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The UK Biobank (UKBB) this week unveiled to scientists the entire genomes of 200,000 people who are part of a long-term British health study.
The trove of genomes, each linked to anonymized medical information, will allow biomedical scientists to scour the full 3 billion base pairs of human DNA for insights into the interplay of genes and health that could not be gleaned from partial sequences or scans of genome markers. “It is thrilling to see the release of this long-awaited resource,” says Stephen Glatt, a psychiatric geneticist at the State University of New York Upstate Medical University.
Other biobanks have also begun to compile vast numbers of whole genomes, 100,000 or more in some cases (see table, below). But UKBB stands out because it offers easy access to the genomic information, according to some of the more than 20,000 researchers in 90 countries who have signed up to use the data. “In terms of availability and data quality, [UKBB] surpasses all others,” says physician and statistician Omar Yaxmehen Bello-Chavolla of the National Institute for Geriatrics in Mexico City.
Enabling your vision to improve public health
Data drives discovery. We have curated a uniquely powerful biomedical database that can be accessed globally for public health research. Explore data from half a million UK Biobank participants to enable new discoveries to improve public health.
This UKBB biobank represents genomes collected from 500,000 middle-age and elderly participants for 2006 to 2010. The genomes are mostly of a European descent. Other large scale genome sequencing ventures like Iceland’s DECODE, which collected over 100,000 genomes, is now a subsidiary of Amgen, and mostly behind IP protection, not Open Access as this database represents.
UK Biobank is a large-scale biomedical database and research resource, containing in-depth genetic and health information from half a million UK participants. The database is regularly augmented with additional data and is globally accessible to approved researchers undertaking vital research into the most common and life-threatening diseases. It is a major contributor to the advancement of modern medicine and treatment and has enabled several scientific discoveries that improve human health.
A summary of some large scale genome sequencing projects are show in the table below:
Biobank
Completed Whole Genomes
Release Information
UK Biobank
200,000
300,000 more in early 2023
TransOmics for Precision Medicien
161,000
NIH requires project specific request
Million Veterans Program
125,000
Non-Veterans Affairs researchers get first access
100,000 Genomes Project
120,000
Researchers must join Genomics England collaboration
All of Us
90,000
NIH expects to release 2022
Other Related Articles on Genome Biobank Projects in this Open Access Online Scientific Journal Include the Following:
Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
Complex rearrangements and oncogene amplification revealed by long-read DNA and RNA sequencing of a breast cancer cell line
Reporter: Stephen J. Williams, PhD
In a Genome Research report by Marie Nattestad et al. [1], the SK-BR-3 breast cancer cell line was sequenced using a long read single molecule sequencing protocol in order to develop one of the most detailed maps of structural variations in a cancer genome to date. The authors detected over 20,000 variants with this new sequencing modality, whereas most of these variants would have been missed by short read sequencing. In addition, a complex sequence of nested duplications and translocations occurred surrounding the ERBB2 (HER2) while full-length transcriptomic analysis revealed novel gene fusions within the nested genomic variants. The authors suggest that combining this long-read genome and transcriptome sequencing results in a more comprehensive coverage of tumor gene variants and “sheds new light on the complex mechanisms involved in cancer genome evolution.”
Genomic instability is a hallmark of cancer [2], which lead to numerous genetic variations such as:
Copy number variations
Chromosomal alterations
Gene fusions
Deletions
Gene duplications
Insertions
Translocations
Efforts such as the Cancer Genome Atlas [3], and the International Genome Consortium (2010) use short-read sequencing technology to detect and analyze thousands of commonly occurring mutations however short-read technology has a high false positive and negative rate for detecting less common genetic structural variations {as high as 50% [4]}. In addition, short reads cannot detect variations in close proximity to each other or on the same molecule, therefore underestimating the variation number.
Methods: The authors used a long-read sequencing technology from Pacific Biosciences (SMRT) to analyze the mutational and structural variation in the SK-BR-3 breast cancer cell line. A split read and within-read mapping approach was used to detect variants of different types and sizes. In general, long-reads have better alignment qualities than short reads, resulting in higher quality mapping. Transcriptomic analysis was performed using Iso-Seq.
Results: Using the SMRT long-read sequencing technology from Pacific Biosciences, the authors were able to obtain 71.9% sequencing coverage with average read length of 9.8 kb for the SK-BR-3 genome.
A few notes:
Most amplified regions (33.6 copies) around the locus spanning the ERBB2 oncogene and around MYC locus (38 copies), EGFR locus (7 copies) and BCAS1 (16.8 copies)
The locus 8q24.12 had the most amplifications (this locus contains the SNTB1 gene) at 69.2 copies
Long-read sequencing showed more insertions than deletions and suggests an underestimate of the lengths of low complexity regions in the human reference genome
Found 1,493 long read variants, 603 of which were between different chromosomes
Using Iso-Seq in conjunction with the long-read platform, they detected 1,692,379 isoforms (93%) mapping to the reference genome and 53 putative gene fusions (39 of which they found genomic evidence)
A table modified from the paper on the gene fusions is given below:
Table 1. Gene fusions with RNA evidence from Iso-Seq and DNA evidence from SMRT DNA sequencing where the genomic path is found using SplitThreader from Sniffles variant calls. Note link in table is GeneCard for each gene.
SplitThreader found two different paths for the RAD51B-SEMA6D gene fusion and for the LINC00536-PVT1 gene fusion. Number of Iso-Seq reads refers to full-length HQ-filtered reads. Alignments of SMRT DNA sequence reads supporting each of these gene fusions are shown in Supplemental Note S2.
References
Nattestad M, Goodwin S, Ng K, Baslan T, Sedlazeck FJ, Rescheneder P, Garvin T, Fang H, Gurtowski J, Hutton E et al: Complex rearrangements and oncogene amplifications revealed by long-read DNA and RNA sequencing of a breast cancer cell line.Genome research 2018, 28(8):1126-1135.
Kandoth C, McLellan MD, Vandin F, Ye K, Niu B, Lu C, Xie M, Zhang Q, McMichael JF, Wyczalkowski MA et al: Mutational landscape and significance across 12 major cancer types. Nature 2013, 502(7471):333-339.
Sudmant PH, Rausch T, Gardner EJ, Handsaker RE, Abyzov A, Huddleston J, Zhang Y, Ye K, Jun G, Fritz MH et al: An integrated map of structural variation in 2,504 human genomes. Nature 2015, 526(7571):75-81.
Other articles on Cancer Genome Sequencing in this Open Access Journal Include:
Bioinformatics Tool Review: Genome Variant Analysis Tools, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. wm2@ebi.ac.uk.
2
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
3
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. fiona@ebi.ac.uk.
Abstract
The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.
Rare diseases can be difficult to diagnose due to low incidence and incomplete penetrance of implicated alleles however variant analysis of whole genome sequencing can identify underlying genetic events responsible for the disease (Nature, 2015). However, a large cohort is required for many WGS association studies in order to produce enough statistical power for interpretation (see post and here). To this effect major sequencing projects have been initiated worldwide including:
And although sequencing costs have dramatically been reduced over the years, the costs to determine the functional consequences of such variants remains high, as thorough basic research studies must be conducted to validate the interpretation of variant data with respect to the underlying disease, as only a small fraction of variants from a genome sequencing project will encode for a functional protein. Correct annotation of sequences and variants, identification of correct corresponding reference genes or transcripts in GENCODE or RefSeq respectively offer compelling challenges to the proper identification of sequenced variants as potential functional variants.
To this effect, the authors developed the Ensembl Variant Effect Predictor (VEP), which is a software suite that performs annotations and analysis of most types of genomic variation in coding and non-coding regions of the genome.
Summary of Features
Annotation: VEP can annotate two broad categories of genomic variants
Sequence variants with specific and defined changes: indels, base substitutions, SNVs, tandem repeats
Larger structural variants > 50 nucleotides
Species and assembly/genomic database support: VEP can analyze data from any species with assembled genome sequence and annotated gene set. VEP supports chromosome assemblies such as the latest GRCh38, FASTA, as well as transcripts from RefSeq as well as user-derived sequences
Transcript Annotation: VEP includes a wide variety of gene and transcript related information including NCBI Gene ID, Gene Symbol, Transcript ID, NCBI RefSeq ID, exon/intron information, and cross reference to other databases such as UniProt
Protein Annotation: Protein-related fields include Protein ID, RefSeq ID, SwissProt, UniParc ID, reference codons and amino acids, SIFT pathogenicity score, protein domains
Noncoding Annotation: VEP reports variants in noncoding regions including genomic regulatory regions, intronic regions, transcription binding motifs. Data from ENCODE, BLUEPRINT, and NIH Epigenetics RoadMap are used for primary annotation. Plugins to the Perl coding are also available to link other databases which annotate noncoding sequence features.
