multigenomic tests for tailoring local therapy: ready for

Multigenomic Tests for Tailoring Local Therapy: Ready for Prime Time?

Monica Morrow MDChief, Breast Surgery Service

Anne Burnett Windfohr Chair of Clinical OncologyMemorial Sloan Kettering Cancer Center

2nd Vienna Breast Surgery DayMedical University of Vienna

Vienna, Austria

17 March 2015

No conflicts of interest to disclose

ER+ HER2+ ER,PR,HER2-

Endocrine rx > 0.5 - 1.0 cm > 0.5 - 1.0 cm

High-risk features CTX + HER2 blockade CTXN+, High Oncotype Dx

CTX ER status

2015

Evolving Treatment Paradigms in Breast Cancer

Tumor Burden in Local TherapyDecision Making

1980s present

Larger

Tumor Burden

Smaller

BCSMastectomy

Tumor Burden Tumor Burden

PMRT None ConventionalWBRT

HypofracAPBI

Larger Smaller Larger Smaller

Potential Roles for Multigenomic Tests in Local Therapy

• Selection for BCT vs Mastectomy• Use of RT

After BCSPMRT

Genetic Signature for Local Recurrence

165 Patients

109 No LR 10yrs 56 LR

111 Gene Classifier

Kreike B, Clin Ca Res 2009;15:4181

Variation in grade, margins, boost, systemic therapy between groups

LR After BCT by Gene Classifier


Validation of Local Recurrence Signature


Sensitivity for LR: 77% (17/22)Specificity for LR: 43% (60/139)

Cox Model LR Classifier Age

EIC LVIMargins Grade

Signature Author Sensitivity Specificity70 gene Van’t Veer 2002 59% 64%76 gene Wang 2005 68% 53%Wound response Chang 2005 75% 41%Hypoxia Chi 2006 57% 28%Radioresistance Khodarev 2004 52% 54%Chromosomal instability Carter 2006 68% 57%Proliferation Whitfield 2006 59% 63%LR after BCT Nimeus-Malmstrom 2008 55% 42%

Predictive Value of Genomic Classifiers of LR


Genomic Tests for Selection of BCT vs Mastectomy

• None sensitive or specific enough for clinical use to identify population at high risk of LR after BCT

• Uncertain if high risk signature for BCT is also high risk after mastectomy

Paik S, J Clin Oncol 2006;24:3726

NSABP B-20 Distant RFS by Recurrence Score

All pts, p=.02 Low risk, p=.61

Inter risk, p=.39High risk, p<.001

651

134

353

164

Mamounas EP, J Clin Oncol 2010;26:1677

LRR and Recurrence ScorePlacebo

n = 355

Impact of Tailored Systemic Rx on LRR


0 2 4 6 8 10

010

2030

40

Time in Years

4.3

7.2

15.8P<0.0001

Rat

e of

LR

R%

RS < 18

RS >31RS 18-30

0 2 4 6 8 100

1020

3040

Time in Years

1.62.7

7.8

P=0.028

Rat

e of

LR

R%

RS < 18

RS >31RS 18-30Tamoxifen Tamoxifen

+ Chemo

NSABP B14 NSABP B20n = 895 n = 424


MVA of Predictors of 10yr LRR in Tam Treated Women


10 Year LRR by Age, Recurrence Score, and Surgery

Recurrence Score and LRRECOG 2197

Solin LJ, Breast Cancer Res Treat 2012;134:683

n = 219p = 0.12

Does Recurrence Score Predict LRR?

• ECOG 2197 underpowered compared to NSABP dataset

• In general, risk of LR parallels risk of distant disease, and multiple datasets show that Recurrence Score predicts risk of distant disease

• ECOG 2197 used AC or AT chemotherapy. NSABP B20 CMF or MF

Prognostic value of RS may be lost/diminished with modern systemic rx and lower rates of LR

How Does RS Aid in Local Therapy Decision Making?

• Not at all!• Identifies low and high risk populations for studies

of less or more therapy

Is there a role for genomic tests in selecting patients for RT?

Do We Need Another Expensive Test?

Benefit of RT after BCS• 15% of N- women• 21% of N+ women

• Multiple randomized trials have failed to identify a subset not benefitting from RT using standard clinical/pathologic predictors

Benefit of RT After BCS

Early Breast Cancer Trialists’ Collaborative Group, Lancet 2011;378:1707

Does Comprehensive Nodal RTImprove Survival?

MA.20 EORTC 22922-10925# patients 1832 4004Interval 5yr 10yr∆ DFS 5.4% p = .002 3.0% p = .044∆ OS 1.6% p = .07 1.6% p = .056

Whelan T, Proc ASCO 2011 Poortmans P, ESMO 2013

Cheng SH, J Clin Oncol 2006;24:4594

Genomic Predictors of LRR After Mastectomy

• 94 patients 1990-2001• No PMRT• Free of LRR at 3 years n=67• Any LRR n=27• Split into training + validation sets

Prognostic Value of 34 Gene Set for LRR



Genomic Predictors of LRRCox Model

Cumulative Incidence of LRR byRS Risk Groups in N+ Patients

0 2 4 6 8 10

0.0

0.1

0.2

0.3

0.4

0.5

Time in Years

Cum

ulat

ive

Inci

denc

e R

ate

P-value < .001

RS LowRS IntermediateRS High

N LRR Events386 16364 25315 39

Mamounas E et al, SSO 2013

3.3%7.2%12.3%

NSABP B28 No PMRT

LRR in Patients with Mastectomy

P-value = 0.64


0 2 4 6 8 10

0.0

0.1

0.2

0.3

0.4

0.5

Time in Years

Cum

ulat

ive

Inci

denc

e R

ate N LRR Events

137 5132 5117 7


N LRR Events75 566 677 18

P-value = 0.006

0 2 4 6 8 10

0.0

0.1

0.2

0.3

0.4

0.5

Time in Years

23.6%

9.6%5.5%

1-3 Positive Nodes(N = 386)

≥ 4 Positive Nodes(N = 218)

Mamounas E et al, SSO 2013

4.1%2.4%

6.0%

No PMRT

Gene Profile for Benefit of PMRTDanish Breast Cancer Group 82bc

Gene Profile

Training Set n=191 LRR n=53Median age 55yrs

72% ER+25% HER2+51% 1-3+ nodes

100% > 7 nodes dissected

Tramm T, Clin Can Res 2014;20:5272


Prognostic Value LRR SignatureNo RT


Predictive ValueLRR Signature

High Risk

Low Risk


Predictive ValueLRR Signature: Validation Set

High Risk

Low Risk

Gene Profile for Benefit of PMRT

• Validation set 4 genes from paraffin sections• Independent of ER, PR, HER2 status• High risk signatures seen in T1-2, N1-3 patients• Low risk signatures seen in >4+ nodes• No overlap with genes in Cheng et al. profile

Requires prospective validationin another dataset

Metaanalysis Trials of Excision ± RT in DCIS

No RT RT p-valueTotal 28.1% 12.9% < .001Invasive 15.4% 6.8% < .001DCIS 14.9% 6.5% < .001

n = 372910 yr IBTR

EBCTCG JNCI Monograph 2010;41:162

Metaanalysis Trials of Excision ± RT in DCIS

10yr Survival Outcomes

No RT RT p-valueAll deaths 8.2 8.4 > .1Death w/o recurrence 5.7 5.4 > .1Cardiac death 1.3 1.5 > .1

EBCTCG JNCI Monograph 2010;41:162

DCIS Score

Solin LJ, JNCI 2013;105:701

Patient CharacteristicsE5194

Premenopausal 24%T > 1cm 20.5%

Margin ≥ 5mm 65%High Grade 34%

ER+ 97%

n=327* No RT

*670 enrolled, 327 blocks available

Risk of Any Breast Event by DCIS Score


Risk of Invasive Breast Event by DCIS Score


Factors Associated With IBTR: Cox Model

Multivariable Analysis of Significant Clinical and Pathologic Factors, Including the DCIS Score

Menopausal statusPremenopausal 1.00 (referent)Postmenopausal 0.49 (0.27 to 0.90)

Tumor size 1.52 (1.11 to 2.01)DCIS Score 2.37 (1.14 to 4.76)


Ontario Cohort: Patient and Sample Disposition for Primary Analysis

Tissue provided to GHIn=828

(with central pathology review)

Final evaluable populationn=571

(BCS alone and negative margins)

257 Histology and lab exclusionsNo tumor: 10Invasive carcinoma: 4Insufficient RNA: 68qPCR Sample Quality: 28Positive margins: 147

Histologically proven DCIS with BCSalone and clinically eligible

n=1,658

Rakovitch E, SABCS 2014

Age ≥ 50 years 459 (81%)Nuclear Grade

Low 55 (10%)Intermediate 332 (58%)High 184 (32%)

Comedo Necrosis 350 (61%)Solid Subtype 358 (63%)Tumor Size

≤ 10mm 150 (26%)> 10mm 140 (25%)Missing 281 (49%)

Multifocality* 114 (20%)ER+ by RT-PCR 541 (95%)

Patient CharacteristicsOntario Cohort (n=571)*Presence of at least 2 foci of DCIS in the same quadrant at least 5 mm apart

*Presence of at least 2 foci of DCIS in the same quadrant at least 5mm apart


Kaplan-Meier 10yr Risk of Local Recurrence by DCIS Score Risk Group

Loca

l Rec

urre

nce

Ris

k (%

)

Years

33.0%27.8%

12.7%


10yr Risk of Invasive and DCISLocal Recurrence by DCIS Score Risk Group

Invasive Local Recurrence DCIS Local Recurrence


Factors Associated With Local Recurrence: Multivariable Analysis

Characteristic N HR (95% CI) P-valueDCIS Score /50 571 1.68 (1.08, 2.62) 0.02Age at diagnosis (yr) 0.03

<50 110 1.75 (1.07, 2.76)≥50 459 1.0

Tumor size 0.01>10mm 140 2.07 (1.15, 3.83)≤10mm 150 1.0

Subtype 0.04Solid 358 1.63 (0.97, 2.88)Cribriform 175 1.0

Multifocality* 0.003Present 114 1.97 (1.27, 3.02)Absent 457 1.0


Comparison of Ontario Cohort to E5194 Cohort: KM 10yr Estimates of Risk of Local Recurrence

ECOG E5194 Ontario DCIS Cohort

Loca

l Rec

urre

nce

Ris

k (%

)

Loca

l Rec

urre

nce

Ris

k (%

)Solin LJ. J Natl Cancer Inst 2013;105:701 Rakovitch E, SABCS 2014

1Solin LJ. J Natl Cancer Inst 2013;105:701 2Rakovitch E, SABCS 2014

121 (21.2%)

355 (62.2%)95 (16.6%)

44 (13.5%)

53 (16.2%)230 (70.3%)

46

LR Risk 10.7%LR Risk 26.7%

LR Risk 25.9%

LR Risk 12.7%LR Risk 33%

LR Risk 27.8%

Is the DCIS Score Clinically Useful?

Is the DCIS Score Clinically Useful?

• We do not know if a high score predicts the benefit of RT

• Risk averse women with DCIS are increasingly choosing bilateral mastectomy

Will they be willing to forego RT based on DCIS score?

Conclusions

• Multigenomic assays offer the potential to tailor the extent of surgery and RT based on biology

• Initial data on risk of LRR after mastectomy, benefit of PMRT most promising

Requires validation in other datasets, clinical trials

• DCIS score provides information that when used in conjunction with traditional clinical and pathologic features may identify a subset with a low risk of LR in the absence of RT

multigenomic tests for tailoring local therapy: ready for

Documents