Haplotype-based profiling of subtle allelic imbalance with SNP arrays

Genetic drivers and clinical consequences of mosaic chromosomal alterations in 1 million individuals

Mosaic chromosomal alterations of the autosomes (aut-mCAs) are large structural somatic mutations which cause clonal hematopoiesis and increase cancer risk. Here, we detected aut-mCAs in 1,011,269 participants across four biobanks. Through integrative analysis of the minimum critical region and inherited genetic variation, we found that proto-oncogenes exclusively drive chromosomal gains, tumor suppressors drive losses, and copy-neutral events can be driven by either. We identified three novel inherited risk loci in CHI3L2, HLA class II, and TERT that modulate aut-mCA risk and ten novel aut-mCA-specific loci. We found specific aut-mCAs are associated with cardiovascular, cerebrovascular, or kidney disease incidence. High-risk aut-mCAs were associated with elevated plasma protein levels of therapeutically actionable targets: NPM1, PARP1, and TACI. Participants with multiple high-risk features such as high clonal fraction, more than one aut-mCA, and abnormal red cell morphology had a 50% cumulative incidence of blood count abnormalities over 2 years. Leveraging inherited variation, we causally established aut-mCAs as premalignant lesions for chronic lymphocytic leukemia. Together, our findings provide a framework integrating somatic mosaicism, germline genetics, and clinical phenotypes to identify individuals who could benefit from preventative interventions.

Impact of cancer therapy on clonal hematopoiesis mutations and subsequent clinical outcomes

ABSTRACT

Exposure to cancer therapies is associated with an increased risk of clonal hematopoiesis (CH). The objective of our study was to investigate the genesis and evolution of CH after cancer therapy. In this prospective study, we undertook error-corrected duplex DNA sequencing in blood samples collected before and at 2 time points after chemoradiation in patients with esophageal or lung cancer recruited from 2013 to 2018. We applied a customized workflow to identify the earliest changes in CH mutation count and clone size and determine their association with clinical outcomes. Our study included 29 patients (87 samples). Their median age was 67 years, and 76% (n = 22) were male; the median follow-up period was 3.9 years. The most mutated genes were DNMT3A, TET2, TP53, and ASXL1. We observed a twofold increase in the number of mutations from before to after treatment in TP53, which differed from all other genes examined (P < .001). Among mutations detected before and after treatment, we observed an increased clone size in 38% and a decreased clone size in 5% of TP53 mutations (odds ratio, 3.7; 95% confidence interval [CI], 1.75-7.84; P < .001). Changes in mutation count and clone size were not observed in other genes. Individuals with an increase in the number of TP53 mutations after chemoradiation experienced shorter overall survival (hazard ratio, 7.07; 95% CI, 1.50-33.46; P = .014). In summary, we found an increase in the number and size of TP53 CH clones after chemoradiation that were associated with adverse clinical outcomes.

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

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.

A pan-tissue survey of mosaic chromosomal alterations in 948 individuals

Genetic mutations accumulate in an organism's body throughout its lifetime. While somatic single-nucleotide variants have been well characterized in the human body, the patterns and consequences of large chromosomal alterations in normal tissues remain largely unknown. Here, we present a pan-tissue survey of mosaic chromosomal alterations (mCAs) in 948 healthy individuals from the Genotype-Tissue Expression project, augmenting RNA-based allelic imbalance estimation with haplotype phasing. We found that approximately a quarter of the individuals carry a clonally-expanded mCA in at least one tissue, with incidence strongly correlated with age. The prevalence and genome-wide patterns of mCAs vary considerably across tissue types, suggesting tissue-specific mutagenic exposure and selection pressures. The mCA landscapes in normal adrenal and pituitary glands resemble those in tumors arising from these tissues, whereas the same is not true for the esophagus and skin. Together, our findings show a widespread age-dependent emergence of mCAs across normal human tissues with intricate connections to tumorigenesis.

Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions

While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)-present in some but not all cells-remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e-4), with recurrent somatic deletions of exons 1-5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5' deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk.

Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes

Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.

Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.

Chromosomal imbalances detected via RNA-sequencing in 28 cancers

MOTIVATION

RNA-sequencing (RNA-seq) of tumor tissue is typically only used to measure gene expression. Here, we present a statistical approach that leverages existing RNA-seq data to also detect somatic copy number alterations (SCNAs), a pervasive phenomenon in human cancers, without a need to sequence the corresponding DNA.

RESULTS

We present an analysis of 4942 participant samples from 28 cancers in The Cancer Genome Atlas (TCGA), demonstrating robust detection of SCNAs from RNA-seq. Using genotype imputation and haplotype information, our RNA-based method had a median sensitivity of 85% to detect SCNAs defined by DNA analysis, at high specificity (∼95%). As an example of translational potential, we successfully replicated SCNA features associated with breast cancer subtypes. Our results credential haplotype-based inference based on RNA-seq to detect SCNAs in clinical and population-based settings.

