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Lecture Materials fromthe TRIG Working Group
Training Residents inGenomics
Production of these materials
made possible by
The Intersociety Council
for Pathology
Information, Inc.
www.pathologytraining.org
Table of Contents
Introduction ............................................................................................................................... 1 Lecture I: Genomic Pathology: An Introduction ........................................................................ 7 Lecture II: Genomic Methods .................................................................................................. 11 Lecture III: Interpreting Genomic Information for Clinical Care .............................................. 21 Lecture IV: Genomic Medicine: Communicating with the Patient .......................................... 31 All the lectures contained in this booklet are available online at www.pathologytraining.org. Production of these materials made possible by The Intersociety Council for Pathology Information, Inc.
June 20, 2012 “It is time to get serious about genomics education for all health care professionals,” wrote Dr. Eric Green, the director of the National Human Genome Research Institute and a pathologist by training, in a recent Commentary in the Journal of the American Medical Association.1 Indeed, a survey conducted by the (Pathology Residency) Program Directors Section (PRODS) of the Association of Pathology Chairs (APC) in the spring of 2010 demonstrated that approximately 70% of pathology residency programs do not have training in genomics. A coordinated effort to meet the genomics education challenge for anatomic pathology and laboratory medicine began in 2010.2,3 The Training Residents in Genomics (TRIG) Working Group was established in the summer of 2010 to facilitate the education of pathology residents in genomics and consists of experts in molecular pathology, genetic counseling and medical education. One of the first goals of the TRIG Working Group was to create four lectures that can be used by training programs to supplement an already existing molecular pathology curriculum for residents. The lectures cover genomic methods, clinical interpretation of genomic testing and communicating and reporting genomic test results to other clinicians and patients. The lectures are presented in this booklet as PowerPoint presentations and will also be freely available (with added notes) online (www.pathologytraining.org) on the website of the Intersociety Council for Pathology Information (ICPI), which also publishes the annual Directory of Pathology Training Programs. For assistance with accessing the online materials, please contact [email protected]. The TRIG Working Group thanks the American Society for Clinical Pathology for administrative and design support and ICPI for providing funding to produce and print this booklet. Please note that the lectures in this booklet and the online materials have not been specifically endorsed by any of the cooperating organizations listed below. They are offered in the hope that they will provide a useful initial introduction to the subject, be helpful in designing a genomic pathology curriculum for graduate training programs, and noting that genomic pathology will continue to evolve with the need to continuously update teaching materials. For your convenience, specific references to content in the four lectures are included on the pages that follow. On behalf of the TRIG Working Group, Richard L. Haspel, MD, PhD Beth Israel Deaconess Medical Center References: 1. Feero WG, Green ED. Genomics education for health care professionals in the 2st century. JAMA
2011; 306:989. 2. Haspel RL, et al. A call to action: training pathology residents in genomics and personalized
medicine. Am J Clin Pathol 2010; 133:832. 3. Tonallato PJ, et al. A national agenda for the future of pathology in personalized medicine: report
of the proceedings of a meeting at the Banbury Conference Center on genome-era pathology, precision diagnostics, and preemptive care: a stakeholder summit. Am J Clin Pathol 2011; 135:668.
1
Training Residents in Genomics (TRIG) Working Group
Roster:
Richard L. Haspel, MD, PhD (Chair) James B. Atkinson, MD Frederic G. Barr, MD, PhD Erynn Gordon, MS, CGC (past member) Karen L. Kaul, MD, PhD Debra G.B. Leonard, MD, PhD Julianne O’Daniel, MS, CGC Henry M. Rinder, MD Joan Scott, MS, CGC Mark E. Sobel, MD, PhD VO Speights, MD
Scientific advisors: Mark S. Boguski, MD, PhD Peter J. Tonellato, PhD Elizabeth Varga, MS, CGC Dennis P. Wall, PhD
The following organizations have cooperated with the TRIG Working Group:
Academy of Clinical Laboratory Physicians and Scientists (ACLPS) American Society for Clinical Pathology (ASCP) American Society for Investigative Pathology (ASIP) Association for Molecular Pathology (AMP) Association of Pathology Chairs (APC) Program Directors Section (PRODS) Undergraduate Medical Educators Section (UMEDS) College of American Pathologists (CAP) Intersociety Council for Pathology Information (ICPI) National Coalition for Health Professional Education in Genetics (NCHPEG) National Society of Genetic Counselors (NSGC) United States and Canadian Academy of Pathologists (USCAP)
ASCP Staff:
Suzanne Ziemnik, MEd Shiwen Song, MD, PhD Nancie Thompson Dan Klosterman Lucy Beck
ASIP Staff:
Priscilla S. Markwood, CAE
2
TRIG Working Group Lecture References Lecture I: Genomic Pathology: An Introduction Authors: Richard L. Haspel, MD, PhD; Mark S. Boguski, MD, PhD 1. Ashley EA, et al. Clinical assessment incorporating a personal genome. Lancet 2010;
375:1525. 2. Choi M, et al. Genetic diagnosis by whole exome capture and massively parallel DNA
sequencing. Proc Natl Acad Sci USA 2009; 106:19096. 3. Feero WG, Green ED. Genomics education for health care professionals in the 21st century.
JAMA 2011; 306:989. 4. Jones SJ, et al. Evolution of an adenocarcinoma in response to selection by targeted kinase
inhibitors. Genome Biol 2010; 11: R82. 5. Kahvejian A. et al. What would you do if you could sequence everything? Nat Biotechnol
2008; 26: 1125. 6. Link DC, et al. Identification of a novel TP53cancer susceptibility mutation through whole-
genome sequencing of a patient with therapy-related AML. JAMA 2011; 305: 1568. 7. Lupski JR, et al. Whole-genome sequencing in a patient with Charcot-Marie-Tooth
neuropathy. N Engl J Med 2010; 362:1181. 8. Macarthur D. Sample swaps at 23andMe: a cautionary tale. Wired. 2010;
http://www.wired.com/wiredscience/2010/06/Sample-swaps-at-23andMe:-a-cautionary-tale; accessed 2/10/12.
9. National Human Genome Research Institute. The human genome project completion: Frequently asked questions. http://www.genome.gov/11006943; accessed 2/10/12.
10. Roychowdhoury S, et al. A model for pathology and genomics. Sci Transl Med 2011; 3: 111ra121.
11. Salari K. The Dawning Era of Personalized Medicine Exposes a Gap in Medical Education. PLoS Medicine 2009; 6: e1000138.
12. Welch JS, et al. Use of whole-genome sequencing to diagnose a cryptic fusion oncogene. JAMA 2011; 305: 1577.
13. Worthey EA, et al. Making a definitive diagnosis: Successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease. Genetics in Medicine 2011; 13: 255.
Lecture II: Genomic Methods Authors: Dennis P. Wall, PhD; Frederic G. Barr, MD, PhD; Debra G.B. Leonard, MD, PhD 1. Alsmadi OA, et al. High accuracy genotyping directly from genomic DNA using a rolling
circle amplification based assay. BMC Genomics 2003 4:21. 2. Duggan DJ, et al. Expression profiling using cDNA microarrays. Nature Genetics 1999;
21:10. 3. Hung RJ, et al. A susceptibility locus for lung cancer maps to nicotinic acetylcholine
receptor subunit genes on 15q25. Nature Genetics 2008; 452: 633. 4. Jones SJ, et al. Evolution of an adenocarcinoma in response to selection by targeted kinase
inhibitors. Genome Biol 2010; 11: R82. 5. Koboldt DC et al. Challenges of sequencing human genomes. Brief Bioinform 2010; 11: 484.
3
6. LaFramboise T. Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances. Nucleic Acids Res 2009; 37: 4181.
7. Li H, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009; 25:2078.
8. Miller DT, et al. Consensus statement: Chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Amer J Hum Genet 2010; 86:749.
9. Ng PC, et al. An agenda for personalized medicine. Nature 2009; 461: 724. 10. Perou CM, et al. Molecular portraits of human breast tumours. Nature 2000; 406: 747. 11. Roychowdhoury S, et al. A model for pathology and genomics. Sci Transl Med 2011; 3:
111ra121. 12. Teo YY. Common statistical issues in genome-wide association studies: a review on power,
data quality control, genotype calling and population structure. Curr Op in Lipidology 2008; 19:133.
13. Welch JS, et al. Use of whole-genome sequencing to diagnose a cryptic fusion oncogene. JAMA 2011; 305: 1577.
14. Online Resources • Online Mendelian Inheritance in Man (http://www.ncbi.nlm.nih.gov/omim) • International HapMap project (http://hapmap.ncbi.nlm.nih.gov) • Human genome mutation database (http://www.hgvs.org/dblist/glsdb.html) • PharmGKB (http://www.pharmgkb.org)
Lecture III: Interpreting Genomic Information for Clinical Care Authors: Richard L. Haspel, MD, PhD; Karen L. Kaul, MD, PhD; Henry M. Rinder, MD 1. Alpha-tocopherol, Beta-carotene Cancer Prevention Study Group. The effect of vitamin E
and beta-carotene on the incidence of lung cancer and other cancers in male smokers. New Engl J Med 1994; 330: 1029.
2. Alsmadi OA, et al. High accuracy genotyping directly from genomic DNA using a rolling circle amplification based assay. BMC Genomics 2003 4:21.
3. Ashley EA, et al. Clinical assessment incorporating a personal genome. Lancet 2010; 375:1525.
4. Cuzick J, et al. Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the genomic health recurrence score in early breast cancer. J Clin Oncol 2011; 29: 4273.
5. Fan HC, et al. Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood. Proc Natl Acad Sci USA 2008; 105: 16266.
6. Hung RJ, et al. A susceptibility locus for lung cancer maps to nicotinic acetylcholine receptor subunit genes on 15q25. Nature Genetics 2008; 452: 633.
7. Jones SJ, et al. Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biol 2010; 11: R82.
8. Lagakos SW. The challenge of subgroup analyses — reporting without distorting. New Engl J Med 2006; 354: 16.
9. Menkes MS, et al. Serum beta-carotene, vitamins A and E, selenium and the risk of lung cancer. New Engl J Med 1986; 315:1250;
4
10. Ng PC, et al. An agenda for personalized medicine. Nature 2009; 461: 724. 11. Pearson TA, Manolio TA. How to interpret a genome-wide association study. JAMA 2008;
299: 1335. 12. Perou CM, et al. Molecular portraits of human breast tumours. Nature 2000; 406: 747. 13. Srinivasan BS, et al. A universal carrier test for the long tail of Mendelian disease. Reprod
Biomed Online 2010; 21: 537-51. 14. Welch JS, et al. Use of whole-genome sequencing to diagnose a cryptic fusion oncogene.
JAMA 2011; 305: 1577. Lecture IV: Genomic Medicine: Communicating with the Patient Authors: Julianne O’Daniel, MS, CGC; Joan Scott, MS, CGC; Elizabeth Varga, MS, CGC; VO Speights, DO; Erynn Gordon, MS, CGC 1. Ashley EA, et al. Clinical assessment incorporating a personal genome. Lancet 2010;
375:1525. 2. Bloss CS, et al. Effect of Direct-to-Consumer Genomewide Profiling to Assess Disease Risk.
New Engl J Med 2011; 364: 524. 3. Macarthur D. Sample swaps at 23andMe: a cautionary tale. Wired. 2010;
http://www.wired.com/wiredscience/2010/06/Sample-swaps-at-23andMe:-a-cautionary-tale; accessed 2/10/12.
4. National Society of Genetic Counselors. Professional status survey. 2010. http://www.nsgc.org/Publications/ProfessionalStatusSurvey/tabid/142/Default.aspx; accessed 2/10/12.
5. Ng PC, et al. An agenda for personalized medicine. Nature 2009; 461: 724. 6. Parmigiani G, et al. Validity of models for predicting BRCA1 and BRCA2 mutations. Ann
Intern Med 2007; 147: 441. 7. Robson ME, et al. American Society of Clinical Oncology Policy Statement Update: Genetic
and Genomic Testing for Cancer Susceptibility J Clin Oncol 2010; 28: 893-901. 8. Roychowdhoury S, et al. A model for pathology and genomics. Sci Transl Med 2011; 3:
111ra121. 9. United State Preventive Services Task Force. Genetic risk assessment and BRCA mutation
testing for breast and ovarian cancer susceptibility. 2005; http://www.uspreventiveservicestaskforce.org/uspstf/uspsbrgen.htm; accessed 2/10/12.
10. UT Southwestern Medical Center. Cancer Gene. http://www8.utsouthwestern.edu/utsw/cda/dept47834/files/73815.html; accessed 2/10/12
11. Online Resources • American Board of Genetic Counseling (www.abgc.net) • American Board of Medical Genetics (www.abmg.org) • National Coalition for Health Professional Education in Genetics (www.nchpeg.org) • American College of Medical Genetics (www.acmg.org) • National Society of Genetic Counselors (www.nsgc.org)
5
NOTES
6
Lecture I:Genomic Pathology: An
I t d tiIntroductionRichard L. Haspel, MD, PhDMark S. Boguski, MD, PhD
1TRIG Curriculum: Lecture 1March 2012
$2,700,000,000
1990-2003
2TRIG Curriculum: Lecture 1March 2012
What do I get for $999?
You get access to your raw data of 50 million DNAbases at high quality (80X coverage). You will haveaccess to new tools and content as they aredeveloped, to take full advantage of your exomesequence data. Most excitingly, you'll be atrailblazer, one of the first people on the planet toknow their personal exome sequence!
3TRIG Curriculum: Lecture 1March 2012
Coming to a clinic near you…
Based on personal experience atStanford University Hospital &Clinics…patients have alreadyp ybegun to arrive at their physician'soffice with 23andMe reports in handseeking expert medical advice.
Salari K. PLoS Medicine. 2009; 6:e1000138
4TRIG Curriculum: Lecture 1March 2012
Coming to a clinic near you…
5TRIG Curriculum: Lecture 1March 2012
DefinitionsWhat is a human genome?
• A “whole genome” consists of ~3 gigabytes (3 billion “base pairs”) of DNA distributed unequally among 46 chromosomes (diploid genome).
• Approximately 98% of the this DNA is “intergenic” (literally “between genes”), does not encode proteins and is of unknown medical relevance.
What is a humanexome?
• Exome refers to the 2% subset of the whole genome that encompasses our 22 000 pairs of genes~22,000 pairs of genes.
• Because each gene, on average, is composed of 8 protein-encoding segments (“exons”), an exome corresponds to 8 x 22,000 = 176,000 segments of DNA.
What is a human transcriptome (a.k.a. gene expression profile)?
• Transcriptome refers collectively to all of the “expressed” RNA “transcripts” of genes based on the “central dogma” of molecular biology, i.e. DNA -> RNA -> protein.
• A transcriptome reflects what a cell is doing at a particular point in time (molecular phenotype) as opposed to what it is capable of doing (genotype).
6TRIG Curriculum: Lecture 1March 2012
7
Medical genetics, preventive medicine
Natural history of disease, response to therapy
Surgical pathology
Genomic Testing as a Universal Diagnostic
MedicalMicrobiology
Molecular mechanisms of disease
Pharmacogenomics
Kahvejian A,.et al. Nat Biotechnol. 2008; 26:1125-33
7TRIG Curriculum: Lecture 1March 2012
Why Pathologists? We have access, we know testing
PersonalizedRisk Prediction,PathologistsMedicationDosing,Diagnosis/Prognosis
Physician sendssample toPathology (blood/tissue)
g
Access to patient’s genome
Genetic CounselorsMedical Geneticists
8TRIG Curriculum: Lecture 1March 2012
“[a patient’s genome] is just another lab value.”
-- D.P. DimmockACMG, Vancouver 18 March 201118 March 2011
Worthey EA, et al. Genetics in Medicine. 2011;13:255
Sample swaps at 23andMe: a cautionary tale
By Daniel MacArthur | June 7, 2010 | 6:00 a.m.
9TRIG Curriculum: Lecture 1March 2012
Pathologists in Familiar Role
?*
Pathologists
10TRIG Curriculum: Lecture 1March 2012
Already considering workflow
11
Roychowdhury S, et al. Sci Transl Med. 2011; 3: 111ra121
TRIG Curriculum: Lecture 1March 2012
What will be the pathology report of the future?
