Digital image analysis and assisted reading of the HER2 score display reduced concordance: pitfalls in the categorisation of HER2-low breast cancer

Evaluation of trophoblast cell surface antigen-2 (TROP2) protein expression in chemotherapy-resistant and metastatic breast carcinomas

Trophoblast cell-surface antigen 2 (TROP2), a transmembrane receptor expressed in many carcinomas, is a target for novel antibody-drug conjugates such as sacituzumab govitecan. TROP2-targeted therapy is used for unresectable locally advanced or metastatic triple-negative and hormone receptor-positive, HER2-negative breast cancers. The role of TROP2 as a predictive marker is yet unclear. Standardized interpretation criteria for TROP2 immunohistochemistry (IHC) are lacking. Here, we compared three antibody clones and two methods for semi-quantitative assessment, aiming to establish reproducible evaluation criteria. First, TROP2 IHC was performed on normal tissues and nine breast cancers, using the BSB-148, EPR20043 and SP293 clones. EPR20043 was selected for subsequent evaluation in 69 breast cancers without pathological complete response to neoadjuvant chemotherapy (NAC). Four pathologists applied the ASCO/CAP guidelines for HER2 IHC testing (designated as the 'membrane score') and the H-score. All H-scores were categorized as low (0-100), intermediate (101-200) and high (201-300). Although the membrane scores strongly correlated with the categorized H-scores, the latter showed higher interobserver variability. Next, TROP2 IHC was performed on 94 breast cancer metastases and evaluated by six pathologists, confirming the strong correlation between the membrane scores and H-scores. In metastases, the interobserver variability was similar for both methods. Our observations support the application of the HER2 ASCO/CAP guidelines for semi-quantitative evaluation of membranous TROP2 protein expression, as this method strongly correlates with the H-score and is less prone to interobserver variability in post-NAC breast resections. Future studies should investigate the association between the TROP2 membrane score and response to TROP2-targeted therapy.

Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study

Aims

This study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria.

Methods and Results

We utilized 330 slides from two institutions for initial AI development and selected 200 slides for the ring study, involving six pathologists (3 senior, 3 junior). Our AI algorithm achieved high accuracy in two independent tests, with accuracies of 0.94 and 0.92. In the ring study, the AI-assisted method improved both accuracy (0.66 vs 0.94) and consistency (kappa=0.48; 95 % CI, 0.443-0.526 vs kappa=0.87; 95 % CI, 0.852-0.885) compared to manual scoring, especially in HER2-low cases (F1-scores: 0.63 vs 0.92). Additionally, in 62.3 % of heterogeneous HER2-positive cases, the interpretation accuracy significantly improved (0.49 vs 0.93). Pathologists, particularly junior ones, experienced enhanced accuracy and consistency with AI assistance.

Conclusions

This is the first study to provide a quantification algorithm for HER2 scoring in UBCa to assist pathologists in diagnosis. The ring study demonstrated that HER2 scoring based on breast cancer criteria can be effectively applied to UBCa. Furthermore, AI assistance significantly improves the accuracy and consistency of interpretations among pathologists with varying levels of experience, even in heterogeneous cases.

Fully Automated Artificial Intelligence Solution for Human Epidermal Growth Factor Receptor 2 Immunohistochemistry Scoring in Breast Cancer: A Multireader Study

PURPOSE

The proven efficacy of human epidermal growth factor receptor 2 (HER2) antibody-drug conjugate therapy for treating HER2-low breast cancers necessitates more accurate and reproducible HER2 immunohistochemistry (IHC) scoring. We aimed to validate performance and utility of a fully automated artificial intelligence (AI) solution for interpreting HER2 IHC in breast carcinoma.

MATERIALS AND METHODS

A two-arm multireader study of 120 HER2 IHC whole-slide images from four sites assessed HER2 scoring by four surgical pathologists without and with the aid of an AI HER2 solution. Both arms were compared with high-confidence ground truth (GT) established by agreement of at least four of five breast pathology subspecialists according to ASCO/College of American Pathologists (CAP) 2018/2023 guidelines.

RESULTS

The mean interobserver agreement among GT pathologists across all HER2 scores was 72.4% (N = 120). The AI solution demonstrated high accuracy for HER2 scoring, with 92.1% agreement on slides with high confidence GT (n = 92). The use of the AI tool led to improved performance by readers, interobserver agreement increased from 75.0% for digital manual read to 83.7% for AI-assisted review, and scoring accuracy improved from 85.3% to 88.0%. For the distinction of HER2 0 from 1+ cases (n = 58), pathologists supported by AI showed significantly higher interobserver agreement (69.8% without AI v 87.4% with AI) and accuracy (81.9% without AI v 88.8% with AI).

CONCLUSION

This study demonstrated utility of a fully automated AI solution to aid in scoring HER2 IHC accurately according to ASCO/CAP 2018/2023 guidelines. Pathologists supported by AI showed improvements in HER2 IHC scoring consistency and accuracy, especially for distinguishing HER2 0 from 1+ cases. This AI solution could be used by pathologists as a decision support tool for enhancing reproducibility and consistency of HER2 scoring and particularly for identifying HER2-low breast cancers.

Understanding the spectrum of HER2 status in breast cancer: From HER2-positive to ultra-low HER2

HER2 (human epidermal growth factor receptor 2) status in breast cancer spans a spectrum from HER2-positive to ultra-low HER2, each category influencing prognosis and treatment decisions differently. Approximately 20 % of breast cancers overexpress HER2, correlating with aggressive disease and poorer outcomes without targeted therapy. HER2 status is determined through immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), guiding therapeutic strategies. HER2-positive breast cancer exhibits HER2 protein overexpression or gene amplification, benefiting from HER2-targeted therapies like trastuzumab and pertuzumab. In contrast, HER2-negative breast cancer lacks HER2 overexpression and amplification, treated based on hormone receptor status. HER2-low breast cancer represents a newly recognized category with low HER2 expression, potentially benefiting from evolving therapies. Ultra-low HER2 cancers, characterized by minimal expression without gene amplification, challenge conventional classifications and treatment paradigms. Their distinct molecular profiles and clinical behaviors suggest unique therapeutic approaches. Recent diagnostic guideline updates refine HER2 assessment, enhancing precision in identifying patients for targeted therapies. Challenges remain in accurately classifying HER2-low tumors and optimizing treatment efficacy, necessitating ongoing research and innovative diagnostic methods. Understanding the heterogeneity and evolving landscape of HER2 status in breast cancer is crucial for advancing personalized treatment strategies and improving patient outcomes.

Artificial intelligence for assisted HER2 immunohistochemistry evaluation of breast cancer: A systematic review and meta-analysis

Accurate assessment of HER2 expression in tumor tissue is crucial for determining HER2-targeted treatment options. Nevertheless, pathologists' assessments of HER2 status are less objective than automated, computer-based evaluations. Artificial Intelligence (AI) promises enhanced accuracy and reproducibility in HER2 interpretation. This study aimed to systematically evaluate current AI algorithms for HER2 immunohistochemical diagnosis, offering insights to guide the development of more adaptable algorithms in response to evolving HER2 assessment practices. A comprehensive data search of the PubMed, Embase, Cochrane, and Web of Science databases was conducted using a combination of subject terms and free text. A total of 4994 computational pathology articles published from inception to September 2023 identifying HER2 expression in breast cancer were retrieved. After applying predefined inclusion and exclusion criteria, seven studies were selected. These seven studies comprised 6867 HER2 identification tasks, with two studies employing the HER2-CONNECT algorithm, two using the CNN algorithm, one with the multi-class logistic regression algorithm, and two using the HER2 4B5 algorithm. AI's sensitivity and specificity for distinguishing HER2 0/1+ were 0.98 [0.92-0.99] and 0.92 [0.80-0.97] respectively. For distinguishing HER2 2+, the sensitivity and specificity were 0.78 [0.50-0.92] and 0.98 [0.93-0.99], respectively. For HER2 3+ distinction, AI exhibited a sensitivity of 0.99 [0.98-1.00] and specificity of 0.99 [0.97-1.00]. Furthermore, due to the lack of HER2-targeted therapies for HER2-negative patients in the past, pathologists may have neglected to distinguish between HER2 0 and 1+, leaving room for improvement in the performance of artificial intelligence (AI) in this differentiation. AI excels in automating the assessment of HER2 immunohistochemistry, showing promising results despite slight variations in performance across different HER2 status. While incorporating AI algorithms into the pathology workflow for HER2 assessment poses challenges in standardization, application patterns, and ethical considerations, ongoing advancements suggest its potential as a widely effective tool for pathologists in clinical practice in the near future.

Immunohistochemical assessment of HER2 low breast cancer: interobserver reproducibility and correlation with digital image analysis

PURPOSE

The recent findings from the DESTINY-Breast04 trial highlighted the clinical importance of distinguishing between HER2 immunohistochemistry (IHC) scores 0 and 1 + in metastatic breast cancer (BC). However, pathologist interpretation of HER2 IHC scoring is subjective, and standardized methodology is needed. We evaluated the consistency of HER2 IHC scoring among pathologists and the accuracy of digital image analysis (DIA) in interpreting HER2 IHC staining in cases of HER2-low BC.

METHODS

Fifty whole-slide biopsies of BC with HER2 IHC staining were evaluated, comprising 25 cases originally reported as IHC score 0 and 25 as 1 +. These slides were digitally scanned. Six pathologists with breast expertise independently reviewed and scored the scanned images, and DIA was applied. Agreement among pathologists and concordance between pathologist scores and DIA results were statistically analyzed using Kendall coefficient of concordance (W) tests.

