Biology subjects

Scheet, P.

Publications and source records attributed to Scheet, P..

4 recordsLinked to original sources

bioRxiv2022

Mosaic chromosomal alterations in blood across ancestries via whole-genome sequencing

Mosaic mutations in blood are common with increasing age and are prognostic markers for cancer, cardiovascular dysfunction and other diseases. This group of acquired mutations include megabase-scale mosaic chromosomal alterations (mCAs). These large mutations have mainly been surveyed using SNP array data from individuals of European (EA) or Japanese genetic ancestry. To gain a better understanding of mCA rates and associated risk factors in genetically diverse populations, we surveyed whole genome sequencing data from 67,390 individuals, including 20,132 individuals of African ancestry (AA), and 7,608 of Hispanic ancestry (HA) with deep (30X) whole genome sequencing data from the NHLBI Trans Omics for Precision Medicine (TOPMed) program. We adapted an existing mCA calling algorithm for application to WGS data, and observed higher sensitivity with WGS data, compared with array-based data, in uncovering mCAs at low mutant cell fractions. As in previous reports, we observed a strong association with age and a non-uniform distribution of mCAs across the genome. The presence of autosomal (but not chromosome X) mCAs was associated with an increased risk of both lymphoid and myeloid malignancies. After adjusting for age, we found that individuals of European ancestry have the highest rates of autosomal mCAs, mirroring the higher rate of leukemia in this group. Our analysis also uncovered higher rates of chromosome X mCAs in AA and HA compared to EA, again after adjusting for age. Germline variants in ATM and MPL showed strong associations with mCAs in cis, including ancestry specific variants. And rare variant gene-burden analysis confirmed the association of putatively protein altering variants in ATM and MPL with mCAs in cis. Individual rare variants in DCPS, ADM17, PPP1R16B, and TET2 were all associated with autosomal mCAs and rare variants in OR4C16 were associated with chromosome X mCAs in females. There was significant enrichment of co-occurrence of CHIP mutations and mCAs both altering cancer associated genes TET2, DNMT3A, JAK2, CUX1, and TP53. Overall, our study demonstrates that rates of mCAs differ across populations and that rare inherited germline variants are strongly associated with mCAs across genetically diverse populations. These results strongly motivate further studies of mCAs in under-represented populations to better understand the causes and consequences of this class of somatic variation.

genomics↗

bioRxiv2022

An atlas of epithelial cell states and plasticity in lung adenocarcinoma

Understanding cellular processes underlying early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation.

cancer biology↗

bioRxiv2020

Resolving the spatial and cellular architecture of lung adenocarcinoma by multi-region single-cell sequencing

Little is known of the geospatial architecture of individual cell populations in lung adenocarcinoma (LUAD) evolution. Here, we perform single-cell RNA sequencing of 186,916 cells from 5 early-stage LUADs and 14 multi-region normal lung tissues of defined spatial proximities from the tumors. We show that cellular lineages, states, and transcriptomic features geospatially evolve across normal regions to LUADs. LUADs also exhibit pronounced intratumor cell heterogeneity within single sites and transcriptional lineage-plasticity programs. T regulatory cell phenotypes are increased in normal tissues with proximity to LUAD, in contrast to diminished signatures and fractions of cytotoxic CD8+ T cells, antigen-presenting macrophages and inflammatory dendritic cells. We further find that the LUAD ligand-receptor interactome harbors increased expression of epithelial CD24 which mediates pro-tumor phenotypes. These data provide a spatial atlas of LUAD evolution, and a resource for identification of targets for its treatment. Statement of significanceThe geospatial ecosystem of the peripheral lung and early-stage LUAD is not known. Our multi-region single-cell sequencing analyses unravel cell populations, states, and phenotypes in the spatial and ecological evolution of LUAD from the lung that comprise high-potential targets for early interception.

cancer biology↗

bioRxiv2019

Pan cancer patterns of allelic imbalance from chromosomal aberrations in 33 tumor types

Somatic copy number alterations (SCNAs), including deletions and duplications, serve as hallmarks of tumorigenesis. SCNAs may span entire chromosomes and typically result in deviations from an expected one-to-one ratio of alleles at heterozygous loci, leading to allelic imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation (CBS) algorithm, providing segment mean copy number estimates from Affymetrix single-nucleotide polymorphism DNA microarray total (log R ratio) intensities, but not allele-specific (\"B allele\") intensities that inform of AI. Here we seek to provide a TCGA-wide description of AI in tumor genomes, including AI induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH), using a powerful haplotype-based method applied to allele-specific intensities. We present AI summaries for all 33 tumor sites and propose an automated adjustment procedure to improve calibration of existing SCNA calls in TCGA for tumors with high levels of aneuploidy where baseline intensities were difficult to establish without annotation of AI. Overall, 94% of tumor samples exhibited AI. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p as well as mixed event types on 8p and 13q. The AI-based approach identified frequent cnLOH on 17p across multiple tumor sites, with additional site-specific cnLOH patterns. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.

genomics↗