Wenan Chen

Although sequencing costs have steadily decreased with advances in technology, they remain high for large scale studies. The design of traditional individual-disease sequencing studies is either case only or cases with relatively few controls, resulting in potential loss of statistical power for discovery of disease associated genes. Here we show that for a given number of sequenced cases, a large control sample size is critical to maximize power for rare variant burden analysis. Furthermore, we have developed an end-to-end workflow based tool (CoCoRV-nf) to facilitate the use of external biobank sequence resources as controls. The modules include consistent variant QC, variant annotation, ancestry population prediction, and gene based burden analysis using summary genotype information, and combined analysis from multiple independent results. The tool supports exomes and genomes from gnomAD and All of Us as controls with preprocessed datasets. We apply the tool in two rare neurological diseases: amyotrophic lateral sclerosis and neuroblastoma. For each disease, two case cohorts are paired with gnomAD and All of Us data, respectively, followed by a combined analysis. Not only did we recapture known genes, but also, we identified new candidate genes for both diseases. By leveraging multiple large external biobank sequence data, we demonstrate the feasibility of using our tool to maximize statistical power to identify new disease predisposition genes.

Determining the clinical relevance of BRCA2 variants of uncertain significance is critical for informed risk management. Recently, two saturation genome editing studies assessed the functional effects of all single nucleotide variants in the BRCA2 C-terminal DNA Binding Domain. To improve the accuracy of functional data used for ACMG/AMP variant classification, we combined results from these studies in four composite models and evaluated the performance of each model using variants with known classifications. Here, we show that an “Integrated VarCall Model”, which combined raw functional data for 6383 variants from the original studies, yielded 98.8% accuracy and out-performed the original studies and other combined data models. Incorporation of the “Integrated VarCall Model” functional data with other sources of evidence according to ClinGen BRCA1/2 variant curation expert panel specifications resulted in classification of 5926 (92.8%) BRCA2 variants as pathogenic ( n = 735) or benign ( n = 5191) and provides valuable insights for individuals with BRCA2 variants.

PURPOSE: Recent studies reveal that 5%-18% of children with cancer harbor pathogenic variants in known cancer-predisposing genes. However, DNA damage repair (DDR) genes, which are frequently somatically altered in pediatric tumors, have not been systematically examined as a source of novel cancer-predisposing signals. METHODS: To address this gap, we interrogated 189 DDR genes for presence of germline predisposing variants (PV) among 5,993 childhood cancer cases and 14,477 adult noncancer controls (discovery cohort). PV were determined using a tiered approach incorporating ClinVar annotations, InterVar classification, and in silico tools (REVEL, CADD, and MetaSVM). Using logistic and firth regression, we identified genes with PV statistically enriched in the germline of children with tumors and replicated findings among 1,497 additional childhood cancer cases across three independent cohorts. RESULTS: Analysis across all cases with cancer revealed enrichment of TP53 PV. Cancer-specific analyses confirmed known associations including germline TP53 PV in adrenocortical carcinoma, high-grade glioma (HGG), and medulloblastoma (MB), PMS2 in HGG and non-Hodgkin lymphoma (NHL), MLH1 in HGG, BRCA2 in NHL, and BARD1 in neuroblastoma. In addition, four novel associations were uncovered, including BRCA1 in ependymoma, SPIDR in HGG, SMC5 in MB, and SMARCAL1 in osteosarcoma (OS). Importantly, the SMARCAL1 :OS association was significant in the discovery (6/230, 2.6%, false discovery rate [FDR] _logistic = 0.0189) as well as all three replication cohorts (Childhood Cancer Survivor Study: 8/275, 2.9%; P _Fisher < .0001; Cancer Predisposition Syndrome-German Childhood Cancer Registry: 4/135, 3%, P _Fisher = .002; Individualized Therapy for Relapsed Malignancies in Childhood: 4/217, 1.8%, P _Fisher = .012). The remaining wild-type SMARCAL1 allele was deleted in three of four OS tumors with available data. CONCLUSION: Our study confirms the relevance DDR genetic variation in pediatric cancer risk and establishes SMARCAL1 as a novel OS predisposing gene, providing insights into tumor biology and creating opportunities to optimize care for patients with this challenging tumor.

Somatic mitochondrial DNA (mtDNA) mutations are frequently observed in tumors, yet their role in pediatric cancers remains poorly understood. The heteroplasmic nature of mtDNA—where mutant and wild-type mtDNA coexist—complicates efforts to define its contribution to disease progression. In this study, bulk whole-genome sequencing of 637 matched tumor-normal samples from the Pediatric Cancer Genome Project revealed an enrichment of functionally impactful mtDNA variants in specific pediatric leukemia subtypes. Collectively, the results from single-cell sequencing of five diagnostic leukemia samples demonstrated that somatic mtDNA mutations can arise early in leukemogenesis and undergo positive selection during disease progression, achieving intermediate heteroplasmy—a “sweet spot” that balances mitochondrial dysfunction with cellular fitness. Network-based systems biology analyses link specific heteroplasmic mtDNA mutations to metabolic reprogramming and therapy resistance. We reveal somatic mtDNA mutations as a potential source of functional heterogeneity and cellular diversity among leukemic cells, influencing their fitness and shaping disease progression.

