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Comparison of IGO- and OGO-derived lesions

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common malignancy, with little improvement in patient outcomes over the past decades. Recently, subtypes of pancreatic cancer with different prognoses have been elaborated; however, the inability to model these subtypes has precluded mechanistic investigation of their origins. Here, we present a xenotransplantation model of PDAC in which neoplasms originate from patient-derived organoids injected directly into murine pancreatic ducts. Our model enables distinction of the two main PDAC subtypes: intraepithelial neoplasms from this model progress in an indolent or invasive manner representing the classical or basal-like subtypes of PDAC, respectively. Parameters that influence PDAC subtype specification in this intraductal model include cell plasticity and hyperactivation of the RAS pathway. Finally, through intratumoral dissection and the direct manipulation of RAS gene dosage, we identify a suite of RAS-regulated secreted and membrane-bound proteins that may represent potential candidates for therapeutic intervention in patients with PDAC.Significance:Accurate modeling of the molecular subtypes of pancreatic cancer is crucial to facilitate the generation of effective therapies. We report the development of an intraductal organoid transplantation model of pancreatic cancer that models the progressive switching of subtypes, and identify stochastic and RAS-driven mechanisms that determine subtype specification.See related commentary by Pickering and Morton, p. 1448.This article is highlighted in the In This Issue feature, p. 1426

Confocal z-stack imaging of immunofluorescent (IF) images of mStrawberry-hT3 grafts 4 weeks after IGO transplantation

Supplementary Table S1 Survival times of IGO and OGO mice Supplementary Table S2 Characteristics of patient-derived organoids, including KRAS, TP53, SMAD4, CDKN2A mutation status and patient stage Supplementary Table S3 Summary of Survival Data, including engraftment rate, mean survival, metastatic frequency Supplementary Table S4 Primers for quantitative PCR

Method Used to Define the Thresholds for Fast- and Slow-Progressing IGO-Derived Tumors

Summary of downsampling study

A distinction between indolent and aggressive disease is a major challenge in diagnostics of prostate cancer. As genetic heterogeneity and complexity may influence clinical outcome, we have initiated studies on single tumor cell genomics. In this study, we demonstrate that sparse DNA sequencing of single-cell nuclei from prostate core biopsies is a rich source of quantitative parameters for evaluating neoplastic growth and aggressiveness. These include the presence of clonal populations, the phylogenetic structure of those populations, the degree of the complexity of copy-number changes in those populations, and measures of the proportion of cells with clonal copy-number signatures. The parameters all showed good correlation to the measure of prostatic malignancy, the Gleason score, derived from individual prostate biopsy tissue cores. Remarkably, a more accurate histopathologic measure of malignancy, the surgical Gleason score, agrees better with these genomic parameters of diagnostic biopsy than it does with the diagnostic Gleason score and related measures of diagnostic histopathology. This is highly relevant because primary treatment decisions are dependent upon the biopsy and not the surgical specimen. Thus, single-cell analysis has the potential to augment traditional core histopathology, improving both the objectivity and accuracy of risk assessment and inform treatment decisions.Significance: Genomic analysis of multiple individual cells harvested from prostate biopsies provides an indepth view of cell populations comprising a prostate neoplasm, yielding novel genomic measures with the potential to improve the accuracy of diagnosis and prognosis in prostate cancer. Cancer Res; 78(2); 348–58. ©2017 AACR.

A PDF file containing Supplementary case reports

Short-read sequencers provide highly accurate reads at very low cost. Unfortunately, short reads are often inadequate for important applications such as assembly in complex regions or phasing across distant heterozygous sites. In this study, we describe novel bench protocols and algorithms to obtain haplotype-phased sequence assemblies with ultra-low error for regions 10 kb and longer using short reads only. We accomplish this by imprinting each template strand from a target region with a dense and unique mutation pattern. The mutation process randomly and independently converts ∼50% of cytosines to uracils. Sequencing libraries are made from both mutated and unmutated templates. Using de Bruijn graphs and paired-end read information, we assemble each mutated template and use the unmutated library to correct the mutated bases. Templates are partitioned into two or more haplotypes, and the final haplotypes are assembled and corrected for residual template mutations and PCR errors. With sufficient template coverage, the final assemblies have per-base error rates below 10–9. We demonstrate this method on a four-member nuclear family, correctly assembling and phasing three genomic intervals, including the highly polymorphic HLA-B gene.

