HER2 Expression In Breast Cancer Research Articles

Precision HER2: a comprehensive AI system for accurate and consistent evaluation of HER2 expression in invasive breast Cancer

BackgroundWith the development of novel anti-HER2 targeted drugs, such as ADCs, it has become increasingly important to accurately interpret HER2 expression in breast cancer. Previous studies have demonstrated high intra-observer and inter-observer variabilities in evaluating HER2 staining by human eyes. There exists a strong requirement to develop artificial intelligence (AI) systems to achieve high-precision HER2 expression scoring for better clinical therapy.MethodsIn the present study, we collected breast cancer tissue samples and stained consecutive sections with anti-Calponin and anti-HER2 antibodies. High-quality digital images were selected from immunohistochemical slides and interpreted as HER2 3+, 2+, 1+, and 0. AI models were trained and assessed using annotated training and testing sets. The AI model was trained to automatically identify ductal carcinoma in situ (DCIS) by Calponin staining and myoepithelial annotation and filter out DCIS components in HER2-stained slides using image-overlapping techniques. Furthermore, we organized two-phase validation studies. In phase one, pathologists interpreted 112 HER2 whole-slide images (WSIs) without AI assistance, whereas in phase two, pathologists read the same slides using the AI system after a washing period of 2 weeks.ResultsOur AI model greatly improved the accuracy of reading (0.902 vs. 0.710). The number of HER2 1 + patients misdiagnosed as HER2 0 was significantly reduced (32/279 vs. 65/279), and they benefitted from ADC drugs. In addition, the AI algorithm improved the intra-group consistency of HER2 readings by pathologists with different years of experience (intra-class correlation coefficient [ICC]: 0.872–0.926 vs. 0.818–0.908), with the improvement most pronounced among junior pathologists (0.885 vs. 0.818).ConclusionsWe proposed a high-precision AI system to identify and filter out DCIS components and automatically evaluate HER2 expression in invasive breast cancer.

PhiHER2: phenotype-informed weakly supervised model for HER2 status prediction from pathological images.

Human epidermal growth factor receptor 2 (HER2) status identification enables physicians to assess the prognosis risk and determine the treatment schedule for patients. In clinical practice, pathological slides serve as the gold standard, offering morphological information on cellular structure and tumoral regions. Computational analysis of pathological images has the potential to discover morphological patterns associated with HER2 molecular targets and achieve precise status prediction. However, pathological images are typically equipped with high-resolution attributes, and HER2 expression in breast cancer (BC) images often manifests the intratumoral heterogeneity. We present a phenotype-informed weakly supervised multiple instance learning architecture (PhiHER2) for the prediction of the HER2 status from pathological images of BC. Specifically, a hierarchical prototype clustering module is designed to identify representative phenotypes across whole slide images. These phenotype embeddings are then integrated into a cross-attention module, enhancing feature interaction and aggregation on instances. This yields a phenotype-based feature space that leverages the intratumoral morphological heterogeneity for HER2 status prediction. Extensive results demonstrate that PhiHER2 captures a better WSI-level representation by the typical phenotype guidance and significantly outperforms existing methods on real-world datasets. Additionally, interpretability analyses of both phenotypes and WSIs provide explicit insights into the heterogeneity of morphological patterns associated with molecular HER2 status. Our model is available at https://github.com/lyotvincent/PhiHER2.

Novel approach to HER2 quantification using phosphor-integrated dots in human breast invasive cancer microarray.

HER2 expression in breast cancer is evaluated to select patients for anti-HER2 therapy. With the advent of newly approved HER2-targeted drugs for low HER2 expression breast cancer, more solid evidence on the whole spectrum of HER2 expression is needed. In this study, we quantitatively assessed HER2 expression from the whole core by combining high-intensity phosphor-integrated dot (PID) immunostaining and whole slide imaging (WSI) analysis. Two types of staining were performed using a 170-core tissue microarray of invasive breast cancer. First, HER2 was stained by immunohistochemistry (IHC), and IHC scores were determined by two practicing pathologists according to the ASCO/CAP HER2 guideline. Second, HER2 was stained with PID, and tentative PID scores were determined by quantitative analysis. The results show that PID can numerically classify HER2 expression status into scores 3+, 2+, 1+, and 0. The HER2 value quantified by PID strongly correlated with the 3, 3'-diaminobenzidine (DAB) IHC score determined by pathologists (R2 = 0.93). PID IHC score 1+ cases included both DAB IHC score 1+ and 0 cases, and low HER2 expression cases appeared to be often evaluated as DAB IHC score 0. Therefore, digital image analysis by PID and WSI can help stratify HER2 IHC. It may also help classify low HER2 expression.

