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Resident Genomic Pathology Workshop Instructor Handbook

A “how-to” guide for curriculum implementation

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Copyright Restrictions The Training Residents in Genomics (TRIG) Working Group and the American Society for Clinical Pathology (ASCP) recognize the value of genomics educational materials and resources to improve resident learning. To this end, the TRIG Working Group and ASCP encourage the responsible use of the TRIG Content for non-profit educational purposes that fall within the guidelines of “fair use” doctrine for teaching, scholarship and research as defined under Section 107 – 118 of the Title 17, U.S Copyright Code. This downloadable TRIG Content is protected by U.S. copyright laws, as well as other state, national and international laws, treaties and regulations, and is for educational uses only. Use of the TRIG Content for commercial purposes is strictly forbidden. Unless provided with written authorization from the TRIG Working Group and ASCP, you may not modify, translate, create derivative works of, market, remove or alter any proprietary notices or labels from, lease, sell, sublicense, transfer, decompile, reverse engineer, or incorporate into any information retrieval system (electronic or mechanical) the TRIG Content, or any portion thereof. Permission for your proposed use of the TRIG Content will be considered on a case-by-case basis and may be requested at [email protected]. Limitation of Liability The TRIG Content is provided “AS IS” and is for educational and informational purposes only. The TRIG Working Group and ASCP make no representations or warranties of any kind concerning the TRIG Content, whether express or implied. This includes, without limitation, warranties of title, merchantability, fitness for a particular purpose, non-infringement, or accuracy. In no event shall the TRIG Working Group or ASCP be liable to you or any party related to you for any indirect, incidental, consequential, special, exemplary, or punitive damages or lost profits, even if user has been advised of the possibility of such damages. Some jurisdictions do not allow the disclaimer of certain warranties. Accordingly, the warranty disclaimer and limitations of liability in this section may not apply to you. Indemnification To the fullest extent permitted by law, you will defend, indemnify, and hold harmless the TRIG Working Group and ASCP from and against all claims arising from or in any way related to a violation by you of these Terms of Use, including any liability or expense, losses, damages (actual and consequential), suits, judgments, litigation costs and attorneys' fees.

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These Terms of Use, together with any revisions, constitutes the entire agreement governing your use of the TRIG Content and supersedes any previous written or oral communication regarding use of the TRIG Content. Contact Us If you have any questions about this Agreement, please feel free to contact us at [email protected]. This Agreement was last modified on November 15, 2014

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Table of Contents

Contributors ......................................................................................................... 7

Introduction .............................................................................................................................. 9

Core Workshop Educational Methods ...................................................................... 11

Previous Resident Genomic Pathology Workshop Evaluation Results .............. 14

Overview of the Genomic Pathology Workshop Components .......................... 18

Structure of Each Exercise .............................................................................................. 20

Preparation Checklist ..................................................................................................... 22

Facilitator Preparation/Tips for Implementation ........................................................... 25

Detailed Instructions for the 4 Exercises ....................................................................... 26

Exercise 1: Single-Gene Testing

Overview, Learning Objectives, and Pre-reading Assignment ....................................... 27

Preparation Questions .................................................................................................... 28

Introduction and Pre-activity Lecture Objectives and References ................................ 29

Genomic Website Links .................................................................................................. 31

Team-based Learning Activity ........................................................................................ 33

Post-activity Lecture, Summary and References ............................................................ 37

References ...................................................................................................................... 39

Exercise 2: Prognostic Gene Panel Testing



Pre-activity Lecture Objectives and References ............................................................. 43




References ...................................................................................................................... 55

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Exercise 3: Design of a Multigene Assay







References ...................................................................................................................... 66

Exercise 4: Whole-Genome Sequencing







References ...................................................................................................................... 83

Workshop Summary Lecture

Learning Objectives, Summary and References ............................................................. 85

References ...................................................................................................................... 87

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Contributors

TRIG Working Group Members:

Richard L. Haspel, MD, PhD (Chair)

James B. Atkinson, MD, PhD

Elizabeth Azzato, MD, PhD, MPH

Frederic Barr, MD, PhD

Andrew Beck, MD, PhD

Karen Frank, MD, PhD

Madhuri Hegde, PhD

Karen Kaul, MD, PhD

Debra Leonard, MD, PhD

John Pfeifer, MD, PhD

Henry Rinder, MD

Iris Schrijver, MD

Mark E. Sobel, MD, PhD

VO Speights, MD

Elizabeth Varga, MS, CGC

Scientific Advisors:

Peter Tonellato, PhD

Dennis Wall, PhD

Lauren Briere, MS, CGC

Outside Reviewers

Phillip Connors, MS, LGC

Loren Joseph, MD

Jennifer Laudadio, MD

Cindy McCloskey, MD

Jill Krejdovsky, MS, LGC

Gaurav Sharma, MD

TRIG Working Group Cooperating

Organizations

Academy of Clinical Laboratory Physicians and

Scientists (ACLPS)

American College of Medical Genetics and

Genomics (ACMG)

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

Pathology (USCAP)

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Participating ASCP Staff

Suzanne Ziemnik, MEd

Eric R. Parks, PhD

Asma Ali, PhD

Lucy Beck

Michelle Martin

Karisa Munoz, MEd

Matthew Smith

Ryan Soles

Jay Wagner

Participating APC Staff

Priscilla S. Markwood, CAE

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Introduction This Resident Genomic Pathology Workshop Instructor Handbook and the accompanying Toolkit provide the materials and guidance needed to implement a structured and field-tested introductory genomic pathology curriculum for residents. The curriculum consists of approximately seven hours of instruction and uses a flipped classroom and team-based learning approach. The instructor leading this workshop does not need to be an expert in genomic pathology, however; a strong background in molecular pathology is recommended. In preparing for the workshop, it is most helpful to review the handbook, PowerPoint lectures, and handouts together – none of these materials are intended to stand alone.

The Need for Genomic Pathology Education In 2003, after more than 10 years and $2 billion spent, scientists sequenced the first human genome. Today, massively parallel methods, due to greatly reduced cost and turn-around-time, have led to genomic testing rapidly entering clinical practice. However, despite the growing application of genomic methods to patient care, there is evidence that many physicians have limited understanding of even relatively uncomplicated single-gene molecular testing, let alone genomic analysis. As major providers of genetic testing, pathologists and medical-laboratory professionals are ideally positioned to guide clinicians and patients alike towards a better understanding of genomic information. This Genomic Pathology Workshop is a unique opportunity for pathology residents to explore genetic and genomic testing and to enable application to patient care. Regardless of residents’ planned sub-specialties, genomic testing will become an important part of their pathology careers. They will need to learn principles related to the development and selection of genomic assays, as well as how to accurately interpret results. The Training Residents in Genomics (TRIG) Working Group was formed in 2010 through the Pathology Residency Program Directors Section (PRODS) of the Association of Pathology Chairs (APC). The TRIG Working Group’s goals are to promote the importance of genomic pathology education, to develop teaching tools for residency programs, and to evaluate progress using novel evaluation tools and the Pathology Resident In-Service Exam (RISE). The efforts of the TRIG Working Group, including the piloting of this curriculum at the 2013 ASCP and 2014 USCAP Annual Meetings and developing this Instructor Handbook and Toolkit, are supported by a research grant from the National Institute of Health (1R25CA168544-01). More information regarding the TRIG Working Group can be found at ascp.org/TRIG. If you have questions regarding this Resident Genomic Pathology Workshop, please email [email protected]. Reference Haspel RL, Olsen RJ, Berry A, et al. Progress and potential: training in genomic pathology. Arch Pathol Lab Med. 2014;138:498-504.

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Workshop Focus

Currently, the primary use of genomic testing is in oncology. Pathologists also play a major role in cancer diagnosis. As such, this workshop focuses on oncologic testing and follows the case of a patient with breast cancer. There is, however, some discussion on other uses of genomic methods, as well as reporting of non-oncologic findings. Major Workshop Objectives:

To demonstrate that genomic tests are, in many ways, “just another laboratory test” and require: o Validation of assays o Confirmation of clinical utility through well-designed studies

To describe the role all pathologists will play in the current genomic medicine era, including: o Selection of samples for testing o Interpretation of variants o Integration of results into pathology reports

To demonstrate the online tools that are available for analyzing and interpreting genomic data, with an emphasis on:

o Constantly changing data o Variable curation quality

Expected Workshop-Participant Characteristics

Although it can be implemented with other learners, the workshop has been designed for Pathology residents. For best outcomes, participants:

a. Should be grouped into teams of 2 to 6 residents each. b. Should have completed or be in the process of completing a molecular-pathology

rotation.

Expected Workshop-Instructor Characteristics

The instructor leading this workshop does not need to be an expert in genomic pathology, however; a strong background in molecular pathology is recommended. The goal of this Instructor Handbook and the accompanying Toolkit is to provide the additional expertise and tools to allow teaching of introductory genomics concepts to pathology residents.

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Core Workshop Educational Methods Flipped Classroom The workshop utilizes the flipped classroom model, as illustrated below

Out-of-Class, Before: Participants are exposed to new knowledge-based content (e.g., watching e-lectures, reviewing PowerPoint presentations, reading journal articles) at their convenience. This workshop consists of 4 exercises. For each exercise, out-of-class tasks will include review of references and answering preparation questions.

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In-Class, During: As opposed to lectures, the live classroom can focus more on the application of the new concepts and knowledge reviewed in the pre-class material. The instructor can then offer more personalized guidance and interaction with participants. The classroom is flipped: the participants learn the factual content at home and do the problem-based homework with the assistance of the instructor during class. For each exercise in this workshop, the in-class structure consists of a pre-activity lecture to review the out-of-class preparation, a team-based learning activity, and a post-activity review of the team responses. Out-of-Class, After: The participant can further apply the concepts and online tools from the in-class experience and explore additional provided references to enhance transfer of both factual knowledge and problem-solving skills.

Benefits of the Flipped Classroom

Participants can learn at their own pace while exploring new concepts and knowledge

As opposed to only lecturing, instructors can spend more 1:1 time with the participants in the classroom assisting them in actively applying their emerging knowledge

Different learning styles are accommodated by the wider variety of teaching approaches (e.g., reading primary literature, viewing lectures, and experiencing hands-on application of knowledge in the classroom)

Participants can learn from, help to teach, and work to solve problems with peers

This teaching model improves participant ability to not simply acquire factual knowledge but to also practically apply that knowledge

Preparation Questions

Preparation questions (also known as readiness-assurance questions) are included with each exercise. In some flipped classroom models, the participant first sees these questions during the in-class component. For this workshop, we include the questions with the pre-class material to help guide reading and to better ensure that residents understand the knowledge that is required for the in-class activity. By reviewing the questions and answers through the in-class pre-activity lecture, the instructor will be able to gauge how much the residents were able to learn from the pre-class material and what additional information may need to be acquired prior to the team-based learning activity.

Team-based Learning (TBL)

The team-based learning (TBL) method has been introduced in healthcare education to foster critical-thinking skills while students work in collaborative teams. TBL is learner centered, with the instructor acting as an expert facilitator but not actively participating in the team discussions. Each team is asked to tackle problems and to work together to reach consensus on an answer. The team then discusses its answers with the other teams and the instructor. TBL provides each participant with the opportunity to work with peers to expose inconsistencies between their current understanding and new experiences, thus stimulating development of a new personal mental framework built upon previous knowledge.

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The learning is active, using relevant problems and group interaction. Teamwork skills are strengthened by focused reflection on new experiences during the team sessions and on teamwork success by providing feedback to group members.

Genomic Websites

Each TBL activity engages residents in the process of using genomic websites that offer various tools to assist in the design of genomic assays, to predict the possible impact of variants on the structure and function of human proteins, to determine the clinical significance of variants, and other tasks. The workshop will provide residents with an introduction to the capabilities of these tools and the application of those tools to their clinical work. The websites accessed by residents during the workshop are listed below. More-detailed descriptions taken directly from the websites and instructions for their use are described later in this handbook within the individual exercises. ClinVar: http://www.ncbi.nlm.nih.gov/clinvar/ Polyphen: http://genetics.bwh.harvard.edu/pph2/ The Kaplan-Meier Plotter website: http://kmplot.com/analysis/index.php?p=service&default=true Catalogue of Somatic Mutations in Cancer (COSMIC) database: http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/ My Cancer Genome: http://www.mycancergenome.org cBioPortal: http://www.cbioportal.org/public-portal/case.do?case_id=TCGA-BH-A1FE&cancer_study_id=brca_tcga OMIM: http://www.ncbi.nlm.nih.gov/omim

References

1. Hrynchak P, Batty H. The educational theory basis of team-based learning. Med Teach.

2012;34:796-801.

2. Center for Teaching and Learning. The flipped classroom. The University of Texas at Austin Web

site. http://ctl.utexas.edu/teaching/flipping-a-class/how. Accessed October 20, 2014.

3. Michaelsen LK, Sweet M. The essential elements of team-based learning. New Dir Teaching

Learning. 2008;116:7-27.

http://www.ncbi.nlm.nih.gov/clinvar/

http://genetics.bwh.harvard.edu/pph2/

http://kmplot.com/analysis/index.php?p=service&default=true

http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

http://www.mycancergenome.org/


http://www.cbioportal.org/public-portal/case.do?case_id=TCGA-BH-A1FE&cancer_study_id=brca_tcga


http://www.ncbi.nlm.nih.gov/omim

http://ctl.utexas.edu/teaching/flipping-a-class/how

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Previous Resident Genomic Pathology Workshop Evaluation Results

Two day-long Resident Genomic Pathology Workshops using a flipped classroom and team-based learning approach (as described in this handbook) were conducted at the 2013 ASCP Annual Meeting and the 2014 United States and Canadian Academy of Pathology (USCAP) Annual Meeting. This section provides evaluation results of these workshops in order to provide instructors with background information on efficacy. Sixty-one pathology residents participated in the workshop; evaluation was based on a survey of 46 residents (response rate, 75%) immediately after the workshops, as well as systematic observations by faculty. Overall perceptions: The workshop was overwhelmingly well received by resident-participants. Highlights from the data show that the workshop accomplished its objectives, that it was a worthwhile use of residents’ time, and that it was relevant to their future careers. Ninety-six percent of residents would recommend the learning experience to others.

Survey Item Agree or Strongly Agree

The workshop met the stated learning objectives.

96%

The workshop helped me to understand the clinical relevance of genomic pathology.

94%

Our workshop discussions were productive.

98%

The workshop was a valuable use of my time.

93%

I would recommend this workshop to other residents.

96%

This workshop will help me as a practicing pathologist.

91%

Individual modules: Reactions to the specific modules were fairly uniform and positive. The data displayed in the following section are for the USCAP workshop in 2014 and are essentially equivalent to the responses from ASCP in 2013, with one important difference. Less than 40% of workshop participants at ASCP in 2013 perceived the modules to be difficult, compared with almost 60% of USCAP participants in 2014. This variation was attributed to the differences between residents’ backgrounds and their preparation for the 2 workshops.

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Group Dynamics: Groups were highly engaged, and residents reported an excellent balance of size and facilitation. Participants believed that the size of the groups (which ranged from 2 to 6 individuals) was appropriate. Facilitator interaction differed qualitatively, in that there was more involvement during the USCAP workshop to assist residents with weaker genomic backgrounds; however, uniformly, residents in both workshops believed that the extent of facilitation was appropriate.

Survey Item Just right Too little Too much

The number of people in my workshop small group was... 43 1 1

The level of participation of my small-group facilitator was... 43 2 1

Just right Too slow Too fast

The pace of the workshop was... 37 6 2

Just right Too easy Too hard

The workshop preparatory questions were... 35 7 4

The application exercises were... 42 2 2

(0)

(20)

(40)

(60)

(80)

(100)

Met objectives Preparatory questionsprepared me

Exercises were difficult Application exerciseswere appropriate

Per

cen

t re

spo

nd

ing

"agr

ee"

or

"str

on

gly

agre

e"

Exercise 1 Exercise 2 Exercise 3 Exercise 4

2013

2014

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Comments about the “best things about the workshop” showed positive reactions to the team-based learning and interactive approach.

What were the best things about the workshop? “Small group interaction” “Team-based learning” “Small group discussion” “group work” “The group-based learning” “Team based learning” “small groups learning” “Hands on practice” “method” “Team work” “active involvement, team-based approach” “Small group discussions” “Interactive nature, teaching” “Practical exercises” “small group” “exercises” “team work” “The exercises were fun. The web resources were very good and I came to know about them for the first time.”

Comments from Workshops

“[The workshop] improved my understanding of available tools for clinical evaluation.”

“It [the workshop] has given me a better perspective on communicating the results with clinicians.”

“[The workshop] deepened my perspective regarding genomic medicine.”

“Keep up the good work! Please repeat!”

“Excellent course. I am taking this back to my home institution.”

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“Word Cloud” Residents were asked to provide 2 adjectives that best describe the workshop. The size of the font is proportional to the number of residents providing that adjective.

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Overview of the Genomic Pathology Workshop Components

The following section describes the workshop components and structure. Four exercises follow the case of a patient with breast cancer and progress in complexity, from single-gene testing to gene panels to whole-exome sequencing. All exercises use a flipped-classroom approach, with the expectation that the residents already will have completed out-of-class preparatory work, including reading and preparation questions. Each in-class exercise begins with the instructor giving a pre-activity lecture that includes a review of the preparation questions and further information to prepare the residents for the team-based learning (TBL) activity. Once the TBL activity is completed, during the post-activity lecture, the instructor will review the resident team’s final-consensus answers to each of the exercise questions and will discuss additional nuances. At the end of the exercise, residents receive a handout containing a list of the references discussed during the exercise. Out-of-class exploration of these references will enhance transfer of both factual knowledge and problem-solving skills. Note that the first exercise also begins with an overall introduction to the curriculum and the last exercise ends with a workshop summary and a discussion of non-oncologic genomic testing. The structure of the workshop is diagrammed below, followed by further details to facilitate implementation. For each exercise, the out-of-class preparation should take residents approximately 60 minutes and the in-class component should take approximately 90 to 120 minutes.

Introductory Lecture

Out-of- Class

In- Class


Team-based Learning Activity

Pre-reading Assignment& Preparation Questions

Post-activity Lecture/ Distribution of

References

Preparation Question Review/ Pre-activity

Lecture

Exercise 1: Single-gene Testing


References

Preparation Questions


Lecture


Exploration of References Out-of-

Class

Exploration of References

In- Class

Out-of- Class

Out-of- Class

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Out-of- Class

Exercise 3: Design of a Multigene Assay


Pre-reading Assignment & Preparation Questions


References


Lecture

Exploration of References Out-of- Class

In- Class In-

Class

Out-of- Class

Exercise 4: Whole-Exome Sequencing

Post-activity Lecture


Lecture


Exploration of References Out-of-

Class

Pre-reading Assignment & Preparation Questions

Summary Lecture/ Distribution of

References

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Structure of Each Exercise

1. Pre-reading/Preparation Questions

At least 24 hours prior to the in-class component of each exercise, resident-participants should receive the pre-reading materials and the pre-exercise preparation handout containing the preparation questions and links to online tools that will be accessed during the exercise. Residents review any pre-reading materials and answer 2 to 3 preparation questions prior to each in-class exercise. Although significant expertise in genomics is not expected prior to the exercise, residents may also begin to explore the links to the online tools. The purposes of these pre-exercise assignments are to engage residents in the educational activity prior to the workshop and to prepare them for the information that they will be learning and actively using during the exercise. This preparatory knowledge will provide fundamental information that will bring the resident-participants to the same level of understanding prior to each exercise. The preparation time for each exercise should be approximately 60 minutes. 2. Pre-Activity Lecture

The pre-TBL lectures are brief PowerPoint presentations given by the instructor that review the answers to the preparation questions, explain key principles, and provide instructions, including an overview of the online tools, all of which are needed to complete the subsequent “Team-Based Learning Activity.” Each lecture should take approximately 15 to 30 minutes. There is also a 15-minute introductory lecture, intended to provide curricular background prior to the exercise 1 pre-activity lecture. 3. Team-Based Learning Activity

The workshop utilizes an interactive team-based approach and practical, hands-on instruction with the use of online genomic tools. The workshop curriculum was designed to allow residents to not only learn factual knowledge but also to apply that knowledge in a collaborative environment with their colleagues. To obtain the maximum benefit from this experience, each team member needs to participate actively in the team-based learning activities and consensus discussions. The instructor should not act as the discussion leader and should only assist if the group is having trouble moving forward. Each team-based learning activity is to be completed within approximately 60 to 75 minutes.

All participants should be given an electronic copy of the team-based learning activity handout and designate a member of the group as a recorder, to enter the team’s final consensus answers on their handout.

Types of questions

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o Reveal questions: For these questions, each team member should write down his or her own answer on paper and then, following the reveal (sharing of written answers with one other), the team should come to a consensus about the single answer on which the team agrees and that it will report to the instructor. This model encourages each resident to commit to a position, thus setting the stage for engagement in the subsequent discussion for the team’s final answer. The questions are indicated as reveal-type in the handouts.

o For other questions, including those involving online tools, all members of the group can work together on the activity and then come to a consensus on the answer that will be reported to the instructor.

Residents should determine final-consensus answers for a question before moving on to the next question.

4. Post-Activity Lecture

During the post-activity lecture, the instructor will review the answers with the team and discuss key take-home points. These lectures take approximately 30 minutes. After the exercise 4 post-activity lecture, there is an additional 30-minute workshop summary lecture, which also includes information on non-oncologic genomic testing. 5. Distribution of References Following the end of each post-activity lecture, residents should receive a handout containing a list of the references discussed during the exercise. Review of these references and further exploration of the online tools will enhance transfer of both factual knowledge and problem-solving skills. Workshop Delivery Options This instructor-facilitator guide is intended for an instructor interacting with a single team of 2 to 6

residents, all of whom the instructor knows and all of whom have a similar knowledge level. Although all

the exercises can be completed in a single day, the recommended presentation of this curriculum is 4

separate 90- to 120-minute sessions over 4 days, each covering 1 exercise, or 2 sessions of 180- to 240-

minute each over 2 days, each covering 2 exercises.

This workshop can be implemented with other learners besides residents. A single instructor can also

lead multiple teams. However, this approach takes some practice and can be especially difficult when

the group has a variable knowledge base. There are also tools such as Google forms that can facilitate

recording of answers from multiple groups. If you would like more information on running a larger team-

based learning session, please contact the TRIG Working Group at [email protected].


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Preparation Checklist

This section describes what you will need (materials, computer requirements, room set-up, etc.) to implement the workshop. Additional materials are available in the accompanying Toolkit. Materials Needed Toolkit consisting of:

o Pre-exercise preparation handouts for the residents. At least 24 hours before each exercise, the handout for that exercise should be given to residents to assist them in completing the out-of-class assignments. These handouts include the preparation questions and links to the online genomic tools that will be used during each exercise.

o Pre-exercise references to be read by the residents o In-class team-based learning activity handouts for the residents. Immediately before each

exercise, the handout for that exercise should be distributed to residents. An electronic version is preferable because it allows easy linking to the online tools.

o PowerPoint presentation files for the instructor, including pre- and post-activity lectures for each exercise, as well as the introductory and summary lectures. Each PowerPoint lecture has notes on the bottom of each slide.

o Reference handouts. The instructor should distribute these to the residents after each exercise.

Equipment

1. Projector and screen 2. A circular table for residents to use to work together on the application activity 3. Personal computers (PCs) with internet access. A separate PC with internet access for each

resident is preferred; however, 1 computer for every 2 or 3 residents is also acceptable. If computers are not available, tablets will also provide access to most, but possibly not all, websites and teaching tools.

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Requirements for PCs are as follows:

Component WINDOWS PC APPLE MACINTOSH

Operating System

Windows 8.1/8/7/Vista (32-bit and 64-bit) Windows XP SP3 (32-bit)

Macintosh OS 10.6 or higher

Productivity Tools

Microsoft Office 2007/2010/2013

Microsoft Office for Mac 2008/2011

Processor Type

MINIMUM MINIMUM

Pentium III/Celeron 866 MHz or equivalent

Intel-based processor

RECOMMENDED RECOMMENDED

Core i7 Processor Core i7 Processor

Memory

MINIMUM MINIMUM

4 GB RAM 4 GB RAM


8 GB RAM 8 GB RAM

Hard Drive

MINIMUM MINIMUM

250 GB 250 GB


500 GB or higher 500 GB or higher

Graphics Card

MINIMUM MINIMUM

512 MB Video Memory or higher

512 MB Video Memory or higher

Browser

PREFERRED PREFERRED

Google Chrome Google Chrome

Note: Internet Explorer, version 9.0 (and above) is also acceptable, but there have occasionally been issues accessing websites

Note: Safari is also acceptable, but there have occasionally been issues accessing websites

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Room Set-up Because of the team-based learning, the recommended size of a group is 2 to 6 residents working together at a circular table. Ideally, each resident should have a computer (preferred) or a tablet with internet access. If necessary, 2 to 3 residents may share a computer. A projector and screen are also needed to deliver the lectures.

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Facilitator Preparation/Tips for Implementation

It is important that residents complete and feel comfortable with the preparatory material. Based on piloting of the workshop, residents who prepared benefitted the most from the exercises. Unprepared residents, given the additional time the instructor spends explaining concepts, also prolong the length of each exercise.

In preparing for the workshop, it is most helpful for the instructor to review the handbook, PowerPoint lectures, and handouts together – none of the materials is intended to stand alone. Also, the instructor may choose to review the additional references included in the pre– and post–team-based learning-activity lectures.

Prior to the session with residents, the instructor should work through the application activities, in conjunction with the PowerPoint lectures, on his/her own (including testing the hyperlinks and accessing the websites). It is critical that instructors are familiar with exercise structure and website mechanics as, during pilot-testing of the workshop, questions regarding navigation were asked most frequently. The post–team-based learning (TBL) activity lectures also include screenshots of the various online tools and expected results, which will assist in understanding the handbook instructions.

Given the constant influx of new data, results may have changed since this document was released. For example, between the first iteration of the workshop and the second, there was a change in the listed clinical significance of a BRCA1 variant. The fact that results may change is an important learning point for residents and is included in the PowerPoint presentations.

For the first team-based activity, it is worthwhile for the instructor to stay in the room to help facilitate the process. The instructor should primarily observe and should only intervene if the team is unable to progress. Once the team is familiar with the process, the instructor may be able to leave the room during application activities and should simply be available by phone or pager if questions arise.

Encourage residents to explore PubMed references that they come across during the activities (e.g., search abstracts and open-access articles for the variant of interest). The “find” option (typically accessed by pressing CTRL-f on the keyboard) enables residents to quickly scan through references.

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Detailed Instructions for the 4 Exercises

The following section of the handbook provides detailed implementation instructions for each of the 4

exercises. Note that all case descriptions in the exercises refer to the same patient.

Each exercise is divided into:

1) An overview

2) Learning objectives

3) Out-of-class resident preparation work (readings and preparation questions)

4) Pre-activity lecture objectives and references included in the lecture

5) A description of the utilized websites

6) The team-based learning activity (with answers shown in italics)

7) Post-activity lecture objectives and references included in the lecture

8) References utilized during the exercise and contained in the reference handout

Before leading the exercises with residents, it is strongly recommended that the instructor review the

guide below, including accessing the online tools, in conjunction with the PowerPoint lectures and

handouts. Each PowerPoint lecture has notes on the bottom of each slide.

Please also note that, given the constant influx of new data, some information may have changed since

this document was released.


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Overview

This exercise first reviews the importance of pathologists in genomic testing and presents several basic molecular pathology concepts in the context of the preparation questions. The team-based learning activity, using the example of BRCA single-gene testing, allows residents to begin to understand the importance of pre-test probability in genetic testing and the utility and limitations of online databases to determine the clinical significance of a variant.

Learning Objectives

In this exercise, residents will be able to:

List the factors that help determine which patients are appropriate candidates for breast-cancer-susceptibility genetic testing

Determine, using online tools, the clinical significance of a variant related to breast-cancer-susceptibility genetic testing

Pre-Reading Assignment

Note: There is no pre-reading assignment for exercise 1, but prior to the in-class session, residents should be given the pre-exercise handout containing the preparation questions and asked to determine the answers.

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Preparation Questions (in pre-exercise preparation handout)

Exercise 1: Single-Gene Testing 1. A nonsense mutation is a single base-pair substitution that leads to:

A. A substitution of one amino acid for another B. A new stop codon C. Incorrect mRNA splicing D. The inability of a tRNA to recognize the sequence

2. What is the basis of Sanger sequencing?

A. Use of DNA nuclease that recognizes specific nucleotides B. Antibody-based recognition of specific nucleotides C. Incorporation of stop codons throughout the length of the

sequence D. Incorporation of chain-terminating nucleotides throughout

the length of the sequence

3. Which of the following represents somatic DNA testing? A. Testing for BRCA1 variants from DNA obtained from a blood

sample B. Testing for trisomy 21 in a fetus using DNA obtained from

amniotic fluid C. Testing for PTEN variants using DNA obtained from a colon

cancer specimen D. Testing for HLA type using DNA obtained from a buccal-swab

specimen

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Introduction and Pre-Activity Lecture Objectives and References

Exercise 1: Single-Gene Testing During this presentation, the instructor will:

1. Review role of pathologists in genomic testing 2. Review principles of genetic counseling/the role of other

healthcare providers 3. Review basic concepts in molecular pathology (Sanger

sequencing, genetic variation, somatic vs. germline testing) Note: depending on resident level of expertise, some of this review may not be necessary.

References included in this lecture:

1. Jones SJM, Laskin J, Li YY, et al. Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biol. 2010;11(8):R82. doi: 10.1186/gb-2010-11-8-r82.

2. Welch JS, Westervelt P, Ding L, et al. Use of whole-genome sequencing to diagnose a cryptic fusion oncogene. JAMA. 2011;305:1577-1584.

3. Ashley EA, Butte AJ, Wheeler MT, et al. Clinical assessment incorporating a personal genome. Lancet. 2010;375:1525-1535.

4. Link DC, Schuettpelz LG, Shen D, et al. Identification of a novel TP53 cancer susceptibility mutation through whole-genome sequencing of a patient with therapy-related AML. JAMA. 2011;305:1568-1576.

5. Worthey EA, Mayer AN, Syverson GD, et al. Making a definitive diagnosis: successful clinical application of whole-exome sequencing in a child with intractable inflammatory bowel disease. Genet Med. 2011;13:255-262.

6. Lupski JR, Reid JG, Gonzaga-Jauregui C, et al. Whole-genome sequencing in a patient with Charcot-Marie-Tooth Neuropathy. N Engl J Med. 2010;362:1181-1191.

7. Bianchi DW, Parker RL, Wentworth J, et al. DNA sequencing versus standard prenatal aneuploidy screening. N Engl J Med. 2014;370:799-808.

8. Roychowdhury S, Iyer MK, Robinson DR, et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med. 2011;3:111ra121. doi: 10.1126/scitranslmed.3003161.

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Introduction and Pre-Activity Lecture Summary


Genomic testing requires a team approach

There are different types of genetic variation

Genomic testing refers to testing large portions of the genome using a single test

o Time consuming and expensive with classic Sanger sequencing (Human Genome Project)

o Next-generation sequencing (massively parallel) is currently in use

Team-Based Learning Activity

To be completed by the team (2-6 residents) over approximately 60 minutes. Answers for the instructor are shown in italics.

For questions 1, 2, and 4, the residents should first write down their answers on the team-based learning activity handout and then “do a reveal.” One resident should write down the team’s final-consensus answers based on the team’s discussion.

For question 3, the residents can explore the online tools together, and 1 resident should write down the team’s final-consensus answer.

During the post-activity lecture, the instructor will review the answers with the residents (approximately 30 minutes).

Remember, the instructor should not act as the discussion leader and should only assist a group if it is having trouble moving forward.

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Genomic Website Links

Exercise 1: Single-Gene Testing Note: Using the screen captures included in the pre-activity lecture, provide an overview of the following websites prior to starting the team-based learning activity. Although significant resident expertise is not expected prior to the exercise, the pre-exercise preparation handout also contains links to online tools that will be accessed during the exercise. 1. ClinVar: http://www.ncbi.nlm.nih.gov/clinvar/ What is ClinVar? “ClinVar aggregates information about sequence variation and its relationship to human health. ClinVar is designed to provide a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. By so doing, ClinVar facilitates access to and communication about the relationships asserted between human variation and observed health status, and the history of that interpretation. ClinVar collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about the submitter, and other supporting data. The alleles described in submissions are mapped to reference sequences, and reported according to the Human Genome Variation Society (HGVS) standard. ClinVar then presents the data for interactive users as well as those wishing to use ClinVar in daily workflows and other local applications. ClinVar works in collaboration with interested organizations to meet the needs of the medical genetics community as efficiently and effectively as possible.” 2. Polyphen: http://genetics.bwh.harvard.edu/pph2/ What is PolyPhen-2? “PolyPhen-2 (Polymorphism Phenotyping v2) is a tool that predicts the possible impact of an amino-acid substitution on the structure and function of human protein using straightforward physical and comparative considerations.”



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3. The Human Gene Mutation Database (HGMD): http://www.hgmd.cf.ac.uk/ac/index.php. (Note: Residents will not directly access this database and are not given a link in the pre-exercise preparation handout; however, it is discussed in the post-activity lecture.) What is the HGMD? “The HGMD represents an attempt to collate known (published) gene lesions responsible for human inherited disease. This database, whilst originally established for the study of mutational mechanisms in human genes, has now acquired a much broader utility in that it embodies an up-to-date and comprehensive reference source to the spectrum of inherited human gene lesions. Thus, HGMD provides information of practical diagnostic importance to (i) researchers and diagnosticians in human molecular genetics, (ii) physicians interested in a particular inherited condition in a given patient or family, and (iii) genetic counselors.”


http://www.hgmd.cf.ac.uk/ac/index.php

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Team-Based Learning Activity #1: Single-Gene Testing Case Presentation The patient is a 39-year-old white, ethnically non-Jewish woman with a new diagnosis of breast cancer, initially discovered on self-examination. The diagnosis was confirmed by biopsy, which showed invasive ductal carcinoma. The patient is referred to the local medical center for evaluation and treatment planning. Given the early age of diagnosis the patient is referred for genetic counseling. The genetic counselor reviews her family history and learns that the patient has no first degree relatives with breast cancer; however, she has a small family with few female relatives. Her father had prostate cancer at 42 and a paternal aunt was diagnosed with breast cancer at age 48. 1. List 2 reasons why BRCA testing is not offered to all women. (REVEAL)

(15 minutes)

1) Waste of resources 2) Management of variants of uncertain clinical significance 3) Possibility for unnecessary worry on the part of patients and their families 4) Potential for genetic discrimination (to be covered further in exercise 4)

2. List 2 ways that knowing the results of BRCA testing could be helpful for this patient. (REVEAL)

(10 minutes)

1) Could influence the decision for bilateral mastectomy vs. unilateral mastectomy/lumpectomy 2) Could indicate the need for oophorectomy 3) Potential for the patient to participate in clinical trials (e.g., poly [ADP ribose] polymerase [PARP]

inhibitors) 4) Counseling of family members 5) Psychological benefit to understanding cause of disease

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3. The patient is found to have a c.4883T>C; p.Met1628Thr (M1628T) variant in the BRCA1 gene. (20 minutes)

a. Using http://www.ncbi.nlm.nih.gov/clinvar/, what is the reported clinical significance of the

variant and based upon what evidence? Please explain using a maximum of 3 sentences. (HINTS: search using “M1628T” and review any PubMed links)

Depending on the difficulty experienced by residents in searching sites on their own, the instructor might share more detailed instructions for using online tools. Note that the “find” option (typically, CTRL-f) enables residents to quickly scan through references for the variant of interest.

1) M1628T can be entered directly into the search bar at the top of the page. Alternatively:

Click “Advanced search” under the search box at the top of the page

Enter on Line 1: brca1

Enter on Line 2: m1628t 2) What is the clinical significance of the variant?

As shown in upper right section of the webpage, clinical significance is indicated as “conflicting data from submitters” and ranges from “benign” to “likely benign” to “uncertain significance”. (Note: in the first workshop, there were actually two entries, one “probably not pathogenic” and the other “pathogenic”.)

Go to bottom of the page. Look under “Clinical Assertions.” The table provides some additional details. Click on “Evidence” tab next to the “Clinical Assertions” tab (residents may need prompting to identify this tab): i. Different “evidence” listed by the various submitters:

Biesecker Laboratory: only indicates 1 patient tested. If cursor moved over “see description”, the text box indicates that the study set was not selected for “affection status” and that pathogenicity is based on “literature curation”.

Invitae: If cursor moved over “see description”, the text box describes frequency data to suggest a likely benign variant; however, there is no additional information on how these data were obtained.

Breast Cancer Information Core: For this entry, it is unclear how clinical significance was determined because there is no listing of observed phenotypes.

Counsyl: link to PubMed references provided. ii. Exploring the references: Many of the 8 references under the Counsyl entry are

for algorithm-based prediction and not clinical outcomes. The following information pertains to several other references. (Note: references and reference order may have changed since the release of this handbook.)


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Reference #1 (Johnston et al.) states that the variant occurred in 1 of 572 participants in an atherosclerosis study who were tested for BRCA1 and BRCA2. This information is only evident if the article is retrieved through PubMed and supplementary table S1 is searched for the Met1628Thr variant. This variant is listed as “unknown pathogenicity” under the “LSDB Classification” column and “disease-causing mutation” in the “HGMD classification” column (see also footnotes e and f).

From Reference #2 (Kuusisto et al.), it appears that the variant may be clinically significant. Table 3 shows that individuals with this variant had breast cancer and family members with breast cancer. There is also a quotation in this paper that indicates some clinical significance (“The clinical significance of the BRCA1 c.4883T > C variant in breast cancer predisposition is uncertain. Our data support the idea that it is a low-penetrant risk allele, because the variant was observed to be three times more common in analyzed high-risk individuals than healthy population controls.”) Recall that ClinVar entries mostly suggested that the variant was benign.

Reference # 4 (Ostrow et al.) is an in vitro study with results that suggest the variant is benign (abstract).

Some residents may access the 8 references provided through the Counsyl entry through the “Clinical Assertions” tab in the table under “Citations.” This approach also includes the following 2 additional references

The Carvalho MA, et al. reference is a functional study in which the M1628T variant was used as a neutral control

The Malone KE, et al. reference is a study of BRCA variants in the general population. Although it is somewhat hard to determine when reading the paper, the M1628T variant was considered a “rare polymorphism” and not deemed to be associated with disease.

i. The following references come up if one does a PubMed search for this variant (typing in “M1628T”). These two functional studies indicate that this variant does not affect transcriptional activity of BRCA1.

Phelan C, et al.

Ostrow KL, et al.

b. Using Polyphen (http://genetics.bwh.harvard.edu/pph2/) what is this variant’s predicted impact on protein function and clinical significance for the patient? (HINT: use “BRCA1” [all capital letters] for “Protein or SNP identifier,” after hitting “Submit Query,” need to hit “Refresh” on following page until job completed)

1) Given that server capacity is variable, it may be best to suggest that groups of 2 to

3 residents perform the search together. 2) Go to http://genetics.bwh.harvard.edu/pph2/.



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3) Input the information to describe the patient’s mutation (Position 1628; Substitution AA1: M & AA2: T).

4) Hit the “Submit Query Button”. 5) Refresh the job until complete and then click “View results”. 6) Click “multiple sequence alignment” under “Details”. The variant was considered

benign because there is significant variation at the amino-acid position across species, which suggests that it is not functionally significant.

4. Based on the available data: (REVEAL)

(15 minutes)

a. In discussing with the patient and determining further medical care, what would you conclude is the clinical significance of the variant (benign or pathogenic)?

Most likely, it is benign; however, there is no absolute right answer—in any case, this question makes the trainees commit to a position. The post-activity lecture discusses inconsistencies in the classification as reported by different databases and the need to refer to multiple sources, including the primary literature. There is further discussion in exercise 4 on whether to report this variant to the ordering physician. b. List 2 appropriate next steps to examine the clinical significance of this variant.

1) Test affected family members for mutation 2) Determine general-population frequency of the variant 3) Perform functional studies


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Post-activity Lecture Summary and References


Many reasons why a test result may be important:

o Establish or confirm a diagnosis (Gaucher disease)

o Reproductive decision making, carrier testing, and fetal testing (cystic fibrosis)

o Prediction of risk for future disease (breast cancer (BRCA), Huntington Disease)

o Aid in management decisions (e.g., BRCA, tumor sequencing)

Determining pre-test probability is important:

o Avoiding waste of resources

o Management of variants of uncertain clinical significance

o Can result in unnecessary worry on the part of patients

Determining clinical significance can be difficult—don’t believe everything in a database

o Information you find may be incorrect

o You know enough to explore the literature

Need for clinical-grade database

o Ease of use

o As data continues to accumulate, content needs to be updated

o Clinically relevant single nucleotide polymorphisms (SNPs)/single nucleotide variants (SNVs)

References in this lecture:

1. Johnston JJ, Rubinstein WS, Facio FM, et al. Secondary variants in individuals undergoing exome sequencing: screening of 572 individuals identifies high-penetrance mutations in cancer-susceptibility genes. Am J Hum Genet. 2012;91:97-108.

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2. Parmigiani G, Chen S, Iversen ES Jr, et al. Validity of models for

predicting BRCA1 and BRCA2 mutations. Ann Intern Med. 2007;147:441-450.

3. Kuusisto KM, Bebel A, Vihinen M, et al. Screening for BRCA1, BRCA2, CHEK2, PALB2, BRIP1, RAD50, and CDH1 mutations in high-risk Finnish BRCA1/2-founder mutation-negative breast and/or ovarian cancer individuals. Breast Cancer Res. 2011;13:R20. doi: 10.1186/bcr2832.

4. Ostrow KL, McGuire V, Whittemore AS, DiCioccio RA. The effects of BRCA1 missense variants V1804D and M1628T on transcriptional activity. Cancer Genet Cytogenet. 2004;153:177-180.

5. Phelan CM, Ðapić V, Tice B, et al. Classification of BRCA1 missense variants of unknown clinical significance. J Med Genet. 2005;42:138-146.

6. Malone KE, Daling JR, Thompson JD, O’Brien CA, Francisco LV, Ostrander EA. BRCA1 mutations and breast cancer in the general population: analyses in women before age 35 years and in women before age 45 years with first-degree family history. JAMA. 1998;279:922-999.

7. Carvalho MA, Marsillac SM, Karchin R, et al. Determination of cancer risk associated with germ line BRCA1 missense variants by functional analysis. Cancer Res. 2007;67:1494-1501.

Notes:

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References (contained in the reference handout to be distributed after the post-activity lecture)

1. Ashley EA, Butte AJ, Wheeler MT, et al. Clinical assessment incorporating a personal genome. Lancet. 2010;375:1525-1535.

2. Berliner JL, Fay AM, Cummings SA, Burnett B, Tillmanns T. NSGC practice guideline: risk assessment and genetic counseling for hereditary breast and ovarian cancer. J Genet Couns. 2013;22:155-163.


4. Carvalho MA, Marsillac SM, Karchin R, et al. Determination of cancer risk associated with germ line BRCA1 missense variants by functional analysis. Cancer Res. 2007;67:1494-1501.

5. Johnston JJ, Rubinstein WS, Facio FM, et al. Secondary variants in individuals undergoing exome sequencing: screening of 572 individuals identifies high-penetrance mutations in cancer-susceptibility genes. Am J Hum Genet. 2012;91:97-108.

6. Jones SJM, Laskin J, Li YY, et al. Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biol. 2010;11(8):R82. doi: 10.1186/gb-2010-11-8-r82.

7. Kuusisto KM, Bebel A, Vihinen M, Schleutker J, Sallinen L. Screening for BRCA1, BRCA2, CHEK2, PALB2, BRIP1, RAD50, and CDH1 mutations in high-risk Finnish BRCA1/2-founder mutation-negative breast and/or ovarian cancer individuals. Breast Cancer Res. 2011;13:R20. doi: 10.1186/bcr2832.

8. Link DC, Schuettpelz LG, Shen D, et al. Identification of a novel TP53 cancer susceptibility mutation through whole-genome sequencing of a patient with therapy-related AML. JAMA. 2011;305:1568-1576.

9. Lupski JR, Reid JG, Gonzaga-Jauregui C, et al. Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. N Engl J Med. 2010;362:1181-1191.

10. Malone KE, Daling JR, Thompson JD, O’Brien CA, Francisco LV, Ostrander EA. BRCA1 mutations and breast cancer in the general population: analyses in women before age 35 years and in women before age 45 years with first-degree family history. JAMA. 1998;279:922-999.

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11. National Comprehensive Cancer Network (NCCN) Guideline. Genetic/Familial High-Risk Assessment: Breast and Ovarian. Version 2.2014.

12. Ostrow KL, McGuire V, Whittemore AS, DiCioccio. The effects of BRCA1 missense variants V1804D and M1628T on transcriptional activity. Cancer Genet Cytogenet. 2004;153:177-180.

13. Pal T, Vadaparampil ST. Genetic risk assessments in individuals at high risk for inherited breast cancer in the breast oncology care setting. Cancer Control. 2012;19:255-266.

14. Parmigiani G, Chen S, Iversen ES Jr, et al. Validity of models for predicting BRCA1 and BRCA2

mutations. Ann Intern Med. 2007;147:441-450.

15. Phelan CM, Ðapić V, Tice B, et al. Classification of BRCA1 missense variants of unknown clinical significance. J Med Genet. 2005;42:138-146.

16. Roychowdhury S, Iyer MK, Robinson DR, et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med. 2011;3:111ra121. doi: 10.1126/scitranslmed.3003161.

17. Welch JS, Westervelt P, Ding L, et al. Use of whole-genome sequencing to diagnose a cryptic fusion oncogene. JAMA. 2011;305:1577-1584.

18. Worthey EA, Mayer AN, Syverson GD, et al. Making a definitive diagnosis: successful clinical application of whole exome sequencing in a child with intractable inflammatory bowel disease. Genet Med. 2011;13:255-262.


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Overview

Using Oncotype DX as an example, this exercise focuses on the use of gene panel RNA expression assays for both prognosis and guiding treatment options. Utilizing an online tool, the exercise also allows residents to understand some of the issues involved in designing these panels.

Learning Objectives


Describe the role of pathologists in facilitating prognostic gene panel testing

Compare the utility of prognostic gene panel testing with that of histologic methods

Interpret a prognostic gene panel report and consider important components of the report to ensure appropriate interpretation by the ordering clinician

Describe the process of selecting genes for expression profiles for clinical use

Pre-reading Assignments

1. Allison KH. Molecular pathology of breast cancer: what a pathologist needs to know. Am J Clin Pathol. 2012;138:770-780.

2. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351:2817-2826.

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1. In which scenario will the Oncotype DX test likely have the most clinical utility?