Replication of tandem repeats of simple sequence motifs, also known as microsatellites, is error prone and variable lengths frequently occur during population expansions. Therefore, microsatellite length variations could serve as markers for cancer. However, accurate error-free quantitation of microsatellite lengths is difficult with current methods because of a high error rate during amplification and sequencing. We have solved this problem by using partial mutagenesis to disrupt enough of the repeat structure so that it can replicate faithfully, yet not so much that the flanking regions cannot be reliably identified. In this work we use bisulfite mutagenesis to convert a C to a U, later read as T. Compared to untreated templates, we achieve three orders of magnitude reduction in the error rate per round of replication. By requiring two independent first copies of an initial template, we reach error rates below one in a million. We discuss potential clinical applications of this method.

Autism arises in high and low-risk families. De novo mutation contributes to autism incidence in low-risk families as there is a higher incidence in the affected of the simplex families than in their unaffected siblings. But the extent of contribution in low-risk families cannot be determined solely from simplex families as they are a mixture of low and high-risk. The rate of de novo mutation in nearly pure populations of high-risk families, the multiplex families, has not previously been rigorously determined. Moreover, rates of de novo mutation have been underestimated from studies based on low resolution microarrays and whole exome sequencing. Here we report on findings from whole genome sequence (WGS) of both simplex families from the Simons Simplex Collection (SSC) and multiplex families from the Autism Genetic Resource Exchange (AGRE). After removing the multiplex samples with excessive cell-line genetic drift, we find that the contribution of de novo mutation in multiplex is significantly smaller than the contribution in simplex. We use WGS to provide high resolution CNV profiles and to analyze more than coding regions, and revise upward the rate in simplex autism due to an excess of de novo events targeting introns. Based on this study, we now estimate that de novo events contribute to 52–67% of cases of autism arising from low risk families, and 30–39% of cases of all autism., Yoon, Munoz, et al. investigate the rate of de novo coding and non-coding variants in families with high- and low-risk for autism using whole-genome sequence data from collections of families with autism. They demonstrate that de novo intronic variants increase the risk of autism, that the contribution of de novo variants is significantly larger in low-risk families, and that de novo variants contribute to 30-39% of cases of all autism.

Tandem repeats of simple sequence motifs, also known as microsatellites, are abundant in the genome. Because their repeat structure makes replication error-prone, variant microsatellite lengths are often generated during germline and other somatic expansions. As such, microsatellite length variations can serve as markers for cancer. However, accurate error-free measurement of microsatellite lengths is difficult with current methods precisely because of this high error rate during amplification. We have solved this problem by using partial mutagenesis to disrupt enough of the repeat structure of initial templates so that their sequence lengths replicate faithfully. In this work, we use bisulfite mutagenesis to convert a C to a U, later read as T. Compared to untreated templates, we achieve three orders of magnitude reduction in the error rate per round of replication. By requiring agreement from two independent first copies of an initial template, we reach error rates below one in a million. We apply this method to a thousand microsatellite loci from the human genome, revealing microsatellite length distributions not observable without mutagenesis.

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common malignancy, with little improvement in patient outcomes over the past decades. Recently, subtypes of pancreatic cancer with different prognoses have been elaborated; however, the inability to model these subtypes has precluded mechanistic investigation of their origins. Here, we present a xenotransplantation model of PDAC in which neoplasms originate from patient-derived organoids injected directly into murine pancreatic ducts. Our model enables distinction of the two main PDAC subtypes: intraepithelial neoplasms from this model progress in an indolent or invasive manner representing the classical or basal-like subtypes of PDAC, respectively. Parameters that influence PDAC subtype specification in this intraductal model include cell plasticity and hyperactivation of the RAS pathway. Finally, through intratumoral dissection and the direct manipulation of RAS gene dosage, we identify a suite of RAS-regulated secreted and membrane-bound proteins that may represent potential candidates for therapeutic intervention in patients with PDAC. Significance: Accurate modeling of the molecular subtypes of pancreatic cancer is crucial to facilitate the generation of effective therapies. We report the development of an intraductal organoid transplantation model of pancreatic cancer that models the progressive switching of subtypes, and identify stochastic and RAS-driven mechanisms that determine subtype specification. See related commentary by Pickering and Morton, p. 1448. This article is highlighted in the In This Issue feature, p. 1426

Copy number alterations (CNAs) play an important role in molding the genomes of breast cancers and have been shown to be clinically useful for prognostic and therapeutic purposes. However, our knowledge of intra-tumoral genetic heterogeneity of this important class of somatic alterations is limited. Here, using single-cell sequencing, we comprehensively map out the facets of copy number alteration heterogeneity in a cohort of breast cancer tumors. Ou/var/www/html/elife/12-05-2020/backup/r analyses reveal: genetic heterogeneity of non-tumor cells (i.e. stroma) within the tumor mass; the extent to which copy number heterogeneity impacts breast cancer genomes and the importance of both the genomic location and dosage of sub-clonal events; the pervasive nature of genetic heterogeneity of chromosomal amplifications; and the association of copy number heterogeneity with clinical and biological parameters such as polyploidy and estrogen receptor negative status. Our data highlight the power of single-cell genomics in dissecting, in its many forms, intra-tumoral genetic heterogeneity of CNAs, the magnitude with which CNA heterogeneity affects the genomes of breast cancers, and the potential importance of CNA heterogeneity in phenomena such as therapeutic resistance and disease relapse., eLife digest Cells in the body remain healthy by tightly preventing and repairing random changes, or mutations, in their genetic material. In cancer cells, however, these mechanisms can break down. When these cells grow and multiply, they can then go on to accumulate many mutations. As a result, cancer cells in the same tumor can each contain a unique combination of genetic changes. This genetic heterogeneity has the potential to affect how cancer responds to treatment, and is increasingly becoming appreciated clinically. For example, if a drug only works against cancer cells carrying a specific mutation, any cells lacking this genetic change will keep growing and cause a relapse. However, it is still difficult to quantify and understand genetic heterogeneity in cancer. Copy number alterations (or CNAs) are a class of mutation where large and small sections of genetic material are gained or lost. This can result in cells that have an abnormal number of copies of the genes in these sections. Here, Baslan et al. set out to explore how CNAs might vary between individual cancer cells within the same tumor. To do so, thousands of individual cancer cells were isolated from human breast tumors, and a technique called single-cell genome sequencing used to screen the genetic information of each of them. These experiments confirmed that CNAs did differ – sometimes dramatically – between patients and among cells taken from the same tumor. For example, many of the cells carried extra copies of well-known cancer genes important for treatment, but the exact number of copies varied between cells. This heterogeneity existed for individual genes as well as larger stretches of DNA: this was the case, for instance, for an entire section of chromosome 8, a region often affected in breast and other tumors. The work by Baslan et al. captures the sheer extent of genetic heterogeneity in cancer and in doing so, highlights the power of single-cell genome sequencing. In the future, a finer understanding of the genetic changes present at the level of an individual cancer cell may help clinicians to manage the disease more effectively.

PURPOSE Copy-number profiling of multiple individual cells from sparse sequencing may be used to reveal a detailed picture of genomic heterogeneity and clonal organization in a tissue biopsy specimen. We sought to provide a comprehensive computational pipeline for single-cell genomics, to facilitate adoption of this molecular technology for basic and translational research. MATERIALS AND METHODS The pipeline comprises software tools programmed in Python and in R and depends on Bowtie, HISAT2, Matplotlib, and Qt. It is installed and used with Anaconda. RESULTS Here we describe a complete pipeline for sparse single-cell genomic data, encompassing all steps of single-nucleus DNA copy-number profiling, from raw sequence processing to clonal structure analysis and visualization. For the latter, a specialized graphical user interface termed the single-cell genome viewer (SCGV) is provided. With applications to cancer diagnostics in mind, the SCGV allows for zooming and linkage to the University of California at Santa Cruz Genome Browser from each of the multiple integrated views of single-cell copy-number profiles. The latter can be organized by clonal substructure or by any of the associated metadata such as anatomic location and histologic characterization. CONCLUSION The pipeline is available as open-source software for Linux and OS X. Its modular structure, extensive documentation, and ease of deployment using Anaconda facilitate its adoption by researchers and practitioners of single-cell genomics. With open-source availability and Massachusetts Institute of Technology licensing, it provides a basis for additional development by the cancer bioinformatics community.

High levels of cancer aneuploidy are frequently associated with poor prognosis. To examine the relationship between aneuploidy and cancer progression, we analyzed a series of congenic cell lines that harbor single extra chromosomes. We found that across 13 different trisomic cell lines, 12 trisomies suppressed invasiveness or were largely neutral, while a single trisomy increased metastatic behavior by triggering a partial epithelial-mesenchymal transition. In contrast, we discovered that chromosomal instability activates cGAS/STING signaling but strongly suppresses invasiveness. By analyzing patient copy-number data, we demonstrate that specific aneuploidies are associated with distinct outcomes, and the acquisition of certain aneuploidies is in fact linked with a favorable prognosis. Thus, aneuploidy is not a uniform driver of malignancy, and different aneuploidies can uniquely influence tumor progression. At the same time, the gain of a single chromosome is capable of inducing a profound cell state transition, thereby linking genomic plasticity, phenotypic plasticity, and metastasis.

Measuring minimal residual disease in cancer has applications for prognosis, monitoring treatment and detection of recurrence. Simple sequence-based methods to detect nucleotide substitution variants have error rates (about 10−3) that limit sensitive detection. We developed and characterized the performance of MASQ (multiplex accurate sensitive quantitation), a method with an error rate below 10−6. MASQ counts variant templates accurately in the presence of millions of host genomes by using tags to identify each template and demanding consensus over multiple reads. Since the MASQ protocol multiplexes 50 target loci, we can both integrate signal from multiple variants and capture subclonal response to treatment. Compared to existing methods for variant detection, MASQ achieves an excellent combination of sensitivity, specificity and yield. We tested MASQ in a pilot study in acute myeloid leukemia (AML) patients who entered complete remission. We detect leukemic variants in the blood and bone marrow samples of all five patients, after induction therapy, at levels ranging from 10−2 to nearly 10−6. We observe evidence of sub-clonal structure and find higher target variant frequencies in patients who go on to relapse, demonstrating the potential for MASQ to quantify residual disease in AML.

We show the use of 5′-Acrydite oligonucleotides to copolymerize single-cell DNA or RNA into balls of acrylamide gel (BAGs). Combining this step with split-and-pool techniques for creating barcodes yields a method with advantages in cost and scalability, depth of coverage, ease of operation, minimal cross-contamination, and efficient use of samples. We perform DNA copy number profiling on mixtures of cell lines, nuclei from frozen prostate tumors, and biopsy washes. As applied to RNA, the method has high capture efficiency of transcripts and sufficient consistency to clearly distinguish the expression patterns of cell lines and individual nuclei from neurons dissected from the mouse brain. By using varietal tags (UMIs) to achieve sequence error correction, we show extremely low levels of cross-contamination by tracking source-specific SNVs. The method is readily modifiable, and we will discuss its adaptability and diverse applications.

Significance Genetics is a major determining factor in autism spectrum disorder (ASD). To date, only the most severe class of de novo mutation, likely gene disruptive (LGD), has been correlated with IQ, a phenotypic characteristic associated with ASD, but not a core feature. A less severe class of de novo mutation, missense, while enriched in individuals with ASD, has been refractory to correlation with any ASD phenotypic feature. In this report, we demonstrate that de novo LGD and missense mutations scored by target gene vulnerability both show significant associations with diminished motor skills., In individuals with autism spectrum disorder (ASD), de novo mutations have previously been shown to be significantly correlated with lower IQ but not with the core characteristics of ASD: deficits in social communication and interaction and restricted interests and repetitive patterns of behavior. We extend these findings by demonstrating in the Simons Simplex Collection that damaging de novo mutations in ASD individuals are also significantly and convincingly correlated with measures of impaired motor skills. This correlation is not explained by a correlation between IQ and motor skills. We find that IQ and motor skills are distinctly associated with damaging mutations and, in particular, that motor skills are a more sensitive indicator of mutational severity than is IQ, as judged by mutational type and target gene. We use this finding to propose a combined classification of phenotypic severity: mild (little impairment of either), moderate (impairment mainly to motor skills), and severe (impairment of both IQ and motor skills).

A distinction between indolent and aggressive disease is a major challenge in diagnostics of prostate cancer. As genetic heterogeneity and complexity may influence clinical outcome, we have initiated studies on single tumor cell genomics. In this study, we demonstrate that sparse DNA sequencing of single-cell nuclei from prostate core biopsies is a rich source of quantitative parameters for evaluating neoplastic growth and aggressiveness. These include the presence of clonal populations, the phylogenetic structure of those populations, the degree of the complexity of copy-number changes in those populations, and measures of the proportion of cells with clonal copy-number signatures. The parameters all showed good correlation to the measure of prostatic malignancy, the Gleason score, derived from individual prostate biopsy tissue cores. Remarkably, a more accurate histopathologic measure of malignancy, the surgical Gleason score, agrees better with these genomic parameters of diagnostic biopsy than it does with the diagnostic Gleason score and related measures of diagnostic histopathology. This is highly relevant because primary treatment decisions are dependent upon the biopsy and not the surgical specimen. Thus, single-cell analysis has the potential to augment traditional core histopathology, improving both the objectivity and accuracy of risk assessment and inform treatment decisions. Significance: Genomic analysis of multiple individual cells harvested from prostate biopsies provides an indepth view of cell populations comprising a prostate neoplasm, yielding novel genomic measures with the potential to improve the accuracy of diagnosis and prognosis in prostate cancer. Cancer Res; 78(2); 348–58. ©2017 AACR.