12
• Ethical issues• Informed consent• Integrated data
interpretationTRIG Curriculum: Lecture 1March 2012
8
Multidisciplinary Teams Needed
• Modern medical technology and practice requires a multidisciplinary approach to medical practicepractice.
• Greater interaction needed between Pathologists, Genetic Counselors and Medical Geneticists
13
Roychowdhury S, et al. Sci Transl Med. 2011; 3: 111ra121
TRIG Curriculum: Lecture 1March 2012
Training Residents in Genomics (TRIG) Working Group
14TRIG Curriculum: Lecture 1March 2012
What we will cover today:• Review present and future
of genomic testing:
– Methods• Single gene• Gene chips• Whole-genome/exome/
transcriptome seqeuncing
– Clinical utility• Prognosis/diagnosis• Risk prediction
– Communicating with the patient
• Single gene• Genomic testing
15TRIG Curriculum: Lecture 1March 2012
9
NOTES
10
Lecture II:Genomic MethodsGenomic Methods
Dennis P. Wall, PhDFrederick G. Barr, MD, PhD
Deborah G.B. Leonard, MD, PhD
1TRiG Curriculum: Lecture 2March 2012
Why Pathologists? We have access, we know testing
PersonalizedRisk Prediction,PathologistsMedicationDosing,Diagnosis/Prognosis
Physician sendssample toPathology (blood/tissue)
g
Access to patient’s genome
Just anotherlaboratory test
2TRiG Curriculum: Lecture 2March 2012
The path to genomic medicine
Sample Pathologists
Sample Collection
Collection
Testing: Sequencing, Gene chips
Analysis
g
Access to patient’s genome
3TRiG Curriculum: Lecture 2March 2012
What we will cover today:
• Types of genetic alterations
• Current and future molecular testing methods
– Cytogenetics, in situ hybridization, PCR
– Gene chips• Genotyping• Expression profiling• Copy number variation
– Next generation sequencing (NGS)
• Whole genome• Transcriptome
4TRiG Curriculum: Lecture 2March 2012
DNA alterations – the small stuff
Point mutation CCTGAGGAG CCTGTGGAG
Example: hemoglobin, beta – sickle cell disease
Repeat alteration
Deletion/Insertion
TTCCAG…(CAG)5…CAGCAA
GAATTAAGAGAAGCA GAAGCA
Example: epidermal growth factor receptor – lung cancer
TTCCAG…(CAG)60…CAGCAA
Example: huntingtin – Huntington disease
5TRiG Curriculum: Lecture 2March 2012
DNA alterations – the bigger stuff
Amplification
Deletion/Insertion
Example: 22q11.2 region –
DiGeorge syndrome
Example: 17q21 1 (ERBB2) –
Translocation
17q21.1 (ERBB2) –Breast cancer
Example:t(11;22)(q24;q12) –
Ewing’s sarcoma
11
22
Der 11
Der 22
6TRiG Curriculum: Lecture 2March 2012
11
Previous strategies to detect DNA alterations
Cytogenetics:Large indels, amplification, translocations
In situ hybridization:large indels, amplification, translocations
EGFR amplification in glioblastomat(6;15) in woman with repeated abortions
http://moon.ouhsc.eduhttp://www.indianmedguru.com
7TRiG Curriculum: Lecture 2March 2012
Previous strategies to detect DNA alterations
PCR-based approaches:Mutations, small indels, repeat alterations,large indels, amplification, translocations
Factor V Leiden mutation
Alsmadi OA, et al. BMC Genomics 2003 4:21
8TRiG Curriculum: Lecture 2March 2012
What we will cover today:
• Types of genetic alterations
• Current and future molecular testing methods
– Cytogenetics, in situ hybridization, PCR
– Gene chips• Genotyping• Expression profiling• Copy number variation
– Next generation sequencing (NGS)
• Whole genome• Transcriptome
9TRiG Curriculum: Lecture 2March 2012
DNA microarray - the basics
• Purpose: multiple simultaneous measurements by hybridization of labeled probe
• DNA elements may be: Oligonucleotides
DNA’ cDNA’s Large insert genomic clones
• Microarray is generated by: Printing Synthesis
10TRiG Curriculum: Lecture 2March 2012
Microarray technologies
11TRiG Curriculum: Lecture 2March 2012
Organization of a DNA microarray
(adapted from Affymetrix)
1.28 cm
1.28 cm
12TRiG Curriculum: Lecture 2March 2012
12
Hybridization of a labeled probeto the microarray
13
(adapted from Affymetrix)
TRiG Curriculum: Lecture 2March 2012
Detection of hybridization on microarray
Light from laser
14
(adapted from Affymetrix)
TRiG Curriculum: Lecture 2March 2012
Hybridization intensities on DNA microarray following laser scanning
15TRiG Curriculum: Lecture 2March 2012
Overview of SNP array technology
LaFramboise T. Nucleic Acids Res. 2009; 37:4181
16TRiG Curriculum: Lecture 2March 2012
Microarray Applications
• DNA analysis Polymorphism/mutation detection –
e.g. Disease susceptibility testingDrug efficacy/sensitivity testing
Copy number detection (comparative genomic hybridization) –
cv
py ( p g y )e.g. Constitutional or cancer karyotyping
Bacterial DNA – e.g. Identification and speciation
• RNA analysis Expression profiling – e.g. Breast cancer prognosis
Cancer of unknown primary origin
17TRiG Curriculum: Lecture 2March 2012
Genome-wide association studies of breast cancer microarray with 317,139 SNP’s
Hung RJ, et al. Nature Genetics. 2008; 452:633
Cases/controlsFrom differentpopulations
18TRiG Curriculum: Lecture 2March 2012
13
Genotype calling
Hybridization intensities translated into genotypesLarge SNP numbers requires automated procedureRecent algorithms – clustering/pooling strategies
• Raw hybridization intensities normalized
• Information combined across different samples at each SNP
• Assign genotypes to entire clusters
• For each sample, estimate probability of each of three genotype calls at each SNP
• Genotype assigned based on defined threshold of probability
• Missing genotypes dependent on algorithm & threshold used
Teo YY, Curr Op in Lipidology. 2008; 19:133
19TRiG Curriculum: Lecture 2March 2012
Genotyping - Limitations & quality control
• Accuracy of algorithm– Depends on number of samples in each cluster
– Prone to errors for small number of samples or SNP’s with rare alleles
• High rates of missing genotypes:g g g yp– Array problems – plating/synthesis issue
– Poor quality DNA – degradation
– Hybridization failure
– Differential performance between SNP’s
• Excess heterozygosity - sample contamination?
Just anotherlaboratory test
20TRiG Curriculum: Lecture 2March 2012
• Analyzed 8,101 genes on chip microarraysmicroarrays
• Reference= pooled cell lines
• Breast cancer subgroups
Perou CM, et al. Nature. 2000; 406, 747
21TRiG Curriculum: Lecture 2March 2012
Original two probe strategy for expression profiling on cDNA arrays
Duggan DJ, et al., Nature Genetics. 1999; 21:10
22TRiG Curriculum: Lecture 2March 2012
Expression profiling: challenges and limitations
Biological• Dynamic & complex nature of gene expression• Heterogeneous nature of tissue samples• Variation in RNA quality
TechnologicalR d ibilit i l tf• Reproducibility across microarray platforms
• Selection of probes – dependence on binding efficiency• Controlling for technical variability
Statistical/bioinformatic• Adequate experimental design• Normalization to remove variability among chips• Multiple testing correction• Validation of results Just another
laboratory test23TRiG Curriculum: Lecture 2March 2012
Copy number variation: Comparative genomic hybridization
CGH
Array-CGH
Tumor DNA Reference DNA
Hybridization
Metaphase Chromosomes
ArrayedDNA’s
Deletion
Gain
Deletion
Gain
24
http://www.advalytix.com/advalytix/hybridization_330.htm
TRiG Curriculum: Lecture 2March 2012
14
Constitutional genomic imbalances detected by copy number arrays
10.9 Mbdeletionat 7q11q
7.2 Mbduplication
on 11q
Miller DT, et al, Amer J Hum Genet. 2010; 86:749
25TRiG Curriculum: Lecture 2March 2012
Copy number - Limitations & quality control
Artifacts may be caused by:
• GC content– Wavy patterns correlate with GC content – Algorithms developed to remove waviness
• DNA sample quantity and quality– Can impact on level of signal noise and false positive rate– Whole genome amplification associated with signal noise
• Sample composition– In cancer studies, normal cells dilute cancer aberrations– Tumor heterogeneity will also affect copy number
26
Just anotherlaboratory test
TRiG Curriculum: Lecture 2March 2012
What we will cover today:
• Types of genetic alterations
• Current and future genetic test methods
– Cytogenetics, in situ hybridization PCRhybridization, PCR
– Gene chips• Genotyping• Expression profiling• Copy number variation
– Next generation sequencing (NGS)
• Whole genome• Transcriptome
27TRiG Curriculum: Lecture 2March 2012
Cancer Treatment: NGS in AML
Welch JS, et al. JAMA, 2011;305, 1577
28TRiG Curriculum: Lecture 2March 2012
Case History
• 39 year old female with APML by morphology
• Cytogenetics and RT-PCR unable to detect PML-RAR fusion
• Clinical question: Treat with ATRA versus allogeneicstem cell transplant
29TRiG Curriculum: Lecture 2March 2012
Methods/Results
• Paired-end NGS sequencing
• Result: Cytogenetically cryptic event: novel fusion protein
• Took 7 weeks
30TRiG Curriculum: Lecture 2March 2012
15
77-kilobase segment from Chr. 15 was inserted en bloc into the second intron of the gene RARA on Chr. 17.
