Genomic data resources of the Brain Somatic Mosaicism Network for neuropsychiatric diseases.

Somatic mosaicism is defined as an occurrence of two or more populations of cells having genomic sequences differing at given loci in an individual who is derived from a single zygote. It is a characteristic of multicellular organisms that plays a crucial role in normal development and disease. To study the nature and extent of somatic mosaicism in autism spectrum disorder, bipolar disorder, focal cortical dysplasia, schizophrenia, and Tourette syndrome, a multi-institutional consortium called the Brain Somatic Mosaicism Network (BSMN) was formed through the National Institute of Mental Health (NIMH). In addition to genomic data of affected and neurotypical brains, the BSMN also developed and validated a best practices somatic single nucleotide variant calling workflow through the analysis of reference brain tissue. These resources, which include >400 terabytes of data from 1087 subjects, are now available to the research community via the NIMH Data Archive (NDA) and are described here.

Efficient reconstruction of cell lineage trees for cell ancestry and cancer.

Mosaic mutations can be used to track cell ancestries and reconstruct high-resolution lineage trees during cancer progression and during development, starting from the first cell divisions of the zygote. However, this approach requires sampling and analyzing the genomes of multiple cells, which can be redundant in lineage representation, limiting the scalability of the approach. We describe a strategy for cost- and time-efficient lineage reconstruction using clonal induced pluripotent stem cell lines from human skin fibroblasts. The approach leverages shallow sequencing coverage to assess the clonality of the lines, clusters redundant lines and sums their coverage to accurately discover mutations in the corresponding lineages. Only a fraction of lines needs to be sequenced to high coverage. We demonstrate the effectiveness of this approach for reconstructing lineage trees during development and in hematologic malignancies. We discuss and propose an optimal experimental design for reconstructing lineage trees.

Control-independent mosaic single nucleotide variant detection with DeepMosaic.

Mosaic variants (MVs) reflect mutagenic processes during embryonic development and environmental exposure, accumulate with aging and underlie diseases such as cancer and autism. The detection of noncancer MVs has been computationally challenging due to the sparse representation of nonclonally expanded MVs. Here we present DeepMosaic, combining an image-based visualization module for single nucleotide MVs and a convolutional neural network-based classification module for control-independent MV detection. DeepMosaic was trained on 180,000 simulated or experimentally assessed MVs, and was benchmarked on 619,740 simulated MVs and 530 independent biologically tested MVs from 16 genomes and 181 exomes. DeepMosaic achieved higher accuracy compared with existing methods on biological data, with a sensitivity of 0.78, specificity of 0.83 and positive predictive value of 0.96 on noncancer whole-genome sequencing data, as well as doubling the validation rate over previous best-practice methods on noncancer whole-exome sequencing data (0.43 versus 0.18). DeepMosaic represents an accurate MV classifier for noncancer samples that can be implemented as an alternative or complement to existing methods.

Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development.

Malformations of cortical development (MCD) are neurological conditions involving focal disruptions of cortical architecture and cellular organization that arise during embryogenesis, largely from somatic mosaic mutations, and cause intractable epilepsy. Identifying the genetic causes of MCD has been a challenge, as mutations remain at low allelic fractions in brain tissue resected to treat condition-related epilepsy. Here we report a genetic landscape from 283 brain resections, identifying 69 mutated genes through intensive profiling of somatic mutations, combining whole-exome and targeted-amplicon sequencing with functional validation including in utero electroporation of mice and single-nucleus RNA sequencing. Genotype-phenotype correlation analysis elucidated specific MCD gene sets associated with distinct pathophysiological and clinical phenotypes. The unique single-cell level spatiotemporal expression patterns of mutated genes in control and patient brains indicate critical roles in excitatory neurogenic pools during brain development and in promoting neuronal hyperexcitability after birth.

Analysis of somatic mutations in 131 human brains reveals aging-associated hypermutability.