MotivationEffective and efficient exploration of numeric data and annotations as a function of genomic position requires specialized software.ResultsWe present G-Graph, an interactive genomic scatter plot viewer. G-Graph stacks or tiles multiple data series in one graph using different colors and markers. It displays gene annotation and other metadata, allows easy changes to the appearance of data series, implements stack-based undo functionality, and saves user-selected application views as image and pdf files. G-Graph delivers smooth and rapid scrolling and zooming even for datasets with millions of points and line segments. The primary target user is a researcher examining many copy number profiles to identify potentially deleterious variants. G-Graph runs under Linux, Mac OSX and Windows.Availabilityhttps://github.com/docpaa/mumdex/ or https://mumdex.com/ggraph/Contactandrewsp@cshl.edu (or paa@drpa.us)

Most human tumors display chromosome-scale copy number alterations, and high levels of aneuploidy are frequently associated with advanced disease and poor patient prognosis. To examine the relationship between aneuploidy and cancer progression, we generated and analyzed a series of congenic human cell lines that harbor single extra chromosomes. We find that different aneuploidies can have distinct effects on invasive behavior: across 13 different cell lines, 12 trisomies suppressed invasiveness or were largely neutral, while a single trisomy increased metastatic behavior by triggering a partial epithelial-mesenchymal transition. In contrast, chromosomal instability, which can lead to the development of aneuploidy, uniformly suppressed cellular invasion. By analyzing genomic copy number and survival data from 10,133 cancer patients, we demonstrate that specific aneuploidies are associated with distinct clinical outcomes, and the acquisition of certain aneuploidies is in fact linked with a favorable prognosis. Thus, aneuploidy is not a uniform driver of malignancy, and different chromosome copy number changes can uniquely influence tumor progression. At the same time, the gain of a single chromosome is capable of inducing a profound cell state transition, underscoring how genomic plasticity can engender phenotypic plasticity and lead to the acquisition of enhanced metastatic properties.

We develop a method of analysis [affected to discordant sibling pairs (A2DS)] that tests if shared variants contribute to a disorder. Using a standard measure of genetic relation, test individuals are compared with a cohort of discordant sibling pairs (CDS) to derive a comparative similarity score. We ask if a test individual is more similar to an unrelated affected than to the unrelated unaffected sibling from the CDS and then, sum over such individuals and pairs. Statistical significance is judged by randomly permuting the affected status in the CDS. In the analysis of published genotype data from the Simons Simplex Collection (SSC) and the Autism Genetic Resource Exchange (AGRE) cohorts of children with autism spectrum disorder (ASD), we find strong statistical significance that the affected are more similar to the affected than to the unaffected of the CDS (P value ∼ 0.00001). Fathers in multiplex families have marginally greater similarity (P value = 0.02) to unrelated affected individuals. These results do not depend on ethnic matching or gender.

We introduce a new protocol, mutational sequencing or muSeq, which randomly deaminates unmethylated cytosines at a fixed and tunable rate. The muSeq protocol marks each initial template molecule with a unique mutation signature that is present in every copy of the template, and in every fragmented copy of a copy. In the sequenced read data, this signature is observed as a unique pattern of C-to-T or G-to-A nucleotide conversions. Clustering reads with the same conversion pattern enables accurate count and long-range assembly of initial template molecules from short-read sequence data. We explore count and low-error sequencing by profiling a 135,000 fragment PstI representation, demonstrating that muSeq improves copy number inference and significantly reduces sporadic sequencer error. We explore long-range assembly in the context of cDNA, generating contiguous transcript clusters greater than 3,000 bp in length. The muSeq assemblies reveal transcriptional diversity not observable from short-read data alone.

SummaryIn individuals with Autism Spectrum Disorder (ASD), de novo mutations have previously been shown to be significantly correlated with lower IQ, but not with the core characteristics of ASD: deficits in social communication and interaction, and restricted interests and repetitive patterns of behavior. We extend these findings by demonstrating in the Simons Simplex Collection that damaging de novo mutations in ASD individuals are also significantly and convincingly correlated with measures of impaired motor skills. This correlation is not explained by a correlation between IQ and motor skills. We find that IQ and motor skills are distinctly associated with damaging mutations and, in particular, that motor skills are a more sensitive indicator of mutational severity, as judged by the type and its gene target. We use this finding to propose a combined classification of phenotypic severity: mild (little impairment of both), moderate (impairment mainly to motor skills) and severe (impairment of both).

Here, we explore the potential of single-cell genomic analysis in blood for early detection of cancer; we consider a method that screens the presence of recurrent patterns of copy number (CN) alterations using sparse single-cell sequencing. We argue for feasibility, based on in silico analysis of existing single-cell data and cancer CN profiles. Sampling procedures from existing diploid single cells can render data for a cell with any given profile. Sampling from multiple published tumor profiles can interrogate cancer clonality via an algorithm that tests the multiplicity of close pairwise similarities among single-cell cancer genomes. The majority of common solid cancers would be detectable in this manner. As any early detection method must be verifiable and actionable, we describe how further analysis of suspect cells can aid in determining risk and anatomic origin. Future affordability rests on currently available procedures for tumor cell enrichment and inexpensive methods for single-cell analysis.

Whole exome sequencing has proven to be a powerful tool for understanding the genetic architecture of human disease. Here we apply it to more than 2,500 simplex families, each having a child with an autistic spectrum disorder. By comparing affected to unaffected siblings, we show that 13% of de novo missense mutations and 43% of de novo likely gene-disrupting (LGD) mutations contribute to 12% and 9% of diagnoses, respectively. Including copy number variants, coding de novo mutations contribute to about 30% of all simplex and 45% of female diagnoses. Almost all LGD mutations occur opposite wild-type alleles. LGD targets in affected females significantly overlap the targets in males of lower intelligence quotient (IQ), but neither overlaps significantly with targets in males of higher IQ. We estimate that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation. LGD targets in the joint class overlap with published targets for intellectual disability and schizophrenia, and are enriched for chromatin modifiers, FMRP-associated genes and embryonically expressed genes. Most of the significance for the latter comes from affected females. © 2014 Macmillan Publishers Limited. All rights reserved., National Institutes of Health: P30CA016359 National Institutes of Health: R01MH101221 National Institutes of Health: RC2HL102923 National Institutes of Health: RC2HL102924 National Institutes of Health: RC2HL102925 National Institutes of Health: RC2HL102926 National Institutes of Health: RC2HL103010 National Institutes of Health: T32GM007266 National Institutes of Health: U54HD083091 National Institutes of Health: UC2HL102923 National Institutes of Health: UC2HL102924 National Institutes of Health: UC2HL102925 National Institutes of Health: UC2HL102926 National Institutes of Health: UC2HL103010 National Institutes of Health: UL1TR000142

We present an open-source algorithm, Scalpel (http://scalpel.sourceforge.net/), which combines mapping and assembly for sensitive and specific discovery of insertions and deletions (indels) in exome-capture data. A detailed repeat analysis coupled with a self-tuning k-mer strategy allows Scalpel to outperform other state-of-the-art approaches for indel discovery, particularly in regions containing near-perfect repeats. We analyzed 593 families from the Simons Simplex Collection and demonstrated Scalpel's power to detect long (≥30 bp) transmitted events and enrichment for de novo likely gene-disrupting indels in autistic children.

The identification of the genetic components of autism spectrum disorders (ASDs) has advanced rapidly in recent years, particularly with the demonstration of de novo mutations as an important source of causality. We review these developments in light of genetic models for ASDs. We consider the number of genetic loci that underlie ASDs and the relative contributions from different mutational classes, and we discuss possible mechanisms by which these mutations might lead to dysfunction. We update the two-class risk genetic model for autism, especially in regard to children with high intelligence quotients.

We present an open-source web platform, Ginkgo (http://qb.cshl.edu/ginkgo), for the analysis and assessment of single-cell copy-number variations (CNVs). Ginkgo automatically constructs copy-number profiles of cells from mapped reads and constructs phylogenetic trees of related cells. We validate Ginkgo by reproducing the results of five major studies and examine the characteristics of three commonly used single-cell amplification techniques to conclude degenerate oligonucleotide-primed PCR to be the most consistent for CNV analysis.