A. ER-negative tumors B. Via FISH, HER2-positive tumors with low histologic grade C. ER-positive low-/intermediate-grade tumors with lymph

nodes that test negative D. ER-positive low-/intermediate-grade tumors with lymph

nodes that test positive

2. Which of the following is recommended for initial pathologic evaluation of infiltrating ductal carcinoma? A. ER, PR, and HER2 B. ER, PR, and KRAS C. ER and PR D. PIK3CA and HER2 E. TP53

3. Which is not a component of the Oncotype DX test? A. ERBB2 (HER2) B. ESR1 (ER) C. PGR (PR) D. MKI67 (Ki-67) E. TP53 (p53)

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Pre-activity Lecture Objectives and References

Exercise 2: Prognostic Gene Panel Testing During this presentation, the instructor will:

1. Distinguish DNA vs. RNA testing

2. Discuss current standard molecular testing for breast cancer

3. Discuss the basis for the Oncotype DX assay

References included in this lecture: None

Pre-activity Lecture Summary

Current initial testing in breast cancer o ER, PR o HER2 (ERBB2)

Prognostic gene-expression panels available for patient care



For questions 1 and 2, the residents should first write down their answers on their team-based learning activity handout and then “do a reveal.” One resident should write down all the team’s final-consensus answers based on the team’s discussion.

For questions 3 and 4, the residents can explore the Oncotype DX report/online tools together, and 1 resident should write down the team’s final-consensus answers.



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Exercise 2: Prognostic Gene Panel Testing Note: Using the screen capture included in the pre-activity lecture, provide an overview of the following website prior to starting the team-based learning activity. Although significant resident expertise is not expected prior to the exercise, the pre-exercise preparation handout also contains links to online tools that will be accessed during the exercise. The Kaplan-Meier Plotter website http://kmplot.com/analysis/index.php?p=service&default=true “The KM plotter is capable of assessing the effect of 22,277 genes on survival in 4,142 patients with breast cancer, 1,464 with ovarian cancer, and 1,715 with lung cancer. The primary purpose of the tool is a meta-analysis based in silico-biomarker assessment.

How does it work?

The background database is manually curated. Gene-expression data and relapse-free and overall survival information are downloaded from several databases. To analyze the prognostic value of a particular gene, the patient samples are split into 2 groups according to various quantile expressions of the proposed biomarker. The 2 patient cohorts are compared by a Kaplan-Meier survival plot, and the hazard ratio with 95% confidence intervals and logrank P value are calculated.

Answers to other frequently asked questions can be found at: http://kmplot.com/analysis/index.php?p=background.”


http://kmplot.com/analysis/index.php?p=background

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Team-Based Learning Activity #2:

Prognostic Gene Panel Testing

Case Presentation The patient’s aunt (who had breast cancer; from exercise 1) tests negative for the BRCA1 variant. The patient elects to proceed with a mastectomy. The tumor is American Joint Committee on Cancer (AJCC) grade 2, stage 1 (T1, N0, Mx), ER 1+, PR 1+, HER2-negative. Prognostic gene panel testing (Oncotype DX) is being considered.

1. Even if the sample is sent to an outside laboratory, list 2 reasons why a pathologist should be involved in molecular testing of tumor samples (such as for Oncotype DX). (REVEAL) (10 minutes) 1) Confirming the presence of invasive cancer on sample 2) Ensuring that the patient fulfills the pathological criteria for testing (ER+, lymph-node

negative) 3) Ensuring that the appropriate type of tissue is sent (i.e., formalin fixed vs frozen) 4) Ensuring that there is adequate sampling of the tumor (i.e., not too much normal tissue) 5) In reporting, ensuring appropriate integration with other pathologic findings/testing (e.g.,

histology)

2. List 2 potential advantages and 2 potential disadvantages to using a prognostic gene panel rather than relying on standard breast-cancer-pathology biomarkers (ER, PR, HER2, grade). (REVEAL) (10 minutes) Potential Advantages:

1) May be more reproducible 2) Presents risk in a quantitative manner 3) Useful for predicting benefit from chemotherapy

Potential Disadvantages:

1) Turnaround-time (longer than immunohistochemistry) 2) Cost 3) Unclear whether it adds clinically significant information to standard pathological analysis

(this is an area of some controversy)

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3. Based on the images from an Oncotype DX report (below): (15 minutes)

a. Is the image 1 graph predictive or prognostic? What about the image 2 graph?

Note: Residents can use a Google search to define “Prognostic” vs. “Predictive” and should not spend more than 5 minutes on this question Image 1: Prognostic Image 2: Predictive

b. If the line on the image 1 graph had a different slope, which of the following slopes

would indicate a useless test: -1, 0, or 1?

A slope of 0 would indicate no difference in risk of recurrence based on recurrence score. As such, the test would not be useful.

c. Would you recommend tamoxifen alone or tamoxifen plus chemotherapy in terms of treatment for this patient? In up to 2 sentences, explain why or why not.

Don’t use chemotherapy because there is no evidence of efficacy. The recurrence rate is not different for patients receiving or not receiving chemotherapy. However, the statement highlighted in image 2 may lead some clinicians to believe that chemotherapy is warranted (note: it is not highlighted in the resident handout).

4. The Kaplan-Meier Plotter website

(http://kmplot.com/analysis/index.php?p=service&default=true) assesses gene expression (over 22,000 genes) and survival data from a large cohort of patients with breast cancer. A list of genes can be entered to determine how well each gene individually or the entire expression signature can differentiate patients with poor vs. good survival. The generated graphs predict survival, with 1 line representing patients with above-the-median expression for the selected gene(s) and the other line representing patients with below-the-median expression for the selected gene(s). The Y axis represents time in years, and the X axis shows the percentage of patients surviving. The hazard ratio (HR) can be interpreted in a similar fashion as a relative risk. The 95% confidence interval for the HR is provided, as well as a P value. (25 minutes)

a. Using the website, would the following gene panel be clinically useful in determining prognosis? Explain your answer in up to 2 sentences.

Instructions: After selecting “Use multigene classifier.” should copy and paste the following list into the box under “Paste list of probeset IDs” and then select “Use mean


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expression of the selected probes.” After selecting “Start analysis using selected probes,” select “only Jetset best probe set” and restrict analysis to “ER positive.” Go to the bottom of the webpage and select “Draw Kaplan-Meier Plot”. ACTN4 ADCY7 AKT2 BNIP3L CD74 CDC2L1 G6PD GNL1 GZMM LGALS3 PDAP1 PRF1 PTBP1 RIOK3 SYNE2 TMSB4X

The panel would not be useful: 1) Small, non–clinically relevant

survival difference 2) Doesn’t account for other

factors (grade, PR, lymph node status, HER2)

3) Despite statistical significance, HR of 0.82, with CI up to 0.98

4) Based on information provided in the next question: no biological basis for signature decreases likelihood of generalizable finding to help guide treatment.

1) Because the server may be slower due to multiple users, you should consider having only 1 person at the table enter data into the Kaplan-Meier tool, with the others observing.

2) Because it takes time to generate the plot, you may consider having the team generate the plot at the beginning of the team-based learning activity and then work on other tasks while the plot is being created.

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b. The panel listed in part a is from a paper describing a signature for genes associated with post-prandial laughter. List 1 reason why such a gene signature could have a P value of <0.05 and appear predictive of breast-cancer survival.

1) The genes could truly be causative of breast-cancer progression and decreased

survival. 2) The results could be simply due to chance, specifically related to only this dataset,

and not reproducible with other data sets. 3) The genes may be correlated with survival but not truly causative of disease. In

several cancers, the expression of a large portion of the tumor genome can be correlated with the expression of critical biological pathways, which results in association with survival; however, in most cases it is not a causal relationship, but a “passenger” relationship. For example, in ER+ breast cancer, approximately 25% of the genome is associated with genes involved in proliferation, but correlation does not imply causation.


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c. The following are a portion of genes used for the Oncotype Dx test (the “proliferation” gene set). Enter and run the list, as for question 4a. Do you believe this gene signature will be clinically useful in predicting prognosis for ER+ breast cancers? Explain your answer in up to 2 sentences.

AURKA MKI67 CCNB1 BIRC5 MYBL2

1) Because the server may be slower due to multiple users, you should consider having only 1person at the table enter data into the Kaplan-Meier tool, with others observing.

2) Because it takes time to generate the plot, you may consider having the team generate the plot at the beginning of the team-based learning activity and then work on other tasks while the plot is being created.

In this case, there is a very significant P value and HR (effect size), and all the genes are related to proliferation. That is, there is a biologic basis for the result.

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d. Run the same analysis, first for ER-negative tumors and then regardless of ER status (leave with default “All”). Would you recommend this panel for a patient with an ER-negative tumor? In up to 2 sentences, explain your answer in the context of the result for “All” tumors.

The panel would not be useful for a patient with an ER-negative tumor. The “All” cancer result has a very strong P value, and the HR is >1. The ER-negative result has a much higher P value and a HR of <1. Because the ER-negative result is part of “All” cancers, shouldn’t there be a similar result? The answer is that the “All” result is driven by the ER-positive tumor result because there are fewer ER-negative cases. Given that the “All” P value is even less than that for ER positive patients, survival of patients having ER-unclassified tumors is also likely strongly predicted by this gene signature (i.e., probably mostly ER positive).

All Tumors ER-negative Tumors

51

Oncotype DX Report

Image 1

52

Image 2


53



The pathologist plays an important role in genomic testing:

o Determination of malignancy o Assess appropriateness of testing o Sample adequacy o Integration with other data

Prognostic gene panel testing may provide advantages over immunohistochemistry:

o Need to evaluate if gene panel adds sufficient relevant information to traditional methods

o Need to validate with prospective trials

How results are reported is critical: o Need to read reports carefully o Can assist other clinicians

Gene panels: careful analysis required o May be associated with survival even if the genes

are not causally involved (e.g., random gene sets in breast cancer)

o Need to understand details of population being evaluated (e.g., result with “All” tumors and patients with ER negativity and ER positivity)

o Small panels may be just as useful as ones containing hundreds of genes, due to reduced cost, complexity of testing, and ease of interpretation

o Data updates can change results References included in this lecture:

1. Dabbs DJ, Klein ME, Mohsin SK, et al. High false-negative rate of HER2 quantitative reverse transcription polymerase chain reaction of the Oncotype DX test: an independent quality assurance study. J Clin Oncol. 2011;29:4279-4285.

2. Cuzick J, Dowsett M, Pineda S, et al. Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2

54

immunohistochemical score and comparison with the Genomic Health recurrence score in early breast cancer. J Clin Oncol. 2011;29:4273-4278.

3. Hayashi T, Urayama O, Kawai K, et al. Laughter regulates gene expression in patients with type 2 diabetes. Psychother Psychosom. 2006;75:62-65.

4. Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol. 2011;7:e1002240. doi: 10.1371/journal.pcbi.1002240.

5. Haibe-Kains B, Desmedt C, Sotiriou C, et al. A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? Bioinformatics. 2008;24:2200-2208.

Notes:

55


References (contained in the reference handout to be distributed after the post-activity lecture)

1. Allison KH. Molecular pathology of breast cancer: what a pathologist needs to know. Am J Clin

Pathol. 2012;138:770-780.

2. Azim HA Jr, Michiels S, Zagouri F, et al. Utility of prognostic genomic tests in breast cancer

practice: The IMPAKT 2012 Working Group Consensus Statement. Ann Oncol. 2013;24(3):647-

654.

3. Cuzick J, Dowset MT, Pineda S, 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;29:4273-4278.

4. Dabbs DJ, Klein ME, Mohsin SK, Tubbs RR, Shuai Y, Bhargava R. High false-negative rate of HER2

quantitative reverse transcription polymerase chain reaction of the Oncotype DX test: an

independent quality assurance study. J Clin Oncol. 2011;29:4273-4285.

5. Haibe-Kains B, Desmedt C, Sotiriou C, Bontempi G. A comparative study of survival models for

breast cancer prognostication based on microarray data: does a single gene beat them all?

Bioinformatics. 2008;24:2200-2208.

6. Hayashi T, Urayama O, Kawai K, et al. Laughter regulates gene expression in patients with type 2

diabetes. Psychother Psychosom. 2006;75(1):62-65.

