Download ESMO E-Learning: Imaging biomarkers in breast

Imaging biomarkers in breast
oncology practice
Elizabeth O’Flynn
Clinical Lecturer and Consultant Radiologist
Institute of Cancer Research and Royal Marsden Hospital,
London, United Kingdom
Background
 Neoadjuvant chemotherapy (NAC):
 Clinical response rates 70–98%1,2
 Pathological complete response (pCR) 3–16%3
Pre-treatment
Pre-treatment
Tumour
Post-treatment
(pCR)
End of treatment
Signal void from marker
1. Powles TJ, et al. J Clin Oncol 1995;13:547–552; 2. Fisher B, et al. J Clin Oncol 1998;16:2672–2685;
3. Swain SM, et al. Cancer Res 1987;47:3889–94.
Images courtesy of Dr Elizabeth O’Flynn.
Predicting response
1. After completion of NAC to guide surgery
2. At baseline (prior to commencing NAC)
3. At an early time point (1 or 2 cycles after commencing NAC)
Predicting response after completion
of NAC
 MRI gives more accurate staging compared to clinical examination
mammography and US1–4
Pooled mean difference
(95% confidence intervals) (cm)
Author
n
MRI
Clinical
exam
Marinovich et al.
2013
19
0.1
(-0.2, 0.3)
-0.3
(-0.7, 0.0)
Mammo
US
0.4
(-0.5, 1.3)
0.1
(-0.1, 0.4)
1. Chagpar AB, et al. Ann Surg 2006;243:257–264; 2. Akawawa K, et al. Breast J 2006;12:130–137; 3. Montemurro F, et al.
Eur Radiol 2005,15:1224–1233; 4. Marinovich ML, et al. Br J Cancer 2013;109:1528–1536.
Underestimation of residual disease
Pre-treatment
Tumour
Images courtesy of Dr Elizabeth O’Flynn.
Post-treatment
Tumour reduced
enhancement
Overestimation of residual disease
Pre-treatment
Tumour
Images courtesy of Dr Elizabeth O’Flynn.
Post-treatment
Residual enhancement
Predicting response after completion
of NAC
n
Sensitivity
(%)
Specificity
(%)
PPV
NPV
AUC
De Los Santos et al.
20131
746
83
47
47
83
74
Hayashi et al.
20132
260
44
90
73
73
78
Ko et al.
20133
166
96
65
-
-
89
Chen et al.
20124
64
39
92
-
-
-
Hylton et al.
20125
216
-
-
-
-
84
Author
1. De Los Santos JF, et al. Cancer 2013;1777–1783; 2. Hayashi Y, et al. Oncol Lett 2013;5:83–89; 3. Ko ES, et al. Ann Surg
Oncol 2013;20:2562–2568; 4. Chen et al. Chin Med J 2012;125:1862–1866; Hylton NM. et al. Radiology 2012;263:663–672.
Predicting response after completion
of NAC
n
Sensitivity
(%)
Specificity
(%)
PPV
NPV
AUC
De Los Santos et al.
20131
746
83
47
47
83
74
Hayashi et al.
20132
260
44
90
73
73
78
Ko et al.
20133
166
96
65
-
-
89
Chen et al.
20124
64
39
92
-
-
-
Hylton et al.
20125
216
-
-
-
-
84
Author
1. De Los Santos JF, et al. Cancer 2013;1777–1783; 2. Hayashi Y, et al. Oncol Lett 2013;5:83–89; 3. Ko ES, et al. Ann Surg
Oncol 2013;20:2562–2568; 4. Chen et al. Chin Med J 2012;125:1862–1866; Hylton NM; et al. Radiology 2012;263:663–672.
Molecular subtypes of breast cancer
Molecular subtype
Biomarker profile
Prevalence
Luminal A
ER+ and/or PR+,
HER2-, low Ki67<14%
42-59%
Luminal B
ER+ and/or PR+ and HER2+
ER+ and/or PR+, HER2-,
high Ki67>14%
6-19%
ER-, PR- , HER2-,
cytokeratin 5/6+ and/or EGFR+
14-20%
ER-, PR-, HER2+
7-12%
Basal-like/ triple negative
HER2
ER = oestrogen receptor
PR = progesterone receptor
HER2 = human epidermal growth factor receptor 2
EGFR = epidermal growth factor receptor
Carey LA, et al. JAMA 2006;295:2492–2502; Nielsen TO, et al. Clin Cancer Res 2004;10:5367–5374; Perou CM, et al. Nature
2000;406:747–752; Rakha EA, et al. Eur J Cancer 2006;42:3149–3156; Sorlie T, et al. Proc Natl Acad Sci USA 2001;
98:10869–10874
MRI predicting response through subtype
Author
n
Findings
Hayashi et al.
20131
264
Better accuracy in triple negative cancers
Ko et al.
20132
166
Worse accuracy in ER positive cancers and low
grade tumours
Chen et al.
20123
50
Better accuracy in triple negative cancers and
high grade tumours
McGuire et al.
20114
203
Better accuracy in ER negative/ HER2 positive
cancers
Worse accuracy in luminal cancers
Kuzucan et al.
20125
54
Worse accuracy in hormone positive cancers and
low grade tumours
De Los Santos et al.
20136
746
Better accuracy in triple negative / HER2 positive
cancers
1. Hayashi Y, et al. Oncol Lett 2013;5:83–89; De Los Santos JF, et al. Cancer 2013;1777–1783; 2. Ko ES, et al. Ann Surg
Oncol 2013;20:2562–2568; 3. Chen et al. Chin Med J 2012;125:1862–1866; 4. McGuire KP et al. Ann Surg Oncol Ann Surg
Oncol 2011;18:3149–3154; 5. Kuzucan A, et al. Clin Breast Cancer 2012;12:110–118; 6. De Los Santos JF, et al. Cancer
2013;1777–1783.
MRI predicting response through subtype
Author
n
Findings
Hayashi et al.
20131
264
Better accuracy in triple negative cancers
Ko et al.
20132
166
Worse accuracy in ER positive cancers and low
grade tumours
Chen et al.
20123
50
Better accuracy in triple negative cancers and
high grade tumours
McGuire et al.
20114
203
Better accuracy in ER negative/ HER2 positive
cancers
Worse accuracy in luminal cancers
Kuzucan et al.
20125
54
Worse accuracy in hormone positive cancers and
low grade tumours
De Los Santos et al.
20136
746
Better accuracy in triple negative / HER2
positive cancers
1. Hayashi Y, et al. Oncol Lett 2013;5:83–89; De Los Santos JF, et al. Cancer 2013;1777–1783; 2. Ko ES, et al. Ann Surg Oncol
2013;20:2562–2568; 3. Chen et al. Chin Med J 2012;125:1862–1866; 4. McGuire KP et al. Ann Surg Oncol 2011;18:3149–
3154; 5. Kuzucan A, et al. Clin Breast Cancer 2012;12:110–118; 6. De Los Santos JF, et al. Cancer 2013;1777–1783.
MRI predicting response through subtype
Author
n
Findings
Hayashi et al.
20131
264
Better accuracy in triple negative cancers
Ko et al.
20132
166
Worse accuracy in ER positive cancers and
low grade tumours
Chen et al.
20123
50
Better accuracy in triple negative cancers and
high grade tumours
McGuire et al.
20114
203
Better accuracy in ER negative/ HER2 positive
cancers
Worse accuracy in luminal cancers
Kuzucan et al.
20125
54
Worse accuracy in hormone positive cancers
and low grade tumours
De Los Santos et al.
20136
746
Better accuracy in triple negative / HER2 positive
cancers
1. Hayashi Y, et al. Oncol Lett 2013;5:83–89; De Los Santos JF, et al. Cancer 2013;1777–1783; 2. Ko ES, et al. Ann Surg Oncol
2013;20:2562–2568; 3. Chen et al. Chin Med J 2012;125:1862–1866; 4. McGuire KP et al. Ann Surg Oncol 2011;18:3149–
3154; 5. Kuzucan A, et al. Clin Breast Cancer 2012;12:110–118; 6. De Los Santos JF, et al. Cancer 2013;1777–1783.
Triple negative breast cancer
High T2 centrally
T2W MRI
Enhancing rim
T1W DCE-MRI
Restricted diffusion rim
DWI b=1150
ADC map
Dogan BE et al. Am J Roentgenol 2010;194:1160–1166; Chen JH, et al. Ann Oncol 2007:18:2042–2043; Wang Y, et al.
Radiology 2008;246:367–375; Noh JM, et al. J Breast Cancer 2013;16:308–314.
Images courtesy of Dr Elizabeth O’Flynn.
MRI accuracy for residual disease
Author
n
Study
Sensitivity
Specificity
Lobbes et al.
20131
35
Systematic
review
Range
25 - 100%
Range
50 - 97%
Meta-analysis
Pooled
estimate
83 - 87%
Pooled
estimate
54 - 83%
Marinovich et al.
20132
44
1. Lobbes MB, et al. Insights Imaging 2013;4:163–175; 2. Marinovich L, et al. J Natl Cancer Inst 2013;105:321–333.
Imaging techniques for predicting pCR in
restaging the axilla post NAC
Technique
n
Author
Sensitivity
Specificity
PPV
NPV
Clinical
exam
32
Arimappamagan
et al. 2004
86
64
40
94
US
32
Arimappamagan
et al. 2004
Heiken et al.
2013
86
100
100
96
58
70
57
71
MRI
88
Heiken et al.
2013
59
61
43
75
Nomogram
291
Schipper et al.
2014
43
88
65
75
Schipper et al. Eur J Radiol 2015;84:41–47
Predicting response at baseline
Author
n
Factors predictive of response
Li et al.
20101
264
Traditional prognostic factors
Uematsu et al.
20102
166
Mass effect and wash-out pattern
Park et al.
20103
50
Lower pre-treatment ADC
Fangberget et al.
20114
264
HER2 overexpression
1. Li SP, et al. Radiology 2010;257:643–652; 2. Uematsu T, et al. Eur Rad 2010;20:2315-–2322; 3. Park SH, et al. Radiology
2010; 257:56–63; 4. Fangberget A, et al. Eur Radiol 2011;21:1188–1199.
Predicting response at an early time point
Standard
Measurements
(RECIST)
Dynamic
Contrast
Enhanced MRI
(DCE-MRI)
MR Spectroscopy
MRI
DiffusionWeighted
Imaging (DWI)
Intrinsic
Susceptibility
Weighted
MRI (R2*)
Tumour diameter
Tumour volume
Standard
Measurements
(RECIST)
Time-signal
Intensity
curves
Ktrans, kep, ve
Dynamic
Contrast
Enhanced MRI
(DCE-MRI)
MRI
b=1150
Apparent Diffusion
Coefficient (ADC)
MR Spectroscopy
Choline
peak at
3.2ppm
DiffusionWeighted
Imaging (DWI)
Images courtesy of Dr Elizabeth O’Flynn.
Intrinsic
Susceptibility
Weighted
MRI (R2*)
R2*=1/T2*
MRI parameters
Tumour Diameter
Tumour Volume
Standard
Measurements
(RECIST)
MRI
Image courtesy of Dr Elizabeth O’Flynn.
MRI parameters
VASCULARITY
Dynamic
Contrast
Enhanced MRI
(DCE-MRI)
Images courtesy of Dr Elizabeth O’Flynn.
