Training Residents in · PDF fileTraining Residents in Genomics ... by training programs to...

Lecture Materials fromthe TRIG Working Group

Training Residents inGenomics

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for Pathology

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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.

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mailto:[email protected]�

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

http://www.wired.com/wiredscience/2010/06/Sample-swaps-at-23andMe:-a-cautionary-tale�


http://www.genome.gov/11006943�

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

http://www.ncbi.nlm.nih.gov/omim�

http://hapmap.ncbi.nlm.nih.gov/�

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



http://www.nsgc.org/Publications/ProfessionalStatusSurvey/tabid/142/Default.aspx�

http://www.uspreventiveservicestaskforce.org/uspstf/uspsbrgen.htm�

http://www8.utsouthwestern.edu/utsw/cda/dept47834/files/73815.html�

http://www.nchpeg.org/�

http://www.acmg.org/�

http://www.nsgc.org/�

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


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!


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




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).


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


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


“[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.


Pathologists in Familiar Role

?*

Pathologists


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


TRIG Curriculum: Lecture 1March 2012

Training Residents in Genomics (TRIG) Working Group


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


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




g


Just anotherlaboratory test


The path to genomic medicine

Sample Pathologists

Sample Collection

Collection

Testing: Sequencing, Gene chips

Analysis

g



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


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


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


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


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




• Current and future molecular testing methods

– Cytogenetics, in situ hybridization, PCR





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


Microarray technologies


Organization of a DNA microarray

(adapted from Affymetrix)

1.28 cm

1.28 cm


12

Hybridization of a labeled probeto the microarray

13


TRiG Curriculum: Lecture 2March 2012

Detection of hybridization on microarray

Light from laser

14



Hybridization intensities on DNA microarray following laser scanning


Overview of SNP array technology

LaFramboise T. Nucleic Acids Res. 2009; 37:4181


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


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


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


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?



• Analyzed 8,101 genes on chip microarraysmicroarrays

• Reference= pooled cell lines

• Breast cancer subgroups

Perou CM, et al. Nature. 2000; 406, 747


Original two probe strategy for expression profiling on cDNA arrays

Duggan DJ, et al., Nature Genetics. 1999; 21:10


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


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


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





• Current and future genetic test methods

– Cytogenetics, in situ hybridization PCRhybridization, PCR





Cancer Treatment: NGS in AML

Welch JS, et al. JAMA, 2011;305, 1577


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


Methods/Results

• Paired-end NGS sequencing

• Result: Cytogenetically cryptic event: novel fusion protein

• Took 7 weeks


15

77-kilobase segment from Chr. 15 was inserted en bloc into the second intron of the gene RARA on Chr. 17.


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


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


Short read output format

Read ID

SSequence

Quality line


Quality control is critical



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.


16

Workflow


Raw Data Analysis




Variant Calling


Annotation


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


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


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


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


Workflow


Raw Data Analysis



Detection of genetic variation(SNPs, Indels, insertions)

Variant Calling


Annotation


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


Workflow


Raw Data Analysis



Detection of genetic variation(SNPs, Indels, insertions)

Variant Calling


Annotation


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


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


18

Cancer Treatment: NGS of Tumor

Jones SJM, et al. Genome Biol. 2010;11:R82.


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


Workflow


Raw Data Analysis




Variant Calling


Annotation


Methods and Results

• Analysis– Whole genome– Transcriptome

• Findings– Upregulation of

RET oncogene– Downregulation of

PTEN


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.


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


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



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






g




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


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



• Review current and future molecular testing:

– Somatic analysis/ Diagnosis/Prognosis

• Cancer

– Laboratory testing• Microbiology• Pre-natal testing

– Risk Assessment• Pathologists involved in

preventive medicine


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


Multi-gene assays in breast cancer

Look familiar?