MotivationStandard genome sequence alignment tools primarily designed to find one alignment per read have difficulty detecting inversion, translocation and large insertion and deletion (indel) events. Moreover, dedicated split read alignment methods that depend only upon the reference genome may misidentify or find too many potential split read alignments because of reference genome anomalies.MethodsWe introduce MUMdex, a Maximal Unique Match (MUM)-based genomic analysis software package consisting of a sequence aligner to the reference genome, a storage-indexing format and analysis software. Discordant reference alignments of MUMs are especially suitable for identifying inversion, translocation and large indel differences in unique regions. Extracted population databases are used as filters for flaws in the reference genome. We describe the concepts underlying MUM-based analysis, the software implementation and its usage.ResultsWe demonstrate via simulation that the MUMdex aligner and alignment format are able to correctly detect and record genomic events. We characterize alignment performance and output file sizes for human whole genome data and compare to Bowtie 2 and the BAM format. Preliminary results demonstrate the practicality of the analysis approach by detecting de novo mutation candidates in human whole genome DNA sequence data from 510 families. We provide a population database of events from these families for use by others.Availabilityhttp://mumdex.com/Contactandrewsp@cshl.edu (or paa@drpa.us)Supplementary informationSupplementary data are available online.

Although heritable factors are an important determinant of risk of early-onset cancer, the majority of these malignancies appear to occur sporadically without identifiable risk factors. Germline de novo copy-number variations (CNVs) have been observed in sporadic neurocognitive and cardiovascular disorders. We explored this mechanism in 382 genomes of 116 early-onset cancer case-parent trios and unaffected siblings. Unique de novo germline CNVs were not observed in 107 breast or colon cancer trios or controls but were indeed found in 7% of 43 testicular germ cell tumor trios; this percentage exceeds background CNV rates and suggests a rare de novo genetic paradigm for susceptibility to some human malignancies.

SummaryExome sequencing of 343 families, each with a single child on the autism spectrum and at least one unaffected sibling, reveal de novo small indels and point substitutions, which come mostly from the paternal line in an age-dependent manner. We do not see significantly greater numbers of de novo missense mutations in affected versus unaffected children, but gene-disrupting mutations (nonsense, splice site, and frame shifts) are twice as frequent, 59 to 28. Based on this differential and the number of recurrent and total targets of gene disruption found in our and similar studies, we estimate between 350 and 400 autism susceptibility genes. Many of the disrupted genes in these studies are associated with the fragile X protein, FMRP, reinforcing links between autism and synaptic plasticity. We find FMRP-associated genes are under greater purifying selection than the remainder of genes and suggest they are especially dosage-sensitive targets of cognitive disorders.

Genomic analysis provides insights into the role of copy number variation in disease, but most methods are not designed to resolve mixed populations of cells. In tumours, where genetic heterogeneity is common, very important information may be lost that would be useful for reconstructing evolutionary history. Here we show that with flow-sorted nuclei, whole genome amplification and next generation sequencing we can accurately quantify genomic copy number within an individual nucleus. We apply single-nucleus sequencing to investigate tumour population structure and evolution in two human breast cancer cases. Analysis of 100 single cells from a polygenomic tumour revealed three distinct clonal subpopulations that probably represent sequential clonal expansions. Additional analysis of 100 single cells from a monogenomic primary tumour and its liver metastasis indicated that a single clonal expansion formed the primary tumour and seeded the metastasis. In both primary tumours, we also identified an unexpectedly abundant subpopulation of genetically diverse 'pseudodiploid' cells that do not travel to the metastatic site. In contrast to gradual models of tumour progression, our data indicate that tumours grow by punctuated clonal expansions with few persistent intermediates.

Cancer progression in humans is difficult to infer because we do not routinely sample patients at multiple stages of their disease. However, heterogeneous breast tumors provide a unique opportunity to study human tumor progression because they still contain evidence of early and intermediate subpopulations in the form of the phylogenetic relationships. We have developed a method we call Sector-Ploidy-Profiling (SPP) to study the clonal composition of breast tumors. SPP involves macro-dissecting tumors, flow-sorting genomic subpopulations by DNA content, and profiling genomes using comparative genomic hybridization (CGH). Breast carcinomas display two classes of genomic structural variation: (1) monogenomic and (2) polygenomic. Monogenomic tumors appear to contain a single major clonal subpopulation with a highly stable chromosome structure. Polygenomic tumors contain multiple clonal tumor subpopulations, which may occupy the same sectors, or separate anatomic locations. In polygenomic tumors, we show that heterogeneity can be ascribed to a few clonal subpopulations, rather than a series of gradual intermediates. By comparing multiple subpopulations from different anatomic locations, we have inferred pathways of cancer progression and the organization of tumor growth.

