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Supplementary Table S22: Segmented b-allele deficits

XLSX file 451K, Somatic mutations identified by whole exome sequencing

Supplementary Tables S16-S19, describing IHC results on NHS/HPFS data

Summary of clinically informative (TARGET) genes.

Supplementary Methods, Figures S1 - S20. Figure S1. Branched evolution leads to tissue-sampling bias in primary tumor samples. Figure S2. Results of ABSOLUTE on samples from patient 418. Figure S3. 2D Bayesian clustering analysis of point-mutation CCF distributions in case 418. Figure S4. Bayesian clustering of private point-mutation CCF distributions in all sequenced tissue samples from case 418. Figure S5. Genetic alterations supporting phylogeny construction in case 418. Figure S6. Phylogenetic tree for case 418 Figure S7. Evolutionary relationships between primary tumor-samples and brain metastasis samples Figure S8. Detection of homozygous deletion in CDKN2A in the brain metastasis of case 24. Figure S9. Amplification of FGFR1 and MYC detected in the brain metastasis of case 331. Figure S10. Additional alterations under investigation for association with various targeted therapies. Figure S11. Power for paired-detection of somatic mutations. Figure S12. Amplification of CCNE1 detected in the brain metastasis of case 314. Figure S13. Amplification of EGFR detected in the brain metastasis of case 314. Figure S14. Amplification of MYC detected in the brain metastasis of case 308. Figure S15. Amplification of MYC detected in the brain metastasis of case 138. Figure S16. Amplifications of CDK6 and MET detected in the brain metastasis of case 138. Figure S17. Amplifications of CCNE1 and AKT2 detected in the brain metastasis of case 138. Figure S18. Amplification of EGFR detected in a regional lymph node from case 296. Figure S19. Power for somatic mutation detection in 86 matched primary-tumor and brain-metastasis samples. Figure S20. Calling of amplifications in primary-tumor samples and paired brain metastases.

Supplementary File 1. Power for detection of somatic point mutations in coding exons of TARGET genes. The description for the plots in Supplementary File 1 can be found in the Legend of Supplementary Figure S1.

PDF file 196K, The Data Supplement contains Supplementary Tables 1-3, which provide additional detail about the clinical trial as well as Supplementary Methods and References

Supplementary Tables S1, S3 through S15, S20, and S23

To understand the genetic drivers of immune recognition and evasion in colorectal cancer, we analyzed 1,211 colorectal cancer primary tumor samples, including 179 classified as microsatellite instability–high (MSI-high). This set includes The Cancer Genome Atlas colorectal cancer cohort of 592 samples, completed and analyzed here. MSI-high, a hypermutated, immunogenic subtype of colorectal cancer, had a high rate of significantly mutated genes in important immune-modulating pathways and in the antigen presentation machinery, including biallelic losses of B2M and HLA genes due to copy-number alterations and copy-neutral loss of heterozygosity. WNT/β-catenin signaling genes were significantly mutated in all colorectal cancer subtypes, and activated WNT/β-catenin signaling was correlated with the absence of T-cell infiltration. This large-scale genomic analysis of colorectal cancer demonstrates that MSI-high cases frequently undergo an immunoediting process that provides them with genetic events allowing immune escape despite high mutational load and frequent lymphocytic infiltration and, furthermore, that colorectal cancer tumors have genetic and methylation events associated with activated WNT signaling and T-cell exclusion.Significance: This multi-omic analysis of 1,211 colorectal cancer primary tumors reveals that it should be possible to better monitor resistance in the 15% of cases that respond to immune blockade therapy and also to use WNT signaling inhibitors to reverse immune exclusion in the 85% of cases that currently do not. Cancer Discov; 8(6); 730–49. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 663

Supplementary File 3. Detailed plots of the data used for phylogenetic inference in each case. The description for the plots in Supplementary File 3 can be found in the Legend of Supplementary Figure S13-S17.

PDF file 249K, Effect of pazopanib on S6K1 phosphorylation in cells expressing activating MTOR mutations

Supplementary File 2. Detailed plots of SCNA calls in TARGET genes. The description for the plots in Supplementary File 2 can be found in the Legend of Supplementary Figure S3.

Supplementary Table 2 from Loss of E-Cadherin Promotes Metastasis via Multiple Downstream Transcriptional Pathways

Supplementary Table 4 from Loss of E-Cadherin Promotes Metastasis via Multiple Downstream Transcriptional Pathways

Supplementary Table 3 from Loss of E-Cadherin Promotes Metastasis via Multiple Downstream Transcriptional Pathways

Supplementary Figure Legends 1-6 from Loss of E-Cadherin Promotes Metastasis via Multiple Downstream Transcriptional Pathways

Supplementary Figures 1-6 from Loss of E-Cadherin Promotes Metastasis via Multiple Downstream Transcriptional Pathways

Loss of the epithelial adhesion molecule E-cadherin is thought to enable metastasis by disrupting intercellular contacts—an early step in metastatic dissemination. To further investigate the molecular basis of this notion, we use two methods to inhibit E-cadherin function that distinguish between E-cadherin's cell-cell adhesion and intracellular signaling functions. Whereas the disruption of cell-cell contacts alone does not enable metastasis, the loss of E-cadherin protein does, through induction of an epithelial-to-mesenchymal transition, invasiveness, and anoikis resistance. We find the E-cadherin binding partner β-catenin to be necessary, but not sufficient, for induction of these phenotypes. In addition, gene expression analysis shows that E-cadherin loss results in the induction of multiple transcription factors, at least one of which, Twist, is necessary for E-cadherin loss–induced metastasis. These findings indicate that E-cadherin loss in tumors contributes to metastatic dissemination by inducing wide-ranging transcriptional and functional changes. [Cancer Res 2008;68(10):3645–53]

Supplementary Table 1 from Loss of E-Cadherin Promotes Metastasis via Multiple Downstream Transcriptional Pathways

Genome-wide association studies (GWAS) have discovered thousands of risk loci for common, complex diseases, each of which could point to genes and gene programs that influence disease. For some diseases, it has been observed that GWAS signals converge on a smaller number of biological programs, and that this convergence can help to identify causal genes1–6. However, identifying such convergence remains challenging: each GWAS locus can have many candidate genes, each gene might act in one or more possible programs, and it remains unclear which programs might influence disease risk. Here, we developed a new approach to address this challenge, by creating unbiased maps to link disease variants to genes to programs (V2G2P) in a given cell type. We applied this approach to study the role of endothelial cells in the genetics of coronary artery disease (CAD). To link variants to genes, we constructed enhancer-gene maps using the Activity-by-Contact model7,8. To link genes to programs, we applied CRISPRi-Perturb-seq9–12to knock down all expressed genes within ±500 Kb of 306 CAD GWAS signals13,14and identify their effects on gene expression programs using single-cell RNA-sequencing. By combining these variant-to-gene and gene-to-program maps, we find that 43 of 306 CAD GWAS signals converge onto 5 gene programs linked to the cerebral cavernous malformations (CCM) pathway—which is known to coordinate transcriptional responses in endothelial cells15, but has not been previously linked to CAD risk. The strongest regulator of these programs isTLNRD1, which we show is a new CAD gene and novel regulator of the CCM pathway.TLNRD1loss-of-function alters actin organization and barrier function in endothelial cellsin vitro, and heart development in zebrafishin vivo. Together, our study identifies convergence of CAD risk loci into prioritized gene programs in endothelial cells, nominates new genes of potential therapeutic relevance for CAD, and demonstrates a generalizable strategy to connect disease variants to functions.

Background: A key goal of precision medicine is to disaggregate common, complex diseases into discrete molecular subtypes. Rare coding variants in the low-density lipoprotein receptor gene ( LDLR ) are identified in 1% to 2% of coronary artery disease (CAD) patients, defining a molecular subtype with risk driven by hypercholesterolemia. Methods: To search for additional subtypes, we compared the frequency of rare, predicted loss-of-function and damaging missense variants aggregated within a given gene in 41 081 CAD cases versus 217 115 controls. Results: Rare variants in LDLR were most strongly associated with CAD, present in 1% of cases and associated with 4.4-fold increased CAD risk. A second subtype was characterized by variants in endothelial nitric oxide synthase gene ( NOS3 ), a key enzyme regulating vascular tone, endothelial function, and platelet aggregation. A rare predicted loss-of-function or damaging missense variants in NOS3 was present in 0.6% of cases and associated with 2.42-fold increased risk of CAD (95% CI, 1.80–3.26; P =5.50×10 −9 ). These variants were associated with higher systolic blood pressure (+3.25 mm Hg; [95% CI, 1.86–4.65]; P =5.00×10 −6 ) and increased risk of hypertension (adjusted odds ratio 1.31; [95% CI, 1.14–1.51]; P =2.00×10 −4 ) but not circulating cholesterol concentrations, suggesting that, beyond lipid pathways, nitric oxide synthesis is a key nonlipid driver of CAD risk. Conclusions: Beyond LDLR , we identified an additional nonlipid molecular subtype of CAD characterized by rare variants in the NOS3 gene.

Molecular profiling studies have enabled discoveries for metastatic prostate cancer (MPC) but have predominantly occurred in academic medical institutions and involved non-representative patient populations. We established the Metastatic Prostate Cancer Project (MPCproject, mpcproject.org), a patient-partnered initiative to involve patients with MPC living anywhere in the US and Canada in molecular research. Here, we present results from our partnership with the first 706 MPCproject participants. While 41% of patient partners live in rural, physician-shortage, or medically underserved areas, the MPCproject has not yet achieved racial diversity, a disparity that demands new initiatives detailed herein. Among molecular data from 333 patient partners (572 samples), exome sequencing of 63 tumor and 19 cell-free DNA (cfDNA) samples recapitulated known findings in MPC, while inexpensive ultra-low-coverage sequencing of 318 cfDNA samples revealed clinically relevant

Linkage disequilibrium (LD) is the correlation among nearby genetic variants. In genetic association studies, LD is often modeled using massive local correlation matrices, but this approach is slow, especially in ancestrally diverse studies. Here, we introduce LD graphical models (LDGMs), which are an extremely sparse and efficient representation of LD. LDGMs are derived from genome-wide genealogies; statistical relationships among alleles in the LDGM correspond to genealogical relationships among haplotypes. We publish LDGMs and ancestry specific LDGM precision matrices for 18 million common SNPs (MAF>1%) in five ancestry groups, validate their accuracy, and demonstrate order-of-magnitude improvements in runtime for commonly used LD matrix computations. We implement an extremely fast multi-ancestry polygenic prediction method, BLUPx-ldgm, which performs better than a similar method based on the reference LD correlation matrix. LDGMs will enable sophisticated methods that scale to ancestrally genetic association data across millions of variants and individuals.

Regulatory relationships between transcription factors (TFs) and their target genes lie at the heart of cellular identity and function; however, uncovering these relationships is often labor-intensive and requires perturbations. Here, we propose a principled framework to systematically infer gene regulation for all TFs simultaneously in cells at steady state by leveraging the intrinsic variation in the transcriptional abundance across single cells. Through modeling and simulations, we characterize how transcriptional bursts of a TF gene are propagated to its target genes, including the expected ranges of time delay and magnitude of maximum covariation. We distinguish these temporal trends from the time-invariant covariation arising from cell states, and we delineate the experimental and technical requirements for leveraging these small but meaningful cofluctuations in the presence of measurement noise. While current technology does not yet allow adequate power for definitively detecting regulatory relationships for all TFs simultaneously in cells at steady state, we investigate a small-scale dataset to inform future experimental design. This study supports the potential value of mapping regulatory connections through stochastic variation, and it motivates further technological development to achieve its full potential.

Recent methods for spatial imaging of tissue samples can identify up to ~100 individual proteins1–3 or RNAs4–10 at single-cell resolution. However, the number of proteins or genes that can be studied in these approaches is limited by long imaging times. Here we introduce Composite In Situ Imaging (CISI), a method that leverages structure in gene expression across both cells and tissues to limit the number of imaging cycles needed to obtain spatially resolved gene expression maps. CISI defines gene modules that can be detected using composite measurements from imaging probes for subsets of genes. The data are then decompressed to recover expression values for individual genes. CISI further reduces imaging time by not relying on spot-level resolution, enabling lower magnification acquisition, and is overall about 500-fold more efficient than current methods. Applying CISI to 12 mouse brain sections, we accurately recovered the spatial abundance of 37 individual genes from 11 composite measurements covering 180 mm2 and 476,276 cells. Spatial transcriptomics is enhanced by using imaging probes for subsets of genes.

