utilidad de la biopsia líquida en cáncer de próstata · utilidad de la biopsia líquida en...
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Utilidad de la biopsia líquida encáncer de próstata
Utilidad de la biopsia líquida encáncer de próstataEnrique González Billalabeitia
Servicio de Hematología y Oncología MédicaHospital G.U. Morales Meseguer
MurciaIX Reunión Nacional Cáncer de próstata, Cáncer renal y
cáncer de Vejiga.Mesa II. Cáncer de Próstata I
Guadalajara, 16 de Junio de 2017
Genetic evolution in Prostate Cancer
Rising PSANo visiblemetastasis
Rising PSANo visiblemetastasis
MetastaticDisease
MetastaticDisease
InvasivediseaseInvasivedisease
Pre-invasivedisease(PIN)
Pre-invasivedisease(PIN)
No diseaseNo disease
Preventionof the disease
Prevention ofinvasion
Local treatment(RP or RT) orobservation(only selected
pts)
“Prevention” ofevident metastatic
disease
Systemictreatment
(ADT, HT, CT)
InflammationOxidative Stress
Telomere shortening
Senescence Castrationresistance
Myc TMPRSS2-ERGPTEN
inactivation
ERK/MAPK activationP53 inactivationRb inactivation
EZH2,P53, Rb inactivation
Lessons from TCGAMolecular subtypes based on driving gene
Focusing on advanced disease…
Rising PSANo visiblemetastasis
Rising PSANo visiblemetastasis
MetastaticDisease
MetastaticDisease
InvasivediseaseInvasivedisease
Pre-invasivedisease(PIN)
Pre-invasivedisease(PIN)
No diseaseNo disease
Preventionof the disease
Prevention ofinvasion
Local treatment(RP or RT) orobservation(only selected
pts)
“Prevention” ofevident metastatic
disease
Systemictreatment
(ADT, HT, CT)
InflammationOxidative Stress
Telomere shortening
Senescence Castrationresistance
Myc TMPRSS2-ERGPTEN
inactivation
ERK/MAPK activationP53 inactivationRb inactivation
EZH2,P53, Rb inactivation
Lessons from Advanced disease
Robinson, D. et al. Cell 161, 1215–1228 (2015).
Summary of changes in key molecular eventsbetween Primary localized and mCRPC
Robinson, D. et al. Cell 161, 1215–1228 (2015).
Neuroendocrine PCa is derived fromdivergent differentiation
Beltran H. Nat Med 2016Dardene E & Beltran H. Cancer Cell 2016
Robinson, D. et al. Cell 161, 1215–1228 (2015).
Liquid biopsy as a reliable source forpersonalized therapies
Wyatt AW and Gleave ME. EMBO Mol Med 2015; 7:878-894
CTC study methods
CTC detection (>4) by CellSearch hasprognostic significance
Danila D. Clin Cancer Res 2007Olmos D. Ann Oncol 2008
CTC response is associated with goodprognosis
Olmos D. Ann Oncol 2008
Detection of any CTC by CellSearchmight be a better meassure
% p
atie
nts e
legi
ble
Prognostic significanceProportion of Eligible Patients Per Endpoint
Adapted form Glenn Heller at 2017 ASCO Annual Meeting
% p
atie
nts e
legi
ble
wC Idex: 0.83 wCI: 0.65
100
80
60
Radi
ogra
phic
Pro
gres
sion-
Free
Surv
ival
Basal CTC detection by AdnaTest isassociated with an adverse outcome
100
80
60
PSA
Prog
ress
ion-
Free
Sur
viva
l
100
80
60Ra
diog
raph
ic P
rogr
essio
n-Fr
ee Su
rviva
l60
40
20
00 6 9 123Ra
diog
raph
ic P
rogr
essio
n-Fr
ee Su
rviva
l
Time (months)
60
40
20
00 6 9 123
PSA
Prog
ress
ion-
Free
Sur
viva
l
Time (months)
60
40
20
00 6 9 123Ra
diog
raph
ic P
rogr
essio
n-Fr
ee Su
rviva
l
Time (months)
CTC yes vs no: median, 7.59 m versus NR,HR, 3.67; 95% CI 1.90-7.10; P < 0.001
median, 12.9 m versus NR;HR, 7.61; 95% CI, 2.80-20.64; P < 0.001
medians NR,HR, 9.51; 95% CI, 1.11-81.52; P = 0.0398
PREMIERE study. ASCO 2017
AR-V7 is associated with resistance tonovel antiandrogens
Antonarakis E. N Engl J Med 2014
AR-V7 has been associated withresistant in pre-treated mCRPC
Antonarakis E. N Engl J Med 2014Scher . JAMA Oncol 2016Antonarakis E. J Clin Oncol 2017
CTC by Epic Science technology is alsoassociated with outcome
Scher J. JAMA Oncol 2016
AR-V7 in CTCs in the PREMIERE trial
Radi
ogra
phic
Prog
ress
ion-
free
Surv
ival
(%)
20
40
60
80
100
PSA
Prog
ress
ion-
free
Surv
ival
(%)
20
40
60
80
100
AR-V7 positive
AR-V7 negative
No CTCs
AR-V7 positive
AR-V7 negative
No CTCs
63 57 45 28 13 1 0
28 18 15 11 5 0 0
5 2 1 0 0 0 0
63 61 53 35 17 3 0
28 22 19 14 8 2 0
5 4 2 0 0 0 0
0 3 6 9 12 15 18
Months
Radi
ogra
phic
Prog
ress
ion-
free
Surv
ival
(%)
0
20
0 3 6 9 12 15 18
Months
PSA
Prog
ress
ion-
free
Surv
ival
(%)
0
20 AR-V7 positive
No. at Risk
No CTCs
AR-V7 negative
AR-V7 positive
No. at Risk
No CTCs
AR-V7 negative
AR-V7 positive
Unpublished
HR = 1.80, 95%CI: 0. 51-6.31 HR =1.88, 95%CI: 0.51-6.87
Treatment response by AR-V7 statusA
Best
PSA
Resp
onse
(%ch
ange
)
0
50
100
AR-V7 negativeAR-V7 positive No CTCs
CTC positive, AR-V7non available
B
Patie
nts a
t bas
elin
e
Best
PSA
Resp
onse
(%ch
ange
)
-50
-100 Patie
nts a
t bas
elin
e
No CTCs
AR-V7 positiveAR-V7 negative
CTC positive, AR-V7non available
0 3 6 9 12 15 18Months
A
(n=98) (n=98) (n=39)
B80%
68%
35%
5%
0
20
40
60
80
100
Early Progression Late Progression
Patie
nts (
%)
CTC-positiveAR-V7 positive
(n=20) (n=19)
P= 0,04
AR-V7 evolution during treatment
C
Months
AR-V7 positive: Routine monitoring; PSA progression; Radiographic progressionAR-V7 negative: Routine monitoring; PSA progressionAR-V7 non available: PSA progression
On-going response
70 55 36 20 4 0
17 5 2 1 0 0
8 0 0 0 0 0
70 61 44 26 8 0
17 9 3 2 0 0
8 3 0 0 0 0
3 6 9 12 15 18Months
Radi
ogra
phic
Prog
ress
ion-
free
Surv
ival
(%)
0
20
40
60
80
100A B
3 6 9 12 15 18
Months
PSA
Prog
ress
ion-
free
Surv
ival
(%)
0
20
40
60
80
100
AR-V7 positive
AR-V7 negative
No CTCs
AR-V7 positive
AR-V7 negative
No CTCs
C
No. at RiskNo CTCsAR-V7 negativeAR-V7 positive
No. at RiskNo CTCsAR-V7 negativeAR-V7 positive
HR = 3. 73, 95%CI: 1. 44 -9.69; p=0.007 HR= 2.49, 95%CI: 0.69-8.99; p=0.16
AR-V7 at 12 weeks of treatment
C
Months
Patie
nts a
t 12
wee
ks o
f tre
atm
ent
3 6 9 12 15 18
No CTCsAR-V7 positiveAR-V7 negativeCTC positive, AR-V7non available
Unpublished
Whole exome sequencing of CTCsprovides a window into metastatic PCa
Lohr et al. Nat Biotech 2014
Dynamic evolution of PSA & PSMAduring ADT treatment
Miyamoto DT. Cancer Discovery 2012
Switch to PSMA expression isassociated with castration resistance
Miyamoto DT. Cancer Discovery 2012
Plasma DNA study is clinically relevant
Wyatt et al, JAMA Onc 2016
DNA tumor fraction is clinically relevant• CtDNA fraction correlates with worse evolution
Median ctDNA fraction = 0.181 Romanel, Gasi Tandefelt et al. Sci Transl Med 2015, 312re10
N=80
Multiple Digital-droplet PCR (ddPCR)
Correlation between ddPCR y la NGSfor AR gain
AR aberrations are associated withadverse outcome
OR: 4.7; 95% CI, 1.17-19.17; P=0.035
OR: 5.0; 95% CI, 1.70-14.91; P=0.003
Condetuda V & Grande E. Annals of Oncology 2017
HR:3.98; 95% CI, 1.74-9.10; P <0.001 HR, 3.81; 95% CI, 2.28-6.37; P <0.001
HR, 2.18; 95% CI, 1.08-4.39; P=0.03 HR, 1.95; 95% CI 1.23-3.11; P=0.01
Condetuda V & Grande E. Annals of Oncology 2017
Patients with AR gain were less likely to have a ≥50%decline in PSA
-100
-50
0
50
100
D
B
C
A
Pros
tate
-spe
cific
antig
ench
ange
(%)
Ove
rall
surv
ival
(%)
PSA
Prog
ress
ion-
free
surv
ival
(%)
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
RA
DPr
ogre
ssio
n-fr
eesu
rviv
al(%
)
ARNormalARGain
83 82(1) 59(18) 38(6) 22(2)11 9(2) 3(6) 1(0) 0(0)
AR Normal
Number at risk
AR Gain83 79(3) 70(4) 48(5) 29(1)11 9(2) 4(5) 2(1) 0(0)
AR Normal
Number at risk
AR Gain
83 81(1) 76(0) 55(1) 32(1)11 11(0) 8(2) 4(1) 0(0)
AR Normal
Number at risk
AR Gain
ARNormal
ARGain
FIGURE 2
-100
-50
0
50
100
D
B
C
A
Pros
tate
-spe
cific
antig
ench
ange
(%)
Ove
rall
surv
ival
(%)
PSA
Prog
ress
ion-
free
surv
ival
(%)
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
RA
DPr
ogre
ssio
n-fr
eesu
rviv
al(%
)
ARNormalARGain
83 82(1) 59(18) 38(6) 22(2)11 9(2) 3(6) 1(0) 0(0)
AR Normal
Number at risk
AR Gain83 79(3) 70(4) 48(5) 29(1)11 9(2) 4(5) 2(1) 0(0)
AR Normal
Number at risk
AR Gain
83 81(1) 76(0) 55(1) 32(1)11 11(0) 8(2) 4(1) 0(0)
AR Normal
Number at risk
AR Gain
ARNormal
ARGain
FIGURE 2
PSA decline ≥ 50% AR gain vs normal: OR, 4.93; 95% CI, 1.30-18.75; P = 0.025
Condetuda V & Grande E. Annals of Oncology 2017
Plasma AR status is associated with worse outcome
-100
-50
0
50
100
D
B
C
A
Pro
stat
e-sp
ecif
ican
tige
nch
ange
(%)
Ove
rall
surv
ival
(%)
PS
AP
rogr
essi
on-f
ree
surv
ival
(%)
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
RA
DP
rogr
essi
on-f
ree
surv
ival
(%)
ARNormalARGain
83 82(1) 59(18) 38(6) 22(2)11 9(2) 3(6) 1(0) 0(0)
AR Normal
Number at risk
AR Gain83 79(3) 70(4) 48(5) 29(1)11 9(2) 4(5) 2(1) 0(0)
AR Normal
Number at risk
AR Gain
83 81(1) 76(0) 55(1) 32(1)11 11(0) 8(2) 4(1) 0(0)
AR Normal
Number at risk
AR Gain
ARNormal
ARGain
FIGURE 2
2a. 2b.
-100
-50
0
50
100
D
B
C
A
Pro
stat
e-sp
ecif
ican
tige
nch
ange
(%)
Ove
rall
surv
ival
(%)
PS
AP
rogr
essi
on-f
ree
surv
ival
(%)
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
RA
DP
rogr
essi
on-f
ree
surv
ival
(%)
ARNormalARGain
83 82(1) 59(18) 38(6) 22(2)11 9(2) 3(6) 1(0) 0(0)
AR Normal
Number at risk
AR Gain83 79(3) 70(4) 48(5) 29(1)11 9(2) 4(5) 2(1) 0(0)
AR Normal
Number at risk
AR Gain
83 81(1) 76(0) 55(1) 32(1)11 11(0) 8(2) 4(1) 0(0)
AR Normal
Number at risk
AR Gain
ARNormal
ARGain
FIGURE 2
2c.
-100
-50
0
50
100
D
B
C
A
Pro
stat
e-sp
ecif
ican
tige
nch
ange
(%)
Ove
rall
surv
ival
(%)
PS
AP
rogr
essi
on-f
ree
surv
ival
(%)
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
RA
DP
rogr
essi
on-f
ree
surv
ival
(%)
ARNormalARGain
83 82(1) 59(18) 38(6) 22(2)11 9(2) 3(6) 1(0) 0(0)
ARNormal
Number at risk
ARGain83 79(3) 70(4) 48(5) 29(1)11 9(2) 4(5) 2(1) 0(0)
ARNormal
Number at risk
ARGain
83 81(1) 76(0) 55(1) 32(1)11 11(0) 8(2) 4(1) 0(0)
ARNormal
Number at risk
ARGain
ARNormal
ARGain
FIGURE 2
-100
-50
0
50
100
D
B
C
A
Pro
stat
e-sp
ecif
ican
tige
nch
ange
(%)
Ove
rall
surv
ival
(%)
PS
AP
rogr
essi
on-f
ree
surv
ival
(%)
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
RA
DP
rogr
essi
on-f
ree
surv
ival
(%)
ARNormalARGain
83 82(1) 59(18) 38(6) 22(2)11 9(2) 3(6) 1(0) 0(0)
AR Normal
Number at risk
AR Gain83 79(3) 70(4) 48(5) 29(1)11 9(2) 4(5) 2(1) 0(0)
AR Normal
Number at risk
AR Gain
83 81(1) 76(0) 55(1) 32(1)11 11(0) 8(2) 4(1) 0(0)
AR Normal
Number at risk
AR Gain
ARNormal
ARGain
FIGURE 2
2a. 2b.
-100
-50
0
50
100
D
B
C
A
Pro
stat
e-sp
ecif
ican
tige
nch
ange
(%)
Ove
rall
surv
ival
(%)
PS
AP
rogr
essi
on-f
ree
surv
ival
(%)
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
RA
DP
rogr
essi
on-f
ree
surv
ival
(%)
ARNormalARGain
83 82(1) 59(18) 38(6) 22(2)11 9(2) 3(6) 1(0) 0(0)
AR Normal
Number at risk
AR Gain83 79(3) 70(4) 48(5) 29(1)11 9(2) 4(5) 2(1) 0(0)
AR Normal
Number at risk
AR Gain
83 81(1) 76(0) 55(1) 32(1)11 11(0) 8(2) 4(1) 0(0)
AR Normal
Number at risk
AR Gain
ARNormal
ARGain
FIGURE 2
2c.
-100
-50
0
50
100
D
B
C
A
Pro
stat
e-sp
ecif
ican
tige
nch
ange
(%)
Ove
rall
surv
ival
(%)
PS
AP
rogr
essi
on-f
ree
surv
ival
(%)
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
0 3 6 9 120
50
100
Months
RA
DP
rogr
essi
on-f
ree
surv
ival
(%)
ARNormalARGain
83 82(1) 59(18) 38(6) 22(2)11 9(2) 3(6) 1(0) 0(0)
ARNormal
Number at risk
ARGain83 79(3) 70(4) 48(5) 29(1)11 9(2) 4(5) 2(1) 0(0)
ARNormal
Number at risk
ARGain
83 81(1) 76(0) 55(1) 32(1)11 11(0) 8(2) 4(1) 0(0)
ARNormal
Number at risk
ARGain
ARNormal
ARGain
FIGURE 2
Median sPFS: 3.60 versus 15.5 monthsHR, 4.33; 95% CI 1.94-9.68; P < 0.001
Median rPFS: 3.90 months versus not reachedHR, 8.06; 95% CI, 3.26-19.93; P < 0.001
Median OS: medians not reachedHR, 11.08; 95% CI, 2.16-56.95; P = 0.004
Condetuda V & Grande E. Annals of Oncology 2017
Multiple Genomic markers Correlatewith TTP
Adapted from Kim Chi at 2017 ASCO Annual Meeting
Includes patients without ctDNA. ** Mutation, deletion or rearrangement. *** includes trial arm, presence of quantifablectDNA and clinical prognostic factors (LDH, ALP, Visceral Mets, ECOG PS).
BRCA2/ATM
Tim
e to
Pro
gres
sion
P53
Tim
e to
Pro
gres
sion
Presented By Kim Chi at 2017 ASCO Annual Meeting
• Truncating mutations or rearrangements (9.2%):Somatic 5.2% (6/115) and germline 4% (8/202)
• Non-truncating monoallelic mutations notassociated with TTP
Tim
e to
Pro
gres
sion
Tim
e to
Pro
gres
sion
Truncating mutations, rearrangements and deletions in56.5% (65/115)81% were early progressors (12 w)
ctDNA can be useful to identifyadquired mechanisms of resistance
Goodhal, Cancer Discovery 2017
ctDNA at relapse in the ToPARP studySubclonal aberrations reverting alterations in BRCA2 and PALB2are shown
Sub-clonal evolution and cross-metastaticseeding is observed in response to therapy
Hong M. Nat Comm 2015
Tumor Biopsy CTCs cfDNAGenomics High Low ModerateGeneexpression
High Moderate -
Protein High High -
The best technique?
Adapted from Joshua Lang presentation at 2017 ASCO Annual Meeting
Protein High High -
Heterogeneity High Moderate Moderate
Source High Moderate -
depends on what you are looking for?
Conclusion• CTCs are useful biomarkers
– Detection is associated with adverse prognosis.– CTC response can be an intermediate outcome for clinical
trials– Molecular characterization can be informative
• AR-V7 is prognostic• AR-V7 is associated with early resistance to
enzalutamide/abiraterone• ctDNA study captures tumor heterogeneity
– Is prognostic– ctDNA aberrations associate with adverse outcome
• AR-gain• Others: P53, RB,
• CTCs are useful biomarkers– Detection is associated with adverse prognosis.– CTC response can be an intermediate outcome for clinical
trials– Molecular characterization can be informative
• AR-V7 is prognostic• AR-V7 is associated with early resistance to
enzalutamide/abiraterone• ctDNA study captures tumor heterogeneity
– Is prognostic– ctDNA aberrations associate with adverse outcome
• AR-gain• Others: P53, RB,
Conclusion
• Further research is needed• We can anticipate that it will be key for
personalized therapy• Biomarkers qualification process
– Parallel to drug approval– Capture tumor dynamics
• Different clinical scenarios
• Further research is needed• We can anticipate that it will be key for
personalized therapy• Biomarkers qualification process
– Parallel to drug approval– Capture tumor dynamics
• Different clinical scenarios
AcknowledgmentsPREMIERE team:Enrique GrandeMaría Piedad Fernández PérezAlbert FontSergio Vázquez EstévezAránzazu González del AlbaBegoña MelladoOvidio Fernández CalvoMaría José Méndez-VidalMiguel Angel ClimentIgnacio DuranEnrique GallardoAngel RodríguezCarmen SantanderM Isabel SáezJavier PuenteTeresa AlonsoJulián TudelaAlberto MartínezDaniel CastellanoEnrique González Billalabeitia
Morales MeseseguerLab:Francisco Ayala de la PeñaHelena García MartínezMaría Piedad Fernández PérezJulián TudelaAlberto MartínezEnrique González Billalabeitia
Attard`s Lab:Gerhardt AttardDaniel WetterskogAnuradha JayaramAnna Wingate
Paolo CremaschiAnjuiKarolina NowakoskaAnjui Wu
PREMIERE team:Enrique GrandeMaría Piedad Fernández PérezAlbert FontSergio Vázquez EstévezAránzazu González del AlbaBegoña MelladoOvidio Fernández CalvoMaría José Méndez-VidalMiguel Angel ClimentIgnacio DuranEnrique GallardoAngel RodríguezCarmen SantanderM Isabel SáezJavier PuenteTeresa AlonsoJulián TudelaAlberto MartínezDaniel CastellanoEnrique González Billalabeitia
IRST team (Meldola)Ugo di GiorgiVinzenca Conteduca
Samantha Sanvi
Johns HopkinsEmmanuel AntonarakisJun Luo
AR Genomic Structural Rearrangements
Presented By Kim Chi at 2017 ASCO Annual Meeting