Determining HER2 Status by Artificial Intelligence: An Investigation of Primary, Metastatic, and HER2 Low Breast Tumors

Artificial Intelligence-Powered Human Epidermal Growth Factor Receptor 2 and Tumor Microenvironment Analysis in Human Epidermal Growth Factor Receptor 2-Amplified Metastatic Colorectal Cancer: Exploratory Analysis of Phase II TRIUMPH Trial

PURPOSE

Human epidermal growth factor receptor 2 (HER2)-targeted therapies have shown promise in treating HER2-amplified metastatic colorectal cancer (mCRC). Identifying optimal biomarkers for treatment decisions remains challenging. This study explores the potential of artificial intelligence (AI) in predicting treatment responses to trastuzumab plus pertuzumab (TP) in patients with HER2-amplified mCRC from the phase II TRIUMPH trial.

MATERIALS AND METHODS

AI-powered HER2 quantification continuous score (QCS) and tumor microenvironment (TME) analysis were applied to the prescreening cohort (n = 143) and the TRIUMPH cohort (n = 30). AI analyzers determined the proportions of tumor cells (TCs) with HER2 staining intensity and the densities of various cells in TME, examining their associations with clinical outcomes of TP.

RESULTS

The AI-powered HER2 QCS for HER2 immunohistochemistry (IHC) achieved an accuracy of 86.7% against pathologist evaluations, with a 100% accuracy for HER2 IHC 3+ patients. Patients with ≥50% of TCs showing HER2 3+ staining intensity (AI-H3-high) exhibited significantly prolonged progression-free survival (PFS; median PFS, 4.4 v 1.4 months; hazard ratio [HR], 0.12 [95% CI, 0.04 to 0.38]) and overall survival (OS; median OS, 16.5 v 4.1 months; HR, 0.13 [95% CI, 0.05 to 0.38]) compared with the AI-H3-low (<50% group). Stratification among patients with AI-H3-high included TME-high (all lymphocyte, fibroblast, and macrophage densities in the cancer stroma above the median) and TME-low (anything below the median), showing a median PFS of 1.3 and 5.6 months for TME-high and TME-low respectively, with an HR of 0.04 (95% CI, 0.01 to 0.19) for AI-H3-high with TME-low compared with AI-H3-low.

CONCLUSION

AI-powered HER2 QCS and TME analysis demonstrated potential in enhancing treatment response predictions in patients with HER2-amplified mCRC undergoing TP therapy.

Low interobserver agreement among subspecialised breast pathologists in evaluating HER2-low breast cancer

AIMS

Metastatic HER2-low breast cancer (HLBC) can be treated by trastuzumab deruxtecan. Assessment of low levels of HER2 protein expression suffers from poor interobserver reproducibility. The aim of the study was to evaluate the interobserver agreement among subspecialised breast pathologists and develop a practical algorithm for assessing HLBC.

METHODS

Six breast pathologists (4 juniors, 2 seniors) evaluated 106 HER2 immunostained slides with 0/1+expression. Two rounds (R1, R2) of ring study were performed before and after training with a modified Ki-67 algorithm, and concordance was assessed.

RESULTS

Agreement with 5% increments increased from substantial to almost perfect (R1: 0.796, R2: 0.804), and remained substantial for three categories (<1% vs 1%-10% vs >10%) (R1: 0.768, R2: 0.764). Seniors and juniors had almost perfect agreement with 5% increments (R1: 0.859 and 0.821, R2: 0.872 and 0.813). For the three categories, agreement remained almost perfect among seniors (R1: 0.837, R2: 0.860) and substantial among juniors (R1: 0.792, R2: 0.768). Binary analysis showed suboptimal agreement, decreasing for both juniors and seniors from substantial (R1: 0.650 and 0.620) to moderate (R2: 0.560 and 0.554) using the 1% cut-off, and increasing from moderate to substantial (R1: 0.478, R2: 0.712) among seniors but remaining moderate (R1: 0.576, R2: 0.465) among juniors using the 10% cut-off. The average scoring time per case was higher (72 vs 92 s).

CONCLUSIONS

Subspecialised breast pathologists have suboptimal agreement for immunohistochemical evaluation of HLBC using the modified Ki-67 methodology. An urgent need remains for a new assay/algorithm to reliably evaluate HLBC.

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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.