RESULTS

Substantial agreement among at least five of the six pathologists was found for 18 of the score 0 cases (72%) and 15 of the score 1 + cases (60%), indicating excellent interobserver agreement (W = 0.828). DIA scores were highly concordant with pathologist scores in 96% of cases (47/49), indicating excellent concordance (W = 0.959).

CONCLUSION

Although breast subspecialty pathologists were relatively consistent in evaluating BC with HER2 IHC scores of 0 and 1 +, DIA may be a reliable supplementary tool to enhance the standardization and quantification of HER2 IHC assessment, especially in challenging cases where results may be ambiguous (i.e., scores 0-1 +). These findings hold promise for improving the accuracy and consistency of HER2 testing.

A pipeline for the development and analysis of extracellular vesicle-based transcriptomic biomarkers in molecular diagnostics

Extracellular vesicles are shed by every cell type and can be found in any biofluid. They contain different molecules that can be utilized as biomarkers, including several RNA species which they protect from degradation. Here, we present a pipeline for the development and analysis of extracellular vesicle-associated transcriptomic biomarkers that our group has successfully applied multiple times. We highlight the key steps of the pipeline and give particular emphasis to the necessary quality control checkpoints, which are linked to numerous available guidelines that should be considered along the workflow. Our pipeline starts with patient recruitment and continues with blood sampling and processing. The purification and characterization of extracellular vesicles is explained in detail, as well as the isolation and quality control of extracellular vesicle-associated RNA. We point out the possible pitfalls during library preparation and RNA sequencing and present multiple bioinformatic tools to pinpoint biomarker signature candidates from the sequencing data. Finally, considerations and pitfalls during the validation of the biomarker signature using RT-qPCR will be elaborated.

Open questions, current challenges, and future perspectives in targeting human epidermal growth factor receptor 2-low breast cancer

Approximately 60% of traditionally defined human epidermal growth factor receptor 2 (HER2)-negative breast cancers express low levels of HER2 [HER2-low; defined as immunohistochemistry (IHC) 1+ or IHC 2+/in situ hybridization (ISH)-]. HER2-low breast cancers encompass a large percentage of both hormone receptor-positive (up to 85%) and triple-negative (up to 63%) breast cancers. The DESTINY-Breast04 trial established that HER2-low tumors are targetable, leading to the approval of trastuzumab deruxtecan (T-DXd) as the first HER2-directed therapy for the treatment of HER2-low breast cancer in the United States and Europe. This change in the clinical landscape results in a number of questions and challenges-including those related to HER2 assessment and patient identification-and highlights the need for careful assessment of HER2 expression to identify patients eligible for T-DXd. This review provides context for understanding how to identify patients with HER2-low breast cancer with respect to sample types, scoring and reporting HER2 status, and testing methods and assays. It also discusses management of important T-DXd-related adverse events. Available evidence supports the efficacy of T-DXd in patients with any history of IHC 1+ or IHC 2+/ISH- scores; however, future research may further refine the population who could benefit from T-DXd or other HER2-directed therapies and identify novel methods for patient identification. Because HER2 expression can change with disease progression or treatment, and variability exists in scoring and interpretation of HER2 status, careful re-evaluation in certain scenarios may help to identify more patients who may benefit from T-DXd.

Intratumoral heterogeneity of Ki67 proliferation index outperforms conventional immunohistochemistry prognostic factors in estrogen receptor-positive HER2-negative breast cancer

In breast cancer (BC), pathologists visually score ER, PR, HER2, and Ki67 biomarkers to assess tumor properties and predict patient outcomes. This does not systematically account for intratumoral heterogeneity (ITH) which has been reported to provide prognostic value. This study utilized digital image analysis (DIA) and computational pathology methods to investigate the prognostic value of ITH indicators in ER-positive (ER+) HER2-negative (HER2-) BC patients. Whole slide images (WSIs) of surgically excised specimens stained for ER, PR, Ki67, and HER2 from 254 patients were used. DIA with tumor tissue segmentation and detection of biomarker-positive cells was performed. The DIA-generated data were subsampled by a hexagonal grid to compute Haralick's texture indicators for ER, PR, and Ki67. Cox regression analyses were performed to assess the prognostic significance of the immunohistochemistry (IHC) and ITH indicators in the context of clinicopathologic variables. In multivariable analysis, the ITH of Ki67-positive cells, measured by Haralick's texture entropy, emerged as an independent predictor of worse BC-specific survival (BCSS) (hazard ratio (HR) = 2.64, p-value = 0.0049), along with lymph node involvement (HR = 2.26, p-value = 0.0195). Remarkably, the entropy representing the spatial disarrangement of tumor proliferation outperformed the proliferation rate per se established either by pathology reports or DIA. We conclude that the Ki67 entropy indicator enables a more comprehensive risk assessment with regard to BCSS, especially in cases with borderline Ki67 proliferation rates. The study further demonstrates the benefits of high-capacity DIA-generated data for quantifying the essentially subvisual ITH properties.

