Validation of a new classifier for the automated analysis of the human epidermal growth factor receptor 2 (HER2) gene amplification in breast cancer specimens

Review of In Situ Hybridization (ISH) Stain Images Using Computational Techniques

Recent advancements in medical imaging have greatly enhanced the application of computational techniques in digital pathology, particularly for the classification of breast cancer using in situ hybridization (ISH) imaging. HER2 amplification, a key prognostic marker in 20-25% of breast cancers, can be assessed through alterations in gene copy number or protein expression. However, challenges persist due to the heterogeneity of nuclear regions and complexities in cancer biomarker detection. This review examines semi-automated and fully automated computational methods for analyzing ISH images with a focus on HER2 gene amplification. Literature from 1997 to 2023 is analyzed, emphasizing silver-enhanced in situ hybridization (SISH) and its integration with image processing and machine learning techniques. Both conventional machine learning approaches and recent advances in deep learning are compared. The review reveals that automated ISH analysis in combination with bright-field microscopy provides a cost-effective and scalable solution for routine pathology. The integration of deep learning techniques shows promise in improving accuracy over conventional methods, although there are limitations related to data variability and computational demands. Automated ISH analysis can reduce manual labor and increase diagnostic accuracy. Future research should focus on refining these computational methods, particularly in handling the complex nature of HER2 status evaluation, and integrate best practices to further enhance clinical adoption of these techniques.

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence

Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.

Clinical application of the HM-1000 image processing for HER2 fluorescence in situ hybridization signal quantification in breast cancer

BACKGROUND

Accurate quantification of human epidermal growth factor receptor 2 (HER2) gene amplification is important for predicting treatment response and prognosis in patients with breast cancer. Fluorescence in situ hybridization (FISH) is the gold standard for the diagnosis of HER2 status, particularly in cases with equivocal status on immunohistochemistry (IHC) staining, but has some limitations of non-classical amplifications and such cases are diagnosed basing on additional IHC and FISH. This study investigated the clinical utility of a novel super-resolution fluorescence microscopy technique for the better FISH signal visualization and HER2 FISH classification.

METHODS

Fourteen breast cancer tissue samples were retrospectively collected between September 2018 and February 2022, and FISH HER2 signal quantification was evaluated by determining the HER2/chromosome 17 centromere (CEP17) ratio and the number of HER2 signals per nucleus in super- versus conventional-resolution images.

RESULTS

Super-resolution images maintained the same overall HER2 diagnosis from routine, but HER2 FISH amplification changed negative to monosomy in two cases. Two Letrozole non-response relapses coincided to monosomy samples. The median number of HER2 signals per nucleus was 7.5 in super-resolution images and 4.0 in conventional-resolution images in HER2-positive samples and 2.8 and 2.1 signals per nucleus, respectively, in HER2-negative samples.

CONCLUSIONS

Super-resolution images improved signal visualization, including a significant difference in the number of countable HER2 and CEP17 signals in a single nucleus compared with conventional-resolution images. Increased accuracy of signal quantification by super-resolution microscopy may provide clinicians with more detailed information regarding HER2 FISH status that allows to better FISH classification such as HER2-low samples.

Quantitative Imaging Analysis Fluorescence In Situ Hybridization Validation for Clinical HER2 Testing in Breast Cancer

CONTEXT.—

Quantitative imaging is a promising tool that is gaining wide use across several areas of pathology. Although there has been increasing adoption of morphologic and immunohistochemical analysis, the adoption of evaluation of fluorescence in situ hybridization (FISH) on formalin-fixed, paraffin-embedded tissue has been limited because of complexity and lack of practice guidelines.

OBJECTIVE.—

To perform human epidermal growth factor receptor 2 (HER2) FISH validation in breast carcinoma in accordance with the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) 2018 guideline.

DESIGN.—

Clinical validation of HER2 FISH was performed using the US Food and Drug Administration-approved dual-probe HER2 IQFISH (Dako, Carpinteria, California) with digital scanning performed on a PathFusion (Applied Spectral Imaging, Carlsbad, California) system. Validation parameters evaluated included z-stacking, classifier, accuracy, precision, software, and hardware settings. Finally, we evaluated the performance of digital enumeration on clinical samples in a real-world setting.