Frequency, phenotype, and citation annotation: VEP searches Ensembl databases containing a large amount of germline variant information and checks variants against the dbSNP single nucleotide polymorphism database. VEP integrates with mutational databases such as COSMIC, the Human Gene Mutation Database, and structural and copy number variants from Database of Genomic Variants. Allele Frequencies are reported from 1000 Genomes and NHLBI and integrates with PubMed for literature annotation. Phenotype information is from OMIM, Orphanet, GWAS and clinical information of variants from ClinVar.
Flexible Input and Output Formats: VEP supports input data format called “variant call format” or VCP, a standard in next-gen sequencing. VEP has the ability to process variant identifiers from other database formats. Output formats are tab deliminated and give the user choices in presentation of results (HTML or text based)
Choice of user interface
Online tool (VEP Web): simple point and click; incorporates Instant VEP Functionality and copy and paste features. Results can be stored online in cloud storage on Ensembl.
VEP script: VEP is available as a downloadable PERL script (see below for link) and can process large amounts of data rapidly. This interface is powerfully flexible with the ability to integrate multiple plugins available from Ensembl and GitHub. The ability to alter the PERL code and add plugins and code functions allows the flexibility to modify any feature of VEP.
VEP REST API: provides robust computational access to any programming language and returns basic variant annotation. Can make use of external plugins.
Watch Video on VES Web Version training on How to Analyze Your Sequence in VEP
Availability of data and materials
The dataset supporting the conclusions of this article is available from Illumina’s Platinum Genomes [93] and using the Ensembl release 75 gene set. Pre-built data sets are available for all Ensembl and Ensembl Genomes species [94]. They can also be downloaded automatically during set up whilst installing the VEP.
Nature. 2015 Mar 12;519(7542):223-8. doi: 10.1038/nature14135. PMID:25533962
Updated 11/15/2018
Research Points to Caution in Use of Variant Effect Prediction Bioinformatic Tools
Although we have the ability to use high throughput sequencing to identify allelic variants occurring in rare disease, correlation of these variants with the underlying disease is often difficult due to a few concerns:
For rare sporadic diseases, classical gene/variant association studies have proven difficult to perform (Meyts et al. 2016)
As Whole Exome Sequencing (WES) returns a considerable number of variants, how to differentiate the normal allelic variation found in the human population from disease-causing pathogenic alleles
For rare diseases, pathogenic allele frequencies are generally low
Therefore, for these rare pathogenic alleles, the use of bioinformatics tools in order to predict the resulting changes in gene function may provide insight into disease etiology when validation of these allelic changes might be experimentally difficult.
In a 2017 Genes & Immunity paper, Line Lykke Andersen and Rune Hartmann tested the reliability of various bioinformatic software to predict the functional consequence of variants of six different genes involved in interferon induction and sixteen allelic variants of the IFNLR1 gene. These variants were found in cohorts of patients presenting with herpes simplex encephalitis (HSE). Most of the adult population is seropositive for Herpes Simplex Virus (HSV) however a minor fraction (1 in 250,000 individuals per year) of HSV infected individuals will develop HSE (Hjalmarsson et al., 2007). It has been suggested that HSE occurs in individuals with rare primary immunodeficiencies caused by gene defects affecting innate immunity through reduced production of interferons (IFN) (Zhang et al., Lim et al.).
References
Meyts I, Bosch B, Bolze A, Boisson B, Itan Y, Belkadi A, et al. Exome and genome sequencing for inborn errors of immunity. J Allergy Clin Immunol. 2016;138:957–69.
Hjalmarsson A, Blomqvist P, Skoldenberg B. Herpes simplex encephalitis in Sweden, 1990-2001: incidence, morbidity, and mortality. Clin Infect Dis. 2007;45:875–80.
Zhang SY, Jouanguy E, Ugolini S, Smahi A, Elain G, Romero P, et al. TLR3 deficiency in patients with herpes simplex encephalitis. Science. 2007;317:1522–7.
Lim HK, Seppanen M, Hautala T, Ciancanelli MJ, Itan Y, Lafaille FG, et al. TLR3 deficiency in herpes simplex encephalitis: high allelic heterogeneity and recurrence risk. Neurology. 2014;83:1888–97.
Genes Immun. 2017 Dec 4. doi: 10.1038/s41435-017-0002-z.
We selected two sets of naturally occurring human missense allelic variants within innate immune genes. The first set represented eleven non-synonymous variants in six different genes involved in interferon (IFN) induction, present in a cohort of patients suffering from herpes simplex encephalitis (HSE) and the second set represented sixteen allelic variants of the IFNLR1 gene. We recreated the variants in vitro and tested their effect on protein function in a HEK293T cell based assay. We then used an array of 14 available bioinformatics tools to predict the effect of these variants upon protein function. To our surprise two of the most commonly used tools, CADD and SIFT, produced a high rate of false positives, whereas SNPs&GO exhibited the lowest rate of false positives in our test. As the problem in our test in general was false positive variants, inclusion of mutation significance cutoff (MSC) did not improve accuracy.
Methodology
Identification of rare variants
Genomes of nineteen Dutch patients with a history of HSE sequenced by WES and identification of novel HSE causing variants determined by filtering the single nucleotide polymorphisms (SNPs) that had a frequency below 1% in the NHBLI Exome Sequencing Project Exome Variant Server and the 1000 Genomes Project and were present within 204 genes involved in the immune response to HSV.
Identified variants (204) manually evaluated for involvement of IFN induction based on IDBase and KEGG pathway database analysis.
In order to validate the predictive value of the software, HEK293T cells, deficient in IRF3, MAVS, and IKKe/TBK1, were cotransfected with the nine variants of the aforementioned genes and a luciferase reporter under control of the IFN-b promoter and luciferase activity measured as an indicator of IFN signaling function. Western blot was performed to confirm the expression of the constructs.
In predicting the pathogenicity of a nonsynonymous single-nucleotide variant (nsSNV), a radical change in amino acid properties is prone to be classified as being pathogenic. However, not all such nsSNVs are associated with human diseases. We generated random forest (RF) models individually for each amino acid substitution to differentiate pathogenic nsSNVs in the Human Gene Mutation Database and common nsSNVs in dbSNP. We named a set of our models ‘Individual Meta RF’ (InMeRF). Ten-fold cross-validation of InMeRF showed that the areas under the curves (AUCs) of receiver operating characteristic (ROC) and precision-recall curves were on average 0.941 and 0.957, respectively. To compare InMeRF with seven other tools, the eight tools were generated using the same training dataset, and were compared using the same three testing datasets. ROC-AUCs of InMeRF were ranked first in the eight tools. We applied InMeRF to 155 pathogenic and 125 common nsSNVs in seven major genes causing congenital myasthenic syndromes, as well as in VANGL1 causing spina bifida, and found that the sensitivity and specificity of InMeRF were 0.942 and 0.848, respectively. We made the InMeRF web service, and also made genome-wide InMeRF scores available online (https://www.med.nagoya-u.ac.jp/neurogenetics/InMeRF/).
Numerous human diseases are caused by mutations in genomic sequences. Since amino acid changes affect protein function through mechanisms often predictable from protein structure, the integration of structural and sequence data enables us to estimate with greater accuracy whether and how a given mutation will lead to disease. Publicly available annotated databases enable hypothesis assessment and benchmarking of prediction tools. However, the results are often presented as summary statistics or black box predictors, without providing full descriptive information. We developed a new semi-manually curated human variant database presenting information on the protein contact-map, sequence-to-structure mapping, amino acid identity change, and stability prediction for the popular UniProt database. We found that the profiles of pathogenic and benign missense polymorphisms can be effectively deduced using decision trees and comparative analyses based on the presented dataset. The database is made publicly available through https://zhanglab.ccmb.med.umich.edu/ADDRESS.
Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.
Notes On Tumor Heterogeneity: Targets and Mechanisms, from the 2015 AACR Meeting in Philadelphia PA
Reporter: Stephen J. Williams, Ph.D.
The following contain notes from the Sunday April 19, 2015 AACR Meeting (Pennsylvania Convention Center, Philadelphia PA) 1 PM Major Symposium Session on Tumor Heterogeneity: Targets and Mechanism chaired by Dr. Charles Swanton.
