Whole genome analysis to support cancer treatment decision making: - - PowerPoint PPT Presentation

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Whole genome analysis to support cancer treatment decision making: - - PowerPoint PPT Presentation

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Whole genome analysis to support cancer treatment decision making: The BC Cancer Agency Personalized Oncogenomics (POG) Project. Marco Marra, PhD, FRSC. Professor and Head, Medical GeneJcs, University of BriJsh Columbia. Director, Genome

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Whole genome analysis to support cancer treatment decision making: The BC Cancer Agency Personalized Oncogenomics (POG) Project.

Marco Marra, PhD, FRSC. Professor and Head, Medical GeneJcs, University of BriJsh Columbia. Director, Genome Sciences Centre, BC Cancer Agency.

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Impact of cancer on Canada and Canadians

  • 2/5 Canadians will get cancer

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Canadian Cancer Society StaJsJcs 2015

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Age is the major risk factor for cancers

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Current Biology 22, #17, p R741-R752 (2012) Canadian Cancer Society Sta;s;cs 2015

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Treatments are increasingly expensive

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BC Cancer Agency

  • Provincial Mandate for Cancer Care and Research
  • Standard opera<ng procedures.
  • Radia<on therapy.
  • Chemotherapy.
  • 6 Regional Cancer Centres
  • Employees: 2,877
  • Medical oncologists: 103
  • Chemo. drug costs: $221M
  • Radia<on therapy: $57M
  • Es
  • Est. ne
  • t. new c

w cases (2014): 25,170 ases (2014): 25,170

  • Me

Metas asta7 a7c >10,000 c >10,000

  • Es
  • Est. ne
  • t. new c

w cases (2028): 35,450 ases (2028): 35,450

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Cancer is a gene;c disease

  • Mutations (“mistakes” in the genetic code) can cause cancers.
  • Inherited “predisposing” mutations
  • Sporadic “acquired” mutations
  • In the tumor DNA, not in the normal DNA
  • Environmental mutagens
  • DNA replication errors
  • …AATCGCGCTACCG… à …AATCGCGCTCCCG…
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DNA can be thought of as the “hard drive” of the cell

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Cytoplasm Nucleus

DNA RNA PROTEIN

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Cancer muta;ons can inform treatment and prognosis

Nature Medicine 15, 1149 - 1152 (2009)

bcr bcr

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NATURE |VOL 409 | 15 FEBRUARY 2001 16 FEBRUARY 2001 VOL 291

The first “draGs” of the human genome sequence

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The evolution of DNA Sequencing

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BCCA Genome Sciences Centre: Sequencing Capacity &Throughput

  • 335 staff including 13 senior scien<sts.
  • Total data generated to date: >1.4

petabases (14,000 human genomes)

  • Annual capacity 1.2 petabases
  • > 12,000 30X human genomes/year
  • 2 secured data centres
  • Compute clusters 1: 800 nodes, 24,000

hyper-threaded cores

  • 16 – 48 GB RAM per node
  • High memory (1.5TB RAM) computers
  • >11 Petabytes on-line disk storage

14,305 Human Genome Equivalents (30X)

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Cancer genomes are complex

  • Base pairs (A-T, G-C) in one genome: ~3 billion
  • Genes: > 20,000
  • Genes mutated / dysregulated in cancers:

1,000s

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Tumors are communi;es of cells 100,000,000 cells / cm3

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100,000,000 cells / cm3 Tumors are communi;es of cells

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  • The constellaJon of somaJc & gene expression alteraJons in

individual cancer paJents cannot be predicted.

  • These must be measured and their impact on pathways assessed.
  • Cancers can “evolve” to become treatment resistant.
  • How can we align the right paJent to the right drug at the right

Jme?

  • What are the geneJc properJes of treatment resistant disease?

Observa;ons from cancer genome studies

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“Medical oncology is an educated guessing game.”

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Drug A Drug B Drug C Drug D

Sequence paJent DNA

DNA sequencing can be used to align pa;ents to treatments

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“Panels” survey small numbers of genes / muta;ons

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Hypothesis Comprehensive whole genome analysis of treatment resistant cancers can explain treatment resistance and reveal cryp7c therapeu7c vulnerabili7es.

