This paper describes updates on personalized medicine in primary liver cancer and has been presented at the EASL meeting 2018

1. Perspectives of personalized medicine in
primary liver cancer
Prof. Eric RAYMOND MD, PhD
Chair of Medical Oncology @ Groupe Hospitalier Paris Saint-Joseph – France
(eraymond@hpsj.fr)

2. Disclosures
• Pfizer
• Novartis
• Eli Lilly
• Ipsen
• Celgen
• AFR-Oncology
• Genoscience Pharma
• SCOR

3. Primary liver cancers
Hepatocellular carcinoma
Cholangiocarcinoma
?

4. Personalized medicine in liver cancers
Molecular-based tumor selection
Biological based tumor selection
Clinically-based tumor selection

5. Patient selection in lung cancer over time

6. Selected
treatment
Molecular analysis
§ Molecular biology
§ RT-PCR
§ Immunohistochemistry
Identification of
TARGETS
The target is the disease… However, trials have been mostly
performed in all comers… Sometime not even with a single biopsy
Biopsy
Surgical specimen
Courtesy of Pr Valérie Paradis, BJN
Molecular biology requires access to tumor biopsy

7. Marusyk A et al. Narure Review Cancer 2012
Tumor heterogeneity:
Evaluation of inter- &
intra-tumoral clonal
variations
Access to tumor biopsy only partly address the question
of tumor heterogeneity

8. Yates et al, Nat Rev Genet 2012
Temporal drift
Clonal variations over
time (spontaneous &
treatment induced)
The tumor biology may change over time: requiring
access to tissue samples immediately prior to therapy

9. Precision medicine in
hepatocellular carcinoma
Curent data and perspectives

10. Mutational profiling of tumors; including hepatocellular
carcinoma
Genome Sequencing in HCC (N = 250)
1. Vogelstein B et al. Science. 2013;339:1546-1558. 2. Schulze K et al. Nat Genet. 2015;47:505-511.
2. Schulze K et al. Nat Genet. 2015;47:505-511. 2. Villanueva A et al. Gastrotenterology. 2012;143:1660-1669.
3. Coulouarn C et al. Hepatology. 2008;47:2059-2067.

11. 1. Vogelstein B et al. Science. 2013;339:1546-1558. 2. Schulze K et al. Nat Genet. 2015;47:505-511.
2. Schulze K et al. Nat Genet. 2015;47:505-511. 2. Villanueva A et al. Gastrotenterology. 2012;143:1660-1669.
3. Coulouarn C et al. Hepatology. 2008;47:2059-2067.
Signaling Pathways (Mut)
•
•
•
•
•
•
•
•
•
•
Telomerase maintenance: 60%
Cell-cycle gene: 49%
Wnt-B–catenin: 54%
Epigenetic modifier: 32%
Akt/mTOR: 51%
MAPK: 43%
Signaling Pathways (Other):
NOTCH: 30%
TGF-beta: 17%
MET: 50%
IGF signaling: 15% (IGF2 epi-driver)
•
•
•
•
•
•
•
•
•
•
Telomerase maintenance: 60%
Cell-cycle gene: 49%
Wnt-B–catenin: 54%
Epigenetic modifier: 32%
Akt/mTOR: 51%
MAPK: 43%
Signaling Pathways (Other):
NOTCH: 30%
TGF-beta: 17%
MET: 50%
IGF signaling: 15% (IGF2 epi-driver)
Landscape of mutations in hepatocellular carcinoma

12. 1. Zucman-Rossi J et al. Gastroenterology. 2015;149:1226-1239.
Molecular Classification of HCC
(FGF 19/CCND1)
mutAnti-CTNNB1
Proliferation Class Nonproliferation Class
Cell lineage features
Prognostic gene
signatures
DNA somatic alterations
Signaling pathway
activation
Epigenetic-based
subtypes
Clinical features
Progenitor-like
EpCAM
S2
Hepatoblastoma-C2
Hepatoblast-like
NOTCH
MET
36 CpG DNA miRNA Class C2
(C19MC)methylation signature
miRNA Class C3
DNA amplif.
Chr 7
Classical WNT7
miRNA Class B
TGF-β
Liver-WNT
HBV
High AFP levels
Poor differentiation
Vascular invasion (+++)
Worse outcome (recurrence/survival)
HCV, Alcohol
Low AFP levels
Well-mod differentiation
Vascular invasion (+)
Better outcome
Chr 11q13 amplif.
Anti-FGFR4
Anti-IGF2
RAS/MAPK inhibitorsAKT/MTOR
MET inhibitors
AKT/MTORRAS inhibitors
Hepatocyte-like
Late TGF-β
Anti–TGF-β
S1
Cluster A
Vascular invasion signature
G1-3/5-gene
Hepatocyte-like
S3
Cluster B
Poly 7 Immune-relatedWNT/CTNNB1
G5-6