Tumor heterogeneity is a common feature of many malignancies, especially the solid tumors and can drive the evolution and adaptation of the growing tumor, complicating therapy and resulting in therapeutic failure, including resistance. This session at AACR described the mechanisms, both genetic and epigenetic, which precipitate intratumor heterogeneity and how mutational processes and chromosomal instability may impact the tumor progression and the origin of driver events during tumor evolution. Finally the session examined possible therapeutic strategies to take advantage of, and overcome, tumor evolution. The session was chaired by Dr. Charles Swanton. For a more complete description of his work, tumor heterogeneity, and an interview on this site please click on the link below:
This paper described the longitudinal Whole Genome Sequencing (WGS) study of a 35 year old female whose primary glioblastoma (GBM) was followed through temozolomide treatment and ultimately recurrence.
In 2008 patient was diagnosed with primary GBM (three biopsies of unrelated sites were Grade II and Grade IV; temozolomide therapy for three years then relapse in 2011
WGS of 2 areas of primary tumor showed extensive mutational and copy number heterogeneity; was able to identify clonal TP53 mutations and clonal IDH1 mutation in primary tumor with different patterns of clonality based on grade
Amplifications on chromosome 4 and 12 (PDGFRA, KIT, CDK4)
After three years of temozolomide multiple translocations found in chromosome 4 and 12 (6 translocations)
Clonal IDH1 R132H mutation in primary tumor only at very low frequency in recurrent tumor
The WGS on recurrent tumor (sequencing took ONLY 9 days from tumor resection to sequence results) showed mutation cluster in KIT/PDGFRA.PI3K.mTOR axis so patient treated with imatinib
However despite rapid sequencing and a personalized approach based on WGS results, tumor progressed and patient died shortly: tumor evolution is HUGE hurdle for personalized medicine
As Dr. Swanton stated:
“we are underestimating the frequency of polyclonal evolution”
analyzed nine cancer types to determine the subclonal frequencies of driver events, to time mutational processes during cancer evolution, and to identify drivers of subclonal expansions.
identified later subclonal “actionable” mutations, including BRAF (V600E), IDH1 (R132H), PIK3CA (E545K), EGFR (L858R), and KRAS (G12D), which may compromise the efficacy of targeted therapy approaches.
> 20% of IDH1 mutations in glioblastomas, and 15% of mutations in genes in the PI3K (phosphatidylinositol 3-kinase)–AKT–mTOR (mammalian target of rapamycin) signaling axis across all tumor types were subclonal
Mutations in the RAS–MEK (mitogen-activated protein kinase kinase) signaling axis were less likely to be subclonal than mutations in genes associated with PI3K-AKT-mTOR signaling
Branched chain can converge on single resistance mechanism; clonal resistance (for example to PI3K inhibitors can get multiple PTEN mutations in various metastases
Targeting Tumor Heterogeneity
Identify high risk occupants (have to know case history)
Mutational landscape interferes with anti-PD1 therapies
Low frequency mutations affect outcome
Notes from Dr. Catherine J. Wu, Dana-Farber Cancer Institute: The evolutionary landscape of CLL: Therapeutic implications
Clonal evolution a key feature of cancer progression and relapse
Hypothesis: evolutionary dynamics (heterogeneity) in chronic lymphocytic leukemia (CLL) contributes to variations in response and disease “tempo”
Used whole exome sequencing and copy number data of 149 CLL cases to discover early and late cancer drivers: clonal patterns (Landau et. al, Cell 2013); some drivers correspond to poor clinical outcome
Methylation studies suggest that there is epigenetic heterogeneity which may drive CLL clonal evolution
Developing methodology to integrate WES to determine mutations with immunogenic potential for development of personalized immunotherapy for CLL and other malignancies
McGranahan N, Favero F, de Bruin EC, Birkbak NJ, Szallasi Z, Swanton C: Clonal status of actionable driver events and the timing of mutational processes in cancer evolution. Science translational medicine 2015, 7(283):283ra254.
Other related articles on Tumor Heterogeneity were published in this Open Access Online Scientific Journal, include the following:
Variant ASGR1 Associated with a Reduced Risk of Coronary Artery Disease
Paul Nioi, Ph.D., Asgeir Sigurdsson, B.Sc., Gudmar Thorleifsson, Ph.D., Hannes Helgason, Ph.D., Arna B. Agustsdottir, B.Sc., Gudmundur L. Norddahl, Ph.D., Anna Helgadottir, M.D., Audur Magnusdottir, Ph.D., Aslaug Jonasdottir, M.Sc., Solveig Gretarsdottir, Ph.D., Ingileif Jonsdottir, Ph.D., Valgerdur Steinthorsdottir, Ph.D., Thorunn Rafnar, Ph.D., Dorine W. Swinkels, M.D., Ph.D., Tessel E. Galesloot, Ph.D., Niels Grarup, Ph.D., Torben Jørgensen, D.M.Sc., Henrik Vestergaard, D.M.Sc., Torben Hansen, Ph.D., Torsten Lauritzen, D.M.Sc., Allan Linneberg, Ph.D., Nele Friedrich, Ph.D., Nikolaj T. Krarup, Ph.D., Mogens Fenger, Ph.D., Ulrik Abildgaard, D.M.Sc., Peter R. Hansen, D.M.Sc., Anders M. Galløe, Ph.D., Peter S. Braund, Ph.D., Christopher P. Nelson, Ph.D., Alistair S. Hall, F.R.C.P., Michael J.A. Williams, M.D., Andre M. van Rij, M.D., Gregory T. Jones, Ph.D., Riyaz S. Patel, M.D., Allan I. Levey, M.D., Ph.D., Salim Hayek, M.D., Svati H. Shah, M.D., Muredach Reilly, M.B., B.Ch., Gudmundur I. Eyjolfsson, M.D., Olof Sigurdardottir, M.D., Ph.D., Isleifur Olafsson, M.D., Ph.D., Lambertus A. Kiemeney, Ph.D., Arshed A. Quyyumi, F.R.C.P., Daniel J. Rader, M.D., William E. Kraus, M.D., Nilesh J. Samani, F.R.C.P., Oluf Pedersen, D.M.Sc., Gudmundur Thorgeirsson, M.D., Ph.D., Gisli Masson, Ph.D., Hilma Holm, M.D., Daniel Gudbjartsson, Ph.D., Patrick Sulem, M.D., Unnur Thorsteinsdottir, Ph.D., and Kari Stefansson, M.D., Ph.D.
N Engl J Med 2016; 374:2131-2141June 2, 2016DOI: 10.1056/NEJMoa1508419
Several sequence variants are known to have effects on serum levels of non–high-density lipoprotein (HDL) cholesterol that alter the risk of coronary artery disease.
We sequenced the genomes of 2636 Icelanders and found variants that we then imputed into the genomes of approximately 398,000 Icelanders. We tested for association between these imputed variants and non-HDL cholesterol levels in 119,146 samples. We then performed replication testing in two populations of European descent. We assessed the effects of an implicated loss-of-function variant on the risk of coronary artery disease in 42,524 case patients and 249,414 controls from five European ancestry populations. An augmented set of genomes was screened for additional loss-of-function variants in a target gene. We evaluated the effect of an implicated variant on protein stability.
We found a rare noncoding 12-base-pair (bp) deletion (del12) in intron 4 of ASGR1, which encodes a subunit of the asialoglycoprotein receptor, a lectin that plays a role in the homeostasis of circulating glycoproteins. The del12 mutation activates a cryptic splice site, leading to a frameshift mutation and a premature stop codon that renders a truncated protein prone to degradation. Heterozygous carriers of the mutation (1 in 120 persons in our study population) had a lower level of non-HDL cholesterol than noncarriers, a difference of 15.3 mg per deciliter (0.40 mmol per liter) (P=1.0×10−16), and a lower risk of coronary artery disease (by 34%; 95% confidence interval, 21 to 45; P=4.0×10−6). In a larger set of sequenced samples from Icelanders, we found another loss-of-function ASGR1 variant (p.W158X, carried by 1 in 1850 persons) that was also associated with lower levels of non-HDL cholesterol (P=1.8×10−3).
ASGR1 haploinsufficiency was associated with reduced levels of non-HDL cholesterol and a reduced risk of coronary artery disease. (Funded by the National Institutes of Health and others.)
Amgen’s deCODE Genetics Publishes Largest Human Genome Population Study to Date
Mark Terry, BioSpace.com Breaking News Staff reported on results of one of the largest genome sequencing efforts to date, sequencing of the genomes of 2,636 people from Iceland by deCODE genetics, Inc., a division of Thousand Oaks, Calif.-based Amgen (AMGN).
There were a total of four studies, published on March 25, 2015 on the online version of Nature Genetics; titled “Large-scale whole-genome sequencing of the Icelandic population[1],” “Identification of a large set of rare complete human knockouts[2],” “The Y-chromosome point mutation rate in humans[3]” and “Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease[4].”
The project identified some new genetic variants which increase risk of Alzheimer’s disease and confirmed some variants known to increase risk of diabetes and atrial fibrillation. A more in-depth post will curate these findings but there was an interesting discrete geographic distribution of certain rare variants located around Iceland. The dataset offers a treasure trove of meaningful genetic information not only about the Icelandic population but offers numerous new targets for breast, ovarian cancer as well as Alzheimer’s disease.
“This work is a demonstration of the unique power sequencing gives us for learning more about the history of our species,” said Kari Stefansson, founder and chief executive officer of deCode and one of the lead authors in a statement, “and for contributing to new means of diagnosing, treating and preventing disease.”
The scale and ambition of the study is impressive, but perhaps more important, the research identified a new genetic variant that increases the risk of Alzheimer’s disease and already had identified an APP variant that is associated with decreased risk of Alzheimer’s Disease. It also confirmed variants that increase the risk of diabetes and a variant that results in atrial fibrillation.
The database of human genetic variation (dbSNP) contained over 50 million unique sequence variants yet this database only represents a small proportion of single nucleotide variants which is thought to exist. These “private” or rare variants undoubtedly contribute to important phenotypes, such as disease susceptibility. Non-SNV variants, like indels and structural variants, are also under-represented in public databases. The only way to fully elucidate the genetic basis of a trait is to consider all of these types of variants, and the only way to find them is by large-scale sequencing.
Curation of Population Genomic Sequencing Programs/Corporate Partnerships
Click on “Curation of genomic studies” below for full Table
Gudbjartsson DF, Helgason H, Gudjonsson SA, Zink F, Oddson A, Gylfason A, Besenbacher S, Magnusson G, Halldorsson BV, Hjartarson E et al: Large-scale whole-genome sequencing of the Icelandic population. Nature genetics 2015, advance online publication.
Sulem P, Helgason H, Oddson A, Stefansson H, Gudjonsson SA, Zink F, Hjartarson E, Sigurdsson GT, Jonasdottir A, Jonasdottir A et al: Identification of a large set of rare complete human knockouts. Nature genetics 2015, advance online publication.
Helgason A, Einarsson AW, Gumundsdottir VB, Sigursson A, Gunnarsdottir ED, Jagadeesan A, Ebenesersdottir SS, Kong A, Stefansson K: The Y-chromosome point mutation rate in humans. Nature genetics 2015, advance online publication.
Steinberg S, Stefansson H, Jonsson T, Johannsdottir H, Ingason A, Helgason H, Sulem P, Magnusson OT, Gudjonsson SA, Unnsteinsdottir U et al: Loss-of-function variants in ABCA7 confer risk of Alzheimer’s disease. Nature genetics 2015, advance online publication.
Other post related to DECODE, population genomics, and NGS on this site include:
8:00AM 11/13/2014 – 10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston
REAL TIME Coverage of this Conference by Dr. Aviva Lev-Ari, PhD, RN – Director and Founder of LEADERS in PHARMACEUTICAL BUSINESS INTELLIGENCE, Boston http://pharmaceuticalintelligence.com
8:00 A.M. Welcome from Gary Gottlieb, M.D.
Opening Remarks:
Partners HealthCare is the largest healthcare organization in Massachusetts and whose founding members are Brigham and Women’s Hospital and Massachusetts General Hospital. Dr. Gottlieb has long been a supporter of personalized medicine and he will provide his vision on the role of genetics and genomics in healthcare across the many hospitals that are part of Partners HealthCare.
IT – GeneInsight – IT goal Clinicians empowered by a workflow geneticist assign cases, data entered into knowledge base, case history, GENEINSIGHT Lab — geneticists enter info in a codified way will trigger a report for the Geneticist – adding specific knowledge standardized report enters Medical Record. Available in many Clinics of Partners members.
Example: Management of Patient genetic profiles – Relationships built between the lab and the Clinician
Variety of Tools are in development
GenInsight Team –>> Pathology –>> Sunquest Relationship
Mass General (MGH) & Brigham Women’s (BWH) — Chart in EM will have the Genetic Profile of a Patients checking in
The Future
Genetic testing –>> other info (Pathology, Exams, Life Style Survey, Meds, Imaging) — Integrated Medical Record
Clinic of the Future-– >> Diagnostics – Genomics data and Variants integrated at the Clinician desk
Why is personalized medicine important to Partners?
From Healthcare system to the Specific Human Conditions
Lab translate results to therapy
Biobank +50,000 specimens links to Medical Records of patients – relevant to Clinician, Genomics to Clinical Applications
Questions from the Podium
test results are not yet available online for patients
clinicians and liability – delays from Lab to decide a variant needs to be reclassified – alert is triggered. Lab needs time to accumulated knowledge before reporting a change in state.
Training Clinicians in above type of IT infrastructure: Labs around the Nations deal with VARIANT RECLASSIFICATION- physician education is a must, Clinicians have access to REFERENCE links.
All clinicians accessing this IT infrastructure — are trained. Most are not yet trained
Coordination within Countries and Across Nations — Platforms are Group specific – PARTNERS vs the US IT Infrastructure — Genomics access to EMR — from 20% to 70% Nationwide during the Years of the Obama Adm.
Shakeout in SW linking Genetic Labs to reach Gold Standard
Genomics in Medicine – Establishing a Patient-Centric View of Genomic Data
Reporter: Aviva Lev-Ari, PhD, RN
UPDATED on 12/13, 2013
Second Annual Genomics in Medicine Establishing a Patient-Centric View of Genomic Data February 13-14, 2014 | San Francisco, CA
Dr. Michael Christman, President and CEO of the Coriell Institute for Medical Research, to Present “Using a Patient’s Genetic Information in the Real World” at the Second Annual Genomics in Medicine Symposium
When a patient needs a new prescription, it will be necessary for the physician to quickly and securely access his/her genetic data to understand drug efficacy prior to dosing. Who will patients and doctors trust to store and interpret the data? Coriell and the CPMC research study have defined several of the key barriers to accelerate the adoption and routine use of genomics in medicine and have proposed solutions that are generally applicable.
Dr. Christman is an expert in genetics and genomics, with a focus on the integration of genome information into the delivery of clinical care. In 2007, he joined Coriell and initiated the Coriell Personalized Medicine Collaborative® (CPMC®), a research study evaluating the utility of using the knowledge of genetics in medicine. Prior to joining Coriell, he served as professor and founding chair of the Department of Genetics and Genomics for Boston University School of Medicine. There he led an international team of scientists in one of the first genome-wide association studies using the Framingham Heart Study cohort, published in Science magazine. Dr. Christman received his bachelor’s degree in chemistry with honors from the University of North Carolina, Chapel Hill, his doctorate in biochemistry from the University of California, Berkeley, and was a Jane Coffin Childs postdoctoral fellow at the Massachusetts Institute of Technology. FINAL AGENDA
RETURNING GENOMIC INFORMATION TO THE PATIENT
KEYNOTE PRESENTATION Incidental Findings in Genomic Medicine: The Debate and the Data Robert C. Green, M.D., MPH, Director, G2P Research Program; Associate Director, Research, Partners Center for Personalized Genetic Medicine, Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
Genomic Medicine Implementations for Primary Care Erwin Bottinger, M.D., The Irene and Dr. Arthur Fishberg Professor of Medicine; Director, The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine, Mount Sinai
Ethical Issues Related to the Return of Incidental Findings in Children/Families Ingrid A. Holm, M.D., MPH, Director, Phenotyping Core, Program in Genomics, Divisions of Genetics and Endocrinology, Boston Children’s Hospital
Beyond Sequence: Integration of Full-Genome Technologies for Personalized Medicine in the Clinic Raphael Lehrer, Founder and Chief Scientist, GeneKey
Targeted NGS of Clinical Samples: Overcoming the Challenges of Obtaining High Quality Data from Low Quality DNA Diane Ilsley, Ph.D., Marketing Manager, Genomic Services, Asuragen Sponsored by: <http://www.asuragen.com/>
BRIDGING THE GAP BETWEEN RESEARCH AND TREATMENT
Genome Sequencing in the Clinic: Found the Variants – Now What? Jennifer Friedman, M.D., Associate Clinical Professor, Neurosciences and Pediatrics, UCSD/Rady Children’s Hospital San Diego
The Answer is There, but I Don’t Understand It: Solutions from the Front Line Vanya Gant, Ph.D., FRCP, FRCPath, Divisional Clinical Director for Infection, The Department of Microbiology, UCLH NHS Foundation Trust
Using a Patient’s Genetic Information in the Real World Michael F. Christman, Ph.D., President and CEO, Coriell Institute for Medical Research
Developing Clinical Sequencing Assays at Einstein-Montefiore Cristina Montagna, Ph.D., Associate Professor, Genetics, Albert Einstein College of Medicine
THE IMPACT OF DTC TESTING
Direct-to-Consumer Genetic Testing: Balancing the Good and the Bad Nazneen Aziz, Ph.D., Director, Molecular Medicine, Transformation Program Office, College of American Pathologists
Crowdsourcing Genetic Discovery Nicholas Eriksson, Ph.D., Principal Scientist, Statistical Genetics, 23andMe
The Ethical and Social Implications of Direct-to-Consumer Genetic Testing Sandra Soo-Jin Lee, Ph.D., Senior Research Fellow, Center for Biomedical Ethics, Stanford University Medical School
THE IMPACT AND EVOLVING ROLE OF GENETIC COUNSELING
Next-Generation Genetic Counseling Ramji Srinivasan, CEO & Co-Founder, Counsyl
TDTC(CC) – Consumers, Clinicians and Counseling Erica Ramos, MS, CGC, Clinical Genomics Specialist, Certified Genetic Counselor, Translational and Consumer Genomics, Illumina, Inc.