Clinical genetic testing identifies variants causal for hereditary cancer, information that is used for risk assessment and clinical management. Unfortunately, some variants identified are of uncertain clinical significance (VUS), complicating patient management. Case-control data is one evidence type used to classify VUS. As an initiative of the Evidence-based Network for the Interpretation of Germline Mutant Alleles (ENIGMA) Analytical Working Group we analyze germline sequencing data of BRCA1 and BRCA2 from 96,691 female breast cancer cases and 302,116 controls from three studies: the BRIDGES study of the Breast Cancer Association Consortium, the Cancer Risk Estimates Related to Susceptibility consortium, and the UK Biobank. We observe 11,207 BRCA1 and BRCA2 variants, with 6909 being coding, covering 23.4% of BRCA1 and BRCA2 VUS in ClinVar and 19.2% of ClinVar curated (likely) benign or pathogenic variants. Case-control likelihood ratio (ccLR) evidence is highly consistent with ClinVar assertions for (likely) benign or pathogenic variants; exhibiting 99.1% sensitivity and 95.3% specificity for BRCA1 and 93.3% sensitivity and 86.6% specificity for BRCA2. This approach provides case-control evidence for 787 unclassified variants; these include 579 with strong or moderate benign evidence and 10 with strong pathogenic evidence for which ccLR evidence is sufficient to alter clinical classification.

PURPOSE: To reduce costs in genomic studies of time-to-event phenotypes like survival, researchers often sequence a subset of samples from a larger cohort. This process usually involves two phases: first, collecting inexpensive variables from all samples, and second, selecting a subset for expensive measurements, for example, sequencing-based biomarkers. Common two-phase designs include nested case-control and case-cohort designs. Additional designs include sampling subjects based on follow-up time, like extreme case-control designs. Recently an optimal two-phase design using a maximum likelihood-based method was proposed, which could accommodate arbitrary sample selection in the second phase. However, direct comparisons of this optimal design with others in terms of power and computational cost is lacking. METHODS: This study performs a direct evaluation of typical two-phase designs, including Tao's optimal design, on type I error, power, effect size estimation, and computational time, using both simulated and real data sets. RESULTS: Results show that the optimal design had the highest power and accurate effect size estimation under the Cox regression model. Surprisingly, logistic regression achieved similar power with much lower computational cost than a more sophisticated method. The study further applied these methods to the MP2PRT study, reporting hazard ratios of cancer subtypes on relapse risk. CONCLUSION: Recommendations for selecting two-phase designs and analysis methods are regarding power, bias of estimated effect size, and computational time.

Germline BRCA2 loss-of function variants, which can be identified through clinical genetic testing, predispose to several cancers^1,2,3,4,5. However, variants of uncertain significance limit the clinical utility of test results. Thus, there is a need for functional characterization and clinical classification of all BRCA2 variants to facilitate the clinical management of individuals with these variants. Here we analysed all possible single-nucleotide variants from exons 15 to 26 that encode the BRCA2 DNA-binding domain hotspot for pathogenic missense variants. To enable this, we used saturation genome editing CRISPR–Cas9-based knock-in endogenous targeting of human haploid HAP1 cells⁶. The assay was calibrated relative to nonsense and silent variants and was validated using pathogenic and benign standards from ClinVar and results from a homology-directed repair functional assay⁷. Variants (6,959 out of 6,960 evaluated) were assigned to seven categories of pathogenicity based on a VarCall Bayesian model⁸. Single-nucleotide variants that encode loss-of-function missense variants were associated with increased risks of breast cancer and ovarian cancer. The functional assay results were integrated into models from ClinGen, the American College of Medical Genetics and Genomics, and the Association for Molecular Pathology⁹ for clinical classification of BRCA2 variants. Using this approach, 91% were classified as pathogenic or likely pathogenic or as benign or likely benign. These classified variants can be used to improve clinical management of individuals with a BRCA2 variant.

PURPOSE: Although cure rates for childhood acute lymphoblastic leukemia (ALL) exceed 90%, ALL remains a leading cause of cancer death in children. Half of relapses arise in children initially classified with standard-risk (SR) disease. MATERIALS AND METHODS: To identify genomic determinants of relapse in children with SR ALL, we performed genome and transcriptome sequencing of diagnostic and remission samples of children with SR (n = 1,381) or high-risk B-ALL with favorable cytogenetic features (n = 115) enrolled on Children's Oncology Group trials. We used a case-control study design analyzing 439 patients who relapsed and 1,057 who remained in complete remission for at least 5 years. RESULTS: Genomic subtype was associated with relapse, which occurred in approximately 50% of cases of PAX5 -altered ALL (odds ratio [OR], 3.31 [95% CI, 2.17 to 5.03]; P = 3.18 × 10 ^–8 ). Within high-hyperdiploid ALL, gain of chromosome 10 with disomy of chromosome 7 was associated with favorable outcome (OR, 0.27 [95% CI, 0.17 to 0.42]; P = 8.02 × 10 ^–10 ; St Jude Children's Research Hospital validation cohort: OR, 0.22 [95% CI, 0.05 to 0.80]; P = .009), and disomy of chromosomes 10 and 17 with gain of chromosome 6 was associated with relapse (OR, 7.16 [95% CI, 2.63 to 21.51]; P = 2.19 × 10 ^–5 ; validation cohort: OR, 21.32 [95% CI, 3.62 to 119.30]; P = .0004). Genomic alterations were associated with relapse in a subtype-dependent manner, including alterations of INO80 in ETV6::RUNX1 ALL, IKZF1 , and CREBBP in high-hyperdiploid ALL and FHIT in BCR::ABL1 -like ALL. Genomic alterations were also associated with the presence of minimal residual disease, including NRAS and CREBBP in high-hyperdiploid ALL. CONCLUSION: Genetic subtype, patterns of aneuploidy, and secondary genomic alterations determine risk of relapse in childhood ALL. Comprehensive genomic analysis is required for optimal risk stratification.