RESULTS.—

The accuracy samples showed a final concordance of 95.3% to 100% across HER2 groups 1 to 5. During clinical implementation for HER2 groups 2, 3, and 4, we achieved a final concordance of 76% (95 of 125). Of these cases, only 8% (10 of 125) had discordances with clinical impact that could be identified algorithmically and triaged for manual review.

CONCLUSIONS.—

Digital FISH enumeration is a useful tool to improve the efficacy of HER2 FISH enumeration and capture genetic heterogeneity across HER2 signals. Excluding cases with high background or poor image quality and manual review of cases with ASCO/CAP group discordances can further improve the efficiency of digital HER2 FISH enumeration.

Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine

Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.

Applications of Artificial Intelligence in Breast Pathology

CONTEXT.—

Increasing implementation of whole slide imaging together with digital workflow and advances in computing capacity enable the use of artificial intelligence (AI) in pathology, including breast pathology. Breast pathologists often face a significant workload, with diagnosis complexity, tedious repetitive tasks, and semiquantitative evaluation of biomarkers. Recent advances in developing AI algorithms have provided promising approaches to meet the demand in breast pathology.

OBJECTIVE.—

To provide an updated review of AI in breast pathology. We examined the success and challenges of current and potential AI applications in diagnosing and grading breast carcinomas and other pathologic changes, detecting lymph node metastasis, quantifying breast cancer biomarkers, predicting prognosis and therapy response, and predicting potential molecular changes.

DATA SOURCES.—

We obtained data and information by searching and reviewing literature on AI in breast pathology from PubMed and based our own experience.

CONCLUSIONS.—

With the increasing application in breast pathology, AI not only assists in pathology diagnosis to improve accuracy and reduce pathologists' workload, but also provides new information in predicting prognosis and therapy response.

Deep learning to automatically evaluate HER2 gene amplification status from fluorescence in situ hybridization images

Human epidermal growth factor receptor 2 (HER2) gene amplification helps identify breast cancer patients who may respond to targeted anti-HER2 therapy. This study aims to develop an automated method for quantifying HER2 fluorescence in situ hybridization (FISH) signals and improve the working efficiency of pathologists. An Aitrox artificial intelligence (AI) model based on deep learning was constructed, and a comparison between the AI model and traditional manual counting was performed. In total, 918 FISH images from 320 consecutive invasive breast cancers were analysed and automatically classified into 5 groups according to the 2018 ASCO/CAP guidelines. The overall classification accuracy was 85.33% (157/184) with a mean average precision of 0.735. In Group 5, the most common group, the consistency was as high as 95.90% (117/122), while the consistency was low in the other groups due to the limited number of cases. The causes of this inconsistency, including clustered HER2 signals, coarse CEP17 signals and some section quality problems, were analysed. The developed AI model is a reliable tool for evaluating HER2 amplification statuses, especially for breast cancer in Group 5; additional cases from multiple centres could further improve the accuracy achieved for other groups.

Singular Nuclei Segmentation for Automatic HER2 Quantification Using CISH Whole Slide Images

Human epidermal growth factor receptor 2 (HER2) quantification is performed routinely for all breast cancer patients to determine their suitability for HER2-targeted therapy. Fluorescence in situ hybridization (FISH) and chromogenic in situ hybridization (CISH) are the US Food and Drug Administration (FDA) approved tests for HER2 quantification in which at least 20 cancer-affected singular nuclei are quantified for HER2 grading. CISH is more advantageous than FISH for cost, time and practical usability. In clinical practice, nuclei suitable for HER2 quantification are selected manually by pathologists which is time-consuming and laborious. Previously, a method was proposed for automatic HER2 quantification using a support vector machine (SVM) to detect suitable singular nuclei from CISH slides. However, the SVM-based method occasionally failed to detect singular nuclei resulting in inaccurate results. Therefore, it is necessary to develop a robust nuclei detection method for reliable automatic HER2 quantification. In this paper, we propose a robust U-net-based singular nuclei detection method with complementary color correction and deconvolution adapted for accurate HER2 grading using CISH whole slide images (WSIs). The efficacy of the proposed method was demonstrated for automatic HER2 quantification during a comparison with the SVM-based approach.

