prediction of recurrence in early-stage NSCLC patients. These findings will require validation in a larger cohort.

BACKGROUND: The multiple de novo copy number variant (MdnCNV) phenotype is described by having four or more constitutional de novo CNVs (dnCNVs) arising independently throughout the human genome within one generation. It is a rare peri-zygotic mutational event, previously reported to be seen once in every 12,000 individuals referred for genome-wide chromosomal microarray analysis due to congenital abnormalities. These rare families provide a unique opportunity to understand the genetic factors of peri-zygotic genome instability and the impact of dnCNV on human diseases. METHODS: Chromosomal microarray analysis (CMA), array-based comparative genomic hybridization, short- and long-read genome sequencing (GS) were performed on the newly identified MdnCNV family to identify de novo mutations including dnCNVs, de novo single-nucleotide variants (dnSNVs), and indels. Short-read GS was performed on four previously published MdnCNV families for dnSNV analysis. Trio-based rare variant analysis was performed on the newly identified individual and four previously published MdnCNV families to identify potential genetic etiologies contributing to the peri-zygotic genomic instability. Lin semantic similarity scores informed quantitative human phenotype ontology analysis on three MdnCNV families to identify gene(s) driving or contributing to the clinical phenotype. RESULTS: In the newly identified MdnCNV case, we revealed eight de novo tandem duplications, each ~ 1 Mb, with microhomology at 6/8 breakpoint junctions. Enrichment of de novo single-nucleotide variants (SNV; 6/79) and de novo indels (1/12) was found within 4 Mb of the dnCNV genomic regions. An elevated post-zygotic SNV mutation rate was observed in MdnCNV families. Maternal rare variant analyses identified three genes in distinct families that may contribute to the MdnCNV phenomenon. Phenotype analysis suggests that gene(s) within dnCNV regions contribute to the observed proband phenotype in 3/3 cases. CNVs in two cases, a contiguous gene duplication encompassing PMP22 and RAI1 and another duplication affecting NSD1 and SMARCC2, contribute to the clinically observed phenotypic manifestations. CONCLUSIONS: Characteristic features of dnCNVs reported here are consistent with a microhomology-mediated break-induced replication (MMBIR)-driven mechanism during the peri-zygotic period. Maternal genetic variants in DNA repair genes potentially contribute to peri-zygotic genomic instability. Variable phenotypic features were observed across a cohort of three MdnCNV probands, and computational quantitative phenotyping revealed that two out of three had evidence for the contribution of more than one genetic locus to the proband's phenotype supporting the hypothesis of de novo multilocus pathogenic variation (MPV) in those families.

BACKGROUND: Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance. METHODS: In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell. RESULTS: Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT). CONCLUSIONS: We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.

BACKGROUND: Cervical screening is transitioning from primary cytology to primary human papillomavirus (HPV) testing. HPV testing is highly sensitive but there is currently no high-specificity triage method for colposcopy referral to detect cervical intraepithelial neoplasia grade 3 or above (CIN3+) in women positive for high-risk (hr) HPV subtypes. An objective, automatable test that could accurately perform triage, independently of sample heterogeneity and age, is urgently required. METHODS: We analyzed DNA methylation at ~850,000 CpG sites across the genome in a total of 1254 cervical liquid-based cytology (LBC) samples from cases of screen-detected histologically verified CIN1-3+ (98% hrHPV-positive) and population-based control women free from any cervical disease (100% hrHPV-positive). Samples were provided by a state-of-the-art population-based cohort biobank and consisted of (i) a discovery set of 170 CIN3+ cases and 202 hrHPV-positive/cytology-negative controls; (ii) a diagnostic validation set of 87 CIN3+, 90 CIN2, 166 CIN1, and 111 hrHPV-positive/cytology-negative controls; and (iii) a predictive validation set of 428 cytology-negative samples (418 hrHPV-positive) of which 210 were diagnosed with CIN3+ in the upcoming 1-4 years and 218 remained disease-free. RESULTS: We developed the WID-CIN (Women's cancer risk IDentification-Cervical Intraepithelial Neoplasia) test, a DNA methylation signature consisting of 5000 CpG sites. The receiver operating characteristic area under the curve (AUC) in the independent diagnostic validation set was 0.92 (95% CI 0.88-0.96). At 75% specificity (≤CIN1), the overall sensitivity to detect CIN3+ is 89.7% (83.3-96.1) in all and 92.7% (85.9-99.6) and 65.6% (49.2-82.1) in women aged ≥30 and <30. In hrHPV-positive/cytology-negative samples in the predictive validation set, the WID-CIN detected 54.8% (48.0-61.5) cases developing 1-4 years after sample donation in all ages or 56.9% (47.6-66.2) and 53.5% (43.7-63.2) in ≥30 and <30-year-old women, at a specificity of 75%. CONCLUSIONS: The WID-CIN test identifies the vast majority of hrHPV-positive women with current CIN3+ lesions. In the absence of cytologic abnormalities, a positive WID-CIN test result is likely to indicate a significantly increased risk of developing CIN3+ in the near future.

BACKGROUND: Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated. METHODS: To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes. RESULTS: Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy. CONCLUSIONS: Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/ . The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway .

Author information: (1)William Harvey Research Institute, Queen Mary University of London, London, UK. (2)MRC Harwell Institute, Harwell, Oxfordshire, UK. (3)Department of Veterinary and Animal Sciences, University of Massachusetts, Amherst, MA, USA. (4)Department of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, TX, USA. (5)Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. (6)The Hospital for Sick Children, The Centre for Phenogenomics, Toronto, Canada. (7)European Molecular Biology Laboratory-European Bioinformatics Institute, Hinxton, UK. (8)Department of Education, Innovation and Technology, Baylor College of Medicine, Houston, TX, USA. (9)Department of Obstetrics and Gynecology, Baylor College of Medicine, Houston, TX, USA. (10)Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, The Centre for Phenogenomics, Toronto, Canada. (11)The Jackson Laboratory, Bar Harbor, ME, USA. (12)The Mary Lyon Centre, MRC Harwell Institute, Harwell, Oxfordshire, UK. (13)Mouse Biology Program, University of California Davis, Davis, CA, USA. (14)William Harvey Research Institute, Queen Mary University of London, London, UK. d.smedley@qmul.ac.uk.