Adam Scott

Protein-structure-guided discovery of functional mutations across 19 cancer types

Local concentrations of mutations are well known in human cancers. However, their three-dimensional spatial relationships in the encoded protein have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and intermolecular clusters, some of which showed tumor and/or tissue specificity. In addition, we identified 369 rare mutations in genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium-recurrence mutations in genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all mapping within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high-throughput phosphorylation data and cell-line-based experimental evaluation. Finally, mutation–drug cluster and network analysis predicted over 800 promising candidates for druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Systematic discovery of complex insertions and deletions in human cancers

Complex insertions and deletions (indels) are formed by simultaneously deleting and inserting DNA fragments of different sizes at a common genomic location. Here we present a systematic analysis of somatic complex indels in the coding sequences of samples from over 8,000 cancer cases using Pindel-C. We discovered 285 complex indels in cancer-associated genes (such as PIK3R1, TP53, ARID1A, GATA3 and KMT2D) in approximately 3.5% of cases analyzed; nearly all instances of complex indels were overlooked (81.1%) or misannotated (17.6%) in previous reports of 2,199 samples. In-frame complex indels are enriched in PIK3R1 and EGFR, whereas frameshifts are prevalent in VHL, GATA3, TP53, ARID1A, PTEN and ATRX. Furthermore, complex indels display strong tissue specificity (such as VHL in kidney cancer samples and GATA3 in breast cancer samples). Finally, structural analyses support findings of previously missed, but potentially druggable, mutations in the EGFR, MET and KIT oncogenes. This study indicates the critical importance of improving complex indel discovery and interpretation in medical research.

Proteogenomic integration reveals therapeutic targets in breast cancer xenografts.

Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities.

Database of evidence for precision oncology portal

Summary: A database of curated genomic variants with clinically supported drug therapies and other oncological annotations is described. The accompanying web portal provides a search engine with two modes: one that allows users to query gene, cancer type, variant type or position for druggable mutations, and another to search for and to visualize, on three‐dimensional protein structures, putative druggable sites that cluster with known druggable mutations. Availability and implementation: http://dinglab.wustl.edu/depo

Corrigendum: Protein-structure-guided discovery of functional mutations across 19 cancer types

Unnur Styrkarsdottir, Hannes Helgason, Asgeir Sigurdsson, Gudmundur L Norddahl, Arna B Agustsdottir, Louise N Reynard, Amanda Villalvilla, Gisli H Halldorsson, Aslaug Jonasdottir, Audur Magnusdottir, Asmundur Oddson, Gerald Sulem, Florian Zink, Gardar Sveinbjornsson, Agnar Helgason, Hrefna S Johannsdottir, Anna Helgadottir, Hreinn Stefansson, Solveig Gretarsdottir, Thorunn Rafnar, Ina S Almdahl, Anne Brækhus, Tormod Fladby, Geir Selbæk, Farhad Hosseinpanah, Fereidoun Azizi, Jung Min Koh, Nelson L S Tang, Maryams Danesphour, Jose I Mayordomo, Corrine Welt, Peter S Braund, Nilesh J Samani, Lambertus A Kiemeney, L Stefan Lohmander, Claus Christiansen, Ole A Andreassen, arcOGEN consortium, Olafur Magnusson, Gisli Masson, Augustine Kong, Ingileif Jonsdottir, Daniel Gudbjartsson, Patrick Sulem, Helgi Jonsson, John Loughlin, Thorvaldur Ingvarsson, Unnur Thorsteinsdottir & Kari Stefansson Nat. Genet.; doi:10.1038/ng.3816; corrected online 17 April 2017

Abstract 61: Sequence and structure-guided approach to identify functional mutations in G-protein coupled receptors

