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We examined 454,712 exomes for genes associated with a wide spectrum of complex traits and common diseases and observed that rare, penetrant mutations in genes implicated by genome-wide association studies confer ∼10-fold larger effects than common variants in the same genes. Consequently, an individual at the phenotypic extreme and at the greatest risk for severe, early-onset disease is better identified by a few rare penetrant variants than by the collective action of many common variants with weak effects. By combining rare variants across phenotype-associated genes into a unified genetic risk model, we demonstrate superior portability across diverse global populations compared to common variant polygenic risk scores, greatly improving the clinical utility of genetic-based risk prediction.One sentence summaryRare variant polygenic risk scores identify individuals with outlier phenotypes in common human diseases and complex traits.

INTRODUCTIONMillions of people have received genome and exome sequencing to date, a collective effort that has illuminated for the first time the vast catalog of small genetic differences that distinguish us as individuals within our species. However, the effects of most of these genetic variants remain unknown, limiting their clinical utility and actionability. New approaches that can accurately discern disease-causing from benign mutations and interpret genetic variants on a genome-wide scale would constitute a meaningful initial step towards realizing the potential of personalized genomic medicine.RATIONALEAs a result of the short evolutionary distance between humans and nonhuman primates, our proteins share near-perfect amino acid sequence identity. Hence, the effects of a protein-altering mutation found in one species are likely to be concordant in the other species. By systematically cataloging common variants of nonhuman primates, we aimed to annotate these variants as being unlikely to cause human disease as they are tolerated by natural selection in a closely related species. Once collected, the resulting resource may be applied to infer the effects of unobserved variants across the genome using machine learning.RESULTSFollowing the strategy outlined above we obtained whole-genome sequencing data for 809 individuals from 233 primate species and cataloged 4.3 million common missense variants. We confirmed that human missense variants seen in at least one nonhuman primate species were annotated as benign in the ClinVar clinical variant database in 99% of cases. By contrast, common variants from mammals and vertebrates outside the primate lineage were substantially less likely to be benign in the ClinVar database (71 to 87% benign), restricting this strategy to nonhuman primates. Overall, we reclassified more than 4 million human missense variants of previously unknown consequence as likely benign, resulting in a greater than 50-fold increase in the number of annotated missense variants compared to existing clinical databases.To infer the pathogenicity of the remaining missense variants in the human genome, we constructed PrimateAI-3D, a semisupervised 3D-convolutional neural network that operates on voxelized protein structures. We trained PrimateAI-3D to separate common primate variants from matched control variants in 3D space as a semisupervised learning task. We evaluated the trained PrimateAI-3D model alongside 15 other published machine learning methods on their ability to distinguish between benign and pathogenic variants in six different clinical benchmarks and demonstrated that PrimateAI-3D outperformed all other classifiers in each of the tasks.CONCLUSIONOur study addresses one of the key challenges in the variant interpretation field, namely, the lack of sufficient labeled data to effectively train large machine learning models. By generating the most comprehensive primate sequencing dataset to date and pairing this resource with a deep learning architecture that leverages 3D protein structures, we were able to achieve meaningful improvements in variant effect prediction across multiple clinical benchmarks. Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

