Breast cancer molecular profiling with single sample predictors: a retrospective analysis

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1 Breast cancer molecular rofiling with single samle redictors: a retrosective analysis Britta Weigelt,* Alan Mackay,* Roger A hern, Rachael Natrajan, David S P Tan, Mitch Dowsett, Alan Ashworth, Jorge S Reis-Filho Summary Background Microarray exression rofiling classifies breast cancer into five molecular subtyes: luminal A, luminal B, basal-like,, and normal breast-like. Three microarray-based single samle redictors (SSPs) have been used to define molecular classification of individual samles. We aimed to establish agreement between these SSPs for identification of breast cancer molecular subtyes. Methods Previously described microarray-based SSPs were alied to one in-house (n=53) and three ublicly available (n=779) breast cancer datasets. Agreement was analysed between SSPs for the whole classification system and for the five molecular subtyes individually in each cohort. Findings Fair-to-substantial agreement between every air of SSPs in each cohort was recorded (κ= ). Of the five molecular subtyes, only basal-like cancers consistently showed almost-erfect agreement (κ> 812). The roortion of cases classified as basal-like in each cohort was consistent irresective of the SSP used; however, the roortion of each remaining molecular subtye varied substantially. Assignment of individual cases to luminal A, luminal B,, and normal breast-like subtyes was deendent on the SSP used. The significance of associations with outcome of each molecular subtye, other than basal-like and luminal A, varied deending on SSP used. However, different SSPs roduced broadly similar survival curves. Interretation Although every SSP identifies molecular subtyes with similar survival, they do not reliably assign the same atients to the same molecular subtyes. For molecular subtye classification to be incororated into routine clinical ractice and treatment decision making, stringent standardisation of methodologies and definitions for identification of breast cancer molecular subtyes is needed. Funding Breakthrough Breast Cancer, Cancer Research UK. Introduction Breast cancers are a diverse and heterogeneous grou of diseases, and clinicoathological features and the status of oestrogen recetor and guide treatment of atients. However, these variables are not sufficient for individualisation of theray. Microarray-based gene exression rofiling has led to a aradigm shift in the way breast cancer is erceived, and has shown conclusively that breast cancer is not a single disease at the molecular level. 1 6 The molecular heterogeneity of breast cancer and its imlications are gradually being incororated into clinical trial design, 7 and treatment strategies are being tailored to secific subgrous of atients with breast cancer whose tumours have articular molecular aberrations (eg, trastuzumab or laatinib for women with -ositive disease). Microarray-based gene exression rofiling has led to a working model for a breast cancer molecular taxonomy, 1 3 which has become widely used. Breast cancers can be classified by hierarchical cluster analysis, using an intrinsic gene list, into one of five molecular subtyes: luminal A, luminal B, basal-like,, and normal breast-like. 1 5,8 However, this aroach can only be alied retrosectively to sufficiently sized cohorts of atients. 8 An alternative strategy for classification of single samles into one of these molecular subtyes is the single samle redictor (SSP), 3 5 which is based on similarities between a given case and molecular subtye centroids. 3,4 Breast cancer molecular subtyes identified by these aroaches have been suggested to have distinct clinical resentations, 9 sites of relase, 1 histological features, 11 resonses to chemotheray, 5,12 and outcomes. 2,4,8,13 For consistent and clinically alicable molecular subtye assignment of breast cancers, a standardised methodology with reroducible results is fundamental. 13 In the ast 1 years, five distinct intrinsic gene lists 1 5 and three different SSPs 3 5 have been described to classify breast cancers into molecular subtye classes. Molecular subtyes identified in searate studies with different SSPs are assumed to be similar, if not identical, with resect to their clinical, biological, and rognostic characteristics (ie, luminal A cancers in study A identified by the SSP-a are synonymous with luminal A cancers in study B identified by the SSP-b). Crucially, however, the validity of this assumtion has not been tested systematically. Nevertheless, some researchers have advocated that this molecular taxonomy should be used to design clinical studies 6,14 and to guide decision-making regarding theray. 14 The aims of this study were to assess the clinical usefulness of SSPs by establishing agreement between different methods of breast cancer molecular subtye Lancet Oncol 21; 11: Published Online February 23, 21 DOI:1.116/S (1)78-5 See Reflection and Reaction age 36 *Contributed equally Cancer Research UK, London Research Institute, London, UK (B Weigelt PhD); The Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, London, UK (A Mackay BSc, R Natrajan PhD, D S P Tan MD, Prof M Dowsett PhD, Prof A Ashworth FRS, J S Reis-Filho FRCPath); Cancer Research UK Clinical Trials & Statistics Unit, Section of Clinical Trials, Institute of Cancer Research, Surrey, UK (R A hern MSc); and Academic Deartment of Biochemistry, The Royal Marsden Hosital, London, UK (Prof M Dowsett) Corresondence to: Dr Jorge S Reis-Filho, The Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, 237 Fulham Road, London SW3 6JB, UK Jorge.Reis-Filho@icr.ac.uk Centroid Mean exression rofiles for each of the five molecular subtyes Vol 11 Aril

2 For the R code used see htt:// rock.icr.ac.uk/collaborations/ Mackay/ For ArrayExress see htt:// See Online for webaendix assignment (ie, do different SSPs assign the same atients to the same molecular subtye?), and to ascertain whether each SSP identifies molecular subtyes with similar associations with outcome. To address these objectives, we analysed a cohort of consecutive invasive ductal carcinomas of no secial tye of histological grade III, which were microdissected to minimise the effect of varying amounts of stromal contamination, 15 and three cohorts of breast cancer samles in the ublic domain Methods Samle collection We obtained 64 anonymised samles of consecutively accrued grade III invasive ductal carcinomas of no secial tye from Hosital La Paz, Madrid, Sain. Details of this cohort are described elsewhere. 15 We defined status with US Food and Drug Administration (FDA)-aroved methods for immuno histochemistry and dual-colour fluorescence in-situ hybridisation (FISH) as reviously described, 19 and we scored status according to guidelines of the American Society of Clinical Oncology and College of American Pathologists (webaendix 1 4). 2 We micro dissected reresentative frozen sections of tumours to ensure a consistent roortion of at least 9% tumour cells, and we extracted RNA with Trizol (Invitrogen, Paisley, UK) as reviously described cases roduced sufficient RNA of otimum quality and underwent geneexression rofiling with the Illumina Human WG6 version 2 array (Illumina Inc, San Diego, USA). Raw gene-exression values were robust-sline normalised with the lumi Bioconductor ackage 21 in R. Gene exression data are ublicly available at ArrayExress (ref E-TABM-543). A detailed descrition of the microarray analysis methods is rovided in the webaendix ( 1 4). The local research ethics committees aroved this roject. We analysed breast cancers from the ublicly available NKI-295 (n=295), 16 Wang (n=286), 17 and TransBig (n=198) 18 datasets for molecular subtye classes and association with outcome. We obtained normalised microarray-based gene-exression data and clinical findings from the internet or ublic reositories (NKI-295, 22 Wang [GEO:GSE234], TransBig [GEO:GSE739]). Details for the cohorts are rovided in the webaendix ( 5). Procedures Methods related to maing of centroid/ssp gene lists and breast cancer datasets and molecular subtye assignment are described in detail in the webaendix ( 1 4). We have attemted to follow details for SSP analysis described in ublished work. 3 5 To minimise the need to make assumtions, we tested several methods of gene and robe annotations and of handling of multile robes for the same gene (webaendix 1 4). We used the method that most accurately reroduced the Grade III invasive ductal carcinomas (n=53) NKI-295 dataset (n=295) Wang dataset (n=286) TransBig dataset (n=198) No cut-off Cut-off < 1* No cut-off Cut-off < 1* No cut-off Cut-off < 1* No cut-off Cut-off < 1* Sorlie SSP Basal-like 13 (25%) 13 (25%) 47 (16%) 46 (16%) 49 (17%) 46 (16%) 3 (15%) 3 (15%) 46 (16%) 39 (13%) 9 (3%) 7 (2%) 11 (6%) 7 (4%) Luminal A 7 (13%) 6 (11%) 96 (33%) 68 (23%) 14 (36%) 85 (3%) 64 (32%) 43 (22%) Luminal B 33 (62%) 29 (55%) 77 (26%) 5 (17%) 124 (43%) 1 (35%) 9 (45%) 65 (33%) Normal breast-like 29 (1%) 15 (5%) 3 (2%) Unclassified NA 5 (9%) NA 77 (26%) NA 48 (17%) NA 53 (27%) Hu SSP Basal-like 14 (26%) 12 (23%) 56 (19%) 53 (18%) 53 (19%) 47 (16%) 4 (2%) 31 (16%) 8 (15%) 37 (13%) 32 (11%) 42 (15%) 21 (7%) 3 (15%) 15 (8%) Luminal A 31 (58%) 14 (26%) 133 (45%) 127 (43%) 171 (6%) 152 (53%) 119 (6%) 11 (56%) Luminal B 48 (16%) 47 (16%) 19 (7%) 17 (6%) 9 (5%) 9 (5%) Normal breast-like 21 (7%) 19 (6%) 1 (<1%) Unclassified NA 27 (51%) NA 17 (6%) NA 49 (17%) NA 33 (17%) Parker SSP Basal-like 13 (24%) 13 (24%) 48 (16%) 47 (16%) 54 (19%) 52 (18%) 41 (21%) 41 (21%) 54 (18%) 53 (18%) 15 (5%) 15 (5%) 11 (6%) 11 (6%) Luminal A 4 (8%) 3 (6%) 9 (31%) 89 (3%) 65 (23%) 61 (21%) 63 (32%) 63 (32%) Luminal B 36 (68%) 35 (66%) 78 (26%) 77 (26%) 139 (49%) 138 (48%) 75 (38%) 73 (37%) Normal breast-like 25 (8%) 24 (8%) 13 (5%) 11 (4%) 8 (4%) 7 (4%) Unclassified NA 2 (4%) NA 5 (2%) NA 9 (3%) NA 3 (2%) Data are n (%). SSP=single samle redictor. NA=not assessable because all samles are classified. *Correlation with centroid/ssp < 1. Table 1: Molecular subtye classifications of breast cancers 34 Vol 11 Aril 21

3 reviously ublished 23,24 molecular subtye assignments of the NKI-295 dataset cancers with SSPs described by Sorlie and colleagues 3 and Hu and coworkers. 4 In brief, annotations of centroid/ssp gene lists and breast cancer datasets were comrehensively udated and maed to build 36 of the human genome (Ensembl assembly 54); annotation overlays were done with Human Genome Organisation (HUGO) gene symbols. We averaged multile robes that identified a gene in a centroid/ssp gene list. To define whether a tumour ertained to the basal-like,, luminal A, luminal B, or normal breastlike molecular subtye class, we correlated the gene exression of each tumour with 5 gene centroids by Sorlie and colleagues 3 (Sorlie s SSP), 36 gene centroids by Hu and coworkers 4 (Hu s SSP), and 5 gene centroids (PAM5) by Parker and colleagues 5 (Parker s SSP). A Sweave document is rovided in the webaendix ( 62 76). Statistical analysis Cohen s κ coefficient 25 was used to measure agreement between molecular subtye assignments. We judged values of 1 2 slight agreement, 21 4 fair agreement, 41 6 moderate agreement, 61 8 substantial agreement, and almost-erfect agreement. 26 Survival curves were calculated by the Kalan-Meier method and comared with the log-rank test. We alied a roortional-hazards model by Cox s regression analysis to estimate s and 95% CI for overall survival (NKI-295 and TransBig datasets) and metastasis-free survival (Wang cohort, in addition to NKI-295 and TransBig cohorts). We fitted SSPs as categorical variables ie, we made no assumtion of a trend across subtyes. In a multivariate analysis, tumour size was fitted as continuous variable, tumour grade was fitted as an ordinal variable, and oestrogen-recetor status and nodal status were fitted as binary variables (ositive vs negative). We judged a two-tailed < 5 significant. We undertook a combined analysis to assess the erformance of SSPs simultaneously in all datasets. In this case, we stratified the Cox model by dataset; this rocess allowed baseline hazard functions to differ in each dataset but constrained the estimated s to be the same across the datasets. This technique is similar to meta-analysis, in which effects are estimated indeendently within datasets and then combined across datasets. It reduces the effect of confounding on hazard ratio estimates caused by inherent differences in rognosis between different datasets but, similar to meta- For Ensembl see htt://www. ensembl.org Sorlie SSP Hu SSP Basal Luminal A Luminal B Normal κ Basal Luminal A Luminal B Normal κ 53 grade III invasive ductal carcinomas Hu SSP 274 ( ) Basal 13 1 Luminal A 7 24 Luminal B Normal 8 Parker SSP 74 ( ) 238 ( ) Basal Luminal A Luminal B Normal NKI-295 dataset Hu SSP 532 ( ) Basal Luminal A Luminal B Normal Parker SSP 528 ( ) 656 ( ) Basal Luminal A Luminal B Normal (Continues on next age) Vol 11 Aril

4 Sorlie SSP Hu SSP Basal Luminal A Luminal B Normal κ Basal Luminal A Luminal B Normal κ (Continued from revious age) Wang dataset Hu SSP 459 ( ) Basal Luminal A Luminal B 1 18 Normal Parker SSP 439 ( ) 393 ( ) Basal Luminal A Luminal B Normal TransBig dataset Hu SSP 44 ( ) Basal 3 1 Luminal A Luminal B 1 8 Normal 21 9 Parker SSP 488 ( ) 475 ( ) Basal Luminal A Luminal B Normal SSP=single samle redictor. Table 2: Agreement between single samle redictors for molecular subtye assignments analysis, it uses all co horts in one analysis, maximising the ower to detect effects. Statistical analyses were done with R version 2.9. and SPSS version Role of the funding source The sonsors had no role in study design, data collection, data analysis, data interretation, writing of the reort, or in the decision to submit for ublication. BW, AM, and JSR-F had full access to raw data. BW, AM, MD, AA, and JSR-F took final resonsibility for the decision to submit the reort for ublication. Results First, we sought to define the exact methods used reviously to classify breast cancers according to three SSPs. In the SSP analysis rocess, arameters and rocedures were not described in sufficient detail in the original ublications. 3 5 To minimise the need to make assumtions on methodology for assignment of molecular subtyes and to reduce inconsistencies in analysis methods, we aimed to reroduce the molecular subtye redictions of the NKI-295 dataset of breast cancers, which were reviously classified into molecular subtyes by the Sorlie or Hu SSPs (webaendix 6 8). 23,24 Agreement in molecular subtye assignment between the methods tested here and those reviously ublished was and deended on three factors: (1) use of different gene annotations for maing of centroid/ssp gene lists and datasets; (2) use of all robes identifying a gene versus averaging of multile robes reresenting the same gene to establish correlations to a centroid/ssp gene list; and (3) inclusion or exclusion of unclassified cases with a correlation less than 1 to any centroid. 3,23 This cut-off was described by Sorlie and colleagues 3 as a conservative aroach to include only cases with a strong correlation to a molecular subtye (webaendix 1 4, 9). In view of the scarcity of detailed guidelines for microarray data rocessing and SSPs and the effect of the methodology used for assignment of cases, our findings indicate that breast cancer molecular subtye classifications done by other investigators with the same datasets might not necessarily reroduce those originally described Vol 11 Aril 21

5 The method that most accurately reroduced the reorted 23,24 molecular subtye assignments of NKI-295 breast cancers (κ 912 [95% CI ] for Sorlie s SSP and 897 [ ]) for Hu s SSP; webaendix 9) was chosen to define subtyes in subsequent analyses (ie, HUGO gene symbol annotations for SSP and dataset maing and averaging of multile robes matching a gene in a centroid/ssp gene list; webaendix 1 4). We retrieved assignments for NKI-295 tumours undertaken reviously; 23,24 only moderate agreement was seen between assignments made with the Sorlie 3,23 and Hu 4,12 SSPs when all cases were included (κ 527 [ ]). Substantial agreement was noted (κ 646 [ ]) after exclusion of 19 unclassified cases with weak correlation (< 1) to any centroids by Sorlie and colleagues 3,23 (webaendix 6 8). This finding suggests that, overall, concordance between different SSPs for classification of breast cancers into molecular subtyes might only be modest. We next ascertained molecular subtye classes (table 1) and agreement between subtye assignments roduced by the three distinct SSPs in the four breast cancer cohorts (table 2, webaendix 1 2). We recorded only fair-to-moderate agreement between classifications rovided by Sorlie s and Hu s SSPs (κ ; table 2), moderate-to-substantial agreement between Sorlie s and Parker s SSPs (κ , table 2), and fair-to-substantial agreement between Hu s and Parker s SSPs (κ ; table 2, webaendix 19 2). Tables 1 and 2 show that in all four cohorts, the number of cases assigned to, luminal A, luminal B, and normal breast-like molecular subtyes differed remarkably, as did the number of unclassified cases with a correlation less than 1 to any centroid (table 1, figures 1 3, webaendix 31 37). Of the five molecular subtyes, only the roortion of basal-like tumours was similar between the three distinct SSPs tested. None of the microdissected grade III invasive ductal carcinomas with a tumour-cell content of at least 9% A Sorlie SSP 3 NKI-295 dataset B 1 9 Sorlie SSP 23 3 Hu SSP 4 8 Parker SSP 5 ER status Histological grade Lymh-node status Tumour size Basal-like Luminal A Luminal B Normal breast-like Patients ER ositive ER negative Grade 1 Grade 2 Grade 3 Node ositive Node negative 2 cm >2 cm Overall survival (%) Basal-like Luminal A 96 Luminal B 77 Normal breast-like 29 < 1 Overall survival (%) C Hu SSP < D Parker SSP < Figure 1: Molecular subtye classification of NKI-295 breast cancers and overall survival of atients assigned to molecular substyes according to three single samle redictors ER=oestrogen recetor. SSP=single samle redictor. Vol 11 Aril

6 A Sorlie SSP 3 Hu SSP 4 Wang dataset B Sorlie SSP 23 3 Parker SSP 5 ER status Basal-like Luminal A Luminal B Normal breast-like Patients ER ositive ER negative Metastasis-free survival (%) = 372 Metastasis-free survival (%) C Hu SSP Basal-like Luminal A Luminal B Normal breast-like 19 1 = = Figure 2: Molecular subtye classification of Wang breast cancers and metastasis-free survival of atients assigned to molecular subtyes according to three single samle redictors ER=oestrogen recetor. SSP=single samle redictor. D Parker SSP 29 5 was assigned to the normal breast-like class by any of the three SSPs (table 1). Seven of 13 -amlified grade III invasive ductal carcinomas as indicated by immuno histochemistry and FISH were assigned to the molecular subtye grou with Hu s SSP, whereas the Sorlie and Parker SSPs assigned all - amlified cases to the luminal B subtye class (webaendix 1). We tested agreement between different SSPs for classification of molecular subtyes individually, for each dataset. Agreement between, luminal A, and luminal B subtyes classified with Sorlie s, Hu s, or Parker s SSPs was generally only fair to moderate (table 3, webaendix 21). In the Wang and TransBig cohorts, concordance in assignment of tumours to luminal B class between Sorlie s and Hu s SSPs, and between Hu s and Parker s SSPs, was only slight. Almost-erfect concordance was noted only for classification of the basal-like subtye by all three SSPs (κ ), which was also recorded when unclassified cases were included in the analysis (webaendix 21). Our findings show that, for assignment of, luminal A, luminal B, and normal breast-like subtye classes, agreement between distinct SSPs is only modest, but concordance for basal-like class is high. To assess whether each SSP identifies molecular subtyes with similar rognostic imlications, tumours of atients in the NKI-295, Wang, and TransBig cohorts were classified into molecular subtye grous and their association with outcome was calculated by Kalan-Meier analysis. Indeendent of the SSP used, molecular classification was associated significantly with overall survival (figure 1) and metastasis-free survival (webaendix 31) in the NKI-295 dataset and with metastasis-free survival in the Wang dataset (no information on overall survival is available ublicly for this cohort; figure 2). These associations were also noted when unclassified cases (ie, correlation < 1 to any centroid/ssp gene list) were included in the analysis, with the excetion of the assignment by Sorlie s SSP in the Wang dataset (webaendix 32 34). In the TransBig data set, molecular subtyes were only associated significantly with overall survival (figure 3) and metastasis-free survival (webaendix 35 37) when classifications were made with Hu s SSP; similar findings were seen when un classified cases were included in the Vol 11 Aril 21

7 A Sorlie SSP 3 TransBig dataset B 1 9 Sorlie SSP 23 3 Hu SSP 4 8 Parker SSP 5 ER status Histological grade Tumour size Basal-like Luminal A Luminal B Normal breast-like Patients ER ositive ER negative Grade 1 Grade 2 Grade 3 2 cm >2 cm Overall survival (%) Basal-like 3 11 Luminal A 64 Luminal B 9 Normal breast-like 3 = 547 C Hu SSP D Parker SSP Overall survival (%) = = Figure 3: Molecular subtye classification of TransBig breast cancers and overall survival of atients assigned to molecular subtyes according to three single samle redictors ER=oestrogen recetor. SSP=single samle redictor. analysis. Imortantly, the number and identity of cases assigned to molecular sub tyes in each cohort differed by the SSP used, with the excetion of basal-like subtye (figures 1 3, tables 1 and 2). Multivariate Cox s hazard analysis including histological grade, tumour size, lymh node status, and oestrogen recetor status showed that, in the NKI-295 dataset, molecular subtyes only added indeendent rognostic information when assigned by the Parker SSP for overall survival and by the Hu and Parker SSPs for metastasis-free survival (webaendix 22 25). Multivariate Cox s hazard analysis of the TransBig dataset (lymh-node negative atients only), which included histological grade, tumour size, and oestrogen recetor status, indicated that molecular subtyes only added indeendent rognostic information when assigned by the Hu SSP for overall survival and metastasis-free survival (webaendix 22 25). Similar results were seen when the 1 cutoff was alied (webaendix 22 25). For the Wang dataset, no information on tumour size and histological grade was available ublicly. To assess associations with outcome of each molecular subtye individually, circumventing the few samles assigned to each molecular subtye and maximising ower to detect effects, the NKI-295, Wang, and TransBig cohorts were combined into one dataset (table 4). This analysis indicated that all SSPs were rognostic for metastasis-free survival, and they were all rognostic for overall survival in the NKI-295 and TransBig cohorts (no overall survival data were available for the Wang dataset). This finding alied to both univariate and multivariate analyses, in which outcome was corrected for oestrogen recetor status, histological grade, and tumour size. Nodal status was not significant in many of the models. Since, in many instances, node ositivity was associated with a slight reduction in hazard, nodal status was omitted from these models. Consistent trends were seen across SSPs for all molecular subtyes. For examle, luminal A and normal breast-like subtyes seemed to have a reduced event rate relative to the basal grou, and the luminal B and subtyes were most likely to be associated with a higher event rate than the luminal A Vol 11 Aril

8 Sorlie vs Hu SSP Sorlie vs Parker SSP Hu vs Parker SSP 53 grade III invasive ductal carcinomas Basal-like 95 ( 854 to 1 47) 1 (1 to 1 ) 95 ( 854 to 1 47) NA NA NA Luminal A 195 ( 54 to 336) 296 ( 86 to 678) 11 ( 2 to 217) Luminal B NA 71 ( 513 to 98) NA Normal breast-like NA NA NA NKI-295 dataset Basal-like 847 ( 767 to 928) 962 ( 92 to 1 5) 837 ( 754 to 92) 482 ( 34 to 625) 591 ( 468 to 715) 729 ( 621 to 837) Luminal A 53 ( 434 to 625) 435 ( 325 to 545) 612 ( 524 to 7) Luminal B 39 ( 268 to 512) 361 ( 242 to 48) 563 ( 451 to 674) Normal breast-like 346 ( 165 to 527) 348 ( 172 to 524) 529 ( 347 to 71) Wang dataset Basal-like 928 ( 872 to 985) 87 ( 795 to 945) 92 ( 861 to 978) 193 ( 46 to 341) 219 ( 14 to 453) 487 ( 329 to 645) Luminal A 542 ( 458 to 626) 384 ( 274 to 494) 318 ( 242 to 393) Luminal B 154 ( 84 to 224) 35 ( 195 to 415) 14 ( 8 to 2) Normal breast-like NA NA 137 ( 14 to 379) TransBig dataset Basal-like 827 ( 724 to 93) 812 ( 76 to 918) 922 ( 855 to 989) 389 ( 198 to 58) 422 ( 154 to 691) 496 ( 39 to 682) Luminal A 463 ( 362 to 563) 478 ( 348 to 69) 473 ( 374 to 572) Luminal B 86 ( 19 to 153) 38 ( 176 to 44) 145 ( 58 to 232) Normal breast-like NA 535 ( 179 to 891) NA Data are κ. SSP=single samle redictor. NA=not alicable. Table 3: Agreement between single samle redictors for classification of each molecular subtye individually subtye. However, the significance of the association between luminal B and normal breast-like grous and metastasis-free survival was deendent on the SSP used for their assignment (table 4). Discussion Our findings show that different SSPs and methods used for molecular subtye assignment 3 5 roduce inconsistent results. The number of cases assigned to each molecular subtye differed deending on the SSP used, and agreement for each class was modest (ranging from fair to substantial), with the excetion of the basal-like subtye. These results indicate that assignment of a given atient to a molecular subtye other than basal-like is strongly deendent on the SSP used and that results from studies of a secific SSP cannot necessarily be generalised. However, the association between molecular subtye and outcome was fairly consistent, irresective of the SSP used for molecular classification. This finding suggests that for whole oulations, distinct SSPs might roduce similar associations with outcome. However, they do not allocate individual samles to a given molecular subtye reroducibly. This result could be accounted for in art by associations between molecular subtyes and clinico atho logical features linked to outcome (webaendix 26 29) and with exression levels of oestrogen recetor,, and roliferationrelated genes (webaendix 38 61). Taken together, our observations lend suort to the idea of a breast cancer molecular classification. However, they suggest that the use of current SSPs to allocate cases into, luminal A, luminal B, and normal breast-like subtyes might be remature for stratification of atients or in the context of clinical trials. The luminal B subtye was not identified reroducibly by different SSPs. Since distinction between luminal A and luminal B tumours seems to be driven by exression of roliferation-related genes, 4,14 the absence of consistency in allocation of these two subtyes should erhas not come as a surrise. Findings of several studies have shown that roliferation in oestrogen recetor-ositive breast cancers is a continuum, 27 and allocation of secific subgrous (eg, luminal A and B) might be arbitrary. One criticism levelled at molecular classification of breast cancer is that normal breast-like tumours could be an artifact derived from analysis of tumour secimens with a high roortion of normal tissue contamination. 4,5,8,14 Indeed, none of the microdissected breast cancer secimens with a tumour-cell content of at least 9% was assigned to the normal breast-like grou; however, the cases included in this study were all histological grade III, and normal breast-like cancers were reorted less frequently to be of grade III in the NKI-295 series (figure 1, webaendix 27). 11 Desite several lines of evidence to suggest that the roortion of stromal cells has a substantial effect on results of exression rofiling analysis and on gene classifiers, and that variable amounts of stromal cells in tumours introduce a confounding factor in interretation of results of microarray analysis, 28 no revious study of molecular taxonomy of breast cancer has taken the degree of stromal contamination into account. Standardisation of tissue comosition is needed if exression rofiling is to be used for breast cancer classification in clinical ractice. Several grous, including ours, have attemted to develo surrogate immunohistochemical markers for the molecular subtyes of breast cancer as defined by microarrays; however, results from these studies should be interreted with caution, in view of the low stability of classes other than basal-like. In fact, validity of multile surrogate markers for luminal B subtyes (oestrogen recetor (ER)-ositive and/or rogesterone recetor (PR)-ositive, and -ositive; 9,31 or ER-ositive and/or PR-ositive and either - ositive and/or high Ki67 exression) 3 remains to be established. Even for basal-like breast cancers, no international consensus exists about how these tumours should be defined by immuno histochemistry. 32,33 Most imortantly, data are scarce for direct comarisons Vol 11 Aril 21

9 Sorlie SSP Hu SSP Parker SSP Univariate Multivariate Univariate Multivariate Univariate Multivariate Metastasis-free survival Overall < 1 3 < 1 5 < 1 1 Basal-like ( ) ( ) ( ) ( ) ( ) ( ) 98 Luminal B 97 ( ) ( ) ( ) ( ) ( ) ( ) 154 Luminal A 54 ( 37 78) 1 73 ( ) ( 43 83) ( ) ( 3 65) < 1 81 ( ) 524 Normal breast-like 38 ( 16 89) ( ) ( ) ( ) ( 22 81) 9 88 ( ) 763 Luminal B vs 1 84 ( ) < ( ) ( ) < ( ) ( ) < ( ) 12 luminal A vs luminal A 3 3 ( ) < ( ) ( ) ( ) ( ) ( ) 62 Histological grade* ( ) ( ) ( ) 148 Oestrogen ( ) ( 4 1 4) ( ) 114 recetor status Tumour size ( ) ( ) ( ) 1 Overall survival Overall < 1 5 < 1 29 < 1 7 Basal-like ( ) ( ) ( ) ( ) ( ) ( ) 422 Luminal B 7 ( ) ( ) ( ) ( ) 21 7 ( ) ( ) 526 Luminal A 26 ( 15 45) < 1 52 ( ) ( 2 52) < 1 76 ( ) ( 14 41) < 1 52 ( ) 84 Normal breast-like 16 ( 5 54) 3 34 ( ) 95 2 ( 6 67) 9 45 ( ) ( 7 53) 2 4 ( ) 114 Luminal B vs 2 67 ( ) ( ) ( ) < ( ) ( ) < ( ) 5 luminal A vs luminal A 4 49 ( ) < ( ) ( ) < ( ) ( ) < ( ) 32 Histological grade* ( ) ( ) ( ) 25 Oestrogen ( 39 98) ( 31 89) ( 34 94) 26 recetor status Tumour size ( ) ( ) ( ) 7 SSP=single samle redictor. *Ordinal 1, 2, or 3. Log (tumour size [mm]). Table 4: Univariate and multivariate Cox s regression analysis of overall and metastasis-free survival for the NKI-295, Wang, and TransBig datasets combined between roosed immuno histochemical surrogates and microarray-defined molecular subtyes. 34 The PAM5 method 5 has rovided an aroach for breast cancer molecular classification, which is ossibly clinically alicable; however, reorted agreement between the PAM5-defined subtye and clinically ositive cases (as ascertained by immunohistochemistry and FISH) was only moderate. 5 Consistent with this observation, of cases classified as -ositive by FDA-aroved methods in our in-house dataset of grade III invasive ductal carcinomas, just over half were assigned to the molecular subtye by Hu s SSP, whereas all these cases were assigned to the luminal B subtye class by the other two SSPs. These results show that the grou, as defined by microarray gene rofiling analysis, does not equate with the clinical subgrou of -ositive breast cancers. Since atients with -ositive cancers are eligible for targeted treatments (ie, trastuzumab or laatinib), extensive misassignment of these cases indicates that microarraybased subtying should not be used to decide treatment for this grou of atients. Although microarray-based gene exression rofiling analysis has been reorted as able to rovide reasonably reroducible results for molecular classification of breast cancer, 3,4 our findings show that without thorough standardisation, these tumours cannot be classified reliably by this aroach. As emhasised by Ioannidis and colleagues, 35 other investigators might only be able to redict molecular subtyes accurately when a detailed descrition of data rocessing and analytical methods is rovided. A roadma similar to the one described for develoment and validation of theraeutically relevant genomic classifiers 36 is needed for introduction of breast cancer molecular taxonomy in clinical ractice. Furthermore, careful standardisation of reanalytical variables that have a direct effect on exression rofiles, such as stromal comonent 28 and tissue rocessing, 37 are equally crucial for develoment of reliable and reroducible classifiers. Vol 11 Aril

10 Limitations of our study include use of retrosectively accrued cohorts, the fact that three cohorts were retrieved from ublic reositories and some clinicoathological features were not available in one of the cohorts (ie, tumour size and grade in the Wang dataset), 17 and varied follow-u length in different datasets These limitations are also alicable to other studies of molecular taxonomy of breast cancer. 1 3 For translation of current knowledge on the molecular features of breast cancer, the mechanisms of action of chemotheray agents, and the availability of many comounds that target secific molecular athways or networks, molecular classifications should have direct functional imlications and be redictive of resonse to secific theraeutic agents, rather than being descritive and rognostic. 8,38,39 This requirement is likely to need an integrative aroach, combining descritive data from sufficiently owered cohorts 4 and many sources, including clinicoathological features, massively arallel DNA and RNA sequencing, and roteomics, with detailed functional data from large anels of cancer models (eg, high throughut RNA interference and chemical screens). 8,38,39,41,42 In conclusion, although SSPs identify molecular subtyes with similar trends for association with outcome, they do not assign reliably the same atients to the same molecular subtyes. Before molecular subtye classification can be incororated into routine clinical ractice and treatment decision making, stringent standardisation of methodologies and definitions for identification of breast cancer molecular subtyes is needed. Contributors BW and JSR-F designed the study. RN rovided study materials and samles. BW and AM gathered data. BW, AM, RA H, MD, AA, and JSR-F interreted data. BW, RA H, DSPT, AA, and JSR-F wrote the reort. All authors gave their final aroval to the reort. Conflicts of interest The authors declared no conflicts of interest. Acknowledgments We thank D S A Nuyten (Netherlands Cancer Institute, Amsterdam, Netherlands) for advice about the centroid analysis, and Richard Marais (Institute of Cancer Research, London, UK) and members of the Molecular Pathology team at Breakthrough Breast Cancer Research Centre for discussions and critical reading of the manuscrit. BW is funded by a Cancer Research UK fellowshi. 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