PAM50 gene signatures and breast cancer prognosis with adjuvant anthracycline- and taxane-based chemotherapy: correlative analysis of C9741 (Alliance)

Abstract

PAM50 intrinsic breast cancer subtypes are prognostic independent of standard clinicopathologic factors. CALGB 9741 demonstrated improved recurrence-free (RFS) and overall survival (OS) with 2-weekly dose-dense (DD) versus 3-weekly therapy. A significant interaction between intrinsic subtypes and DD-therapy benefit was hypothesized. Suitable tumor samples were available from 1,471 (73%) of 2,005 subjects. Multiplexed gene-expression profiling generated the PAM50 subtype call, proliferation score, and risk of recurrence score (ROR-PT) for the evaluable subset of 1,311 treated patients. The interaction between DD-therapy benefit and intrinsic subtype was tested in a Cox proportional hazards model using two-sided alpha=0.05. Additional multivariable Cox models evaluated the proliferation and ROR-PT scores as continuous measures with selected clinical covariates. Improved outcomes for DD therapy in the evaluable subset mirrored results from the complete data set (RFS; hazard ratio=1.20; 95% confidence interval=0.99-1.44) with 12.3-year median follow-up. Intrinsic subtypes were prognostic of RFS (P<0.0001) irrespective of treatment assignment. No subtype-specific treatment effect on RFS was identified (interaction P=0.44). Proliferation and ROR-PT scores were prognostic for RFS (both P<0.0001), but no association with treatment benefit was seen (P=0.14 and 0.59, respectively). Results were similar for OS. The prognostic value of PAM50 intrinsic subtype was greater than estrogen receptor/HER2 immunohistochemistry classification. PAM50 gene signatures were highly prognostic but did not predict for improved outcomes with DD anthracycline- and taxane-based therapy. Clinical validation studies will assess the ability of PAM50 and other gene signatures to stratify patients and individualize treatment based on expected risks of distant recurrence.

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Minetta C Liu1, Brandelyn N Pitcher2, Elaine R Mardis3, Sherri R Davies4, Paula N Friedman5, Jacqueline E Snider4, Tammi L Vickery3, Jerry P Reed3, Katherine DeSchryver6, Baljit Singh7, William J Gradishar8, Edith A Perez9, Silvana Martino10, Marc L Citron11,Larry Norton12, Eric P Winer13, Clifford A Hudis12, Lisa A Carey14, Philip S Bernard15, Torsten O Nielsen16, Charles M Perou17, Matthew J Ellis18 and William T Barry19

PAM50 intrinsic breast cancer subtypes are prognostic independent of standard clinicopathologic factors. CALGB 9741 demonstrated improved recurrence-free (RFS) and overall survival (OS) with 2-weekly dose-dense (DD) versus 3-weekly therapy. A signicant interaction between intrinsic subtypes and DD-therapy benet was hypothesized. Suitable tumor samples were available from 1,471 (73%) of 2,005 subjects. Multiplexed gene-expression proling generated the PAM50 subtype call, proliferation score, and risk of recurrence score (ROR-PT) for the evaluable subset of 1,311 treated patients. The interaction between DD-therapy benet and intrinsic subtype was tested in a Cox proportional hazards model using two-sided alpha = 0.05. Additional multivariable Cox models evaluated the proliferation and ROR-PT scores as continuous measures with selected clinical covariates. Improved outcomes for DD therapy in the evaluable subset mirrored results from the complete data set (RFS; hazard ratio = 1.20; 95% condence interval = 0.991.44) with 12.3-year median follow-up. Intrinsic subtypes were prognostic of RFS (Po0.0001) irrespective of treatment assignment. No subtype-specic treatment effect on RFS was identied (interaction P = 0.44). Proliferation and ROR-PT scores were prognostic for RFS (both Po0.0001), but no association with treatment benet was seen (P = 0.14 and 0.59, respectively). Results were similar for OS. The prognostic value of PAM50 intrinsic subtype was greater than estrogen receptor/HER2 immunohistochemistry classication. PAM50 gene signatures were highly prognostic but did not predict for improved outcomes with DD anthracycline- and taxane-based therapy. Clinical validation studies will assess the ability of PAM50 and other gene

npj Breast Cancer (2016) 2, 15023; doi:http://dx.doi.org/10.1038/npjbcancer.2015.23

Web End =10.1038/npjbcancer.2015.23 ; published online 6 January 2016

INTRODUCTION

The clinical and genomic heterogeneity of early-stage breast cancer is well-recognized. Tumor characterization beyond hormone receptor status, HER2 status, tumor size, and extent of nodal involvement may improve prognostication and guide systemic therapy. Intrinsic breast cancer subtypes derived through global gene-expression analysis are prognostic independent of standard clinicopathological variables and identify the subgroup(s) of patients most likely to benet from a given adjuvant chemotherapy regimen.15

The luminal A (LumA), luminal B (LumB), HER2-enriched (HER2-E), basal-like, and normal-like breast cancer subtypes were initially dened through unsupervised clustering analysis of global gene

2016 Breast Cancer Research Foundation/Macmillan Publishers Limited

ARTICLE OPEN

PAM50 gene signatures and breast cancer prognosis with adjuvant anthracycline- and taxane-based chemotherapy: correlative analysis of C9741 (Alliance)

signatures to stratify patients and individualize treatment based on expected risks of distant recurrence.

expression from RNA extracted from frozen tissue.1 A 50-gene qPCR assay (PAM50) was developed to identify the intrinsic biological subtypes using RNA isolated from more readily available formalin-xed, parafn-embedded (FFPE) tissue. These subtypes can also be assessed using a multiplexed gene-expression proling technology (NanoString Technologies; Seattle, WA, USA). The PAM50 assay was used to develop a prognostic risk of relapse score based on the relative distance to the centroid of each subtype;6 a proliferation score based on a subset of genes related to cell cycle progression;7 and composite scores that include tumor size with molecular phenotypes.6,7 Although each has prognostic capability, the utility of these scores to predict for specic treatment benet and select therapy has not been studied.

1Department of Oncology, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA; 2Department of Biostatistics and Bioinformatics, Alliance Statistics and Data Center, Duke University Medical Center, Durham, NC, USA; 3The Genome Institute, Washington University, St. Louis, MO, USA; 4Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA; 5Alliance for Clinical Trials in Oncology, University of Chicago, Chicago, IL, USA; 6Department of Pathology, Washington University, St. Louis, MO, USA; 7Department of Pathology, New York University Medical Center, New York, NY, USA; 8Department of Medicine, Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; 9Department of Medicine, Mayo Clinic, Jacksonville, FL, USA; 10The Angeles Clinic and Research Institute, Santa Monica, CA, USA; 11Department of Medical Oncology, Hofstra North Shore-LIJ School of Medicine, ProHEALTH Care Associates, Lake Success, NY, USA; 12Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA; 13Department of Medicine, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; 14Department of Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA; 15Department of Pathology, Huntsman Cancer Center, University of Utah, Salt Lake City, UT, USA; 16Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, USA; 17Department of Genetics, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA; 18Department of Medical Oncology, Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA and 19Department of Biostatistics and Computational Biology, Alliance Statistics and Data Center, Dana Farber Cancer Institute, Boston, MA, USA.

Correspondence: MC Liu (mailto:[email protected]

Web End [email protected])

This work was presented in part at the 2012 CTRC-AACR San Antonio Breast Cancer Symposium.

Received 23 June 2015; revised 15 October 2015; accepted 16 November 2015

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Table 1. Patient and tumor characteristics

Variable All treated C9741 patients (N = 1,972)a Subset evaluable by PAM50 (N = 1,311)b P-valuec

Number of positive nodes; median (IQR) 3 (1, 5) 3 (1, 5) 0.655 Age in years; median (IQR) 50 (43, 57) 50 (43, 57) 0.697

Tumor size 2 cm 787 (40%) 478 (36%) o0.001 42 cm 1140 (58%) 803 (61%)

Missing 45 (2%) 30 (2%)

ER statusPositive 1275 (65%) 822 (63%) 0.022 Negative 663 (33%) 468 (36%)

Missing 34 (2%) 21 (1%)

PgR statusPositive 1108 (56%) 706 (54%) 0.008 Negative 821 (42%) 578 (44%)

Missing 43 (2%) 27 (2%)

Menopausal statusPre 976 (49%) 642 (49%) 0.513 Post 996 (51%) 669 (51%)

Treatment armSequentialq3 483 (25%) 314 (24%) 0.408 Sequentialq2 493 (25%) 343 (26%)

Concurrentq3 501 (25%) 330 (25%)

Concurrentq2 495 (25%) 324 (25%)

Recurrence-free survival at3 years (95% CI) 0.84 (0.82, 0.85) 0.83 (0.81, 0.85) 0.402 5 years (95% CI) 0.77 (0.75, 0.79) 0.76 (0.74, 0.79)10 years (95% CI) 0.67 (0.65, 0.69) 0.67 (0.64, 0.69)

Overall survival at3 years (95% CI) 0.92 (0.90, 0.93) 0.91 (0.90, 0.93) 0.403 5 years (95% CI) 0.85 (0.83, 0.86) 0.84 (0.82, 0.86)10 years (95% CI) 0.72 (0.70, 0.74) 0.72 (0.69, 0.74)

Abbreviations: CI, condence interval; ER, estrogen receptor; IQR, interquartile range.

aN = 1,973 patients were reported in the primary manuscript, but one patient was later excluded having never begun treatment.

bN = 1,311 because 10 patients with PAM50 genomic results never started protocol directed therapy.

cP-values are for comparisons of the 1,311 patients evaluable for PAM50 versus the 661 treated patients who were not evaluable. Comparisons for categorical variables use Pearson's 2 test; for continuous variables use Wilcoxon rank-sum tests; and for time-to-event variables use logrank tests.

The CALGB (Alliance) 9741 adjuvant node-positive breast cancer trial randomized treatment with doxorubicin (A), cyclophosphamide (C), and paclitaxel (T) using a 2 2 factorial design. The two factors were (i) 2-weekly (dose dense; DD) versus 3-weekly administration and (ii) sequential (A T C) versus concurrent (AC T) chemotherapy. DD therapy improved recurrence-free survival (RFS) and overall survival (OS).8 No survival differences were observed between concurrent and sequential administration, and no interaction between density and sequence was identied. An unplanned retrospective subset analysis suggested an interaction between estrogen receptor (ER) status and DD-therapy benet.9 We hypothesized that the increased prognostic accuracy of PAM50 would allow for the prediction of benet with DD scheduling.

RESULTSPatient and tumor characteristicsPAM50 intrinsic subtype calls were generated for 1,321 of 1,471 patients (90%) with evaluable blocks or slides. There was a slight, but statistically signicant, enrichment of ER-negative and

progresterone receptor-negative cancers (both Po0.05) in the PAM50 sample set relative to the treated study population (N = 1,972). On average, tumor size was larger in the PAM50 subset (Po0.001), as expected with considerations for sample acquisition and processing. Treatment assignment and other patient characteristics remain well balanced in the evaluable population (Table 1).

At 12.3 years median follow-up in all treated patients, 664 recurrences or deaths have been recorded in C9741, 452 of which occur in the subset evaluable by PAM50. With updated outcomes information, the overall treatment effects are consistent with the primary C9741 clinical trial results.8 No differences in RFS or OS were observed between sequential versus concurrent chemotherapy: hazard ratio (HR) = 1.06 (95% condence interval (CI) = 0.911.24, P = 0.43) and HR = 1.04 (95% CI = 0.891.23, P = 0.63), respectively. Improved outcomes were seen in patients who received DD treatment: HR = 1.26 for RFS (95% CI = 1.081.47, P = 0.003) and HR = 1.21 for OS (95% CI = 1.031.43, P = 0.019). The effect of dose density on RFS and OS is slightly attenuated in the PAM50 subset (HR = 1.20 (95% CI = 0.991.44) and HR = 1.15 (95% CI = 0.951.40), respectively) and with the smaller sample size does not reach statistical signicance at the 0.05 level.

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Time from study entry (yrs)

LuminalA LuminalB Her2Enriched

BasalLike

Number at risk414 385 335 293 256 125 338 295 253 203 172 81 266 207 171 146 126 57 293 219 195 173 159 69

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0.4

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logrankp < 0.0001

0.0

Time from study entry (yrs)

LuminalA LuminalB Her2Enriched

BasalLike

Number at risk

414 407 363 319 277 148 338 330 288 232 195 97 266 236 196 164 139 64 293 243 205 181 164 80

Figure 1. (a) KaplanMeier plot of RFS in C9741 patients classied by PAM50 intrinsic subtype: basal-like, HER2-E, LumA, and LumB. (b) KaplanMeier plot of OS in C9741 patients classied by PAM50 intrinsic subtype: basal-like, HER2-E, LumA, and LumB. OS, overall survival; RFS, recurrence-free survival.

Distribution of PAM50 subtypes and prognostic value for RFS and OS

PAM50 generated 414 (32%) LumA, 338 (26%) LumB, 266 (20%) HER2-E, and 293 (22%) basal-like calls. Patient characteristics were broadly distributed across intrinsic subtypes (Supplementary Table1). The relationship between PAM50 intrinsic subtype and clinical prognostic factors was consistent with previous reports, including the enrichment of LumA cancers in postmenopausal patients and among smaller tumors. Randomized treatment assignment was well balanced across subtypes.

The prognostic relationship between intrinsic subtype and RFS was statistically signicant (logrank P = 0.0001) and demonstrated patterns consistent with previous studies (Figure 1a).6 A higher rate of recurrence was observed with LumB (HR = 1.50, 95% CI = 1.161.93), HER2-E (HR = 1.70, 95% CI = 1.302.22), and basal-like (HR = 1.66, 95% CI = 1.282.16) tumors relative to LumA tumors. Furthermore, basal-like and HER2-E subtypes have substantially higher rates of recurrence than LumA and LumB in

years 03 and lower rates of recurrence afterwards. The independent prognostic value of intrinsic subtype after adjusting for the number of positive nodes and menopausal status is summarized in Table 2. A similar relationship between intrinsic subtype and OS is demonstrated (Figure 1b).

PAM50 intrinsic subtype does not predict benet with DD therapy The ability of PAM50 subtype to predict for benet with adjuvant

DD chemotherapy was evaluated as a test of interaction between dose density and the four subtype calls. No statistically signicant association with RFS (3 df, P = 0.44) or OS benet (3 df, P = 0.65) was identied. As an exploratory analysis, levels of RFS benet from DD treatment were evaluated within patient subsets dened by PAM50 and patient/tumor characteristics (Figure 2). The forest plot suggests that the benet of dose density was most substantial in the basal-like and HER2-E subtypes. A larger study is required to conrm this effect.

PAM50 proliferation and ROR-PT scores: prognostic and predictive value

Proliferation score and ROR-PT score were considered as continuous variables in all inferential tests because the thresholds for classication (i.e., cutoff values for high/intermediate/low risk) had not been established in this patient population. A strong positive correlation was observed between proliferation score and ROR-PT score (r = 0.72), and each is associated with intrinsic subtype as expected from the shared genomic features to each algorithm (Figure 3a).

Proliferation and ROR-PT scores were strongly prognostic for RFS in C9741 when evaluated as linear terms in the Cox proportional hazard model. For proliferation score, a 0.5-unit change corresponded to an 18% increase in risk of recurrence (HR = 1.17, 95% CI = 1.101.26, Po0.0001, Figure 3b). For ROR-PT score, a 10-unit change corresponded to a 12% increase in risk of recurrence (HR = 1.12, 95% CI = 1.071.18, Po0.0001, Figure 3c).

Menopausal status did not affect the prognostic value of these scores (Supplementary Figure 2). Conversely, no statistically signicant associations with DD therapy were seen using interaction tests in the bivariable Cox models (1 df, P = 0.14 and0.58, respectively). Similar prognostic relationships between RFS and intrinsic subtype, proliferation score, and ROR-PT score were found in HER2-negative patients as a planned subset analysis (N = 848; data not shown). No interaction between dose density and RFS was observed, driven partly by the lack of overall benet observed in this patient subgroup (HR = 1.03 for RFS, 95% CI = 0.821.30, P = 0.7958).

The hazard of breast cancer recurrence is known to change over time by intrinsic subtype10 and proliferation,11 and this was observed in C9741 (Grambsch and Therneau, Po0.0001 for each).

Therefore, we performed a sensitivity analysis of the predictive value of the PAM50 assay for early recurrence by 3 years and all recurrences by 10 years. The relative benet of DD therapy for early and late recurrence was explored across each PAM50 molecular score using nonparametric spline regression models (Supplementary Figure 3). The signicant increases in overall risk of recurrence are seen most strongly in the lower ranges of proliferation and ROR-PT scores, whereas nonsignicant trends of DD-therapy benet are seen only with higher scores. The KaplanMeier estimates and hazard ratios of DD therapy are displayed in Figures 2 and 3 using cut-points that give approximately equally sized tertiles. The greatest prognostic difference is between low versus intermediate/high, whereas the nonsignicant trends of predicting DD-therapy benet occur only in patients with intermediate/high scores (Supplementary Figures 3A and D). Determinations of optimal thresholds and statistical signicance will require validation in independent data sets and were not performed as part of this exploratory analysis.

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Table 2. Multivariable Cox proportional hazard models of RFS and OS

Variable (contrast) Recurrence-free survival Overall survival

Hazard ratio P-value Hazard ratio P-value

Number of positive nodes (sqrt) 2.17 (1.82, 2.60) o0.0001 2.18 (1.82, 2.63) o0.0001 Menopausal status (pre/post) 0.91 (0.75, 1.10) 0.3276 0.88 (0.72, 1.08) 0.2284

Dose density (q3wk/q2wk) 1.20 (0.99, 1.45) 0.0582 1.15 (0.94, 1.40) 0.1671 Sequence of therapy (con/seq) 0.98 (0.81, 1.18) 0.8314 0.98 (0.80, 1.19) 0.8174

PAM50 intrinsic subtypeBasal-like versus LumA 1.83 (1.40, 2.38) o0.0001 1.91 (1.44, 2.53) o0.0001 HER2-E versus LumA 1.63 (1.24, 2.15) 1.69 (1.27, 2.26)

LumB versus LumA 1.47 (1.14, 1.91) 1.47 (1.12, 1.94)

Abbreviations: con, concurrent; HER2-E, HER2-enriched; Lum, luminal; q2wk, every two weeks or 2-weekly; q3wk, every 3 weeks or 3-weekly; seq, sequential; sqrt, square root.

N = 1,299 because 11 patients were missing information about the number of positive nodes or menopausal status.

Comparison of PAM50 phenotypes to immunohistochemistry assessments of ER/HER2, Ki67, CK5/6, epidermal growth factor receptor

Substantial agreement was seen between site-determined and centralized assessments of ER by tissue microarray (Cohens = 0.78). Common relationships between intrinsic subtypes are noted for the 1,024 cases with both PAM50 subtype call and ER/HER2 immunohistochemistry (IHC) results (Table 3). LumA and LumB tumors were predominantly ER-positive. Basal-like tumors were predominantly ER-negative. The distribution of intrinsic subtypes did not vary by HER2 IHC staining when stratied by ER status (MantelHaenszel 2, P = 0.43). Ki67-positive tumors were highly enriched in basal-like and to a lesser degree in HER2-E subtypes relative to the luminal subtypes (Pearsons 2,

Po0.0001). Similar patterns were seen for cytokeratin (CK) 5/6 and epidermal growth factor receptor (EGFR) 1+/2+ tumors (mean score 2, Po0.0001).

When considering breast cancer subtypes by PAM50 and clinicopathologic variables using the multivariable Cox models in Table 2, the prognostic value of PAM50 intrinsic subtype remained statistically signicant in a model including subtype by both assessments (P = 0.004). Conversely, RFS did not vary signicantly by IHC subtype dened by ER/HER2 alone (P = 0.31) or in conjunction with Ki67, CK5/6, and EGFR (P = 0.12). Thus, the cumulative prognostic value of PAM50 and ER/HER2 by IHC is largely captured by intrinsic subtype alone in this cohort of patients (data not shown).

DISCUSSIONPrecision medicine in oncology has been spurred in part by the availability of multigene-based mRNA expression assays intended to add prognostic and predictive value to traditional markers of risk (e.g., tumor size, nodal status). For breast cancer, the identication of several molecular subgroups with distinct clinical outcomes is possible through commercial assays.1215 Because of similar prognostic performance, it is likely these signatures are derived from similar biologic principles. Unfortunately, none reliably predict for benet with specic chemotherapeutics, including the addition of taxanes. Practically speaking, there is great need for a single platform that can be applied to all breast primaries, performed on small amounts of routinely processed tissue (e.g., FFPE), assessed in local laboratories, and easily interpreted for general clinical use.

High quality tumor samples from a large, representative subset of participants in C9741 were available for this study. PAM50 was assessed with the same nanotechnology-based nCounter

digital gene-expression platform as the Prosigna Breast Cancer Prognostic Gene Signature Assay. All subtypes were represented in a distribution similar to that of other populations unselected for hormone receptor or HER2 status.6 Our ndings conrm the prognostic value of the PAM50 intrinsic subtype identied in smaller studies of patients treated with contemporary adjuvant anthracycline and taxane-based regimens, including GEICAM/ 9906.16 Intrinsic subtype, proliferation score, and ROR-PT score were strongly associated with RFS and OS irrespective of treatment assignment and independent of standard clinicopatho-logic variables. Comparison of subtypes dened by PAM50 or IHC demonstrates that prognosis is most reliably determined by intrinsic subtype as opposed to conventional assessments of ER/ HER2 status.

PAM50 testing also identied patterns of recurrence. Higher rates of recurrence were observed with the non-luminal versus luminal subtypes between years 0 and 3, but lower rates of recurrence were observed thereafter. This is consistent with clinical observations of late recurrences in endocrine responsive breast cancer and early recurrences in the poor prognosis subset of triple-negative and untreated HER2-positive disease, suggesting that intrinsic subtyping is more precisely informative.

The updated survival benets associated with DD AC T remain unchanged.8,17 The strong prognostic differences by PAM50 intrinsic subtype, proliferation score, and ROR-PT score were seen regardless of treatment assignment, but no test was specically predictive of subgroups with greater or lesser benet from DD scheduling. As expected, a suggestion of greatest benet was observed in the chemotherapy sensitive (i.e., basal-like and HER2-E) subtypes and with higher ROR-PT scores and proliferation. Therefore, one cannot denitively conclude that PAM50 does not predict for DD-therapy benet, as this study may be under-powered to detect such an interaction. (Neo)adjuvant chemotherapy benet in the higher PAM50 recurrence risk subtypes has been previously reported.6,18,19 Available data are concordant in that the LumA subtype is associated with a more favorable natural history and greater sensitivity to endocrine therapy, whereas the basal-like and HER2-E subtypes are associated with poorer clinical outcomes and greater sensitivity to chemotherapy. In contrast, the prognosis at 10 years for the LumB group is as poor as the HER2-E and basal-like groups.

Intrinsic subtyping was prognostic in this study, but improved survival with DD treatment in patient subgroups dened by intrinsic subtype did not reach statistical signicance in a full interaction model. This may be attributed to a lack of power in the smaller evaluable subset of patients treated in C9741, and to the strong prognostic differences between LumA and LumB versus

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VariableNo. Pos. Nodes

134 or more Tumor size

<=2cm>2cm PAM50 Subtype

BasalLike Her2Enriched LuminalA LuminalB Proliferation score

<= 0> 0 RORPT

<= 50> 50IHC Subtype

ER HER2 ER HER+ ER+ HER2 ER+ HER2+ IHC Ki67

Neg Pos IHC CK5/6

012IHC EGFR

0

1

2

Total

N

778 520

478 803

293 266 414 338

581 730

421 860

269

93 516 146

672 370

652 308

71

822 182

63

1311

HR (95% CI)

1.30 (0.98, 1.72)1.13 (0.88, 1.45)

1.17 (0.83, 1.66)1.18 (0.94, 1.47)

1.43 (0.99, 2.09)1.36 (0.92, 1.99)1.16 (0.80, 1.68)0.98 (0.69, 1.40)

1.08 (0.80, 1.46)1.29 (1.02, 1.63)

0.93 (0.64, 1.34)1.30 (1.04, 1.61)

1.18 (0.79, 1.76)1.46 (0.74, 2.89)0.98 (0.73, 1.31)1.10 (0.62, 1.95)

1.04 (0.80, 1.35)1.19 (0.85, 1.66)

1.00 (0.77, 1.29)1.14 (0.79, 1.65)1.60 (0.58, 4.41)

1.07 (0.85, 1.36)1.33 (0.83, 2.13)0.75 (0.32, 1.80)

1.19 (0.99, 1.44)

0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25

DD is better

DD is worse

Figure 2. Forest plot displaying hazard ratios (HR) and 95% condence intervals (CI) for RFS with DD therapy in patient subgroups from C9741 dened by tumor characteristics (number of positive nodes and tumor size), PAM50 assay (intrinsic subtype, proliferation score, and ROR-PT score), and immunohistochemistry (ER/HER2, Ki67, CK5/6, and EGFR). CK, cytokeratin; DD, dose dense; EGFR, epidermal growth factor receptor; ER, estrogen receptor; IHC, immunohistochemistry; N, number of subjects; RFS, recurrence-free survival; ROR-PT, risk of recurrence score.

Hazard Ratio

the basal-like and HER-E subtypes. The prediction of treatment benet remains a key goal, and clinical validation studies will further assess the ability of the PAM50 gene signature to stratify patients on the risk of distant recurrence and maximize the reliable identication of patients (i.e., the LumA population) with such favorable long-term outcomes that they should be spared unnecessary adjuvant chemotherapy.

MATERIALS AND METHODSPatient enrollment, sample acquisition, clinical outcome

Cancer and Leukemia Group B (CALGB) 9741 was conducted in collaboration with the Eastern Cooperative Group, Southwest Oncology Group, and North Central Cancer Treatment Group, accruing 2,005 subjects between September 1997 and March 1999.8 Clinical endpoints included OS and RFS, dened as the interval from study entry until rst local or distant recurrence or death owing to any cause.20 Survival analyses are based on updated clinical outcomes data collected through January 2012.

A total of 1,652 patients had FFPE primary breast tumor samples archived at the CALGB Pathology Coordinating Ofce (PCO), of which 1,471 were suitable for inclusion in this study. Gene-expression proles were generated for 1,321 of 1,471 patient samples (90%). Ten randomized subjects did not receive treatment and were excluded. The primary analysis therefore includes 1,311 patients in total (REMARK diagram,21

Supplementary Figure 1).

Sample preparation and multiplexed gene-expression proling The CALGB PCO provided batches of 96 tumor samples as block punches or slide material. To avoid technical batch effects, all available high, moderate, and poor slide materials were randomly assigned to batches by the CALGB (Alliance) Statistical Center using permuted-blocks. FFPE samples were sent to Washington University CLIA molecular laboratories for macrodissection of slide material (if needed) and RNA extraction using an RNA isolation kit and procedures provided by NanoString Technologies. Optical density of total RNA was measured at 260 and 280 nm to determine yield and purity using a low-volume spectrophotometer. RNA samples passed quality control if the measured concentration was 12.5 ng/l and the A260/280 ratio was 1.72.5. A second optical density measurement was taken for RNA samples that failed to meet the quality metrics before exclusion. Gene-expression proling was performed on a research-use-only nCounter Analysis System using the research-use-only PAM50 probe set. The hybridization reaction was performed according to procedures provided by NanoString Technologies using a nominal RNA input of 250 ng. The hybridization time was 1521 h using a bench-top thermocycler set to 65 C with a heated lid set to 70 C. Manufacturer's specications were used for the nCounter Prep Station, which prepares the hybridized products for imaging. The nCounter Digital Analyzer reports the digital counts representing the number of molecules labeled with a uorescent barcode for each probe-targeted transcript. The Digital Analyzer was set to scan at the max sensitivity setting dened as 1155 FOV (elds of view).

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100

80

Riskofrelapse (RORPT)

60

40

20

0

2 1

0

1 2

Proliferation Score

1.0

1.0

Proportion recurrencefree

0.8

0.8

Proportion recurrencefree

0.6

0.6

0.4

0.4

Prolif.

Low

Inter.

High

5yr RFS (95% CI)

0.85 (0.81, 0.88)

0.74 (0.70, 0.78)

0.70 (0.66, 0.75)

RORPT

Low

Inter.

High

5yr RFS (95% CI)

0.84 (0.80, 0.87)

0.73 (0.69, 0.77)

0.72 (0.67, 0.76)

0.2

0.2

0.0

0.0

0.0 2.5 5.0 7.5 10.0 12.5

0.0 2.5 5.0 7.5 10.0 12.5

Time from study entry (yrs)

Time from study entry (yrs)

Low Inter High

Number at risk437 402 353 302 265 124 438 364 310 258 225 107 436 340 291 255 223 101

Low Inter High

Number at risk444 403 359 314 277 136 424 340 292 244 215 95 413 339 280 239 206 95

Figure 3. (a) Scatterplot of proliferation and ROR-PT scores labeled by intrinsic subtype: basal-like (red), HER2-E (pink), LumA (dark blue), and LumB (light blue). Cutpoints that divide each score into tertiles are shown in gray. (b) KaplanMeier plot and 5-year RFS estimates for the low, intermediate (inter.), and high subgroups of proliferation (prolif.) scores. (c) KaplanMeier plot and 5-year RFS estimates for the low, intermediate, and high subgroups of ROR-PT scores. CI, condence interval; RFS, recurrence-free survival; ROR-PT, risk of recurrence score.

Raw gene-expression data (RCC les) were evaluated using pre-specied quality metrics and have been deposited in the Gene Expression Omnibus (GSE74821). The geometric mean of eight housekeeping genes was required to be above a minimum threshold to ensure gene-expression signal levels sufcient for accurate and precise results. Data that passed sample and assay quality metrics were provided in a blinded fashion to NanoString Technologies for normalization and analysis with a proprietary PAM50 algorithm.22 Gene-expression proles were returned as a four-level classier (LumA, LumB, basal-like, and HER2-E) based upon Pearsons distance to centroids re-trained on the nCounter platform; a proliferation score that represents an average of expression values for the subset of proliferation-related genes (expanded from 11 in the original model to 18 in the NanoString version);7 and a ROR-PT score expanded from the original risk of relapse model.6 The NanoString ROR-PT algorithm includes distance to all centroids, proliferation score, and gross pathologic tumor size as terms to the model. ROR-PT scores were calculated by NanoString Technologies assuming that samples were from small (2 cm) or large (42 cm) tumors to maintain the blind-to-patient information.

Immunohistochemical analysesCentralized whole-section analysis results for HER2 were available from 1,224 of the 1,652 C9741 patients with submitted FFPE primary breast tumor samples. HER2 staining was performed with the CB11 monoclonal antibody (BioGenex Laboratories, San Ramon, CA, USA; #MU134-UC). Cases were considered positive with staining of 50% carcinoma cells.23,24

Blocks suitable for inclusion on a tissue microarray were obtained from 1,231 C9741 patients and reviewed to identify representative areas of viable invasive breast carcinoma. Replicate 0.6 mm cores from each case were extracted and assembled into separate tissue microarrays at the

CALGB PCO using established methods.25 Duplicate blocks (i.e., two cores per patient) were used. A total of 26 sections (4 microns each) were cut and shipped to the Genetic Pathology Evaluation Centre, British Columbia Cancer Agency (Vancouver, BC, Canada) or the University of Colorado, School of Medicine (Denver, CO, USA). Guidelines for IHC-staining conditions and interpretation of the ER (LabVision, Fremont, CA, USA; #RM-9101), Ki67 (LabVision, #RM-9106), CK5/6 (Zymed Laboratories, San Francisco, CA, USA; Clone D5/16B4, #180267), and EGFR (Dako Corporation, Carpinteria, CA, USA; #K1492) assays were pre-specied.26,27 Staining

was performed within 1 week of tissue microarray sectioning, and all biomarker scoring was performed by pathologists blinded to patient data. For continuously quantied variables (ER, Ki67), the average between replicate cores was used. For semiquantitative variables (CK5/6, EGFR), the higher score was taken. Centralized IHC staining of ER and HER2 was available on 1,124 C9741 patients, including 1,024 of the cases successfully proled for PAM50.

Statistical analysisDescriptive statistics were used to summarize clinical and molecular endpoints. Contrasts of demographics and tumor characteristics between patient subgroups were evaluated using Pearsons 2 test with continuity correction for categorical variables, and Wilcoxon rank-sum tests for continuous variables. Survival functions for time-to-event endpoints and median follow-up were summarized using the KaplanMeier product limit estimator. HRs and CIs were estimated using univariable and multivariable Cox proportion hazards models. Planned prospective analyses of the interaction between dose density and PAM50 intrinsic subtype (categorical), proliferation score (continuous), and ROR-PT score (continuous) were performed using score tests for bivariable Cox proportional hazard models.

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Table 3. Prevalence of molecular phenotypes by IHC and PAM50 intrinsic subtype

Variable Basal-like (N = 293)

EPW, CAH, and WTB were involved in the data analysis and interpretation. All the authors contributed to data collection and assembly. All the authors provided nal manuscript approval for submission.

COMPETING INTERESTS

SRD has stock or other ownership in NanoString Techologies. PSB has a patent, intellectual property, or royalties from Bioclassier. TON has a consulting or advisory role with Bioclassier and a relationship with Nanostring Technologies. CMP has a leadership role and stock or other ownership with Bioclassier. MJE has a leadership role and stock or other ownership with Bioclassier, and he has a consulting or advisory role with NanoString Technologies. The remaining authors declare no conict of interest.

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2. Prat, A., Ellis, M. J. & Perou, C. M. Practical implications of gene-expression-based assays for breast oncologists. Nat. Rev. Clin. Oncol. 9, 4857 (2012).

3. Prat, A. et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 12, R68 (2010).

4. Prat, A. & Perou, C. M. Deconstructing the molecular portraits of breast cancer. Mol. Oncol. 5, 523 (2011).

5. Sorlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98, 1086910874 (2001).

6. Parker, J. S. et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27, 11601167 (2009).

7. Nielsen, T. O. et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor-positive breast cancer. Clin. Cancer Res. 16, 52225232 (2010).

8. Citron, M. L. et al. Randomized trial of dose-dense versus conventionally scheduled and sequential versus concurrent combination chemotherapy as postoperative adjuvant treatment of node-positive primary breast cancer: rst report of Intergroup Trial C9741/Cancer and Leukemia Group B Trial 9741. J. Clin. Oncol. 21, 14311439 (2003).

9. Berry, D. A. et al. Estrogen-receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer. JAMA 295, 16581667 (2006).

10. Jatoi, I. et al. Breast cancer adjuvant therapy: time to consider its time-dependent effects. J. Clin. Oncol. 29, 23012304 (2011).

11. Bianchini, G. et al. Proliferation and estrogen signaling can distinguish patients at risk for early versus late relapse among estrogen receptor positive breast cancers. Breast Cancer Res. 15, R86 (2013).

12. Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 28172826 (2004).

13. van't Veer, L. J. et al. Gene expression proling predicts clinical outcome of breast cancer. Nature 415, 530536 (2002).

14. Sotiriou, C. et al. Gene expression proling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J. Natl Cancer Inst. 98, 262272 (2006).

15. Ma, X. J. et al. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5, 607616 (2004).

16. Martin, M. et al. PAM50 proliferation score as a predictor of weekly paclitaxel benet in breast cancer. Breast Cancer Res. Treat. 138, 457466 (2013).

17. Norton, L. A Gompertzian model of human breast cancer growth. Cancer Res. 48, 70677071 (1988).

18. Cheang, M. C. et al. Responsiveness of intrinsic subtypes to adjuvant anthracycline substitution in the NCIC.CTG MA.5 randomized trial. Clin. Cancer Res. 18, 24022412 (2012).

19. Esserman, L. J. et al. Chemotherapy response and recurrence-free survival in neoadjuvant breast cancer depends on biomarker proles: results from the I-SPY 1 TRIAL (CALGB 150007/150012; ACRIN 6657). Breast Cancer Res. Treat. 132, 10491062 (2012).

20. Hudis, C. A. et al. Proposal for standardized denitions for efcacy end points in adjuvant breast cancer trials: the STEEP system. J. Clin. Oncol. 25, 21272132 (2007).

21. McShane, L. M. et al. Reporting recommendations for tumor marker prognostic studies. J. Clin. Oncol. 23, 90679072 (2005).

22. Dowsett, M. et al. Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy.J. Clin. Oncol. 31, 27832790 (2013).

HER2-E (N = 266)

LumA (N = 414)

LumB (N = 338)

IHC subtypea

Unknown 64 53 118 52ER HER2 161 (70%) 85 (40%) 15 (5%) 8 (3%) ER HER2+ 50 (22%) 27 (13%) 11 (4%) 5 (2%) ER+ HER2 14 (6%) 80 (38%) 208 (70%) 214 (75%) ER+ HER2+ 4 (2%) 21 (10%) 62 (21%) 59 (21%)

Ki67

Unknown 60 54 111 44 Neg. (o13.5%) 67 (29%) 112 (53%) 291 (96%) 202 (69%)

Pos. (13.5%) 166 (71%) 100 (47%) 12 (4%) 92 (31%)

CK5/6

Unknown 61 52 121 470 75 (32%) 121 (57%) 225 (77%) 231 (79%) 1 99 (43%) 88 (41%) 61 (21%) 60 (21%) 2 58 (25%) 5 (2%) 7 (2%) 1 (0%)

EGFR

Unknown 56 48 101 390 76 (32%) 161 (74%) 297 (95%) 288 (96%) 1 118 (50%) 41 (19%) 13 (4%) 10 (3%) 2 43 (18%) 16 (7%) 3 (1%) 1 (0%)

Abbreviations: CK, cytokeratin; EGFR, epidermal growth factor receptor; HER2-E, HER2-enriched; IHC, immunohistochemistry; Lum, luminal; Neg., negative; Pos., positive.

Column percentages are computed excluding samples with unknown status by IHC.

aER-positivity is dened by 1% positive tumor nuclei. HER2-positivity is dened by staining of 450% carcinoma cells.

PAM50 gene signatures and adjuvant chemotherapy MC Liu et al

Planned comparisons of molecular phenotypes by PAM50 and IHC were performed using likelihood ratio tests for nested multivariable Cox models. Correlation between molecular phenotypes was evaluated using Pearsons 2 tests for binary covariates and MantelHaenszel 2 tests for ordinal covariates and stratied models. All the tests used a two-sided type I error of alpha = 0.05. Exploratory analyses of molecular phenotypes were performed using nonlinear knotted cubic spline function (knots at evenly spaced quintiles) and logistic regression models for 3- and 10-year rates of recurrence.28

All statistical analyses were performed using SAS v9.2 (Cary, NC, USA). Graphics were generated in R version 2.15.0.29

ACKNOWLEDGMENTS

We are grateful to the patients for generously participating in the related studies. We also thank the CALGB, ECOG, NCCTG, and SWOG investigators and research coordinators for their efforts on behalf of the patients and protocol. This work was supported by grants from the National Cancer Institute for Strategic Partnering to Evaluate Cancer Signatures (SPECS; MJE, CMP, PSB, and TON; CA114722), to the Washington University Pathology Coordinating Ofce (CA114736), to the Alliance for Clinical Trials in Oncology (Monica M. Bertagnolli; Chair; CA31946), to the Alliance Statistics and Data Center (Daniel J. Sargent; CA33601), and to the NCCTG (CA025224), ECOG (CA21115), and SWOG (CA32102). Additional funding was provided by the Breast Cancer Research Foundation. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the ofcial views of the National Cancer Institute. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE74821 (https://www.ncbi.nlm.nih.gov/geo/query/ acc.cgi?acc=GSE74821).

CONTRIBUTIONS

MCL and WTB are the guarantors of the research ndings presented. MCL, EPW, CAH, LAC, CMP, MJE, and WTB were involved in the study concept and design. MCL, BNP,

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PAM50 gene signatures and adjuvant chemotherapy MC Liu et al

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23. Thor, A. D. et al. ErbB-2, p53, and efcacy of adjuvant therapy in lymph node-positive breast cancer. J. Natl Cancer Inst. 90, 13461360 (1998).

24. Hayes, D. F. et al. HER2 and response to paclitaxel in node-positive breast cancer.N. Engl. J. Med. 357, 14961506 (2007).25. Rimm D. L. et al. Cancer and Leukemia Group B Pathology Committee guidelines for tissue microarray construction representing multicenter prospective clinical trial tissues. J. Clin. Oncol. 2011; 29: 22822290.

26. Cheang, M. C. et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J. Natl Cancer Inst. 101, 736750 (2009).

27. Cheang, M. C. et al. Basal-like breast cancer dened by ve biomarkers has superior prognostic value than triple-negative phenotype. Clin. Cancer Res. 14, 13681376 (2008).

28. Harrell F. E. Regression modeling strategies: R package version 3.6-3. http://CRAN.R-project.org/package = rms (2013).

29. R Development Core Team. R: A Language and Environment for Statistical Computing. The R Foundation for Statistical Computing: Vienna, Austria (2012).

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