Dr Gill heads in the Cancer Diagnosis and Pathology Research Group at the University of Sydney and is the anatomical pathologist for the Australian Pancreatic Genome Initiative (APGI), part of the International Cancer Genome Consortium (ICGC) effort to sequence human cancers. In this presentation at the Cirdan Pathology Horizons conference 2015, he presents the key results, the challenges and failures of this project and what it will mean in routine clinical care.

1. Anthony Gill MD FRCPA
Pathologist
PaLMS, Royal North Shore Hospital
& University of Sydney
Sydney Australia
Lessons learnt for pathology from the
ICGC (International Cancer Genome
Consortium)
Dr Gill has no conflicts of interest to disclose

2. SYDNEY
Studies show the greatest jet lag is 9hrs west to east

3. Sydney Harbour Bridge

6. CRICKET PITCH
RNSH Hospital Cricket Team

8. Graveyard

9. OLD RNSH HOSPITAL

10. NEW RNSH HOSPITAL
$750 million to $1Billion

11. ADMINISTRATORS

12. VIEW FROM MEDICAL ADMINISTRATION

13. PATIENTS

14. VIEW FROM THE WARDS
PATIENTS
Harbour Bridge

15. PATHOLOGISTS

16. VIEW FROM PATH DEPT
PATHOLOGISTS

17. PATHOLOGISTS

18. Royal North Shore
• Endocrine Path
• GIT Path
• Interest in
hereditary
endocrine disease

19. RNSH University of Sydney
Endocrine Surgery Database
1957-2015
Records all:
Parathyroid
Thyroid
1984-2015
Records all:
Adrenal surgery

20. RNSH University of Sydney
Endocrine Surgery Database
50 000 Procedures recorded
With follow up for all
malignant cases

28. RNSH University of Sydney
Endocrine Surgery Unit
CURRENTLY each year:
1500 Thyroid
400 Parathyroids
100 Adrenals
800 Consultation cases

29. Royal North Shore
• Endocrine Path
• GIT Path
• Interest in
hereditary
endocrine disease
Largest volume pancreatic
surgery unit in Australia

30. What is genomics?
• The study of the structure of the entire
genome, rather than single mutations etc.

31. The human genome
How many base pairs are there in a normal
human genome?
How much did it cost to sequence the first human
genome?
How long did it take to sequence the first
genome?
When was the first genome sequence
completed?
Whose genome was it?

32. The human genome
How many base pairs are there in a normal
human genome? 3 billion
How much did it cost to sequence the first human
genome?
How long did it take to sequence the first
genome?
When was the first genome sequence
completed?
Whose genome was it?

33. The human genome
How many base pairs are there in a normal
human genome? 3 billion
How much did it cost to sequence the first human
genome? $2.7 billion
How long did it take to sequence the first
genome?
When was the first genome sequence
completed?
Whose genome was it?

34. The human genome
How many base pairs are there in a normal
human genome? 3 billion
How much did it cost to sequence the first human
genome? $2.7 billion
How long did it take to sequence the first
genome? 13 years
When was the first genome sequence
completed?
Whose genome was it?

35. The human genome
How many base pairs are there in a normal
human genome? 3 billion
How much did it cost to sequence the first human
genome? $2.7 billion
How long did it take to sequence the first
genome? 13 years
When was the first genome sequence
completed? 2000-2003
Whose genome was it?

36. The human genome
How many base pairs are there in a normal
human genome? 3 billion
How much did it cost to sequence the first human
genome? $2.7 billion
How long did it take to sequence the first
genome? 13 years
When was the first genome sequence
completed? 2000-2003
Whose genome was it? A volunteer from Buffalo USA

37. Why the interest in genomics?
• Massive advances in DNA sequencing
technology
– 1st
generation Sanger sequencing
– 2nd
generation Automated capillary sequencing
– 3rd
generation next generation sequencing

38. Fragments were cloned:

39. x millionsx millions

40. x millionsx millions

41. Sanger SequencingSanger Sequencing
5’
T G C G C G G C C C A
Primer
A C G C G C C G G G T ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
5’3’