Use of MRI Radiomics Models in Evaluating the Low HER2 Expression in Breast Cancer.

To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer. The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model. The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value. The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans

Abstract PO3-13-03: RNAScope: a practical approach and promising alternative to immunohistochemistry to quantify HER2 expression in breast cancer

Abstract T-DXd has been approved by the FDA to treat patients with metastatic HER2-low and -positive breast cancer. The utility of current HER2 immunohistochemistry (IHC) assays in evaluating HER2-low tumors is not clear. A simple and objective method to evaluate HER2 expression in breast cancer is urgently needed. RNAScope can detect HER2 RNA levels by in situ hybridization using one regular unstained FFPE slide and processed using the Leica BOND-III autostainer that is readily available in many clinical laboratories. RNA level detected by RNAScope can be quantified by dots/cell using publicly available software. Therefore, RNAScope is a practical assay and could be a promising alternative to IHC to quantify HER2 levels in breast cancer. We evaluated HER2 levels in 605 breast cancer tissue microarray cores using RNAScope and the two most commonly used FDA approved HER2 IHC assays: Ventana PATHWAY (PATHWAY) and Dako HercepTest (HercepTest). Clinical data were available for 505 cores from 347 patients. RNA level (dots/cell) by RNAScope was quantified using publicly available software QuPath. IHC assays were scored as 0, ultralow (UL, >0% but ≤10% incomplete membranous staining), 1+, 2+ and 3+. In addition, HER2 protein levels (AQUA protein level) were quantified from 100 cores through regression analysis, using AQUA score against cell line arrays with pre-calibrated HER2 protein levels determined by mass spectrometry. We used ANOVA to assess the differences in RNAScope results among the five IHC scores, and linear regressions to correlate RNAScope with HER2 AQUA protein levels. We finally evaluated 41 RNAScope whole-slide images (IHC 1+: n=5; 2+: n=26; 3+: n=10) of metastatic tumors from 31 patients treated with T-DXd. No significant differences of RNAScope results were observed among the 0, UL, and 1+ cores in both IHC assays, indicating the current IHC assays cannot differentiate HER levels in HER2-low tumors. However, statistically significant differences (p < 0.0001) were found among the IHC 1+, 2+, and 3+ cores and higher RNAScope dots/cell was associated with higher stage of the tumors. There was a strong correlation (R2 = 0.610) between the RNAScope results and quantitative HER2 AQUA protein level in the 100 cores. There were significantly higher HER2 RNA levels in the 41 metastatic biopsies with higher IHC scores (p < 0.05). When we used RNAScope to measures HER2 levels in metastases right before T-DXd treatment, there was numerically (p=0.881) higher HER2 RNA levels in responders (5.60±8.82 dots/cell) vs non-responders (5.20±5.31). Interestingly, the HER2 RNA levels in bone metastases was statistically higher (p=0.030) in non-responders (5.24±2.87, n=3) than in responders (1.55±0.81, n=5); although number of patients was low. For the non-bone metastases (esophagus, lymph node, liver, brain, lung), HER2 RNAScope values were numerically higher (p=0.261) in responders (9.65±2.87, n=5) than non-responders (5.19±5.74, n=15). In these non-bone metastatic cases, the response rates by IHC scores were 100% in 1+ cases, 24% in 2+ and 33% in 3+. When we used AI assisted categories based on RNAScope results, the response rates were 20% in RNAScope 1+ cases, 20% in 2+, 50% in 3+ cases. Our study shows that current IHC assays are unable to differentiate HER2 levels between IHC 0 and 1+ breast cancer cases, which is a critical issue in properly identifying patients who will benefit from T-DXd treatment. RNAScope results strongly correlate with HER2 protein levels and showed similar RNA levels among IHC 0 and 1+ cases. RNAScope is a simple and objective assay to quantify HER2 levels by dots/cell using publicly available software and may help better identify which patients benefit from T-DXd treatment. Other factors besides HER2 level may also contribute to the response rate in patients treated with T-DXd. RNAScope results in association with immunohistochemistry (IHC) scores by PATHWAY, HercepTest, and in biopsies from patients treated with T-DXd (T-DXd cohort). XX XX No significant (ns) difference of RNAScope dots/cell were seen among 0, UL and 1+ cases by both PATHWAY and HercepTest assays. There were significant differences of dots/cell comparing 1+, 2+ and 3+ cases. Note: ns not significant (p>0.05), * p<0.05, *** p<0.001, **** p<0.0001 Citation Format: Xiaoxian Li, Ji-Hoon Lee, Yuan Gao, Jilun Zhang, Katherine Bates, David Rimm, Huina Zhang, Geoffrey Smith, Diane Lawson, Jane Meisel, Jenny Chang, Lei Huo. RNAScope: a practical approach and promising alternative to immunohistochemistry to quantify HER2 expression in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-13-03.

Abstract PO3-13-11: Quantitative Multiplex Immunofluorescence Assay for TROP2 and HER2 Expression in Breast Cancer: Towards Guiding Patient Selection for Antibody Drug Conjugate Therapies

Abstract Emerging antibody drug conjugate (ADC) therapies targeting human trophoblast cell-surface antigen (TROP2) and human epidermal growth factor receptor 2 (HER2) are transforming the treatment landscape for breast cancer. Sacituzumab govitecan (SG) and trastuzumab deruxtecan (T-DXd) have gained approval for an overlapping set of "HER2-low" metastatic breast cancers, including hormone receptor (HR)-positive HER2 non-amplified and "triple-negative" subtypes. Nevertheless, the optimal selection of patients and treatment sequencing for these ADC therapies remains a clinical challenge. Clinical trial objective response rates to SG are approximately 30%, compared to 30-90% for T-DXd depending on HER2 expression levels. While both drugs are thought to be targeted therapies, the value of measuring the target and the best methods to do so are still not established. We believe that quantitative measurement of TROP2 and HER2 antigen expression levels could establish thresholds for responders, enabling more effective patient selection for ADC therapies. Here, we present a TROP2, high-sensitivity HER2, and cytokeratin (CK) quantitative immunofluorescence (QIF) multiplex assay. Using a ten-cell line standard array and proteomic mass spectrometry, we can convert tumor compartment QIF intensity to protein concentration in fmol/mm2 for tissue specimens. Anti-HER2, anti-TROP2 antibodies, and fluorescence detection systems were titrated and combined to maximize signal-to-noise ratio on our cell line standard array and breast cancer tissue microarrays (TMA). The multiplex assay was designed for automated slide stainers (Leica BOND Rx) and fluorescence slide scanning (Rarecyte CyteFinder II HT). We perform our analyses in QuPath using an image processing plugin developed for automated QIF/IHC analysis (Qymia). Reproducible TROP2 and HER2 QIF scoring (R² > 0.95) was achieved across multiple staining batches using serial sections of breast cancer TMAs and cell standard arrays. This assay has a TROP2 linear range between 0.63 - 9.17 fmol/mm2 (about 1 million TROP2 receptors/cell) and HER2 linear range between 0.09 - 0.565 fmol/mm2 (about 60,000 HER2 receptors/cell). We then applied this multiplex assay to two serial retrospective primary breast cancer cohorts from Yale University to quantitatively measure TROP2 and HER2 expression (338 clinical cases). We find a weak negative correlation between TROP2 and HER2 expression in our breast cancer cohorts (Pearson r = -0.14, p = 0.0097, n = 338). TROP2 expression levels were above the limit of detection (LOD) in 90.2% of cases, with 4.1% exceeding the limit of linearity (LOL), and a mean TROP2 expression of 4.05 fmol/mm2. For HER2, 67.2% of cases were above the LOD, with 7.1% exceeding the LOL, and a mean HER2 expression of 0.186 fmol/mm2. Both TROP2 and HER2 were below the LOD in 3.0% of cases, which we define as “negative”. We found 29.9% expressed TROP2 and were HER2-negative, and 6.8% expressed HER2 and were TROP2-negative. Our future studies will aim to quantitatively define expression thresholds for T-DXd and SG response with the goal to produce a clinical grade assay for ADC patient selection and determine the value of the assay to help select which ADC to give first. HER2 and TROP2 protein expression summary in Yale breast cancer cohort Table 1: Summary of HER2 and TROP2 protein expression levels in serial retrospective primary breast cancer cohort of 338 cases using our high-sensitivity HER2 and TROP2 multiplex immunofluorescent assay Citation Format: Charles Robbins, Mengni He, Revekka Khaimova, Katherine Bates, Nay Nwe Nyein Chan, Daniel Liebler, Regan Fulton, David Rimm. Quantitative Multiplex Immunofluorescence Assay for TROP2 and HER2 Expression in Breast Cancer: Towards Guiding Patient Selection for Antibody Drug Conjugate Therapies [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-13-11.

