Ki67 immunohistochemistry (IHC), commonly used as a proliferation marker in breast cancer, has limited value for treatment decisions due to questionable analytical validity. The International Ki67 in Breast Cancer Working Group (IKWG) consensus meeting, held in October 2019, assessed the current evidence for Ki67 IHC analytical validity and clinical utility in breast cancer, including the series of scoring studies the IKWG conducted on centrally stained tissues. Consensus observations and recommendations are: 1) as for estrogen receptor and HER2 testing, preanalytical handling considerations are critical; 2) a standardized visual scoring method has been established and is recommended for adoption; 3) participation in and evaluation of quality assurance and quality control programs is recommended to maintain analytical validity; and 4) the IKWG accepted that Ki67 IHC as a prognostic marker in breast cancer has clinical validity but concluded that clinical utility is evident only for prognosis estimation in anatomically favorable estrogen receptor–positive and HER2-negative patients to identify those who do not need adjuvant chemotherapy. In this T1-2, N0-1 patient group, the IKWG consensus is that Ki67 5% or less, or 30% or more, can be used to estimate prognosis. In conclusion, analytical validity of Ki67 IHC can be reached with careful attention to preanalytical issues and calibrated standardized visual scoring. Currently, clinical utility of Ki67 IHC in breast cancer care remains limited to prognosis assessment in stage I or II breast cancer. Further development of automated scoring might help to overcome some current limitations.

In breast cancer, immunohistochemical assessment of proliferation using the marker Ki67 has potential use in both research and clinical management. However, lack of consistency across laboratories has limited Ki67’s value. A working group was assembled to devise a strategy to harmonize Ki67 analysis and increase scoring concordance. Toward that goal, we conducted a Ki67 reproducibility study. Eight laboratories received 100 breast cancer cases arranged into 1-mm core tissue microarrays—one set stained by the participating laboratory and one set stained by the central laboratory, both using antibody MIB-1. Each laboratory scored Ki67 as percentage of positively stained invasive tumor cells using its own method. Six laboratories repeated scoring of 50 locally stained cases on 3 different days. Sources of variation were analyzed using random effects models with log2-transformed measurements. Reproducibility was quantified by intraclass correlation coefficient (ICC), and the approximate two-sided 95% confidence intervals (CIs) for the true intraclass correlation coefficients in these experiments were provided. Intralaboratory reproducibility was high (ICC = 0.94; 95% CI = 0.93 to 0.97). Interlaboratory reproducibility was only moderate (central staining: ICC = 0.71, 95% CI = 0.47 to 0.78; local staining: ICC = 0.59, 95% CI = 0.37 to 0.68). Geometric mean of Ki67 values for each laboratory across the 100 cases ranged 7.1% to 23.9% with central staining and 6.1% to 30.1% with local staining. Factors contributing to interlaboratory discordance included tumor region selection, counting method, and subjective assessment of staining positivity. Formal counting methods gave more consistent results than visual estimation. Substantial variability in Ki67 scoring was observed among some of the world’s most experienced laboratories. Ki67 values and cutoffs for clinical decision-making cannot be transferred between laboratories without standardizing scoring methodology because analytical validity is limited.

The four intrinsic subtypes of breast cancer, defined by differential expression of 50 genes (PAM50), have been shown to be predictive of risk of recurrence and benefit of hormonal therapy and chemotherapy. Here we describe the development of Prosigna™, a PAM50-based subtype classifier and risk model on the NanoString nCounter Dx Analysis System intended for decentralized testing in clinical laboratories. 514 formalin-fixed, paraffin-embedded (FFPE) breast cancer patient samples were used to train prototypical centroids for each of the intrinsic subtypes of breast cancer on the NanoString platform. Hierarchical cluster analysis of gene expression data was used to identify the prototypical centroids defined in previous PAM50 algorithm training exercises. 304 FFPE patient samples from a well annotated clinical cohort in the absence of adjuvant systemic therapy were then used to train a subtype-based risk model (i.e. Prosigna ROR score). 232 samples from a tamoxifen-treated patient cohort were used to verify the prognostic accuracy of the algorithm prior to initiating clinical validation studies. The gene expression profiles of each of the four Prosigna subtype centroids were consistent with those previously published using the PCR-based PAM50 method. Similar to previously published classifiers, tumor samples classified as Luminal A by Prosigna had the best prognosis compared to samples classified as one of the three higher-risk tumor subtypes. The Prosigna Risk of Recurrence (ROR) score model was verified to be significantly associated with prognosis as a continuous variable and to add significant information over both commonly available IHC markers and Adjuvant! Online. The results from the training and verification data sets show that the FDA-cleared and CE marked Prosigna test provides an accurate estimate of the risk of distant recurrence in hormone receptor positive breast cancer and is also capable of identifying a tumor's intrinsic subtype that is consistent with the previously published PCR-based PAM50 assay. Subsequent analytical and clinical validation studies confirm the clinical accuracy and technical precision of the Prosigna PAM50 assay in a decentralized setting.

Introduction: Tumor infiltrating lymphocytes may indicate an immune response to cancer development, but their significance remains controversial in breast cancer. We conducted this study to assess CD8+ (cytotoxic T) lymphocyte infiltration in a large cohort of invasive early stage breast cancers, and to evaluate its prognostic effect in different breast cancer intrinsic subtypes. Methods: Immunohistochemistry for CD8 staining was performed on tissue microarrays from 3992 breast cancer patients. CD8+ tumor infiltrating lymphocytes were counted as intratumoral when in direct contact with tumor cells, and as stromal in adjacent locations. Kaplan-Meier functions and Cox proportional hazards regression models were applied to examine the associations between tumor infiltrating lymphocytes and breast cancer specific survival. Results: Among 3403 cases for which immunohistochemical results were obtained, CD8+ tumor infiltrating lymphocytes were identified in an intratumoral pattern in 32% and stromal pattern in 61% of the cases. In the whole cohort, the presence of intratumoral tumor-infiltrating lymphocytes was significantly correlated with young age, high grade, estrogen receptor negativity, human epidermal growth factor receptor-2 positivity and core basal intrinsic subtype, and was associated with superior breast cancer specific survival. Multivariate analysis indicated that the favorable prognostic effect of CD8+ tumor infiltrating lymphocytes was significant only in the core basal intrinsic subgroup (Hazard ratio, HR = 0.35, 95% CI = 0.23-0.54). No association with improved survival was present in those triple negative breast cancers that lack expression of basal markers (HR = 0.99, 95% CI = 0.48-2.04) nor in the other intrinsic subtypes. Conclusions: CD8+ tumor infiltrating lymphocytes are an independent prognostic factor associated with better patient survival in basal-like breast cancer, but not in non-basal triple negative breast cancers nor in other intrinsic molecular subtypes.

NanoString’s Prosigna™ Breast Cancer Prognostic Gene Signature Assay is based on the PAM50 gene expression signature. The test outputs a risk of recurrence (ROR) score, risk category, and intrinsic subtype (Luminal A/B, HER2-enriched, Basal-like). The studies described here were designed to validate the analytical performance of the test on the nCounter Analysis System across multiple laboratories.

Background: Many methodologies have been used in research to identify the “intrinsic” subtypes of breast cancer commonly known as Luminal A, Luminal B, HER2-Enriched (HER2-E) and Basal-like. The PAM50 gene set is often used for gene expression-based subtyping; however, surrogate subtyping using panels of immunohistochemical (IHC) markers are still widely used clinically. Discrepancies between these methods may lead to different treatment decisions. Methods: We used the PAM50 RT-qPCR assay to expression profile 814 tumors from the GEICAM/9906 phase III clinical trial that enrolled women with locally advanced primary invasive breast cancer. All samples were scored at a single site by IHC for estrogen receptor (ER), progesterone receptor (PR), and Her2/neu (HER2) protein expression. Equivocal HER2 cases were confirmed by chromogenic in situ hybridization (CISH). Single gene scores by IHC/CISH were compared with RT-qPCR continuous gene expression values and “intrinsic” subtype assignment by the PAM50. High, medium, and low expression for ESR1, PGR, ERBB2, and proliferation were selected using quartile cut-points from the continuous RT-qPCR data across the PAM50 subtype assignments. Results: ESR1, PGR, and ERBB2 gene expression had high agreement with established binary IHC cut-points (area under the curve (AUC) ≥ 0.9). Estrogen receptor positivity by IHC was strongly associated with Luminal (A and B) subtypes (92%), but only 75% of ER negative tumors were classified into the HER2-E and Basal-like subtypes. Luminal A tumors more frequently expressed PR than Luminal B (94% vs 74%) and Luminal A tumors were less likely to have high proliferation (11% vs 77%). Seventy-seven percent (30/39) of ER-/HER2+ tumors by IHC were classified as the HER2-E subtype. Triple negative tumors were mainly comprised of Basal-like (57%) and HER2-E (30%) subtypes. Single gene scoring for ESR1, PGR, and ERBB2 was more prognostic than the corresponding IHC markers as shown in a multivariate analysis. Conclusions: The standard immunohistochemical panel for breast cancer (ER, PR, and HER2) does not adequately identify the PAM50 gene expression subtypes. Although there is high agreement between biomarker scoring by protein immunohistochemistry and gene expression, the gene expression determinations for ESR1 and ERBB2 status was more prognostic.