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The first case

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Medical Oncology Janessa Laskin Deepa Wadhwa Lawrence Lee Simon Chan Andy Mungall Abdul Al-Tourah Lyly Le Tamana Walia Hector Li Chang Carolyn Ch'ng Brad Nelson Helen Anderson Christopher Lee HuiLi Wong Pedro Farinha Eric Chuah Cydney Nielsen Vanessa Bernstein Ursula Lee Muhammad Zulfigar Malcolm Hayes Richard CorbeO Julie Nielsen Sylvie Bourque Howard Lim Ann Tan Tadaaki Hiruki An He Jacquie Schein Barbara Campling Jenny Ko Sara Taylor Hugo Horlings MarSn Jones Colin Schlosser Angela Chan ChrisSan Kollmannsberger Brian Thiessen David Huntsman Steven Jones Sohrab Shah Theresa Chan Caroline Lohrisch Anna Tinker Diana Ionescu Katayoon Kasaian Liz Starks Sylvia Cheng Nicol Macpherson Dorothy Uhlman Hoang Lien Ji-Young Kim Yongjun Zhao Winson Cheung Barb Melosky Medical GeneScs Nikita Makretsov Sreeja Leelakumari Social Science Kim Chi John Paul McGhie Linlea Armstrong Nissreen Mohammad Jake Lever Anita Charters Stephen Chia Corey Metcalf Ian Bosdet Greg Naus Yvonne Li Peter Chow-White Joseph Connors Deepu Mirchandani Gillian Mitchell Tony Ng William Long Dung Ha Janine Davies Nevin Murray Sean Young Torsten Nielsen Yussanne Ma Dean Regier Rebecca Deyell Sujaatha Narayanan Intan Schrader Tomo Osako Karen Mungall Deirdre Weymann Thuan Do Thao Nguyen Clinical Ethics Amir Rahemtulla Brandon Pierce Project Management/CoordinaSon Bernhard Eigl Conrad Oja Alice Virani David Schaeffer Erin Pleasance Leslie Alfaro Susan Ellard Gary Pansegrau Radiology Brandon Sheffield Cara Reisle Charlene Appleby Xiaolan Feng Maryse Power Francois Bernard Sona Sihra Yaoqing Shen Balvir Deol David Fenton Bradley Proctor Colin Mar Brian Skinnider Greg Taylor Nancy Ferguson Daygen Finch Sanjay Rao Montgomery MarSn Graham Slack Nina Thiessen Colleen Fitzgerald Paul Galbraith Rod Rassekh John Myo Peyman Tavassoli Tina Wong Cathy Fitzpatrick Karen Gelmon Daniel Renouf Pharmacy Basile Tessier-ClouSer Wei Zhang Alexandra Fok Alina Gerrie Paul Rogers Shirin Abadi Tom Thomson Eric Zhao Colleen Jantzen Sharlene Gill David Sanford Pathology Tracy Tucker Amir Zadeh Jas Kandola Karmjit Gill Delia Sauciuc Yazeed Alwalaie Emilija Todorovic Kelsey Zhu Julie LoreOe Anagha Gurjal Kerry Savage Daiana Becker-Santos Dirk van Niekerk Genome Science Katherine Mui Edward Hardy Ravinder Sawhney Ian Bosdet Suzanne Vercauteren Sam Aparicio Jessica Nelson Jason Hart Asif Shaikh Kathy Ceballos Carlos Vilamil ScoO Brown Robyn Roscoe Cheryl Ho Wen Wen Shan Andy Churg Joanne Wright Robin Coope Payal Sipahimalani Donna Hogge Tamara Shenkier Bakul Dalal Stephen Yip Peter Eirew June Song Paul Hoskins ChrisSne Simmons Christopher Dunham Chen Zhou Bruno Grande Isabel TrapagaAvancena Michael Humphreys Kevin Song John English BioinformaScs MarSn Hirst Peggy Tsang Bal Johal Caron Strahlendorf Patrice Eydoux Jianghong An Rob Holt Hagen Kennecke Sophie Sun Anthony Karnezis DusSn Bleile Christopher Hughes Kong Khoo Isabella Tai Aly Karsan Melika Bonakdar Richard Moore Meg Knowling Joanna Vergidis Helga Klein-Parker Pinaki Bose Gregg Morin Doran Ksienski Diego Villa Anna Lee Morgan Bye Ryan Morin

All pa7ents & their families

Acknowledgements

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POG process

Oncology consult & consent Biopsy (Metasta7c site) Pathology review Sample prepara7on Tumour (80x) Normal (40x) RNA (200M)

Targeted alignment analysis WGS / WTS sequencing Sample acquisi7on Informed consent

‘In silico panel’ report

Clinical ac7on

Follow up consult & clinical decision

Tumour board discussion

Review of genomic findings Discussion of poten7al for clinical ac7on

Integra7ve analysis

Genomic events of poten7al biological and therapeu7c relevance (in context to pa7ent disease)

