Hormone receptor expression in breast cancer: postanalytical issues

Contrast-Enhanced Mammography (CEM) Capability to Distinguish Molecular Breast Cancer Subtypes

With breast cancer ranking first among the most common malignant neoplasms in the world, new techniques of early detection are in even more demand than before. Our awareness of tumors' biology is expanding and may be used to treat patients more efficiently. A link between radiology and pathology was searched for in our study, as well as the answer to the question of whether a tumor type can be seen on contrast-enhanced mammography and if such knowledge may serve as part of precision medicine.

Comparison of immunohistochemistry and RT-qPCR for assessing ER, PR, HER2, and Ki67 and evaluating subtypes in patients with breast cancer

PURPOSE

Currently, the most commonly applied method for the determination of breast cancer subtypes is to test estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and Ki67 by immunohistochemistry (IHC). However, the IHC method has substantial intraobserver and interobserver variability. ESR1, PGR, ERBB2, and MKi67 mRNA tests by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) assay may improve the diagnostic objectivity and efficiency. Here, we compared the concordance between RT-qPCR and IHC for assessment of the same biomarkers and evaluated the subtypes.

METHODS

A total of 265 eligible cases were divided into a training cohort and a validation cohort, and the expressions of ER/ESR1, PR/PGR, HER2/ERBB2, and Ki67/MKI67 were tested by IHC and RT-qPCR. Then, the appropriate cutoff of RT-qPCR was calculated in the training cohort. The concordance between RT-qPCR and IHC was calculated for individual marker. In addition, we investigated the subtypes based on the RT-qPCR results.

RESULTS

The Spearman correlation coefficients between ER/ESR1, PR/PGR, HER2/ERBB2, and Ki67/MKI67 by IHC and RT-qPCR were 0.768, 0.699, 0.762, and 0.387, respectively. The cutoff values for the RT-qPCR assay of ESR1 (1%), PGR (1%), ERBB2, and MKi67 (14%) were 35.539, 32.139, 36.398, and 29.176, respectively. The overall percent agreement (OPA) between ER/ESR1, PR/PGR, HER2/ERBB2, and Ki67/MKI67 by IHC and RT-qPCR was 92.48%, 73.68%, 92.80%, and 74.44%, respectively. A total of 224 (84.53%) specimens were concordant for the breast cancer subtypes (IHC-based type) by RT-qPCR.

CONCLUSION

Evaluation of breast cancer biomarker status by RT-qPCR was highly concordant with IHC. RT-qPCR can be used as a supplementary method to detect molecular markers of breast cancer.

Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer

Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.

Androgen receptor expression in breast cancer: Implications on prognosis and treatment, a brief review

Approximately 70%-85% of breast cancers express androgen receptors (ARs). The role of AR in breast cancer pathogenesis is currently in exploration. Both androgens and anti-androgens have demonstrated variable inhibitory and stimulatory effects in AR-positive breast cancer depending on estrogen receptor and HER2 co-expression. Androgen signaling pathways interact with other critical cellular pathways, such as the PI3K/AKT/mTOR, Ras/Raf/MAPK/ERK, Wnt/β-catenin, and estrogen signaling pathways. Therapeutic exploitation of AR has been the crux of management of prostate cancer for decades. In recent years there has been increasing interest in AR as a novel therapeutic target in breast cancer. There have been many early phase clinical trials evaluating the safety and efficacy of various AR-targeted agents in breast cancer. Some of these studies have shown promising clinical benefits. Studies of biomarkers to identify the patients likely to benefit from AR-targeted therapies are currently in progress. Besides, AR expression may be an important prognostic and predictive marker for breast cancer, which needs to be defined better in future studies.

Decision theory for precision therapy of breast cancer

Correctly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

Metformin Restores the Drug Sensitivity of MCF-7 Cells Resistant Derivates via the Cooperative Modulation of Growth and Apoptotic-Related Pathways

The phenomenon of the primary or acquired resistance of cancer cells to antitumor drugs is among the key problems of oncology. For breast cancer, the phenomenon of the resistance to hormonal or target therapy may be based on the numerous mechanisms including the loss or mutation of estrogen receptor, alterations of antiapoptotic pathways, overexpression of growth-related signaling proteins, etc. The perspective approaches for overcoming the resistance may be based on the usage of compounds such as inhibitors of the cell energetic metabolism. Among the latter, the antidiabetic drug metformin exerts antitumor activity via the activation of AMPK and the subsequent inhibition of mTOR signaling. The experiments were performed on the ERα-positive MCF-7 breast cancer cells, the MCF-7 sublines resistant to tamoxifen (MCF-7/T) and rapamycin (MCF-7/Rap), and on triple-negative MDA-MB-231 breast cancer cells. We have demonstrated metformin’s ability to enhance the cytostatic activity of the tamoxifen and rapamycin on both parent MCF-7 cells and MCF-7-resistant derivates mediated via the suppression of mTOR signaling and growth-related transcriptional factors. The cooperative effect of metformin and tested drugs was realized in an estrogen-independent manner, and, in the case of tamoxifen, was associated with the activation of apoptotic cell death. Similarly, the stimulation of apoptosis under metformin/tamoxifen co-treatment was shown to occur in the MCF-7 cells after steroid depletion as well as in the ERα-negative MDA-MB-231 cells. We conclude that metformin co-treatment may be used for the increase and partial restoration of the cancer cell sensitivity to hormonal and target drugs. Moreover, the combination of metformin with tamoxifen induces the apoptotic death in the ERα-negative breast cancer cells opening the additional perspectives in the treatment of estrogen-independent breast tumors.

Quality assurance guidance for scoring and reporting for pathologists and laboratories undertaking clinical trial work

While pathologists have always played a pivotal role in clinical trials ensuring accurate diagnosis and staging, pathology data from prognostic and predictive tests are increasingly being used to enrol, stratify and randomise patients to experimental treatments. The use of pathological parameters as primary and secondary outcome measures, either as standalone classifiers or in combination with clinical data, is also becoming more common. Moreover, reporting of estimates of residual disease, termed 'pathological complete response', have been incorporated into neoadjuvant clinical trials. Pathologists have the expertise to deliver this essential information and they also understand the requirements and limitations of laboratory testing. Quality assurance of pathology-derived data builds confidence around trial-specific findings and is necessarily focused on the reproducibility of pathological data, including 'estimates of uncertainty of measurement', emphasising the importance of pathologist education, training, calibration and demonstration of satisfactory inter-observer agreement. There are also opportunities to validate objective image analysis tools alongside conventional histological assessments. The ever-expanding portfolio of clinical trials will demand more pathologist engagement to deliver the reliable evidence-base required for new treatments. We provide guidance for quality assurance of pathology scoring and reporting in clinical trials.

Co-expressed genes enhance precision of receptor status identification in breast cancer patients

PURPOSE

Therapeutic decisions in breast cancer patients crucially depend on the status of estrogen receptor, progesterone receptor and HER2, obtained by immunohistochemistry (IHC). These are known to be inaccurate sometimes, and we demonstrate how to use gene-expression to increase precision of receptor status.

METHODS

We downloaded data from 3241 breast cancer patients out of 36 clinical studies. For each receptor, we modelled the mRNA expression of the receptor gene and a co-gene by logistic regression. For each patient, predictions from logistic regression were merged with information from IHC on a probabilistic basis to arrive at a fused prediction result.

RESULTS

We introduce Sankey diagrams to visualize the step by step increase of precision as information is added from gene expression: IHC-estimates are qualified as 'confirmed', 'rejected' or 'corrected'. Additionally, we introduce the category 'inconclusive' to spot those patients in need for additional assessments so as to increase diagnostic precision and safety.

CONCLUSIONS

We demonstrate a sound mathematical basis for the fusion of information, even if partly contradictive. The concept is extendable to more than three sources of information, as particularly important for OMICS data. The overall number of undecidable cases is reduced as well as those assessed falsely. We outline how decision rules may be extended to also weigh consequences, being different in severity for false-positive and false-negative assessments, respectively. The possible benefit is demonstrated by comparing the disease free survival between patients whose IHC could be confirmed versus those for which it was corrected.

Immunohistochemical Performance of Estrogen and Progesterone Receptor Antibodies on the Dako Omnis Staining Platform: Evaluation in Multicenter Studies

The analysis of estrogen receptor (ER) and progesterone receptor (PR) expression levels by immunohistochemistry is an important part of the initial evaluation of breast cancer and critically important in treatment planning. Anti-ERα (clone EP1) and anti-PR (clone PgR 1294) antibodies are in development for the Dako Omnis automated staining platform. These antibodies are not yet commercially available and are in performance evaluation, including the 4 international, multicenter studies reported here. For each antibody, a reproducibility study and a method comparison study was done in a randomized manner in order to test the antibodies under conditions closest to real-world user conditions. The reproducibility studies included 5 staining runs on the Dako Omnis with 20 formalin-fixed and paraffin-embedded human breast carcinoma specimens in 3 independent laboratories, and the method comparison studies included several hundred specimens stained on the Dako Omnis and on the Autostainer Link 48 platforms. Stained slides were evaluated for nuclear ER or PR expression according to American Society of Clinical Oncology/College of American Pathologists guidelines (≥1% cut-off for positive) by pathologists who were blinded from the staining method and specimen ID. For both anti-ERα (clone EP1) and anti-PR (clone PgR 1294) on the Dako Omnis, high reproducibility agreement rates were obtained on the interrun, interlaboratory, and interobserver endpoints. High concordance rates were observed between the specimens stained on the Dako Omnis platform and the Autostainer Link 48 platform. Staining quality was excellent for both anti-ERα (clone EP1) and anti-PR (clone PgR 1294) on the Dako Omnis. These results suggest that these antibodies are reliable and reproducible tools for immunohistochemistry analysis of ER and PR expression levels in formalin-fixed and paraffin-embedded breast carcinoma tissues on the Dako Omnis platform.

An international reproducibility study validating quantitative determination of ERBB2, ESR1, PGR, and MKI67 mRNA in breast cancer using MammaTyper®

Background

Accurate determination of the predictive markers human epidermal growth factor receptor 2 (HER2/ERBB2), estrogen receptor (ER/ESR1), progesterone receptor (PgR/PGR), and marker of proliferation Ki67 (MKI67) is indispensable for therapeutic decision making in early breast cancer. In this multicenter prospective study, we addressed the issue of inter- and intrasite reproducibility using the recently developed reverse transcription-quantitative real-time polymerase chain reaction-based MammaTyper® test.

Methods

Ten international pathology institutions participated in this study and determined messenger RNA expression levels of ERBB2, ESR1, PGR, and MKI67 in both centrally and locally extracted RNA from formalin-fixed, paraffin-embedded breast cancer specimens with the MammaTyper® test. Samples were measured repeatedly on different days within the local laboratories, and reproducibility was assessed by means of variance component analysis, Fleiss’ kappa statistics, and interclass correlation coefficients (ICCs).

Results

Total variations in measurements of centrally and locally prepared RNA extracts were comparable; therefore, statistical analyses were performed on the complete dataset. Intersite reproducibility showed total SDs between 0.21 and 0.44 for the quantitative single-marker assessments, resulting in ICC values of 0.980–0.998, demonstrating excellent agreement of quantitative measurements. Also, the reproducibility of binary single-marker results (positive/negative), as well as the molecular subtype agreement, was almost perfect with kappa values ranging from 0.90 to 1.00.

Conclusions

On the basis of these data, the MammaTyper® has the potential to substantially improve the current standards of breast cancer diagnostics by providing a highly precise and reproducible quantitative assessment of the established breast cancer biomarkers and molecular subtypes in a decentralized workup.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-017-0848-z) contains supplementary material, which is available to authorized users.

Estrogen receptor, progesterone receptor, interleukin-6 and interleukin-8 are variable in breast cancer and benign stem/progenitor cell populations

Background

Estrogen receptor positive breast cancers have high recurrence rates despite tamoxifen therapy. Breast cancer stem/progenitor cells (BCSCs) initiate tumors, but expression of estrogen (ER) or progesterone receptors (PR) and response to tamoxifen is unknown. Interleukin-6 (IL-6) and interleukin-8 (IL-8) may influence tumor response to therapy but expression in BCSCs is also unknown.

Methods

BCSCs were isolated from breast cancer and benign surgical specimens based on CD49f/CD24 markers. CD44 was measured. Gene and protein expression of ER alpha, ER beta, PR, IL-6 and IL-8 were measured by proximity ligation assay and qRT-PCR.

Results

Gene expression was highly variable between patients. On average, BCSCs expressed 10-10⁶ fold less ERα mRNA and 10-10³ fold more ERβ than tumors or benign stem/progenitor cells (SC). BCSC lin-CD49f⁻CD24⁻cells were the exception and expressed higher ERα mRNA. PR mRNA in BCSCs averaged 10-10⁴ fold less than in tumors or benign tissue, but was similar to benign SCs. ERα and PR protein detection in BCSCs was lower than ER positive and similar to ER negative tumors. IL-8 mRNA was 10-10⁴ higher than tumor and 10² fold higher than benign tissue. IL-6 mRNA levels were equivalent to benign and only higher than tumor in lin-CD49f⁻CD24⁻cells. IL-6 and IL-8 proteins showed overlapping levels of expressions among various tissues and cell populations.

Conclusions

BCSCs and SCs demonstrate patient-specific variability of gene/protein expression. BCSC gene/protein expression may vary from that of other tumor cells, suggesting a mechanism by which hormone refractory disease may occur.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2407-14-733) contains supplementary material, which is available to authorized users.

Molecular and cellular heterogeneity in breast cancer: challenges for personalized medicine

Breast cancer is noted for disparate clinical behaviors and patient outcomes, despite common histopathological features at diagnosis. Molecular pathogenesis studies suggest that breast cancer is a collection of diseases with variable molecular underpinnings that modulate therapeutic responses, disease-free intervals, and long-term survival. Traditional therapeutic strategies for individual patients are guided by the expression status of the estrogen and progesterone receptors (ER and PR) and human epidermal growth factor receptor 2 (HER2). Although such methods for clinical classification have utility in selection of targeted therapies, short-term patient responses and long-term survival remain difficult to predict. Molecular signatures of breast cancer based on complex gene expression patterns have utility in prediction of long-term patient outcomes, but are not yet used for guiding therapy. Examination of the correspondence between these methods for breast cancer classification reveals a lack of agreement affecting a significant percentage of cases. To realize true personalized breast cancer therapy, a more complete analysis and evaluation of the molecular characteristics of the disease in the individual patient is required, together with an understanding of the contributions of specific genetic and epigenetic alterations (and their combinations) to management of the patient. Here, we discuss the molecular and cellular heterogeneity of breast cancer, the impact of this heterogeneity on practical breast cancer classification, and the challenges for personalized breast cancer treatment.