AVAILABILITY AND IMPLEMENTATION

The analyses presented use the data publicly available from TCGA Research Network (http://cancergenome.nih.gov/). See Methods for details regarding data downloads. hapLOHseq software is freely available under The MIT license and can be downloaded from http://scheet.org/software.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell genome-wide concurrent haplotyping and copy-number profiling through genotyping-by-sequencing

Single-cell whole-genome haplotyping allows simultaneous detection of haplotypes associated with monogenic diseases, chromosome copy-numbering and subsequently, has revealed mosaicism in embryos and embryonic stem cells. Methods, such as karyomapping and haplarithmisis, were deployed as a generic and genome-wide approach for preimplantation genetic testing (PGT) and are replacing traditional PGT methods. While current methods primarily rely on single-nucleotide polymorphism (SNP) array, we envision sequencing-based methods to become more accessible and cost-efficient. Here, we developed a novel sequencing-based methodology to haplotype and copy-number profile single cells. Following DNA amplification, genomic size and complexity is reduced through restriction enzyme digestion and DNA is genotyped through sequencing. This single-cell genotyping-by-sequencing (scGBS) is the input for haplarithmisis, an algorithm we previously developed for SNP array-based single-cell haplotyping. We established technical parameters and developed an analysis pipeline enabling accurate concurrent haplotyping and copy-number profiling of single cells. We demonstrate its value in human blastomere and trophectoderm samples as application for PGT for monogenic disorders. Furthermore, we demonstrate the method to work in other species through analyzing blastomeres of bovine embryos. Our scGBS method opens up the path for single-cell haplotyping of any species with diploid genomes and could make its way into the clinic as a PGT application.

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

Somatic copy number alterations (SCNAs) serve as hallmarks of tumorigenesis and often result in deviations from one-to-one allelic ratios at heterozygous loci, leading to allelic imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation algorithm, providing segment mean copy number estimates from single-nucleotide polymorphism DNA microarray total intensities (log R ratio), but not allele-specific intensities ("B allele" frequencies) that inform of AI. Our approach provides more sensitive identification of SCNAs by modeling the "B allele" frequencies jointly, thereby bolstering the catalog of chromosomal alterations in this widely utilized resource. Here we present AI summaries for all 33 tumor sites in TCGA, including those induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH). We identified AI in 94% of the tumors, higher than in previous reports. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p, as well as mixed event types on 8p and 13q. We also observed both site-specific and pan-cancer (spanning 17p) cnLOH, patterns which have not been comprehensively characterized. The identification of such cnLOH events elucidates tumor suppressors and multi-hit pathways to carcinogenesis. We also contrast the landscapes inferred from AI- and total intensity-derived SCNAs and propose an automated procedure to improve and adjust SCNAs in TCGA for cases where high levels of aneuploidy obscured baseline intensity identification. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.

Prevalence of cancer susceptibility variants in patients with multiple Lynch syndrome related cancers

Along with early-onset cancers, multiple primary cancers (MPCs) are likely resulting from increased genetic susceptibility; however, the associated predisposition genes or prevalence of the pathogenic variants genes in MPC patients are often unknown. We screened 71 patients with MPC of the stomach, colorectal, and endometrium, sequencing 65 cancer predisposition genes. A subset of 19 patients with early-onset MPC of stomach and colorectum were further evaluated for variants in cancer related genes using both normal and tumor whole exome sequencing. Among 71 patients with MPCs, variants classified to be pathogenic were observed in 15 (21.1%) patients and affected Lynch Syndrome (LS) genes: MLH1 (n = 10), MSH6 (n = 2), PMS2 (n = 2), and MSH2 (n = 1). All carriers had tumors with high microsatellite instability and 13 of them (86.7%) were early-onset, consistent with LS. In 19 patients with early-onset MPCs, loss of function (LoF) variants in RECQL5 were more prevalent in non-LS MPC than in matched sporadic cancer patients (OR = 31.6, 2.73–1700.6, p = 0.001). Additionally, there were high-confidence LoF variants at FANCG and CASP8 in two patients accompanied by somatic loss of heterozygosity in tumor, respectively. The results suggest that genetic screening should be considered for synchronous cancers and metachronous MPCs of the LS tumor spectrum, particularly in early-onset. Susceptibility variants in non-LS genes for MPC patients may exist, but evidence for their role is more elusive than for LS patients.

Deciphering the genetic and epidemiological landscape of mitochondrial DNA abundance

Mitochondrial (MT) dysfunction is a hallmark of aging and has been associated with most aging-related diseases as well as immunological processes. However, little is known about aging, lifestyle and genetic factors influencing mitochondrial DNA (mtDNA) abundance. In this study, mtDNA abundance was estimated from the weighted intensities of probes mapping to the MT genome in 295,150 participants from the UK Biobank. We found that the abundance of mtDNA was significantly elevated in women compared to men, was negatively correlated with advanced age, higher smoking exposure, greater body-mass index, higher frailty index as well as elevated red and white blood cell count and lower mortality. In addition, several biochemistry markers in blood-related to cholesterol metabolism, ion homeostasis and kidney function were found to be significantly associated with mtDNA abundance. By performing a genome-wide association study, we identified 50 independent regions genome-wide significantly associated with mtDNA abundance which harbour multiple genes involved in the immune system, cancer as well as mitochondrial function. Using mixed effects models, we estimated the SNP-heritability of mtDNA abundance to be around 8%. To investigate the consequence of altered mtDNA abundance, we performed a phenome-wide association study and found that mtDNA abundance is involved in risk for leukaemia, hematologic diseases as well as hypertension. Thus, estimating mtDNA abundance from genotyping arrays has the potential to provide novel insights into age- and disease-relevant processes, particularly those related to immunity and established mitochondrial functions.

Multiomics profiling of primary lung cancers and distant metastases reveals immunosuppression as a common characteristic of tumor cells with metastatic plasticity

BACKGROUND

Metastasis is the primary cause of cancer mortality accounting for 90% of cancer deaths. Our understanding of the molecular mechanisms driving metastasis is rudimentary.