Genomic data genera7on

SNV, CNV, SV Expression Other analyses

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Comprehensive knowledgebase

POG_AGBT_2017 23

GOAL: Deliver comprehensive, high-quality genomic characteriza<on for clinical interpreta<on

GSC Knowledgebase Leverages publically available resources where possible Updated from research and outcome of POG case analyses BioFX

Gene MutaJons Copy number Variants Structural Variants Expression Variants Biological DiagnosJc PrognosJc TherapeuJc

Curated Literature

Events References 9400 Cancer ‘Events’

Gene annota<ons (1108) Gene muta<ons (6473) Copy number variants (412) Structural Variants (1342) Expression Variants (65)

1093 References

Occurrence (42) Biological (289) Diagnos<c (28) Prognos<c (75) Therapeu<c (428)

Sequencing Alignment ReposiJoning Splilng Alignment & Merging Germline variant calling De novo structural variant calling SNV & Indel calling Gene & Exon expression SV merging Expression cohort correlaJon SomaJc CNV & LOH calling SomaJc SNV calling Microbial content & integraJon Indel merging Targeted alignment Germline Review CNV & LOH summariy Combined expression summary Combined indel summary Combined structural summary Microbial report SomaJc SNV summary Drug Target Analysis Tumour transcriptome fastq Tumour & Normal fastqs Transcriptome bams Transcriptome indel file Merged Tumour bam Merged normal bam Gene expression databasec

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  • >40% pa<ents treated demonstrated par<al

response (30 pts) or stable disease (22 pts)

Clinical ac;on

POG_AGBT_2017 24

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% All Clinical Trial Standard Tx Off-label Not evaluable Progressive disease Mixed response Stable disease ParJal response Complete response

79% acJonable (n=307) 26% wait & watch (n=76) 36% not acJoned (n=112) 38% treated (n=119) 34% off-label (n=40) 52 % SOC (n=62) 14% Trials (n=17) ReposiJoned to more appropriate standard of care

Erin Pleasance PhD.

386 pa<ents sequenced and analyzed (23 pediatric)

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Mul;ple driver altera;ons are common

8-25 5 10 15 20 25 Expression Copy number Muta<on and fusion

Erin Pleasance

98% of cases have more than one driver altera<on. 67% have more than five driver altera<ons. Mu Mul7 l7ple p le path thways d s driv rive c e can ancer d cer develop elopmen ment in t in almos almost all c t all cases. ases.

Number of driver altera7ons Pa7ent samples

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From genes to gene;c networks

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Caralyn Reisle; Eric Zhao

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Mul;ple drivers open mul;ple pathways for cancer growth

POG_SAC_2017 27 BCCA Confiden<al - For Research Purposes Only

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POG Products

  • A popula<on-based plaeorm for discovery, interpreta<on and clinical

hypothesis tes<ng across cancers.

– Educa7on and engagement of oncology diagnos7c & treatment community. – 84 (82%) of medical oncologists in BC can triage and consent pa7ents. – Established clinical infrastructure from REB to delivery of POG data to physicians.

  • A demonstra<on that the plaeorm can impact treatment decision

making for pa<ents with “incurable” cancer.

– Ac7onable observa7ons in ~79% of cases; 33% receive POG-informed treatment.

  • A unique, portable and expanding “Knowledgebase”, assembled from
  • ur observa<ons, that will facilitate learning and automa<on.

– E.g. IBM Watson, CIVIC, MD Anderson.

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Looking ahead

  • ~800 pa<ents so far. REB approval in place for 5,000.

– Linked with REB approval from BC Children’s Hospital for pediatric POG.

– Decrease turnaround <me: 22 days to automated “1st look”. 79 days for manual interpreta<on. – Scale biopsy capacity beyond 10 / week.

  • Improve access to treatments informed by POG.

– Off label drugs and improved access to clinical trials.

  • Use the POG plaeorm to understand mechanisms of response and

treatment resistance arising in pa<ents treated with emerging therapies.

POG_SAC_2017 30 BCCA Confiden<al - For Research Purposes Only

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Benefits

  • Pa<ent benefits

– Access to leading edge analy<cs to inform the best choice of cancer therapies – Ra<onal stra<fica<on to clinical trials

  • Data

– An accessible database of detailed genomic informa<on linked to treatment will drive cancer research and treatment.

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POG and The Nature of Things: Feb 23, 8 pm hrp://www.cbc.ca/natureoshings/episodes/cracking-cancer Produced by Sue Rideout Directed by Judith Pyke Dreamfilm (dreamfilm.ca)

The POG program: Whole genome analysis of treatment resistant cancers

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Acknowledgements