Think "HER2" different: integrative diagnostic approaches for HER2-low breast cancer

This work explores the complex field of HER2 testing in the HER2-low breast cancer era, with a focus on methodological aspects. We aim to propose clear positions to scientific societies, institutions, pathologists, and oncologists to guide and shape the appropriate diagnostic strategies for HER2-low breast cancer. The fundamental question at hand is whether the necessary tools to effectively translate our knowledge about HER2 into practical diagnostic schemes for the lower spectrum of expression are available. Our investigation is centered on the significance of distinguishing between an immunohistochemistry (IHC) score 0 and score 1+ in light of the clinical implications now apparent, as patients with HER2-low breast cancer become eligible for trastuzumab-deruxtecan treatment. Furthermore, we discuss the definition of HER2-low beyond its conventional boundaries and assess the reliability of established diagnostic procedures designed at a time when therapeutic perspectives were non-existent for these cases. In this regard, we examine potential complementary technologies, such as gene expression analysis and liquid biopsy. Ultimately, we consider the potential role of artificial intelligence (AI) in the field of digital pathology and its integration into HER2 testing, with a particular emphasis on its application in the context of HER2-low breast cancer.

High inter-laboratory variability in the assessment of HER2-low breast cancer: a national registry study on 50,714 Danish patients

BACKGROUND

Considering the recent advancements in the treatment of breast cancer with low expression of human epidermal growth factor receptor 2 (HER2), we aimed to examine inter-laboratory variability in the assessment of HER2-low breast cancer across all Danish pathology departments.

METHODS

From the Danish Breast Cancer Group, we obtained data on all women diagnosed with primary invasive breast cancer in 2007-2019 who were subsequently assigned for curatively intended treatment.

RESULTS

Of 50,714 patients, HER2 score and status were recorded for 48,382, among whom 59.2% belonged to the HER2-low group (score 1+ or 2+ without gene amplification), 26.8% had a HER2 score of 0, and 14.0% were HER2 positive. The proportion of HER2-low cases ranged from 46.3 to 71.8% among pathology departments (P < 0.0001) and from 49.3 to 65.6% over the years (P < 0.0001). In comparison, HER2 positivity rates ranged from 11.8 to 17.2% among departments (P < 0.0001) and from 12.6 to 15.7% over the years (P = 0.005). In the eight departments with the highest number of patients, variability in HER2-low cases increased from 2011 to 2019, although the same immunohistochemical assay was used. By multivariable logistic regression, the examining department was significantly related to both HER2 score 0 and HER2 positivity (P < 0.0001) but showed greater dispersion in odds ratios in the former case (range 0.25-1.41 vs. 0.84-1.27).

CONCLUSIONS

Our data showed high inter-laboratory variability in the assessment of HER2-low breast cancer. The findings cast doubt on whether the current test method for HER2 is robust and reliable enough to select HER2-low patients for HER2-targeted treatment in daily clinical practice.

HER2-Low Breast Cancer: Current Landscape and Future Prospects

More than 50% of breast cancers are currently defined as "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", with HER2 immunohistochemistry (IHC) scores of +1 or +2 with a negative fluorescence in situ hybridization (FISH) test. In most studies that compared the clinical and biological characteristics of HER2-low BC with HER2-negative BC, HER2-low was not associated with unique clinical and molecular characteristics, and it seems that the importance of HER2 in these tumors is being a docking site for the antibody portion of antibody drug conjugates (ADCs). Current pathological methods may underestimate the proportion of BCs that express low levels of HER2 due to analytical limitations and tumor heterogeneity. In this review we summarize and contextualize the most recent literature on HER2-low breast cancers, including clinical and translational studies We also review the challenges of assessing low HER2 expression in BC and discuss the current and future therapeutic landscape for these tumors.

Pathological identification of HER2-low breast cancer: Tips, tricks, and troubleshooting for the optimal test

The introduction of novel anti-HER2 antibody-drug conjugates (ADC) for the treatment of HER2-low breast cancers has transformed the traditional dichotomy of HER2 status to an expanded spectrum. However, the identification of HER2-low (i.e., immunohistochemistry (IHC) score 1 + or IHC score 2+, without gene amplification) tumors is challenged by methodological and analytical variables that might influence the sensitivity and reproducibility of HER2 testing. To open all possible therapeutic opportunities for HER2-low breast cancer patients the implementation of more accurate and reproducible testing strategies is mandatory. Here, we provide an overview of the existing barriers that may trouble HER2-low identification in breast cancer and discuss practical solutions that could enhance HER-low assessment.