RESULTS

We perform whole exome sequencing (WES), RNA sequencing, methylation microarray, and immunohistochemistry (IHC) on 8 pairs of non-small cell lung cancer (NSCLC) primary tumors and matched distant metastases. Furthermore, we analyze published WES data from 35 primary NSCLC and metastasis pairs, and transcriptomic data from 4 autopsy cases with metastatic NSCLC and one metastatic lung cancer mouse model. The majority of somatic mutations are shared between primary tumors and paired distant metastases although mutational signatures suggest different mutagenesis processes in play before and after metastatic spread. Subclonal analysis reveals evidence of monoclonal seeding in 41 of 42 patients. Pathway analysis of transcriptomic data reveals that downregulated pathways in metastases are mainly immune-related. Further deconvolution analysis reveals significantly lower infiltration of various immune cell types in metastases with the exception of CD4+ T cells and M2 macrophages. These results are in line with lower densities of immune cells and higher CD4/CD8 ratios in metastases shown by IHC. Analysis of transcriptomic data from autopsy cases and animal models confirms that immunosuppression is also present in extracranial metastases. Significantly higher somatic copy number aberration and allelic imbalance burdens are identified in metastases.

CONCLUSIONS

Metastasis is a molecularly late event, and immunosuppression driven by different molecular events, including somatic copy number aberration, may be a common characteristic of tumors with metastatic plasticity.

Inherited causes of clonal haematopoiesis in 97,691 whole genomes

Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown1. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer2-4 and coronary heart disease5-this phenomenon is termed clonal haematopoiesis of indeterminate potential (CHIP)6. Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.

Bayesian copy number detection and association in large-scale studies

BACKGROUND

Germline copy number variants (CNVs) increase risk for many diseases, yet detection of CNVs and quantifying their contribution to disease risk in large-scale studies is challenging due to biological and technical sources of heterogeneity that vary across the genome within and between samples.

METHODS

We developed an approach called CNPBayes to identify latent batch effects in genome-wide association studies involving copy number, to provide probabilistic estimates of integer copy number across the estimated batches, and to fully integrate the copy number uncertainty in the association model for disease.

RESULTS

Applying a hidden Markov model (HMM) to identify CNVs in a large multi-site Pancreatic Cancer Case Control study (PanC4) of 7598 participants, we found CNV inference was highly sensitive to technical noise that varied appreciably among participants. Applying CNPBayes to this dataset, we found that the major sources of technical variation were linked to sample processing by the centralized laboratory and not the individual study sites. Modeling the latent batch effects at each CNV region hierarchically, we developed probabilistic estimates of copy number that were directly incorporated in a Bayesian regression model for pancreatic cancer risk. Candidate associations aided by this approach include deletions of 8q24 near regulatory elements of the tumor oncogene MYC and of Tumor Suppressor Candidate 3 (TUSC3).

CONCLUSIONS

Laboratory effects may not account for the major sources of technical variation in genome-wide association studies. This study provides a robust Bayesian inferential framework for identifying latent batch effects, estimating copy number, and evaluating the role of copy number in heritable diseases.

Integrated case-control and somatic-germline interaction analyses of soft-tissue sarcoma

PURPOSE

The contribution of rare genetic variation in the development of soft-tissue sarcoma (STS) remains underexplored. To address this gap, we conducted a whole-exome case-control and somatic-germline interaction study to identify and characterise STS susceptible genes.

METHODS

The study involved 219 STS cases from The Cancer Genome Atlas and 3507 controls. All cases and controls were matched genetically onEuropean ancestry based on the 1000 Genomes project. Cross-platform technological stratification was performed with XPAT and gene-based association tests with VAAST 2.

RESULTS

NF1 exhibited the strongest genome-wide signal across the six subtypes, with p=1×10^-5. We also observed nominally significant association signals for three additional genes of interest, TP53 (p=0.0025), RB1 (p=0.0281), and MSH2 (p=0.0085). BAG1, which has not previously been implicated in STS, exhibited the strongest genome-wide signal after NF1, with p=6×10^-5. The association signals for NF1 and MSH2 were driven primarily by truncating variants, with ORs of 39 (95% CI: 7.1 to 220) for NF1 and 33 (95% CI: 2.4 to 460) for MSH2. In contrast, the association signals for RB1 and BAG1 were driven primarily by predicted damaging missense variants, with estimated ORs of 12 (95% CI: 2.4 to 59) for RB1 and 20 (95% CI: 1.4 to 300) for BAG1.

CONCLUSIONS

Our results confirm that pathogenic variants in NF1, RB1 and TP53 confer large increases in the risk of developing multiple STS subtypes, provide support for the role of MSH2 in STS susceptibility and identify BAG1 as a novel candidate STS risk gene.

Large-scale analysis of acquired chromosomal alterations in non-tumor samples from patients with cancer

Mosaicism, the presence of subpopulations of cells bearing somatic mutations, is associated with disease and aging and has been detected in diverse tissues, including apparently normal cells adjacent to tumors. To analyze mosaicism on a large scale, we surveyed haplotype-specific somatic copy number alterations (sCNAs) in 1,708 normal-appearing adjacent-to-tumor (NAT) tissue samples from 27 cancer sites and in 7,149 blood samples from The Cancer Genome Atlas. We find substantial variation across tissues in the rate, burden and types of sCNAs, including those spanning entire chromosome arms. We document matching sCNAs in the NAT tissue and the adjacent tumor, suggesting a shared clonal origin, as well as instances in which both NAT tissue and tumor tissue harbor a gain of the same oncogene arising in parallel from distinct parental haplotypes. These results shed light on pan-tissue mutations characteristic of field cancerization, the presence of oncogenic processes adjacent to cancer cells.

Immune Profiling of Premalignant Lesions in Patients With Lynch Syndrome

Importance

Colorectal carcinomas in patients with Lynch syndrome (LS) arise in a background of mismatch repair (MMR) deficiency, display a unique immune profile with upregulation of immune checkpoints, and response to immunotherapy. However, there is still a gap in understanding the pathogenesis of MMR-deficient colorectal premalignant lesions, which is essential for the development of novel preventive strategies for LS.

Objective

To characterize the immune profile of premalignant lesions from a cohort of patients with LS.

Design, Setting, and Participants

Whole-genome transcriptomic analysis using next-generation sequencing was performed in colorectal polyps and carcinomas of patients with LS. As comparator and model of MMR-proficient colorectal carcinogenesis, we used samples from patients with familial adenomatous polyposis (FAP). In addition, a total of 47 colorectal carcinomas (6 hypermutants and 41 nonhypermutants) were obtained from The Cancer Genome Atlas (TCGA) for comparisons. Samples were obtained from the University of Texas MD Anderson Cancer Center and "Regina Elena" National Cancer Institute, Rome, Italy. All diagnoses were confirmed by genetic testing. Polyps were collected at the time of endoscopic surveillance and tumors were collected at the time of surgical resection. The data were analyzed from October 2016 to November 2017.

Main Outcomes and Measures

Assessment of the immune profile, mutational signature, mutational and neoantigen rate, and pathway enrichment analysis of neoantigens in LS premalignant lesions and their comparison with FAP premalignant lesions, LS carcinoma, and sporadic colorectal cancers from TCGA.

Results

The analysis was performed in a total of 28 polyps (26 tubular adenomas and 2 hyperplastic polyps) and 3 early-stage LS colorectal tumors from 24 patients (15 [62%] female; mean [SD] age, 48.12 [15.38] years) diagnosed with FAP (n = 10) and LS (n = 14). Overall, LS polyps presented with low mutational and neoantigen rates but displayed a striking immune activation profile characterized by CD4 T cells, proinflammatory (tumor necrosis factor, interleukin 12) and checkpoint molecules (LAG3 [lymphocyte activation gene 3] and PD-L1 [programmed cell death 1 ligand 1]). This immune profile was independent of mutational rate, neoantigen formation, and MMR status. In addition, we identified a small subset of LS polyps with high mutational and neoantigen rates that were comparable to hypermutant tumors and displayed additional checkpoint (CTLA4 [cytotoxic T-lymphocyte-associated protein 4]) and neoantigens involved in DNA damage response (ATM and BRCA1 signaling).

Conclusions and Relevance

These findings challenge the canonical model, based on the observations made in carcinomas, that emphasizes a dependency of immune activation on the acquisition of high levels of mutations and neoantigens, thus opening the door to the implementation of immune checkpoint inhibitors and vaccines for cancer prevention in LS.

Driver Mutations in Normal Airway Epithelium Elucidate Spatiotemporal Resolution of Lung Cancer

Rationale: Uninvolved normal-appearing airway epithelium has been shown to exhibit specific mutations characteristic of nearby non-small cell lung cancers (NSCLCs). Yet, its somatic mutational landscape in patients with early-stage NSCLC is unknown.Objectives: To comprehensively survey the somatic mutational architecture of the normal airway epithelium in patients with early-stage NSCLC.Methods: Multiregion normal airways, comprising tumor-adjacent small airways, tumor-distant large airways, nasal epithelium and uninvolved normal lung (collectively airway field), matched NSCLCs, and blood cells (n = 498) from 48 patients were interrogated for somatic single-nucleotide variants by deep-targeted DNA sequencing and for chromosomal allelic imbalance events by genome-wide genotype array profiling. Spatiotemporal relationships between the airway field and NSCLCs were assessed by phylogenetic analysis.Measurements and Main Results: Genomic airway field carcinogenesis was observed in 25 cases (52%). The airway field epithelium exhibited a total of 269 somatic mutations in most patients (n = 36) including key drivers that were shared with the NSCLCs. Allele frequencies of these acquired variants were overall higher in NSCLCs. Integrative analysis of single-nucleotide variants and allelic imbalance events revealed driver genes with shared "two-hit" alterations in the airway field (e.g., TP53, KRAS, KEAP1, STK11, and CDKN2A) and those with single hits progressing to two in the NSCLCs (e.g., PIK3CA and NOTCH1).Conclusions: Tumor-adjacent and tumor-distant normal-appearing airway epithelia exhibit somatic driver alterations that undergo selection-driven clonal expansion in NSCLC. These events offer spatiotemporal insights into the development of NSCLC and, thus, potential targets for early treatment.

Directional allelic imbalance profiling and visualization from multi-sample data with RECUR

MOTIVATION

Genetic analysis of cancer regularly includes two or more samples from the same patient. Somatic copy number alterations leading to allelic imbalance (AI) play a critical role in cancer initiation and progression. Directional analysis and visualization of the alleles in imbalance in multi-sample settings allow for inference of recurrent mutations, providing insights into mutation rates, clonality and the genomic architecture and etiology of cancer.

RESULTS

The REpeat Chromosomal changes Uncovered by Reflection (RECUR) is an R application for the comparative analysis of AI profiles derived from SNP array and next-generation sequencing data. The algorithm accepts genotype calls and 'B allele' frequencies (BAFs) from at least two samples derived from the same individual. For a predefined set of genomic regions with AI, RECUR compares BAF values among samples. In the presence of AI, the expected value of a BAF can shift in two possible directions, reflecting an increased or decreased abundance of the maternal haplotype, relative to the paternal. The phenomenon of opposite haplotype shifts, or 'mirrored subclonal allelic imbalance', is a form of heterogeneity, and has been linked to clinico-pathological features of cancer. RECUR detects such genomic segments of opposite haplotypes in imbalance and plots BAF values for all samples, using a two-color scheme for intuitive visualization.

AVAILABILITY AND IMPLEMENTATION

RECUR is available as an R application. Source code and documentation are available at scheet.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genomic landscape of allelic imbalance in premalignant atypical adenomatous hyperplasias of the lung

Background

Genomic investigation of atypical adenomatous hyperplasia (AAH), the only known precursor lesion to lung adenocarcinomas (LUAD), presents challenges due to the low mutant cell fractions. This necessitates sensitive methods for detection of chromosomal aberrations to better study the role of critical alterations in early lung cancer pathogenesis and the progression from AAH to LUAD.

Methods

We applied a sensitive haplotype-based statistical technique to detect chromosomal alterations leading to allelic imbalance (AI) from genotype array profiling of 48 matched normal lung parenchyma, AAH and tumor tissues from 16 stage-I LUAD patients. To gain insights into shared developmental trajectories among tissues, we performed phylogenetic analyses and integrated our results with point mutation data, highlighting significantly-mutated driver genes in LUAD pathogenesis.

Findings

AI was detected in nine AAHs (56%). Six cases exhibited recurrent loss of 17p. AI and the enrichment of 17p events were predominantly identified in patients with smoking history. Among the nine AAH tissues with detected AI, seven exhibited evidence for shared chromosomal aberrations with matched LUAD specimens, including losses harboring tumor suppressors on 17p, 8p, 9p, 9q, 19p, and gains encompassing oncogenes on 8q, 12p and 1q.

Interpretation

Chromosomal aberrations, particularly 17p loss, appear to play critical roles early in AAH pathogenesis. Genomic instability in AAH, as well as truncal chromosomal aberrations shared with LUAD, provide evidence for mutation accumulation and are suggestive of a cancerized field contributing to the clonal selection and expansion of these premalignant lesions.

Fund

Supported in part by Cancer Prevention and Research Institute of Texas (CPRIT) grant RP150079 (PS and HK), NIH grant R01HG005859 (PS) and The University of Texas MD Anderson Cancer Center Core Support Grant.

System for Quality-Assured Data Analysis: Flexible, reproducible scientific workflows

The reproducibility of scientific processes is one of the paramount problems of bioinformatics, an engineering problem that must be addressed to perform good research. The System for Quality-Assured Data Analysis (SyQADA), described here, seeks to address reproducibility by managing many of the details of procedural bookkeeping in bioinformatics in as simple and transparent a manner as possible. SyQADA has been used by persons with backgrounds ranging from expert programmer to Unix novice, to perform and repeat dozens of diverse bioinformatics workflows on tens of thousands of samples, consuming over 80 CPU-months of computing on over 300,000 individual tasks of scores of projects on laptops, computer servers, and computing clusters. SyQADA is especially well-suited for paired-sample analyses found in cancer tumor-normal studies. SyQADA executable source code, documentation, tutorial examples, and workflows used in our lab is available from http://scheet.org/software.html.

Assessing inter-component heterogeneity of biphasic uterine carcinosarcomas

OBJECTIVE

Uterine carcinosarcoma (UCS) is a rare and aggressive form of uterine cancer. It is bi-phasic, exhibiting histological features of both malignant epithelial (carcinoma) and mesenchymal (sarcoma) elements, reflected in ambiguity in accepted treatment guidelines. We sought to study the genomic and transcriptomic profiles of these elements individually to gain further insights into the development of these tumors.

METHODS

We macro-dissected carcinomatous, sarcomatous, and normal tissues from formalin fixed paraffin embedded uterine samples of 10 UCS patients. Single nucleotide polymorphism microarrays, targeted DNA sequencing and whole-transcriptome RNA-sequencing were performed. Somatic chromosomal alterations (SCAs), point mutation and gene expression profiles were compared between carcinomatous and sarcomatous components.

RESULTS

In addition to TP53, other recurrently mutated genes harboring putative driver or loss-of-function mutations included PTEN, FBXW7, FGFR2, KRAS, PIK3CA and CTNNB1, genes known to be involved in UCS. Intra-patient somatic mutation and SCA profiles were highly similar between paired carcinoma and sarcoma samples. An epithelial-mesenchymal transition (EMT) signature tended to differentiate components, with EMT-like status more common in advanced-stage patients exhibiting higher inter-component SCA heterogeneity.

CONCLUSIONS

From DNA analysis, our results indicate a monoclonal disease origin for this cohort. Yet expression-derived EMT statuses of the carcinomatous and sarcomatous components were often discrepant, and advanced cases displayed greater genomic heterogeneity. Therefore, separately-profiled components of UCS tumors may better inform disease progression or potential.

Insights into clonal haematopoiesis from 8,342 mosaic chromosomal alterations

The selective pressures that shape clonal evolution in healthy individuals are largely unknown. Here we investigate 8,342 mosaic chromosomal alterations, from 50 kb to 249 Mb long, that we uncovered in blood-derived DNA from 151,202 UK Biobank participants using phase-based computational techniques (estimated false discovery rate, 6-9%). We found six loci at which inherited variants associated strongly with the acquisition of deletions or loss of heterozygosity in cis. At three such loci (MPL, TM2D3-TARSL2, and FRA10B), we identified a likely causal variant that acted with high penetrance (5-50%). Inherited alleles at one locus appeared to affect the probability of somatic mutation, and at three other loci to be objects of positive or negative clonal selection. Several specific mosaic chromosomal alterations were strongly associated with future haematological malignancies. Our results reveal a multitude of paths towards clonal expansions with a wide range of effects on human health.