SLC35A2 expression is associated with HER2 expression in breast cancer

The role of SLC35A2 in breast cancer remains poorly understood, with limited available information on its significance. This study aimed to investigate the expression of SLC35A2 and clinicopathological variables in breast cancer patients. Immunohistochemical analysis of SLC35A2 protein was conductedon 40 adjacent non-neoplastic tissues and 320 breast cancer tissues. The study also assesed the association between SLC35A2 expression and breast cancer clinicopathological features of breast cancer, as well as its impact on overall survival. In comparison to adjacent non-neoplastic tissues, a significantly higher expression of SLC35A2 was observed in breast cancer tissues (P = 0.020), and this expression was found to be independently correlated with HER2 positivity (P = 0.001). Survival analysis indicated that patients with low SLC35A2 expression had a more favorable prognosis in HER2-positive subtype breast cancer (P = 0.017). These results suggest that SLC35A2 is overexpressed in breast cancer tissues compared to adjacent non-neoplastic tissues and may serve as a potential prognostic marker for HER2-positive subtype breast cancer. Furthermore, breast cancer patients with the HER2 positive subtype who exhibited decreased levels of SLC35A2 expression demonstrated improved long-term prognostic outcomes.

Development and Validation of MRI Radiomics Models to Differentiate HER2-Zero, -Low, and -Positive Breast Cancer.

BACKGROUND. Breast cancer HER2 expression has been redefined using a three-tiered system, with HER2-zero cancers considered ineligible for HER2-targeted therapy, HER2-low cancers considered candidates for novel HER2-targeted drugs, and HER2-positive cancers treated with traditional HER2-targeted medications. OBJECTIVE. The purpose of this study was to assess MRI radiomics models for a three-tiered classification of HER2 expression of breast cancer. METHODS. This retrospective study included 592 patients with pathologically confirmed breast cancer (mean age, 47.0 ± 18.0 [SD] years) who underwent breast MRI at either of a health system's two hospitals from April 2016 through June 2022. Three-tiered HER2 status was pathologically determined. Radiologists assessed the conventional MRI features of tumors and manually segmented the tumors on multiparametric sequences (T2-weighted images, DWI, ADC maps, and T1-weighted delayed contrast-enhanced images) to extract radiomics features. Least absolute shrinkage and selection operator analysis was used to develop two radiomics signatures, to differentiate HER2-zero cancers from HER2-low or HER2-positive cancers (task 1) as well as to differentiate HER2-low cancers from HER2-positive cancers (task 2). Patients from hospital 1 were randomly assigned to a discovery set (task 1: n = 376; task 2: n = 335) or an internal validation set (task 1: n = 161; task 2: n = 143); patients from hospital 2 formed an external validation set (task 1: n = 55; task 2: n = 50). Multivariable logistic regression analysis was used to create nomograms combining radiomics signatures with clinicopathologic and conventional MRI features. RESULTS. AUC, sensitivity, and specificity in the discovery, internal validation, and external validation sets were as follows: for task 1, 0.89, 99.4%, and 69.0%; 0.86, 98.6%, and 76.5%; and 0.78, 100.0%, and 0.0%, respectively; for task 2, 0.77, 93.8%, and 32.3%; 0.75, 92.9%, and 6.8%; and 0.77, 97.0%, and 29.4%, respectively. For task 1, no nomogram was created because no clinicopathologic or conventional MRI feature was associated with HER2 status independent of the MRI radiomics signature. For task 2, a nomogram including an MRI radiomics signature and three pathologic features (histologic grade of III, high Ki-67 index, and positive progesterone receptor status) that were independently associated with HER2-low expression had an AUC of 0.87, 0.83, and 0.80 in the three sets. CONCLUSION. MRI radiomics features were used to differentiate HER2-zero from HER2-low cancers or HER2-positives cancers as well as to differentiate HER2-low cancers from HER2-positive cancers. CLINICAL IMPACT. MRI radiomics may help select patients for novel or traditional HER2-targeted therapies, particularly those patients with ambiguous results of immunohistochemical staining results or limited access to fluorescence in situ hybridization.

Evaluation of Approaches for the Assessment of HER2 Expression in Breast Cancer by Radionuclide Imaging Using the Scaffold Protein [99mTc]Tc-ADAPT6.