Background: Elevated maternal body mass index (BMI) in pregnancy is associated with adverse maternal and fetal outcomes. Identification of alterations in the placental transcriptome may elucidate molecular markers of the biological pathways underlying these associations. Methods: We investigated the association of first trimester maternal BMI with the placental transcriptome in Gen3G, a large population-based pregnancy cohort. We completed RNA sequencing of 449 placental samples and performed differential gene expression analysis of over 15,000 genes (Limma Voom). We used maternal BMI as a continuous variable, with estimates reported as change in normalized gene expression per 1-unit increase in BMI (kg/m2), as well as a categorical variable (participants with normal weight (N=270) vs. obesity (N=82)), with RNA expression estimates reported as log2 fold changes. We adjusted models for maternal age, fetal sex, gestational age and 6 surrogate variables inferred from the expression data to adjust for batch effects and other unmeasured sources of variability. We accounted for multiple testing using False Discovery Rate (FDR). Sensitivity analyses were performed by removing cases of gestational diabetes and hypertensive disorders in pregnancy. Results: At enrollment in early pregnancy, participants had a mean ± SD age of 28.2 ± 4.4 years and measured BMI of 25.4 ± 5.5 kg/m 2 ; 57% of participants were categorized in normal weight, 22% in overweight and 18% in obesity BMI categories. Maternal BMI was significantly associated with decreased expression of EPYC , a gene that regulates fibrillogenesis (slope= -0.14, FDR-adjusted P=1.68x10 -5 for continuous BMI; log2 fold change= -1.71, FDR-adjusted P=1.06x10 -2 for obesity vs. normal weight). Both sensitivity analyses with the removal of gestational diabetes and hypertensive disorders revealed similar associations for EPYC . Conclusions: We identified an association between elevated maternal BMI and downregulation of EPYC expression in the placenta. Additional studies are required to examine the role of placental EPYC expression in maternal and fetal health.

Background: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants. Methods: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics). Results: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10 -11 . These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, β=0.61±0.05, P =3.27×10 -30 ) and MMP-3 (β=-0.60±0.05, P =1.67×10 -32 ), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, β=0.34±0.04, P =1.34×10 -17 ) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure. Conclusions: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis (MESA) which included several hundred individuals with whole genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, ten years apart. We evaluated each multi-omic phenotype’s ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62x and rare frameshift variants 216x as frequently as controls, compared to 13x to 27x for expression or protein effects alone. We developed a Bayesian hierarchical model to prioritize specific rare variants underlying multi-omic signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer’s disease.

Post-translational modifications (PTMs) play key roles in regulating cell signaling in health and disease processes. Recent advances in mass spectrometry enable comprehensive investigation of PTMs. However, identifying functionally relevant PTMs and protein domains implicated in disease remains a key challenge. We present CLUMPS-PTM, an algorithm for spatial clustering of PTMs to propose functional domains. We apply CLUMPS-PTM to the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset (one of the largest proteogenomic pan-cancer PTM datasets) comprising 1110 samples spanning 11 tumor types. First, we map over 14K phospho-sites and 13K acetyl-sites detected in the CPTAC dataset to protein data bank (PDB) files. We expand this to the predicted AlphaFold proteome and recover additional 12K phospho-sites and 10K acetyl-sites exceeding model confidence (pLDDT) of 70%. Consistent with previous research, phospho-sites are found on residues with lower AlphaFold prediction confidence than acetyl-sites due to their abundance in unstructured domains (p-value < 1e-4). To identify functionally relevant protein domains in the pan-cancer context, we characterize the 1110 samples by applying SignatureAnalyzer, a Bayesian non-negative matrix factorization method, to discover multi-omic patterns. This analysis identifies strong signatures associated with DNA damage response (DDR) and stratifies the CPTAC cohort into DDR-high (n=442) and DDR-low (n=668) groups. Next, we applied CLUMPS-PTM to differentially modified phospho/acetyl-sites between these groups. We identified 22 proteins (including GMPS, NCBP1, and ANPEP (CD13)) with significant clusters of differentially phosphorylated sites (q-value < 0.1) in DDR-high samples. ANPEP is a zinc-dependent aminopeptidase with a significant cluster (q-value = 0.0129) of differentially phosphorylated sites in the ERAP1-C domain that faces peptidase active sites known to have proliferative activity via the MAPK pathway. CLUMPS-PTM also identified a significant cluster of acetyl-sites on ARID1A (a SWI/SNF chromatin remodeler known to be mutated in cancer) in DDR-high samples (q-value = 0.085). The ARID1A cluster is on the C-terminal tail of the protein within the glucocorticoid receptor (GR) domain, an upstream regulator of proteins that increase catabolism, reduce inflammation, and increase cell survival. Increased acetylation may block a ubiquitination site at the C-terminus of the protein, resulting in increased protein levels, likely due to reduced ARID1A degradation, and increased GR signaling. Overall, CLUMPS-PTM is an open-source tool that allows near proteome-wide spatial analysis with the growing availability of PTM data. We anticipate that it will be useful to the broader proteomic community for the discovery of novel targets and generation of insights into functional mechanisms. Citation Format: Shankara K. Anand, Yifat Geffen, Yo Akiyama, Francois Aguet, Gad Getz. CLUMPS-PTM: Spatial clustering of post-translational modifications across cancer types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3132.