G-protein coupled receptors (GPCRs) account for about 4% of all encoded genes in the human genome with over 800 different types. They activate signal transduction pathways inside the cell that regulate a wide variety of cellular responses and physiological processes. GPCRs are known to play a role in disease progression and are the target of about 40% of drugs on market. However, the implication of GPCRs in tumor initiation and/or progression has not been extensively studied and remains unknown. Due to GPCRs being major drug targets, identifying functional mutations in GPCRs that may lead to tumorigenesis has large therapeutic implications. Nearly 20% of human tumors harbor mutations in GPCRs. However, individual GPCRs may harbor few recurrent mutations making it hard to detect potential functional mutations. Due to this lack of statistical power, it is beneficial to analyze the protein family as a whole and look at recurrent mutations at the same structural site. We can use sequence conservation, protein structure, and cancer mutation data to investigate the mutational landscape of GPCRs and identify conserved mutation hotspots that might be involved in tumorigenesis. We use the positions of structural domains to guide our protein family alignment. Entropy scores, which are used to measure conservation, and somatic mutation densities are computed based on the structure-guided alignment for each position in each domain. Positions that exhibit high somatic mutation density and low entropy are prioritized as possible functional mutations. We analyzed GPCRs from 3,281 tumor samples in over 12 cancer types and have identified three residues located in highly conserved motifs: the DRY, NPxxY, and CWxP motifs that may be involved in the activation of oncogenic pathways. The arginine residue in the DRY motif serves as an “ionic lock”, which keeps GPCRs in an inactive state. Mutations at the arginine could disrupt the inactive conformation leading to a ligand-independent active form and constitutive activation. The NPxxY and CWxP motifs are known to control the equilibrium between the inactive and active states of GPCRs. We plan to further develop our methodology by integrating specific amino acid changes and mutation expression levels during the prioritization process. We will generalize our methodology into a tool called AnaConDAS (Analysis of Conservation in Domain Alignments and Structure) for major druggable protein families. Our lab has created a new tool called HotSpot3D, which clusters mutations and associated drugs based on proximity on a 3D protein structure. We plan to integrate AnaConDAS and HotSpot3D by focusing on the hotpot residues identified by AnaConDAS and analyzing mutations that cluster around them. This approach is novel because it harnesses the statistical power of studying mutations across a protein family and uses 3D protein structure/proximity based analyses to uncover functional mutations in cancer. Citation Format: Sohini Sengupta, Kai Ye, Adam D. Scott, Beifang Niu, Matthew H. Bailey, Michael D. McLellan, Michael C. Wendl, Matthew A. Wyczalkowski, Li Ding. Sequence and structure-guided approach to identify functional mutations in G-protein coupled receptors. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 61. doi:10.1158/1538-7445.AM2015-61

CharGer: clinical Characterization of Germline variants

Summary: CharGer (Characterization of Germline variants) is a software tool for interpreting and predicting clinical pathogenicity of germline variants. CharGer gathers evidence from databases and annotations, provided by local tools and files or via ReST APIs, and classifies variants according to ACMG guidelines for assessing variant pathogenicity. User‐designed pathogenicity criteria can be incorporated into CharGers flexible framework, thereby allowing users to create a customized classification protocol. Availability and implementation: Source code is freely available at https://github.com/ding‐lab/CharGer and is distributed under the GNU GPL‐v3.0 license. Software is also distributed through the Python Package Index (PyPI) repository. CharGer is implemented in Python 2.7 and is supported on Unix‐based operating systems. Supplementary information: Supplementary data are available at Bioinformatics online.

Corrigendum: Proteogenomic integration reveals therapeutic targets in breast cancer xenografts.

Nature Communications 8: Article number: 14864 (2017)); Published: 28 March 2017; Updated: 25 April 2017 The original version of this Article contained a typographical error in the spelling of the author Beifang Niu, which was incorrectly given as Beifung Niu. This has now been corrected in both thePDF and HTML versions of the Article.

Abstract 1939: Discovery and proteogenomic investigation of genetic variants in human cancers

A significant fraction of cancers have a heritable component, and require an interplay between somatic and germline variants. Common and rare germline variants have been investigated by previous GWAS and family based studies. However, a comprehensive analysis of both somatic and germline variants in cancer using high throughput sequencing data to discover genetic variants of functional relevance is lacking. Herein, we investigated the potential role of somatic and germline variants from over 20 major cancer types from large-scale studies such as TCGA and ICGC and discovered thousands of somatic and germline variants in cancer genomes. To link these genetic variants to cancer phenotypes, we analyzed the proteomics data in breast, ovarian and colorectal cancers generated by the Clinical Proteomic Tumour Analysis Consortium (CPTAC) using a PepScan pipeline that can detect whether a genomic variant is observed at the peptide level. The pipeline incorporated QUILTS to construct patient-specific protein database and MS-GF+ to identify peptide sequences in the database from MS spectra. Our analysis validated roughly 2% of non-synonymous genetic variants in peptides with matched spectra. We correlated the effect of genetic mutations on proteomic subtypes based on global protein expression levels. Additionally, we assessed the role of genetic variations in kinase genes using phosphoproteome profiles, and identified downstream markers that may be candidate targets for diagnosis or treatment. This analysis also helped us prioritize kinase variants that are likely functional candidates for experimental validation. In conclusion, the comprehensive study of genetic variants utilizing an integrated proteogenomic approach revealed genetic variants with potential functional impacts in cancer. Citation Format: Kuan-lin Huang, Jaiyin Wang, Song Cao, Mingchao Xie, Reyka Jayasinghe, Jie Ning, Michael McLellan, Michael Wendl, Adam Scott, Kimberly Johnson, Sherri Davies, David Fenyo, Reid Townsend, Feng Chen, Jeffrey Parvin, Matthew Ellis, Li Ding. Discovery and proteogenomic investigation of genetic variants in human cancers. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1939. doi:10.1158/1538-7445.AM2015-1939