Computer-Aided Scoring Of () Gene Amplification Status In Breast Cancer

Background

Identification of HER2 protein overexpression and/or amplification of the HER2 gene are required to qualify breast cancer patients for HER2 targeted therapies. In situ hybridization (ISH) assays that identify HER2 gene amplification function as a stand-alone test for determination of HER2 status and rely on the manual quantification of the number of HER2 genes and copies of chromosome 17 to determine HER2 amplification.

Methods

To assist pathologists, we have developed the uPath HER2 Dual ISH Image Analysis for Breast (uPath HER2 DISH IA) algorithm, as an adjunctive aid in the determination of HER2 gene status in breast cancer specimens. The objective of this study was to compare uPath HER2 DISH image analysis vs manual read scoring of VENTANA HER2 DISH-stained breast carcinoma specimens with ground truth (GT) gene status as the reference. Three reader pathologists reviewed 220, formalin-fixed, paraffin-embedded (FFPE) breast cancer cases by both manual and uPath HER2 DISH IA methods. Scoring results from manual read (MR) and computer-assisted scores (image analysis, IA) were compared against the GT gene status generated by consensus of a panel of pathologists. The differences in agreement rates of HER2 gene status between manual, computer-assisted, and GT gene status were determined.

Results

The positive percent agreement (PPA) and negative percent agreement (NPA) rates for image analysis (IA) vs GT were 97.2% (95% confidence interval [CI]: 95.0, 99.3) and 94.3% (95% CI: 90.8, 97.3) respectively. Comparison of agreement rates showed that the lower bounds of the 95% CIs for the difference of PPA and NPA for IA vs MR were –0.9% and –6.2%, respectively. Further, inter- and intra-reader agreement rates in the IA method were observed with point estimates of at least 96.7%.

Conclusions

Overall, our data show that the uPath HER2 DISH IA is non-inferior to manual scoring and supports its use as an aid for pathologists in routine diagnosis of breast cancer.

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Image analysis algorithm for HER2 amplification using Bright-field ISH in Breast.

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Automated tumor cell selection and quantitation within pathologist defined ROI.

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The image analysis algorithm is non-inferior to manual scoring.

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Integrated solution to support pathologists in determining HER2 gene status.

Her2-Positive Cancers and Antibody-Based Treatment: State of the Art and Future Developments

HER2 positive breast cancer represent about 20% of all breast cancer subtypes and it was considered the subtype with the worst prognosis until the discovery of therapies directed against the HER2 protein. The determination of the status of the HER2 must be very precise and well managed to identify this subtype, and there are very specific and updated guides that allow its characterization to be adjusted. Treatment in local disease has been considerably improved with less aggressive and highly effective approaches and very high cure rates. In metastatic disease, average median survival rates of 5 years have been achieved. New highly active molecules have also been discovered that allow disease control in very complicated situations. This article reviews all these options that can be used for the management of this disease.

Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.

Automatic quantification of HER2 gene amplification in invasive breast cancer from chromogenic in situ hybridization whole slide images

Human epidermal growth factor receptor 2 (HER2), a transmembrane tyrosine kinase receptor encoded by the ERBB2 gene on chromosome 17q12, is a predictive and prognostic biomarker in invasive breast cancer (BC). Approximately 20% of BC are HER2-positive as a result of ERBB2 gene amplification and overexpression of the HER2 protein. Quantification of HER2 is performed routinely on all invasive BCs, to assist in clinical decision making for prognosis and treatment for HER2-positive BC patients by manually counting gene signals. We propose an automated system to quantify the HER2 gene status from chromogenic in situ hybridization (CISH) whole slide images (WSI) in invasive BC. The proposed method selects untruncated and nonoverlapped singular nuclei from the cancer regions using color unmixing and machine learning techniques. Then, HER2 and chromosome enumeration probe 17 (CEP17) signals are detected based on the RGB intensity and counted per nucleus. Finally, the HER2-to-CEP17 signal ratio is calculated to determine the HER2 amplification status following the ASCO/CAP 2018 guidelines. The proposed method reduced the labor and time for the quantification. In the experiment, the correlation coefficient between the proposed automatic CISH quantification method and pathologist manual enumeration was 0.98. The p -values larger than 0.05 from the one-sided paired t -test ensured that the proposed method yields statistically indifferent results to the reference method. The method was established on WSI scanned by two different scanners. Through the experiments, the capability of the proposed system has been demonstrated.

Automated density-based counting of FISH amplification signals for HER2 status assessment

BACKGROUND

Automated image analysis can make quantification of FISH signals in histological sections more efficient and reproducible. Current detection-based methods, however, often fail to accurately quantify densely clustered FISH signals.

METHODS

We propose a novel density-based approach to quantifying FISH signals. Instead of detecting individual signals, this approach quantifies FISH signals in terms of the integral over a density map predicted by Deep Learning. We apply the density-based approach to the task of counting and determining ratios of ERBB2 and CEN17 signals and compare it to common detection-based and area-based approaches.

RESULTS

The ratios determined by our approach were strongly correlated with results obtained by manual annotation of individual FISH signals (Pearson's r = 0.907). In addition, they were highly consistent with cutoff-scores determined by a pathologist (balanced concordance = 0.971). The density-based approach generally outperformed the other approaches. Its superiority was particularly evident in the presence of dense signal clusters.

CONCLUSIONS

The presented approach enables accurate and efficient automated quantification of FISH signals. Since signals in clusters can hardly be detected individually even by human observers, the density-based quantification performs better than detection-based approaches.

High-content, cell-by-cell assessment of HER2 overexpression and amplification: a tool for intratumoral heterogeneity detection in breast cancer

Immunohistochemistry and fluorescence in situ hybridization are the two standard methods for human epidermal growth factor receptor 2 (HER2) assessment. However, they have severe limitations to assess quantitatively intratumoral heterogeneity (ITH) when multiple subclones of tumor cells co-exist. We develop here a high-content, quantitative analysis of breast cancer tissues based on microfluidic experimentation and image processing, to characterize both HER2 protein overexpression and HER2 gene amplification at the cellular level. The technique consists of performing sequential steps on the same tissue slide: an immunofluorescence (IF) assay using a microfluidic protocol, an elution step for removing the IF staining agents, a standard FISH staining protocol, followed by automated quantitative cell-by-cell image processing. Moreover, ITH is accurately detected in both cluster and mosaic form using an analysis of spatial association and a mathematical model that allows discriminating true heterogeneity from artifacts due to the use of thin tissue sections. This study paves the way to evaluate ITH with high accuracy and content while requiring standard staining methods.

Intratumoral heterogeneity of ERBB2 amplification and HER2 expression in micropapillary urothelial carcinoma

Micropapillary urothelial carcinoma (MPUC) is a rare but an aggressive variant of urothelial carcinoma. MPUC has been shown to commonly exhibit ERBB2 amplification and HER2 protein overexpression, but the frequency and distribution of these findings within micropapillary (MP) and not otherwise specified (NOS) components of tumors with mixed histology have not been addressed. Therefore, we evaluated ERBB2 amplification and HER2 expression in 43 MPUC cases by fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC). Of the 35 tumors containing both MP and NOS components, ERBB2 amplification was present in both the MP and NOS components of 12 tumors (34.3%), in only the MP component of 11 tumors (31.4%), and exclusively in the NOS component of 4 tumors (11.4%). HER2 protein overexpression was significantly more commonly present in the MP component compared to the NOS component within the same tumor (68.6% versus 34.3%, P = .012). Overall, there was a moderately positive correlation between HER2 protein expression and ERBB2 amplification in both MP (ρ = 0.59, P < .001) and NOS (ρ = 0.70, P < .001) components. All MP/NOS areas with IHC score 3+ and none of MP/NOS areas with IHC score 0 were associated with ERBB2 amplification. We conclude that ERBB2 amplification and HER2 overexpression are preferentially but not exclusively identified in the MP component compared to the NOS component within the same tumor. Our findings identify the presence of intratumoral heterogeneity of ERBB2 amplification and HER2 expression in MPUC and provide grounds for further investigation into the mechanisms underlying the development of MPUC.