31TRiG Curriculum: Lecture 2March 2012
Workflow
Image processing and base calling
Raw Data Analysis
Alignment to reference genome
Whole Genome Mapping
Detection of genetic variation(SNPs, Indels, SV)
Variant Calling
Linking variants to biological information
Annotation
32TRiG Curriculum: Lecture 2March 2012
Overview of Paired End Sequencing
Short Insert
Random
Adapters Ligated
Annealed to Surface
Sequenced
Synthesized
DNA
Shearing
Sequencing done with labeled NTPs and massively parallel
33TRiG Curriculum: Lecture 2March 2012
Short read output format
Read ID
SSequence
Quality line
34TRiG Curriculum: Lecture 2March 2012
Quality control is critical
Just anotherlaboratory test
35TRiG Curriculum: Lecture 2March 2012
Measuring Accuracy• Phred is a program that assigns a quality score to
each base in a sequence. These scores can then be used to trim bad data from the ends, and to determine how good an overlap actually is.
• Phred scores are logarithmically related to the probability of an error: a score of 10 means a 10% probability of an error: a score of 10 means a 10% error probability; 20 means a 1% chance, 30 means a 0.1% chance, etc.
– A score of 20 is generally considered the minimum acceptable score.
36TRiG Curriculum: Lecture 2March 2012
16
Workflow
Image processing and base calling
Raw Data Analysis
Alignment to reference genome
Whole Genome Mapping
Detection of genetic variation(SNPs, Indels, SV)
Variant Calling
Linking variants to biological information
Annotation
37TRiG Curriculum: Lecture 2March 2012
Alignment/Mapping
…CCATAGGCTATATGCGCCCTATCGGCAATTTGCGGTATAC…GCGCCCTA
GCCCTATCGGCCCTATCG
CCTATCGGACTATCGGAAA
AAATTTGCAAATTTGC
TTTGCGGTTTGCGGTA
GCGGTATA
GTATAC…
TCGGAAATTCGGAAATTT
CGGTATAC
TAGGCTATAAGGCTATATAGGCTATATAGGCTATAT
GGCTATATGCTATATGCG
…CC…CC…CCA…CCA…CCAT
ATAC…C…C…
…CCAT…CCATAG TATGCGCCC
GGTATAC…CGGTATAC
GCCCTATCGGCCCTATCG
CCTATCGGACTATCGGAAA
AAATTTGCAAATTTGC
TTTGCGGT
TCGGAAATTCGGAAATTTCGGAAATTT
GGAAATTTG
…CCATAGGCTATATGCGCCCTATCGGCAATTTGCGGTATAC…ATAC……CC
GAAATTTGC
Read depth is critical for accurate reconstruction
38TRiG Curriculum: Lecture 2March 2012
Alignment approachesAligner DescriptionIllumina platform
ELAND Vendor-provided aligner for Illumina dataBowtie Ultrafast, memory-efficient short-read aligner for Illumina data
Novoalign A sensitive aligner for Illumina data that uses the Needleman–Wunsch algorithm
SOAP Short oligo analysis package for alignment of Illumina dataMrFAST A mapper that allows alignments to multiple locations for CNV
detectionSOLiD platform
Corona lite Vendor provided aligner for SOLiD dataCorona-lite Vendor-provided aligner for SOLiD dataSHRiMP Efficient Smith–Waterman mapper with colorspace correction
454 PlatformNewbler Vendor-provided aligner and assembler for 454 data
SSAHA2 SAM-friendly sequence search and alignment by hashing program
BWA-SW SAM-friendly Smith–Waterman implementation of BWA for long reads
Multi-platformBFAST BLAT-like fast aligner for Illumina and SOLiD data
BWA Burrows-Wheeler aligner for Illumina, SOLiD, and 454 dataMaq A widely used mapping tool for Illumina and SOLiD; now
deprecated by BWA
Koboldt DC, et al. Brief Bioinform 2010 Sep;11(5):484-98
39TRiG Curriculum: Lecture 2March 2012
Short read alignment
Given a reference and a set of reads, report at least one “good” local alignment for each read if one exists Approximate answer to question: where in genome did read
originate?
h “ d”
…TGATCATA…GATCAA
…TGATCATA…GAGAAT
better than
• What is “good”? For now, we concentrate on:
…TGATATTA…GATcaT
…TGATCATA…GTACAT
better than
– Fewer mismatches = better– Failing to align a low-quality
base is better than failing to align a high-quality base
40TRiG Curriculum: Lecture 2March 2012
Post alignment: what do you get?
Alignment of reads including read pairs
CIGAR fi ld
SAM file
Read Pair
CIGAR field
Simplified pileup output
Li H, et al. Bioinformatics. 2009;25:2078
41TRiG Curriculum: Lecture 2March 2012
Workflow
Image processing and base calling
Raw Data Analysis
Alignment to reference genome
Whole Genome Mapping
Detection of genetic variation(SNPs, Indels, insertions)
Variant Calling
Linking variants to biological information
Annotation
42TRiG Curriculum: Lecture 2March 2012
17
Discovering Genetic Variation
SNPs
ATCCTGATTCGGTGAACGTTATCGACGATCCGATCGACGGTGAACGTTATCGACGATCCGATCGAACTGTCAGCGGTGAACGTTATCGACGTTCCGATCGAACTGTCAGCGTGAACGTTATCGACGTTCCGATCGAACTGTCAGCGGCTGAACGTTATCGACGTTCCGATCGAACTGTCAGCGGCTGAACGTTATCGACGTTCCGATCGAACTGTCAGCGGC
GTTATCGACGATCCGATCGAACTGTCAGCGGCAAGCTTTATCGACGATCCGATCGAACTGTCAGCGGCAAGCT
ATCCTGATTCGGTGAACGTTATCGACGATCCGATCGA
ATCCTGATTCGGTGAACGTTATCGACGATCCGATCGAACTGTCAGCGGCAAGCTGATCGATCGATCGATGCTAGTG
TTATCGACGATCCGATCGAACTGTCAGCGGCAAGCTTCGACGATCCGATCGAACTGTCAGCGGCAAGCTGAT
ATCCGATCGAACTGTCAGCGGCAAGCTGATCG CGATTCCGATCGAACTGTCAGCGGCAAGCTGATCG CGATC TCCGATCGAACTGTCAGCGGCAAGCTGATCGATCGA
GATCGAACTGTCAGCGGCAAGCTGATCG CGATCGA AACTGTCAGCGGCAAGCTGATCG CGATCGATGCTA
TGTCAGCGGCAAGCTGATCGATCGATCGATGCTAG
INDELsTCAGCGGCAAGCTGATCGATCGATCGATGCTAGTG
reference genome
43TRiG Curriculum: Lecture 2March 2012 44TRiG Curriculum: Lecture 2March 2012
45TRiG Curriculum: Lecture 2March 2012
Workflow
Image processing and base calling
Raw Data Analysis
Alignment to reference genome
Whole Genome Mapping
Detection of genetic variation(SNPs, Indels, insertions)
Variant Calling
Linking variants to biological information
Annotation
46TRiG Curriculum: Lecture 2March 2012
Where to go to annotate genomic data, determine clinical relevance?