Semi-automated analysis of HER2 immunohistochemistry in invasive breast carcinoma using whole slide images: utility for interpretation in clinical practice

Human epidermal growth factor receptor 2 (HER2) gene amplification and subsequent protein overexpression is a strong prognostic and predictive biomarker in invasive breast carcinoma (IBC). ASCO/CAP recommended tests for HER2 assessment include immunohistochemistry (IHC) and/or in situ hybridization (ISH). Accurate HER2 IHC scoring (0, 1+, 2+, 3+) is key for appropriate classification and treatment of IBC. HER2-targeted therapies, including anti-HER2 monoclonal antibodies and antibody drug conjugates (ADC), have revolutionized the treatment of HER2-positive IBC. Recently, ADC have also been approved for treatment of HER2-low (IHC 1+, IHC 2+/ISH-) advanced breast carcinoma, making a distinction between IHC 0 and 1+ crucial. In this focused study, 32 IBC with HER2 IHC scores from 0 to 3+ and HER2 FISH results formed a calibration dataset, and 77 IBC with HER2 IHC score 2+ and paired FISH results (27 amplified, 50 non-amplified) formed a validation dataset. H&E and HER2 IHC whole slide images (WSI) were scanned. Regions of interest were manually annotated and IHC scores generated by the software QuantCenter (MembraneQuant application) by 3DHISTECH Ltd. (Budapest, Hungary) and compared to the microscopic IHC score. H-scores [(3×%IHC3+) +(2×%IHC2+) +(1×%IHC1+)] were calculated for semi-automated (MembraneQuant) analysis. Concordance between microscopic IHC scoring and 3DHISTECH MembraneQuant semi-automated scoring in the calibration dataset showed a Kappa value of 0.77 (standard error 0.09). Microscopic IHC and MembraneQuant image analysis for the detection of HER2 amplification yielded a sensitivity of 100% for both and a specificity of 56% and 61%, respectively. In the validation set of IHC 2+ cases, only 13 of 77 cases (17%) had discordant results between microscopic and MembraneQuant images, and various artifacts limiting the interpretation of HER2 IHC, including cytoplasmic/granular staining and crush artifact were noted. Semi-automated analysis using WSI and microscopic evaluation yielded similar HER2 IHC scores, demonstrating the potential utility of this tool for interpretation in clinical practice and subsequent accurate treatment. In this study, it was shown that semi-automatic HER2 IHC interpretation provides an objective approach to a test known to be quite subjective.

The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review

This systematic review aims to address the research gap in the performance of computational algorithms for the digital image analysis of HER2 images in clinical settings. While numerous studies have explored various aspects of these algorithms, there is a lack of comprehensive evaluation regarding their effectiveness in real-world clinical applications. We conducted a search of the Web of Science and PubMed databases for studies published from 31 December 2013 to 30 June 2024, focusing on performance effectiveness and components such as dataset size, diversity and source, ground truth, annotation, and validation methods. The study was registered with PROSPERO (CRD42024525404). Key questions guiding this review include the following: How effective are current computational algorithms at detecting HER2 status in digital images? What are the common validation methods and dataset characteristics used in these studies? Is there standardization of algorithm evaluations of clinical applications that can improve the clinical utility and reliability of computational tools for HER2 detection in digital image analysis? We identified 6833 publications, with 25 meeting the inclusion criteria. The accuracy rate with clinical datasets varied from 84.19% to 97.9%. The highest accuracy was achieved on the publicly available Warwick dataset at 98.8% in synthesized datasets. Only 12% of studies used separate datasets for external validation; 64% of studies used a combination of accuracy, precision, recall, and F1 as a set of performance measures. Despite the high accuracy rates reported in these studies, there is a notable absence of direct evidence supporting their clinical application. To facilitate the integration of these technologies into clinical practice, there is an urgent need to address real-world challenges and overreliance on internal validation. Standardizing study designs on real clinical datasets can enhance the reliability and clinical applicability of computational algorithms in improving the detection of HER2 cancer.

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.

Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry

Artificial intelligence (AI) is a reality of our times, and it has been successfully implemented in all fields, including medicine. As a relatively new domain, all efforts are directed towards creating algorithms applicable in most medical specialties. Pathology, as one of the most important areas of interest for precision medicine, has received significant attention in the development and implementation of AI algorithms. This focus is especially important for achieving accurate diagnoses. Moreover, immunohistochemistry (IHC) serves as a complementary diagnostic tool in pathology. It can be further augmented through the application of deep learning (DL) and machine learning (ML) algorithms for assessing and analyzing immunohistochemical markers. Such advancements can aid in delineating targeted therapeutic approaches and prognostic stratification. This article explores the applications and integration of various AI software programs and platforms used in immunohistochemical analysis. It concludes by highlighting the application of these technologies to pathologies such as breast, prostate, lung, melanocytic proliferations, and hematologic conditions. Additionally, it underscores the necessity for further innovative diagnostic algorithms to assist physicians in the diagnostic process.

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.