Semi-automated scoring of triple-probe FISH in human sperm using confocal microscopy

Structural and numerical sperm chromosomal aberrations result from abnormal meiosis and are directly linked to infertility. Any live births that arise from aneuploid conceptuses can result in syndromes such as Kleinfelter, Turners, XYY and Edwards. Multi-probe fluorescence in situ hybridization (FISH) is commonly used to study sperm aneuploidy, however manual FISH scoring in sperm samples is labor-intensive and introduces errors. Automated scoring methods are continuously evolving. One challenging aspect for optimizing automated sperm FISH scoring has been the overlap in excitation and emission of the fluorescent probes used to enumerate the chromosomes of interest. Our objective was to demonstrate the feasibility of combining confocal microscopy and spectral imaging with high-throughput methods for accurately measuring sperm aneuploidy. Our approach used confocal microscopy to analyze numerical chromosomal abnormalities in human sperm using enhanced slide preparation and rigorous semi-automated scoring methods. FISH for chromosomes X, Y, and 18 was conducted to determine sex chromosome disomy in sperm nuclei. Application of online spectral linear unmixing was used for effective separation of four fluorochromes while decreasing data acquisition time. Semi-automated image processing, segmentation, classification, and scoring were performed on 10 slides using custom image processing and analysis software and results were compared with manual methods. No significant differences in disomy frequencies were seen between the semi automated and manual methods. Samples treated with pepsin were observed to have reduced background autofluorescence and more uniform distribution of cells. These results demonstrate that semi-automated methods using spectral imaging on a confocal platform are a feasible approach for analyzing numerical chromosomal aberrations in sperm, and are comparable to manual methods. © 2017 International Society for Advancement of Cytometry.

Automated Image Analysis of HER2 Fluorescence In Situ Hybridization to Refine Definitions of Genetic Heterogeneity in Breast Cancer Tissue

Human epidermal growth factor receptor 2 gene- (HER2-) targeted therapy for breast cancer relies primarily on HER2 overexpression established by immunohistochemistry (IHC) with borderline cases being further tested for amplification by fluorescence in situ hybridization (FISH). Manual interpretation of HER2 FISH is based on a limited number of cells and rather complex definitions of equivocal, polysomic, and genetically heterogeneous (GH) cases. Image analysis (IA) can extract high-capacity data and potentially improve HER2 testing in borderline cases. We investigated statistically derived indicators of HER2 heterogeneity in HER2 FISH data obtained by automated IA of 50 IHC borderline (2+) cases of invasive ductal breast carcinoma. Overall, IA significantly underestimated the conventional HER2, CEP17 counts, and HER2/CEP17 ratio; however, it collected more amplified cells in some cases below the lower limit of GH definition by manual procedure. Indicators for amplification, polysomy, and bimodality were extracted by factor analysis and allowed clustering of the tumors into amplified, nonamplified, and equivocal/polysomy categories. The bimodality indicator provided independent cell diversity characteristics for all clusters. Tumors classified as bimodal only partially coincided with the conventional GH heterogeneity category. We conclude that automated high-capacity nonselective tumor cell assay can generate evidence-based HER2 intratumor heterogeneity indicators to refine GH definitions.

Agreement of Different Methods for Tissue Based Detection of HER2 Signal in Invasive Breast Cancer

Breast cancer is the second leading cause of cancer mortality amongst American women. The HER2 gene encodes a cell surface receptor that affects cell proliferation and has been recognized as a diagnostic factor in treatment selection for invasive breast cancer. Examine accuracy in HER2 detection between manual count, computer assisted, and automated tiling algorithm. 42 randomly selected invasive breast cancer specimens were enumerated by fluorescence in situ hybridization (FISH)for HER2 and CEP17 markers using the Vysis HER2 assay (AbbotLaboratory, North Chicago, IL). Specimens were tested using three methods: Manual, computer assisted nuclei selection (Tissue FISH MetaSystems, Newton, MA), and automated enumeration (MetaSystems, Newton, MA). The greatest bias and widest agreement limits for HER2 and CEP17 were seen in Automatic versus Manual, the gold standard. HER2 values greater than 6 possessed the greatest bias and widest agreement limits. CEP17 comparison showed similar bias and agreement limits for each comparison. Kappa values indicated good agreement for all methods although Tissue FISH and Manual possessed better agreement. Higher agreement at lower HER2 & CEP17 count maybe due to fewer chromosomal aberrations, in which selection of field of views has less variation between methods. Alternatively, increased background signals seen in polyploidy may be responsible for the variations in signal count. Manual and Tissue FISH demonstrated good agreement amongst by both Altman Bland and Cohen's Kappa. While the automatic method has good agreement at lower HER2, the sharp increase in variability at higher HER2 counts illustrates a limitation of the automatic method.

Estrogen receptor alpha gene amplification in breast cancer: 25 years of debate

Twenty-five years ago, Nembrot and colleagues reported amplification of the estrogen receptor alpha gene (ESR1) in breast cancer, initiating a broad and still ongoing scientific debate on the prevalence and clinical significance of this genetic aberration, which affects one of the most important genes in breast cancer. Since then, a multitude of studies on this topic has been published, covering a wide range of divergent results and arguments. The reported prevalence of this alteration in breast cancer ranges from 0% to 75%, suggesting that ESR1 copy number analysis is hampered by technical and interpreter issues. To date, two major issues related to ESR1 amplification remain to be conclusively addressed: (1) The extent to which abundant amounts of messenger RNA can mimic amplification in standard fluorescence in situ hybridization assays in the analysis of strongly expressed genes like ESR1, and (2) the clinical relevance of ESR1 amplification: Such relevance is strongly disputed, with data showing predictive value for response as well as for resistance of the cancer to anti-estrogen therapies, or for subsequent development of cancers in the case of precursor lesions that display amplification of ESR1. This review provides a comprehensive summary of the various views on ESR1 amplification, and highlights explanations for the contradictions and conflicting data that could inform future ESR1 research.

Advantages and disadvantages of technologies for HER2 testing in breast cancer specimens

OBJECTIVES

Human epidermal growth factor receptor 2 (HER2) plays a central role as a prognostic and predictive marker in breast cancer specimens. Reliable HER2 evaluation is central to determine the eligibility of patients with breast cancer to targeted anti-HER2 therapies such as trastuzumab and lapatinib. Presently, several methods exist for the determination of HER2 status at different levels (protein, RNA, and DNA level).

METHODS

In this review, we discuss the main advantages and disadvantages of the techniques developed so far for the evaluation of HER2 status in breast cancer specimens.

RESULTS

Each technique has its own advantages and disadvantages. It is therefore not surprising that no consensus has been reached so far on which technique is the best for the determination of HER2 status.

CONCLUSIONS

Currently, emphasis must be put on standardization of procedures, internal and external quality control assessment, and competency evaluation of already existing methods to ensure accurate, reliable, and clinically meaningful test results. Development of new robust and accurate diagnostic assays should also be encouraged. In addition, large clinical trials are warranted to identify the technique that most reliably predicts a positive response to anti-HER2 drugs.

Automated Analysis of 1p/19q Status by FISH in Oligodendroglial Tumors: Rationale and Proposal of an Algorithm

Objective

To propose a new algorithm facilitating automated analysis of 1p and 19q status by FISH technique in oligodendroglial tumors with software packages available in the majority of institutions using this technique.

