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INSERM 1162 - Paris 5
Génomique fonctionnelle des
tumeurs solides
Pierre Nahon
Service d’Hépatologie
Hôpital Jean Verdier
Bondy – Université Paris 13
Chicago, ILCA 2019
Assessment of risk and decision
analysis
Financial Disclosures
• Honoraria or consultation fees: Abbvie, Astra-Zeneca, Bayer,
Bristol-Myers Squibb, Gilead Sciences, IPSEN
Opportunities in HCC Risk assessment
• Large prospective multicentre cohorts and consortiums
• Evidence for effective surveillance, intervention and prevention strategies in high risk individuals
• Design for chemoprevention and/or improved surveillance trials
• Modeling cost-effectiveness and burden of disease by stratifying the population by risk and intervention
• New risk assessment methodologies and evaluation techniques
• Promising new biomarkers
• Progress in research for communicating risk, decision-making and decision aids
Opportunities in HCC Risk assessment
• Large prospective multicentre cohorts and consortiums
• Evidence for effective HCC surveillance, intervention and prevention strategies in high risk individuals
• Design for chemoprevention and/or improved surveillance trials
• Modeling cost-effectiveness and burden of disease by stratifying the population by risk and intervention
• New risk assessment methodologies and evaluation techniques
• Promising new biomarkers
• Progress in research for communicating risk, decision-making and decision aids
2018: HCC surveillance becomes a collective responsability
Extension of at-risk poulation
Level of evidence: multicentre cohort studies taking into accountlead-time bias Costentin et al, Gastroenterology 2018
CirVir CO12
Compliance Early detection
• Compliance for long-term monitoring
• Reduced cost-effectiveness in specific subgroups
• Pitfalls of US screening
• Sensitivity (15-20% of patients diagnosed outside Milan)
• Performance in obese individuals
• Lack of reliable biomarkers for prediction/early detection
Risk-based strategy incorporating precision medicine
Refinement of periodicity/modality of surveillance
Cooper, Gastroenterology 2018
Refinement of screening strategies
LowHCC risk
Moderate/ High HCC risk
Recommendedsemi-annualultrasound
• Patients education• Practitionners training • Enlistment of primary careproviders
Improving compliance
Promoting education
• Systems-level interventions• Dedicated clinical pathways• Navigation programs• Mailed outreach
Increasing surveillance ratesRisk stratification
Screening usingContrast-enhanced
imaging
Early diagnosisbiomakers
Machine learningPredictionbiomakers
Reviewed in Singal, Lampertico, Nahon. J Hepatology (in press)
407 patients with cirrhosisat « high HCC risk » (>5%/yr)
With both US and MRI for surveillance
Kim et al, Hepatology 2019
Cost-effectiveness study
Cancer risk-based models and surveillance: the example of lung cancer in the
general population
Allocation of HCC risk classes
High HCC risk
Personalisation of HCC screening
<1.5% 1.5 - 3% >3%
Intermediate HCC riskLow HCC risk
Allocation of HCC risk classes
High HCC risk
Reinforced US surveillance• Education programs• Mailed outreach• Dedicated clinical pathway
Optimization of surveillance modality• Imaging (CT scan, MRI)?• Biomarkers for early detection?• shorter interval?
Decision
Personalisation of HCC screening
Recommended US surveillanceOr
Dropping surveillance?
<1.5% 1.5 - 3% >3%
COSTS
Intermediate HCC riskLow HCC risk
HCCLiver-related
mortalityExtra-hepatic
mortality
Insult
Liver injury
VirusMetabolic syndromeAlcoholHistological features
GenderAgeEthnicityGenetics
Environmentalfactors
Host factors
? ?
Determinants and outcomes
6.7%
18.1%
2.9%
2.7%
HBV (n=528)
HCV (n=1372)
Alcohol (n=652)
NASH (n=7068)
A « global » annual incidence ranging from 1.5% to 3% in cirrhosis in 2019*
Papatheodiris, Hepatology 2017
Ganne-Carrié, J Hepatology 2018
Nahon, Gastroenterology 2017
Ioannou, J Hepatology 2019
*Based on European multicentre prospective cohorts of patients included in surveillance programs
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Training set: 3584 patients (REVEAL cohort)
Validation set: 1505 patients (Hong Kong and Korea)
REACH-B score
17-point scoring system:
- Male: 2
- Per 5-year increase above 35: 1
- ALAT >15: 1; ALT >45: 2
- HBeAg (+): 2
- DNA >4 log: 3; >5 log: 5
HCC-risk assessment models
Yang HI, et al. Lancet Oncol 2011;12:568–74.
REACH-B, Risk Estimation for HCC in Chronic Hepatitis B; ALAT, alanine
aminotransferase; ALT, alanine transaminase
Cum
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tive
ris
k s
co
re a
nd
asso
cia
ted 5
-ye
ar
risk o
f d
eve
lop
ing H
CC
in p
atie
nts
with
CH
B
HBV-controlled caucasians with cirrhosis?
Brichler et al, JVH 2018
Older men with cirrhosis !
Papatheodiris J Hepatol 2016
PAGE-B
HCC risk models in non-viral cirrhosis
Ioannou, J Hepatology 2019
HCV: Can we “predict” HCC risk at
the individual level?