Jon Stroup (Companies A-K) Manager, Business Development
Cambridge Healthtech Institute
T: (+1) 781-972-5483
E: jstroup@healthtech.com
Joseph Vacca (Companies L-Z) Manager, Business Development
Cambridge Healthtech Institute
T: (+1) 781-972-5431
E: jvacca@healthtech.com
Cambridge Healthtech Institute’s Second Annual
Part of the 21st Annual Molecular Medicine Tri-Conference February 13-14, 2014 | Westin St. Francis | San Francisco, CA
Cambridge Healthtech Institute’s Second Annual Genomics in Medicine symposium will provide insight into common implementation issues as they relate to practicing clinicians, as well as address the evolving role of genomics in guiding diagnoses and treatments. Special focus will be given to processing and delivering complex data to the practicing physician. Integration of decision-making tools with existing patient records will also be discussed. This symposium will provide a forum for those hoping to learn more about genomic medicine as well as those currently practicing and looking for an update on the field’s latest advances.
Thursday, February 13
7:30 am Registration and Morning Coffee
RETURNING GENOMIC INFORMATION TO THE PATIENT
9:00 Chairperson’s Opening Remarks
9:05 KEYNOTE PRESENTATION:
Incidental Findings in Genomic Medicine: The Debate and the Data
Robert C. Green, M.D., MPH, Director, G2P Research Program; Associate Director, Research, Partners Center for Personalized Genetic Medicine, Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School
Genomics is being rapidly integrated into medicine with many unanswered questions about how and how much risk information should be communicated, and how such information will influence physician and patient behaviors, health outcomes and health care costs. This presentation will summarize data from over 10 years of experimental work in translational genomics and health outcomes, discuss recent ACMG recommendations for incidental findings and preview results from our newest NIH-funded studies, the ongoing MedSeq Project and the recently funded BabySeq Project.
9:35 Genomic Medicine Implementations for Primary Care
Erwin Bottinger, M.D., The Irene and Dr. Arthur Fishberg Professor of Medicine; Director, The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine, Mount Sinai
Increasingly, genomic discoveries provide opportunities to personalize medication use and prediction and prevention of common chronic diseases. However, effective integration of genomic medicine in busy primary care practices is hampered by multiple barriers, including provider education gaps and negative impact on clinical workflow. Innovative programs for real-time, point-of-care integration of genomic medicine for primary care providers through genome-informed clinical decision support enabled in electronic health records will be presented.
10:05 Ethical Issues Related to the Return of Incidental Findings in Children/Families
Ingrid A. Holm, M.D., MPH, Director, Phenotyping Core, Program in Genomics, Divisions of Genetics and Endocrinology, Boston Children’s Hospital
10:35 Coffee Break with Exhibit and Poster Viewing
EMERGING TOOLS TO ENABLE PHYSICIAN USE
11:05 Reducing the Complexity of Clinical Omics Reporting for Clinicians and Laboratories
Jonathan Hirsch, Founder & President, Syapse
Syapse has built a cloud-based software platform that enables the use of omics at the point of care through an interactive web portal. We will describe how clinical omics labs use the Syapse platform to maintain an evolving omics knowledgebase which drives updated clinical reporting through interactive, intuitive interfaces designed for ease of use and comprehension. We will describe how hospitals use the Syapse platform to place omics results in the context of clinical guidelines, enabling physicians to easily adopt and integrate omics into their clinical workflow.
11:35 Beyond Sequence: Integration of Full-Genome Technologies for Personalized Medicine in the Clinic
Raphael Lehrer, Founder and Chief Scientist, GeneKey
Here we describe how we have used a combination of multiple full genome technologies to triangulate on key dysregulated mechanisms in a patient’s sample. By using a combination of systems biology and statistical analysis, we are able to draw conclusions far more precise than one could from sequence alone. We describe how we have applied in the clinic with patients and their oncologists and what we have seen/learned to date, including cases where the dysfunction is not mutation-based.
Sponsored by
12:05 pm Targeted NGS of Clinical Samples: Overcoming the Challenges of Obtaining High Quality Data from Low Quality DNA
12:35 Luncheon Presentation (Sponsorship Opportunity Available) or Lunch on Your Own
1:05 Session Break
BRIDGING THE GAP BETWEEN RESEARCH AND TREATMENT
1:50 Chairperson’s Remarks
1:55 Genome Sequencing in the Clinic: Found the Variants – Now What?
Jennifer Friedman, M.D., Associate Clinical Professor, Neurosciences and Pediatrics, UCSD/Rady Children’s Hospital San Diego
Advances in genome sequencing hold tremendous promise for providing answers and tailored therapies for undiagnosed patients. How to interpret, transmit and act upon volumes of complex data remains a challenge for sequencing providers, physicians and their patients. This presentation will use case-based examples to demonstrate promises and pitfalls encounter along the way.
2:25 The Answer is There but I Don’t Understand It: Solutions from the Front Line
Vanya Gant, Ph.D., FRCP, FRCPath, Divisional Clinical Director for Infection, The Department of Microbiology, UCLH NHS Foundation Trust
This talk will introduce the concept and fundamental problem of how to present complex NGS datasets to clinicians – and how this will be critical for rapid uptake. A case study outlining the principles behind a very new and innovative pathology project and way of delivering healthcare diagnostics will also be presented.
2:55 Refreshment Break with Exhibit and Poster Viewing
3:25 Using a Patient’s Genetic Information in the Real World
Michael F. Christman, Ph.D., President and CEO, Coriell Institute for Medical Research
When a patient needs a new prescription, it will be necessary for the physician to quickly and securely access his/her genetic data to understand drug efficacy prior to dosing. Who will patients and doctors trust to store and interpret the data? Coriell and the CPMC research study have defined several of the key barriers to accelerate the adoption and routine use of genomics in medicine and have proposed solutions that are generally applicable.
3:55 Developing Clinical Sequencing Assays at Einstein-Montefiore
We developed a program to introduce Next-Generation Sequencing (NGS) to address the needs of individuals receiving clinical care at Montefiore Medical Center. After extensive dialogue with clinicians, we designed a custom gene panel, spanning 5Mb and consisting of 650 genes targeting known Mendelian loci, some pediatric diseases and several hotspot genes in various cancer types. By building a basic infrastructure for transitioning NSG in the clinic we have encountered roadblocks and established protocols to overcome these.
4:25 Breakout Discussions (see website for details)
Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn
Curator and Writer: Stephen J. Williams, Ph.D.
In an earlier post entitled “Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing” the heterogenic nature of solid tumors was discussed. There resulted an excellent discussion in the Oncology Pharma forum on LinkedIn so I curated the comments (below article) to foster further discussion. To summarize the original post, this was a discussion of Dr. Charles Swanton’s paper[1] in which he and colleagues had noticed that individual biopsies from primary renal tumors displayed a variety of mutations of the same and different tumor suppressor genes (TSG), thereby not only revealing the heterogeneity of individual tumors but also how tumors can evolve. Thus it was suggested that individual cells of a primary tumor can represent individual clones, each evolving on a distinct pathway to tumorigenicity and metastasis as each clone would have accumulated different passenger mutations. It is these passenger mutations which have been posited to be responsible for a tumor’s continued growth (as discussed in the following post Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers). Indeed, as Dr. Swanton mentioned in the posting that it is very likely a solid tumor contains discrete clones with different driver and passenger mutations and possibly different mutated TSG but also this intra-tumor heterogeneity would have great implications for personalized chemotherapeutic strategies, not only against the primary tumor but against resistant outgrowth clones, and to the metastatic disease, as Swanton and colleagues had found that the metastatic disease displayed tremendously increased genomic instability than the underlying primary disease.