Methods

We documented all green/red (G/R) probe signal combinations in a retrospective series of 53 oligodendroglial tumors according to literature guidelines (Algorithm 1) and selected only the most significant combinations for a new algorithm (Algorithm 2). This second algorithm was then validated on a prospective internal series of 45 oligodendroglial tumors and on an external series of 36 gliomas.

Results

Algorithm 2 utilizes 24 G/R combinations which represent less than 40% of combinations observed with Algorithm 1. The new algorithm excludes some common G/R combinations (1/1, 3/2) and redefines the place of others (defining 1/2 as compatible with normal and 3/3, 4/4 and 5/5 as compatible with imbalanced chromosomal status). The new algorithm uses the combination + ratio method of signal probe analysis to give the best concordance between manual and automated analysis on samples of 100 tumor cells (91% concordance for 1p and 89% concordance for 19q) and full concordance on samples of 200 tumor cells. This highlights the value of automated analysis as a means to identify cases in which a larger number of tumor cells should be studied by manual analysis. Validation of this algorithm on a second series from another institution showed a satisfactory concordance (89%, κ = 0.8).

Conclusion

Our algorithm can be easily implemented on all existing FISH analysis software platforms and should facilitate multicentric evaluation and standardization of 1p/19q assessment in gliomas with reduction of the professional and technical time required.

Fully automated fluorescent in situ hybridization (FISH) staining and digital analysis of HER2 in breast cancer: a validation study

HER2 assessment is routinely used to select patients with invasive breast cancer that might benefit from HER2-targeted therapy. The aim of this study was to validate a fully automated in situ hybridization (ISH) procedure that combines the automated Leica HER2 fluorescent ISH system for Bond with supervised automated analysis with the Visia imaging D-Sight digital imaging platform. HER2 assessment was performed on 328 formalin-fixed/paraffin-embedded invasive breast cancer tumors on tissue microarrays (TMA) and 100 (50 selected IHC 2+ and 50 random IHC scores) full-sized slides of resections/biopsies obtained for diagnostic purposes previously. For digital analysis slides were pre-screened at 20x and 100x magnification for all fluorescent signals and supervised-automated scoring was performed on at least two pictures (in total at least 20 nuclei were counted) with the D-Sight HER2 FISH analysis module by two observers independently. Results were compared to data obtained previously with the manual Abbott FISH test. The overall agreement with Abbott FISH data among TMA samples and 50 selected IHC 2+ cases was 98.8% (κ = 0.94) and 93.8% (κ = 0.88), respectively. The results of 50 additionally tested unselected IHC cases were concordant with previously obtained IHC and/or FISH data. The combination of the Leica FISH system with the D-Sight digital imaging platform is a feasible method for HER2 assessment in routine clinical practice for patients with invasive breast cancer.

Incident urothelial cancer in the Malmö Diet and Cancer Study: cohort characteristics and further validation of ezrin as a prognostic biomarker

BACKGROUND

Reduced membranous expression of the cytoskeleton-associated protein ezrin has previously been demonstrated to correlate with poor prognosis in urothelial bladder cancer in several independent studies. The present study provides a first description of clinicopathological characteristics of incident urothelial cancers, not only located to the bladder, in the prospective, population-based cohort study Malmö Diet and Cancer. In addition, the prognostic value of ezrin expression is validated in primary tumours, and the longitudinal expression of ezrin examined in a subset of primary and recurrent tumours (n=28).

METHODS

Among a total number of 355 incident tumours registered up until Dec 31 2010, 335 were located to the bladder. Immunohistochemical expression of cytoplasmic and membranous ezrin was evaluated in tissue microarrays with primary tumours from 272 cases and recurrent tumours from 28 cases. A combined score of the minimum, mean and maximum fraction and percentage of staining was calculated. Classification regression tree analysis was applied for selection of prognostic cutoff. Kaplan-Meier analysis, log rank test, univariable and multivariable Cox regression proportional hazards' modeling were used to evaluate the impact of ezrin expression on 5-year overall survival (OS).