We analyzed 131 human brains (44 neurotypical, 19 with Tourette syndrome, 9 with schizophrenia, and 59 with autism) for somatic mutations after whole genome sequencing to a depth of more than 200×. Typically, brains had 20 to 60 detectable single-nucleotide mutations, but ~6% of brains harbored hundreds of somatic mutations. Hypermutability was associated with age and damaging mutations in genes implicated in cancers and, in some brains, reflected in vivo clonal expansions. Somatic duplications, likely arising during development, were found in ~5% of normal and diseased brains, reflecting background mutagenesis. Brains with autism were associated with mutations creating putative transcription factor binding motifs in enhancer-like regions in the developing brain. The top-ranked affected motifs corresponded to MEIS (myeloid ectopic viral integration site) transcription factors, suggesting a potential link between their involvement in gene regulation and autism.

Comprehensive identification of somatic nucleotide variants in human brain tissue.

BACKGROUND: Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. RESULTS: Here, the Brain Somatic Mosaicism Network conducts a coordinated, multi-institutional study to examine the ability of existing methods to detect simulated somatic single-nucleotide variants (SNVs) in DNA mixing experiments, generate multiple replicates of whole-genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual, devise strategies to discover somatic SNVs, and apply various approaches to validate somatic SNVs. These efforts lead to the identification of 43 bona fide somatic SNVs that range in variant allele fractions from ~ 0.005 to ~ 0.28. Guided by these results, we devise best practices for calling mosaic SNVs from 250× whole-genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrate that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. CONCLUSIONS: This study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

SCELLECTOR: ranking amplification bias in single cells using shallow sequencing.

BACKGROUND: The study of mosaic mutation is important since it has been linked to cancer and various disorders. Single cell sequencing has become a powerful tool to study the genome of individual cells for the detection of mosaic mutations. The amount of DNA in a single cell needs to be amplified before sequencing and multiple displacement amplification (MDA) is widely used owing to its low error rate and long fragment length of amplified DNA. However, the phi29 polymerase used in MDA is sensitive to template fragmentation and presence of sites with DNA damage that can lead to biases such as allelic imbalance, uneven coverage and over representation of C to T mutations. It is therefore important to select cells with uniform amplification to decrease false positives and increase sensitivity for mosaic mutation detection. RESULTS: We propose a method, Scellector (single cell selector), which uses haplotype information to detect amplification quality in shallow coverage sequencing data. We tested Scellector on single human neuronal cells, obtained in vitro and amplified by MDA. Qualities were estimated from shallow sequencing with coverage as low as 0.3× per cell and then confirmed using 30× deep coverage sequencing. The high concordance between shallow and high coverage data validated the method. CONCLUSION: Scellector can potentially be used to rank amplifications obtained from single cell platforms relying on a MDA-like amplification step, such as Chromium Single Cell profiling solution.

Complex mosaic structural variations in human fetal brains.

Somatic mosaicism, manifesting as single nucleotide variants (SNVs), mobile element insertions, and structural changes in the DNA, is a common phenomenon in human brain cells, with potential functional consequences. Using a clonal approach, we previously detected 200-400 mosaic SNVs per cell in three human fetal brains (15-21 wk postconception). However, structural variation in the human fetal brain has not yet been investigated. Here, we discover and validate four mosaic structural variants (SVs) in the same brains and resolve their precise breakpoints. The SVs were of kilobase scale and complex, consisting of deletion(s) and rearranged genomic fragments, which sometimes originated from different chromosomes. Sequences at the breakpoints of these rearrangements had microhomologies, suggesting their origin from replication errors. One SV was found in two clones, and we timed its origin to ∼14 wk postconception. No large scale mosaic copy number variants (CNVs) were detectable in normal fetal human brains, suggesting that previously reported megabase-scale CNVs in neurons arise at later stages of development. By reanalysis of public single nuclei data from adult brain neurons, we detected an extrachromosomal circular DNA event. Our study reveals the existence of mosaic SVs in the developing human brain, likely arising from cell proliferation during mid-neurogenesis. Although relatively rare compared to SNVs and present in ∼10% of neurons, SVs in developing human brain affect a comparable number of bases in the genome (∼6200 vs. ∼4000 bp), implying that they may have similar functional consequences.

Molecular characterization of colorectal adenomas with and without malignancy reveals distinguishing genome, transcriptome and methylome alterations.

The majority of colorectal cancer (CRC) arises from precursor lesions known as polyps. The molecular determinants that distinguish benign from malignant polyps remain unclear. To molecularly characterize polyps, we utilized Cancer Adjacent Polyp (CAP) and Cancer Free Polyp (CFP) patients. CAPs had tissues from the residual polyp of origin and contiguous cancer; CFPs had polyp tissues matched to CAPs based on polyp size, histology and dysplasia. To determine whether molecular features distinguish CAPs and CFPs, we conducted Whole Genome Sequencing, RNA-seq, and RRBS on over 90 tissues from 31 patients. CAPs had significantly more mutations, altered expression and hypermethylation compared to CFPs. APC was significantly mutated in both polyp groups, but mutations in TP53, FBXW7, PIK3CA, KIAA1804 and SMAD2 were exclusive to CAPs. We found significant expression changes between CAPs and CFPs in GREM1, IGF2, CTGF, and PLAU, and both expression and methylation alterations in FES and HES1. Integrative analyses revealed 124 genes with alterations in at least two platforms, and ERBB3 and E2F8 showed aberrations specific to CAPs across all platforms. These findings provide a resource of molecular distinctions between polyps with and without cancer, which have the potential to enhance the diagnosis, risk assessment and management of polyps.

Inferring modes of evolution from colorectal cancer with residual polyp of origin.

Besides the classical evolutionary model of colorectal cancer (CRC) defined by the stepwise accumulation of mutations in which normal epithelium transforms through an intermediary polyp stage to cancer, a few studies have proposed alternative modes of evolution (MOE): early eruptive subclonal expansion, branching of the subclones in parallel evolution, and neutral evolution. However, frequencies of MOEs and their connection to mutational characteristics of cancer remain elusive. In this study, we analyzed patterns of somatic single nucleotide variations (SNVs) and copy number aberrations (CNAs) in CRC with residual polyp of origin from 13 patients in order to determine this relationship. For each MOE we defined an expected pattern with characteristic features of allele frequency distributions for SNVs in cancers and their matching adenomas. From these distinct patterns, we then assigned an MOE to each CRC case and found that stepwise progression was the most common (70% of cases). We found that CRC with the same MOE may exhibit different mutational spectra, suggesting that different mutational mechanisms can result in the same MOE. Inversely, cancers with different MOEs can have the same mutational spectrum, suggesting that the same mutational mechanism can lead to different MOEs. The types of somatic substitutions, distribution of CNAs across genome, and mutated pathways did not correlate with MOEs. As this could be due to small sample size, these relations warrant further investigation. Our study paves the way to connect MOE with clinical and mutational characteristics not only in CRC but also to neoplastic transformation in other cancers.

Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis.

Somatic mosaicism in the human brain may alter function of individual neurons. We analyzed genomes of single cells from the forebrains of three human fetuses (15 to 21 weeks postconception) using clonal cell populations. We detected 200 to 400 single-nucleotide variations (SNVs) per cell. SNV patterns resembled those found in cancer cell genomes, indicating a role of background mutagenesis in cancer. SNVs with a frequency of >2% in brain were also present in the spleen, revealing a pregastrulation origin. We reconstructed cell lineages for the first five postzygotic cleavages and calculated a mutation rate of ~1.3 mutations per division per cell. Later in development, during neurogenesis, the mutation spectrum shifted toward oxidative damage, and the mutation rate increased. Both neurogenesis and early embryogenesis exhibit substantially more mutagenesis than adulthood.