Therefore it may behoove the clinical oncologist to view solid tumors as a collection of multiple clones, each having their own mutagenic spectrum and tumorigenic phenotype. Each of these clones may acquire further mutations which provide growth advantage over other clones in the early primary tumor. In addition, branched evolution of a clone most likely depends more on genomic instability and epigenetic factors than on solely somatic mutation.
This is echoed in a report in Carcinogenesis back in 2005[3] Lorena Losi, Benedicte Baisse, Hanifa Bouzourene and Jean Benhatter had shown some similar results in colorectal cancer as their abstract described:
“In primary colorectal cancers (CRCs), intratumoral genetic heterogeneity was more often observed in early than in advanced stages, at 90 and 67%, respectively. All but one of the advanced CRCs were composed of one predominant clone and other minor clones, whereas no predominant clone has been identified in half of the early cancers. A reduction of the intratumoral genetic heterogeneity for point mutations and a relative stability of the heterogeneity for allelic losses indicate that, during the progression of CRC, clonal selection and chromosome instability continue, while an increase cannot be proven.”
Therefore if a tumor had evolved in time closer to the initial driver mutation multiple therapies may be warranted while tumors which had not yet evolved much from their driver mutation may be tackled with an agent directed against that driver, hence the branched evolution as shown in the following diagram:
An article written by Drs. Andrei Krivtsov and Scott Armstron entitled “Can One Cell Influence Cancer Heterogeneity”[4] commented on a study by Friedman-Morvinski[5] in Inder Verma’s laboratory discussed how genetic lesions can revert differentiated neorons and glial cells to an undifferentiated state [an important phenotype in development of glioblastoma multiforme].
In particular it is discussed that epigenetic state of the transformed cell may contribute to the heterogeneity of the resultant tumor. Indeed many investigators (initially discovered and proposed by Dr. Beatrice Mintz of the Institute for Cancer Research, later to be named the Fox Chase Cancer Center) show the cellular microenvironment influences transformation and tumor development[6-8].
Briefly the Friedman-Morvinski study used intra-cerebral ventricular (ICV) injection of lentivirus to introduce oncogenes within the CNS and produced tumors of multiple cell origins including neuronal and glial cell origin (neuroblastoma and glioma). The important takeaway was differentiated somatic cells which acquire genetic lesions can transform to form multiple tumor types. As the authors state, “cellular differentiation and specialization are accompanied by gradual changes in epigenetic programs” and that “the cell of origin may influence the epigenetic state of the tumor”. In essence this means that the success of therapy may depend on the cellular state (whether stem cell, progenitor cell, or differentiated specialized cell) at time of transformation. In other words tumors arising from cells with an epigenetic state seen in stem cells would be more resistant to therapy unless given an epigenetic therapy, such as azacytididne, retinoic acid or HDAC inhibitors.
So as the Oncology Pharma forum on LinkedIn was such an excellent discussion I would like to post the comments for curation purposes and foster further discussion. I would like to thank everyone’s great comments below. I would especially like to thank Dr. Emanuel Petricoin from George Mason and Dr. David Anderson for supplying extra papers which will be the subject of a future post. I had curated each comment with inserted LIVE LINKS to make it easier to refer to a paper and/or company mentioned in the comment.
The comments seemed to center on three main themes:
1. Clinicians pondering the benefit to mutational spectrum analysis to determine personalized therapy and develop biomarkers of early disease
2. A shift in the clinicians paradigm of cancer development, diagnoses, and treatment from strictly histologic evaluation to a genetic and altered cellular pathway view
3. Use of proteomics, microarray and epigenetics as an alternative to mutational analysis to determine aberrant cellular networks in various stages of tumor development
Victor Levenson • Thanks for posting this! To be honest, I am puzzled by the insistence on sequencing as a tool for tumor analysis – we know that expression patterns rather than mutations in a limited number of genes determine tumor physiology (or, even more, physiology of any tissue). Since the AACR-2012 we know that different tumors have similar or even identical mutations, so >functional< rather than >structural< differences are important. Frankly, I’d be much more excited learning about expression pattern heterogeneity in tumors.Granted that is much more challenging than NGS sequencing, but the value of the data would be incomparable, especially in its application to biomarker development.
Stephen J. Williams, Ph.D. • Dear Dr. Levenson, thanks for your comments. I agree with you and in no way am insisting on the releiance of sequencing mutations in cancer as the sole means for determining therapy. It is extremely true that tumors will show tremendous heterogeneity of mRNA expression. There are a number of studies (one which I will post on pharmaceuticalintelligence.com) that individual tumor cells will have differing expression patterns based on the levels of regional hypoxia within the tumor as well as other microenvironmental factors. I do have two posts on pharmaceuticalintelligence.com on this matter, curating various programs around the world which are using microarray expression analysis of tumors to determine personalized strategies. I believe the reliance on mutational analysis is based on the drugs that have been developed (such as Gleevec and crizotinib) which are based on mutant forms of BCR-Abl and ALK, respectively. However (as per two posts I did based on Mike Martin on our site “Mathematical Models of Driver and Passenger mutations) where he discusses how certain driver mutations will get the senescent cell over the hump to get to fully transformed and contribute to a certain level of growth while subsequent passengers are responsible for the sustained survival and expansion of the tumor.
Victor Levenson • Dr. Williams, thanks for the comments. Driving a senescent cell into proliferative stage is a tremendous change, which >may< begin with a mutation, but involves dramatic restructuring of transcription patterns that will drive the process. Hypoxia will definitely contribute to variations in the patterns, although will probably not be the main driver of the process. As to whether a mutation or a change in transcription pattern initiate the process, I am not sure we will ever be able to determine <grin>.
Vanisree Staniforth • Thanks for posting! Certainly a thought provoking article with regard to the future of personalized cancer therapies.
Dr. Raj Batra • If we follow Dr Levenson’s proposed conceptual approach (which we also published in 2009 and 2010), we are MUCH more likely to significantly impact tumor morbidity and mortality.
Stephen J. Williams, Ph.D. • Thanks Vanisiree and Dr. Batra for your comments. Hopefully we will see, from the future cancer statistics, how personlized therapy have improved outcomes for the solid tumors, like the hematologic cancers. 26 days ago
Emanuel Petricoin • The issue about intra and inter tumor heterogeneity is very important however since it is unknown which mutations are true drivers, an explanation of the results found in these studies simply could be the variances are all in the inconsequential mutations and the commonality is the driver mutations. Moreover, at the end of the day, its not the mRNA expression that we really care about but the functional protein signaling -phosphoprotein driven signaling architecture, that we care about since these are the drug targets directly.
Mohammad Azhar Aziz,PhD • This article addresses the potential complexity of dealing with cancer which is apparently increasing proportionally with the amount of data generated. Intratumor heterogeneity will remain there and even multiple biopsies that are randomly chosen will offer no conclusive solution.Mutations,expression profiles and functional protein signaling (as discussed above) alone can not provide any breakthrough. It will be a composite picture of all these and many other components (e.g. microenvironment, alternative splicing, epigenetics,non-coding RNAs etc.) that will hold the promises in the future. We have made phenomenal advances in understanding each of these aspects separately but definitely lack the tools to integrate all these. Developing tools to integrate all these data may provide some breakthrough in understanding and thus treating cancer.
Emanuel Petricoin • I agree Mohammad in a systems biology approach however the current compendium of drugs largely are kinase inhibitors or enzymatic inhibitors. Since most studies have shown little correlation between gene mutation and protein levels and phosphoprotein levels, for example, it is no wonder why the recent spate of failed trials (e.g. stratification by PIK3CA mutation or PTEN mutation for AKT-mTOR inhibitors) should come as any shock. We will be publishing work using protein pathway activation mapping coupled to laser dissection of a number of intra and inter tumoral analysis that indicates that the signaling architecture appears much more stable.