HER2-low breast cancer and response to neoadjuvant chemotherapy: a population-based cohort study

About half of breast cancers (BC) without amplification of the human epidermal growth factor receptor 2 (HER2) have a low HER2 protein expression level (HER2-low). The clinical impact of HER2-low and the response to neoadjuvant chemotherapy (NAC) is unclear. This study aimed to assess the association between HER2-low BC and pathological response to NAC. Data from the Dutch Pathology Registry were collected for 11,988 BC patients treated with NAC between 2014 and 2022. HER2-low BC was defined as an immunohistochemical score of 1+ or 2+ and a negative molecular reflex test. We compared clinicopathological features of HER2-0 versus HER2-low BC and assessed the correlation between HER2 status and the pathological complete response (pCR) rate after NAC, including overall survival. Among hormone receptor (HR)-positive tumours, 67% (n=4,619) were HER2-low, compared to 47% (n=1,167) in the HR-negative group. Around 32% (n=207) of patients had a discordant HER2 status between the pre-NAC biopsy and the corresponding post-NAC resection, within which 87% (n=165) changed from HER2-0 to HER2-low or vice versa. The pCR rate was significantly lower in HER2-low BC compared to HER2-0 BC within the HR-positive group (4% versus 5%; p=0.022). However, the absolute difference was limited, so the clinical relevance is questionable. In HR-negative cases, the difference in pCR was not significant (32% versus 34%; p=0.266). No significant difference in overall survival was observed between HER2-low and HER2-0 tumours, regardless of hormone receptor status. The antibody-drug conjugate trastuzumab deruxtecan (T-DXd) has improved survival outcomes of patients with HER2-low metastatic BC. The finding that one-third of the patients in this study had a discordant HER2 status between the pre-NAC biopsy and the post-NAC resection specimen could impact clinical decision-making should T-DXd be used in early BC treatment.

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.

Digital counting of tissue cells for molecular analysis: the QuANTUM pipeline

The estimation of tumor cellular fraction (TCF) is a crucial step in predictive molecular pathology, representing an entry adequacy criterion also in the next-generation sequencing (NGS) era. However, heterogeneity of quantification practices and inter-pathologist variability hamper the robustness of its evaluation, stressing the need for more reliable results. Here, 121 routine histological samples from non-small cell lung cancer (NSCLC) cases with complete NGS profiling were used to evaluate TCF interobserver variability among three different pathologists (pTCF), developing a computational tool (cTCF) and assessing its reliability vs ground truth (GT) tumor cellularity and potential impact on the final molecular results. Inter-pathologist reproducibility was fair to good, with overall Wk ranging between 0.46 and 0.83 (avg. 0.59). The obtained cTCF was comparable to the GT (p = 0.129, 0.502, and 0.130 for surgical, biopsies, and cell block, respectively) and demonstrated good reliability if elaborated by different pathologists (Wk = 0.9). Overall cTCF was lower as compared to pTCF (30 ± 10 vs 52 ± 19, p < 0.001), with more cases < 20% (17, 14%, p = 0.690), but none containing < 100 cells for the algorithm. Similarities were noted between tumor area estimation and pTCF (36 ± 29, p < 0.001), partly explaining variability in the human assessment of tumor cellularity. Finally, the cTCF allowed a reduction of the copy number variations (CNVs) called (27 vs 29, - 6.9%) with an increase of effective CNVs detection (13 vs 7, + 85.7%), some with potential clinical impact previously undetected with pTCF. An automated computational pipeline (Qupath Analysis of Nuclei from Tumor to Uniform Molecular tests, QuANTUM) has been created and is freely available as a QuPath extension. The computational method used in this study has the potential to improve efficacy and reliability of TCF estimation in NSCLC, with demonstrated impact on the final molecular results.

Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases

BACKGROUND

Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations.

METHODS

AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment.

RESULTS

Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance.

CONCLUSIONS

This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.

Prognostic impact of HER2 biomarker levels in trastuzumab-treated early HER2-positive breast cancer

BACKGROUND

Overexpression of human epidermal growth factor receptor 2 (HER2) caused by HER2 gene amplification is a driver in breast cancer tumorigenesis. We aimed to investigate the prognostic significance of manual scoring and digital image analysis (DIA) algorithm assessment of HER2 copy numbers and HER2/CEP17 ratios, along with ERBB2 mRNA levels among early-stage HER2-positive breast cancer patients treated with trastuzumab.

METHODS

This retrospective study comprised 371 early HER2-positive breast cancer patients treated with adjuvant trastuzumab, with HER2 re-testing performed on whole tumor sections. Digitized tumor tissue slides were manually scored and assessed with uPath HER2 Dual ISH image analysis, breast algorithm. Targeted ERBB2 mRNA levels were assessed by the Xpert® Breast Cancer STRAT4 Assay. HER2 copy number and HER2/CEP17 ratio from in situ hybridization assessment, along with ERBB2 mRNA levels, were explored in relation to recurrence-free survival (RFS).