Semi-quantitative parameters:
Time-signal intensity curves
Maximum signal intensity
Absolute MRI signal intensity
Relative MRI signal intensity
Normalised MRI signal intensity
Initial Area Under the Gadolinium Curve (IAUGC)
Enhancement Fraction
Pharmacokinetically modelled parameters:
Ktrans
kep
Ve
Textural analysis
Dynamic contrast enhanced (DCE)-MRI
Gd-DTPA
iv injection
Endothelial cells3
renal excretion
Blood plasma
Ktrans
Transfer constant
kep
rate constant
Pericyte – red3
Ve
extravascular extracellular
space
Pharmacokinetic modelling1,2
Normal
Tumour
1. Courtesy of O’Flynn EAM, et al. Breast Cancer Research 2011;13:204; 2. Tofts PS, et al. J Magn Reson Imaging
1999;10:223–232; 3. McDonald DM, et al. Nat Med 2003;9:713–725. Reprinted by permission from Macmillan Publishers Ltd:
Nature Medicine, © 2003
DCE-MRI Assessing treatment response
Author
Ah-See et al.
20081
Pickles et al.
20052
Padhani et al.
20063
Yu et al.
20074
Yu et al.
20105
Time point
imaged after
NAC
n
Responders
Nonresponders
19
Ktrans ↓ 40%
kep ↓ 33%
Ktrans ↑ 18%
kep ↑ 7%
2 cycles
68
Ktrans ↓ 20%
kep ↓ 20%
ve ↑ 4%
Ktrans ↓ 20%
kep ↓ 36%
ve ↑ 28%
“early time
point”
15
Ktrans ↓ 22%
kep ↓ 62%
Ktrans ↓ 7%
kep ↓ 25%
1 cycle
2 cycles
29
Ktrans ↓ 5%
kep ↓ 14%
Ktrans ↑ 8%
kep ↑ 7%
1 cycle
32
Ktrans ↓ 49%
ve ↓ 27%
Ktrans ↓ 18%
ve ↓ 13%
2 cycles
1. Ah-See ML, et al. Clin Can Res 2008;14:6580–6589; 2. Pickles MD, et al. Breast Cancer Res Treat 2005;91:1–10; 3.Padhani
et al. Radiology 2006;239:361–374; 4. Yu JH, et al. J Magn Reson Imaging 2007;26:615–623; 5.Yu Y, et al. Radiology
2010;257:47–55.
MRI parameters
DWI b=1150
ADC map
CELLULARITY
DiffusionWeighted
Imaging (DWI)
Images courtesy of Dr Elizabeth O’Flynn.
ADC = Apparent Diffusion Coefficient
Diffusion-Weighted Imaging (DWI)
 Sensitive to factors affecting microscopic motion of water
Tumour
Normal tissue
●
●
Random Brownian
Motion
cell ● water molecule
Free diffusion
Low signal intensity DWI
High signal ADC
●
Restricted diffusion
High signal intensity
DWI
Low signal ADC
ADC = Apparent Diffusion Coefficient
DWI
b=0
b = 350
In (S/S0
b = 700
b = 1150
ADC map
S = S0e-b.ADC
b (s/mm2)
ADC = Apparent Diffusion Coefficient
Images courtesy of Dr Elizabeth O’Flynn.
DWI
Normalised
 ADC values reproducible1
 DWI has shown an increase in ADC prior to any change in breast
tumour size2
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.6
0.5
Rise in ADC
Tumour size static
0
1
Treatment cycles
ADC (normalised)
2
Diameter (normalised)
1. O’Flynn EA, et al. Eur Radiol 2012;22:1512–1518; 2. Pickles MD, et al. Magn Reson Imaging 2006;24:843–847
DWI – Assessing treatment response
Author
n
Responders
Time point imaged after
NAC
Pickles et al.
20061
10
ADC ↑ 16%
ADC↑ 27%
1 cycle
2 cycles
ADC ↑ 15%
ADC ↑ 27%
ADC ↑35%
1 cycle
2 cycles
3 cycles
14
Sharma et al.