7. Oldenhuis CNAM, Oosting SF, Gietema JA, de Vries EGE. Prognostic versus predictive value of

biomarkers in oncology. Eur J Cancer. 2008;44:946-953.

8. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-

negative breast cancer. N Engl J Med. 2004;351:2817-2826.

9. Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly

associated with breast cancer outcome. PLoS Comput Biol. 2011;7:e1002240. doi:

10.1371/journal.pcbi.1002240.

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Exercise 3: Design of a Multigene Assay (Cancer-Gene Panel)

Overview

This exercise focuses on the design of a cancer-gene panel to assess drug-treatment options. The application activity allows residents to understand the differences, utility and limitations of polymerase chain reaction (PCR)-based, Sanger sequencing-based and next generation sequencing-based methods, as well as an approach to validation.

Learning Objectives


Describe different methods for detecting DNA variants (PCR vs. Sanger-based vs. next-generation sequencing [NGS]–based)

Determine the appropriate methodology for a selected gene panel

Describe the factors that determine the utility of inclusion of a specific gene in a multigene assay (in this case, a cancer-gene panel)


1. Halling KC, Schrijver I, Persons D. Test verification and validation for molecular

diagnostic assays. Arch Pathol Lab Med. 2012;136:11-13.

2. Siddiqui AD, Piperdi B. KRAS mutation in colon cancer: a marker of resistance to

EGFR-I therapy. Ann Surg Oncol. 2010;17:1168-1176.

57



1. In colon cancer, why has KRAS DNA mutation testing, as opposed to EGFR protein immunohistochemistry (IHC), become the standard to determine whether a patient’s colon cancer will respond to anti-EGFR antibody therapy?

A. EGFR targeted therapies will not work if there are activating KRAS mutations.

B. Antibodies for EGFR immunochemistry are not commercially available.

C. KRAS mutation testing has more rapid turnaround time and is less expensive.

D. EGFR IHC requires a tumor sample, whereas KRAS mutation testing does not.

2. Which of the following performance characteristics does not need to be verified by the laboratory performing an FDA-approved molecular test?

A. Precision B. Analytic sensitivity C. Accuracy D. Reportable range

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Exercise 3: Design of a Multigene Assay (Cancer-Gene Panel) During this presentation, the instructor will:

1. Review standard methods for detection of DNA variants and

introduce NGS methods.

2. Review fundamentals of the validation process for basic

molecular tests.


1. Siddiqui AD, Piperdi B. KRAS Mutation in colon cancer: a marker

of resistance to EGFR-I Therapy. Ann Surg Oncol. 2010;17:1168-

1176.

2. Shackelford W, Deng S, Murayama K, Wang J. A new technology

for mutation detection. Ann N Y Acad Sci. 2004;1022:257-262.

3. Dias-Santagata D, Akhavanfard S, David SS, et al. Rapid targeted

mutational analysis of human tumours: a clinical platform to

guide personalized cancer medicine. EMBO Mol Med.

2010;2:146-158.

4. Meyerson M, Gabriel S, Getz G. Advances in understanding

cancer genomes through second-generation sequencing. Nat Rev

Genet. 2010;11:685-696.

5. Integrative Genomics Viewer.

http://www.broadinstitute.org/igv/. Accessed October 24, 2015.

6. Frampton GM, Fichtenholtz A, Otto GA, et al. Development and

validation of a clinical cancer genomic profiling test based on

massively parallel DNA sequencing. Nat Biotechnol.

2013;31:1023-1031.

7. Pont-Kingdon G, Gedge F, Wooderchak-Donahue W, et al. Design

and analytical validation of clinical DNA sequencing assays. Arch

Pathol Lab Med. 2012;136:41-46.

http://www.broadinstitute.org/igv/

59



Multiple methods to detect DNA variants o Cytogenetics/FISH o Allele-specific PCR o Sanger sequencing o NGS o All have advantages and disadvantages

Different indications for Sanger sequencing vs. non-sequencing assays (e.g, allele-specific PCR) vs. NGS assays

o Sensitivity (limited for Sanger) o Need to analyze entire gene vs. detect specific variant

Validation procedure as for the rest of laboratory testing o PARR+AS+AS o There can still be errors



For question 1, the residents should first write down their answer on the team-based learning activity handout and then “do a reveal.” One resident should write down the team’s final-consensus answer based on team’s discussion.

For questions 2 and 3, the residents can explore the online tools together, and 1 resident should write down the team’s final-consensus answers.



60


Exercise 3: Design of a Multigene Assay (Cancer-Gene Panel) Note: Using the screen captures included in the pre-activity lecture, provide an overview of the following websites prior to starting the team-based learning activity. Although significant resident expertise is not expected prior to the exercise, the pre-exercise preparation handout also contains links to online tools that will be accessed during the exercise. 1. Catalogue of Somatic Mutations in Cancer (COSMIC) database


What is COSMIC?

“All cancers arise because of the acquisition of a series of fixed DNA-sequence abnormalities, or mutations, many of which ultimately confer a growth advantage on the cells in which they have occurred. A vast amount of information is available in the published scientific literature about these changes. COSMIC is designed to store and display somatic-mutation information and related details and contains information relating to human cancers.

Some key features of COSMIC are:

Contains information on publications, samples, and mutations. Includes samples that have tested negative for mutations during screening, therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.

Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumors, recurrences, metastases, and cancer-cell lines.

The mutation data and associated information are extracted from the primary literature and entered into the COSMIC database. To provide a consistent view of the data, histology and tissue ontology have been performed, and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology, or gene and displayed as a graph, as a table or exported in various formats.”


61

2. My Cancer Genome http://www.mycancergenome.org What is My Cancer Genome?

“My Cancer Genome is a personalized cancer-medicine knowledge

resource for physicians, patients, caregivers, and researchers.

My Cancer Genome gives up-to-date information on which mutations make cancers grow and the related therapeutic implications, including the results of available clinical trials.

My Cancer Genome is a one-stop tool that matches tumor mutations to therapies, making information accessible and convenient for busy clinicians.”




62

Team-Based Learning Activity #3: Design of a Multigene Assay (Cancer-Gene Panel)

Case Presentation After 6 months, the patient (from the previous exercises) develops an enlarged lymph node, which is removed. Pathologic testing results reveal early metastases to the axillary lymph nodes. The patient has been researching options and would like to have her tumor cells from the lymph-node metastasis tested, to guide selection of the most helpful therapy. The oncologist would like to order a gene panel to test for somatic mutations.

1. List 2 criteria to guide the selection of variants that should be on the gene panel for this patient. (REVEAL) (15 minutes)

1) Targeted drug therapy potentially available 2) Quality of evidence that variant is associated with disease 3) Quality of evidence that variant is deleterious 4) Variant frequency 5) Reimbursement (not a major focus for this workshop)

2. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) database

(http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/): (HINT: Search via “Cancer Browser” as “carcinoma” for histology and “include all” for subtissue and subhistology.) (30 minutes)

a. Which genes are mutated in greater than 5% of breast cancers?

1) Go to COSMIC: http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

2) Click: “Search by tissue”: http://cancer.sanger.ac.uk/cosmic/browse/tissue

3) For Tissue Selection, click and highlight “Breast”

4) For sub-tissue selection, click and highlight “Include all”

5) For Histology selection, click and highlight “Carcinoma”

6) For sub-Histology selection, click and highlight “Include all”

7) Click “Go”

8) Note: blue bar shows number of samples with a mutation and red bar shows total samples

analyzed for mutations in that gene

Answer: PIK3CA (26%), TP53 (23%), CDH1 (11%), GATA3 (7%), MLL3 (7%)



http://cancer.sanger.ac.uk/cosmic/browse/tissue

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b. What is the most common type of breast cancer mutation (e.g., frameshift, nonsense)?

1) Click “Distribution” tab

2) Answer: Missense substitution

3. We are designing a test for PIK3CA and TP53 mutation analysis. We are deciding whether to use

a targeted PCR-based genotyping method that can measure up to 5 different single nucleotide

variants (SNVs) per gene or to use a next-generation sequencing (NGS)–based method that

could sequence mutations across the exons for the entire gene. Based on data from the

COSMIC database:

a. Which test would you recommend for each gene? Explain your answer in up to 3

sentences.

1. PIK3CA (select PCR-based or NGS-based)

2. TP53 (select PCR-based or NGS-based)

1) Under “top genes” tab, click on “PIK3CA” and “TP53” and compare/contrast the

distribution and types of mutations in these genes. Can also click on “mutations” tab to obtain information on the actual nucleotide changes.

2) PIK3CA – Answer: Targeted PCR-based genotyping to cover the 2 major hotspot mutations. Assay is less expensive, more sensitive, and would be effective. PCR is extremely sensitive because the PCR reaction is specifically enriched for that set of mutations (which may be especially important for samples with a low percentage of tumor). The NGS or Sanger-based method does not have a PCR-amplification step designed for specific mutations but instead sequences the selected genes. (However, NGS could potentially overcome this issue with increased depth of coverage.)

3) TP53 – Answer: Full exon sequencing to cover wide distribution of mutations (although, bear in mind, this method may also pick up non–clinically significant variations).

b. Is each of the following genes a tumor suppressor, an oncogene, or both? Explain

your answer in up to 3 sentences.

1. PIK3CA

2. TP53

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A tumor suppressor only needs to be inactivated. As such, many different types of mutations in many areas of the protein can lead to an inactive or non-expressed protein. In contrast, oncogene variants typically lead to increased activity of the protein (e.g., a kinase). As such, the areas that can be mutated to lead to this phenotypic change are more limited. Based on this reasoning, TP53 is a tumor suppressor and PIK3CA is an oncogene.

4. Using mycancergenome.org, of the top 3 genes mutated in breast cancer (based on your answer

to question 2a), for which ones are there ongoing clinical trials? For each gene with multiple

associated trials, assuming the patient harbors mutations, which trial would you select for her?

(15 minutes)

1) Under “Find Clinical Trials”: enter “breast cancer” and gene of interest.

2) There are trials for PIK3CA, TP53, and CDH1.

3) The most useful trial would be one with a drug targeting the mutation of interest.

Many of the trials, however, are not actually related to a treatment option (e.g., just

observational). There is a clear trial related to a PI3K inhibitor.



65



A number of criteria on which to base gene panel

selection

o Drug targets

o Frequency

Different indications for Sanger sequencing vs. non-

sequencing assays (e.g, allele-specific PCR) vs. NGS

assays

o Sensitivity (limited for Sanger)

o Need to analyze entire gene vs. detect specific

variant

Can use available databases

o COSMIC for percentage of variants associated

with a particular cancer

o Mycancergenome.org for information on genes

and clinical trials

o Database information needs clinical

interpretation and data updates affect results

References used in this lecture: None

Notes:

66

Exercise 3: Design of a Multigene Assay (Cancer-Gene Panel) References (contained in the reference handout to be distributed after the post-activity lecture)

1. Cunningham D, Humblet Y, Siena S, et al. Cetuximab monotherapy and cetuximab plus irinotecan in irinotecan-refractory metastatic colorectal cancer. New Engl J Med. 2004;351:337-345.

2. Dias-Santagata D, Akhavanfard S, David SS, et al. Rapid targeted mutational analysis of human tumours: a clinical platform to guide personalized cancer medicine. EMBO Mol Med. 2010;2:146-158.

3. Duncavage EJ, Abel HJ, Szankasi P, Kelley TW, Pfeifer JD. Targeted next generation sequencing of clinically significant gene mutations and translocations in leukemia. Mod Pathol. 2012;25:795-804.

4. Ellis MJ, Perou CM. The genomic landscape of breast cancer as a therapeutic roadmap. Cancer Discov. 2013;3:27-34.

5. Frampton GM, Fichtenholtz A, Otto GA, et al. Development and validation of a clinical cancer

genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol. 2013;31:1023-1031.

6. Halling KC, Schrijver I, Persons DL. Test verification and validation for molecular diagnostic assays.

Arch Pathol Lab Med. 2012;136:11-13.

