Arriagada, r. breast cancer

Predictive factors of the effect of adjuvant systemic treatments in breast cancer

R. Arriagada 1-3 , S. Michiels 2

1 Karolinska Institutet and University Hospital, Stockholm, Sweden 2 Institut Gustave-Roussy

3 Université de Paris-Sud, France

Predictive factors of the effect of adjuvant systemic treatments

• Why only systemic treatments ?

• Hormonal receptors

• HER2

• Molecular classifications

• Genomics signatures

• Methodological considerations

Radiotherapy in invasive breast cancer Isolated loco-regional recurrences in the trials of

any type of radiotherapy (RT) versus no RT

Isolated local recurrenceAbsolute difference in risk of isolated local recurrence: 20%, mostly within the first 5 years.

EBCTCG, Lancet 366: 2087-2106, 2005

Prognostic and predictive factors

• Prognostic factors: those that in multivariate

analysis show an independent effect on the

studied event

• May be studied in large retrospective series

• Predictive factors: those that are shown to be

significantly related to treatment effect

• They should be studied in large randomised

series testing the treatment (subgroup analysis),

or taking tumour response as event (advanced

disease or neo-adjuvant setting)

Hormonal receptors and hormonoresistance

• ER: good predictors but not enough

• Still about 50% of patients with ER+ are non-

responders

• About 10% of patients with ER- are responders

• ER and PR are not enough

• Others markers, pathways and cross-talks

Variations of treatment effects according to covariates

Intrinsic resistance to hormonal treatments

HR N Rate 95% CI

ER+ PR+ 102 / 319 32 % 27 - 37

( ER - PR + 15 / 26 58 % 37 – 77 )

ER + PR - 151 / 223 68 % 61 - 74

ER - PR - 179 / 197 91 % 86 - 94

* Osborne K. Breast, 1991

Hormonoresistance breast cancerRationale

• Two-thirds of tumours have positive ER and/or PR

• ER good predictor of tumour response (50%), but

only a part of the puzzle

- ERα

- ERβ (Gustafsson et al)

- Other factors (EGFR / HER2 receptors)

- Crosstalk ER and GFR pathways

Hormonoresistance breast cancerRationale

• EGFR / HER2 pathway may play a role in

resistance to SERMs (e.g. tamoxifen)

• ER complex with other transcription factors (Fos

and Jun proteins), alter gene transcription (cyclyn

D, ILGF-1,…)

• P53 mutation: poor response to tamoxifen

HormonoresistanceTamoxifen: largely used for 30 years

Hormonoresistance

• Loss of ER expression

• Modification of expression oestrogen-related

genes such as those coding GF or GF receptors

• Altered expression of transcriptional cofactors

associated with ER α• Deterioration of ER α circulation

• Implication of ER β• Alterations of tamoxifen metabolism

• ……

HormonoresistanceTamoxifen: gold standard for 30 years

• Agonistic and antagonistic effects

• Prevents the binding of oestrogen to its receptor

• Intrinsic (50% of ER+) and acquired resistance

• Agonistic effect: increase some side effects

• Adjuvant use for 5 years (however, some patients

could benefit from a longer treatment)

• More accurate and selective predictive factors are

needed.

HormonoresistanceAnti-aromatase inhibitors (AIs)

• Exemestane, anastrazole, letrozole

• More effective than TAM in postmenopausal pts

• Optimal treatment and sequencing to be defined

• Adverse effects: joint disorders

• ER+, HER2+ : AIs ?

• ER+, PR+ : sequence of TAM and AIs ?

HormonoresistanceFulvestrant

• Steroidal ER antagonist with no agonist effect

• It binds, blocks and accelerates degradation of

ER protein

• As effective as anastrazole and tamoxifen in

advanced ER+ breast cancer

• Well tolerated

• Lacks cross-resistance with TAM and AIs

• Sequential regimens ?

HormonoresistanceAdditional predictive factors

• ERα + and PR+ only used for practical indications

• Functional genomics: subgroups of gene profile

(e.g. Paik et al)

• Proteomics: study complex protein mixtures with

high resolution, SELDI-TOF-MS and antibody

array (Linderholm et al): 5 new potential

biomarkers

HormonoresistancePractical implications of prediction

• Tumours resistant to TAM could be sensitive to

AIs and viceversa

• The same for the indication of Fulvestrant

• Definition of optimal sequencing (BIG 01-98)

• Knowledge about mechanisms of resistance: new

drugs or treatment of hormonal resistance

J 0

Frozen biopsyUSPET*MRI

Frozen biopsyUSPET*MRI

1 month

Surgery at 4 months if OR < 50%

Surgery at 6 months

Frozen sample

USPET*MRI

USPET*MRI

* : optional

CARMINA: Neo-adjuvant studyT2 - 4 N0-3, M0 patients

Anastrozole or fulvestrant

5 yrs adjuvant hormonal treatment

Domain Function Homology A/B The regulatory domain 18%

C The DNA-binding domain 97%

D The hinge 30%

E The ligand-binding domain 59%

F The F region 18%

Oestrogen receptor family

Human Estrogen Receptor α: 6q25.1

Human Estrogen Receptor β : 14q22-24

A/B C D E F -COOHNH2-

1 148 214 304 500 530

NH2- A/B C D E F -COOH

1 185 251 355 549 595

• In ductal cells of the mammary gland, ERα and ERβ oppose each other on proliferation

• The proliferative response to oestrogens is determined by the ratio of ERα / ERβ

• Functions of ERβ in the breast are probably related to both its anti-proliferative and its pro-differentiation functions

• Breast cancer in postmenopausal women: high expression of ERα (cancer cells and normal ducts) indicate a normal elevation of ERα in the absence of its ligand, estradiol

Oestrogen receptor family I

• ERα expression in normal postmenopausal breast is not elevated • ERβ expressed > 60% of breast epithelial cells • In some women, ERβ is not detected but the splice variant ERβ cx may be very abundant • Breast cancer sections showed that ERβ is lost

and ERα is gained as we go from normal tissue to cancer• A decreased level of ERβ mRNA may be associated with breast tumourigenesis• DNA methylation: important mechanism for ERβ gene silencing in breast cancer

Oestrogen receptor family II

Pharmacogenetic Tools (TAM)Efficacy

• Polymorphism of TAM metabolising genes : – CYP2D6 : TAM OH-Tam, N-desmethylTam ⇨ ⇨

4 OH-N-desmethylTam (endoxifen) • Antidepressant (selective serotonin re-uptake

inhibitors), currently used for hot flushes linked to TAM, are CYP2D6 inhibitors.

• Plasma concentration of Endoxifen ⇩ if CYP2D6 *4/*4 genotype or paroxetin use.

Goetz et al, 2005,2007; Jin et al, 2005; Borges et al, 2006; Wegman et al, 2007

• AGE• Hormonal receptors• HER2• Others ?

Adjuvant chemotherapy effectPredictive factors


Intrinsic resistance to chemotherapyIBCSG study: 1275 pts N- , periop CT

Factor N HR (CT vs no) P (Cox)

Grade 1 200 2.11 0.04 2 519 0.76 3 432 0.75ER + 608 0.87 0.01 - 379 0.58PR + 459 0.67 0.03PR - 466 0.80

* Neville et al, JCO 10: 696-705, 1992


Adjuvant setting: Hormonal receptors

• In more recent randomised data, HR+ appears as a

marker of intrinsic chemoresistance:

♦ French studies *

♦ IBCSG IX study **

* Arriagada R et al. Ann Oncol, 13: 1378-86, 2002

Arriagada R et al. Acta Oncol, 44: 458-66, 2005

** IBCSG JNCI 94: 1054-1065, 2002


Chemotherapy

• Drug type: anthracycline vs CMF-like

regimens (EBCTCG)

• Anthracycline dose: Belgian and French trials*

• Hormonal receptors: French study**

• Age (EBCTCG)

* Piccart M et al, JCO 19: 3103-10, 2001; Bonneterre J et al,

JCO 19: 602-11, 2001

** Arriagada R et al. Acta Oncol 44: 458-66, 2005

DFS according to ER

ER Poor 27/79 41/76 -10.2 16.8

ER ++ 47/157 63/159 -9.2 27.4

ER +++ 85/250 84/249 -0.2 42.2

Total 159/486 188/484 -19.6 86.4

Category CT NoCT O-E Variance (CT:NoCT) ( SD)

No. Events/ No. Entered Relative Risk Risk Redn

CT effect 2P=0.04

CT better|NoCT betterTest for heterogeneity: 2P=0.09

Test for trend: 2P=0.03

20 % 10

0.0 0.5 1.0 1.5 2.0

TrendP = 0.03

Arriagada R et al. Acta Oncol 44: 458-66, 2005

DFS according to ER

InteractionP = 0.005

Comforti R et al. Ann Oncol (in press)

DFS in ER- (CT vs no CT)



DFS according to HER2

InteractionP = 0.34


DFS according to molecular subclassification



Overcoming drug resistanceDrug Doses

Lower doses of anthracyclines have a lower effect

than higher doses

1. CALGB *

2. Belgian trial **

3. French trial ***

4. Counterpart: AML ?

* Budman et al. JNCI 90: 1205-11, 1998** Piccart et al. JCO 19: 3103-10, 2001*** Bonneterre et al. JCO 19: 602-11, 2001**** Ann Oncol 14: 663-5, 2003

Adjuvant anthracyclin-based CTHER2 as a prognostic factor

Pritchard KI et al. N Engl J Med 354: 2103-11, 2006

Adjuvant anthracyclin-based CTHER2 as a predictive factor

Pritchard KI et al. N Engl J Med 354: 2103-11, 2006

Adjuvant trastuzumabHER2

• HER2 +++ predictive of treatment effect• 0 / 1 effect ? Determinism ?• NSABP-31: HER++ FISH (–) also a similar effect• New trial for these patients• BETH trial: adding bevacizumab for N+, HER2+++

Piccart M et al. N Engl J Med 353: 1659-72, 2005

HERA trial

Definition of new predictive factors Genomics / Proteomics

1. Knowledge about the human genoma

2. Knowledge about functional genes

3. Investigation of gene expression: proteins

4. Technical facilities: Micro-arrays

1. Frozen tissues

2. Fresh tissues

These investigations will be introduced in

prospective randomised trials (e.g. MINDACT)

Definition of new predictive factors Genomics / Micro-arrays

1. Determination of 25,000 genes

2. Selection of a genetic profile (or signature)

based on 70 genes

3. Used in limited database and evaluated as a

prognostic factor *

4. The introduction in randomised trials will allow

to evaluate their predictive value

* van’t der Veer L et al. Nature, 2002

Van der Vijver M et al. NEJM, 2003

NSABP 21-gene RS (Oncotype DX) Tam versus TAM + CT (CMF or MF)

651 pts

DMF interval

A) TotalB) Low riskC) IntermediateD) High risk (25 %)

Paik S et al.J Clin Oncol24: 2006

Definition of new predictive factors BIG/TRANSBIG: Mindact

US Intergroup TAILORx trial

• Thousands of N- patients to be included

• Europe: Amsterdam signature

• US: Oncotype

• Randomising adjuvant CT and HT

• Expensive +++

• Feasibility ?

Prognostic signature challengesC. Sotiriou (Brussels)

• 10 – 20% discordance between labs• Molecular classification: suboptimal

reproducibility• Fine-tuning needed• Very small gene overlap• Some validations

• Most prognostic genes are markers of proliferation

Statistical challenges related to micro-chips

Hopes and false positive results

S. Michiels, S. Koscielny, T. Boulet, C. Hill

Biostatistics and Epidemiology Department

16 April 2007

Some issues

Molecular signature

• Limited number of genes defining patient groups• Predictive signature for a defined metastatic risk

assumes the existence of an unique genetic combination for this risk

The old story of numbers

• Analysed series with a small number of patients and thousands of covariates

• Statistical power issues, interpretation and results validation

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Prediction quality

95% CI

Mean rate of misclassification

• Proportion of misclassification according to the number of number of patients in the learning sample

( van’t Veer, 2002)

Data of the validation 1 study

n=234 (55 with distant metastases)

Survival without distant metastasis, Cox model

(Dunkler, Michiels, Schemper. Eur J Cancer, 2007)

Explained variability of the pioneer study

R2

Model without factors 0%

Model with only conventional factors 16 % (±5%)

Model with only molecular signature 12% (±4%)

Model with conventional factors AND the molecular signature

19 % (±5%)

Added value of the molecular signature 3 %

Predictive factors of the effect of systemic treatments Conclusions

• Useful predictive factors: HR, HER2• However, they explain only a part of the

variability• They give probabilities and are not

deterministic• Biomics signatures: Hopes and a large field of

research• Clinical application: only robust results

Arriagada, r. breast cancer

Health & Medicine

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