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

0

5

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45

50

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

ula

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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60

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80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

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

0.00

0.10

0.20

Cum

ula

tive

in

cid

en

ce o

f H

CC

(H

CV

)

0.30

0.40

0.50

0.60

Time since inclusion (months)0 12 24 36 48 60 72 84

0

10

20

HC

C r

isk (

%)

30

40

50

60

70

80

90

100

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*

0.00

0.25

0.50

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

0.00

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)