Cancer Genome Biology at the Broad Institute: A Team of Teams Levi A. - - PowerPoint PPT Presentation

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Sep 23, 2023 194 likes •371 views

Cancer Genome Biology at the Broad Institute: A Team of Teams Levi A. Garraway, M.D., Ph.D. Overarching Goals of Broad Cancer Genome Characterization Efforts h ff A complete catalogue of significant and A complete catalogue of

SLIDE 1

Cancer Genome Biology at the Broad Institute: A “Team of Teams”

Levi A. Garraway, M.D., Ph.D.

SLIDE 2

Overarching Goals of Broad Cancer h ff Genome Characterization Efforts

A complete catalogue of significant and
A complete catalogue of significant and

impactful tumor genomic alterations T dd j ti i bi l

To address major questions in cancer biology

using genomics

Clinical applications of genome sequencing data

SLIDE 3

Platforms Leveraged by Broad h Research Teams

Biological Samples Platform

Biological Samples Platform

Chemical Biology Platform
Genome Sequencing Platform
Genome Sequencing Platform
Genetic Analysis Platform

I i Pl tf

Imaging Platform
Metabolite Profiling Platform
Proteomics Platform
RNAi Platform

SLIDE 4

Cancer Genome Characterization at h d h “ ” the Broad Institute: The “Core” Team

3 Senior Associate Members (faculty)
3 Senior Associate Members (faculty)
4 Associate Members (faculty)

4 6 R h S i ti t

4‐6 Research Scientists
>20 Computational Biologists
>20 Postdoctoral fellows/students
Many technicians, project managers, software

engineers, etc.

Many collaborators

SLIDE 5

Characteristics of Cancer Team Science h d Projects at the Broad Institute

Many cancer genome projects, large & small

y g p j , g

– TCGA (GCC and GDAC) – NHGRI Sequencing center‐initiated projects d f l d – Broad faculty‐driven initiatives – Collaborator‐driven initiatives – Academic‐industry collaborations (e.g., CCLE) Academic industry collaborations (e.g., CCLE) – “Clinical sequencing” projects – Philanthropic projects

Considerable breadth and diversity of genomic data

– Whole genome, whole exome, “targeted” exome, transcriptome (“RNA‐seq”) methylome transcriptome ( RNA seq ), methylome…

SLIDE 6

Cancer Genome Sequencing Process Flow

Sample Intake

Genotype Characterization

Illumina Sequencing Data QC

Cancer Genome Sequencing Process Flow

Compliance Review
Quantification
Quality Check
Fingerprinting
Whole Exome
Whole Genome
Custom Targeted
RNAseq
Preliminary SNPs
Concordance
Indel cleaning
dbSNP%

Integration with

ther omic data

Findings that

Base mutations
Insertions/deletions

“Firehose” pipeline Validation/

Findings that may impact cancer biology or

Copy number

alterations

Rearrangements
Pathgens

extension Experimental

clinical

ncology

Experimental follow‐up

SLIDE 7

Cancer Genome Projects: Specific Hurdles to Overcome Hurdles to Overcome

Process oversight and “de‐mystification”

– Who controls the queue/timetable? q

“Lost in the ether”

– What happened to my samples/data?

P d ti l l “ d i t ”

Production‐level “admixture”

– WGS on Monday; WES on Tuesday, RNA‐seq on Wed…

Bureaucratic and logistical delays

Bureaucratic and logistical delays

– Shifting consent form criteria, personnel absences, etc.

Managing computational bandwidth

/ ( h l )! – 1 TB per 60X T/N pair (whole genome)!

Efficiency of mutation validation/extension

– We need 200 more T/N pairs now! We need 00 more T/N pairs now!

Publication/authorship considerations

– Who gets to be 1st and last on this 60+ author paper?

SLIDE 8

Cancer Genome Projects: h “ l ” The “Operational Unit”

Disease biology sease b o ogy “champion” Analytical “champion” Project manager

A triad of “champions” owns each project
Disease biology: often postdocs, grad students

l l b h h h

Analytical: both post‐ or pre‐PhD with supervision
Presumes dual first and senior authorship

SLIDE 9

Cancer Genome Projects: h “ l ” The “Operational Unit”

SLIDE 10

Cancer Genome Projects: h “ l ” The “Operational Unit”

???

