BACKGROUND: There has been a growing appreciation recently that mutagenic processes can be studied through the lenses of mutational signatures, which represent characteristic mutation patterns attributed to individual mutagens. However, the causal links between mutagens and observed mutation patterns as well as other types of interactions between mutagenic processes and molecular pathways are not fully understood, limiting the utility of mutational signatures. METHODS: To gain insights into these relationships, we developed a network-based method, named GENESIGNET that constructs an influence network among genes and mutational signatures. The approach leverages sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes. RESULTS: Applying GENESIGNET to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer-related processes. Our results are consistent with previous findings, such as the impact of homologous recombination deficiency on clustered APOBEC mutations in breast cancer. The network identified by GENESIGNET also suggest an interaction between APOBEC hypermutation and activation of regulatory T Cells (Tregs), as well as a relation between APOBEC mutations and changes in DNA conformation. GENESIGNET also exposed a possible link between the SBS8 signature of unknown etiology and the Nucleotide Excision Repair (NER) pathway. CONCLUSIONS: GENESIGNET provides a new and powerful method to reveal the relation between mutational signatures and gene expression. The GENESIGNET method was implemented in python, and installable package, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/GeneSigNet.

BACKGROUND: The incidence of early-onset colorectal cancer (EOCRC; patients < 50 years old) has been rising rapidly, whereas the EOCRC genetic susceptibility remains incompletely investigated. Here, we aimed to systematically identify specific susceptible genetic variants for EOCRC. METHODS: Two parallel GWASs were conducted in 17,789 CRC cases (including 1490 EOCRC cases) and 19,951 healthy controls. A polygenic risk score (PRS) model was built based on identified EOCRC-specific susceptibility variants by using the UK Biobank cohort. We also interpreted the potential biological mechanisms of the prioritized risk variant. RESULTS: We identified 49 independent susceptibility loci that were significantly associated with the susceptibility to EOCRC and the diagnosed age of CRC (both P < 5.0×10-4), replicating 3 previous CRC GWAS loci. There are 88 assigned susceptibility genes involved in chromatin assembly and DNA replication pathways, mainly associating with precancerous polyps. Additionally, we assessed the genetic effect of the identified variants by developing a PRS model. Compared to the individuals in the low genetic risk group, the individuals in the high genetic risk group have increased EOCRC risk, and these results were replicated in the UKB cohort with a 1.63-fold risk (95% CI: 1.32-2.02, P = 7.67×10-6). The addition of the identified EOCRC risk loci significantly increased the prediction accuracy of the PRS model, compared to the PRS model derived from the previous GWAS-identified loci. Mechanistically, we also elucidated that rs12794623 may contribute to the early stage of CRC carcinogenesis via allele-specific regulating the expression of POLA2. CONCLUSIONS: These findings will broaden the understanding of the etiology of EOCRC and may facilitate the early screening and individualized prevention.

BACKGROUND: Sex chromosome aneuploidies (SCAs) give rise to a broad range of phenotypic traits and diseases. Previous studies based on peripheral blood samples have suggested the presence of ripple effects, caused by altered X chromosome number, affecting the methylome and transcriptome. Whether these alterations can be connected to disease-specific tissues, and thereby having clinical implication for the phenotype, remains to be elucidated. METHODS: We performed a comprehensive analysis of X chromosome number on the transcriptome and methylome in blood, fat, and muscle tissue from individuals with 45,X, 46,XX, 46,XY, and 47,XXY. RESULTS: X chromosome number affected the transcriptome and methylome globally across all chromosomes in a tissue-specific manner. Furthermore, 45,X and 47,XXY demonstrated a divergent pattern of gene expression and methylation, with overall gene downregulation and hypomethylation in 45,X and gene upregulation and hypermethylation in 47,XXY. In fat and muscle, a pronounced effect of sex was observed. We identified X chromosomal genes with an expression pattern different from what would be expected based on the number of X and Y chromosomes. Our data also indicate a regulatory function of Y chromosomal genes on X chromosomal genes. Fourteen X chromosomal genes were downregulated in 45,X and upregulated in 47,XXY, respectively, in all three tissues (AKAP17A, CD99, DHRSX, EIF2S3, GTPBP6, JPX, KDM6A, PP2R3B, PUDP, SLC25A6, TSIX, XIST, ZBED1, ZFX). These genes may be central in the epigenetic and genomic regulation of sex chromosome aneuploidies. CONCLUSION: We highlight a tissue-specific and complex effect of X chromosome number on the transcriptome and methylome, elucidating both shared and non-shared gene-regulatory mechanism between SCAs.