Integrated case-control and somatic-germline interaction analyses of melanoma susceptibility genes

While a number of genes have been implicated in melanoma susceptibility, the role of protein-coding variation in melanoma development and progression remains underexplored. To better characterize the role of germline coding variation in melanoma, we conducted a whole-exome case-control and somatic-germline interaction study involving 322 skin cutaneous melanoma cases from The Cancer Genome Atlas and 3607 controls of European ancestry. We controlled for cross-platform technological stratification using XPAT and conducted gene-based association tests using VAAST 2. Four established melanoma susceptibility genes achieved nominal statistical significance, MC1R (p = .0014), MITF (p = .0165) BRCA2 (p = .0206), and MTAP (p = .0393). We also observed a suggestive association for FANCA (p = .002), a gene previously implicated in melanoma survival. The association signal for BRCA2 was driven primarily by likely gene disrupting (LGD) variants, with an Odds Ratio (OR) of 5.62 (95% Confidence Interval (CI) 1.03-30.1). In contrast, the association signals for MC1R and MITF were driven primarily by predicted pathogenic missense variants, with estimated ORs of 1.4 to 3.0 for MC1R and 4.1 for MITF. MTAP exhibited an excess of both LGD and predicted damaging missense variants among cases, with ORs of 5.62 and 3.72, respectively, although neither category was significant. For individuals with known or predicted damaging variants, age of disease onset was significantly lower for two of the four genes, MC1R (p = .005) and MTAP (p = .035). In an analysis of germline carrier status and overlapping copy number alterations, we observed no evidence to support a two-hit model of carcinogenesis in any of the four genes. Although MC1R carriers were represented proportionally among the four molecular tumor subtypes, these individuals accounted for 69% of ultraviolet (UV) radiation mutational signatures among triple-wild type tumors (p = .040), highlighting the increased sensitivity to UV exposure among individuals with loss-of-function variants in MC1R.

DNA isolation protocol effects on nuclear DNA analysis by microarrays, droplet digital PCR, and whole genome sequencing, and on mitochondrial DNA copy number estimation

Potential bias introduced during DNA isolation is inadequately explored, although it could have significant impact on downstream analysis. To investigate this in human brain, we isolated DNA from cerebellum and frontal cortex using spin columns under different conditions, and salting-out. We first analysed DNA using array CGH, which revealed a striking wave pattern suggesting primarily GC-rich cerebellar losses, even against matched frontal cortex DNA, with a similar pattern on a SNP array. The aCGH changes varied with the isolation protocol. Droplet digital PCR of two genes also showed protocol-dependent losses. Whole genome sequencing showed GC-dependent variation in coverage with spin column isolation from cerebellum. We also extracted and sequenced DNA from substantia nigra using salting-out and phenol / chloroform. The mtDNA copy number, assessed by reads mapping to the mitochondrial genome, was higher in substantia nigra when using phenol / chloroform. We thus provide evidence for significant method-dependent bias in DNA isolation from human brain, as reported in rat tissues. This may contribute to array "waves", and could affect copy number determination, particularly if mosaicism is being sought, and sequencing coverage. Variations in isolation protocol may also affect apparent mtDNA abundance.

Rapid and powerful detection of subtle allelic imbalance from exome sequencing data with hapLOHseq

Motivation: The detection of subtle genomic allelic imbalance events has many potential applications. For example, identifying cancer-associated allelic imbalanced regions in low tumor-cellularity samples or in low-proportion tumor subclones can be used for early cancer detection, prognostic assessment and therapeutic selection in cancer patients. We developed hapLOHseq for the detection of subtle allelic imbalance events from next-generation sequencing data.

Results: Our method identified events of 10 megabases or greater occurring in as little as 16% of the sample in exome sequencing data (at 80×) and 4% in whole genome sequencing data (at 30×), far exceeding the capabilities of existing software. We also found hapLOHseq to be superior at detecting large chromosomal changes across a series of pancreatic samples from TCGA.

Availability and Implementation:hapLOHseq is available at scheet.org/software, distributed under an open source MIT license.

Contact:pscheet@alum.wustl.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Genomic Landscape of Colorectal Mucosa and Adenomas

The molecular basis of the adenoma-to-carcinoma transition has been deduced using comparative analysis of genetic alterations observed through the sequential steps of intestinal carcinogenesis. However, comprehensive genomic analyses of adenomas and at-risk mucosa are still lacking. Therefore, our aim was to characterize the genomic landscape of colonic at-risk mucosa and adenomas. We analyzed the mutation profile and copy number changes of 25 adenomas and adjacent mucosa from 12 familial adenomatous polyposis patients using whole-exome sequencing and validated allelic imbalances (AI) in 37 adenomas using SNP arrays. We assessed for evidence of clonality and performed estimations on the proportions of driver and passenger mutations using a systems biology approach. Adenomas had lower mutational rates than did colorectal cancers and showed recurrent alterations in known cancer driver genes (APC, KRAS, FBXW7, TCF7L2) and AIs in chromosomes 5, 7, and 13. Moreover, 80% of adenomas had somatic alterations in WNT pathway genes. Adenomas displayed evidence of multiclonality similar to stage I carcinomas. Strong correlations between mutational rate and patient age were observed in at-risk mucosa and adenomas. Our data indicate that at least 23% of somatic mutations are present in at-risk mucosa prior to adenoma initiation. The genomic profiles of at-risk mucosa and adenomas illustrate the evolution from normal tissue to carcinoma via greater resolution of molecular changes at the inflection point of premalignant lesions. Furthermore, substantial genomic variation exists in at-risk mucosa before adenoma formation, and deregulation of the WNT pathway is required to foster carcinogenesis. Cancer Prev Res; 9(6); 417-27. ©2016 AACR.