20092
24
29
Nilsen et al.
20103
25
ADC ↑ 25%
4 cycles
Fangberget et al.
20114
29
ADC ↑ 40%
4 cycles
1. Pickles MD, et al. Magn Reson Imaging 2006;24:843–847; 2. Sharma U, et al. NMR Biomed 2009;22:104–113;
3. Nilsen l, et al. Acta Oncol 2010;49:354–360; 4. Fangberget A, et al. Eur Radiol 2011;21:1188–1199.
MRI parameters
METABOLISM
MR
Spectroscopy
Magnified spectrum illustrates
a positive Choline peak at a
frequency of 3.2 ppm
Voxel over
enhancing tumour
Graph courtesy of Dr Geoffrey Payne
Image courtesy of Dr Elizabeth O’Flynn
Spectroscopy –
Assessing treatment response
Author
n
Responders
Non-responders
Time point
imaged after
NAC
Jacobs et al.
20111
18
[Cho] ↓ 35%
[Cho] ↓ 11%
1 cycle
Tozaki et al.
20102
16
[Cho] ↓ 56%
[Cho] ↓ 24%
2 cycles
Baek et al.
20093
35
[Cho] ↓ 68%
[Cho] ↓ 37%
[Cho] ↓ 28%
[Cho] ↓ 49%
[Cho] ↓ 94%
[Cho] ↓ 8%
[Cho] ↓ 16%
[Cho] ↓ 39%
Danishad et al.
20104
25
2 cycles
1 cycle
2 cycles
3 cycles
1. Jacobs MA, et al. Breast Cancer Res Treat 2011;128:119–126; 2. Tozaki M, et al. J Magn Reson Imaging 2010 ;31:895–902;
3. Baek HM, et al. Radiology 2009;251:653–662; 4. Danishad, et al. NMR Biomed 2010;23:233–241.
MRI parameters
OXYGENATION
T2* sequence
multiple echo
times
R2* map
Intrinsic
Susceptibility
Weighted
MRI (R2*)
Images courtesy of Dr Elizabeth O’Flynn.
R2*=1/T2*
Intrinsic susceptibility weighted
imaging / R2*
 Breast cancer patients R2* lower in tumour than normal parenchyma
prior to chemo
Author
n
Responders
Time point imaged after NAC
Li et al.
2010
27
↑ 10%
2 cycles
Li SP, et al. Radiology 2010;257:643–652.
Early prediction of response
Authors
Prevos et al.
20121
Authors
Hylton et al.
20122
Study
n
Systematic
review
15
Study
n
I-SPY
216
Findings
Most frequently studies parameters:
Tumour diameter or volume
Ktrans, kep, ve, ADC
Findings
MR Imaging findings stronger
predictor of pCR than clinical exam
Greatest advantage observed using
volumetric measurement of tumour
response early in treatment
I-SPY TRIAL: Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis
1. Prevos R, et al. Eur Rad 2012;22:2607–2616; Hylton NM, et al. Radiology 2012;263:663–672.
Multiparametric MRI
 Improved diagnostic
accuracy combining
DCE-MRI, DWI
and MR Spectroscopy
 Change in enhancement
fraction after 2 cycles
was the best
discriminator of response
Images courtesy of Dr Elizabeth O’Flynn
1. Pinker K, et al. Invest Radiol 2014;49:421–430;
2. O’Flynn EA, et al. ISMRM Proceedings. 2014.
Summary
 Many MRI parameters and time points at which to predict response
 No definite advantage of MRI assessment over US but more larger
scale studies needed
 Tumour subtype plays a crucial role accuracy of MRI
 Assessment of tumour volume and the ADC hold most potential for
incorporation into routine clinical practice
 Standardisation required
Acknowledgments
 ICR The Institute of Cancer research
 Cancer Research UK
 The Royal Marsden – NHS Foundation Trust
THANK YOU!