Staging and prognostic systems: beyond BCLC?

Alessandro Vitale, MD, PhD, FEBS

U.O.C. di Chirurgia Epatobiliare e dei Trapianti Epatici,

Department of Surgery, Oncology and Gastroenterology, University of Padua, Padua; Italy

ALESSANDRO VITALE, MD, PhDAzienda Ospedaliera e Università di Padova

Il sottoscritto dichiara di non aver avuto negli ultimi 12 mesi conflitto d’interesse in relazione a questa presentazione

e

che la presentazione non contiene discussionedi farmaci in studio o ad uso off-label

alessandro.vitale@unipd.it

(1) Prognosis for individual patients.

(2) Common scale for treatment selection for individualpatients. Avoiding under and overtreatment.

(3) Common scale for the comparison of outcomes amongtreatment methods and institutions and for RCT design.

(4) A graph contrasting outcomes of transplantation to long-term outcomes of preexisting treatment methods for decidingindication of liver transplantation (Transplant benefit).

Kudo M, et al. Dig Dis 2011;29:339–364

Importance of HCC prognostic systems

HCC Prognostic Systems

1. PROGNOSTIC SCORES are “conventional ” prognosticscores that incorporate variables that were significant inmultivariable (Cox, parametric models) survival analyses. The prognostic “weights” of the variables are used to construct the score (DATA BASED).

2. STAGING SYSTEMS typically based on systematic reviewsof the literature and/or expert opinions (EVIDENCE BASED). These systems stratify the HCC population in evolutionary stages exclusively or mainly defined by tumorcharacteristics.

Farinati F, et al. PLoS Med 2016; 13(4):e1002006

Staging and prognostic systems: beyond BCLC?

• Data based prognostic scores

• Evidence based staging systems

• Combined prognostic systems

• The issue of treatment allocation

Data based Prognostic Scores

Liu PH, et al. J Hepatol 2016; 64: 601

OKUDA

FRENCH

CLIP TOKYO

Data based Prognostic Scores

Faria SC, et al. Abdominal Imaging 2014

Yang JD, et al. HEPATOLOGY 2012;56:614-621

Data based Prognostic Scores

Model to Estimate Survival In Ambulatory HCC patients (MESIAH)

Chan WHA, et al. Liver Int 2016. In press

Johnson PJ, et al. JCO 2015; 33: 550

Data based Prognostic Scores

PRO: objective and reproducible variables and rigorousstatistical methodology. Accurate survival prediction

CONS: often they are not suitably generalizable to populations different from the one that generated the score, and they don’ t define tumor stages for treatment selection

Data based Prognostic Scores

Farinati F, et al. PLoS Med 2016; 13(4):e1002006

Prognostic score(multivariate analysis)

Staging system(literature based)

Data based Prognostic Scores

Staging and prognostic systems: beyond BCLC?

• Data based prognostic scores

• Evidence based staging systems

• Combined prognostic systems

• The issue of treatment allocation

Minagawa M, et al. Ann Surg 2007; 245: 909

T definition originally refersonly to HCC pathologicalcharacteristics of patientsreceiving liver resection

There are 3 TNM surgical stagingsystems:

1) Liver Cancer Study Group of Japan

(LCSGJ)2) American Joint Committee on Cancer(AJCC) and International Union against

Cancer(UICC)3) United Network for Organ Sharing

(UNOS)

Evidence based staging systems

AJCC-UICC TNM 5th edition, 1997AJCC-UICC TNM 6th edition, 2002

UNOS TNM, 2002

Evidence based staging systems

The Barcelona Clinic Liver Cancer(BCLC) Staging Classification for HCC

BCLC stageTumor volume,

number and invasivenessPerformance

status Child-Pugh

0 Very earlySingle < 2 cm

Carcinoma in situ0 A

A Early Single or 3 nodules < 3 cm 0 A – B

B Intermediate Multinodular 0 A – B

C Advanced Portal invasion N1M1 1 – 2 A – B

D Terminal Any of above > 2 C

Cillo U, Vitale A, et al. J Hepatol 2004Cillo U, Vitale A, et al. J Hepatol 2006

Evidence based staging systems

PRO: They are useful to link stages to guidelines for the management of patients with HCC and the design of clinical trials.

CONS: However, these systems are not based on a strong statistical methodology (variables are not weightened) and they often lack prognostic power

Evidence based staging systems

Farinati F, et al. PLoS Med 2016; 13(4):e1002006

Liu PH, et al. J Hepatol 2016; 64: 601

Evidence based staging systems

Hsu CY, et al. Hepatology 2013

ECOG PST 1 classified as BCLC B (in original BCLC stage C)

Prognostic pitfalls of BCLC classification

Hsu CY, et al. Liver Int 2016. In press

Prognostic pitfalls of BCLC classification

Prognostic score(multivariate analysis)

Staging system(literature based)

Evidence based staging systems

Staging and prognostic systems: beyond BCLC?

• Data based prognostic scores

• Evidence based staging systems

• Combined prognostic systems

• The issue of treatment allocation

Prognosis Treatment

CombinedPrognostic

SystemPrognostic score(multivariate analysis)

Staging system(literature based)

Combined prognostic systems

• Japan TNM poorperformance in western patients

• No PST• No AFP

Combined prognostic systems

Kudo M, et al. Dig Dis 2011;29:339–364

From 2003

� Kaplan-Meier, log-rank test

� Multivariate log-logistic parametric survival model: ITA.LI.CA staging construction

� Multivariate log-logistic parametric survival model: comparison between systems

� Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)

� 5183 HCC patients (database ITA.LI.CA. - Italian Liver Cancer)

� Training cohort (3628 pts) / Internal validation cohort (1555 pts)

� External validation cohort: 2651 pts (database from Taipei –Taiwan, 2000-2012)

M.F = 3:1

Age (median): 68 yrs

Child (median): 6

MELD (median): 11

HCV+ 60%, HBV+ 17%, Alcool 26%

Diameter max (median): 3 cm

Multifocal: 22%

Metastases: 3%

BCLC: 0 7%, A 33%, B 12%, C 42%, D 6%

Main Treatment:

Resection 11% , Transplant 2%,

Ablation 30% ,TACE 26%,

Sorafenib 3%,

Other 8%, BSC 20%

Staging systems did not respect Proportional Hazard Assumption

• Tumor stage classification based on the literature• Final score based on multivariate analysis

Combined prognostic systems

Hong Kong Liver Cancer Staging System BCLC B HCC: Proposal for a Subclassification

Variables 0 A B1 B2 B3 C

Diameter(cm) < 2 ≤ 3 ≤ 5 3-5 > 5 3-5 > 5 > 5 Any Any

N° nodules 1 2-3 1 2-3 1 > 3 2-3 > 3 Any Any

Vascular invasion

or metastases

no no no no no no no no Intra Exta

UNOS TNM, 2002

ITA.LI.CA TUMOR STAGING

Farinati F, et al. PLoS Med 2016; 13(4):e1002006

Combined prognostic systems

Combined prognostic systems

Farinati F, et al. PLoS Med 2016; 13(4):e1002006

Combined prognostic systems

Farinati F, et al. PLoS Med 2016; 13(4):e1002006

Staging and prognostic systems: beyond BCLC?

• Data based prognostic scores

• Evidence based staging systems

• Combined prognostic systems

• The issue of treatment allocation

1. Llovet JM, et al. Lancet 2003;362:1907–1917. 2. Marrero JA, et al. Clin Liver Dis 2006;10:339–351. 3. Marrero JA, et al. Hepatology 2005;41:707–716. 4. Llovet JM, et al. Semin Liver Dis 1999;19:329–338.

5. Leung T, et al. Cancer 2002;94:1760–1769. 6. Chevret S, et al. J Hepatol 1999;31:133–141. 7. Schafer DF, et al. Lancet 1999;353:1253–1257. 8. CLIP. Hepatology 1998;28:751–755.

9. Makuuchi M, et al. World J Gastroenterol 2006;12:828–829.

� Prognosis of HCC1

� Most patients have underlyingliver disease

� Key prognostic indicatorsare not clearly defined

� Prognostic indicators vary during the course of disease

� Factors affecting HCC prognosis2,3

� Tumour stage� Liver function� Health status

Patient

TumourLiver

ECOGPS

Child-Pugh

TNM

BCLC4

Okuda 7

CLIP8

JIS9

CUPI5

GRETCH6

HCC Prognostic Factors

The issue of treatment allocation

• … both the stage and the various types of intervention shouldideally be built into the prognostic system..

• There are four main factors affecting prognosis: (a) the stage, aggressiveness and growth rate of the tumor; (b) the general health of the patient; (c) the liver function of the patient; and (d) the specific intervention

• The .. optimal solution would be to develop a prognostic model for each relevant evolutionary stage of the disease (early, intermediate- advanced and terminal) and model into eachstage the variables related to each specific intervention.

Bruix J and Sherman M, et al. Hepatology 2005

The issue of treatment allocation

Treatment selectionPre-determined

Staging System

The issue of treatment allocation

Treatment selectionStaging System

PROBLEM 1: Tumor, liver function, patient related vari ablesdifferently influence Treatment selection and patient p rognosis

PROBLEM 1: PROGNOSTIC PROBLEM

Bruix J and Sherman M. Gastroenterology 2016

?

?

? ? ?

?

The issue of treatment allocation

PROBLEM 1: PROGNOSTIC PROBLEM

Combined prognostic systems

Farinati F, et al. PLoS Med 2016; 13(4):e1002006

PROBLEM 1: PROGNOSTIC PROBLEM

Yau T, et al. Gastroenterology 2014

13 pointsscore??

The issue of treatment allocation

PROBLEM 1: PROGNOSTIC PROBLEM

Yau T, et al. Gastroenterology 2014

The issue of treatment allocation

PROBLEM 1: PROGNOSTIC PROBLEM

Treatment selectionPre-determined

Staging System

The issue of treatment allocation

Treatment selectionStaging System

PROBLEM 2: Treatment selection criteria change with ti me and should be inclusive (indications better than algorithms )

PROBLEM 2: THERAPEUTIC PROBLEM

“The tempting simplicity of the BCLC classification came at a price of low clinical utility by compromising the importance of liver transplantation and locoregional therapies in medicalmanagement of HCC.”

“Designed using data mostly acquired in small Western patientpopulations, the BCLC classification lacks universal applicability in terms of discriminatory ability and prognostic accuracy with regard to treatment recommendations. In fact, the BCLC system precludespatients with more advanced disease from receiving radical therapiesout of safety considerations.”

Chapiro J, et al. Nat Rev Gastroenterol Hepatol 2014; 1 1: 334

The issue of treatment allocation

PROBLEM 2: THERAPEUTIC PROBLEM

Bruix J and Sherman M. Gastroenterology 2016

? ?

?

? ? ?

?

The issue of treatment allocation

PROBLEM 2: THERAPEUTIC PROBLEM

Roayaie S, et al. Hepatology 2015; 62: 440

The issue of treatment allocation

PROBLEM 2: THERAPEUTIC PROBLEM

1302 BCLC A patients undergoing resection

Roayaie S, et al. Hepatology 2015; 62: 440

The issue of treatment allocation

PROBLEM 2: THERAPEUTIC PROBLEM

Kim KM, et al. Liver Int 2016.

A total of 3515 treatment-naıve, newly diagnosed HCC patients at a

single centre were analyzed

The issue of treatment allocationPROBLEM 2: THERAPEUTIC PROBLEM

The issue of treatment allocation

Kudo M, et al. Dig Dis 2011;29:339–364

SOLUTION 1: INDEPENDENT ALGORITHM

Bolondi L, et al. Sem Liv Dis 2012

The issue of treatment allocation

SOLUTION 2: TREATMENT INDICATIONS

� TREATMENT SELECTION as end-point: multivariate logistic regression models

� SURVIVAL BENEFIT as end-point: multivariate loglogistic parametric survival modelsTreatment selection and survival benefit were combined using Inverse Probability Weight (IPW)

� EVIDENCE BASED approach (literature – multiple societies document)

� New ITA.LI.CA 2015 database including 6669 HCC patients (database ITA.LI.CA. - ItalianLiver Cancer)

�Inclusion criteria:- Cirrhotic patients- Complete follow-up data- Period 2002 – 2015

� Study population: 4867 HCC patients

� External validation cohort: 2651 pts (database from Taipei –Taiwan, 2002-2012)

ITA.LI.CA treatment indications (no algorithm ) were based on:

The issue of treatment allocation

SOLUTION 3: TREATMENT INDICATIONS+DATA BASED SURVIVAL BENEFIT

Variables 0 A B1 B2 B3 C Any

Functional

score (FS)

FS≤ 2: CTP AB and PST 0; CPT ≤ 7 and PST ≤ 2 FS > 2:

CTP C/PST > 2

Diameter (cm) < 2 ≤ 3 ≤ 5 3-5 > 5 3-5 > 5 > 5 Any Any Any

N° nodules 1 2-3 1 2-3 1 > 3 2-3 > 3 Any Any Any

VI / meta no no no no no no no no Intra Extra Any

Median survival71 55 46 33 16 14 8

Therapy

LT

LR

ABL

IAT

SOR

BSC

Therapy

LT

LR

ABL

IAT

SOR

BSC

102

77

64

120

76

61

120

50

64

46

120

33

50

40

120

33

25

28

18

16 7

15

5

102

31

6

Neg

49

21

6

65

Neg

18

0

74

Neg

17

7

87

0

Neg

12

2

0 Neg

1

5

94

Variables 0 A B1 B2 B3 C Any

Functional

score (FS)

FS≤ 2: CTP AB and PST 0; CPT ≤ 7 and PST ≤ 2 FS > 2:

CTP C/PST > 2

Diameter (cm) < 2 ≤ 3 ≤ 5 3-5 > 5 3-5 > 5 > 5 Any Any Any

N° nodules 1 2-3 1 2-3 1 > 3 2-3 > 3 Any Any Any

VI / meta no no no no no no no no Intra Extra Any

Median survival71 55 46 33 16 14 8

LIVER RESECTION

SORAFENIB

LIVER TRANSPLANTATION

ABLATION

TACE/TARE

LT

The issue of treatment allocation

Following ITA.LI.CA indications: 3153 pts (65%)vs 43% BCLC vs 55% HKLC algorithms

In (MS 41 mo)

Out (MS 41 mo)

The issue of treatment allocation

SOLUTION 3: TREATMENT INDICATIONS+DATA BASE SURVIVAL BENEFIT

CONCLUSIONS• There is not worldwide consensus on the best prognostic

system for HCC patients

• The ITA.LI.CA prognostic score showed the best predictive

ability in large western and eastern cohorts

• Beyond BCLC?:

- YES, for intrinsic prognostic pitfals (evidence based and

treatment dependent system)

- YES, for treatment related pitfals (distance from best

clinical practice and personalized approach)

• ITA.LI.CA treatment indications could represent a potential

solution ??

Department of General Surgery and Organ Transplantation,

Hepatobiliary Surgery and Liver Transplantation Unit, University Hospital of Padua, Padua; Italy

Director: Prof. Umberto Cillo

THANK YOU