• Online Mendelian Inheritance in Man(http://www.ncbi.nlm.nih.gov/omim)
• International HapMap project (http://hapmap.ncbi.nlm.nih.gov)( p p p g )
• Human genome mutation database (http://www.hgvs.org/dblist/glsdb.html)
• PharmGKB (http://www.pharmgkb.org)• Scientific literature
47TRiG Curriculum: Lecture 2March 2012
Case-control study design = variable results
•Need for Clinical Grade DatabaseNeed for Clinical Grade Database•Ease of use•Continually updated•Clinically relevant SNPs/variations
48
Ng PC, et al. Nature. 2009; 461: 724
TRiG Curriculum: Lecture 2March 2012
18
Cancer Treatment: NGS of Tumor
Jones SJM, et al. Genome Biol. 2010;11:R82.
49TRiG Curriculum: Lecture 2March 2012
Case History
• 78 year old male• Poorly differentiated
papillary adenocarcinoma of tongueg
• Metastatic to lymph nodes
• Failed chemotherapy• Decision to use next-
generation sequencing methods
50TRiG Curriculum: Lecture 2March 2012
Workflow
Image processing and base calling
Raw Data Analysis
Alignment to reference genome
Whole Genome Mapping
Detection of genetic variation(SNPs, Indels, SV)
Variant Calling
Linking variants to biological information
Annotation
51TRiG Curriculum: Lecture 2March 2012
Methods and Results
• Analysis– Whole genome– Transcriptome
• Findings– Upregulation of
RET oncogene– Downregulation of
PTEN
52TRiG Curriculum: Lecture 2March 2012
Transcriptome and Whole-exome
• Transcriptome– Convert RNA to cDNA– Perform sequencing– Only expressed genes– Can get expression Can get expression
levels
• Whole-exome– Use selection procedure
to enrich exons– No intron data– Results depends on
selection procedure
53
Martin JA, Wang Z. Nat Rev Genet. 2011; 12:671.
TRiG Curriculum: Lecture 2March 2012
A few words about samples…
• Can use formalin-fixed paraffin-embedded tissue for whole-exome or transcriptome sequencing
• Need frozen tissue for • Need frozen tissue for whole-genome sequencing– Better quality DNA
• Small quantity of DNA needed – For whole-exome
sequencing, amount off a few slides
54TRiG Curriculum: Lecture 2March 2012
19
Summary• Gene chips
– SNPs– Expression profiling– Copy number variation
• Major steps in NGS– Base calling– Alignmentg– Variant calling– Annotation
• Technology will change but just another test– Accuracy– Precision– Need to validate findings with
traditional methods
Roychowdhury S, et al. Sci Transl Med. 2011; 3: 111ra121
55TRiG Curriculum: Lecture 2March 2012
20
Lecture III: Interpreting genomic information for
li i l clinical careRichard L. Haspel, MD, PhD
Karen L. Kaul, MD, PhDHenry M. Rinder, MD, PhD
TRiG Curriculum: Lecture 3 1March 2012
Coming to a clinic near you…
2TRiG Curriculum: Lecture 3March 2012
Why Pathologists? We have access, we know testing
PersonalizedRisk Prediction,PathologistsMedicationDosing,Diagnosis/Prognosis
Physician sendssample toPathology (blood/tissue)
g
Access to patient’s genome
Just anotherlaboratory test
3TRiG Curriculum: Lecture 3March 2012
What we could test for? Same Stuff
• Somatic analysis– Tumor genomics
• Diagnosis/Prognosis• Response to treatment
M h / – May change/ evolve/require repeat testing
• Laboratory testing– Microbiology
– Pre-natal testinghttp://www.bcm.edu/breastcenter/pathology/index.cfm?pmid=11149
4TRiG Curriculum: Lecture 3March 2012
What we could test for? Something New
• Risk prediction– Pathologists involved
in preventive medicine• Predict risk of disease• Predict drug response g p
(pharmacogenomics)
• Germline– Heritable genomic
targets– Does not change
during lifetime Just anotherlaboratory test
5TRiG Curriculum: Lecture 3March 2012
What we will cover today:
• Review current and future molecular testing:
– Somatic analysis/ Diagnosis/Prognosis
• Cancer
– Laboratory testing• Microbiology• Pre-natal testing
– Risk Assessment• Pathologists involved in
preventive medicine
6TRiG Curriculum: Lecture 3March 2012
21
Diagnosis/Prognosis Timeline: Cancer
• Single gene– HER2
• Multi-gene assays– Breast cancerBreast cancer
• Gene chips/Next generation sequencing of tumors– Expression profiling– Exome– Transcriptome– Whole genome
7TRiG Curriculum: Lecture 3March 2012
Multi-gene assays in breast cancer
Look familiar?
8TRiG Curriculum: Lecture 3March 2012
Multi-gene assays to determine risk score, need for additional chemo
For use in ER+, node negative cancer
9TRiG Curriculum: Lecture 3March 2012
• Oncotype similar predictive value to
combined four immunohistochemicalcombined four immunohistochemical
stains (ER,PR, HER2, Ki-67)
• May offer standardization lacking in IHC
• Need to validate
– Prospective trialsJust anotherlaboratory test
10TRiG Curriculum: Lecture 3
Cuzick J, et al. J Clin Oncol. 2011; 29: 4273
March 2012
• Analyzed 8,101 genes on chip microarraysmicroarrays
• Reference= pooled cell lines
• Breast cancer subgroups
Perou CM, et al. Nature. 2000; 406, 747
11TRiG Curriculum: Lecture 3March 2012
Cancer Treatment: NGS in AML
Welch JS, et al. JAMA, 2011;305, 1577
12TRiG Curriculum: Lecture 3March 2012
22
Case History
• 39 year old female with APML by morphology
• Cytogenetics and RT-PCR y gunable to detect PML-RAR fusion
• Clinical question: Treat with ATRA versus allogeneic stem cell transplant
13TRiG Curriculum: Lecture 3March 2012
The Findings: Led to appropriate treatment
• Analysis– Paired-end NGS
• Findings – Cytogenetically
ti t l cryptic event: novel fusion
• Analysis took 7 weeks
• ATRA Treatment• Patient still alive 15
months later14TRiG Curriculum: Lecture 3March 2012
Cancer Treatment: NGS of Tumor
Jones SJM, et al. Genome Biol. 2010;11:R82
15TRiG Curriculum: Lecture 3March 2012
Case History
• 78 year old male• Poorly differentiated
papillary adenocarcinoma of tongueg
• Metastatic to lymph nodes
• Failed chemotherapy• Decision to use next-
generation sequencing methods
16TRiG Curriculum: Lecture 3March 2012
Methods and Results
• Analysis– Whole genome– Transcriptome
• Findings– Upregulation of
RET oncogene– Downregulation of
PTEN
17TRiG Curriculum: Lecture 3March 2012
X
1 month pre-anti-RET Anti-RET added 1 month on anti-Ret18TRiG Curriculum: Lecture 3March 2012
23
X
19TRiG Curriculum: Lecture 3March 2012
Why Pathologists? We have access, we know testing
PersonalizedTumor PathologistsTreatmentPlan
Would like to identify tumor, know prognosis,treatment options
g
Access to tumor genome
20TRiG Curriculum: Lecture 3March 2012
Why pathologists?
“However, to fully use this potentiallytransformative technology to makeinformed clinical decisions, standards
ill h b d l d h ll fwill have to be developed that allow forCLIA-CAP certification of whole-genome sequencing and for directreporting of relevant results to treatingphysicians.”