Since many single-cell RNA-seq (scRNA-seq) data are obtained after cell sorting, such as when investigating immune cells, tracking cellular landscape by integrating single-cell data with spatial transcriptomic data is limited due to cell type and cell composition mismatch between the two datasets. We developed a method, spSeudoMap, which utilizes sorted scRNA-seq data to create virtual cell mixtures that closely mimic the gene expression of spatial data and trains a domain adaptation model for predicting spatial cell compositions. The method was applied in brain and breast cancer tissues and accurately predicted the topography of cell subpopulations. spSeudoMap may help clarify the roles of a few, but crucial cell types.

BACKGROUND: Rapidly and efficiently identifying critically ill infants for whole genome sequencing (WGS) is a costly and challenging task currently performed by scarce, highly trained experts and is a major bottleneck for application of WGS in the NICU. There is a dire need for automated means to prioritize patients for WGS. METHODS: Institutional databases of electronic health records (EHRs) are logical starting points for identifying patients with undiagnosed Mendelian diseases. We have developed automated means to prioritize patients for rapid and whole genome sequencing (rWGS and WGS) directly from clinical notes. Our approach combines a clinical natural language processing (CNLP) workflow with a machine learning-based prioritization tool named Mendelian Phenotype Search Engine (MPSE). RESULTS: MPSE accurately and robustly identified NICU patients selected for WGS by clinical experts from Rady Children's Hospital in San Diego (AUC 0.86) and the University of Utah (AUC 0.85). In addition to effectively identifying patients for WGS, MPSE scores also strongly prioritize diagnostic cases over non-diagnostic cases, with projected diagnostic yields exceeding 50% throughout the first and second quartiles of score-ranked patients. CONCLUSIONS: Our results indicate that an automated pipeline for selecting acutely ill infants in neonatal intensive care units (NICU) for WGS can meet or exceed diagnostic yields obtained through current selection procedures, which require time-consuming manual review of clinical notes and histories by specialized personnel.

BACKGROUND: Next-generation sequencing (NGS) based population screening holds great promise for disease prevention and earlier diagnosis, but the costs associated with screening millions of humans remain prohibitive. New methods for population genetic testing that lower the costs of NGS without compromising diagnostic power are needed. METHODS: We developed double batched sequencing where DNA samples are batch-sequenced twice - directly pinpointing individuals with rare variants. We sequenced batches of at-birth blood spot DNA using a commercial 113-gene panel in an explorative (n = 100) and a validation (n = 100) cohort of children who went on to develop pediatric cancers. All results were benchmarked against individual whole genome sequencing data. RESULTS: We demonstrated fully replicable detection of cancer-causing germline variants, with positive and negative predictive values of 100% (95% CI, 0.91-1.00 and 95% CI, 0.98-1.00, respectively). Pathogenic and clinically actionable variants were detected in RB1, TP53, BRCA2, APC, and 19 other genes. Analyses of larger batches indicated that our approach is highly scalable, yielding more than 95% cost reduction or less than 3 cents per gene screened for rare disease-causing mutations. We also show that double batched sequencing could cost-effectively prevent childhood cancer deaths through broad genomic testing. CONCLUSIONS: Our ultracheap genetic diagnostic method, which uses existing sequencing hardware and standard newborn blood spots, should readily open up opportunities for population-wide risk stratification using genetic screening across many fields of clinical genetics and genomics.

BACKGROUND: Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. METHODS: We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. RESULTS: In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e-4 for progression-free survival (PFS) and 3.63e-4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fisher's exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e-4 for PFS and 3.66e-4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient using in vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage ( http://www.wang-lab-hkust.com:3838/TMZEP ). CONCLUSIONS: We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs.