Identifying the Best Ki67 Cut-Off for Determining Luminal Breast Cancer Subtypes Using Immunohistochemical Analysis and PAM50 Genomic Classification - European Medical Journal
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Identifying the Best Ki67 Cut-Off for Determining Luminal Breast Cancer Subtypes Using Immunohistochemical Analysis and PAM50 Genomic Classification

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EMJ Oncology 8.1 2020 Feature Image
Authors:
Roberto Andrés Escala-Cornejo,1,2 María García Muñoz,2,3 *Alejandro Olivares-Hernández,2,3 Magdalena Sancho de Salas,4 María A. Gómez Muñoz,4 Juncal Claros Ampuero,2,3 Luis Figuero-Pérez,2,3 Elena Escalera Martín,2,3 Beatriz Barrios Collado,2,3 Germán Martín García,2,3 Raquel Seijas-Tamayo,2,3 Amalia Gómez Bernal,2,3 Juan J. Cruz Hernández,2,3 César A. Rodríguez Sánchez2,3
Disclosure:

The others have declared no conflicts of interest.

Citation
EMJ Oncol. ;8[1]:47-48. Abstract Review No. AR3.

Each article is made available under the terms of the Creative Commons Attribution-Non Commercial 4.0 License.

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BACKGROUND AND AIMS

Gene expression profiling has a significant impact on understanding the biology of breast cancer (BC). Over the past 15 years, four main intrinsic molecular subtypes of BC have been identified.1-3 At the 13th Saint Gallen International Breast Cancer Consensus, a surrogate classification of BC molecular subtypes by immunohistochemistry (IHC) was established.4 The most controversial point was the difference between the luminal A and luminal B subtypes according to the Ki67 values.5 Commonly, 14% is the Ki67 cut-off that has been established for differentiating BC subtypes; however, in later studies this value has been questioned and a cut-off of 20% has been proposed.6 This study aimed to analyse the correlation between the surrogate BC subtypes using IHC and PAM50 gene expression assay,7-10 considering Ki67 as an independent factor to identify the best Ki67 cut-off.

MATERIALS AND METHODS

Included in the study were females diagnosed between 2015 and 2020 with early stage luminal BC whose samples underwent genomic testing using PAM50/Prosigna® (NanoString Technologies, Seattle, Washington, USA). A total of 143 samples were analysed at a single institution. The IHC subtypes were classified using two independent Ki67 cut-offs, 14% and 20%, and these were compared to the subtypes identified by PAM50.

RESULTS

Using the Ki67 cut-off >14% (Figure 1A), a correlation of 70.6% with a sensitivity of 79.1% and a specificity of 55.8% was observed, as well as a positive predictive value of 75.8% and a negative predictive value of 60.4%. By modifying the Ki67 cut-off to be >20% (Figure 1B), the percentage of well-classified tumours as determined by IHC was 76.2%, improving the agreement by 6.2%. The sensitivity was 93.4%, but the specificity was 46.1%. The positive predictive value was 75.2% and the negative predictive value was 80.0%, which suggests that IHC has a high probability of diagnosing luminal A and luminal B.

Figure 1: Concordance between immunohistochemistry and PAM50 using two different cut-off values.
A) Concordance between IHC (Ki67 cut-off 14%) and PAM50. B) Concordance between IHC (Ki67 cut-off 20%) and PAM50.
IHC: immunohistochemistry.

CONCLUSIONS

Based on the results from this study, the authors demonstrated that modifying the Ki67 cut-off to >20% provides a better surrogate classification by IHC and a higher sensitivity for classifying the luminal subtypes than ≥14%. The authors propose that the Ki67 cut-off should be globally modified to >20% as an independent factor; however, due to the low specificity, other factors, such as progesterone receptor expression, should be considered.

References
Prat A et al. Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast. 2015;24(Suppl 2) :S26-35. Perou C et al. Molecular portraits of human breast tumours. Nature. 2000;406:747-52. Sørlie T et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98(19): 10869-74. Harbeck N et al. St. Gallen 2013: Brief preliminary summary of the consensus discussion. Breast Care (Basel). 2013;8(2):102-9. Cheang M et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J Natl Cancer Inst. 2009;101:737-50. Cardoso F et al. Early breast cancer: ESMO clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2019;30:1194-220. Prat A et al. Practical implications of gene-expression-based assays for breast oncolo-gists. Nat Rev Clin Oncol. 2011;9:48-57. 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. 2013;31:2783-90. Gnant M et al. Predicting distant recurrence in receptor-positive breast cancer patients with limited clinicopathological risk: using the PAM50 Risk of Recurrence score in 1478 postmenopausal patients of the ABCSG-8 trial treated with adjuvant endocrine therapy alone. Ann Oncol. 2014;25:339-45. Parker JS et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27:1160-7.