21TRiG Curriculum: Lecture 3
Welch JS, et al. JAMA, 2011;305:1577
March 2012
What we will cover today:
• Review current and future molecular testing:
– Somatic analysis/ Diagnosis/Prognosis
• Cancer
– Laboratory testing• Microbiology• Pre-natal testing
– Risk Assessment• Pathologists involved in
preventive medicine
22TRiG Curriculum: Lecture 3March 2012
Laboratory Testing: Micro
• Identifying outbreak source
– Serotyping
– Pulsed field electrophoresis
– Next-generation sequencing analysis
23TRiG Curriculum: Lecture 3March 2012
Laboratory testing: Pre-natal• Amniocentesis/ Chorionic
villus sampling– Karyotyping– Single gene testing
• Multigene assaysg y– “Universal Genetic Test”
available for 100+ diseases
• Next generation methods– Fetal DNA in maternal
plasma, detection of Trisomy 21
Fan HC, et al. PNAS. 2008;105:16266Srinivasan BS, et al. Reprod Biomed Online. 2010;21:537-51
24TRiG Curriculum: Lecture 3March 2012
24
What we will cover today:• Review current and
future molecular testing:
– Somatic analysis/ Diagnosis/Prognosis
• Cancer
– Laboratory testing• Microbiology• Pre-natal testing
– Risk Assessment• Pathologists involved in
preventive medicine
25TRiG Curriculum: Lecture 3March 2012
Risk Prediction: Timeline
• Single gene
• Multigene assaysDirect to
Factor V Leiden
– Direct-to-consumer
• Next generation sequencing
Alsmadi OA, et al. BMC Genomics 2003 4:21
26TRiG Curriculum: Lecture 3March 2012
27TRiG Curriculum: Lecture 3March 2012
Hereditary Risk Prediction: How is risk calculated?
• Analysis of SNPs (up to a million)– Genome wide
association studies (GWAS)
• Case-control studies– Odds ratios
• Using odds ratios to determine individual patient risk
28TRiG Curriculum: Lecture 3March 2012
Just another test: Case-control study
• Adequate selection criteria for cases/controls
• # of patients = reasonable ORs (<=1.3)
• Assays appropriate– Enough variation– Proper controls
• Statistics appropriate• Detect known variants• Reproducible results
– Different populations– Different samples
• Pathophysiologic basisPearson TA, Manolio TA. JAMA 2008; 298:1335
29TRiG Curriculum: Lecture 3March 2012
Just another test: Selection• Lung cancer risk• “Old School Study”
– Cases and controls were matched based on age, smoking status, race and month of blood collection
Menkes MS, et al. NEJM 1986;315:1250; Hung RJ, et al. Nature Genetics. 2008; 452:633
blood collection
• “Genomic Study”: – Cases and controls
were frequency matched by sex, age center, referral (or of residence) area and period of recruitment
30TRiG Curriculum: Lecture 3March 2012
25
Statistics: Classic case-control study
Lung Cancer+ -
+ A B
Vitamin ELow Level
+
- C D
AD/BC = Odds ratio (OR) ~ Relative risk (RR)31TRiG Curriculum: Lecture 3March 2012
GWAS: (Case-control)N
Lung Cancer+ -
+ A B+
- C D
SNP 1
32TRiG Curriculum: Lecture 3March 2012
GWAS: (Case-control)N
+ -
+ A B
Lung Cancer
+
- C D
SNP 2
33TRiG Curriculum: Lecture 3March 2012
GWAS: (Case-control)N
+ -
+ A B
Lung Cancer
+
- C D
SNP 3
34TRiG Curriculum: Lecture 3March 2012
GWAS: (Case-control)N
+ -
+ A B
Lung Cancer
+
- C D
X
Up to1,000,000 SNPs (howevermany on chip)
SNP X
35TRiG Curriculum: Lecture 3March 2012
A word about statistics…• 20 tests, “significant” if
p=0.05– (.95)N = chance all tests
“not significant”– 1- (.95)N = chance one
test “significant– 1- (.95)20= 64% – Bonferroni correction p = – Bonferroni correction p =
0.0025
• Need to adjust for number of tests run– For 1 million SNP
GWAS p< 0.00000005Just anotherlaboratory test
Lagakos SW. NEJM 2006;354:16
36TRiG Curriculum: Lecture 3March 2012
26
Other criteria: Reproducibility: only single populationPhysiologic hypothesis: anti-oxidant (determined pre-study)
37TRiG Curriculum: Lecture 3March 2012
Table 1 | Lung cancer risk and rs8034191 genotype
Cases/controlsFrom differentpopulations
Other criteria:Reproducibility: many populationsPhysiologic hypothesis: mutation in carcinogen binding receptor (determined post-study)
38TRiG Curriculum: Lecture 3March 2012
Why Pathologists? We have access, we know testing
PersonalRisk PathologistsPrediction
Would like to determine patientrisk for disease
g
Access to patient’schip results
Not so simple!!39TRiG Curriculum: Lecture 3March 2012
Risk Prediction: Not easy to do!!
• Based on case-control study design = variable results
• No quality control of associations
Need for Clinical Grade – Need for Clinical Grade Database
• Ease of use
• Continually updated
• Clinically relevant SNPs/variations
• Pre-test probability assessment
40TRiG Curriculum: Lecture 3
Ng PC, et al. Nature. 2009; 461: 724
March 2012
DTC: A simplistic calculation
How about family history? Environment?
Pre-test probability
Post-test probability
41TRiG Curriculum: Lecture 3
Ng PC, et al. Nature. 2009; 461: 724
March 2012
Calculating pre-test
probability is probability is not so simple
TRiG Curriculum: Lecture 3 42
Parmigiani G, et al. Ann Intern Med. 2007; 147: 441
March 2012
27
• “Avg” (average risk for your ethnic group = pre-test probability): 8%
• OR from SNP is 0.75***25% decreased risk****
• “You” (post-test probability): 8% x 0.75 = 6%
• Absolute decreased risk: = 2%
• Same OR if 80% vs. 60%
• Absolute decreased risk: 20%
Just another laboratory test
43TRiG Curriculum: Lecture 3March 2012
Hereditary Risk Prediction: NGS
• 40 year old male with family history of CAD and sudden cardiac death
• Whole genome sequencing performed on DNA from whole blood
• How to approach analysis?
44TRiG Curriculum: Lecture 3
Ashley EA, et al. Lancet. 2010; 375: 1525
March 2012
Pharmacogenomics may guide care
Need validation in clinical trials
45TRiG Curriculum: Lecture 3March 2012
Other variants detected
46TRiG Curriculum: Lecture 3March 2012
Clinical Risk determination (prevalence X post test probability = clinical risk)
Pre-testprobability
Post-testprobability
47TRiG Curriculum: Lecture 3March 2012
Why Pathologists? We have access, we know testing
PersonalRisk PathologistsPrediction
Would like to determine patientrisk for disease
g
Access to patient’swhole genome!
Not so simple!!48TRiG Curriculum: Lecture 3March 2012
28
Risk Prediction: Not easy to do!!
• Based on case-control study design = variable results
• No quality control of associations– Need for Clinical Grade
Database• Ease of use
• Continually updated
• Clinically relevant SNPs/variations
• Pre-test probability assessment
49TRiG Curriculum: Lecture 3March 2012
• “No methods exist for statistical integration of such conditionally d d t dependent risks”
• Strength of association based on # of Medline articles
50TRiG Curriculum: Lecture 3March 2012
In the end: Is the info actionable?
NEJM. 1994;330:1029
51TRiG Curriculum: Lecture 3March 2012
Summary
• Genomic-era technologies involve– Typical roles of pathologists
• Cancer diagnosis/prognosis/guide treatment
• Laboratory testing (e.g., microbiology)– New roles for pathologists
• Predict disease risk• Predict drug response
– We control the specimens
• Just another test– Issues with case-control studies– Issues of pre- and post-test probability
• Accurately assessing pre-test probability
– Need to validate
52TRiG Curriculum: Lecture 3
Roychowdhury S, et al. Sci Transl Med. 2011; 3: 111ra121
March 2012
29
NOTES
30
Lecture IV: Genomic Medicine: Communicating
ith th P ti t with the Patient Julianne O’Daniel, MS, CGC
Joan Scott, MS, CGCElizabeth Varga, MS, CGC
VO Speights, DOErynn Gordon, MS, CGC
March 2012 TRiG Curriculum: Lecture 4 1
What will be the pathology report of the future?
