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Is the current design of precision medicine studies the right one:

lessons from the SHIVA trialPRO

Christophe Le Tourneau, MD, PhD

Institut Curie – Paris & Saint-Cloud – France

Department of Medical Oncology

Head of Early Phase Clinical Trials

Versailles-Saint-Quentin-en-Yvelines University

WIN symposium – Paris – June 27, 2016

• Nothing to disclose

Conflicts of interest

Precision medicine trials

Stratified trials Algorithm-testing trials

Molecularly-

stratified

Histology-

stratified

Non-

randomized

Randomized

Tumor types 1 N 1 or N

Molecular

Alterations

N 1 or N N

Treatments N 1 N

Test Test drugs efficacy Test algorithm efficiency

Le Tourneau et al. Chin Clin Oncol 2014;3:13

Introduction

• CUSTOM trial (Advanced thoracic malignancies)

Introduction

Lopez-Chavezet al. JCO 2015;33:1000-7



Molecularly-

stratified

Histology-

stratified

Non-

randomized

Randomized


Molecular

Alterations

N 1 or N N

Treatments N 1 N



Introduction

• WINTHER

Introduction

Introduction

• M-PACT

R

Targeted therapy based on molecular

profiling

Conventional therapy at

physicians‘ choice

NGS+

Cytoscan HD

+IHC

Bioinformatics

Tumor biopsy

Informed

consent

signed

Specific

therapy

available

Molecular

biology

board

YESNO

Non eligible

patient

Eligible

patient

Informed

consent

signed

Patients with refractory

cancer (all tumor types)

Imatinib

Everolimus

Sorafenib

Erlotinib

Dasatinib

Lapatinib

Trastuzumab

Vemurafenib

Tamoxifen

Letrozole

Abiraterone

Cross-over

Le Tourneau et al. Lancet Oncol 2015;16:1324-34


Introduction



Molecularly-

stratified

Histology-

stratified

Non-

randomized

Randomized


Molecular

Alterations

N 1 or N N

Treatments N 1 N



Introduction

Was the design of the SHIVA trial the right one?

Is the current design of precision

medicine studies the right one? PRO

Outline


medicine studies the right one? PRO1) The SHIVA trial asked a real-life question

Outline

Real-life question

Ciriello et al. Nature Genet 2013;45:1127-33

• Pilot study by von Hoff et al.

Real-life question

von Hoff et al. JCO 2010;28:4877-83

Real-life question

von Hoff et al. JCO 2010;28:4877-83

• 18/66 patients (27%): PFS ratio>1.3

Real-life question

Tsimberidou et al. CCR 2012;18:6373-83

Patients receiving matched targeted therapy Patients receiving no matched targeted therapy

Real-life question

Tsimberidou et al. CCR 2012;18:6373-83

Failure-free survival Overall survival

Patients receiving matched targeted

therapy

Patients receiving matched

targeted therapy

Patients receiving no matched targeted therapy

>?

Real-life question



2) The SHIVA trial was designed to evaluate a specific

prespecified treatment algorithm

Outline

Treatment algorithm

Molecular

profile

Molecular

alteration

Targeted agentTargeted agentTargeted agentTargeted agent Targeted agent Targeted agent Targeted agent

= TREATMENT ALGORITHM

Treatment algorithm

Le Tourneau et al. JNCI [epub ahead of print on November 23, 2015]

Treatment algorithm

Targets Molecular alterations MTAs

ER, PR Protein expression >10% IHC Tamoxifen or Letrozole

AR Protein expression >10% IHC Abiraterone

PI3KCA, AKT1

AKT2/3, mTOR, RICTOR, RAPTOR

PTEN

STK11

INPP4B

Mutation/Amplification

Amplification

Homozygous deletion

Heterozygous deletion + mutation or IHC

Homozygous deletion

Heterozygous deletion + mutation

Homozygous deletion

Everolimus

BRAF Mutation/Amplification Vemurafenib

KIT, ABL1/2, RET Mutation/Amplification Imatinib

PDGFRA/B, FLT3 Mutation/Amplification Sorafenib

EGFR Mutation/Amplification Erlotinib

HER-2 Mutation/Amplification Lapatinib + Trastuzumab

SRC

EPHA2, LCK, YES1


AmplificationDasatinib

HORMONE

RECEPTOR PATHWAY

PI3K/AKT/mTOR

PATHWAY

RAF/MEK

PATHWAY

Treatment algorithm


Treatment algorithm

• Variants of interest:

- validated hotspots mutations* frequency: >4% for SNVs and >5% for indels

* coverage: >30X for SNVs and >100X for indels

- non targeted variants* outside a hotspot

* frequency >10%

* no synonymous mutations

* no polymorphisms

Treatment algorithm

• Amplifications:

- Gene copy number* diploid tumor: >6

* tetraploid tumor: >7

- Amplicon size* <1 Mb

* <10 Mb if protein overexpression/or loss of expression

is validated in IHC

Treatment algorithm


Treatment algorithm




PI3KCA, AKT1


PTEN

STK11

INPP4B


Amplification

Homozygous deletion


Homozygous deletion


Homozygous deletion

Everolimus






SRC

EPHA2, LCK, YES1



Treatment algorithm

PFS – Hormone receptor pathway


Treatment algorithm

• 72 yo female AR+ breast cancer

M0 Abiraterone M14

Treatment algorithm




PI3KCA, AKT1


PTEN

STK11

INPP4B


Amplification

Homozygous deletion


Homozygous deletion


Homozygous deletion

Everolimus






SRC

EPHA2, LCK, YES1



Treatment algorithm

Treatment algorithm

PFS – PI3K/AKT/mTOR pathway


Treatment algorithm

• 41 yo female PI3KCA-mutated cervical ca

M0 Everolimus M3

Treatment algorithm




PI3KCA, AKT1


PTEN

STK11

INPP4B


Amplification

Homozygous deletion


Homozygous deletion


Homozygous deletion

Everolimus






SRC

EPHA2, LCK, YES1



Treatment algorithm


PFS – RAF/MEK pathway

Treatment algorithm

Treatment algorithm


Treatment algorithm

• Multiple molecular alterations:- DNA alterations were considered of a higher impact than

hormone receptors expression

- If AR and ER/PR were both overexpressed, the hormone

receptor with the highest expression level was taken into

account

- If >2 DNA alterations were identified, clinically validated

alterations prevailed (i.e. HER-2 amplification)

- Erlotinib was not given in case of KRAS mutation



2) The SHIVA trial was designed to evaluate a specific

prespecified treatment algorithm

3) The cross-over data of the SHIVA trial suggest that

taking each patient as his/her own control is a relevant

strategy for precision medicine studies

Outline

Cross-over data

Le Tourneau et al. ASCO 2016 (#2535)

25% =25/100

72% =70/97

Cross-over data

Le Tourneau et al. ASCO 2016 (#2535)

N PFS ratio >1.3 PFS ratio >2

TPC MTAHormone receptor pathway

PI3K/AKT/mTOR pathway

RAF/MEK pathway

6831

30

7

41%35%

38%

54%

25%20%

19%

18%

MTA TPC 25 56% 28%

Conclusions

• The current design of precision medicine

studies is relevant

• Using the PFS ratio as a primary end point

might be a relevant strategy for precision

medicine trials

• Treatment algorithms will need to be

refined to further improve patients’ outcome

Acknowledgments• Direction

Thierry Philip

Claude Huriet

Pierre Teillac

Daniel Louvard

• ICGEX

Olivier Delattre

Thomas Rio Frio

Virginie Bernard

• UGEC

Patricia Tresca

Sebastien Armanet

Fabrice Mulot

• Biostatistics

Xavier Paoletti

Lisa Belin

Corine Plancher

Cécile Mauborgne

• Pathology

Anne Salomon

Odette Mariani

Frédérique Hammel

Xavier Sastre

Didier Meseure

• Translational research

Maud Kamal

David Gentien

Sergio Roman-Roman

• Radiology

Vincent Servois

Daniel Szwarc

• Bioinformatics

Philippe Huppé

Nicolas Servant

Julien Romejon

Emmanuel Barillot

Philippe La Rosa

Alexandre Hamburger

Pierre Gestraud

Fanny Coffin

Séverine Lair

Bruno Zeitouni

Alban Lermine

Camille Barette

• Comunication

Céline Giustranti

Catherine Goupillon-Senghor

Cécile Charre

• Genetics

Ivan Bièche

Gaëlle Pierron

Etienne Rouleau

Céline Callens

Marc-Henri Stern

• Surgery

Thomas Jouffroy

José Rodriguez

Angélique Girod

Pascale Mariani

Virginie Fourchotte

Fabien Reyal

• Foundation

Hélène Bongrain- Meng

Ifrah El-Alia

Véronique Masson

Agnès Hubert

• Clinical research

Malika Medjbahri

• Sampling

Solène Padiglione

• Pharmacy

Laurence Escalup

• Oncology

Alain Livartowski

Suzy Scholl

Laurent Mignot

Philippe Beuzeboc

Paul Cottu

Jean-Yves Pierga

Véronique Diéras

Valérie Laurence

Sophie Piperno-Neumann

Catherine Daniel

Wulfran Cacheux

Bruno Buecher

Emmanuel Mitry

Astrid Lièvre

Coraline Dubot

Etienne Brain

Barbara Dieumegard

Frédérique Cvitkovic

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