Reproducibility of mRNA-Based Testing of ESR1, PGR, ERBB2, and MKI67 Expression in Invasive Breast Cancer-A Europe-Wide External Quality Assessment

Breast Cancer Molecular Subtyping in Practice: A Real-World Study of the APIS Breast Cancer Subtyping Assay in a Consecutive Series of Breast Core Biopsies

The APIS Breast Cancer Subtyping Kit is an mRNA-based assessment of the seven parameters including three biomarkers routinely assessed in all the newly diagnosed breast cancers (BC), oestrogen receptor (ER), progesterone receptor (PR) and HER-2 and an additional four genes that create a novel proliferation signature, MKI67, PCNA, CCNA2 and KIF23. Taken together, the data are used to produce a molecular subtype for every sample. The kit was evaluated against the current standard protocol of immunohistochemistry (IHC) and/or in situ hybridisation (ISH) in breast cancer patients. The data were presented at the weekly breast multidisciplinary team (MDT) meeting. A total of 98 consecutive cases of pre-operative breast cancer core biopsies and two core biopsies of nodal metastases yielding 100 cases were assessed. IHC and APIS results were available for 100 and 99 cases. ER was concordant in 97% cases, PR was concordant in 89% and HER-2 results were concordant with IHC/ISH in 100% of the cases. Ki-67 IHC was discordant in 3% of cases when compared with MK167 alone but discordant in 24% when compared with the four-gene proliferation signature. In conclusion, our study indicates that the APIS Breast Cancer Subtyping Kit is highly concordant when compared to the results produced for ER/PR/HER-2 by IHC and/or ISH. The assay could play a role in the routine assessment of newly diagnosed breast cancer (BC) specimens.

Validation of the Xpert Breast Cancer STRAT 4 Assay on the GeneXpert instrument to Assess Hormone Receptor, Ki67, and HER2 Gene Expression Status in Breast Cancer Tissue Samples

Breast cancer is the commonest cause of cancer-related mortality in African females where patients often present later and with advanced disease. Causes for delayed diagnosis include restricted diagnostic access and international controversy on interpretation of ancillary tests like immunohistochemistry (IHC). Fine needle aspirates (FNAC) are an attractive alternative although may have reduced sensitivity. The Xpert Breast Cancer STRAT4 (STRAT4) (CE-IVD*) assay (Cepheid, Sunnyvale) is a semi-quantitative reverse-transcription polymerase chain reaction assay which detects messenger RNA (mRNA) expression in breast samples for estrogen receptor ( ESR1 ), progesterone receptor ( PGR1 ), human epidermal growth factor receptor/Erb-B2 receptor tyrosine kinase 2 (HER2/ ERBB2 ) and the proliferation marker, MKi67 . We assessed the performance of this assay on both formalin-fixed paraffin-embedded (FFPE, n=31) and matched FNAC (n=20) samples from patients presenting with breast cancer to the Johannesburg academic hospitals. IHC and Fluorescent in situ hybridization analysis (performed on HER2-indeterminate samples) was compared with the mRNA expression of the corresponding target genes in FFPE samples, and mRNA expression on FNAC samples was compared with the FFPE results for both mRNA expression and IHC. Concordance between IHC/FISH and Xpert Breast Cancer STRAT4 in FFPE and FNAC samples using the Quick lysis (Q) method (a research-use-only modification of the validated FFPE-lysis method), showed an overall percentage agreement for ESR1 expression of 90.3% and 81.3%, and for PGR1 expression at 86.7% and 81.3% respectively in FFPE and FNAC samples. Concordance was lowest for Ki67 expression, using a binary IHC cutoff for Ki67 positivity at ≥20% staining) at 83.9% and 62.5%, for FFPE and FNAC samples, respectively. This suggests that the STRAT4 assay may be a useful ancillary test in determining HR and Ki67 status in FFPE samples and that use on FNAC samples may be feasible. Future studies should expand the sample numbers and establish locally relevant cutoffs.

Evaluation of ERBB2 mRNA Expression in HER2-Equivocal (2+) Immunohistochemistry Cases

Xpert Breast Cancer STRAT4 is a RT-qPCR platform that studies the mRNA expression of ESR1, PGR, MKI67 and ERBB2, providing a positive or negative result for each of these breast cancer biomarkers. Its concordance with immunohistochemistry (IHC) and in situ hybridization (ISH) has been previously demonstrated, but none of the previous works was focused on HER2-equivocal (2+) cases identified by IHC. Thus, we studied the concordance between IHC/ISH and STRAT4 results for 112 HER2 2+ IBC samples, using 148 HER2 0+, 1+ and 3+ (no-HER2 2+) samples for comparison. We found 91.3% accuracy for the determination of HER2 status globally, 99.3% for no-HER2 2+ samples and 80.7% for HER2 2+ samples. Regarding the other biomarkers, we obtained 96.4% accuracy for estrogen receptor, 84.1% for progesterone receptor and 58.2% for Ki67. Our results suggest that the use of ERBB2 mRNA for the evaluation of HER2 2+ cases is not a reliable reflex method to assess the ERBB2 amplification status.

Breast cancer molecular diagnostics in Rwanda: a cost-minimization study of immunohistochemistry versus a novel GeneXpert® mRNA expression assay

Objective

To compare the financial and time cost of breast cancer biomarker analysis by immunohistochemistry with that by the Xpert^® STRAT4 assay.

Methods

We estimated costs (personnel, location, consumables and indirect) and time involved in breast cancer diagnosis at the Butaro Cancer Centre of Excellence, Rwanda, using time-driven activity-based costing. We performed a cost-minimization analysis to compare the cost of biomarker analysis for estrogen receptor, progesterone receptor and human epidermal growth factor receptor-2 status with immunohistochemistry versus STRAT4. We performed sensitivity analyses by altering laboratory-specific parameters for the two methods.

Findings

We estimated that breast cancer diagnosis in Rwanda costs 138.29 United States dollars (US$) per patient when conducting biomarker analysis by immunohistochemistry. At a realistic immunohistochemistry antibody utilization efficiency of 70%, biomarker analysis comprises 48.7% (US$ 67.33) of diagnostic costs and takes 33 min. We determined that biomarker analysis with STRAT4 yields a reduction in diagnosis cost of US$ 7.33 (10.9%; 7.33/67.33), and in pathologist and technician time of 20 min (60.6%; 20/33), per patient. Our sensitivity analysis revealed that no cost savings would be made in laboratories with antibody utilization efficiencies over 90%, or where only estrogen and/or progesterone receptor status are assessed; however, such operational efficiencies are unlikely, and more laboratories are pursuing human epidermal growth factor receptor-2 analysis as targeted therapies become increasingly available.

Conclusion

Breast cancer biomarker analysis with STRAT4 has the potential to reduce the required human and capital resources in sub-Saharan African laboratories, leading to improved treatment selection and better clinical outcomes.

XPERT® breast cancer STRAT4 as an alternative method of identifying breast cancer phenotype in Cape Verde (preliminary results)

Introduction

Breast cancer (BC) is a public health problem in developing countries, including Cape Verde. Immunohistochemistry (IHC) is the gold standard technique used for BC phenotypic characterisation to support efficient therapeutic decisions. However, IHC is a demanding technique that requires knowledge, trained technicians, expensive antibodies and reagents, controls, and results validation. The low number of cases in Cape Verde increases the risk of expiring the validity of the antibodies, and manual procedures often jeopardise the quality of the results. Thus, IHC is limited in Cape Verde, and an alternative technically easy solution is needed. A point-of-care messenger RNA (mRNA) STRAT4 BC assay to assess estrogen (ER), progesterone (PR), hormone growth factor 2 receptor (HER2), and Ki67, using the GeneXpert platform, has been recently validated on tissues from internationally accredited laboratories, showing excellent concordance with IHC results.To assess whether this technology can be implemented in Cape Verde to guide BC treatment we decided to study the level of agreement between the findings yielded by BC STRAT4 and the results are the same cases obtained by IHC.

Methods

Formalin-fixed and paraffin-embedded (FFPE) tissue samples from 29 Cabo Verdean BC patients diagnosed in Agostinho Neto University Hospital were analysed by applying IHC and BC STRAT4 assay. The time between sample collection and pre-analytic procedures is unknown. All the samples were pre-processed in Cabo Verde (fixed in formalin and embedded in paraffin). IHC studies were performed in referenced laboratories in Portugal. STRAT4 and IHC result concordance was assessed by calculating the percentage of results agreement and Cohen's Kappa (K) statistics.

Results

STRAT4 assay failed in 2 out of the 29 analysed samples. Of the 27 successfully analysed samples, STRAT4/IHC results for ER, PR, HER2, and Ki67 were concordant in 25, 24, 25, and 18 cases, respectively. Ki67 was indeterminate in three cases, and PR was indeterminate once.The percentage of agreement between STRAT4 and IHC results for ER, PR, HER2, and Ki67 was 92.59%, 92.31%, 92.59% and 81.82%, respectively. The Cohen's K statistic coefficients for each biomarker were 0.809, 0.845, 0.757 and 0.506, respectively.

Conclusions

According to our preliminary results, a point-of-care mRNA STRAT4 BC assay may be an alternative in laboratories unable to provide quality and/or cost-efficient IHC services. However, more data and improvement on sample pre-analytic processes are required to implement this BC STRAT4 Assay in Cape Verde.

The g3mclass is a practical software for multiclass classification on biomarkers

The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation-maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer. We showed the g3mclass output's accuracy, robustness, scalability, and interpretability. The user-friendly interface and free dissemination of this multi-platform software aim to ease its use by research laboratories, biomedical pharma, companion diagnostic developers, and healthcare regulators. Furthermore, the g3mclass automatic extracting information through probabilistic modeling is adaptable for blending with machine learning and artificial intelligence.