March 2012 2TRiG Curriculum: Lecture 4
Why Pathologists? We have access, we know testing
PersonalizedRisk Prediction,PathologistsMedicationDosing,Diagnosis/Prognosis
Physician sendssample toPathology (blood/tissue)
g
Access to patient’s genome
Genetic CounselorsMedical Geneticists
March 2012 3TRiG Curriculum: Lecture 4
Workflow must include communication
March 2012 4TRiG Curriculum: Lecture 4
Roychowdhury S, et al. Sci Transl Med. 2011; 3: 111ra121
Multidisciplinary Teams Needed
• Modern medical technology and practice requires a multidisciplinary approach to medical pppractice.
• Greater interaction needed between Pathologists, Genetic Counselors and Medical Geneticists
March 2012 5TRiG Curriculum: Lecture 4
Roychowdhury S, et al. Sci Transl Med. 2011; 3: 111ra121
What we will cover today:
• Geneticist training and professional roles
• Current and future of reporting and communicating genetic test results
– Case 1• Single gene tests• Direct-to-consumer
(DTC) gene chip testing
– Case 2• Whole genome
sequencing
March 2012 6TRiG Curriculum: Lecture 4
31
The approach
Pre-test Counseling
Informed Consent
Ethical and Legal Issues
Post-test Counseling
Testing
March 2012 7TRiG Curriculum: Lecture 4
Medical Geneticists
• MD or DO– Separate residency
training or in combination with other medical other medical specialty
• MD, DO or PhD– Biochemical
Genetics
– Molecular Genetics– Cytogenetics
March 2012 8TRiG Curriculum: Lecture 4
www.abmg.orgwww.acmg.org
Genetic Counselors• “Genetic counseling is the
process of helping people understand and adapt to the medical, psychological and familial implications of genetic contributions to disease.”
• Masters-prepared – Medical genetics– Risk assessment– Communication – Psychosocial assessment
• Board certification
• Licensed in a growing number of states
March 2012 9TRiG Curriculum: Lecture 4
www.abgc.net
Genetic Counselors
• Many work settings:
• Scope of practice statement:– To provide expertise in clinical
genetics– To counsel and communicate
ith ti t tt f with patients on matters of clinical genetics;
– To provide genetic counseling services in accordance with professional ethics and values.”
• Clinical expertise spans:– Prenatal, pediatric, cancer,
adult-onset conditions
March 2012 10TRiG Curriculum: Lecture 4
www.nsgc.org
Geneticists as a Resource
• Bridge gap between healthcare providers, pathologists, and patients D l d ti l • Develop educational material
• Consult about ethical issues
• Develop provider and patient-friendly reports
March 2012 11TRiG Curriculum: Lecture 4
http://www.nsgc.org/Publications/ProfessionalStatusSurvey/tabid/142/Default.aspx
What we will cover today:• Geneticist training and
professional roles
• Current and future of reporting and communicating genetic test results
– Case 1• Single gene tests• Direct-to-consumer
(DTC) gene chip testing
– Case 2• Whole genome
sequencing
March 2012 12TRiG Curriculum: Lecture 4
32
Case 1 – Single Gene Testing
• A 38 year old woman has a strong family history of breast cancer.
Breast ca 45
• Affected family members not available for testing
• She is referred for genetic counseling
Breast ca 42Breast ca 36
d.52 ovarian ca
March 2012 13TRiG Curriculum: Lecture 4
Guidelines exist for BRCA testing
The USPSTF recommends that women whose family history is associated with an increased risk for deleterious mutations in for deleterious mutations in BRCA1 or BRCA2 genes be referred for genetic counseling and evaluation for BRCA testing. Rating: B Recommendation.
March 2012 14TRiG Curriculum: Lecture 4
http://www.uspreventiveservicestaskforce.org/uspstf/uspsbrgen.htm
• ~ 1 in 8 US women will develop breast cancer– Not uncommon to
have >1 sporadic case in a family
Genetic Counseling for Breast Cancer
Pre-test Counseling
Ethical and Legal Issues
in a family– Only 1 in 300 to 400
have a known mutation
– Several steps in the process of genetic counseling
Informed Consent
Post-test counseling
Testing
March 2012 15TRiG Curriculum: Lecture 4
Pre-test counseling is crucial
• Gathering information– Medical, Family and
Social histories
• Risk assessment i l d includes: – Family Hx/Family
member testing– Medical Hx– Risk scoring
programs (BRCAPRO, Gail)
March 2012 16TRiG Curriculum: Lecture 4
Parmigiani G, et al. Ann Intern Med. 2007; 147: 441
BRCAPRO Analysis
http://www8.utsouthwestern.edu/utsw/cda/dept47834/iles/73815.html
March 2012 17TRiG Curriculum: Lecture 4
Pre-test counseling in crucial
• Presenting information– Suspected differential
and/or testing strategy– Testing process,
optionsoptions– Facilitate decision-
making– Patient/family
concerns and expectations about genetics and testing
March 2012 18TRiG Curriculum: Lecture 4
33
Implications of Results:Context Matters
• The informational and counseling needs depend on context and indications for testing– Examples:
• Establish or confirm a diagnosis (Gaucher Disease)
• Reproductive decision-making, carrier testing, fetal testing (cystic fibrosis)
• Predict risk for future disease (BRCA, Huntington’s Disease)
• Aid management decisions (e.g., tumor sequencing)
• Ethical issues vary by context
March 2012 19TRiG Curriculum: Lecture 4
Ethics: Use of Genetic Info
• Genetic Information Nondiscrimination Act (GINA)– Protects against use of
genetic information in health and employmentand employment
– Does not protect in disability or life insurance or symptomatic disease
– Active duty military and federal employees are currently exempt from GINA protections
– Has not been tested in the courts
March 2012 20TRiG Curriculum: Lecture 4
Ethics: Prenatal and Pediatric Testing
• Pre-implantation genetic diagnosis (PGD)– Identify unaffected embryos for
implantation– What types of genetic disease
may be included?
• Testing of fetuses or minors for adult-onset disorders– Generally NOT recommended in
the absence of increased risk for pediatric symptoms or treatment options.
– Even carrier testing may have consequences.
March 2012 21TRiG Curriculum: Lecture 4
www.acmg.org
The approach
Pre-test Counseling
Informed Consent
Ethical and Legal Issues
Post-test counseling
Testing
March 2012 22TRiG Curriculum: Lecture 4
Informed Consent May be Required
• Regulations for genetic tests vary by state
• May be institutional requirement for review by Risk Management
• Consent forms include practical issues:issues:– What and why testing is being done– Risk, benefits, and limitations of
testing– How results will be conveyed– Potential results and implications
• Effect on relatives– Cost and insurance coverage– Privacy and confidentiality
March 2012 23TRiG Curriculum: Lecture 4
Robson ME, et al. JCO. 2010; 28: 893
March 2012 24TRiG Curriculum: Lecture 4
34
The approach
Pre-test Counseling
Informed Consent
Ethical and Legal Issues
Post-test counseling
Testing
March 2012 25TRiG Curriculum: Lecture 4
What Geneticists Need to Know about a Test
• Is the lab reliable?• Coordinate testing
– Sample collection– Communications with lab
• What are the limitations in terms of answering the clinical question?– Detection rate
– Method/Coverage of target
• Potential need for follow-up testing
March 2012 26TRiG Curriculum: Lecture 4
Possible test results• Positive (known pathogenic
mutation)
• Negative – Absence of known pathogenic
mutation--Need to discuss residual disease risk
Ab f k f ili l
If our patient tested negative, this would be appropriate (no affected family tested)
– Absence of known familial pathogenic mutation--Need to discuss population risks and screening
• Unknown Significance– There is no or limited evidence about
the pathogenicity of the identified variant(s)
– Additional testing and/or future re-interpretation is necessary
Breast ca 42
Breast ca 36d.52 ovarian ca
Breast ca 45
March 2012 27TRiG Curriculum: Lecture 4
Post-test Counseling
• Review reasons for testing
• Review test result(s)• Assess comprehension Assess comprehension
and coping• Discuss plan for
additional evaluation and/or treatment
• Follow-up
March 2012 28TRiG Curriculum: Lecture 4
Implications of Results:Context Matters
• The informational and counseling needs depend on context and indications for testing– Examples:
• Establish or confirm a diagnosis (Gaucher Disease)
• Reproductive decision-making, carrier testing, fetal testing (cystic fibrosis)
• Predict risk for future disease (BRCA, Huntington’s Disease)
• Aid management decisions (e.g., tumor sequencing)
• Ethical issues vary by context
March 2012 29TRiG Curriculum: Lecture 4
Case 1– Single Gene Testing
• Patient tests positive for BRCA– S1970X (6137C>A)
mutation in BRCA2. (Not an Ashkenazi
Breast ca 45
Jewish mutation)
– Implications for patient
• Ethical issue– Other family
members?
