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Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Clonal evolution drives cancer progression and therapeutic resistance. Recent studies have revealed divergent longitudinal trajectories in gliomas, but early molecular features steering posttreatment cancer evolution remain unclear. Here, we collected sequencing and clinical data of initial-recurrent tumor pairs from 544 adult diffuse gliomas and performed multivariate analysis to identify early molecular predictors of tumor evolution in three diffuse glioma subtypes. We found that CDKN2A deletion at initial diagnosis preceded tumor necrosis and microvascular proliferation that occur at later stages of IDH-mutant glioma. Ki67 expression at diagnosis was positively correlated with acquiring hypermutation at recurrence in the IDH-wild-type glioma. In all glioma subtypes, MYC gain or MYC-target activation at diagnosis was associated with treatment-induced hypermutation at recurrence. To predict glioma evolution, we constructed CELLO2 (Cancer EvoLution for LOngitudinal data version 2), a machine learning model integrating features at diagnosis to forecast hypermutation and progression after treatment. CELLO2 successfully stratified patients into subgroups with distinct prognoses and identified a high-risk patient group featured by MYC gain with worse post-progression survival, from the low-grade IDH-mutant-noncodel subtype. We then performed chronic temozolomide-induction experiments in glioma cell lines and isogenic patient-derived gliomaspheres and demonstrated that MYC drives temozolomide resistance by promoting hypermutation. Mechanistically, we demonstrated that, by binding to open chromatin and transcriptionally active genomic regions, c-MYC increases the vulnerability of key mismatch repair genes to treatment-induced mutagenesis, thus triggering hypermutation. This study reveals early predictors of cancer evolution under therapy and provides a resource for precision oncology targeting cancer dynamics in diffuse gliomas.

Objective: Extra-axial cavernous hemangiomas (ECHs) are sporadic and rare intracranial occupational lesions that usually occur within the cavernous sinus. The aetiology of ECHs remains unknown. Methods: Whole-exome sequencing was performed on ECH lesions from 12 patients (discovery cohort) and droplet digital polymerase-chain-reaction (ddPCR) was used to confirm the identified mutation in 46 additional cases (validation cohort). Laser capture microdissection (LCM) was carried out to capture and characterise subgroups of tissue cells. Mechanistic and functional investigations were carried out in human umbilical vein endothelial cells and a newly established mouse model. Results: We detected somatic GJA4 mutation (c.121G>T, p.G41C) in 5/12 patients with ECH in the discovery cohort and confirmed the finding in the validation cohort (16/46). LCM followed by ddPCR revealed that the mutation was enriched in lesional endothelium. In vitro experiments in endothelial cells demonstrated that the GJA4 mutation activated SGK-1 signalling that in turn upregulated key genes involved in cell hyperproliferation and the loss of arterial specification. Compared with wild-type littermates, mice overexpressing the GJA4 mutation developed ECH-like pathological morphological characteristics (dilated venous lumen and elevated vascular density) in the retinal superficial vascular plexus at the postnatal 3 weeks, which were reversed by an SGK1 inhibitor, EMD638683. Conclusions: We identified a somatic GJA4 mutation that presents in over one-third of ECH lesions and proposed that ECHs are vascular malformations due to GJA4-induced activation of the SGK1 signalling pathway in brain endothelial cells.

In the article by Li et al, "De Novo Germline and Somatic Variants Convergently Promote Endothelial-to-Mesenchymal Transition in Simplex Brain Arteriovenous Malformation,"which published in the October 15, 2021 issue of the journal (Circ Res. 2021;129:825-839. doi: 10.1161/CIRCRESAHA.121.319004), a correction was needed. There was an error in the Abstract and Introduction: The definition of AVM was incorrectly displayed as "arteriovenous- malformation-like"when it should have been "arteriovenous-malformation". The journal apologizes for the error. The corrected version of this article is available at www.ahajournals.org/doi/10.1161/CIRCRESAHA.121.319004. © 2021 American Heart Association, Inc.

Cerebral cavernous malformations (CCMs) are vascular disorders that affect up to 0.5% of the total population. About 20% of CCMs are inherited because of familial mutations in CCM genes, including CCM1/KRIT1, CCM2/MGC4607, and CCM3/PDCD10, whereas the etiology of a majority of simplex CCM-affected individuals remains unclear. Here, we report somatic mutations of MAP3K3, PIK3CA, MAP2K7, and CCM genes in CCM lesions. In particular, somatic hotspot mutations of PIK3CA are found in 11 of 38 individuals with CCMs, and a MAP3K3 somatic mutation (c.1323C>G [p.Ile441Met]) is detected in 37.0% (34 of 92) of the simplex CCM-affected individuals. Strikingly, the MAP3K3 c.1323C>G mutation presents in 95.7% (22 of 23) of the popcorn-like lesions but only 2.5% (1 of 40) of the subacute-bleeding or multifocal lesions that are predominantly attributed to mutations in the CCM1/2/3 signaling complex. Leveraging mini-bulk sequencing, we demonstrate the enrichment of MAP3K3 c.1323C>G mutation in CCM endothelium. Mechanistically, beyond the activation of CCM1/2/3-inhibited ERK5 signaling, MEKK3 p.Ile441Met (MAP3K3 encodes MEKK3) also activates ERK1/2, JNK, and p38 pathways because of mutation-induced MEKK3 kinase activity enhancement. Collectively, we identified several somatic activating mutations in CCM endothelium, and the MAP3K3 c.1323C>G mutation defines a primary CCM subtype with distinct characteristics in signaling activation and magnetic resonance imaging appearance.

Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering clinical translation, lacking effective methods for multimodal and multisource data integration is becoming a bottleneck. Here we proposed DeepDRK, a machine learning framework for deciphering drug response through kernel-based data integration. To transfer information among different drugs and cancer types, we trained deep neural networks on more than 20 000 pan-cancer cell line-anticancer drug pairs. These pairs were characterized by kernel-based similarity matrices integrating multisource and multi-omics data including genomics, transcriptomics, epigenomics, chemical properties of compounds and known drug-target interactions. Applied to benchmark cancer cell line datasets, our model surpassed previous approaches with higher accuracy and better robustness. Then we applied our model on newly established patient-derived cancer cell lines and achieved satisfactory performance with AUC of 0.84 and AUPRC of 0.77. Moreover, DeepDRK was used to predict clinical response of cancer patients. Notably, the prediction of DeepDRK correlated well with clinical outcome of patients and revealed multiple drug repurposing candidates. In sum, DeepDRK provided a computational method to predict drug response of cancer cells from integrating pharmacogenomic datasets, offering an alternative way to prioritize repurposing drugs in precision cancer treatment. The DeepDRK is freely available via https://github.com/wangyc82/DeepDRK.