RESULTS

The analysis showed that patients with tumors with the highest and lowest manually counted HER2 copy number levels had worse RFS than those with intermediate levels (HR = 2.7, CI 1.4-5.3, p = 0.003 and HR = 2.1, CI 1.1-3.9, p = 0.03, respectively). A similar trend was observed for HER2/CEP17 ratio, and the DIA algorithm confirmed the results. Moreover, patients with tumors with the highest and the lowest values of ERBB2 mRNA had a significantly worse prognosis (HR = 2.7, CI 1.4-5.1, p = 0.003 and HR = 2.8, CI 1.4-5.5, p = 0.004, respectively) compared to those with intermediate levels.

CONCLUSIONS

Our findings suggest that the association between any of the three HER2 biomarkers and RFS was nonlinear. Patients with tumors with the highest levels of HER2 gene amplification or ERBB2 mRNA were associated with a worse prognosis than those with intermediate levels, which is of importance to investigate in future clinical trials studying HER2-targeted therapy.

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.

Impact of the OncotypeDX score and HER2 RNA PCR levels on HER2-low IHC levels in primary and metastasized tumors

PURPOSE

One-half of hormone receptor-positive (HR +) breast cancer (BC) patients have low expression of HER2 (HER2-low) and may benefit from trastuzumab deruxtecan (TDXd). This study aimed to identify parameters associated with HER2-low levels in primary and metastatic tumors. We specifically sought to determine whether OncotypeDX and HER2 mRNA levels could identify patients who would otherwise be considered HER2-negative by immunohistochemistry (IHC).

METHODS

This retrospective analysis of all consecutive HR + patients who underwent OncotypeDX from January 2004 to December 2020 was conducted in a single medical center (n = 1429). We divided HER2-negative cases into HER2-low (IHC = 1 + or 2 + and non-amplified fluorescent situ hybridization) and HER2-0 (IHC = 0). HER2 RT-PCR was evaluated from the OncotypeDX results.

RESULTS

HER2-low cases exhibited significantly higher HER2 RT-PCR scores (p = 2.1e-9), elevated estrogen receptor (ER) levels (p = 0.0114), and larger tumor sizes compared to HER2-0 cases (> 2 cm; 36.6% vs. 22.1%, respectively, p < 0.00001). Primary tumors > 2 cm were more likely to be HER2-low (OR = 2.07, 95% CI: 1.6317 to 2.6475, p < 0.0001). Metastatic BCs expressed higher HER2 IHC scores compared with primary BCs (Wilcoxon signed-rank, p = 0.046). HER2 IHC scores were higher for low-risk vs. medium-risk OncotypeDX (p = 0.0067). No other clinical or pathological parameters were associated with the increase in HER2 levels in the metastatic samples.

CONCLUSION

It might be beneficial to use clinical data from the primary tumor, including the HER2 RT-PCR score, to determine a HER2-low status.

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.

ELISA-like QDB method to meet the emerging need of Her2 assessment for breast cancer patients

Inherent issues of subjectivity and inconsistency have long plagued immunohistochemistry (IHC)-based Her2 assessment, leading to the repeated issuance of guidelines by the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) for its standardization for breast cancer patients. Yet, all these efforts may prove insufficient with the advent of Trastuzumab deruxtecan (T-Dxd), a drug with the promise to expand to tumors traditionally defined as Her2 negative (Her2-). In this study, we attempted to address these issues by exploring an ELISA-like quantitative dot blot (QDB) method as an alternative to IHC. The QDB method has been used to measure multiple protein biomarkers including ER, PR, Ki67, and cyclin D1 in breast cancer specimens. Using an independent cohort (cohort 2) of breast cancer formalin-fixed paraffin-embedded (FFPE) specimens, we validated cutoffs developed in cohort 1 (Yu et al., Scientific Reports 2020 10:10502) with overall 100% specificity (95% CI: 100-100) and 97.56% sensitivity (95% CI: 92.68-100) in cohort 2 against standard practice with the dichotomized absolutely quantitated values. Using the limit of detection (LOD) of the QDB method as the putative cutoff point, tumors with no Her2 expression were identified with the number comparable to those of IHC 0. Our results support further evaluation of the QDB method as an alternative to IHC to meet the emerging need of identifying tumors with low Her2 expression (Her2-low) in daily clinical practice.

Editorial on Special Issue "Artificial Intelligence in Pathological Image Analysis"

The artificial intelligence (AI), especially deep learning models, is highly compatible with medical images and natural language processing and is expected to be applied to pathological image analysis and other medical fields [...].