Project description:Identifying genes where a variant allele is preferentially expressed in tumors could lead to a better understanding of cancer biology and optimization of targeted therapy. However, tumor sample heterogeneity complicates standard approaches for detecting preferential allele expression. We therefore developed a novel approach combining genome and transcriptome sequencing data from the same sample that corrects for sample heterogeneity and identifies significant preferentially expressed alleles. We applied this analysis to epithelial ovarian cancer samples consisting of matched primary ovary and peritoneum and lymph node metastasis. We find that preferentially expressed variant alleles include germline and somatic variants, are shared at a relatively high frequency between patients and are in gene networks known to be involved in cancer processes. Analysis at a patient level identifies patient-specific preferentially expressed alleles in genes that are targets for known drugs. Analysis at a site level identifies patterns of site specific preferential allele expression with similar pathways being impacted in the primary and metastasis sites. We conclude that genes with preferentially expressed variant alleles can act as cancer drivers and that targeting those alleles could lead to new therapeutic strategies. Three cancer patients, three tumor samples per patient from different sites, two normal tissue samples from two different patients, four cell lines.
Project description:Identifying genes where a variant allele is preferentially expressed in tumors could lead to a better understanding of cancer biology and optimization of targeted therapy. However, tumor sample heterogeneity complicates standard approaches for detecting preferential allele expression. We therefore developed a novel approach combining genome and transcriptome sequencing data from the same sample that corrects for sample heterogeneity and identifies significant preferentially expressed alleles. We applied this analysis to epithelial ovarian cancer samples consisting of matched primary ovary and peritoneum and lymph node metastasis. We find that preferentially expressed variant alleles include germline and somatic variants, are shared at a relatively high frequency between patients and are in gene networks known to be involved in cancer processes. Analysis at a patient level identifies patient-specific preferentially expressed alleles in genes that are targets for known drugs. Analysis at a site level identifies patterns of site specific preferential allele expression with similar pathways being impacted in the primary and metastasis sites. We conclude that genes with preferentially expressed variant alleles can act as cancer drivers and that targeting those alleles could lead to new therapeutic strategies.
Project description:RNA sequencing and other experimental methods producing large amounts of data are increasingly dominant in molecular biology. However, the noise properties of these techniques are not fully appreciated. We assessed how reproducible are the measurements of allele-specific expression between replicate RNA-seq experiments from the same RNA sample. Surprisingly, estimates of allelic imbalance (AI) varied between technical replicates up to 8-fold higher than expected from commonly applied noise models. We show that AI overdispersion substantially varies between replicates and experimental series, appears to arise during the construction of sequencing libraries, and can be measured by comparing technical replicates. We demonstrate that compensation for AI overdispersion greatly reduces technical variation and enables reliable differential analysis of allele-specific expression across samples and across experiments. Conversely, not taking AI overdispersion into account can lead to a substantial number of false positives in analysis of allele-specific gene expression.
Project description:KRAS mutations in renal cell carcinoma (RCC) are rare and are associated with the histologic subtype "papillary renal neoplasm with reverse polarity" (PRNRP). Despite this association, the broader clinical and molecular implications of KRAS mutation in RCC are not well understood. This study aimed to characterize the clinical and molecular features of KRAS-mutated RCC. KRAS-mutant RCC patients were identified within the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) and The Cancer Genome Atlas Kidney Renal Papillary Cell Carcinoma (TCGA-KIRP) repository. Fraction and allele-specific copy number estimates from tumor sequencing (FACETS) was performed to obtain copy number alterations. Available samples were used for immunohistochemistry and RNA-sequencing analysis. Seventeen patients were included. Three distinct KRAS-mutant RCC subtypes were identified: KRAS-mutant PRCC (35%), KRAS-mutant URCC (35%), and PRNRP (29%). Seven patients (41%) had metastatic disease; none were PRNRP. RNAseq-based deconvolution analysis revealed that PRNRP had enrichment in distal nephron components, whereas KRAS-mutant PRCC was enriched in proximal tubule cells (p = 0.02). IHC staining of L1CAM was positive in PRNRP but negative in KRAS-mutant PRCC, supporting their distinct cell-of-origin phenotypes. This study is limited by its cohort size, which influences the availability of tissue samples. PRNRP represents a distinct KRAS-mutant RCC subtype with unique metabolic and genomic features linked to its distal nephron origin. This contrasts with the genomic complexity and aggressive clinical behavior observed in KRAS-mutant PRCC and URCC, highlighting the need for subtype-specific diagnostic criteria and therapeutic strategies.
Project description:Inborn errors of immunity (IEIs) are genetic disorders that underlie susceptibility to infection, autoimmunity, autoinflammation, allergy, and/or malignancy. Incomplete penetrance is common among IEIs despite their monogenic basis. Herein we investigate the contribution of autosomal random monoallelic expression (aRMAE), a somatic commitment to expression of one allele, to phenotypic variability observed in families with IEIs. Using a clonal primary T cell system to assess aRMAE status of genes in healthy individuals, we find 4.30% of IEI genes and 5.20% of all genes undergo aRMAE. Perturbing H3K27me3 and DNA methylation alters allele expression commitment, supporting two proposed mechanisms for regulation of aRMAE. We tested PBMCs from individuals sharing genetic lesions but with discordant clinical phenotypes in seven families with different IEIs for aRMAE. Among relatives heterozygous for a mutation in PLCG2, an antibody deficiency phenotype corresponds with selective mutant allele expression in B cells. In contrast, among relatives heterozygous for a mutation in JAK1, the unaffected carrier T cells predominantly expressed the wild-type JAK1 allele, while the affected carrier T cells exhibited biallelic expression. Selective expression of a single allele was documented in phenotypically discordant family members carrying an AD Loss-of-function (LoF) mutation in STAT1, CARD11, as well as gain-of-function (GoF) mutations in STAT1. This study highlights the importance of not only considering genotype but also the “transcriptotype” in the analyses of the penetrance and expressivity of monogenic disorders.
Project description:Investigation of non-synonymous mutation as a major driver for cancer is essential for discovery of biomarkers and therapeutics. Although, many cancer-associated mutations have been identified through genomics, there is a poor understanding of how they manifest in the proteome. In fact, detection of mutation derived peptides is confounded by technical and biological factors. In this study cancer associated mutations from the COSMIC database were integrated into proteins, to generate cell line specific sequence databases. We used a robust workflow for high-throughput processing of 375 cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE) to identify mutant peptides and generate PRIDE compatible mzTab results. This includes a peptide to genome mapping tool, PepGenome, allowing targeted mapping of mutant peptides. Examining the allelic paired non-mutant reference peptides, we were able to profile significantly more potential mutant peptides and determine allelic biases in the expression and observation of mutant peptides. We present quantification, genomic mapping, and evaluation of 1,336 cancer associated mutant peptides and infer protein allelic bias for 31,219 mutations in. We highlight the problems faced in detecting and characterising mutant peptides by mass spectrometry and show that allelic bias plays a significant role in suppressing the expression of mutant proteoforms.
Project description:This study describes the systematic transcriptomic and expression and interaction proteomic analysis of isogenic HCT116 colorectal cancer cells with either mutant CTNNB1/Beta-catenin allele disrupted or wild-type CTNNB1/Beta-catenin allele disrupted.