Relationship between complete pathologic response to ... · 1 Relationship between complete pathologic response to neoadjuvant chemotherapy and survival in triple negative breast

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Relationship between complete pathologic response to neoadjuvant chemotherapy and

survival in triple negative breast cancer

Christos Hatzisa, W. Fraser Symmansb, Ya Zhangb, Rebekah E. Gouldb, Stacy L. Moulderc, Kelly

K. Huntd, Maysa Abu-Khalafa, Erin W. Hofstattera, Donald Lannine, Anees B. Chagpare, Lajos

Pusztaia

aDepartment of Internal Medicine, Yale School of Medicine, Yale Cancer Center, New Haven,

CT

bDepartment of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX

cDepartment of Breast Medical Oncology, University of Texas MD Anderson Cancer Center,

Houston, TX

dDepartment of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston,

TX

eDepartment of Surgery, Yale School of Medicine, Yale Cancer Center, New Haven, CT

Running Title: Pathologic complete response and survival benefit in cancer

Keywords: Breast cancer, neoadjuvant studies, trial design, pathologic complete response,

survival

Financial Support

This work was supported in part by grants from The Breast Cancer Research Foundation (WFS, LP) and Susan G. Komen for The Cure (KG081680 to WFS).

Research. on June 7, 2020. © 2015 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on August 18, 2015; DOI: 10.1158/1078-0432.CCR-14-3304

http://clincancerres.aacrjournals.org/

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Corresponding Authors

Christos Hatzis ([email protected]) or Lajos Pusztai ([email protected] )

Breast Medical Oncology

Yale School of Medicine

333 Cedar Street

PO Box 208032

New Haven, CT 06520-8032

Tel: 203-737-8309

Fax: 203-785-5792

Conflicts of Interest

The authors declare that they have no competing interests to disclose.

Word Count

Abstract: 247 words

Body: 2830 words




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Translational Relevance

Pathologic complete response (pCR) to neoadjuvant chemotherapy is indicative of the cytotoxic

efficacy of a drug, but patient survival is influenced by several other factors. The answer to the

often-quoted question, “Is pCR a surrogate for long term survival?” is yes at the individual

patient level. For the subtly different question “If increased pCR rate is a surrogate for improved

survival in a trial arm”, the answer depends on the absolute improvement in pCR rate, baseline

prognosis of the trial population, interaction of pCR with prognostic variables and efficacy of

post-neoadjuvant treatment modalities . Our framework helps estimating the impact of increased

pCR rate on survival and could help designing and powering the next generation of neoadjuvant

trials, particularly if the goal is to demonstrate improved survival from a regimen that results in

higher pCR rate.




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Abstract Background: Pathologic complete response (pCR) to neoadjuvant chemotherapy reflects the

cytotoxic efficacy of a drug, but patient survival is influenced by many other factors. The

purpose of this study was to assess the relationship between increased pCR rate and trial-level

survival benefit in triple negative breast cancer (TNBC).

Methods: We used bootstrap resampling from a neoadjuvant trial to simulate trials with different

pCR rates. We used estimates from AdjuvantOnline to simulate trial populations with different

baseline prognosis and estimated survival improvements associated with changes in pCR rate.

Results: Assuming that survival is similar for patients with pCR regardless of treatment arm, a

linear relationship exists between increasing pCR rate and increasing RFS. The slope is equal to

the difference in survival between those with pCR and RD which in turn is influenced by (i) the

baseline prognosis of the trial population, (ii) interactions between prognostic variables and pCR,

and (iii) the efficacy of the post-neoadjuvant therapies. For example, if the pCR rates are 30%

and 60% (OR=3.5) and the 10-year RFS of the control arm is 0.74, the trial would require 3550

patients per arm, whereas if the RFS is 0.54 the trial would only require 425 patients per arm to

detect significant survival benefit.

Conclusions: We provide a framework for understanding the relationship between pCR and

overall survival benefit that can help inform the design of neoadjuvant trials aiming to

demonstrate improved survival from a regimen that results in higher pCR rate.




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Introduction

Breast cancer patients who achieve pathologic complete response (pCR) defined as complete

eradication of invasive cancer from the breast and lymph nodes have excellent survival

regardless of hormone receptor status or molecular subtype while those with residual invasive

disease (RD) have variable prognosis depending on disease subtype and clinical stage.(1-4)

Triple negative breast cancer (TNBC) patients with RD have a poor prognosis that is

proportionate to the extent of residual disease, but some patients with estrogen receptor (ER)

positive cancer have excellent survival even with RD after neoadjuvant chemotherapy.(3, 5, 6)

One could expect that more effective cytotoxic therapies that produce higher pCR rates will lead

to improved survival compared to less effective cytotoxic therapies, as they push more patients

into the favorable survival group. Indeed, inclusion of taxanes in neoadjuvant chemotherapy

increased pCR rates and also increased survival in much larger adjuvant trials.(7, 8) The

inclusion of trastuzumb with a broad range of chemotherapy drugs doubled the pCR rates

compared to chemotherapy alone, and also increased survival.(9, 10) The NOAH study for

locally advanced breast cancer, directly demonstrated improvement in pCR rate (38% vs 19%)

followed by an improvement in disease free survival (DFS; 71% vs 56%) in the same trial.(11)

Based on these and other supportive data, the US Food and Drug Administration (FDA) is

currently considering pCR as an endpoint to support accelerated drug approval for early stage

breast cancer.(12, 13)

However, the relationship between improvement in pCR rate and trial-arm level survival

benefit is less than straightforward. Even studies that directly or indirectly support a positive

association show substantially greater absolute improvements in pCR rate than in survival.

Furthermore, the largest randomized neoadjuvant trial, the NSABP B-27 (n=2411), failed to




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demonstrate a significant improvement in 5-year DFS (71% v 68%) or overall survival (OS),

despite a significant improvement in pCR rate (26% vs 13%) with the inclusion of docetaxel

with anthracycline/cyclophosphamide neoadjuvant therapy.(2) Several other randomized

neoadjuvant trials with sample sizes between 200 to 2000, and including both estrogen receptor

(ER) positive and negative cases, also failed to demonstrate improvements in survival despite

increased pCR rates in the experimental arms.(14, 15) The FDA-led meta-analysis of 12

neoadjuvant clinical trials (CTNeoBC) confirmed the patient-level prognostic value of pCR but it

could not demonstrate a significant correlation between pCR odds ratio and event-free survival

hazard ratio at the trial-level across the studies.(16) More recently, the ALTTO adjuvant trial

(n=6281) that compared one year of anti-HER2 therapy with lapatinib alone, trastuzumab alone

or the combination of the two drugs concomitant with or after chemotherapy in HER2-positive

early breast cancer also failed to show statistically significant improvement in survival,(17)

despite a significant improvement in pCR rate (51% vs 29% and 24%) with the dual HER2

blockade in the neoadjuvant NeoALTTO trial.(18) Clearly, substantial and statistically

significant improvements in pCR rate can translate in minor, non-significant improvements in

patient survival in large trials.

At least five factors could contribute to why a regimen with increased cytotoxic activity

would fail to improve survival. First, the baseline prognosis of the patient cohort has a major

impact on the absolute survival benefit; patients with good initial prognosis (e.g. earlier clinical

stage) can only have small gains in survival. Second, the frequency of pCR may be higher in

patients with good initial prognosis, meaning that increased pCR rate may have a diminished

impact on overall survival at the trial level. Third, effective post-neoadjuvant therapy, such as

adjuvant endocrine therapy for ER positive cancers, will improve the outcome in the RD group.




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Fourth, patients with minimal residual cancer burden after neoadjuvant chemotherapy often have

the same excellent prognosis as patients who achieve pCR;(6) if a regimen increases the pCR

rate primarily by moving patients who would have with minimal residual cancer burden after

standard therapy to the pCR group in the experimental arm, the net survival benefit from the

increase in pCR rate would be minimal. Fifth, 5-6% of patients who achieve pCR will experience

distant recurrence, which further limits its early surrogate function. All the above factors co-exist

and influence survival in a clinical trial and most are poorly understood and difficult to model.

The lack of independence between many prognostic and predictive markers further complicates

building theoretical models, which has motivated us to use real patient data to assess the

relationship between pCR and survival. In real patient data, the hidden biomarker associations

and above confounders are already accounted for. We also assessed our model on simulated

survival data generated by Adjuvant!Online predictions to examine the impact of anatomical

prognostic risk variables on the relationship between pCR and survival.

The purpose of this study was to evaluate the relationship between increased pCR rate

and trial-level survival using bootstrap resampling from real and simulated neoadjuvant trial

patient outcome data. We varied the pCR frequency through biased resampling to simulate trials

with different pCR rates. We also varied the survival of the RD cohort to simulate TNBC trial

populations with different baseline prognoses.

Materials and Methods

Patient data




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We used survival data from 127 clinical stage I-III TNBC cases from our previously published

cohort from M.D. Anderson Cancer Center (6) with recently updated survival outcomes.(19)

TNBC status was defined as ER and PR < 1% positivity by routine immunohistochemistry (IHC)

and lack of HER2 amplification by FISH (HER2:CEP17 copy number ratio < 2.0) or HER2 IHC

score of 0. All patients received preoperative chemotherapy with weekly paclitaxel and

fluorouracil, doxorubicin (or epirubicin), and cyclophosphamide, as previously reported.

Pathologic complete response was defined as no residual invasive cancer in the breast or lymph

nodes; the pCR rate for TNBC patients was 35%. The median follow up for event-free survivors

was 9.3 years (range 0.5 to 13.4 years). The 5-year distant recurrence-free survival (DRFS) for

the pCR and RD groups was 0.93 (95%CI: 0.86-1.00) and 0.58 (95%CI: 0.49-0.69), respectively.

Patient characteristics are presented in Supplementary Table 1.

We also generated simulated survival data sets for TNBC using Adjuvant!Online (AO)

predictions (20) to vary the baseline prognosis of the trial population. In these data sets all

patients were designated as 60 years of age with average comorbidities, grade 3, ER negative

cancers and treated with 3rd generation adjuvant chemotherapy (i.e. control arm). Clinical tumor

size and clinical nodal status were considered in brackets of increasing disease burden T1bN0,

T1cN0, T2N0, or T2N1. Adjuvant!Online only provides recurrence free survival (RFS) estimates

and not DRFS, therefore in these data sets we report RFS as outcome. The overall 10-year RFS

associated with the 4 distinct prognostic groups were 0.782, 0.741, 0.657, and 0.548, respectively

(Table 1).

Statistical analysis




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We used biased bootstrap resampling to simulate 1:1, two-arm randomized clinical trial

scenarios with N=127 patients per arm. For each scenario, 1000 bootstrap replicates were drawn

from the reference cohort with replacement, stratified by response group to keep the pCR rate at

35% in the control arm. To simulate the experimental arm, 1000 different datasets were

generated from the same TNBC reference cohort using biased resampling of the pCR and RD

groups from the TNBC reference cohort to achieve various pre-specified pCR rates. This

simulation strategy requires two assumptions: (i) the survival of cases with pCR is similar

regardless of which treatment arm they are, and (ii) the survival of patients with residual disease

is also similar in both arms. These assumptions are supported by observations from several

randomized neoadjuvant clinical trials. (2,17) We used the Kaplan-Meier estimator and

univariate Cox regression to estimate the 5-year DRFS for the control and experimental arms and

the treatment-related DRFS hazard ratio, respectively. Means and 95% confidence intervals were

estimated from the 1000 bootstrapped datasets for each scenario. The power to detect a

significant DRFS hazard ratio was estimated by the proportion of datasets for which the Wald

statistic for the hazard ratio was significant (p<0.05). The bootstrap procedure is equivalent to

generating event and censoring times from the Kaplan-Meier estimator of the survival and

censoring distributions.(21, 22)

To evaluate the effect of baseline prognosis on survival, we generated cohorts for the

control arm that matched the 10-year RFS predicted by Adjuvant!Online for the specified patient

characteristics. We further assumed independent exponential relapse times for the pCR and RD

patient groups in either arm.(23) We assumed that the pCR rate for the control arm (TNBC

treated with 3rd generation chemotherapy) is 30% and that the 10-year RFS of patients who

achieved pCR is 82% for both arms corresponding to an exponential hazard rate of 0.0198




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(events/year).(19) Under these assumptions, the survival function of the entire cohort for each

arm will be the mixture of the survival functions of the pCR and RD groups. Based on this we

then calculated the 10-year RFS of patients with RD as

(10| ) = 11 − (10) − 1 − (10| )

where (10) is the 10-year RFS for the overall cohort as predicted by Adjuvant! Online, (10| ) and (10| ) are the 10-year RFS for the pCR and RD subgroups and is the

pCR rate. The exponential hazard rate for the RD group is then estimated as = − ( | ). The values used in the simulated scenarios are summarized in Table 1. We modeled censoring

times for both pCR and RD cohorts as being distributed according to an exponential distribution

with rate = 0.5 , which corresponded to the censoring pattern observed in our reference

cohort. The R (v.3.2.0) statistical software was used for all computations.(24)

Results

The relationship between trial-level pCR rate and survival benefit

We consider a two-arm randomized neoadjuvant trial where the experimental treatment has

greater cytotoxic efficacy compared to the control treatment, as reflected by an increase in pCR

rate of ∆ (Figure 1). If the survival functions for the control and experimental arms are ( )

and ( ), then ( ) = ( | ) + (1 − ) ( | ) and ( ) = ( + Δ ) ( | ) + (1 − − Δ ) ( | )

Assuming that the survival outcome of patients with pCR is the same in both treatment arms, the

survival benefit at the study level is




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Δ ( ) = ( ) − ( ) = Δ ( | ) − ( | ) + (1 − ) ( | ) − ( | ) ,

indicating a linear association between trial-level survival benefit (Δ ) and improvement in

pCR rate (∆ ), conditional on all other prognostic factors between the two arms being held equal.

The magnitude of the trial-level survival benefit derived from an improvement in pCR is driven

by the patient-level survival benefit between the pCR and RD groups in the experimental arm. If

the baseline prognosis of the overall patient cohort is good, the difference in outcome between

pCR and RD groups can only be small and therefore even a large ∆ translates into a minimal

trial-level survival benefit. A more effective cytotoxic therapy, in addition to pushing more

patients into the pCR response category, may also reduce the extent of residual disease in those

patients who did not achieve pCR. The second term in the expression is the secondary

contribution of this residual disease down staging on the trial-level survival benefit.

Association between odds for pCR and DRFS benefit in TNBC

Figures 2A and 2B illustrate a scenario where doubling of the pCR rate (from 35% to 70%) is

accompanied by a sizable patient level survival benefit (Figure 2A) yet it translates into only a

small improvement in trial-arm level survival (Figure 2B).

The impact of pCR odds ratio (OR) on the DRFS hazard ratio (HR) and DRFS difference

at 5 years was determined through simulations of a large number of trial scenarios. Higher OR

results in smaller DRFS hazard ratios (Figure 2C) or greater DRFS difference at 5 years (Figure

2D). Power analysis suggests that an experimental treatment that increases the pCR rate from

35% to 65% (pCR OR of 3.45) would have a 50%, 76%, and 90% power to detect a significant

survival benefit at the 95% confidence level in 1:1 randomized trials with 127, 250 or 350

patients per arm, respectively (Figure 2E). These results suggest that the power to detect




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significant survival HR effect is quite low (<50%) for studies with less than 350 patients per arm

unless the pCR OR is high (OR > 2).

Power to detect significant survival benefit does not only depend on pCR odds ratio but also on

the baseline prognosis of patients

To evaluate how baseline prognostic risk impacts the trial-level survival benefit, we simulated a

series of scenarios where the baseline prognosis of patients differed by adjusting anatomical risk

variables (T size and N stage). We assumed a pCR rate of 30% in the control arm for all baseline

risk groups, as the effect of tumor size and nodal status on pCR rate is modest.(16) The study-

level survival benefit, measured by the difference in 10-year RFS, increased with increasing pCR

rate effect but was considerably larger in patients with the worst prognosis (T3N2 – Figure 3A).

A 100 patient per arm randomized trial that includes only T1cN0 patients, or any patient

population with similar good baseline prognosis, would fail to show significant survival benefit

unless the absolute pCR rate increase is > 60% (corresponding to an OR > 20; Figure 3A). Even

studies with 1000 patients per arm would require an absolute pCR increase > 40% (OR > 5.4) to

demonstrate a significant survival benefit in a good risk patient population (Figure 3A). Figure

3B provides a more systematic exploration of the effect of baseline prognostic characteristics on

the power to detect significant survival benefit. It shows that if a trial targets high-risk patients

only, clinically T3N2 or similar high risk population, then 300 patients per arm could yield 80%

power to detect significant survival benefit for an absolute pCR rate increase of > 20%

corresponding to an OR of 2.3.

Table 2 provides sample size estimates for 1:1 randomized trials to detect a significant

improvement at 5% significance level in trial-level RFS with 80% or 90% power as a function of




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(i) the improvement in pCR in the experimental arm and (ii) the overall baseline risk of the trial

population. Power estimates for the extreme hypothetical scenario of 100% pCR in the

experimental group versus 0% pCR in the control group are provided in Supplemental Table 2

for comparison. Even in this unrealistic scenario, a study involving only low risk T1cN0 patients

will require 333 patients to show significant survival benefit with 80% power. It is apparent that

the size of a phase III trial powered to demonstrate significant survival benefit depends not only

on the expected pCR rate improvement but also to a large degree on the baseline risk of the

treated cohort.

Discussion

There is confusion and contradictory data in the literature about how increase in pCR rate may

impact survival difference between arms in a randomized trial. We show that trial-level survival

benefit is a complex function of many factors beyond the difference in pCR rate. We used

survival data from real patients participating in neoadjuvant studies and simulated patient

outcome derived from AdjuvantOnline estimates to assess the relationship between pCR rate and

survival for TNBC. Assuming that the survival outcome is similar for those who achieve pCR

regardless of which treatment arm they are in a linear relationship exists between trial-level pCR

rate effect and RFS benefit. The slope is equal to the difference in survival between those with

pCR and RD, or the patient-level survival benefit, in the experimental arm, which in turn is

influenced by the baseline prognosis of the trial population and the effectiveness of post-

neoadjuvant therapies. Patients with favorable TNM stage derive little benefit from even highly

efficacious neoadjuvant therapies. Similarly, patients with highly endocrine sensitive cancers




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will derive a large survival benefit from adjuvant endocrine therapy and therefore their initial

response to neoadjuvant cytotoxic therapy is less important.

There are molecular variables that could simultaneously influence both the probability of

achieving pCR and the prognostic risk of a patient (i.e. risk of recurrence in the absence of any

systemic therapy). For example, high tumor proliferation rate is associated with higher

probability of pCR but also with worse prognosis, (25) while high tumor infiltrating lymphocyte

count is associated higher pCR rate and also better prognosis.(26, 27) Many similar, yet to be

identified, interactions between prognostic markers and pCR may exist. Some could be breast

cancer subtype specific and may also differ by treatment and hard to model. (28) We focused our

analysis on TNBC because the pCR and survival relationship is the clearest in this disease subset

due to the lack of effective post-neoadjuvant systemic therapies. We also assumed that the

experimental therapy behaves like a third generation chemotherapy in terms of its hidden

interaction with prognostic and predictive markers but it is more effective. This implies that the

power and sample size relationship that we report here could be different for neoadjuvant

therapies that have substantially different mechanism of action because different interactions

could exist between the experimental therapy and prognostic and predictive markers. Our model

relies on a commonly used constant hazard function to generate exponential survival estimates,

although we recognize that the hazard of distant recurrence for TNBC peaks initially and then

diminishes over time.

We recognize that clinical trials accrue patients with different baseline prognoses, and we

selected a set of specific T/N stage groups to illustrate the dependence on baseline prognosis of

the trial population and to provide some general guidelines. Power calculations for actual trials

will have to consider the specific TNM stage distribution of the intended patient population.




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We did not elaborate in our analysis on the effects of down staging of residual cancer on

the association between pCR and survival benefit. An effective treatment could improve survival

in the RD group by reducing the amount of residual disease without affecting the overall pCR

rate resulting in a smaller trial-level survival benefit. Alternatively, a treatment could increase

the pCR rate by shifting patients with minimal residual cancer into the pCR category which

would impact the net survival benefit only marginally as patients with minimal residual cancer

burden have the same excellent prognosis as patients who achieve pCR.(6) The net effect of

residual cancer down staging on the association between pCR rate and trial-level survival will

need to be evaluated in neoadjuvant studies that also monitor levels of residual disease.

In summary, our results indicate that modest improvements in pCR rate translate into

very small improvements in survival in low and moderate risk trial populations, consistent with

the existing literature. Almost all trials included in the CTNeoBC pooled analysis had pCR OR <

1.5 and included many ER positive patients. These results do not undermine the value of pCR as

an efficacy measure to identify more effective cytotoxic therapies in early stage breast cancer but

highlight the importance of selectively administering more effective novel treatments to those

who remain high risk for recurrence despite current best therapies.




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Figure Legends Figure 1. Patient-level (first plot) versus trial-level (second plot) survival analysis for a 1:1 two

arm randomized clinical study. Patient-level analysis shows a survival benefit associated with

patients who achieve pathologic complete response (pCR) compared to patients who have

residual disease (RD). The trial-level survival benefit in the experimental treatment arm depends

on the increase in pCR rate and also on the patient-level survival benefit, which varies depending

on baseline prognostic risk of the trial population. In both clinical trial scenarios shown, the pCR

rate increase in the experimental arm is 20% (pCR odds ratio = 2.33), but scenario A is assumed

to involve a low prognostic risk population (T1c, N0), whereas scenario B a high risk population

(T3, N2). The prognostic risk characteristics of these populations are shown in Table 1. The

survival functions of the pCR and RD groups are assumed to be exponentials.

Figure 2. The Kaplan-Meier plots in panel A illustrate patient-level survival by responder group

in each arm (pCR subgroups shown in solid lines and RD subgroups in dashed lines), and panel

B shows the trial-level survival of the two arms for one typical bootstrap sample in the MDACC

TNBC cohort. The control arms (n=127) were generated through unbiased stratified bootstrap to

have 35% pCR rate, and the experimental arms were generated by biased bootstrap resampling to

yield a pCR rate of 70%. The association between pCR odds ratio and trial-level survival benefit

in terms of the DRFS hazard ratio or the difference in 5-yr DRFS between the study arms is

shown in panels C and D, respectively. The points represent the means and the vertical lines the

95% confidence intervals of the survival outcome estimates obtained by 1000 bootstrap

replicates. Panel E shows the power to detect significance in DRFS hazard ratio at 95%




22

confidence level as a function of pCR odds ratio for studies of different sample sizes (number of

patients per arm: circles: N=127, triangles: N=250, squares: N=350).

Figure 3. Power to detect significant survival benefit from scenarios for 1:1 randomized trials

involving TNBC patient cohorts with different baseline risk. Simulated scenarios included 30 to

3200 patients per arm. The pCR rate of the control arm was set at 30% and the 10-year RFS of

patients with pCR to 82%. Ten-year RFS estimates of four different prognostic risk populations

T1cN0 (circles), T2N1 (triangles), T3N1 (squares), and T3N2 (crosses) were obtained from

Adjuvant! Online and listed in Table 1. Panels show the estimated power to detect by Cox

proportional hazards based on the Wald test a significant RFS hazard ratio between the

experimental and control arms of the trial as a function of the between-arm difference in pCR

rate, the baseline prognosis of the trial population, and the number of patients per study arm (N).




Figure 1

A

B




Figure 2




N = 30 N = 60 N = 100

N = 180 N = 320 N = 560

N = 1000 N = 1800 N = 3200

0.00

0.25

0.50

0.75

1.00

0.00

0.25

0.50

0.75

1.00

0.00

0.25

0.50

0.75

1.00

0.2 0.4 0.6 0.2 0.4 0.6 0.2 0.4 0.6

Difference in pCR Rate (Experimental - Baseline)

Po

we

r

Baseline Group

T1c,N0

T2,N1

T3,N1

T3,N2

Figure 3

N = 100

N = 320

N = 1000

0.0

0.2

0.4

0.0

0.2

0.4

0.0

0.2

0.4

0.2 0.4 0.6

Difference in pCR Rate

RF

S D

iffe

ren

ce

at

10

ye

ars

A B




Table 1. Patient parameters used in simulated scenarios (all scenarios assumed 60-year

old patients with average comorbidities and grade 3 ER-negative tumors treated with 3rd

generation chemotherapy with a pCR rate of 30%).

Clinical T-stage

Clinical N-stage

10-year RFS Hazard Rate (1/year)

Overall (Adjuvant!

online)

pCR cohort

RD cohort

pCR cohort RD cohort

T1c N0 0.741 0.82 0.707 0.0198 0.0347

T2 N1 0.545 0.82 0.427 0.0198 0.0851

T3 N1 0.456 0.82 0.300 0.0198 0.1204

T3 N2 0.350 0.82 0.149 0.0198 0.1908




Table 2. Sample size per arm required for 1:1 randomized trials to detect improvement

in trial-level RFS at an one-sided 5% significance level as a function of the overall

baseline risk of the trial population and the improvement in pCR rate in the experimental

arm (the pCR rate of control arm is assumed to be 30% and the 10-year RFS of the pCR

subgroup to be 82%). The power to detect significant survival benefit between treatment

arms using unadjusted Cox proportional hazards regression analysis based on the Wald

test (hazard ratio for RFS between arms < 1) was estimated by 1000 simulations of each

baseline and pCR improvement scenarios with study sizes ranging from 30 to 3200

patients per arm. Natural spline interpolation was then used to obtain the sample sizes for

80% and 90% power. No adjustments were made for patient accrual time.

Baseline Risk Group

Power Improvement in pCR Rate in Experimental Arm

0.1 0.2 0.3 0.4 0.5 0.6

T1cN0 0.8 >4,000 >4,000 2680 1551 929 673

0.9 >4,000 >4,000 3554 2056 1258 903

T2N1 0.8 3078 717 315 168 105 71

0.9 >4,000 942 425 223 137 94

T3N1 0.8 1788 460 186 103 66 46

0.9 2320 598 247 136 88 58

T3N2 0.8 1122 284 124 75 45 31

0.9 1488 364 159 95 56 42




Published OnlineFirst August 18, 2015.Clin Cancer Res Christos Hatzis, W. Fraser Symmans, Ya Zhang, et al. cancer

breastneoadjuvant chemotherapy and survival in triple negative Relationship between complete pathologic response to

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