RESULTS

Ezrin expression could be evaluated in 263/272 primary and all 28 recurrent tumours. Membranous but not cytoplasmic ezrin was significantly reduced in recurrent compared to primary tumours (p < 0.001). Low cytoplasmic and membranous ezrin expression were associated with more advanced T-stage (p = 0.004, p < 0.001) and high-grade tumours (p = 0.025, p < 0.001), but not with age, sex, tumour location or smoking status. Both low cytoplasmic and membranous ezrin staining were associated with a significantly reduced 5-year OS (HR = 1.65; 95% CI 1.06-2.57 and HR = 2.51, 95% CI 1.52-4.17), but only low membranous ezrin remained prognostic after adjustment for age, sex, stage, grade and smoking status (HR = 1.69, 95% CI 1.00-2.85).

CONCLUSIONS

This study provides a first description of the clinicopathological characteristics of 355 incident urothelial cancers in the Malmö Diet and Cancer Study up until 2010. In addition, the value of ezrin expression as a prognostic biomarker is further consolidated in this type of cancer.

VIRTUAL SLIDES

The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_189.

Multi-colour FISH in oesophageal adenocarcinoma-predictors of prognosis independent of stage and grade

BACKGROUND

Oesophageal adenocarcinoma or Barrett's adenocarcinoma (EAC) is increasing in incidence and stratification of prognosis might improve disease management. Multi-colour fluorescence in situ hybridisation (FISH) investigating ERBB2, MYC, CDKN2A and ZNF217 has recently shown promising results for the diagnosis of dysplasia and cancer using cytological samples.

METHODS

To identify markers of prognosis we targeted four selected gene loci using multi-colour FISH applied to a tissue microarray containing 130 EAC samples. Prognostic predictors (P1, P2, P3) based on genomic copy numbers of the four loci were statistically assessed to stratify patients according to overall survival in combination with clinical data.

RESULTS

The best stratification into favourable and unfavourable prognoses was shown by P1, percentage of cells with less than two ZNF217 signals; P2, percentage of cells with fewer ERBB2- than ZNF217 signals; and P3, overall ratio of ERBB2-/ZNF217 signals. Median survival times for P1 were 32 vs 73 months, 28 vs 73 months for P2; and 27 vs 65 months for P3. Regarding each tumour grade P2 subdivided patients into distinct prognostic groups independently within each grade, with different median survival times of at least 35 months.

CONCLUSIONS

Cell signal number of the ERBB2 and ZNF217 loci showed independence from tumour stage and differentiation grade. The prognostic value of multi-colour FISH-assays is applicable to EAC and is superior to single markers.

Significance of ERβ expression in different molecular subtypes of breast cancer

Purpose

This study is to investigate the estrogen receptor β (ERβ) expression in molecular subtypes of breast cancer and clinic significance of ERβ expression.

Method

The ERβ expression was detected in 730 cases of breast cancer tissue specimens by immunohistochemistry. Twenty-one patients were censored during 2–10 years follow-up. The difference in ERβ expression was analyzed by Pearson Chi-square Test. Its correlation with estrogen receptor α (ERα), progesterone receptor (PR) and human epidermal growth factor receptor 2 (Her-2) was analyzed by Spearman rank correlation. The accumulative tumor-free survival rate was calculated by Kaplan-Meier method and difference in survival rate was analyzed by Log-rank test. Cox regression was used for multi-factor analysis.

Result

The ERβ expression was significantly different among the molecular subtypes of breast cancer (P < 0.05). The ERβ expression in breast cancer was positively correlated with Her-2 (P < 0.05) while it had no correlation with ERα and Her-2. The expression of ERα was negatively correlated with Her-2 (P < 0.01) whereas positively correlated with PR (P < 0.01). The expression of PR was negatively correlated with Her-2 (P < 0.05). The tumor-free survival rate in patients with positive ERβ expression was significantly lower than that in patients with negative ERβ expression.

Conclusion

Positive ERβ expression is a poor prognostic factor of breast cancer.

Virtual slides

The virtual slides for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1084557586106833