Stephen J. Williams, Ph.D. • Thank you Dr. Pettricoin for your comments. I eagerly await the publication of your results concerning proteomic evaluation of multiple biopsies of a tumor. I am very interested that you found limited intratuoral heterogeneity of signaling pathways given the diversity of intratumoral microenvironmental stresses (changes in regional hypoxia, blood flow, and populations of cancer stem cells). I agree with you and Mohammed that proteomic profiling will be imperative in determining personalized approaches for targeted therapy. Dr. Swanton had informed me that they had used IHC to determine if mTOR signaling had correlated with the mutational spectrum they had seen. In addition he had mentioned that there was enhanced genomic instability in the metastatic disease relative to the primary tumor and it would be very interesting to see how signaling pathways change in cohorts of matched metastatic and primary tumors. A few years ago we were looking at genes which were completely lost upon transformation of ovarian epithelial cells and worked up one of those genes (CRBP1) in cohorts of human ovarian cancer samples, using expression analysis in conjunction with laser capture microdissection and backed up by IHC analysis, and found that the expression pattern of CRBP1 was uniform in a tumor, either there was a complete loss in all cells in a tumor of CRBP1 or all the cells expressed the protein. Therefore I am curious if intratumor heterogeneity is dependent on the cell lineage and evolution of the transformed cell into a full tumor or a function of a discrete population of stem cells with varied genomic instability. Your results might suggest a more clonal evolution rather than a branched evolution which was found in this paper.
It is interesting that you mention the tough trials with the PTEN/PI3K/AKT axis of inhibitors. In high grade serous ovarian cancer we were never able to find any PI3K, PTEN, nor AKT mutations yet PI3K activity is usually overactive. If feel both your and Mohammed’s assessment that a systems biology approach instead of just relying on DNA mutational analysis will be more important in the future. In addition, there is nice work from Dr. Jefferey Peterson at Fox Chase and the development of a database of kinase inhibitors and activity effects on the kinome, showing the vast amount of crosstalk between once thought linear enzyme systems. If TKI’s will be the brunt of pharma’s development I feel they need to quickly develop as many TKI’s as they can now before we get to a clinical problem (resistance and lack of available therapeutics).
Emanuel Petricoin • Thanks Steven- yes, we are working with Charlie Swanton and Marco on the renal sets- our other studies are from breast and colon cancers. I think one of the things we do that really no one else is doing, unfortunately, is to laser capture microdissect the tumor cells from these specimens so that we have a more pure and accurate view of the signaling architecture. One confounder from the proteomic stand-point is the fact that pre-analytical variables such as post-excision delay times where the tissue is a hypoxic wound and signaling changes fluctuating as the tissue reacts to the ex-vivo condition can really effect things. When we look at tissue sets where the tissue is biopsied and immediately frozen we really dont see big differences in the signaling – the within tumor architecture is much more similar then between. We use the reverse phase array technology we invented to provide quantitative analysis on hundreds of phosphoproteins at once – so a nice view of the functional protein activation network. Your results of CRBP1 in ovarian tumors and the IHC data are very interesting. We will see how this all plays out. Of course once other confounder with the mutational data is that we really dont know what are the drivers and what are the passengers…
Yes I know Jeff Peterson’s work- its fantastic. In the end the hope I think- and in my personal opinion- will be rationally combined therapeutics based on the signaling architecture of each individual patient.
Incidentally, we just published a paper that you may be interested in from a “systems biology” standpoint-
SYSTEMS ANALYSIS OF THE NCI-60 CANCER CELL LINES BY ALIGNMENT OF PROTEIN PATHWAY ACTIVATION MODULES WITH “-OMIC” DATA FIELDS AND THERAPEUTIC RESPONSE SIGNATURES.
also- we published a paper that speaks directly to your point where we compared the signaling network activation of patient-matched primary colorectal cancers and synchronous liver mets. indeed there is huge systemic differences in the liver metastasis compared to the primary. there is no doubt in my mind that we will need to biopsy the metastasis to know how to treat. Looking at the primary tumor as a guide for therapy is a fools errand. here is the paper reference:
Protein pathway activation mapping of colorectal metastatic progression reveals metastasis-specific network alterations.
Center for Applied Proteomics and Molecular Medicine, George Mason University, 10900 University Blvd., Manassas, VA, 20110, USA.
Abstract
The mechanism by which tissue microecology influences invasion and metastasis is largely unknown. Recent studies have indicated differences in the molecular architecture of the metastatic lesion compared to the primary tumor, however, systemic analysis of the alterations within the activated protein signaling network has not been described. Using laser capture microdissection, protein microarray technology, and a unique specimen collection of 34 matched primary colorectal cancers (CRC) and synchronous hepatic metastasis, the quantitative measurement of the total and activated/phosphorylated levels of 86 key signaling proteins was performed. Activation of the EGFR-PDGFR-cKIT network, in addition to PI3K/AKT pathway, was found uniquely activated in the hepatic metastatic lesions compared to the matched primary tumors. If validated in larger study sets, these findings may have potential clinical relevance since many of these activated signaling proteins are current targets for molecularly targeted therapeutics. Thus, these findings could lead to liver metastasis specific molecular therapies for CRC.
Adrian Anghel • I think both patterns (protein phosphorylation and mRNA) should be important in this complicated equation of heterogeneity. Let’s not forget the so-called functional miRNA-mRNA regulatory modules (FMRMs). Also I think we have different patterns of this heterogeneity for different evolutive stages of the tumour.
Alvin L. Beers, Jr., M.D. • This is a great study, but bad news for attempting to tailor treatment based on molecular markers. Dr. Swanton’s comment: “herterogeneity is likely to complicate matters” is an understatement. Intratumoral heterogeneity, branched, instead of linear, evolution of mutational events portends a nightmare in trying to predict location and volume of biopsies. I am reminded of a series of articles in Nature 491 (22 November 2012) “Physical Scientists take on Cancer”. There is a great comment by Jennie Dusheck: “Cancer researchers now recognize that taming wild cancer cells – populations of cells that evolve, cooperate, and roam freely through the body-demand a wider-angle view than molecular biology has been able to offer. Cross-disciplinary collaborations can approach cancer a greater spatial and temporal scales, using mathematical methods more typical of engineering, physics, ecology and evolutionary biology. The sense of failure so evident five years ago is giving way to the excitement of a productive intellectual partnership.” I’m not certain how well the “productive partnership” is going, but this Swanton study confirms the limitations of molecular biology.
Stephen J. Williams, Ph.D. • Thanks Dr. Beers for adding in your comment and adding in Jennie’s comment. Certainly it is something to be aware of if a cancer center’s strategy is to rely solely on gene arrays to genotype tumors. I think Dr. Pettricoin’s work on using proteomics might give some resolution to the matter however, in communicating with Dr. Swanton, I did not get the feeling of an “all hope is lost” but just that, in the case of solid tumors like renal, that careful monitoring of tumors after treatment may be warranted and, more interestingly, from a scientific standpoint, is the genetic complexity surrounding the origin of the disease, and not simple mutational spectrum of a single clone.
Burke Lillian • This is clinically a very important issue. Right now, sequencing or massive approaches such as pan-phosphorylation studies are helpful because, although we know many of the drivers, these studies are actually identifying new genes or new pathways that are activated. After a few (or several years), we truly will know which genes are typically activated and there will be panels to look for these.
Emanuel Petricoin • yes, I agree. In fact, the company that I co-founded, Theranostics Health, Inc– is launching a CLIA based protein pathway activation mapping test at ASCO that measures actionable drug targets (e.g. phospho HER2, EGFR, HER3, AKT, ERK, JAK, STAT, p70S6) and total HER2, EGFR, HER3 and PTEN. So these tests are coming even now.
Alvin L. Beers, Jr., M.D. • I do not think that “all hope is lost” nor did I have the impression that Dr. Swanton feels that way with regards to molecular profiling of cancer. I certainly applaud further research into the molecular aspects of cancer biology. But I do not believe that this will be sufficient. Integrating physicial sciences into cancer biology makes perfect sense toward better understanding of this complex disease.
Eleni Papadopoulos-Bergquist • I have enjoyed reading these comments and different ideas regarding genetic testing and profiling. As a nurse and researcher at heart, this is information that will make a huge impact on drug protocols, therefore allowing the best and most specific treatment to each individual rather than having a standard treatment protocol. Even with the scientific complexity of specifying genotypes of particular cancers, there is still the question of each individuals body responding to treatment. I’d love to have some dialogue regarding immune response.
Bradford Graves • I too have enjoyed reading this discussion. I am not a clinician but as a drug discovery researcher I have been struck by some parallels to the concept of virus fitness in virology – particularly as applied to HIV. Drug discovery cannot wait for the final answers to the many important questions being addressed in the discussion initiated by Dr. Williams. The best we can do is to pursue a broad range of therapeutics that will give the clinicians the armament they will need to either cure a given cancer or to at least turn it into a chronic as opposed to an acute disease. There has been a measure of success in the HIV field and it seems like it will be achievable for cancer. Obviously, to the extent that the labels of driver and passenger mutations can be correctly applied will help to prioritize the targets we address.
David W. Anderson • I would suggest that you look at the following publications:
Boguski et al. (2009) Customized care 2020: how medical sequencing and network biology will enable personalized medicine. F1000 Bio Report 1:7.