Genomic Landscape Established by Allelic Imbalance in the Cancerization Field of a Normal Appearing Airway

Visually normal cells adjacent to, and extending from, tumors of the lung may carry molecular alterations characteristics of the tumor itself, an effect referred to as airway field of cancerization. This airway field has been postulated as a model for early events in lung cancer pathogenesis. Yet the genomic landscape of somatically acquired molecular alterations in airway epithelia of lung cancer patients has remained unknown. To begin to fill this void, we sought to comprehensively characterize the genomic architecture of chromosomal alterations inducing allelic imbalance (AI) in the airway field of the most common type of lung tumors, non-small cell lung cancer (NSCLC). To do so, we conducted a genome-wide survey of multiple spatially distributed normal-appearing airways, multiregion tumor specimens, and uninvolved normal tissues or blood from 45 patients with early-stage NSCLC. We detected alterations in airway epithelia from 22 patients, with an increased frequency in NSCLCs of squamous histology. Our data also indicated a spatial gradient of AI in samples at closer proximity to the NSCLC. Chromosome 9 displayed the highest levels of AI and comprised recurrent independent events. Furthermore, the airway field AI included oncogenic gains and tumor suppressor losses in known NSCLC drivers. Our results demonstrate that genome-wide AI is common in the airway field of cancerization, providing insights into early events in the pathogenesis of NSCLC that may comprise targets for early treatment and chemoprevention. Cancer Res; 76(13); 3676-83. ©2016 AACR.

Early Events in the Molecular Pathogenesis of Lung Cancer

The majority of cancer-related deaths in the United States and worldwide are attributed to lung cancer. There are more than 90 million smokers in the United States who represent a significant population at elevated risk for lung malignancy. In other epithelial tumors, it has been shown that if neoplastic lesions can be detected and treated at their intraepithelial stage, patient prognosis is significantly improved. Thus, new strategies to detect and treat lung preinvasive lesions are urgently needed in order to decrease the overwhelming public health burden of lung cancer. Limiting these advances is a poor knowledge of the earliest events that underlie lung cancer development and that would constitute markers and targets for early detection and prevention. This review summarizes the state of knowledge of human lung cancer pathogenesis and the molecular pathology of premalignant lung lesions, with a focus on the molecular premalignant field that associates with lung cancer development. Lastly, we highlight new approaches and models to study genome-wide alterations in human lung premalignancy in order to facilitate the discovery of new markers for early detection and prevention of this fatal disease. Cancer Prev Res; 9(7); 518-27. ©2016 AACR.

Extensive Hidden Genomic Mosaicism Revealed in Normal Tissue

Genomic mosaicism arising from post-zygotic mutation has recently been demonstrated to occur in normal tissue of individuals ascertained with varied phenotypes, indicating that detectable mosaicism may be less an exception than a rule in the general population. A challenge to comprehensive cataloging of mosaic mutations and their consequences is the presence of heterogeneous mixtures of cells, rendering low-frequency clones difficult to discern. Here we applied a computational method using estimated haplotypes to characterize mosaic megabase-scale structural mutations in 31,100 GWA study subjects. We provide in silico validation of 293 previously identified somatic mutations and identify an additional 794 novel mutations, most of which exist at lower aberrant cell fractions than have been demonstrated in previous surveys. These mutations occurred across the genome but in a nonrandom manner, and several chromosomes and loci showed unusual levels of mutation. Our analysis supports recent findings about the relationship between clonal mosaicism and old age. Finally, our results, in which we demonstrate a nearly 3-fold higher rate of clonal mosaicism, suggest that SNP-based population surveys of mosaic structural mutations should be conducted with haplotypes for optimal discovery.

Concurrent whole-genome haplotyping and copy-number profiling of single cells

Methods for haplotyping and DNA copy-number typing of single cells are paramount for studying genomic heterogeneity and enabling genetic diagnosis. Before analyzing the DNA of a single cell by microarray or next-generation sequencing, a whole-genome amplification (WGA) process is required, but it substantially distorts the frequency and composition of the cell's alleles. As a consequence, haplotyping methods suffer from error-prone discrete SNP genotypes (AA, AB, BB) and DNA copy-number profiling remains difficult because true DNA copy-number aberrations have to be discriminated from WGA artifacts. Here, we developed a single-cell genome analysis method that reconstructs genome-wide haplotype architectures as well as the copy-number and segregational origin of those haplotypes by employing phased parental genotypes and deciphering WGA-distorted SNP B-allele fractions via a process we coin haplarithmisis. We demonstrate that the method can be applied as a generic method for preimplantation genetic diagnosis on single cells biopsied from human embryos, enabling diagnosis of disease alleles genome wide as well as numerical and structural chromosomal anomalies. Moreover, meiotic segregation errors can be distinguished from mitotic ones.

Preliminary whole-exome sequencing reveals mutations that imply common tumorigenicity pathways in multiple endocrine neoplasia type 1 patients

BACKGROUND

Whole-exome sequencing studies have not established definitive somatic mutation patterns among patients with sporadic hyperparathyroidism (HPT). No sequencing has evaluated multiple endocrine neoplasia type 1 (MEN1)-related HPT. We sought to perform whole-exome sequencing in HPT patients to identify somatic mutations and associated biological pathways and tumorigenic networks.

METHODS

Whole-exome sequencing was performed on blood and tissue from HPT patients (MEN1 and sporadic) and somatic single nucleotide variants (SNVs) were identified. Stop-gain and stop-loss SNVs were analyzed with Ingenuity Pathways Analysis (IPA). Loss of heterozygosity (LOH) was also assessed.

RESULTS

Sequencing was performed on 4 MEN1 and 10 sporadic cases. Eighteen stop-gain/stop-loss SNV mutations were identified in 3 MEN1 patients. One complex network was identified on IPA: Cellular function and maintenance, tumor morphology, and cardiovascular disease (IPA score = 49). A nonsynonymous SNV of TP53 (lysine-to-glutamic acid change at codon 81) identified in a MEN1 patient was suggested to be a driver mutation (Cancer-specific High-throughput Annotation of Somatic Mutations; P = .002). All MEN1 and 3/10 sporadic specimens demonstrated LOH of chromosome 11.

CONCLUSION

Whole-exome sequencing revealed somatic mutations in MEN1 associated with a single tumorigenic network, whereas sporadic pathogenesis seemed to be more diverse. A somatic TP53 mutation was also identified. LOH of chromosome 11 was seen in all MEN1 and 3 of 10 sporadic patients.

Identification of allelic imbalance with a statistical model for subtle genomic mosaicism

Genetic heterogeneity in a mixed sample of tumor and normal DNA can confound characterization of the tumor genome. Numerous computational methods have been proposed to detect aberrations in DNA samples from tumor and normal tissue mixtures. Most of these require tumor purities to be at least 10-15%. Here, we present a statistical model to capture information, contained in the individual's germline haplotypes, about expected patterns in the B allele frequencies from SNP microarrays while fully modeling their magnitude, the first such model for SNP microarray data. Our model consists of a pair of hidden Markov models--one for the germline and one for the tumor genome--which, conditional on the observed array data and patterns of population haplotype variation, have a dependence structure induced by the relative imbalance of an individual's inherited haplotypes. Together, these hidden Markov models offer a powerful approach for dealing with mixtures of DNA where the main component represents the germline, thus suggesting natural applications for the characterization of primary clones when stromal contamination is extremely high, and for identifying lesions in rare subclones of a tumor when tumor purity is sufficient to characterize the primary lesions. Our joint model for germline haplotypes and acquired DNA aberration is flexible, allowing a large number of chromosomal alterations, including balanced and imbalanced losses and gains, copy-neutral loss-of-heterozygosity (LOH) and tetraploidy. We found our model (which we term J-LOH) to be superior for localizing rare aberrations in a simulated 3% mixture sample. More generally, our model provides a framework for full integration of the germline and tumor genomes to deal more effectively with missing or uncertain features, and thus extract maximal information from difficult scenarios where existing methods fail.

Accuracy evaluation of the unified P-value from combining correlated P-values

Meta-analysis methods that combine P-values into a single unified P-value are frequently employed to improve confidence in hypothesis testing. An assumption made by most meta-analysis methods is that the P-values to be combined are independent, which may not always be true. To investigate the accuracy of the unified P-value from combining correlated P-values, we have evaluated a family of statistical methods that combine: independent, weighted independent, correlated, and weighted correlated P-values. Statistical accuracy evaluation by combining simulated correlated P-values showed that correlation among P-values can have a significant effect on the accuracy of the combined P-value obtained. Among the statistical methods evaluated those that weight P-values compute more accurate combined P-values than those that do not. Also, statistical methods that utilize the correlation information have the best performance, producing significantly more accurate combined P-values. In our study we have demonstrated that statistical methods that combine P-values based on the assumption of independence can produce inaccurate P-values when combining correlated P-values, even when the P-values are only weakly correlated. Therefore, to prevent from drawing false conclusions during hypothesis testing, our study advises caution be used when interpreting the P-value obtained from combining P-values of unknown correlation. However, when the correlation information is available, the weighting-capable statistical method, first introduced by Brown and recently modified by Hou, seems to perform the best amongst the methods investigated.

Detecting statistical interaction between somatic mutational events and germline variation from next-generation sequence data

The two-hit model of carcinogenesis provides a valuable framework for understanding the role of DNA repair and tumor suppressor genes in cancer development and progression. Under this model, tumor development can initiate from a single somatic mutation in individuals that inherit an inactivating germline variant. Although the two-hit model can be an overgeneralization, the tendency for the pattern of somatic mutations to differ in cancer patients that inherit predisposition alleles is a signal that can be used to identify and validate germline susceptibility variants. Here, we present the Somatic-Germline Interaction (SGI) tool, which is designed to identify statistical interaction between germline variants and somatic mutational events from next-generation sequence data. SGI interfaces with rare-variant association tests and variant classifiers to identify candidate germline susceptibility variants from case-control sequencing data. SGI then analyzes tumor-normal pair next-generation sequence data to evaluate evidence for somatic-germline interaction in each gene or pathway using two tests: the Allelic Imbalance Rank Sum (AIRS) test and the Somatic Mutation Interaction Test (SMIT). AIRS tests for preferential allelic imbalance to evaluate whether somatic mutational events tend to amplify candidate germline variants. SMIT evaluates whether somatic point mutations and small indels occur more or less frequently than expected in the presence of candidate germline variants. Both AIRS and SMIT control for heterogeneity in the mutational process resulting from regional variation in mutation rates and inter-sample variation in background mutation rates. The SGI test combines AIRS and SMIT to provide a single, unified measure of statistical interaction between somatic mutational events and germline variation. We show that the tests implemented in SGI have high power with relatively modest sample sizes in a wide variety of scenarios. We demonstrate the utility of SGI to increase the power of rare variant association studies in cancer and to validate the potential role in cancer causation of germline susceptibility variants.