SLIDE 11

1 7

Phasing increases efficiency of personnel utilization

2 8

personnel utilization

3 9

~months

3 9 4 10 5 11 6 12

SLIDE 12

The Cancer Genome Steering Committee

4 faculty 6 staff scientists (2 co‐chairs)

4 faculty, 6 staff scientists (2 co chairs)

Strategic >> operational guidance

S i ifi i j “ i i ”

Scientific input at project “pivot points”

– (e.g., experimental plan after a key genomic h d ) insight is made)

Identification of systematic errors/issues
Resource management

SLIDE 13

Cumulative Cancer Samples Sequenced at Broad

12,000

Approx. 5,000 Tumor/Normal Pairs complete!

ICGC= 1,100 Cases

10,000

ed CGC , 00 Cases TCGA = 5,143 Cases (2,900 from Broad)

6,000 8,000

s Complete

Whole Genome Whole Exome 4,000

Samples

Whole Exome ‐ 2,000

2009 2009 2010 2010 2010 2010 2011 2011 2011 2011 2012 2009 Q3 2009 Q4 2010 Q1 2010 Q2 2010 Q3 2010 Q4 2011 Q1 2011 Q2 2011 Q3 2011 Q4 2012 Q1

SLIDE 14

Output (past ~2‐3 years) Output (past 2 3 years)

~60 papers (several others submitted/in press)

60 papers (several others submitted/in press)

>5000 cancer genomes
Multiple new sources of funding
Multiple new sources of funding

– NHGRI Sequencing center grant renewal U01 in Exploratory Clinical Sequencing (with help from NCI) – U01 in Exploratory Clinical Sequencing (with help from NCI) – Other R01, U01, P01, R33 grants Multiple foundation grants – Multiple foundation grants – Industry‐sponsored research – Other philanthropy – Other philanthropy

SLIDE 15

Framework for vetting and prioritizing the next wave of large‐scale projects?

C lli i ifi i l

Compelling scientific rationale
Potential for high impact (scientific or clinical)
Deeply invested collaborator(s)
Local “champion”
Technical feasibility (e.g., the samples are

ready to go, the consent form is kosher, protocol active)

Validation/follow‐up plan
Funding is in place

SLIDE 16 Relevance: biological insights of importance? Timely, opportunity Yes

Flagship Project Concept: (Prostate Cancer, early 2011)

Address key biological or clinical questions Indolent versus lethal disease; resistance to antiandrogen therapies, relationship between somatic genomics patterns and ancestry Systems in place for such questions Yes Study Design: comprehensive in breadth or depth Ability to expand to multi‐dimensional genomics Yes Discovery cohort in place Nearly 300 samples in place, most are frozen tissue availability / access to extension cohort Extensive collaborative network in place, both FFPE, frozen tissues and derivative cells Model system – comparative oncogenomics GEMM systems that model leading genetic/biological drivers Follow‐through: Coordinated efforts /collaborations Functional validations Yes active and ongoing Functional validations Yes, active and ongoing Model systems Yes, established and emerging ones Path to translation Extensive translational / clinical‐trial investigators engaged Logistics: funding, staffing Funding for genomic discovery CIP Funding for extension studies Sources available (PCF, Movember, DOD…) Funding for downstream studies Funded Starr, DOD grants, SPORE application likely, PCF Faculty champions Levi Garraway Biology champions Sylvan Baca Analysis champions Mike Lawrence Disease experts collaborators Kantoff/Rubin/Tewari/Balk/Bubley/Taplin

SLIDE 17

Broad Cancer Genome Sequencing: d f Lessons Learned for Team Science

Deep and sustained collaborations are essential

Deep and sustained collaborations are essential

All parties must “buy‐in” and receive due credit
“Ground level” ownership by nimble teams
“Ground level” ownership by nimble teams
Data generation is the easy part!

h k l k b l l k /

Think like a biologist, act like a CEO/COO
“Hub” model for team science research?
High‐level team science cannot happen

everywhere