Jones, S et al. (2010). Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biology. 11:R82. Marco Marra’s group in Toronto.
Also look at how companies and organizations like Foundation Medicine, Caris, Clarient, and CollabRx who are using genomics and sequencing on a large scale to address cancer from a personalized/individual approach.
Cancer is/will be a chronic disease requiring individualized/combinatorial therapies in many cases.
Alvin L. Beers, Jr., M.D. • David. These are excellent articles by Paul Bunn and Mark Boguski regarding integrating molecular markers into diagnostic evaluation, and I’ve seen other papers of similiar elk, and likely there will be more to come. Particularly in NSC lung cancer, the SOC is to use these markers up front. Diagnosis based on histology alone can no longer be recommended. The challenge for the future is how to integrate other aspects of cell biology with these markers. It remains daunting that not only do we see heterogeneity in molecular within tumors at a particularly point in time, but that there is often an evolution of markers over time, ie, a “plasticity” of markers, whether treatment is given or not. We know that targeted agents, TKI’s, enzyme inhibitors are not curative, but do give an improvement in PFS. A great deal of this resistance has to do with this “moving target” aspect of cancer cell biology..
References:
1. Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P et al: Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. The New England journal of medicine 2012, 366(10):883-892.
5. Friedmann-Morvinski D, Bushong EA, Ke E, Soda Y, Marumoto T, Singer O, Ellisman MH, Verma IM: Dedifferentiation of neurons and astrocytes by oncogenes can induce gliomas in mice. Science 2012, 338(6110):1080-1084.
NEW YORK (GenomeWeb News) – A Memorial Sloan-Kettering Cancer Center-led team has taken an exome- and genome-sequencing centered look at the mutations that may be found in the salivary gland cancer adenoid cystic carcinoma, or ACC.
As they reported in Nature Genetics online yesterday, the researchers did exome or genome sequencing on five-dozen matched ACC tumor and normal pairs.
Their analysis unearthed relatively few glitches in each tumor’s protein-coding sequences. But the group found suspicious mutations to several main pathways, including some — such as the PI3-kinase, fibroblast growth factor, and insulin-like growth factor-containing pathway— that may make promising treatment targets.
“Our discovery of genomic alterations in targetable pathways suggests potential avenues for novel treatments to address a typically chemoresistant malignancy,” corresponding author Timothy Chan, an oncology researcher at Memorial Sloan-Kettering, and his colleagues wrote, noting that “[v]erified ACC cell lines are needed to further substantiate the clinical usefulness of the mutations identified here.”
A few genetic glitches have been linked to ACC in the past, the team noted, including a fusion between the transcription factor genes MYB and NFIB. The tumors are also notorious for having higher-than-usual expression of certain genes, such as the epidermal growth factors. Even so, there is still a ways to go in characterizing and treating the aggressive cancer.
To get a better sense of the nature and frequency of mutations involved in ACC, the researchers used Illumina’s HiSeq2000 to do exome sequencing on 55 matched ACC and normal samples, as well as whole-genome sequencing on five more tumor-normal pairs.
For the exome sequencing experiments, they used Agilent SureSelect kits to capture protein-coding portions of the genome prior to sequencing. In the subsequent analyses, meanwhile, the group relied on Life Tehnologies’ SOLiD and Illumina’s MiSeq platforms to verify apparent single nucleotide glitches and small insertions and deletions.
With 106-fold coverage of the exomes, on average, and 37-fold average coverage of the genomes, the group was able to track down a mean of almost two-dozen somatic coding alterations per tumor.
When they used an algorithm called CHASM to distinguish between driver and passenger mutations in a set of 710 validated non-synonymous mutations, the researchers saw an over-representation of apparent driver mutations affecting genes known for processes ranging from chromatin regulation and DNA damage response to signaling and metabolism.
For instance, more than one-third of the tumors harbored mutations to chromatin regulators or chromatin state modifying genes such as SMARCA2, CREBBP, and KDM6A. Similarly, the researchers tracked down multiple mutations to genes coding for enzymes involved in adding or removing methylation and acetylation marks to histones.
Glitches to DNA damage response pathways also turned up in multiple tumors, they reported, as did mutations involving genes from the FGF-IGF-PI3K and other signaling pathways.
Some 57 percent of the tumors tested contained the MYB-NFIB fusion that had been implicated in ACC previously. But the new analysis also turned up mutations affecting genes that interact with MYB and in the NFIB gene itself, pointing to widespread — and perhaps complex — involvement for the two transcription factors in ACC.
“Our data highlight MYB as an active oncogenic partner in fusion transcripts in ACC,” the study’s authors said, “but also suggest a separate role for NFIB, given the presence of mutations specific to this gene.”
Going forward, the group hopes to see further analyses on alterations uncovered in the current study, particularly those falling in pathways that might be prone to clinical interventions.
“[O]ur data provide insights into the genetic framework underlying ACC oncogenesis,” the researchers concluded, “and establish a foundation for identifying new therapeutic strategies.”
I briefly review 3 important articles that recently appeared, each touching on important controversies in the use of whole genome sequencing:
1. Should Healthy People Have Their Genomes Sequenced At This Time? Wall Street Journal, February 15, 2013.
2. A Genetic Code for Genius? Wall Street Journal, February 15, 2013.
3. Francke U, Djamco C, Kiefer AK, et al. Dealing with the unexpected: consumer responses to direct-access BRCA mutation testing. PeerJ. 2012;1:e8. DOI 10.7717/peerj.8
Welcome to another segment on genomic medicine. Today, I want to get into 3 different articles: 2 from the Wall Street (“Medical”) Journal and the other from a new open access journal, PeerJ. All of them are related to the issues of genome sequencing.
First, there was a debate about whether all healthy people should have their genomes sequenced. It was a debate between Atul Butte from Stanford and Robert Green from Harvard. In this debate, they made a number of really good points, and I have linked you to that article if you’re interested.
Basically, is it too early to get sequencing because we need millions of people to have whole genome sequencing who are healthy in order for that information to be truly informative. The price continues to drop. So even though the sequencing that is done today would still be valid if it’s done accurately, the problem we have, of course, is a lack of enough people who are phenotyped with a particular condition to extract all the best information that is truly informative from whole genome sequencing.
it’s unlikely that even 2000 individuals with high IQ will be particularly informative but also, of course, what this could do from a bioethical standpoint. I’ll leave that to your imagination and thoughts as to where this could go – that is, trying to understand, even with limited power, the genomics of intelligence.
The third article, which is also very interesting, comes from this new journal called PeerJ. I’m on the editorial board of that journal, and I think it’s terrific to see open access, high-quality biomedical articles.
This one comes from the company 23andMe. From a very large number of individuals – now over 200,000 and rapidly approaching 1 million – who have had genome scans, a large number of women had information about the BRCA gene and whether they had a significant mutation. From these women who volunteered to participate in this study, we learned that they had no serious psychological repercussions from knowledge of this highly actionable BRCA pathogenic mutation.
This goes along with the previous study that we had done at Scripps led by my colleague Cinnamon Bloss in the New England Journal of Medicine, where, in thousands of individuals who had genome scans and had such data as ApoE4 status known to them for the first time, there were no significant negative psychological repercussions.
Should Healthy People Have Their Genomes Sequenced At This Time?
‘Patients in Waiting’
Injecting so much uncertain genetic information into the doctor-patient relationship could create legions of “patients in waiting” leading to unnecessary tests, harmful outcomes and lifelong anxiety. As private software companies compete to provide more genomic “findings” to a medical culture that is trained to search for diagnostic fire when they smell the smoke of disease risk, there are potential benefits. But there is also a real possibility that medical resources will be squandered and patients could be harmed.
Perhaps we all underestimated how complicated it would be to move genomic knowledge into the practice of medicine and public health. Now is the time to make sure we get this right through rigorous basic and clinical studies that define which mutations are dangerous, and distinguish useful from unnecessary interventions. Soon, genomic insights will give us early warnings about life-threatening illnesses that we may be able to prevent. Soon, standards will be available to guide doctors about which findings are meaningful and which are not.
Soon, there may be evidence to support the benefits of screening healthy individuals. But not today.
Table 1. Performance values for genome sequencing technologies including Sanger methods and Massively Parallel Seqeuncing methods. Sinville, R. and Soper, S. A. High resolution DNA separations using microchip electrophoresis. J. Sep. Sci. 2007, 30, 1714 – 1728 Morozova,O. and Marra, M. A. Applications of next-generation sequencing technologies in functional genomics. Genomics. 92 (2008) 255–264 (Photo credit: Wikipedia)
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