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

1 Mins
Oncology
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

1. Department of Medical Oncology, Complejo Asistencial de Ávila, Ávila, Spain
2. Institute for Biomedical Research of Salamanca (IBSAL), Salamanca, Spain
3. Department of Medical Oncology, Complejo Asistencial Universitario de Salamanca, Salamanca, Spain
4. Department of Pathology, Complejo Asistencial Universitario de Salamanca, Salamanca, Spain
*Correspondence to [email protected]

Disclosure:

The others have declared no conflicts of interest.

Citation:
EMJ Oncol. ;8[1]:47-48. Abstract Review No. AR3.
Keywords:
Breast cancer (BC), immunohistochemistry (IHC), luminal, PAM50.

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

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.

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