Multi-gene assays to determine risk score, need for additional chemo

For use in ER+, node negative cancer


• 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


Cancer Treatment: NGS in AML

Welch JS, et al. JAMA, 2011;305, 1577


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


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


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


Methods and Results

• Analysis– Whole genome– Transcriptome

• Findings– Upregulation of

RET oncogene– Downregulation of

PTEN


X

1 month pre-anti-RET Anti-RET added 1 month on anti-Ret18TRiG Curriculum: Lecture 3March 2012

23

X



PersonalizedTumor PathologistsTreatmentPlan

Would like to identify tumor, know prognosis,treatment options

g

Access to tumor genome


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.”


Welch JS, et al. JAMA, 2011;305:1577

March 2012


• Review current and future molecular testing:


• Cancer



preventive medicine


Laboratory Testing: Micro

• Identifying outbreak source

– Serotyping

– Pulsed field electrophoresis

– Next-generation sequencing analysis


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


24

What we will cover today:• Review current and

future molecular testing:


• Cancer



preventive medicine


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



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


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


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


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



+ -

+ A B

Lung Cancer

+

- C D

SNP 2



+ -

+ A B

Lung Cancer

+

- C D

SNP 3



+ -

+ A B

Lung Cancer

+

- C D

X

Up to1,000,000 SNPs (howevermany on chip)

SNP X


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


26

Other criteria: Reproducibility: only single populationPhysiologic hypothesis: anti-oxidant (determined pre-study)


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)



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


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


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


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?


Ashley EA, et al. Lancet. 2010; 375: 1525

March 2012

Pharmacogenomics may guide care

Need validation in clinical trials


Other variants detected


Clinical Risk determination (prevalence X post test probability = clinical risk)

Pre-testprobability

Post-testprobability



PersonalRisk PathologistsPrediction

Would like to determine patientrisk for disease

g

Access to patient’swhole genome!

Not so simple!!48TRiG Curriculum: Lecture 3March 2012

28



• No quality control of associations– Need for Clinical Grade

Database• Ease of use





• “No methods exist for statistical integration of such conditionally d d t dependent risks”

• Strength of association based on # of Medline articles


In the end: Is the info actionable?

NEJM. 1994;330:1029


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



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


March 2012 2TRiG Curriculum: Lecture 4




g


Genetic CounselorsMedical Geneticists


Workflow must include communication




• Modern medical technology and practice requires a multidisciplinary approach to medical pppractice.





• 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


31

The approach

Pre-test Counseling

Informed Consent

Ethical and Legal Issues

Post-test Counseling

Testing


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


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


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


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


http://www.nsgc.org/Publications/ProfessionalStatusSurvey/tabid/142/Default.aspx

What we will cover today:• Geneticist training and

professional roles





sequencing


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


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.


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


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


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)


Parmigiani G, et al. Ann Intern Med. 2007; 147: 441

BRCAPRO Analysis

http://www8.utsouthwestern.edu/utsw/cda/dept47834/iles/73815.html


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


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


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


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.


www.acmg.org

The approach

Pre-test Counseling

Informed Consent



Testing


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


Robson ME, et al. JCO. 2010; 28: 893


34

The approach

Pre-test Counseling

Informed Consent



Testing


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


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


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


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


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


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.


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.



DTC: A simplistic calculation

How about family history? Environment?

Pre-test probability

Post-test probability


Ng PC, et al. Nature. 2009; 461: 724



• No quality control of associations– Need for Clinical Grade

Database• Ease of use





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


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.


• No evidence for anxiety• No evidence for change


Bloss CS, et al. NEJM. 2011; 364: 524


• Geneticist training and professional roles





sequencing


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


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


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?


Clinical Risk determination (prevalence X post test probability = clinical risk)

Pre-testprobability

Post-testprobability


Pre-test Probability: Population vs Individual


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


To the Nth power for genomics

Pre-test Counseling

Informed Consent

Ethical and Legal Issues N


Testing


38

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


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


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?


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.”


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


Testing



• Modern medical technology and practice requires a multidisciplinary approach to medical pppractice.




39



• Ethical issues• Informed consent• Integrated data

interpretation

40

NOTES

41

NOTES

42

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