42. Sanger Sequencing ReactionsSanger Sequencing Reactions
.
Includes regular nucleotides (A, C, G, T) for extension, but also includes
dideoxy nucleotides – which induce a stop.
A
A
A
A
A
A
A
G
A
T
C
C
C
C
C
C
C
T
T
T
T
T
G
G
G
G
G
G
Regular Nucleotides
Dideoxy Nucleotides
A
A
A
A
AT
C
C
C
T
T
T
T
G
G
G
G
G
1. Labeled
2. Terminators

43. Sanger SequencingSanger Sequencing
5’
T G C G C G G C C C A
Primer
A C G C G C C G G G T ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
5’3’

44. Sanger SequencingSanger Sequencing
A C G C G C C G G G T C A G A A C C C G A T C G C G
5’3’
5’
T G C G C G G C C C A
Primer
G T C T T G G G C T

45. Sanger SequencingSanger Sequencing
G T C T T G G G C T A G C G C
A C G C G C C G G G T C A G A A C C C G A T C G C G
5’3’
5’
T G C G C G G C C C A
Primer
G T C T T G G G C T
5’
T G C G C G G C C C A 21 bp

46. Sanger SequencingSanger Sequencing
A C G C G C C G G G T C A G A A C C C G A T C G C G
5’3’
G T C T T G G G C T A G C G C
5’
T G C G C G G C C C A
G T C T T G G G C T
5’
T G C G C G G C C C A 21 bp
26 bp
5’
T G C G C G G C C C A
Primer
G T C T T G G G C T A

48. Sanger Throughput LimitationsSanger Throughput Limitations
• Must have 1 colony picked for every 2 reactions
• Must do 1 DNA prep for every 2 reactions
• Must have 1 PCR tube for each reaction
• Must have 1 gel lane for each reaction
from The Economist

49. Sanger SequencingSanger Sequencing
A C G C G C C G G G T C A G A A C C C G A T C G C G
5’3’
G T C T T G G G C T A G C G C
5’
T G C G C G G C C C A
G T C T T G G G C T
5’
T G C G C G G C C C A 21 bp
26 bp
5’
T G C G C G G C C C A G T C T T G G G C T A 22 bp
5’
T G C G C G G C C C A
Primer
G

50. Sanger SequencingSanger Sequencing
A C G C G C C G G G T C A G A A C C C G A T C G C G
5’3’
G T C T T G G G C T A G C G C
5’
T G C G C G G C C C A
G T C T T G G G C T
5’
T G C G C G G C C C A 21 bp
26 bp
5’
T G C G C G G C C C A G T C T T G G G C T A 22 bp
5’
T G C G C G G C C C A G 12 bp
5’
T G C G C G G C C C A
Primer
G T C T T G G G C

51. Sanger SequencingSanger Sequencing
A C G C G C C G G G T C A G A A C C C G A T C G C G
5’3’
G T C T T G G G C T A G C G C
5’
T G C G C G G C C C A
G T C T T G G G C T
5’
T G C G C G G C C C A 21 bp
26 bp
5’
T G C G C G G C C C A G T C T T G G G C T A 22 bp
5’
T G C G C G G C C C A G 12 bp
5’
T G C G C G G C C C A G T C T T G G G C 20 bp
5’
T G C G C G G C C C A
Primer
G T C T T

52. Sanger SequencingSanger Sequencing
A C G C G C C G G G T C A G A A C C C G A T C G C G
5’3’
G T C T T G G G C T A G C G C
5’
T G C G C G G C C C A
G T C T T G G G C T
5’
T G C G C G G C C C A 21 bp
26 bp
5’
T G C G C G G C C C A G T C T T G G G C T A 22 bp
5’
T G C G C G G C C C A G 12 bp
5’
T G C G C G G C C C A G T C T T G G G C 20 bp
5’
T G C G C G G C C C A G T C T T 16 bp

53. Sanger SequencingSanger Sequencing
A C G C G C C G G G T ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?
5’3’
? ? ? ? ? ? ? ? ? ? ? ? ? ? C
5’
T G C G C G G C C C A
? ? ? ? ? ? ? ? ? T
5’
T G C G C G G C C C A 21 bp
26 bp
5’
T G C G C G G C C C A ? ? ? ? ? ? ? ? ? ? A 22 bp
5’
T G C G C G G C C C A G 12 bp
5’
T G C G C G G C C C A ? ? ? ? ? ? ? ? C 20 bp
5’
T G C G C G G C C C A ? ? ? ? T 16 bp

54. 5’
T G C G C G G C C C A G T C T T G G G 19 bp
5’
T G C G C G G C C C A G T C T T G G G C T A 22 bp
Sanger SequencingSanger Sequencing
G T C T T G G G C T
5’
T G C G C G G C C C A 21 bp
5’
T G C G C G G C C C A G T C T T G G G C 20 bp
5’
T G C G C G G C C C A G 12 bp
5’
T G C G C G G C C C A G T 13 bp
5’
T G C G C G G C C C A G T C T T 16 bp
5’
T G C G C G G C C C A G T C 14 bp
5’
T G C G C G G C C C A G T C T 15 bp
5’
T G C G C G G C C C A G T C T T G 17 bp
5’
T G C G C G G C C C A G T C T T G G 18 bp

55. Sanger Sequencing OutputSanger Sequencing Output
Each sequencing reaction gives us a chromatogram, usually ~600-1000 bp:

56. Massive Parallel Sequencing
Complete genome copiesFragmented genome chunks

57. Massive Parallel Sequencing
Fragmented genome chunks

58. Genomic
Fragment
Adapters
Shotgun Massive Parallel

60. A C G C G C C G G G T C A G A A C C C G A T C G C G
5’3’
5’
T G C G C G G C C C A
Primer
Only give polymerase one nucleotide at a time:
If that nucleotide is incorporated, enzymes turn by-products into light:
T C A G T C A G T C A G
G T C T T
G
GG
G G
G G G
The real power of
this method is that
it can take place in
millions of tiny
wells in a single
plate at once.
Raw 454 data

61. ATTGTTCCCACAGACCG
CGGCGAAGCATTGTTCC ACCGTGTTTTCCGACCG
TTTCCGACCGAAATGGCTTGTTCCCACAGACCGTGAGCTCGATGCCGGCGAAG
ATGCCGGCGAAGCATTGT
TAATGCGACCTCGATGCC
ACAGACCGTGTTTCCCGA
AAGCATTGTTCCCACAG TGTTTTCCGACCGAAAT
CCGACCGAAATGGCTCCTGCCGGCGAAGCCTTGT
Assembly, aka
17 bp
66 bp

62. ATTGTTCCCACAGACCG
CGGCGAAGCATTGTTCC ACCGTGTTTTCCGACCG
TTTCCGACCGAAATGGCTTGTTCCCACAGACCGTGAGCTCGATGCCGGCGAAG
ATGCCGGCGAAGCATTGT
TAATGCGACCTCGATGCC
ACAGACCGTGTTTCCCGA
AAGCATTGTTCCCACAG TGTTTTCCGACCGAAAT
CCGACCGAAATGGCTCCTGCCGGCGAAGCCTTGT
Assembly
TAATGCGACCTCGATGCCGGCGAAGCATTGTTCCCACAGACCGTGTTTTCCGACCGAAATGGCTCC
Consensus:

63. ATTGTTCCCACAGACCG
CGGCGAAGCATTGTTCC ACCGTGTTTTCCGACCG
TTTCCGACCGAAATGGCTTGTTCCCACAGACCGTGAGCTCGATGCCGGCGAAG
ATGCCGGCGAAGCATTGT
TAATGCGACCTCGATGCC
ACAGACCGTGTTTCCCGA
AAGCATTGTTCCCACAG TGTTTTCCGACCGAAAT
CCGACCGAAATGGCTCCTGCCGGCGAAGCCTTGT
Assembly
TAATGCGACCTCGATGCCGGCGAAGCATTGTTCCCACAGACCGTGTTTTCCGACCGAAATGGCTCC
Consensus:
Coverage: # of reads underlying the consensus

64. ATTGTTCCCACAGACCG
CGGCGAAGCATTGTTCC ACCGTGTTTTCCGACCG
TTTCCGACCGAAATGGCTTGTTCCCACAGACCGTGAGCTCGATGCCGGCGAAG
ATGCCGGCGAAGCATTGT
TAATGCGACCTCGATGCC
ACAGACCGTGTTTCCCGA
AAGCATTGTTCCCACAG TGTTTTCCGACCGAAAT
CCGACCGAAATGGCTCCTGCCGGCGAAGCCTTGT
Assembly
TAATGCGACCTCGATGCCGGCGAAGCATTGTTCCCACAGACCGTGTTTTCCGACCGAAATGGCTCC
Consensus:
6x coverage
100% identity
Coverage: # of reads underlying the consensus

65. ATTGTTCCCACAGACCG
CGGCGAAGCATTGTTCC ACCGTGTTTTCCGACCG
TTTCCGACCGAAATGGCTTGTTCCCACAGACCGTGAGCTCGATGCCGGCGAAG
ATGCCGGCGAAGCATTGT
TAATGCGACCTCGATGCC
ACAGACCGTGTTTCCCGA
AAGCATTGTTCCCACAG TGTTTTCCGACCGAAAT
CCGACCGAAATGGCTCCTGCCGGCGAAGCCTTGT
Assembly
TAATGCGACCTCGATGCCGGCGAAGCATTGTTCCCACAGACCGTGTTTTCCGACCGAAATGGCTCC
Consensus:
5x coverage
80% identity
Coverage: # of reads underlying the consensus

66. ATTGTTCCCACAGACCG
CGGCGAAGCATTGTTCC ACCGTGTTTTCCGACCG
TTTCCGACCGAAATGGCTTGTTCCCACAGACCGTGAGCTCGATGCCGGCGAAG
ATGCCGGCGAAGCATTGT
TAATGCGACCTCGATGCC
ACAGACCGTGTTTCCCGA
AAGCATTGTTCCCACAG TGTTTTCCGACCGAAAT
CCGACCGAAATGGCTCCTGCCGGCGAAGCCTTGT
Assembly
TAATGCGACCTCGATGCCGGCGAAGCATTGTTCCCACAGACCGTGTTTTCCGACCGAAATGGCTCC
Consensus:
2x coverage
50% identity
Coverage: # of reads underlying the consensus

67. ATTGTTCCCACAGACCG
CGGCGAAGCATTGTTCC ACCGTGTTTTCCGACCG
TTTCCGACCGAAATGGCTTGTTCCCACAGACCGTGAGCTCGATGCCGGCGAAG
ATGCCGGCGAAGCATTGT
TAATGCGACCTCGATGCC
ACAGACCGTGTTTCCCGA
AAGCATTGTTCCCACAG TGTTTTCCGACCGAAAT
CCGACCGAAATGGCTCCTGCCGGCGAAGCCTTGT
Assembly
TAATGCGACCTCGATGCCGGCGAAGCATTGTTCCCACAGACCGTGTTTTCCGACCGAAATGGCTCC
Consensus:
1x coverage
Coverage: # of reads underlying the consensus

68. Tumour
Normal
Somatic Substitutions
Somatic KRAS Codon12 C-T

69. FIRST GENERATION SANGER
SEQUENCING

70. SECOND GENERATION
CAPILLARY SEQUENCING

71. NEXT GENERATION
(MASSIVE PARALLEL SEQUENCING)

73. ICGC
>600 citations

74. • Prevent reduplication
• Standardized approach to allow data sharing
• Different cancer vary across the world
• Provide a bioethical framework
Cannot make IP claims to primary data
Open access to data to other researchers
Reasons for formation

75. International Cancer Genome Consortium
(ICGC)

77. Acknowledgements
APGI
Garvan Institute
Andrew Biankin
David Chang
Venessa Chin
Adnan Nagrial
Angela Chou
Lorraine Chantrill
Mark Pinese
Jeremy Humphris
Marc Cowley
Jianmin Wu
Amber Johns
Mary-Anne Brancato
Chris Toon
Mona Martyn-Smith
James Kench
Sarah Rowe
BTF
Garvan Institute
Michael Pickering
Carlie Crawford
Anthony Gill
Jas Samra
Nick Williams
Lyn Barrett
Nancy Consoli
Marie Wessell
PaLMs Anatomical
Pathology
Sydney, NSW
Duncan Mcleod
Virginia James
Vincent Lam
Henry Pleass
ICPMR Anatomical
Pathology
Perth, WA
Krishna Epari
Michael Texler
Tze Khor
David Fletcher
Cindy Forrest
Maria Beilin
Lisa Spalding
Nik Zeps
PathWest Laboratory
Medicine- Fremantle
Hospital
Brisbane, QLD
Andrew Barbour
Tom O’Rourke
Jonathon Fawcett
Neil Merrett
Rachel Neale
Lisa Braadvedt
Fran Millar
Andrew Clouston
Patrick Martin
Envoi Pathology
Adelaide, SA
Mark Brooke-Smith
Chris Worthley
John Chen
Nam Nguyen
Andrew Ruskeiwicz
Carly Burgstad
Tamara Debrencini
Institute for Molecular
Bioscience, UQ
Sean Grimmond
Nicola Waddell
Karin Kassahn
Katia Nones
Peter Wilson
John Pearson
David Miller
Flow facility
Rob Salomon
David Snowden
Nikki Alling
APGI
Garvan Institute
Angela Steinmann
Calan Spielman
Renee Di Pietro
Clare Watson
Rachel Wong
Jessica Pettitt
Marc Jones
Christopher Scarlett
Ilse Rooman
Scott Mead

78. Australian Pancreatic Cancer Genome Initiative
(Australian Pancreatic Cancer Network)
~ 400 cases
Amber Johns and Team

79. APGI Timeline
Sites Initiated-
first patient
recruited
First 100 pts-
National sites
grow- SA, QLD
First 150 genomes
sequenced
50 genomes in
DCC
Hit target of
350 eligible 597
Patients
June 2009 July 2010 March 2011 February 2012 October 2012
May 2013
Collections hit
200
All national sites
active
2009 2011 2012 2013
250
Sequenced
Nature
publication- Global
landscape of PC

80. Genome
Tumor
& normal (>50% TC)
40x /60-80x fold
Exome
Tumor & normal
(>20-50%)
>200 fold
Transcriptome
Expression array,
mRNAseq,
miRNAseq
Tumor tissue
& adjacent normal
~100 million reads
Epigenome
Methyl Miner
enrichment, methyl
seq
Tumor & adjacent
normal : 450K
array (>20% TC)
mRNA
small
RNAs
4 Hiseqs (Illumina) (max 3.6-5.5Tb / month)
SNP/CNV Chip analysis,
gDNA sequencing
High cellularity
Exome sequencing
Low cellularity
Sequencing Strategy

81. Cancer Research Program
Progress in Sample Acquisition
– 597 prospectively recruited patients
– 6000 analytes; 2000 samples shipped
– 355 ICGC compliant PDACs
• Cohorts:
– Mets: 23
– Primary/Met Pairs: 11 (more FPFE)
– Neoadjuvant tx: ~25
– Immortalised derivatives:
• 90 Xenografts
• >15 cell lines
– 208 exomes complete (108 in DCC)
– 27 cell line/normal pairs sequenced

82. APGI Progress by Site
N=366

87. https://www.ebi.ac.uk/ega/
http://icgc.org
http://cancer.sanger.ac.uk/cancergenome/projects/cosmic
/
Data Accessibility

88. What have I learnt from my involvement in
the APGI / ICGC?

89. 1. All cancers have different frequencies of
mutations
LB Alexandrov, S Nik-Zainal, DC Wedge, et al Signatures of mutational processes in human cancer Nature 2013; 500:415-421

90. Genome wide mutation rate in PDAC
LB Alexandrov, S Nik-Zainal, DC Wedge, et al Signatures of mutational processes in human cancer Nature 2013; 500:415-421

91. 2. Cancers can be classified by mutation
“signatures”
LB Alexandrov, S Nik-Zainal, DC Wedge, et al Signatures of mutational processes in human cancer Nature 2013; 500:415-421

92. • I used to think that cancer is caused by
somatic mutations . . .
. . . Now I appreciate that cancers are
caused by somatic mutations and
genomic instability which leads to more
mutations
This genomic instability fits into different
patterns called ‘signatures’
Cancers can be classified by mutation
“signatures”

93. What is a mutational signature?
• There are four base pairs in DNA.
Therefore there are only six types of base
substitutions:
C>A
C>G
C>T
T>A
T>C
T>G

94. What is a mutational signature?
Types of substitutions can be further
classified based on the nucleotides on
either side:
ACT>ATT is different to ACC>ATC
ACA>ATA is different to TCA>TTA
That is, taking into account the base pairs on either side,
there are only 96 different substitutions

95. LB Alexandrov, S Nik-Zainal, DC Wedge, et al Signatures of mutational processes in human cancer Nature 2013; 500:415-421

96. Signature 2 is characterised by C>T and C>G at TpCpN trinucleotides
Similar mutant profile to that seen in APOBEC – involved in defence against viruses
Signature 1 is characterised by C>T and NpCpG trinucleotides
This seams to be the signature associated with deamination which occurs in aging

97. Signature 3 is more or less equal across the genome
This seams to be the signature associated the homologous recombination repair deficiency
Signature 6 is dominated by C>T
This is associated with microsatellite instability

98. Signature 4 is caused by smoking . . .
Signature 7 is caused by ultraviolet light . . .

99. APOBEC
Deamination
BRCA pathway
defective
Possibly age related
signature
Genes ?
Microsatellite
instability
Defects in DNA
mismatch repair
Genes: MLH1, MLH3, MSH2,
MSH6, PMS1
Defects in dsb DNA
repair
Genes: BRCA1, BRCA2,
ATM?, PALB2?, RAD51?
DNA de-aminating
enzymes involved in
viral defense
Genes: APOBEC3 implicated
Mining mutagenic signatures in PDAC
LB Alexandrov, S Nik-Zainal, DC Wedge, et al Signatures of mutational processes in human cancer Nature 2013; 500:415-421

100. LB Alexandrov, S Nik-Zainal, DC Wedge, et al Signatures of mutational processes in human cancer Nature 2013; 500:415-421

101. LB Alexandrov, S Nik-Zainal, DC Wedge, et al Signatures of mutational processes in human cancer Nature 2013; 500:415-421

102. APOBEC
Deamination
BRCA pathway
defective
Possibly age related
signature
Genes ?
Microsatellite
instability
Defects in DNA
mismatch repair
Genes: MLH1, MLH3, MSH2,
MSH6, PMS1
Defects in dsb DNA
repair
Genes: BRCA1, BRCA2,
ATM?, PALB2?, RAD51?
DNA de-aminating
enzymes involved in
viral defense
Genes: APOBEC3 implicated
Mining mutagenic signatures in PDAC
LB Alexandrov, S Nik-Zainal, DC Wedge, et al Signatures of mutational processes in human cancer Nature 2013; 500:415-421

103. 3. Pancreatic cancer is highly
heterogeneous malignancy
Bainkin et al Pancreatic Cancer Genomces Reveal Aberrations in Axon Guidance pathway genes Nature 2012; 491:399-405

105. Genes affected by Inter-
chromosomal translocations
FGFR1 (bi-allelic)
LYPD6B
NRXN3
SFTPB
TNPO1
TP53BP2
ZNF468
Expressed Fusion transcript
ATE1 – KLRAQ1
Genes affected by intra-
chromosomal breakpoint
133 genes
Differential Methylation &
Expression
1800 genes
Somatic simple
mutations
ABCC9
ADAMTS20
AMAC1L2
B3GALT4
BLID
BRCC3
C3orf62
C11orf94
CACNA1C
CAPN11
CENPE
COLEC11
CTCF
FRMD6
GPR137B
IQCH
KIR3DX1
KLKB1
LEMD2
PIK3CD
PXDN
RPA1
SIGLECP3
SLC26A5
TIMELESS
ZNF432
ZNF132
Pancreatic Genome Report

107. 4. Pancreatic cancer may be classified
into four major groups based on structural
variation of the chromosomes
Waddell et al Whole genomes redefine the mutational landscape of pancreatic cancer Nature 2015; 518:495-5015

108. N Waddell et al. Nature 518, 495-501 (2015) doi:10.1038/nature14169
Subtypes of pancreatic cancer.
<50 structural variations Significant event on one two chromosomes50-200 structural variations >200 structural variations

109. Defects in homologous
recombination repair

110. BRCA
• BRCA1
• BRCA2
Genes associated with hereditary breast
cancer
Together account of 5% of breast cancer

111. BRCA and DNA repair
• BRCA1 and BRCA2 perform homologus
recombination which repairs dsDNA
breaks
• Some CTX (platinum/mitomycin) induce
dsDNA breaks – therefore BRCA mutated
tumours should be susceptible

112. PARP inhibitors
• poly(adenosine diphosphate–ribose) polymerase (PARP)
• PARPS are a family of enzymes (PARP1
is most common)
• PARP is needed to repair double stranded
breaks
• Therefore PARP inhibitors should
potentiate CTX with platinum in BRCA
tumours

113. BRCAness of Cancer
BRCAness refers to traits that some cancers including
sporadic cancers share with BRCA1/BRCA2 related
tumours – particularly certain poor homologous DNA
repair
Tumours which display BRCAness should respond to
certain CTX (eg: platinum)
This response may be augmented by PARP inhibitors
5% of breast ca BRCA,
but ? 20% have BRCAness

114. Candidate Drivers – Structural Rearrangements

115. Structural Variation – Platinum/MMC Response

116. Pan-Genomic Instability: Platinum Response
On Phenotype Responses: 4/4;
Off or Unknown Phenotype Responses: 0/5

117. Pan-Genomic Instability – Platinum Response

118. 5. Everyone talks about personalized medicine but
applying it in the real world is difficult . . .
Chantrill LA et al Precision Medicine for Advanced Pancreas Cancer: The Individualized Molecular Pancreatic Cancer
Therapy (IMPaCT) Trial et al Whole genomes redefine the mutational landscape of pancreatic cancer Clinical Cancer
Research Clinical Cancer Research 2015; 21:2029-37

119. Actionable Phenotype Therapeutic Molecular Characterization Prevalence
Gemcitabine Responsive Gemcitabine High hENT1, hCNT1, hCNT3 outliers 14%
DDR deficient Platinum; MMC; PARPi Pan-Genomic Instability
BRCA2/ATM/PALB2 mutations
30%
4%
nab-paclitaxel responsive nab-paclitaxel SPARC expression 11%
5-FU Responsive 5-Fluorouracil; Capecitabine Unknown 3%
Anti-EGFR Responsive Erlotinib KRASwt; Epithelial signature 5%
IrinotecanResponsive Irinotecan Topoisomerase 1 overexpression 2%
HER2 Amplified Trastuzumab HER2 amplification 2%
Hedgehog SMO inhibitors HH pathway mutations 4%
STK11/LKB1 null mTOR inhibitors Loss of STK11/LKB1 expression 3%
PTENnull / AKT activated mTOR inhibitor Loss of PTENexpression 12%
METAmplified MET inhibitors METAmplification 2%
ROCKAmplified Fasudil ROCKamplification 12%?
FGFR2 Amplified FGF Inhibitor FGFR2 Amplification 2%
CDK6 Amplified CDK Inhibitors CDK6 Amplification 2%
PIK3CA Amplified PI3K/AKT Inhibitors PIK3CA Amplification 2%
PIK3R3 Amplified ? Inhibitor PIK3R3 Amplification 2%
CSF1Rmutated Sunitinib CSF1Rmutation 1%
KIT Imatinib KIT overexpression; KIT mutation 0.2%
MDSlike 5-AZA Chromatin modifier mutations 9%
EML4-ALK Fusion Crizotinib Translocation: EML4-ALK Fusion 0%
Actionable Molecular Phenotypes of PDAC

120. The original IMPaCT trial schema.
Lorraine A. Chantrill et al. Clin Cancer Res 2015;21:2029-2037
©2015 by American Association for Cancer Research

121. An overview of the number of cases successfully screened for eligibility for the IMPaCT trial.
Lorraine A. Chantrill et al. Clin Cancer Res 2015;21:2029-2037
©2015 by American Association for Cancer Research

122. Barriers to enrollment.
Lorraine A. Chantrill et al. Clin Cancer Res 2015;21:2029-2037
©2015 by American Association for Cancer Research

123. Summary
• We’ve come a long way . . .
. . . But we’ve got a long way to go

125. Acknowledgements
APGI
Garvan Institute
Andrew Biankin
David Chang
Venessa Chin
Adnan Nagrial
Angela Chou
Lorraine Chantrill
Mark Pinese
Jeremy Humphris
Marc Cowley
Jianmin Wu
Amber Johns
Mary-Anne Brancato
Chris Toon
Mona Martyn-Smith
James Kench
Sarah Rowe
BTF
Garvan Institute
Michael Pickering
Carlie Crawford
Anthony Gill
Jas Samra
Nick Williams
Lyn Barrett
Nancy Consoli
Marie Wessell
PaLMs Anatomical
Pathology
Sydney, NSW
Duncan Mcleod
Virginia James
Vincent Lam
Henry Pleass
ICPMR Anatomical
Pathology
Perth, WA
Krishna Epari
Michael Texler
Tze Khor
David Fletcher
Cindy Forrest
Maria Beilin
Lisa Spalding
Nik Zeps
PathWest Laboratory
Medicine- Fremantle
Hospital
Brisbane, QLD
Andrew Barbour
Tom O’Rourke
Jonathon Fawcett
Neil Merrett
Rachel Neale
Lisa Braadvedt
Fran Millar
Andrew Clouston
Patrick Martin
Envoi Pathology
Adelaide, SA
Mark Brooke-Smith
Chris Worthley
John Chen
Nam Nguyen
Andrew Ruskeiwicz
Carly Burgstad
Tamara Debrencini
Institute for Molecular
Bioscience, UQ
Sean Grimmond
Nicola Waddell
Karin Kassahn
Katia Nones
Peter Wilson
John Pearson
David Miller
Flow facility
Rob Salomon
David Snowden
Nikki Alling
APGI
Garvan Institute
Angela Steinmann
Calan Spielman
Renee Di Pietro
Clare Watson
Rachel Wong
Jessica Pettitt
Marc Jones
Christopher Scarlett
Ilse Rooman
Scott Mead

126. Bioinformatics:
John Pearson
Lynn Fink
Darrin Taylor
David Wood
Conrad Leonard
Oliver Holmes
Qinying Xu
Matthew Anderson
Scott Wood
Felicity Newell
Nick Waddell
GenomeSeq:
David Miller
Angelika Christ
Tim Bruxner
Craig Nourse
Ehsan Nourbakhsh
Suzanne Manning
Ivon Harliwong
Senel Idrisoglu
Shivangi Wani
Karin Kassahn
Nicole Cloonan
Anita Steptoe
Keerthana Krishnan
Jason Steen
Muhammad Fadlullah
Brooke Gardiner
Sarah Song
Genome Biology:
Ann-Marie Patch
Peter Bailey
Katia Nones
Mike Quinn
Maely Gauthier
Shivashanka Nagaraj
Kelly Quek
Alan Roberston
Peter Wilson &
Deborah Gywnne
Acknowledgements
Sean Grimmond

127. Garvan:
Andrew Biankin
Rob Sutherland
Liz Musgrove
Roger Daly
James Kench
Marc Jones
Jianmin Wu
Anthony Gill
Page Tobelman
Jeremy Humphris
Mark Pinese
Angela Chou
David Chang*
Mark Cowley*
Chris Scarlett*
& APGI collaborators
(John Fawcett, O’Rourke, Barbour,
Henry, Kelly Slater)
Amber Johns
Scott Mead
Michelle Thomas
Chris Toon
Mary-Anne Brancato
Cathy Axford
Emily Colvin
Amanda Mawson
Johana Susanto
Marina Pajic
Mona Martyn-Smith
Lorraine Chantrill
Adnan Nagrial
Venessa Chin
Acknowledgements

128. OICR:
Leadership:
John McPherson
Lincoln Stein
Nicole Onetto
Thomas Hudson
Ming-Sound Tsao
Informatics:
Timothy Beck
Kimberly Begley
Richard De Borja
Tony DeBat
Robert Denroche
Fouad Yousif
Christina Yung
BHGSC:
Richard A Gibbs David A. Wheeler
Marie-Claude Gingras,
Nipun Kakkar Fengmei Zhao
Yuan Qing Wu Min Wang
Donna M. Muzny William E. Fisher
Sally E. Hodges Jennifer Drummond
Kyle Chang Yi Han
Lora L. Lewis Huyen Dinh
Christian J. Buhay F. Charles Brunicardi
Genomics:
Andrew Brown
Nicholas Buchner
Debabrata
Mukhopadhyay
Lakshmi Muthuswamy
Jessica Miller
Laura Mullen
Karen Ng
Deepa Pai
Ami Panchal
Michelle Sam
Lee Timms
Clinicians:
Steve Gallinger
Gloria Petersen
Patricia Shaw
Acknowledgements
Verona:
Aldo Scarpa
Claudio Bassi
Paolo Pederzoli
Rita Lawlor
Johns Hopkins:
Ralph Hruban
Jim Eshleman
Anirban Maitra
Chris Iacobuzio-Donahue
WTSI:
Ludmil Alexandrov
Serena Nik-Zainal
Peter Campbell
Mike Stratton