Breast ca 42
Breast ca 36d.52 ovarian ca
March 2012 30TRiG Curriculum: Lecture 4
Cousin
35
Dear [ ]:
I recently had genetic testing to help me understand my risk of developing cancer. I was tested for inherited changes (or mutations) in the BRCA1 and BRCA2 genes. My test identified a mutation that runs in our family (relatives related by blood).This test result means that I have Hereditary Breast and Ovarian Cancer syndrome Fortunately there are medical Ovarian Cancer syndrome. Fortunately, there are medical options to reduce my risks, which is why knowing about this mutation will be very helpful.
It is possible that someone in our family besides me may have a BRCA1 or BRCA2 mutation. I am writing to all of the relatives who may be at risk to also have this mutation. You may want to talk to your doctor about whether genetic testing makes sense for you.
March 2012 31TRiG Curriculum: Lecture 4
Case 1– Single Gene TestingThe patient’s 35 year old cousin is interested in learning more about her breast cancer risk but decides to pursue testing on her own through a DTC company.
Does not include all mutationssuch as S1970X (6137C>A) mutation in BRCA2.
March 2012 32TRiG Curriculum: Lecture 4
March 2012 33TRiG Curriculum: Lecture 4
DTC: A simplistic calculation
How about family history? Environment?
Pre-test probability
Post-test probability
March 2012 34TRiG Curriculum: Lecture 4
Ng PC, et al. Nature. 2009; 461: 724
Risk Prediction: Not easy to do!!
• Based on case-control study design = variable results
• No quality control of associations– Need for Clinical Grade
Database• Ease of use
• Continually updated
• Clinically relevant SNPs/variations
• Pre-test probability assessment
March 2012 35TRiG Curriculum: Lecture 4
Ng PC, et al. Nature. 2009; 461: 724
DTC Genetic Tests: Positive Aspects?
• Educate consumers• Provide direct access• Empower consumers to
take charge of their health
• Motivate behavior change to improve health outcomes
• Provide confidential testing
March 2012 36TRiG Curriculum: Lecture 4
36
DTC Genetic Tests: Points to Consider
• Questionable benefits
• Qualification of the lab• Clinical validity of the test(s)
• Claims made about the test(s)
P i
Sample swaps at 23 and Me: i l• Privacy
• Clinical application– Appropriateness of tests ordered– Interpretation of results– False reassurance if “normal” or
unwarranted concern if “positive”– Forgoing standard treatments– Seeking unnecessary treatments
a cautionary tale
By Daniel MacArthur | June 7, 2010 | 6:00 a.m.
March 2012 37TRiG Curriculum: Lecture 4
• No evidence for anxiety• No evidence for change
March 2012 38TRiG Curriculum: Lecture 4
Bloss CS, et al. NEJM. 2011; 364: 524
What we will cover today:
• Geneticist training and professional roles
• Current and future of reporting and communicating genetic test results
– Case 1• Single gene tests• Direct-to-consumer
(DTC) gene chip testing
– Case 2• Whole genome
sequencing
March 2012 39TRiG Curriculum: Lecture 4
Case 2 – Risk Prediction with NGS
• Patient assessed by cardiologist and genetic counselor due y g gto family history of vascular disease and sudden death– 40 yo, no significant past medical history
• Exercises regularly, no medications• Normal clinical exam• Normal lipid panel, electrocardiogram, echocardiogram and
cardiopulmonary exercise test
• Social history: highly educated male, professor of bioengineering
March 2012 40TRiG Curriculum: Lecture 4
Ashley EA, et al. Lancet. 2010; 375: 1525
• 4-generation pedigree constructed
– Coronary artery disease, abdominal aortic aneurysm sudden cardiac death in 1st and 2nd degree relatives
March 2012 41TRiG Curriculum: Lecture 4
Pharmacogenomics may guide care
• 64 clinically relevant pharmacogenomic variants,12 novel, non-conservative amino acid changing SNPs
• Most relevant to current care:– CYP2C19 variant- clopidogrel
• Most relevant to future care– Variants affecting warfarin dosing, response to statinsMarch 2012 42TRiG Curriculum: Lecture 4
37
Rare Cardiac Variants – Possibly Pathogenic
• MYBPC3 Arg326Gln- originally associated with late onset hypertrophic cardiomyopathy– Later found in multiple controls– Conclusion: probably benign
• TMEM42 Met41Val- in 1 of 150 probands with ARVD/C– Test other family members to help assess relevance?
• DSP Arg1838His- entirely new variantMarch 2012 43TRiG Curriculum: Lecture 4
Unexpected Findings
• 3 variants in 2 genes associated with hemochromatosis– No family history– No current clinical evidence of disease in patient– How should management be changed? Serum ferritin monitoring?
• Gene variant implicated in parathyroid pathology – Osteoarthritis in family and knee pain in patient related to this?
March 2012 44TRiG Curriculum: Lecture 4
Clinical Risk determination (prevalence X post test probability = clinical risk)
Pre-testprobability
Post-testprobability
March 2012 45TRiG Curriculum: Lecture 4
Pre-test Probability: Population vs Individual
March 2012 46TRiG Curriculum: Lecture 4
Parmigiani G, et al. Ann Intern Med. 2007; 147: 441 Ashley EA, et al. Lancet. 2010; 375: 1525
• “No methods exist for statistical integration of such conditionally d d t dependent risks”
• Strength of association based on # of Medline articles
March 2012 47TRiG Curriculum: Lecture 4
To the Nth power for genomics
Pre-test Counseling
Informed Consent
Ethical and Legal Issues N
Post-test counseling
Testing
March 2012 48TRiG Curriculum: Lecture 4
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Counseling: Genomic Testing• Same principles
– Setting appropriate expectations– WGS is limited by our current
biological understanding
• Communicating multiple complex results effectively– Prioritization of most significant results– Timing of communication (how much
at once?)– Method of communication (written,
verbal, electronic)– Incidental findings – Familial implications of results– Need for ongoing communication as
interpretation of results evolves
March 2012 49TRiG Curriculum: Lecture 4
Counseling: Genomic Testing
• Difficulties in interpretation– Large amount of
data/results with limited models for clinical interpretation
– Integration with family history, environmental risks
– Error rates, validation processes
March 2012 50TRiG Curriculum: Lecture 4
Ng PC, et al. Nature. 2009; 461: 724
Ethics: To the Nth Power• Informational overload and
patient comprehension
• Patient autonomy to learn/refuse results
• What information should be returned?returned?
• Lab and clinical liabilities if results are not disclosed or genomic regions analyzed?
• How to store results in an accessible but private manner?
• What information to return when testing minors?
March 2012 51TRiG Curriculum: Lecture 4
Genomics Requires New Clinical Models
• Lengthy, multiple pre-/post-test sessions
• Reimbursement for broad-based testing
• Data reporting and storage in Data reporting and storage in medical record systems
• Informed consent• “The patient received education and
counseling before signing the consent form and throughout testing and follow-up.”
March 2012 52TRiG Curriculum: Lecture 4
Ashley EA, et al. Lancet. 2010; 375: 1525Roychowdhury S, et al. Sci Transl Med. 2011; 3: 111ra121
Summary: To the Nth Power
• Core principles apply
• Huge amount of data
Pre-test Counseling
Ethical and Legal Issues N
• Difficulties in interpretation
• Reimbursement• Multiple ethical
issues– Informed consent
Informed Consent
Post-test counseling
Testing
March 2012 53TRiG Curriculum: Lecture 4
Multidisciplinary Teams Needed
• Modern medical technology and practice requires a multidisciplinary approach to medical pppractice.
• Greater interaction needed between Pathologists, Genetic Counselors and Medical Geneticists
March 2012 54TRiG Curriculum: Lecture 4
Roychowdhury S, et al. Sci Transl Med. 2011; 3: 111ra121
39
What will be the pathology report of the future?
March 2012 55TRiG Curriculum: Lecture 4
• Ethical issues• Informed consent• Integrated data
interpretation
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NOTES
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NOTES
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