7. Integrative Genomics Viewer. http://www.broadinstitute.org/igv/. Accessed October 24, 2014.

8. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010;11:685-696.

9. Pont-Kingdon G, Gedge F, Wooderchak-Donahue W, et al. Design and analytical validation of

clinical DNA sequencing assays. Arch Pathol Lab Med. 2012;136:41-46.

10. Shackelford W, Deng S, Murayama K, Wang J. A new technology for mutation detection. Ann N Y Acad Sci. 2004;1022:257-262.

11. Siddiqui AD, Piperdi B. KRAS mutation in colon cancer: a marker of resistance to EGFR-I therapy.

Ann Surg Oncol. 2010;17:1168-1176.

http://www.broadinstitute.org/igv/

67


Overview

In this exercise, the general next-generation sequencing workflow is discussed, along with ethical issues involved in genomic testing. Residents are given the opportunity to explore the patient’s genome to determine treatment options, as well as to decide whether to call and report variants, including a variant of uncertain significance, based on the sequencing data. In addition, residents utilize databases to determine the clinical significance of non-oncologic findings and to discuss whether to include them in the final pathology report.

Learning Objectives


Describe key aspects of informed consent for genomic analyses

Describe the process of NGS-data analysis

Describe the benefits and limitations of integrative genomic analyses for patients

with advanced cancer

Describe the reporting issues related to incidental findings

Use online tools to interpret the clinical significance of genomic data


1. Korf BR, Rehm HL. New approaches to molecular diagnosis. JAMA. 2013;309:1511-

1521.

2. Haimovich AD. Methods, challenges, and promise of next-generation sequencing in

cancer biology. Yale J Biol Med. 2011;84:439-446.

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1. What does “depth of coverage” refer to when discussing next-

generation sequencing?

A. The fraction of total genes of the genome sequenced

B. The ratio of the number of exons sequenced to number of

introns sequenced

C. The degree of alignment with the reference genome

D. The average number of times each nucleotide was

sequenced

2. For which of the following cases would whole-exome sequencing

be more clinically advantageous than a targeted single-gene

assay?

A. A screen for the sickle-cell-disease hemoglobin variant

B. Determining genetic risk for diabetes in a healthy

individual

C. Determining the causative mutation in a very rare

genetic syndrome of unknown etiology

D. BCR-ABL monitoring in patients with chronic myelogenous

leukemia

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Exercise 4: Whole-Exome Sequencing During this presentation, the instructor will:

1. Discuss the use of NGS in cancer

2. Explain the NGS workflow

Reference used in this lecture:

1. Jones SJM, Laskin J, Li YY, et al. Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biol. 2010;11:R82. doi: 10.1186/gb-2010-11-8-r82.

2. Anderson MW, Shrijver I. Next generation DNA sequencing and the future of genomic medicine. Genes. 2010;1:38-69.


Whole-exome/genome sequencing of tumors has yielded benefit

Limited to case report

Workflow for NGS consists of raw data analysis, sequence alignment, variant calling, and annotation


To be completed by the team (2-6 residents) over approximately 75 minutes. Answers for the instructor version are shown in italics.

For questions 1, 3a, 3b, 4c, and 4d, the residents should first write down their answers on the team-based learning activity handout and then “do a reveal.” One resident should write down the team’s final-consensus answers based on the team’s discussion.

For the other questions, the residents can explore the online tools together, and 1 resident should write down the team’s final-consensus answers.



70


Exercise 4: Whole-Exome Sequencing Note: Using the screen captures included in the pre-activity lecture, provide an overview of the following websites prior to starting the team-based learning activity. Although significant resident expertise is not expected prior to the exercise, the pre-exercise preparation handout also contains links to online tools that will be accessed during the exercise. 1. cBioPortal: http://www.cbioportal.org/public-

portal/case.do?case_id=TCGA-BH-A1FE&cancer_study_id=brca_tcga

What is the cBioPortal for Cancer Genomics?

“The portal stores genomic data from large-scale, integrated cancer

genomic data sets. It allows explorative data analysis (e.g., Is my

gene of interest altered in a specific cancer type? How frequently is

EGFR amplified in glioblastoma? Do mutations of BRCA1 and BRCA2

in ovarian cancer co-occur?) and provides simple download of small

data slices (user-defined gene and sample sets; no need to download

entire data sets).”

The portal also stores data from individual patients. For this exercise,

we are using it to review the genomic sequence data from our

‘patient.’”

2. My Cancer Genome: http://www.mycancergenome.org

What is My Cancer Genome?

“My Cancer Genome is a personalized cancer-medicine knowledge resource for physicians, patients, caregivers, and researchers.

My Cancer Genome gives up-to-date information on which mutations make cancers grow and the related therapeutic implications, including the results of available clinical trials.

My Cancer Genome is a one-stop tool that matches tumor mutations to therapies, making information accessible and convenient for busy clinicians.”





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3. Catalogue of Somatic Mutations in Cancer (COSMIC) database: http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

What is COSMIC?

“All cancers arise because of the acquisition of a series of fixed DNA-

sequence abnormalities, or mutations, many of which ultimately

confer a growth advantage on the cells in which they have occurred.

A vast amount of information is available in the published scientific

literature about these changes. COSMIC is designed to store and

display somatic mutation information and related details and

contains information relating to human cancers.

Some key features of COSMIC are:

Contains information on publications, samples, and mutations.

Includes samples that have tested negative for mutations

during screening, therefore enabling frequency data to be

calculated for mutations in different genes in different cancer

types.

Samples entered include benign neoplasms and other benign

proliferations, in situ and invasive tumors, recurrences,

metastases, and cancer-cell lines.

The mutation data and associated information are extracted from

the primary literature and entered into the COSMIC database. To

provide a consistent view of the data, histologic and tissue ontology

have been performed, and all mutations are mapped to a single

version of each gene. The data can be queried by tissue, histology, or

gene and displayed as a graph, as a table, or exported in various

formats.”

4. OMIM: http://www.ncbi.nlm.nih.gov/omim

What is OMIM?

“OMIM is a comprehensive, authoritative compendium of human

genes and genetic phenotypes that is freely available and updated

daily. OMIM is authored and edited at the McKusick-Nathans

Institute of Genetic Medicine, Johns Hopkins University School of

Medicine, under the direction of Dr. Ada Hamosh. Its official home is

http://www.omim.org.”



http://www.omim.org/

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5. ClinVar: http://www.ncbi.nlm.nih.gov/clinvar/

What is ClinVar?

“ClinVar aggregates information about sequence variation and its relationship to human health. ClinVar is designed to provide a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. By so doing, ClinVar facilitates access to and communication about the relationships asserted between human variation and observed health status, and the history of that interpretation. ClinVar collects reports of variants found in patient samples, assertions made regarding the clinical significance of those samples, information about the submitter, and other supporting data. The alleles described in submissions are mapped to reference sequences and reported according to the Human Genome Variation Society (HGVS) standard. ClinVar then presents the data for interactive users, as well as for those wishing to use ClinVar in daily workflows and other local applications. ClinVar works in collaboration with interested organizations to meet the needs of the medical genetics community as efficiently and effectively as possible.”



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Team-Based Learning Activity #4: Whole-Exome Sequencing Case Presentation The patient (from the previous exercises) develops a metastasis to her ovary and undergoes surgery. She is also enrolled in a study that uses tumor and blood DNA to perform whole-exome sequencing to identify somatic and germline variation. The tumor sample submitted contains approximately 80% tumor cells.

1. List 3 key components in the informed-consent process for whole-exome and whole-genome sequencing studies. (REVEAL) (10 minutes)

1) Expected benefits to patient (e.g., what is being detected) 2) Potential harm to patient (e.g., privacy issues) 3) Alternatives 4) Opportunity to allow questions/evaluate comprehension 5) Effects on family members 6) Incidental findings

The first four answers are a standard part of the informed consent process for any testing or procedure. The other answers listed are somewhat unique for genetic testing.

2. As part of the trial, the patient’s data are entered into cBioPortal:

http://www.cbioportal.org/public-portal/case.do?case_id=TCGA-BH-A1FE&cancer_study_id=brca_tcga (20 minutes)

a. Using the “Summary” tab,

1. What proportion of the patient’s genome is affected by copy-number alterations

(CNAs)? 4.3% of the genome Copy number alterations involve gain or loss of genetic material. Common CNAs in cancer include: amplification of oncogenes (e.g. ERBB2 (HER2)) and deletion of tumor suppressors (e.g. PTEN). CNVs can be detected by next generation sequencing based methods (e.g. exome sequencing) or by array-based methods (e.g. aCGH). This workshop



74

has predominantly focused on SNVs, but CNVs are a major driver of carcinogenesis (see Zack TI, et al.)

2. How many genes have non-synonymous somatic mutations? 10 genes

b. Using the “Mutations” tab, what type of ERBB2 alteration does the patient have (e.g, nonsense, frameshift) ?

The patient has a point mutation in ERBB2 leading to a missense mutation (D769Y)

c. Using http://www.mycancergenome.org, what is the predicted effect of this alteration on protein function? Why was it missed by standard pathologic HER2/neu analysis?

Search under “Find a Cancer Mutation”,“Disease”“Breast Cancer”. Select “HER2” from the “Gene” dropdown menu and “HER2 c. 2305G>T (D769Y)” from the “Variant” dropdown menu. Review the Bose et al. reference in the table under “Properties”“Location of Mutation”; also located on the bottom of the page where potential drug sensitivity based on pre-clinical data is listed. The patient has a kinase-activating point mutation in ERBB2 (D769Y) that may be sensitive to HER2-targeting therapies. The patient does not have HER2 genomic amplification or overexpression, so this alteration would not be detected via FISH or IHC analysis. Note there is only 1% mRNA expression which may confuse residents in that they may believe this fact is the reason the variant was not picked up by standard testing.

d. This patient received HER2-targeting therapy but eventually developed additional distant metastases and died. Based on your comprehensive genomic analysis, list 2 reasons why the patient may have failed to respond to HER2-targeting therapy.

1) Low expression level, so may not be an important mutation (although activating

mutations may not require high expression) 2) The patient has 9 other mutations in cancer genes and many copy-number alterations,

which may cause cancer progression despite HER2-targeting therapy 3) Treatment can also cause selective advantage for aggressive tumor sub-clones


75

3. You are asked to specifically review sequencing data to determine whether to call and report

specific variants (see the following images; image 1 represents one variant and image 2 represents another variant). (20 minutes) Note: it is very important to go slowly through the images during the pre-activity lecture (especially in describing the data in the “total count” boxes) so residents can answer the following questions.

a. List 3 criteria you would you use to call a variant. (REVEAL)

1. Percentage of reads showing the particular allele 2. Depth of coverage 3. Percentage of tumor in the sample 4. Quality of the read mapping

b. List 2 criteria you would use to report a variant. (REVEAL)

1. Whether the variant has been previously reported 2. Known clinical significance 3. Known functional significance 4. Predictive of response to drug therapy

c. Using the COSMIC website (http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/)

1. Determine whether the image 2 variant has ever been reported.

2. Then, determine whether it has clinical significance. (also use http://www.mycancergenome.org)

1) In search box on COSMIC home page, type in “PTEN g127r” 2) Click on “c.379g>a” in the syntax box 3) Click on “Samples” tab for number of samples (4) 4) Click on “References” tab to reveal a single reference (related to

endometrial cancer and doesn’t particularly discuss clinical significance) 5) On the mycancergenome.org website, in the top search area, type in

“breast cancer” and “PTEN” (note: the g127r mutation is not listed) 6) In the website, although the specific mutation isn’t there, the residents can

identify that PTEN is a tumor suppressor (under the “what is PTEN?” tab)



76

77

78

d. Based on the criteria you listed, for the variant in each of the 2 images: a) Would you call

this variant? Explain your answer in up to 2 sentences. b) Would you report the variant? Explain your answer in up to 2 sentences. If yes, also include up to 2 sentences of sample text explaining how you would report it.