Gene regulation in the human genome is controlled by distal enhancers that activate specific nearby promoters(1). One model for this specificity is that promoters might have sequence-encoded preferences for certain enhancers, for example mediated by interacting sets of transcription factors or cofactors(2). This “biochemical compatibility” model has been supported by observations at individual human promoters and by genome-wide measurements in Drosophila(3-9). However, the degree to which human enhancers and promoters are intrinsically compatible has not been systematically measured, and how their activities combine to control RNA expression remains unclear. Here we designed a high-throughput reporter assay called ExP STARR-seq (enhancer x promoter self-transcribing active regulatory region sequencing) and applied it to examine the combinatorial compatibilities of 1,000 enhancer and 1,000 promoter sequences in human K562 cells. We identify simple rules for enhancer-promoter compatibility: most enhancers activated all promoters by similar amounts, and intrinsic enhancer and promoter activities combine multiplicatively to determine RNA output (R(2)=0.82). In addition, two classes of enhancers and promoters showed subtle preferential effects. Promoters of housekeeping genes contained built-in activating motifs for factors such as GABPA and YY1, which decreased the responsiveness of promoters to distal enhancers. Promoters of variably expressed genes lacked these motifs and showed stronger responsiveness to enhancers. Together, this systematic assessment of enhancer-promoter compatibility suggests a multiplicative model tuned by enhancer and promoter class to control gene transcription in the human genome.

Human Molecular Genetics and Genomics Genomic research has evolved from seeking to understand the fundamentals of the human genetic code to examining the ways in which this code varies among people...

A DARPA-like culture at NIH can drive biomedical and health advances

Single-cell quantification of RNAs is important for understanding cellular heterogeneity and gene regulation, yet current approaches suffer from low sensitivity for individual transcripts, limiting their utility for many applications. Here we present Hybridization of Probes to RNA for sequencing (HyPR-seq), a method to sensitively quantify the expression of hundreds of chosen genes in single cells. HyPR-seq involves hybridizing DNA probes to RNA, distributing cells into nanoliter droplets, amplifying the probes with PCR, and sequencing the amplicons to quantify the expression of chosen genes. HyPR-seq achieves high sensitivity for individual transcripts, detects nonpolyadenylated and low-abundance transcripts, and can profile more than 100,000 single cells. We demonstrate how HyPR-seq can profile the effects of CRISPR perturbations in pooled screens, detect time-resolved changes in gene expression via measurements of gene introns, and detect rare transcripts and quantify cell-type frequencies in tissue using low-abundance marker genes. By directing sequencing power to genes of interest and sensitively quantifying individual transcripts, HyPR-seq reduces costs by up to 100-fold compared to whole-transcriptome single-cell RNA-sequencing, making HyPR-seq a powerful method for targeted RNA profiling in single cells.

Characterizing the interactions that SARS-CoV-2 viral RNAs make with host cell proteins during infection can improve our understanding of viral RNA functions and the host innate immune response. Using RNA antisense purification and mass spectrometry, we identified up to 104 human proteins that directly and specifically bind to SARS-CoV-2 RNAs in infected human cells. We integrated the SARS-CoV-2 RNA interactome with changes in proteome abundance induced by viral infection and linked interactome proteins to cellular pathways relevant to SARS-CoV-2 infections. We demonstrated by genetic perturbation that cellular nucleic acid-binding protein (CNBP) and La-related protein 1 (LARP1), two of the most strongly enriched viral RNA binders, restrict SARS-CoV-2 replication in infected cells and provide a global map of their direct RNA contact sites. Pharmacological inhibition of three other RNA interactome members, PPIA, ATP1A1, and the ARP2/3 complex, reduced viral replication in two human cell lines. The identification of host dependency factors and defence strategies as presented in this work will improve the design of targeted therapeutics against SARS-CoV-2., Interactions between SARS-CoV-2 viral RNAs and host cell proteins during infection are evaluated to improve our understanding of viral RNA functions and the host innate immune response.

Structural variants in more than 17,000 human genomes are mapped and characterized using whole-genome sequencing, showing how this type of variation contributes to rare deleterious coding and noncoding alleles. A key goal of whole-genome sequencing for studies of human genetics is to interrogate all forms of variation, including single-nucleotide variants, small insertion or deletion (indel) variants and structural variants. However, tools and resources for the study of structural variants have lagged behind those for smaller variants. Here we used a scalable pipeline(1)to map and characterize structural variants in 17,795 deeply sequenced human genomes. We publicly release site-frequency data to create the largest, to our knowledge, whole-genome-sequencing-based structural variant resource so far. On average, individuals carry 2.9 rare structural variants that alter coding regions; these variants affect the dosage or structure of 4.2 genes and account for 4.0-11.2% of rare high-impact coding alleles. Using a computational model, we estimate that structural variants account for 17.2% of rare alleles genome-wide, with predicted deleterious effects that are equivalent to loss-of-function coding alleles; approximately 90% of such structural variants are noncoding deletions (mean 19.1 per genome). We report 158,991 ultra-rare structural variants and show that 2% of individuals carry ultra-rare megabase-scale structural variants, nearly half of which are balanced or complex rearrangements. Finally, we infer the dosage sensitivity of genes and noncoding elements, and reveal trends that relate to element class and conservation. This work will help to guide the analysis and interpretation of structural variants in the era of whole-genome sequencing.