Background: Diffuse midline gliomas (DMGs) are a universally fatal brain tumor of childhood. While histone mutations are a critical tumor initiating event, they are insufficient to drive gliomagenesis. Histone mutations co-occur with somatic alterations in other pathways including TP53, MAPK, and MYC signaling. However, the mechanisms through which these pathways are activated have not been fully elucidated. Methods: We applied an integrative approach using transcriptomics, epigenetics, proteomics, in vitro cancer models, and in vivo mouse models to systematically evaluate how FOXR2 mediates gliomagenesis. Results: We have recently found that a subset of DMGs aberrantly express FOXR2, a forkhead transcription factor. FOXR2 is both sufficient to enhance tumor formation, and necessary for FOXR2-expressing DMGs. While FOXR2 indeed enhances MYC protein stability, FOXR2 exerts oncogenesis through MYC-independent functions and specifically hijacks E26-transformation specific (ETS) transcriptional circuits and FOXR2 DNA-binding is highly enriched at ETS motifs. We have performed proteomic and phospho-proteomic analysis of FOXR2-expressing human neural stem cells to identify proteins and phospho-sites that are highly enriched in FOXR2-expressing cells. Conclusion: Taken together, this study elucidates how FOXR2 interacts with ETS transcription factors to mediate oncogenesis, and further highlights a role for FOXR2 in activating ETS and MAPK signaling. Citation Format: Jessica W. Tsai, Paloma Cejas, Marissa Coppola, Dayle K. Wang, Smruti Patel, David W. Wu, Phonepasong Arounleut, Xin Wei, Ningxuan Zhou, Sudeepa Syamala, Frank P. Dubois, Kristine Pelton, Jayne Vogelzang, Cecilia Sousa, Audrey Baguette, Xiaolong Chen, Alexandra L. Condurat, Sarah E. Dixon-Clarke, Annarah Charles, Kevin N. Zhou, Sophie D. Lu, Elizabeth M. Gonzalez, Madison S. Chacon, Jeromy J. Digiacomo, Rushil Kumbhani, Dana Novikov, Maria Tsoli, David S. Ziegler, Uta Dirksen, Natalie Jager, Gnana Prakash Balasubramanian, Christof M. Kramm, Michaela Nathrath, Stefan Bielack, Suzanne J. Baker, Jinghui Zhang, James M. McFarland, Gad Getz, Francois Aguet, Nada Jabado, Olaf Witt, Stefan M. Pfister, Keith L. Ligon, Volker Hovestadt, Claudia Kleinman, Henry Long, David T. Jones, Pratiti Bandopadhayay, Timothy N. Phoenix. Dissecting mechanisms underlying FOXR2-mediated gliomagenesis in diffuse midline gliomas. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3562.

BACKGROUND: Understanding how aberrant transcription factors (TFs) hijack normal development to induce oncogenesis is a critical question in oncology. Forkhead box (FOX) proteins are a superfamily of transcriptional regulators characterized by a forkhead DNA-binding domain. Within this family, Forkhead Box R2 (FOXR2) has been identified as a candidate structural variant (SV) driver in a subset of pediatric cancers including CNS embryonal tumors and peripheral neuroblastoma. While FOXR2 has been shown to stabilize MYC isoforms, the mechanistic details through which it enhances tumor formation, other non-SV mechanisms of activating aberrant expression, and the true extent of its role as an oncogene across all cancers have not been systematically evaluated. METHODS: We applied an integrative approach using transcriptomics, epigenetics, in vitro cancer models, and in vivo mouse models to systematically evaluate the mechanisms by which FOXR2 is activated across human cancers. RESULTS: We performed a pan-cancer analysis of FOXR2 activation across over 10,000 adult and pediatric cancer samples, and surprisingly found FOXR2 to be aberrantly upregulated in 70% of all cancer types (including diffuse midline gliomas), and 8% of all individual tumors. FOXR2 expression occurred predominantly in the absence of rearrangement/fusions, single nucleotide variants, or copy number aberrations at the DNA level. Transcriptomic and epigenomic analyses show the vast majority of tumors (78%) aberrantly express FOXR2 through a previously undescribed epigenetic mechanism via hypomethylation of a novel promoter. Using both in vitro and in vivo models, we demonstrate that FOXR2 expression is both sufficient and necessary for transformation across multiple lineages, including DMGs. CONCLUSION: Taken together, this study demonstrates that FOXR2 is a novel and potent oncogene across pediatric and adult cancers, and highlights a new epigenetic mechanism by which its expression is activated.

Patients with smoldering multiple myeloma (SMM) are observed until progression, but early treatment may improve outcomes. We conducted a phase II trial of elotuzumab, lenalidomide, and dexamethasone (EloLenDex) in patients with high-risk SMM and performed single-cell RNA and T cell receptor (TCR) sequencing on 149 bone marrow (BM) and peripheral blood (PB) samples from patients and healthy donors (HDs). We find that early treatment with EloLenDex is safe and effective and provide a comprehensive characterization of alterations in immune cell composition and TCR repertoire diversity in patients. We show that the similarity of a patient's immune cell composition to that of HDs may have prognostic relevance at diagnosis and after treatment and that the abundance of granzyme K (GZMK)

Background: Understanding how aberrant transcription factors (TFs) hijack normal development to induce oncogenesis is a critical question in oncology. Forkhead box (FOX) proteins are a superfamily of transcriptional regulators characterized by a forkhead DNA-binding domain. Within this family, Forkhead Box R2 (FOXR2) expression has been associated with a subset of cancers including CNS and peripheral neuroblastoma. While FOXR2 has been shown to stabilize MYC isoforms, the mechanistic details through which it enhances tumor formation and the true extent of its role as an oncogene across all cancers have not been systematically evaluated. Methods: We applied an integrative approach using transcriptomics, epigenetics, in vitro cancer models, and in vivo mouse models to systematically evaluate the mechanisms by which FOXR2 is activated across human cancers. Results: We performed a pan-cancer analysis of FOXR2 activation across over 10,000 adult and pediatric cancer samples, and found FOXR2 to be aberrantly upregulated in 70% of all cancer types, and 8% of all individual tumors. We identified genetic and epigenetic mechanisms that induce its expression, including hypomethylation of a novel promoter in the vast majority (78%) of FOXR2-expressing cases. We demonstrate that FOXR2 expression is both sufficient and necessary for transformation across multiple lineages, using both in vitro and in vivo models. Conclusion: Taken together, this study demonstrates the role of FOXR2 as a potent oncogene across human cancers, and highlights a novel mechanism by which its expression is activated. Citation Format: Jessica W. Tsai, Paloma Cejas, Dayle K. Wang, Smruti Patel, David W. Wu, Phonepasong Arounleut, Xin Wei, Ningxuan Zhou, Sudeepa Syamala, Frank P. Dubois, Kristine Pelton, Jayne Vogelzang, Cecilia Sousa, Audrey Baguette, Xiaolong Chen, Alexandra L. Condurat, Sarah E. Dixon-Clarke, Kevin N. Zhou, Sophie D. Lu, Elizabeth M. Gonzalez, Madison S. Chacon, Jeromy J. Digiacomo, Rushil Kumbhani, Dana Novikov, J'Ya Hunter, Maria Tsoli, David S. Ziegler, Uta Dirksen, Natalie Jager, Gnana Prakash Balasubramanian, Christof M. Kramm, Michaela Nathrath, Stefan Bielack, Suzanne J. Baker, Jinghui Zhang, James M. McFarland, Gad Getz, Francois Aguet, Nada Jabado, Olaf Witt, Stefan M. Pfister, Keith L. Ligon, Claudia Kleinman, Henry Long, David T. Jones, Pratiti Bandopadhayay, Timothy N. Phoenix. FOXR2 is an oncogenic driver across adult and pediatric cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5730.

We evaluated ancestry effects on mutation rates, DNA methylation, and mRNA and miRNA expression among 10,678 patients across 33 cancer types from The Cancer Genome Atlas. We demonstrated that cancer subtypes and ancestry-related technical artifacts are important confounders that have been insufficiently accounted for. Once accounted for, ancestry-associated differences spanned all molecular features and hundreds of genes. Biologically significant differences were usually tissue-specific but not specific to cancer. However, admixture and pathway analyses suggested some of these differences are causally related to cancer. Specific findings included an increased FBXW7 mutations in patients of African origin, decreased VHL and PBRM1 mutations in renal cancer patients of African origin, and decreased immune activity in bladder cancer patients of East Asian origin.

The inevitable emergence of acquired resistance is a major limitation to the clinical benefit of precision medicine strategies. Single-lesion tumor biopsies have long been the mainstay of understanding acquired resistance, but recent data suggest tumor biopsies may under-represent the molecular heterogeneity of acquired resistance. Alternatively, studies have suggested that liquid biopsy approaches analyzing cell-free DNA (cfDNA) may offer significant advantages, but extensive prospective comparisons of matched liquid vs. tumor biopsies obtained at the time of acquired resistance are lacking. Here, we assess systematic liquid biopsy upon acquired resistance to targeted therapy in 44 patients across seven molecularly defined subtypes of gastrointestinal cancers. Liquid biopsy at disease progression identified at least one functionally validated molecular mechanism of resistance in 75% of patients, wherein 52% exhibited >1 resistance alteration (range 2-9, median 3 per patient). In 23 patients in whom a matched post-progression tumor biopsy could be obtained, tumor biopsy was less effective than liquid biopsy in identifying resistance mechanisms, with resistance alterations detected in only 48% of patients, and multiple resistance mechanisms detected in only 9% of cases. In matched cases, liquid biopsy detected at least one resistance alteration not detected in tumor biopsy in 78% of cases. Targeted analysis and whole-exome sequencing of serial cfDNA, multiple post-progression biopsies, and rapid autopsy specimens from select cases revealed key insights into the geographic and complex characteristics of heterogeneity captured by liquid biopsy in the setting of acquired resistance. These data illustrate that acquired resistance is characterized by frequent and profound tumor heterogeneity, and suggests that liquid biopsy may more effectively identify heterogeneous clinically relevant resistance alterations compared to standard tumor biopsy. Citation Format: Aparna R. Parikh, Ignaty Leshchiner, Liudmila Elagina, Lipika Goyal, Chaya Levovitz, Giulia Siravegna, Dimitri Livitz, Kahn Rhrissorrakrai, Liz Martin, Emily E. Van Seventer, Megan Hanna, Kara Slowik, Filippo Utro, Christopher J. Pinto, Alicia Wong, Brian P. Danysh, Ferran Fece de la Cruz, Isobel J. Fetter, Brandon Nadres, Heather A. Shahzade, Jill N. Allen, Lawrence S. Blaszkowsky, Jeffrey W. Clark, Bruce Giantonio, Janet E. Murphy, Ryan D. Nipp, Eric Roeland, David P. Ryan, Colin D. Weekes, Eunice L. Kwak, Jason E. Faris, Francois Aguet, Ipsita Guha, Mehlika Hazar-Rethinam, Dora Dias-Santagata, David T. Ting, Andrew X. Zhu, Theodore S. Hong, Todd R. Golub, A J. Iafrate, Viktor Adalsteinsson, Alberto Bardelli, Laxmi Parida, Dejan Juric, Gad Getz, Ryan B. Corcoran. Liquid biopsy versus tissue biopsy to assess acquired resistance and tumor heterogeneity in gastrointestinal cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-257.