Sohini Sengupta

Comprehensive Characterization of Cancer Driver Genes and Mutations

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified >3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that >300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors.

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 Functional Annotation of Somatic Mutations in Cancer

The functional impact of the vast majority of cancer somatic mutations remains unknown, representing a critical knowledge gap for implementing precision oncology. Here, we report the development of a moderate-throughput functional genomic platform consisting of efficient mutant generation, sensitive viability assays using two growth factor-dependent cell models, and functional proteomic profiling of signaling effects for select aberrations. We apply the platform to annotate >1,000 genomic aberrations, including gene amplifications, point mutations, indels, and gene fusions, potentially doubling the number of driver mutations characterized in clinically actionable genes. Further, the platform is sufficiently sensitive to identify weak drivers. Our data are accessible through a user-friendly, public data portal. Our study will facilitate biomarker discovery, prediction algorithm improvement, and drug development.

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

Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges

The advent of next‐generation sequencing has dramatically decreased the cost for whole‐genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics communitys ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.

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

Integrative omics analyses broaden treatment targets in human cancer

BackgroundAlthough large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers.MethodsTo overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO.ResultsWithin the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability.ConclusionsOur results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients.