Precision Medicine: From stratified therapies to personalized therapies
From pathology research to stratified medicine trials. JB... · “Stratified medicine requires...
Transcript of From pathology research to stratified medicine trials. JB... · “Stratified medicine requires...
From pathology research
to stratified medicine
trials
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Dr. John BartlettProgram Director Transformative Pathology C
CCTG Breast Group Steering CommitteeASCO-CAP HER2 Panel
“As is your Pathology, so is your Medicine.” Sir William Osler. 1849-1919.
San Antonio Breast Cancer Symposium, December 9-13, 2014
Cancer = “the big C”?
1950-70s Cancer as 1 diseaseDNA structure discovered 1953
1970-80s Cancer as “site specific”
“one size fits all”
1990-2000sCancer “subtypes”
Herceptin – targeted treatments
2010s: Molecular complexity and Heterogeneity
Genomic data overload –
4.1Million mutations in 1000 cancers..
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Cancer Treatment: 20th century
Surgery
Radiotherapy
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Pan Cancer projects: Unlocking cancer
genomic data
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Information at the pathway level
Version: 26Apr2012
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Insights into global cancer processes
• Mutation vs CNAs
– a fork in the cancer
genome road map?
• “M” vs “C” class cancers?
– Mutations and CNAs
represent fundamentally
different aspects of cancer.
– Most cancers are “mixed”
with low numbers of
alterations– Ciriello G, et al, Nat Genetics 2013
45:1127-1133
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7Nature Genetics 45, 1127–1133 (2013)
M-Class Cancers
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C-Class Cancers
Nature Genetics 45, 1127–1133 (2013)
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The challenge posed by ICGC:
Cancers are heterogeneous:
Between patient heterogeneity – requires stratified
treatment approaches;
Within tumour heterogeneity – target multiple clones –
requires combination/multi-agent targeted therapy;
Selection of resistant clones during treatment requires
adaptive therapies.
NB Cancer has not changed – our understanding has
improved – current treatments are effective in the context of
molecular heterogeneity.
ICGC puts widely recognized and existing therapeutic and
treatment challenges in a molecular context – enabling a rational targeted therapeutic approach.
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Cancer Paradoxes or cancer successes?
TCGA colorectal cancer
4 molecular subgroups
ULTRA mutated cancers
Genetically unstable, multiple driver mutations
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Cancer Paradoxes or cancer successes?
Version: 26Apr2012
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PIK3CA mutations: One gene –two results
0 100 200 300 400 500 600 700
0.0
0.2
0.4
0.6
0.8
1.0
Time, days
EVE.PIK3CA.WT
EVE.PIK3CA.Alt
PBO.PIK3CA.WT
PBO.PIK3CA.Alt
Pro
bab
ilit
y o
f
Pro
gre
ssio
n-F
ree S
urv
ival
In early breast cancer PIK3CA mutation is linked to good outcome, in metastatic
breast cancer PIK3CA is linked to poor outcome – what changed?
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The challenge of Context:
It is not enough to model gene function in isolation.
Treatment of different cancers, even with similar
molecular drivers, varies.
Molecular drivers, viewed in isolation, may have
different impacts at different stages of disease.
The relationship between the molecular make up
of a cancer and outcome for a patient reflects a
complex integration of multiple treatment and
response variables.
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Cancer is highly complex.
10,048 cancers
An average of 400 mutations per cancer
Over 100 different druggable mutations
9.3x10157
permutations!
If only 10% are
present =
3.6x106
permutations
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To summarize….
Personalised medicine –
progress to date
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HER2 – the test that came in from the
cold:
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HER2 – selective for Herceptin Benefit (2005!)
87%85%
67%
75%
%
HR=0.48,2P=3x10-12
AC TH
AC T
Years From Randomisation
Herceptin/Trastuzumab:
“Humanised” mouse antibody
Targets BRCA driver gene HER2
Reverses poor outcome in some
“HER2 positive” BRCA (15%)
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Successful personalised drug development
Rapid identification of potential targets
For “druggable” targetsInterval from target to clinical candidate is shorter
For clinically important targetsInterval from FIH to drug approval is short
Chin L et al. Nature Medicine 17: 297-303, 2011
FD
A A
pp
ro
val
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Personalized medicine?
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In 2015 the FDA
Approved 15 new therapeutic agents
Approved 11 new applications of existing
therapeutic agents
Approved one new diagnostic test
for existing EGFr mutation detection
“As is your Pathology, so is your Medicine.” Sir
William Osler. 1849-1919.
“As is your molecular pathology – so is your
molecular medicine” - David Huntsman
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CDK/CCND pathway
CDK4/CCND1 –
amplified 15-20% breast
cancer
Luminal ER+ BC cells
sensitive to CDK4/6
inhibitor palbociclib
Palbociclib + letrozole
approved for ER+/HER2-
breast cancer
No biomarkers to select
patients for treatment
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PI3K/AKT/mTOR pathway
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Most commonly altered
pathway in BC
PIK3CA mutated in 35-
40% ER+ BC
AKT1 2-4% ER+ BC
PTEN 29-44% ER+ BC
Resistance to endocrine
therapy
Everolimus +
exemestane approved
for ER+/HER2- breast
cancer
5-15% benefit from therapy
benefit from therapy
do not benefit try alternate therapyIdentify new target
Current paradigm
response to therapy no response to therapy
Treat
Test
F4LBC Dream Team
Don’t Treat
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5-15% benefit from therapy
benefit from therapy
do not benefit
Current reality
Test
F4LBC Dream Team
Treat
Treat
response to therapy no response to therapy
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Multiple targets - multiple opportunities
F4LBC Dream Team
Zardavas D, et al. Nat Rev Clin Oncol. 2013;10(4):191-210.
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How do we treat breast cancer
Stage
BiologyRisk
Chemotherapy benefit estimate from proportional hazards
Grade + receptors
NPI Adjuvant!
Assumes that breast cancer is a single disease (One size fits all)
Assumes that chemotherapy benefit is present in ALL
cancer subtypes. Biology
Endocrine therapyHER2-targeted therapy
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How should we treat cancer?
5-15% benefit from therapy
Current therapy
Alternate therapies
Treat
Don’t treat
TEST Less therapy
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Lack of Personalised Medicine:
One Size Fits All
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0 5 10 15
Bre
as
t C
an
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)
Years
None
Tamoxifen (5yrs)
3.6%
7.9%
9.2% Benefit from
TamoxifenTreat 11 – “benefit” 1.
25% exhibit or acquireTamoxifen/endocrineresistanceNEW TREATMENT
65% “cured” by surgery/RxREDUCE TREATMENT
For each treatment “success”3 patients experience resistance7 patients are treated unnecessarily<10% of patients gain benefit
ALL risk side effects
Reducing treatment:
Integrate risk and reduce
treatment for low risk patients
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Oncotype DX: RS as Continuous
Predictor in tam treated patients
0%
5%
10%
15%
20%
25%
30%
35%
40%
0 5 10 15 20 25 30 35 40 45 50
Dis
tan
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ecu
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at
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ars
Recurrence Score
Low-Risk Group High-Risk Group Intermediate-
Risk Group
Data from NSABP B14: Paik NEJM 2004, 351:2817
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B-20 Results: Relative benefit of chemo on 10 Year DDFS
Paik et al. J Clin Oncol. 2006.
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Optimal Personalised Treatment of early breast
cancer usIng Multi-parameter Analysis
Objectives1. To establish a method of selecting patients with hormone sensitive
primary breast cancer who are likely to benefit or not benefit from post-operative chemotherapy.
2. To establish the cost-effectiveness of alternative test-guided treatment strategies compared to standard practise
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OPTIMA Design
Radiotherapy given according to local practise
Adequate surgery
Age ≥ 40
ER +ve, HER2 –ve
N+/ N0 & T>30mm
Central confirmation of ER
& HER2 Endocrine therapy continued to 5+ yrs
Permitted chemotherapy: FEC75-100 x 6 cyclespre-specified by patient TC x 4 cyclesbefore randomization FEC100-T
E-CMF
Permitted endocrine therapy: postmenopausal - any AI premenopausal - GnRH agonist (3 yrs) + tamoxifen
Exclusion: advanced
stage = ≥ 10 N+/ IM+
Sample size to demonstrate non-inferiority (-3%) = 1860 per arm
chemo.
endocrine
R
endocrine
chemo. endocrine
1
1 Test
Patients receiving chemotherapy blind to randomisation
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Stratified medicine – and diagnostics
5-15% benefit from therapy
Current therapy
Alternate therapies
Treat
Don’t treat
TEST Less therapy
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Use existing Phase III trial of current SOC to develop
stratified medicine approaches to breast cancer
We rarely know why patients respond to treatment;
We more often know why they do not – and it is
usually different for different patients.
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Gene Hazard Ratio 95% CI Freq(%)UPF3B 8.40 1.98-35.64 0.73CBLB 5.83 1.40-24.36 0.87ERBB2 4.60 1.64-12.87 2.04BRCA1 2.32 1.18-4.56 7.57USH2A 2.16 1.22-3.83 11.35TP53 1.93 0.96-3.91 8.01AKT1 0.77 0.18-3.15 3.78MET 0.69 0.28-1.72 9.17PIK3CA 0.66 0.37.1.18 30.1GATA3 0.61 0.08-4.46 2.91EPHA5 0.53 0.17-1.68 7.13
F4LBC Dream Team 37
TEAM trial: integrating genomics
CGH/CNV
ad
just
ed
HR
targeted sequencing mRNA profiles
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PIK3CA pathway: mutation, CNV, mRNA
CNV
PIK3CA/AKT
gain, PTEN loss
PIK3CA
AKT1
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A Functional Focus: Pathways are paramount:
Molecular alterations rarely exist in isolation
50-200 mutations/CNAs in each cancer.
Intracellular regulatory pathways have key mutational nodes
Evaluation of multiple events/nodes is essential
Mutation
Copy number change
Expression
Proteins etc etc.
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Developing novel stratified diagnostics:
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Pathway and target oriented.
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Umbrella or basket?
Biankin et al, Nature 2015
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Stratified clinical trials
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HIG
H R
ISK
BR
EAST
CA
NC
ER Arm 1: Conventional treatment
Arm 2: Experimental treatment A
Arm 3: Experimental treatment B
Arm 4: Experimental treatment C
Ran
do
mise
Mo
lecular d
iagno
stic screen
Pre-operative Neo-adjuvant Adjuvant2-3 weeks 3-4 months 3-5 years
Low risk – conventional or reduced treatment
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Stratified medicine – and diagnostics
5-15% benefit from therapy
Current therapy
Alternate therapies
Treat
Don’t treat
TEST Less therapy
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To Dream the impossible dream?
RNA
CNA
CpG
Mut
mRNA
miRNA
Mutation
CNA
CpG
Ptn
“a new era of medicine — one that delivers the right treatment at the right time, to the right patient”
“Stratified medicine requires stratified diagnostics”Failure to improve diagnostic pathology, both in the quantity and the quality of information derived will continue to restrict the development of personalised medicine.
“As is your Pathology, so is your Medicine.” William Osler 1848-1919
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“I want the country
that eliminated polio
and mapped the
human genome to
lead a new era of
medicine — one that
delivers the right
treatment at the right
time,”
President Obama 21st
Jan 2015.
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OICR Diagnostic Development:
“mi casa es su casa”
IHC and FISH:
Ventana, Dako autostainer, FISH
Light and fluorescence microscopy
Bioview (FISH), Ariol, Definiens
mRNA:
Multiplex PCR/Nanostring q-mi/mRNA
RNAseq
DNA – targeted seq/CNV MLPA etc
Preclinical – cell culture, high throughput drug screening, in vitro, invivo.
Tissue:-
Laser capture micro/manual macro – 10,000s samples
ONTARIO TUMOUR BANK 15,000 samples
Breast/Prostate tissues (20-30,000)
TMAs, frozen section, etc etc…
Funding for the Ontario Institute for Cancer Research
is provided by the Government of Ontario
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The Tumor Ecosystem: Joan Brugge
Hypothesis: there is a limit to the number of insults that the ecosystem can withstand.
Need to identify critical set of ‘perturbants’ that push the ecosystem to point beyond its ability to adapt.
Susan Komen Lecture
The facts expressed here belong to everybody, the opinions to me. The distinction is yours to draw.