13. Features Sensitivity? Resistance?
Disease etiology Hepatitis C Hepatitis B
Disease pattern Extra-hepatic spread
High tumor burden
Radiological features Hypervascularity pattern?
Tumor tissue markers VEGF?
Blood biomarkers High baseline HGF
Bruix et al, J Hepatol 2017
Bouattour et al, ILCA 2016
Llovet et al, Clin Cancer Res 2012
36) and with other agents inhibiting VEGFR-2 in HCC (37,
38) and other tumor types (39–41). Treatment with the
anti-VEGF antibody bevacizumab has yielded mixed results,
with increases in VEGF observed in some studies (40, 42)
and decreases in VEGFR noted in others, including HCC
(43, 44). Increases in VEGF, and associated decreases in
VEGFR-2, have also been observed in nontumor-bearing
mice after treatment with a VEGFR-2 inhibitor (45), sug-
cancer (47) and those with non–small cell lung cancer
(NSCLC; ref. 48), as well as those with advanced HCC
(49). Our biomarker analysis suggests that both molecules
are independent predictors of survival in patients with HCC
and provides a basis for novel opportunities for combina-
tion therapy in these patients.
Elevated HGF concentration was also identified as indic-
ative of poor prognosis in the present study, although HGF
30
40
20
10
0 1 32 4 7 98 10 11 12 13 14 16 17 18 19 20 2165 15
HR = 0.90
(95% CI, 0.66–1.22
117107 96 82 68 61 58 52 47 44 35 28 25 20 13 10 8 4 4 2 0
119108 97 86 77 72 68 64 58 50 47 34 29 24 18 11 8 5 1 1 0
Months
Patients at risk
Placebo
Sorafenib
Survival
90
100
80
70
60
50
30
40
20
10
0 1 32 4 7 98 10 11 12 13 14 16 17 18 19 20 2165 15
HR = 0.69
(95% CI, 0.53–0.90)
Sorafenib (n = 187)
Median OS = 12.4 mo
Placebo (n = 179)
Median OS = 9.8 mo
176164 151 138 125 103 90 82 74 56 45 38 32 21 15 11 5 5 2 0
183172 164 155 144 126 118 113 99 85 66 56 46 36 23 18 12 4 1 0
Months
Low baseline HGF
Patients at risk
Placebo
Sorafenib
C
Survivalprobability(%)
30
40
20
10
0 1 32 4 7 98 10 11 12 13 14 16 17 18 19 20 2165 15
HR = 0.58
(95% CI, 0.41–0.81)
129121 112 104 93 88 75 66 58 51 39 29 23 18 12 7 8 1 7 0
112107 104 101 93 89 80 73 72 63 48 39 33 27 22 14 12 9 4 0
Months
Patients at risk
Placebo
Sorafenib
Survival
90
100
80
70
60
50
30
40
20
10
0 1 32 4 7 98 10 11 12 13 14 16 17 18 19 20 2165 15
HR = 1.10
(95% CI, 0.72–1.67)
Sorafenib (n = 50)
Median OS = 6.3 mo
Placebo (n = 72)
Median OS = 5.3 mo
69 63 56 47 35 34 29 27 23 21 18 12 10 6 4 2 1 0 0 0
48 43 37 32 26 25 22 19 16 13 9 6 5 4 3 1 1 1 1 0
Months
High baseline HGF
Patients at risk
Placebo
Sorafenib
D
Survivalprobability(%)
Figure 2. Analysis of baseline biomarkers as predictive factors for sorafenib benefit (OS). Low s-c-KIT (A) and high s-c-KIT (B), P value for biomarker
treatment interaction ¼ 0.081. C, low HGF and (D) high HGF, P value for biomarker treatment interaction ¼ 0.073.
Faivre, Beaujon
VEGFR-MKI: what do we know? The sorafenib experience

14. HCC SK-sora cells pre-exposed to sorafenib display cMET overexpression
and exquisite sensitivity to MET inhibition
Tijeras-Raballand et al, ILCA 2013

15. Tolerability and Activity of Second-Line Tepotinib, a Potent and Highly Selective c-Met Inhibitor, in Patients with
Advanced Hepatocellular Carcinoma Previously Treated with Sorafenib
Faivre et al. World GI 2016
CT after 2 cycles showed objective
response by RECIST (-48%)
PET scan after 2 cycles showed significant
decrease of size and metabolic activity
30
10
-10
-20
-40
-50
-60
Bestrelativechangeinsum
oflongestdiameter
inbaseline(%)
Tepotinib 300 mg
Dose level
Tepotinib 500 mg
20
0
-30

16. Galunisertib (TGFβRI Inhibitor) in Patients With Hepatocellular Carcinoma
n/N (%) Median
AFP responders 25/103 (24%) 21.4 mo
AFP non-responders 78/103 (76%) 6.8 mo
Overall survival
AFP responders = patients who
decreased circulating AFP levels by
>20%
AFP non responders
AFP responders
Faivre S. et al.
Pres. ASCO GI 2014 and ASCO 2016, submitted
Part A
AFP ≥1.5 ULN
Part B
AFP <1.5 ULN
AFP>200
at Baseline

17. REACH-2 trial: Phase 3 of ramucirumab vs placebo in 2L for advanced HCC with elevated
baseline AFP
04/04/2018: Lilly Announces CYRAMZA® (ramucirumab) Phase 3 REACH-2 Study in Second-
Line Hepatocellular Carcinoma Patients Met Overall Survival Endpoint
Ramucirumab
ü Monoclonal antibody designed to bind VEGFR2
ü Binding antagonist of VEGF receptor ligands VEGF-A, VEGF-C, and VEGF-D

18. Pivotal REACH trial: overall survival according to AFP baseline level
Zhu AX et al, Lancet 2015
AFP>400
at Baseline

19. Hepatocellular Carcinoma (HCC) and FGF19
HCC is a world wide medical need
• Multi-kinase inhibitors provide OS < 1 year
FGF19 - a potential HCC driver
• FGF19 is a mitogen that signals via FGFR4 and KLB
• Normal liver and HCC express FGFR4 and KLB
• Aberrant FGF19 expression may drive HCC
tumorigenesis and confer poor prognosis
Treatment for advanced disease
sorafenib
1st-line
regorafenib
2nd-line
IHC ~30% HCC FISH ~7% HCC
FGFR4 KLB
FGF19
• ~850,000 cases/per year
• ~800,000 deaths/year
Kim R et al, ESMO 2017

20. BLU-554: a highly potent and selective FGFR4 inhibitor
Hep3B xenograft model
FGF19 overexpression with amplification
Study days
2,000
1,500
1,000
500
0
5
Tumorvolume(mm3)
0 10 15 20 25
BLU-554 Sorafenib Regorafenib
Vehicle
BLU-554 30 mg/kg
BLU-554 100 mg/kg
BLU-554 200 mg/kg
BLU-554 10 mg/kg
LIX-066 PDX model
FGF19 overexpression without amplification
0
5
Tumorvolume(mm3)
0 10 15 20 25
1,000
500
Inhibitory
Mechanism
TEL-FGFR4
IC50 nM
Cellular
BLU-554
Type 1
Irreversible
3.5
sorafenib
Type 2
Reversible
4,142
regorafenib
Type 2
Reversible
3,021
Kim R et al, ESMO 2017

21. BLU-554: first-in-human study
Key objectives
• Define MTD, safety profile, pharmacokinetics and pharmacodynamics
• Assess preliminary anti-tumour activity in relation to FGF19 IHC and FISH status
Part 1: Dose escalation – completed Part 2: Dose expansion – enrolling
NCT02508467
Advanced HCC
•Child Pugh A
•ECOG PS 0-1
•No ascites
• prior sorafenib
MTD
• 3+3 dose escalation (140-900 mg PO QD)
• 600 mg established as MTD
I
H
C
FGF19 IHC- (n ~15)
FGF19 IHC+(n~50)
Retrospective FGF19 FISH
Kim R et al, ESMO 2017

22. FGF19 Immunohistochemistry identifies aberrant pathway activation
107/395 FGF19 IHC+ ≥ 1%
Aberrant pathway activation in 27% of HCCCentral Laboratory IHC
277
107
11
0
50
100
150
200
250
300
FGF19 IHC- FGF19 IHC+ Not evaluable
Numbersamplesanalyzed
IHC-negative
0%
IHC+ 1%
IHC+ 15% IHC+ 50%
Kim R et al, ESMO 2017

23. BLU-554: first-in-human study IHC-positivity enriches for radiographic
tumor reduction and response
*4 confirmed responses 17
Kim R et al, ESMO 2017

24. Precision medicine in
cholangiocarcinoma
Curent data and perspectives
?

25. Reported frequency of FGFR alterations in cholangiocarcinoma
Valle J et al, Cancer Discov 2017, 7: 943-962

26. Precision medicine for patients with advanced biliary tract cancers:
MOSCATO-01 trial
L. Verlingue et al. / European Journal of Cancer 87 (2017) 122e1

31. Conclusions
• Unlike in many other tumor types, treatments of hepatocellular carcinoma
and cholangiocarcinoma are not yet molecularly driven
• Biopsies performed for diagnosis – but more importantly biopsy performed
before therapy that are more likely to describe molecular drifts occurring
during progression – are not yet generalized, preventing understanding
better biological drivers that are responsible for tumor progression
• Approaches investigating the inhibition of major oncogenic events (MET,
FGFR4/FGF19, …) appear very promising in identifying subtype of tumors for
precision medicine

32. Thanks for your attention