Concordance between Immunohistochemistry and Microarray Gene Expression Profiling for Estrogen Receptor, Progesterone Receptor, and HER2 Receptor Statuses in Breast Cancer Patients in Lebanon

The prognostic and predictive value of ESR1 fusion gene transcripts in primary breast cancer

Background

In breast cancer (BC), recurrent fusion genes of estrogen receptor alpha (ESR1) and AKAP12, ARMT1 and CCDC170 have been reported. In these gene fusions the ligand binding domain of ESR1 has been replaced by the transactivation domain of the fusion partner constitutively activating the receptor. As a result, these gene fusions can drive tumor growth hormone independently as been shown in preclinical models, but the clinical value of these fusions have not been reported. Here, we studied the prognostic and predictive value of different frequently reported ESR1 fusion transcripts in primary BC.

Methods

We evaluated 732 patients with primary BC (131 ESR1-negative and 601 ESR1-positive cases), including two ER-positive BC patient cohorts: one cohort of 322 patients with advanced disease who received first-line endocrine therapy (ET) (predictive cohort), and a second cohort of 279 patients with lymph node negative disease (LNN) who received no adjuvant systemic treatment (prognostic cohort). Fusion gene transcript levels were measured by reverse transcriptase quantitative PCR. The presence of the different fusion transcripts was associated, in uni- and multivariable Cox regression analysis taking along current clinico-pathological characteristics, to progression free survival (PFS) during first-line endocrine therapy in the predictive cohort, and disease- free survival (DFS) and overall survival (OS) in the prognostic cohort.

Results

The ESR1-CCDC170 fusion transcript was present in 27.6% of the ESR1-positive BC subjects and in 2.3% of the ESR1-negative cases. In the predictive cohort, none of the fusion transcripts were associated with response to first-line ET. In the prognostic cohort, the median DFS and OS were respectively 37 and 93 months for patients with an ESR1-CCDC170 exon 8 gene fusion transcript and respectively 91 and 212 months for patients without this fusion transcript. In a multivariable analysis, this ESR1-CCDC170 fusion transcript was an independent prognostic factor for DFS (HR) (95% confidence interval (CI): 1.8 (1.2–2.8), P = 0.005) and OS (HR (95% CI: 1.7 (1.1–2.7), P = 0.023).

Conclusions

Our study shows that in primary BC only ESR1-CCDC170 exon 8 gene fusion transcript carries prognostic value. None of the ESR1 fusion transcripts, which are considered to have constitutive ER activity, was predictive for outcome in BC with advanced disease treated with endocrine treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09265-1.

An Efficient Algorithm for the Detection of Outliers in Mislabeled Omics Data

High dimensionality and noise have made it difficult to detect related biomarkers in omics data. Through previous study, penalized maximum trimmed likelihood estimation is effective in identifying mislabeled samples in high-dimensional data with mislabeled error. However, the algorithm commonly used in these studies is the concentration step (C-step), and the C-step algorithm that is applied to robust penalized regression does not ensure that the criterion function is gradually optimized iteratively, because the regularized parameters change during the iteration. This makes the C-step algorithm runs very slowly, especially when dealing with high-dimensional omics data. The AR-Cstep (C-step combined with an acceptance-rejection scheme) algorithm is proposed. In simulation experiments, the AR-Cstep algorithm converged faster (the average computation time was only 2% of that of the C-step algorithm) and was more accurate in terms of variable selection and outlier identification than the C-step algorithm. The two algorithms were further compared on triple negative breast cancer (TNBC) RNA-seq data. AR-Cstep can solve the problem of the C-step not converging and ensures that the iterative process is in the direction that improves criterion function. As an improvement of the C-step algorithm, the AR-Cstep algorithm can be extended to other robust models with regularized parameters.

Association between low estrogen receptor positive breast cancer and staining performance

Estrogen receptor (ER) expression in breast carcinomas, determined by immunohistochemistry, indicates statistically significant benefit to endocrine therapy in patients with tumors expressing ER in ≥1% of tumor cells. Rare cases with low ER expression (1-10%) lead to the dilemma of treating these tumors as ER positive or negative. We hypothesize that low ER positive result from poor staining performance and that we may detect this artefact by assessing the average dynamic range of normal ducts adjacent to low ER positive tumors. Using quantitative tools, we compare the dynamic range of normal background ER expression in patients with low (1-10%) ER tumors to dynamic range of ER expression in normal epithelium from control patient populations, to determine if low ER cases are accompanied by decreased dynamic range. Low ER cases were infrequent (1% of invasive breast carcinomas). Twenty-one cases with low ER staining and two control cohorts, including a tissue microarray (TMA) of 10 benign breast sections and a group of 34 control breast carcinomas (reported as ER negative or >10% ER positive) with normal background epithelium, were digitally scanned. QuPath was utilized to quantify ER staining for each cell as the mean optical density of nuclear DAB staining. The dynamic range of ER expression in normal epithelium surrounding low ER tumors was significantly lower (range 2-240, median 16.5) than that of the benign epithelium in the control tumors (range 3-475, median 30.8; p < 0.001) and benign TMA sections (range 38-212, median 114; p < 0.001) suggesting inconsistent stainer performance.