1. Image 1 variant

Calling: This variant is in an area that shows a low level of coverage (only 31 total reads). In addition, only 2 of these reads (6%) show the variant. As such, this variant would likely not be called. Reporting: Aside from the fact that you would be unlikely to call this variant, the variant is in a non-protein coding region, making it of unlikely clinical significance and unlikely to predict drug response. As such, this variant would likely not be reported; if reported, one would want to include many caveats.

2. Image 2 variant

Calling: This variant is in an area that shows a high level of coverage (548 total reads). In addition, 460 (84%) of these reads show the variant. As such, this variant would likely be called. Reporting: This variant would be called, based on the high depth of coverage and high-variant allele frequency. In addition, this variant may also be reported because the mutation changes the amino-acid composition of a known tumor suppressor gene and the mutation has been reported previously as a somatic mutation in cancer. PTEN also has a role in the PI3K signaling pathway, for which drugs exist. However, very little is known of the biological and clinical significance of this particular PTEN variant in cancer. As such, this variant would likely be reported with information related to what is known regarding biological and clinical significance.

4. The following germline results are obtained:

BRCA1: Met1628Thr (M1628T; same as in exercise 1) MYLK: Ser1759Pro (S1759P) CFTR: No variant detected

(25 minutes)

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a. Using OMIM, (http://www.ncbi.nlm.nih.gov/omim), list the disease(s) with which MYLK is associated. (HINT: search using “mylk”.)

For OMIM, the gene name can just be typed into the query box. MYLK is associated with Familial Thoracic Aortic Aneurysm, type 7

b. Using ClinVar (http://www.ncbi.nlm.nih.gov/clinvar/), is the MYLK S1759P variant clinically significant? (HINTS: search using “s1759p”; review any PubMed links)

1) S1759P can be entered directly into the search bar at the top of the page.

Alternatively:

Click “Advanced search” under the search box at the top of the page

Enter on Line 1: mylk

Enter on Line 2: S1759P 2) There is a single result, suggesting that the variant is pathogenic. 3) The PubMed reference demonstrates a pedigree and functional data. 4) Of note, the aorta does not enlarge in this variant.

c. Which of the results in the previous question would you include in your genomic pathology report? Explain why, in up to 2 sentences for each result. (REVEAL)

1) The MYLK variant is associated with thoracic aortic aneurysms. The ACMG guidance

document on incidental findings recommends reporting known or expected clinically significant variants in this gene.

2) The BRCA variant is the same as that in exercise 1. The residents can decide what information about this variant they would include. In previous workshops, as opposed to with the MYLK variant, it was difficult for teams to reach a consensus; this is unsurprising because there is no clear answer. If there is uncertainty regarding the clinical significance of the variant, reporting will alert the clinician regarding the need for additional follow-up (e.g., as more data is accumulated, there may be additional clarification of the variant’s clinical significance). On the other hand, reporting may lead to uncertainty and anxiety for patients, as well as the possibility of a healthcare provider unfamiliar with this type of information recommending inappropriate treatment. The ACMG guidance document on incidental findings recommends reporting known or expected clinically significant variants in this gene.

3) The CFTR result is negative but relates to question 3d. The ACMG guidance document on incidental findings does not recommend reporting variants in this gene; however, it has implications for pre-conception testing.



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We have placed the next question on a separate page so the residents will only read it after completing the previous questions.

d. The patient is known to have a CFTR variant. List 2 reasons why the variant may not have

been detected using whole-exome sequencing. (REVEAL)

1) There was not enough coverage of the CFTR gene to detect the variant (sensitivity). 2) The variant involves a DNA alteration not easily detected with NGS (e.g., whole-exon

deletion). 3) The variant may occur in an intronic or regulatory region.


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Genetic testing raises ethical concerns o Disclosure/Incidental findings o Pediatric o GINA o To the Nth power with Genomics

Online tools can help identify variants

The pathologist plays an important role in determining what is reported

o What to report is not always clear


1. Roychowdhury S, Iyer MK, Robinson DR, et al. Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med. 2011;3:111ra121.

2. Robson ME, Storm CD, Weitzel J, Wollins DS, Offit K. American Society of Clinical Oncology policy statement update: genetic and genomic testing for cancer susceptibility. J Clin Oncol. 2010;28:893-901.

3. Bose R, Kavuri SM, Searleman AC, et al. Activating HER2 mutations in HER2 gene amplification negative breast cancer. Cancer Discov. 2013;3:224-237.

4. Peterson LM, Kipp BR, Halling KC, et al. Molecular characterization of endometrial cancer: a correlative study assessing microsatellite instability, MLH1 hypermethylation, DNA mismatch repair protein expression, and PTEN, PIK3CA, KRAS, and BRAF mutation analysis. Int J Gynecol Pathol. 2012;31:195-205.

5. Wang L, Guo D-C, Cao J, et al. Mutations in myosin light chain kinase cause familial aortic dissections. Am J Hum Genet. 2010;87:701-707.

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6. Green RC, Berg JS, et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. 2013. http://www.acmg.net/docs/ACMG_Releases_Highly-Anticipated_Recommendations_on_Incidental_Findings_in_Clinical_Exome_and_Genome_Sequencing.pdf. Accessed October 24, 2014.

7. Clarification to Green et al. reference above. 2013. http://www.acmg.net/docs/Incidental_Findings_in_Clinical_Genomics_A_Clarification.pdf. Accessed October 24, 2014.

Notes:

http://www.acmg.net/docs/ACMG_Releases_Highly-Anticipated_Recommendations_on_Incidental_Findings_in_Clinical_Exome_and_Genome_Sequencing.pdf



http://www.acmg.net/docs/Incidental_Findings_in_Clinical_Genomics_A_Clarification.pdf


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References (contained in the reference handout to be distributed after the workshop summary lecture)

1. Anderson MW, Shrijver I. Next generation DNA sequencing and the future of genomic medicine. Genes. 2010;1:38-69.

2. Bose R, Kavuri SM, Searleman AC, et al. Activating HER2 mutations in HER2 gene amplification negative breast cancer. Cancer Discov. 2013;3:224-237.

3. Gargis AS, Kalman L, Berry MW, et al. Assuring the quality of next-generation sequencing in clinical laboratory practice. Nature Biotechnology. 2012;30:1033-1036.

4. Green RC, Berg JS, Grody WW, et al. ACMG Recommendations for reporting of incidental findings in clinical exome and genome sequencing. 2013. http://www.acmg.net/docs/ACMG_Releases_Highly-Anticipated_Recommendations_on_Incidental_Findings_in_Clinical_Exome_and_Genome_Sequencing.pdf. Accessed October 24, 2014.

5. Clarification to Green et al., ACMG Recommendations for reporting of incidental findings in clinical exome and genome sequencing. http://www.acmg.net/docs/Incidental_Findings_in_Clinical_Genomics_A_Clarification.pdf. Accessed October 24, 2014.

6. Haimovich AD. Methods, challenges, and promise of next-generation sequencing in cancer

biology. Yale J Biol Med. 2011;84:439-446.

7. Jones SJM, Laskin J, Li YY, et al. Evolution of an adenocarcinoma in response to selection by

targeted kinase inhibitors. Genome Biol. 2010;11:R82. doi: 10.1186/gb-2010-11-8-r82.

8. Korf BR, Rehm HL. New approaches to molecular diagnosis. JAMA. 2013;309:1511-1521.

9. Peterson LM, Kipp BR, Halling KC, et al. Molecular characterization of endometrial cancer: a

correlative study assessing microsatellite instability, MLH1 hypermethylation, DNA mismatch repair protein expression, and PTEN, PIK3CA, KRAS, and BRAF mutation analysis. Int J Gynecol Pathol. 2012;31:195-205.

10. Robson ME, Storm CD, Weitzel J, Wollins DS, Offit K. American Society of Clinical Oncology policy

statement update: genetic and genomic testing for cancer susceptibility. J Clin Oncol. 2010;28:893-901.





84

11. Roychowdhury S, Iyer MK, Robinson DR, et al. Personalized oncology through integrative high-

throughput sequencing: a pilot study. Sci Transl Med. 2011;3:111ra121. doi: 10.1126/scitranslmed.3003161.

12. Wang L, Guo D-C, Cao J, et al. Mutations in myosin light chain kinase cause familial aortic dissections. Am J Hum Genet. 2010;87:701-707.

13. Zack TI, Schumacher SE, Carter SL, et al. Pan-cancer patterns of somatic copy number alteration.

Nat Genet. 2013;45:1134-1140.


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Objectives

During the presentation, you will:

1. Summarize key workshop points

2. Review uses of genomic testing outside of oncology

3. Discuss limitations for some of this testing

Summary and References

Genomic testing is entering clinical practice o Oncology, rare diseases, pre-natal diagnosis, and

microbiology o Currently limited utility for polygenic diseases

“Just another laboratory test” o Need to validate assays o Need to confirm clinical utility

Pathologists play an important role in genomic testing o Selecting samples for testing o Interpreting variants o Integrating pathology reports o Not simply developing assays

Online tools available o Data are constantly changing o Databases may not be accurate o You know enough to explore the primary data (e.g.,

PubMed)


1. Fan HC, Blumenfeld YJ, Chitkara U, Hudgins L, Quake SR.

Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood. Proc Natl Acad Sci U S A. 2008;105:16266-16271.

2. Morain S, Greene MF, Mello MM. A new era in noninvasive prenatal testing. N Engl J Med. 2013;369:499-501.

86

3. Bianchi DW, Parker RL, Wentworth J, et al. DNA

sequencing versus standard prenatal aneuploidy screening. N Engl J Med. 2014;370:799-808.

4. Long SW, Williams D, Valson C, et al. A genomic day in the

life of a clinical microbiology laboratory. J Clin Microbiol. 2013;51:1272-1277.

5. Wilson MR, Naccache SN, Samayoa E, et al. Actionable

diagnosis of neuroleptospirosis by next-generation sequencing. N Engl J Med. 2014;370:2408-2417.


7. Yang Y, Muzny DM, Reid JG, et al. Clinical whole-exome

sequencing for the diagnosis of mendelian disorders. N Engl J Med. 2013;369:1502-1511.

8. Nissen SE. Pharmacogenomics and clopidogrel: irrational

exuberance? JAMA. 2011;306:2727-2728.

9. Kimmel SE, French B, Kasner SE, et al. A pharmacogenetic versus a clinical algorithm for warfarin dosing. N Engl J Med. 2013;369:2283-2293.

10. Ng PC, Murray SS, Levy S, Vanter JC. An agenda for

personalized medicine. Nature. 2009;461:724-726.

Notes:

87


References (contained in the reference handout to be distributed after the workshop summary lecture)


2. Fan HC, Blumenfeld YJ, Chitkara U, Hudgins L, Quake SR. Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood. Proc Natl Acad Sci U S A. 2008;105:16266-16271.

3. Kimmel SE, French B, Kasner SE, et al. A pharmacogenetic versus a clinical algorithm for warfarin dosing. N Engl J Med. 2013;369:2283-2293.

4. Long SW, Williams D, Valson C, et al. A genomic day in the life of a clinical microbiology

laboratory. J Clin Microbiol. 2013;51:1272-1277.

5. Morain S, Greene MF, Mello MM. A new era in noninvasive prenatal testing. N Engl J Med. 2013;369:499-501.

6. Ng PC, Murray SS, Levy S, Vanter JC. An agenda for personalized medicine. Nature.

2009;461:724-726.

7. Nissen SE. Pharmacogenomics and clopidogrel: irrational exuberance? JAMA. 2011;306:2727-2728.

8. Wilson MR, Naccache SN, Samayoa E, et al. Actionable diagnosis of neuroleptospirosis by next-

generation sequencing. N Engl J Med. 2014;370:2408-2417.


10. Yang Y, Muzny DM, Reid JG, et al. Clinical whole-exome sequencing for the diagnosis of

mendelian disorders. N Engl J Med. 2013;369:1502-1511.

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