2010 April 6, Short hairpin RNAs (shRNAs) capable of stably suppressing gene function by RNA interference (RNAi) can mimic tumor-suppressor-gene loss in mice. By selecting for shRNAs capable of accelerating lymphomagenesis in a well-characterized mouse lymphoma model, we identified over ten candidate tumor suppressors, including Sfrp1, Numb, Mek1, and Angiopoietin 2. Several components of the DNA damage response machinery were also identified, including Rad17, which acts as a haploinsufficient tumor suppressor that responds to oncogenic stress and whose loss is associated with poor prognosis in human patients. Our results emphasize the utility of in vivo RNAi screens, identify and validate a diverse set of tumor suppressors, and have therapeutic implications.

We examined copy number changes in the genomes of B cells from 58 patients with chronic lymphocytic leukemia (CLL) by using representational oligonucleotide microarray analysis (ROMA), a form of comparative genomic hybridization (CGH), at a resolution exceeding previously published studies. We observed at least 1 genomic lesion in each CLL sample and considerable variation in the number of abnormalities from case to case. Virtually all abnormalities previously reported also were observed here, most of which were indeed highly recurrent. We observed the boundaries of known events with greater clarity and identified previously undescribed lesions, some of which were recurrent. We profiled the genomes of CLL cells separated by the surface marker CD38 and found evidence of distinct subclones of CLL within the same patient. We discuss the potential applications of high-resolution CGH analysis in a clinical setting.

Deletions on chromosome 8p are common in human tumors, suggesting that one or more tumor suppressor genes reside in this region. Deleted in Liver Cancer 1 (DLC1) encodes a Rho-GTPase activating protein and is a candidate 8p tumor suppressor. We show that DLC1 knockdown cooperates with Myc to promote hepatocellular carcinoma in mice, and that reintroduction of wild-type DLC1 into hepatoma cells with low DLC1 levels suppresses tumor growth in situ. Cells with reduced DLC1 protein contain increased GTP-bound RhoA, and enforced expression a constitutively activated RhoA allele mimics DLC1 loss in promoting hepatocellular carcinogenesis. Conversely, down-regulation of RhoA selectively inhibits tumor growth of hepatoma cells with disabled DLC1. Our data validate DLC1 as a potent tumor suppressor gene and suggest that its loss creates a dependence on the RhoA pathway that may be targeted therapeutically.

Autism is among the most clearly genetically determined of all cognitive-developmental disorders, with males affected more often than females. We have analyzed autism risk in multiplex families from the Autism Genetic Resource Exchange (AGRE) and find strong evidence for dominant transmission to male offspring. By incorporating generally accepted rates of autism and sibling recurrence, we find good fit for a simple genetic model in which most families fall into two types: a small minority for whom the risk of autism in male offspring is near 50%, and the vast majority for whom male offspring have a low risk. We propose an explanation that links these two types of families: sporadic autism in the low-risk families is mainly caused by spontaneous mutation with high penetrance in males and relatively poor penetrance in females; and high-risk families are from those offspring, most often females, who carry a new causative mutation but are unaffected and in turn transmit the mutation in dominant fashion to their offspring.

Copy number variants (CNVs) underlie a significant amount of genetic diversity and disease. CNVs can be detected by a number of means, including chromosomal microarray analysis (CMA) and whole-genome sequencing (WGS), but these approaches suffer from either limited resolution (CMA) or are highly expensive for routine screening (both CMA and WGS). As an alternative, we have developed a next-generation sequencing-based method for CNV analysis termed SMASH, for short multiply aggregated sequence homologies. SMASH utilizes random fragmentation of input genomic DNA to create chimeric sequence reads, from which multiple mappable tags can be parsed using maximal almost-unique matches (MAMs). The SMASH tags are then binned and segmented, generating a profile of genomic copy number at the desired resolution. Because fewer reads are necessary relative to WGS to give accurate CNV data, SMASH libraries can be highly multiplexed, allowing large numbers of individuals to be analyzed at low cost. Increased genomic resolution can be achieved by sequencing to higher depth.