Colorectal Cancer with Residual Polyp of Origin: A Model of Malignant Transformation.

The majority of colorectal cancers (CRCs) arise from adenomatous polyps. In this study, we sought to present the underrecognized CRC with the residual polyp of origin (CRC RPO+) as an entity to be utilized as a model to study colorectal carcinogenesis. We identified all subjects with biopsy-proven CRC RPO+ that were evaluated over 10 years at Mayo Clinic, Rochester, MN, and compared their clinical and pathologic characteristics to CRC without remnant polyps (CRC RPO-). Overall survival and disease-free survival overlap with an equivalent hazard ratio between CRC RPO+ and RPO- cases when age, stage, and grade are adjusted. The somatic genomic profile obtained by whole genome sequencing and the gene expression profiles by RNA-seq for CRC RPO+ tumors were compared with that of age -and gender-matched CRC RPO- evaluated by The Cancer Genome Atlas. CRC RPO+ cases were more commonly found with lower-grade, earlier-stage disease than CRC RPO-. However, within the same disease stage and grade, their clinical course is very similar to that of CRC RPO-. The mutation frequencies of commonly mutated genes in CRC are similar between CRC RPO+ and RPO- cases. Likewise, gene expression patterns are indistinguishable between the RPO+ and RPO- cases. We have confirmed that CRC RPO+ is clinically and biologically similar to CRC RPO- and may be utilized as a model of the adenoma to carcinoma transition.

Restoration of paclitaxel resistance by CDK1 intervention in drug-resistant ovarian cancer.

Epithelial ovarian cancer (EOC) commonly acquires resistance to chemotherapy, and this is the major obstacle to the better prognosis. Elucidating the molecular targets altered by chemotherapy is critically required to understand and overcome drug resistance. As a drug combination including paclitaxel is a prevalent prescription for treatment of EOC, to uncover gene expression altered in paclitaxel-resistant EOC, we analyzed multidirectional microarray profiles in both EOC cell lines and patients with paclitaxel resistance. Cyclin-dependent kinase 1 (CDK1) was found to be a potential target of transcription factors to regulate paclitaxel resistance. As a result of the subsequent pharmacogenomics analysis, CDK1 inhibitor alsterpaullone was also indicated as a promising chemical that may be used in combinatorial therapies to reverse paclitaxel-induced chemoresistance. Although a CDK1 inhibitor has the potential to kill cancer cells, short-term treatment over 2 weeks at sublethal doses effectively induced cell death only upon additional treatment with paclitaxel. A prominent reduction in the tumor growth rate was observed upon paclitaxel subsequent to alsterpaullone treatment in EOC xenograft model. Thus, we suggest that inhibition of CDK1 with alsterpaullone may be a novel therapeutic method to reverse paclitaxel-induced resistance in ovarian cancer cells.

Transcriptome analysis indicates TFEB1 and YEATS4 as regulatory transcription factors for drug resistance of ovarian cancer.

Ovarian cancer is an intractable disease because patients with ovarian cancer frequently develop drug resistance after long-term chemotherapy. Despite the availability of cumulative information on drug-resistant patients, strategies to reverse drug resistance have still not been established. In this study, we analyzed drug resistance-associated transcription factors (TFs) in ovarian cancer. Gene expression profiles of 15 drug-resistant and 11 drug-sensitive patients with ovarian cancer were compared. Our results showed that TFs TFEB1 and YEATS4 regulated the expression of downstream target genes. These 2 TFs have already been implicated in tumorigenesis or metastasis. To our knowledge, this is the first study to evaluate the involvement of these TFs in drug resistance of ovarian cancer. Interestingly, 70% knockdown of each of these TFs with siRNAs resulted in approximately 20%~30% recovery of drug sensitivity. Further, combination treatment of ovarian cancer cells with TFEB1 and YEATS4 siRNAs resulted in 35% reversal of drug resistance. The effect of these TFs on chemoresistance seemed to be associated with intrinsic apoptosis-related pathways, such as p53 activation, and not with the suppression of drug transport. Thus, we suggest a novel approach to reverse chemoresistance of ovarian cancer by suppressing TFEB1 and YEATS4.

Identification of upstream regulators for prognostic expression signature genes in colorectal cancer.

BACKGROUND: Gene expression signatures have been commonly used as diagnostic and prognostic markers for cancer subtyping. However, expression signatures frequently include many passengers, which are not directly related to cancer progression. Their upstream regulators such as transcription factors (TFs) may take a more critical role as drivers or master regulators to provide better clues on the underlying regulatory mechanisms and therapeutic applications. RESULTS: In order to identify prognostic master regulators, we took the known 85 prognostic signature genes for colorectal cancer and inferred their upstream TFs. To this end, a global transcriptional regulatory network was constructed with total >200,000 TF-target links using the ARACNE algorithm. We selected the top 10 TFs as candidate master regulators to show the highest coverage of the signature genes among the total 846 TF-target sub-networks or regulons. The selected TFs showed a comparable or slightly better prognostic performance than the original 85 signature genes in spite of greatly reduced number of marker genes from 85 to 10. Notably, these TFs were selected solely from inferred regulatory links using gene expression profiles and included many TFs regulating tumorigenic processes such as proliferation, metastasis, and differentiation. CONCLUSIONS: Our network approach leads to the identification of the upstream transcription factors for prognostic signature genes to provide leads to their regulatory mechanisms. We demonstrate that our approach could identify upstream biomarkers for a given set of signature genes with markedly smaller size and comparable performances. The utility of our method may be expandable to other types of signatures such as diagnosis and drug response.

Identification of master regulator candidates in conjunction with network screening and inference.

We developed a procedure for identifying transcriptional Master Regulators (MRs) related to special biological phenomena, such as diseases, in conjunction with network screening and inference. Network screening is a system for detecting activated transcriptional regulatory networks under particular conditions, based on the estimation of graph structure consistency with the measured data. Since network screening utilises the known Transcriptional Factor (TF)-gene relationships as the experimental evidence for molecular relationships, its performance depends on the ensemble of known TF networks used for its analysis. To compensate for its restrictions, a network inference method, the path consistency algorithm, is concomitantly utilised to identify MRs. The performance is illustrated by means of the known MRs in brain tumours that were computationally inferred and experimentally verified. As a result, the present procedure worked well for identifying MRs, in comparison to the previous computational selection for experimental verification.

Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug.

BACKGROUND: The process of drug discovery and development is time-consuming and costly, and the probability of success is low. Therefore, there is rising interest in repositioning existing drugs for new medical indications. When successful, this process reduces the risk of failure and costs associated with de novo drug development. However, in many cases, new indications of existing drugs have been found serendipitously. Thus there is a clear need for establishment of rational methods for drug repositioning. RESULTS: In this study, we have established a database we call "PharmDB" which integrates data associated with disease indications, drug development, and associated proteins, and known interactions extracted from various established databases. To explore linkages of known drugs to diseases of interest from within PharmDB, we designed the Shared Neighborhood Scoring (SNS) algorithm. And to facilitate exploration of tripartite (Drug-Protein-Disease) network, we developed a graphical data visualization software program called phExplorer, which allows us to browse PharmDB data in an interactive and dynamic manner. We validated this knowledge-based tool kit, by identifying a potential application of a hypertension drug, benzthiazide (TBZT), to induce lung cancer cell death. CONCLUSIONS: By combining PharmDB, an integrated tripartite database, with Shared Neighborhood Scoring (SNS) algorithm, we developed a knowledge platform to rationally identify new indications for known FDA approved drugs, which can be customized to specific projects using manual curation. The data in PharmDB is open access and can be easily explored with phExplorer and accessed via BioMart web service (http://www.i-pharm.org/, http://biomart.i-pharm.org/).