Ganne-Carrié et al, Hepatology 2016
P <0.0001
Score ≤5: low
Score 6–10: intermediate
Score 11–14: high
Score >14: maximal
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HC
C R
ISK
(%
)
RISK SCORE
1-year risk
3-year risk
5-year risk
CirVir CO12
• Age >50 years
• Alcohol
• GGT >N
• Plat <100 103
• SVR Risk modelling
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0.20
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tive
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cid
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f H
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(H
CV
)
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Time since inclusion (months)0 12 24 36 48 60 72 84
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HC
C r
isk (
%)
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1-year risk
3-year risk
5-year risk
Score ≤5
6 ≤ score ≤10
11 ≤score ≤14
Score >14
Specific HCC risk factors in patients with SVR?
Nahon P, et al. Gastroenterology 2017
Influence of metabolic syndrome
according to SVR status1000 SVR patients followed 5.7 yrs: 842 cirrhotics,158 bridging fibrosis
Van der Meer AJ, et al. J Hepatol 2016
How can we improve risk stratification?
• Limits of conventional analytic approaches (Cox models)
• Useful to quantify the relative importance of independent predictors (HRs)
• Unfit to deal with highly heterogeneous populations and to detect specific relationships in specific subgroups
• Decision-tree based approaches using Machine-learning
• Effective for modeling complex relationships between correlated variables
• Automatic detection of optimal thresholds• High illustrative value
Identifying residual risk of HCC following HCV eradication in compensated
cirrhosis: Machine learning approaches (decision tree analysis)
CirVir CO12
JCO, in revision
N=836
Forest plots (or variable importance plots): hierachisation of risk
factors taking into account their interactions and internal validation in an ensemble of 1000 trees (stability, robustness)
External validation and calibration of models are essential*
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0.75
1.00
Cum
u lat
ive
inci
d en c
eof
HCC
0 12 24 36 48 60
Time (months)
High predicted risk Moderate predicted risk Low predicted risk
0% 25% 50% 75% 100%
0%
25%
50%
75%
100%
Predicted HCC probability
Ob
se
rve
d H
CC
sta
tus
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0.25
0.50
0.75
1.00
Cum
u lat
ive
inci
d ence
ofHCC
0 12 24 36 48 60
Time (months)
High predicted risk Moderate predicted risk Low predicted risk
A. HCC cumulative incidence: Cox proportional hazards model
0% 25% 50% 75% 100%
0%
25%
50%
75%
100%
Predicted HCC probability
Ob
se
rve
d H
CC
sta
tus
B. Calibration plot: Cox proportional hazards model
F. Calibration plot: random survival forestE. HCC cumulative incidence: random survival forest
Cox model
Machine learning
*Validation in the CO22 Hepather cohort (Carrat F et al, Lancet 2019, n=668 patients with HCV-related cirrhosis)
Random survival forest (RSF) combining 1000 decision trees
C-Index=0.66
C-Index=0.74
Non invasive biomarkers for HCC risk stratification (and
early diagnosis)
CirrhosisPrecancerousFocal lesion
HCC
Identifysubroups at
different risks
FacilitateHCC earlydetection
Improvement of stagingand prediction
of treatment response
Liquid biopsy
Gastroenterology 2011SNP + clinical data
HALT-C cohort
Hepatology 2005
• N = 816• Follow-up: 6,1 yrs• HCC=66
Pat
ien
ts s
ans
CH
C (
%)
Integration of genetic data into HCC-risk assessment models: which incremental
value?
Guyot et al, J Hepatol 2013Tr
ue
po
siti
ve f
ract
ion
False positive fraction
0.00.0 0.2 0.4 0.6 0.8 1.0
0.2
0.4
0.6
0.8
1.0
PNPLA3 (rs738409 C>G)
Age + Gender + BMI + Diabetes
PNPLA3 + Clinical factors
0.36
•♀=95%• BMI=24 kg/m2
• Diabetes=13,5%• PNPLA3(GG)=5%
•♂=100%• BMI=31,6 kg/m2
• Diabetes=56,1%• PNPLA3(GG)=22,5%
Refinement of risk predictionby reclassification of individuals
Manolio T, NEJM 2010
Reviewed in Trépo, Romero, Zucman-Rossi, Nahon; J Hepatol 2016
Towards individualized HCC risk assessment: « user-friendly » interface for
decision-making process
SNPs+
clinical data
Prévenir et Réduire le risque de CHC dans le VHCMardi 4 juin - Paris
Conclusions et perspectives (1)
• HCC incidence tends to be globally similar in non-viral and viral cirrhosis
following HBV control/HCV eradication
• HCC risk factors in these patients include various features related to 1) host
characteristics, 2) environmental factors, 3) liver tests impairment
• The incremental values of circulating biomarkers (genetic variants) to improve
HCC risk assessment remains to be demonstrated
• Combining these simple routine parameters using classical logistic regression is
able to stratify cirrhotic patients into distinct HCC risk classes but only provides
information on average global effects
• Machine learning approaches enable :
• more effective combinations between HCC risk factors by better accounting for patient’s
complexity
• the identification of unexpected “extreme phenotypes”
• High illustrative value of the long course of cirrhosis
• HCC risk stratification will form the basis for future trials exploring:
• Refinement of surveillance modalities
• The identification of new biomarkers useful for HCC prediction/early diagnosis/classification
• Prevention strategies
• Cost-effectiveness strategies in HCC management
Conclusions et perspectives (2)