Prion disease is a rare, fatal, and exceptionally rapid neurodegenerative disease. Although incurable, prion disease follows a clear pathogenic mechanism, in which a single gene gives rise to a single prion protein (PrP) capable of converting into the sole causal disease agent, the misfolded prion. As efforts progress to leverage this mechanistic knowledge toward rational therapies, a principal challenge will be the design of clinical trials. Previous trials in prion disease have been done in symptomatic patients who are often profoundly debilitated at enrolment. About 15% of prion disease cases are genetic, creating an opportunity for early therapeutic intervention to delay or prevent disease. Highly variable age of onset and absence of established prodromal biomarkers might render infeasible existing models for testing drugs before disease onset. Advancement of near-term targeted therapeutics could crucially depend on thoughtful design of rigorous presymptomatic trials.

Genome-wide association studies have associated thousands of genetic variants with complex traits and diseases, but pinpointing the causal variant(s) among those in tight linkage disequilibrium with each associated variant remains a major challenge. Here, we use seven experimental assays to characterize all common variants at the multiple disease-associated TNFAIP3 locus in five disease-relevant immune cell lines, based on a set of features related to regulatory potential. Trait/disease-associated variants are enriched among SNPs prioritized based on either: (1) residing within CRISPRi-sensitive regulatory regions, or (2) localizing in a chromatin accessible region while displaying allele-specific reporter activity. Of the 15 trait/disease-associated haplotypes at TNFAIP3, 9 have at least one variant meeting one or both of these criteria, 5 of which are further supported by genetic fine-mapping. Our work provides a comprehensive strategy to characterize genetic variation at important disease-associated loci, and aids in the effort to identify trait causal genetic variants., While genome-wide association studies have yielded thousands of trait-associated loci, identifying causal variants remains challenging. Here, the authors perform seven genomics assays in various cell types to prioritize genetic variants in the TNFAIP3 locus, and report high-priority variants within disease-associated haplotypes.

Despite rare cancers accounting for 25% of adult tumors1, they are difficult to study due to the low disease incidence and geographically dispersed patient populations, which has resulted in significant unmet clinical needs for patients with rare cancers. We assessed whether a patient-partnered research approach using online engagement can overcome these challenges, focusing on angiosarcoma, a sarcoma with an annual incidence of 300 cases in the United States. Here we describe the development of the Angiosarcoma Project (ASCproject), an initiative enabling US and Canadian patients to remotely share their clinical information and biospecimens for research. The project generates and publicly releases clinically annotated genomic data on tumor and germline specimens on an ongoing basis. Over 18 months, 338 patients registered for the ASCproject, which comprises a large proportion of all patients with angiosarcoma. Whole-exome sequencing (WES) of 47 tumors revealed recurrently mutated genes that included KDR, TP53, and PIK3CA. PIK3CA-activating mutations were observed predominantly in primary breast angiosarcoma, which suggested a therapeutic rationale. Angiosarcoma of the head, neck, face and scalp (HNFS) was associated with a high tumor mutation burden (TMB) and a dominant ultraviolet damage mutational signature, which suggested that for the subset of patients with angiosarcoma of HNFS, ultraviolet damage may be a causative factor and that immune checkpoint inhibition may be beneficial. Medical record review revealed that two patients with HNFS angiosarcoma had received off-label therapeutic use of antibody to the programmed death-1 protein (anti-PD-1) and had experienced exceptional responses, which highlights immune checkpoint inhibition as a therapeutic avenue for HNFS angiosarcoma. This patient-partnered approach has catalyzed an opportunity to discover the etiology and potential therapies for patients with angiosarcoma. Collectively, this proof-of-concept study demonstrates that empowering patients to directly participate in research can overcome barriers in rare diseases and can enable discoveries. A framework of patient-partnered research allows patients with angiosarcoma to share their samples and clinical records securely to accelerate molecular characterization of tumors and identification of therapeutic approaches.

For Alzheimer’s disease–a leading cause of dementia and global morbidity–improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer’s disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer’s disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer’s disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer’s disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer’s disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution.