Agreement between molecular subtyping and surrogate subtype classification: a contemporary population-based study of ER-positive/HER2-negative primary breast cancer

Christine Lundgren, Pär-Ola Bendahl, Åke Borg, Anna Ehinger, Cecilia Hegardt, Christer Larsson, Niklas Loman, Martin Malmberg, Helena Olofsson, Lao H Saal, Tobias Sjöblom, Henrik Lindman, Marie Klintman, Jari Häkkinen, Johan Vallon-Christersson, Mårten Fernö, Lisa Rydén, Maria Ekholm, Christine Lundgren, Pär-Ola Bendahl, Åke Borg, Anna Ehinger, Cecilia Hegardt, Christer Larsson, Niklas Loman, Martin Malmberg, Helena Olofsson, Lao H Saal, Tobias Sjöblom, Henrik Lindman, Marie Klintman, Jari Häkkinen, Johan Vallon-Christersson, Mårten Fernö, Lisa Rydén, Maria Ekholm

Abstract

Purpose: Oestrogen receptor-positive (ER+) and human epidermal receptor 2-negative (HER2-) breast cancers are classified as Luminal A or B based on gene expression, but immunohistochemical markers are used for surrogate subtyping. The aims of this study were to examine the agreement between molecular subtyping (MS) and surrogate subtyping and to identify subgroups consisting mainly of Luminal A or B tumours.

Methods: The cohort consisted of 2063 patients diagnosed between 2013-2017, with primary ER+/HER2- breast cancer, analysed by RNA sequencing. Surrogate subtyping was performed according to three algorithms (St. Gallen 2013, Maisonneuve and our proposed Grade-based classification). Agreement (%) and kappa statistics (κ) were used as concordance measures and ROC analysis for luminal distinction. Ki67, progesterone receptor (PR) and histological grade (HG) were further investigated as surrogate markers.

Results: The agreement rates between the MS and St. Gallen 2013, Maisonneuve and Grade-based classifications were 62% (κ = 0.30), 66% (κ = 0.35) and 70% (κ = 0.41), respectively. PR did not contribute to distinguishing Luminal A from B tumours (auROC = 0.56). By classifying HG1-2 tumours as Luminal A-like and HG3 as Luminal B-like, agreement with MS was 80% (κ = 0.46). Moreover, by combining HG and Ki67 status, a large subgroup of patients (51% of the cohort) having > 90% Luminal A tumours could be identified.

Conclusions: Agreement between MS and surrogate classifications was generally poor. However, a post hoc analysis showed that a combination of HG and Ki67 could identify patients very likely to have Luminal A tumours according to MS.

Keywords: Breast cancer; Gene expression; Intrinsic subtype; Molecular subtyping; Surrogate marker.

Conflict of interest statement

Author Anna Ehinger has received remuneration from Roche, Amgen and Novartis. Author Lao H. Saal has received remuneration and has stock ownership in SAGA Diagnostics AB. Author Maria Ekholm has had a consultant/advisory role in Pfizer and Novartis. The other authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Flow chart of the study cohort

References

    1. Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D. Molecular portraits of human breast tumours. Nature. 2000;406(6797):747–752. doi: 10.1038/35021093.
    1. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Lonning PE, Borresen-Dale AL. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98(19):10869–10874. doi: 10.1073/pnas.191367098.
    1. Curigliano G, Burstein HJ, Winer PE. De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017. Ann Oncol. 2017;28(8):1700–1712. doi: 10.1093/annonc/mdx308.
    1. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush JF, Stijleman IJ, Palazzo J, Marron JS, Nobel AB, Mardis E, Nielsen TO, Ellis MJ, Perou CM, Bernard PS. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27(8):1160–1167. doi: 10.1200/jco.2008.18.1370.
    1. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, Baehner FL, Walker MG, Watson D, Park T, Hiller W, Fisher ER, Wickerham DL, Bryant J, Wolmark N. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817–2826. doi: 10.1056/NEJMoa041588.
    1. van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–536. doi: 10.1038/415530a.
    1. Sparano JA, Gray RJ, Makower DF, et al. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N Engl J Med. 2018;379(2):111–121. doi: 10.1056/NEJMoa1804710.
    1. Cardoso F, van’t Veer LJ, Bogaerts J, et al. 70-Gene signature as an aid to treatment decisions in early-stage breast cancer. N Engl J Med. 2016;375(8):717–729. doi: 10.1056/NEJMoa1602253.
    1. NanoString Technologies (2018) Prosigna-Breast cancer prognostic gene signature assay. . Accessed 15 Dec 2018
    1. Gnant M, Harbeck N, Thomssen C. St. Gallen 2011: summary of the consensus discussion. Breast Care (Basel) 2011;6(2):136–141. doi: 10.1159/000328054.
    1. Goldhirsch A, Winer EP, Coates AS, et al. Personalizing the treatment of women with early breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013;24(9):2206–2223. doi: 10.1093/annonc/mdt303.
    1. Maisonneuve P, Disalvatore D, Rotmensz N, Curigliano G, Colleoni M, Dellapasqua S, Pruneri G, Mastropasqua MG, Luini A, Bassi F, Pagani G, Viale G, Goldhirsch A. Proposed new clinicopathological surrogate definitions of luminal A and luminal B (HER2-negative) intrinsic breast cancer subtypes. Breast Cancer Res. 2014;16(3):R65. doi: 10.1186/bcr3679.
    1. Ehinger A, Malmström P, Bendahl P-O, Elston CW, Falck A-K, Forsare C, Grabau D, Rydén L, Stål O, Fernö M. Histological grade provides significant prognostic information in addition to breast cancer subtypes defined according to St. Gallen 2013. Acta Oncol. 2017;56(1):68–74. doi: 10.1080/0284186X.2016.1237778.
    1. Saal LH, Vallon-Christersson J, Häkkinen J, Hegardt C, Grabau D, Winter C, Brueffer C, Tang MHE, Reuterswärd C, Schulz R, Karlsson A, Ehinger A, Malina J, Manjer J, Malmberg M, Larsson C, Rydén L, Loman N, Borg Å. The Sweden Cancerome Analysis Network—Breast (SCAN-B) Initiative: a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine. Genome Med. 2015;7(1):20. doi: 10.1186/s13073-015-0131-9.
    1. Rydén LA-O, Loman N, Larsson C, Hegardt C, Vallon-Christersson J, Malmberg M, Lindman H, Ehinger A, Saal LH, Borg Å. Minimizing inequality in access to precision medicine in breast cancer by real-time population-based molecular analysis in the SCAN-B initiative. Br J Surg. 2018;105(2):e158–e168. doi: 10.1002/bjs.10741.
    1. Regionala Cancercentrum i Samverkan (2019) Nationellt kvalitetsregister för bröstcancer och bröstrekonstruktion. . Accessed 14 Apr 2019
    1. Romero Q, Bendahl PO, Klintman M, Loman N, Ingvar C, Ryden L, Rose C, Grabau D, Borgquist S. Ki67 proliferation in core biopsies versus surgical samples—a model for neo-adjuvant breast cancer studies. BMC Cancer. 2011;11:341. doi: 10.1186/1471-2407-11-341.
    1. Knutsvik G, Stefansson IM, Aziz S, Arnes J, Eide J, Collett K, Akslen LA. Evaluation of Ki67 expression across distinct categories of breast cancer specimens: a population-based study of matched surgical specimens, core needle biopsies and tissue microarrays. PLoS ONE. 2014;9(11):e112121. doi: 10.1371/journal.pone.0112121.
    1. Brueffer C, Vallon-Christersson J, Grabau D, Ehinger A, Häkkinen J, Hegardt C, Malina J, Chen Y, Bendahl P-O, Manjer J, Malmberg M, Larsson C, Loman N, Rydén L, Borg Å, Saal LH. Clinical value of RNA sequencing-based classifiers for prediction of the five conventional breast cancer biomarkers: a report from the population-based multicenter Sweden Cancerome Analysis Network—Breast Initiative. JCO Precis Oncol. 2018;2:1–18. doi: 10.1200/PO.17.00135.
    1. Regionala Cancercentrum i Samverkan (2018) Kvalitetsdokument för patologi. . Accessed 30 Nov 2018
    1. Elston CW, Ellis IO. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology. 1991;19(5):403–410. doi: 10.1111/j.1365-2559.1991.tb00229.x.
    1. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–174. doi: 10.2307/2529310.
    1. Boiesen P, Bendahl PO, Anagnostaki L, Domanski H, Holm E, Idvall I, Johansson S, Ljungberg O, Ringberg A, Ostberg G, Fernö M. Histologic grading in breast cancer: reproducibility between seven pathologic departments. Acta Oncol. 2000;39(1):41–45. doi: 10.1080/028418600430950.
    1. Ekholm M, Grabau D, Bendahl PO, Bergh J, Elmberger G, Olsson H, Russo L, Viale G, Ferno M. Highly reproducible results of breast cancer biomarkers when analysed in accordance with national guidelines—a Swedish survey with central re-assessment. Acta Oncol. 2015;54(7):1040–1048. doi: 10.3109/0284186X.2015.1037012.
    1. Focke CM, Burger H, van Diest PJ, Finsterbusch K, Glaser D, Korsching E, Decker T. Interlaboratory variability of Ki67 staining in breast cancer. Eur J Cancer. 2017;84:219–227. doi: 10.1016/j.ejca.2017.07.041.
    1. Bartlett JM, Bayani J, Marshall A, Dunn JA, Campbell A, Cunningham C, Sobol, Hall PS, Poole CJ, Cameron DA, Earl HM, Rea DW, Macpherson IR, Canney P, Francis A, McCabe C, Pinder SE, Hughes-Davies L, Makris A, Stein RC. Comparing breast cancer multiparameter tests in the OPTIMA Prelim Trial: No test is more equal than the others. J Natl Cancer Inst. 2016;108(9):djw050. doi: 10.1093/jnci/djw050.
    1. Wallden B, Storhoff J, Nielsen T, Dowidar N, Schaper C, Ferree S, Liu S, Leung S, Geiss G, Snider J, Vickery T, Davies SR, Mardis ER, Gnant M, Sestak I, Ellis MJ, Perou CM, Bernard PS, Parker JS. Development and verification of the PAM50-based Prosigna breast cancer gene signature assay. BMC Med Genomics. 2015;8:54. doi: 10.1186/s12920-015-0129-6.

Source: PubMed

3
Abonnieren