Due to its small size and high affinity binding, the engineered scaffold protein ADAPT6 is a promising targeting probe for radionuclide imaging of human epidermal growth factor receptor type 2 (HER2). In a Phase I clinical trial, [99mTc]Tc-ADAPT6 demonstrated safety, tolerability and capacity to visualize HER2 expression in primary breast cancer. In this study, we aimed to select the optimal parameters for distinguishing between breast cancers with high and low expression of HER2 using [99mTc]Tc-ADAPT6 in a planned Phase II study. HER2 expression was evaluated in primary tumours and metastatic axillary lymph nodes (mALNs). SPECT/CT imaging of twenty treatment-naive breast cancer patients was performed 2 h after injection of [99mTc]Tc-ADAPT6. The imaging data were compared with the data concerning HER2 expression obtained by immunohistochemical evaluation of samples obtained by core biopsy. Maximum Standard Uptake Values (SUVmax) afforded the best performance for both primary tumours and mALNs (areas under the receiver operating characteristic curve (ROC AUC) of 1.0 and 0.97, respectively). Lesion-to-spleen ratios provided somewhat lower performance. However, the ROC AUCs were still over 0.90 for both primary tumours and mALNs. Thus, lesion-to-spleen ratios should be further evaluated to find if these could be applied to imaging using stand-alone SPECT cameras that do not permit SUV calculations.

Multi-directional Changes in 8-OHdG Concentration in Saliva during HER2 Expression in Breast Cancer

The purpose of this work was to evaluate changes in the structure of 8-OHdG in saliva in patients with breast cancer depending on molecular biological subtypes. The findings show that decreased salivary 8-OHdG concentrations in the HER2-positive group rapidly correlated with decreased cell differentiation and high Ki-67 levels, reflecting the aggressive nature of the tumor disease and poor survival prognosis. Thus, the presence of high levels of 8-OHdG in the oral cavity may lead to the development of the role of a biological marker of the HER2-positive group with a poor prognosis of breast cancer and can be used as an additional method of diagnosis, patient condition and response to therapy.

Correlation of HER2 Protein Level With mRNA Level Quantified by RNAscope in Breast Cancer

Trastuzumab deruxtecan (T-DXd) has been approved by the US Food and Drug Administration (FDA) to treat patients with metastatic HER2-positive and HER2-low breast cancer, and clinical trials are examining its efficacy against early-stage breast cancer. Current HER2 immunohistochemical (IHC) assays are suboptimal in evaluating HER2-low breast cancers and identifying which patients would benefit from T-DXd. HER2 expression in 526 breast cancer tissue microarray (TMA) cores was measured using the FDA-approved PATHWAY and HercepTest IHC assays, and the corresponding RNA levels were evaluated by RNAscope. HER2 protein levels by regression analysis using a quantitative immunofluorescence score against cell line arrays with known HER2 protein levels determined by mass spectrometry were available in 48 of the cores. RNAscope was also performed in 32 metastatic biopsies from 23 patients who were subsequently treated with T-DXd, and the results were correlated with response rate. HER2 RNA levels by RNAscope strongly correlated with HER2 protein levels (P < .0001) and with HER2 IHC H-scores from the PATHWAY and HercepTest assays (P < .0001). However, neither protein levels nor RNA levels significantly differed between cases scored 0, ultralow, and 1+ by PATHWAY and HercepTest. The RNA levels were significantly higher (P = .030) in responders (6.4 ± 8.2 dots/cell, n = 12) than those in nonresponders (2.6 ± 2.2, n = 20) to T-DXd. RNAscope is a simple assay that can be objectively quantified and is a promising alternative to current IHC assays in evaluating HER2 expression in breast cancers, especially HER2-low cases, and may identify patients who would benefit from T-DXd.

Vision Transformers for Breast Cancer Human Epidermal Growth Factor Receptor 2 Expression Staging without Immunohistochemical Staining

Accurate staging of human epidermal growth factor receptor 2 (HER2) expression is vital for evaluating breast cancer treatment efficacy. However, it typically involves costly and complex immunohistochemical staining, along with hematoxylin and eosin staining. This work presents customized vision transformers for staging HER2 expression in breast cancer using only hematoxylin and eosin-stained images. The proposed algorithm comprised three modules: a localization module for weakly localizing critical image features using spatial transformers, an attention module for global learning via vision transformers, and a loss module to determine proximity to a HER2 expression level based on input images by calculating ordinal loss. Results, reported with 95% CIs, reveal the proposed approach's success in HER2 expression staging: area under the receiver operating characteristic curve, 0.9202±0.01; precision, 0.922±0.01; sensitivity, 0.876±0.01; and specificity, 0.959±0.02 over fivefold cross-validation. Comparatively, this approach significantly outperformed conventional vision transformer models and state-of-the-art convolutional neural network models (P<0.001). Furthermore, it surpassed existing methods when evaluated on an independent test data set. This work holds great importance, aiding HER2 expression staging in breast cancer treatment while circumventing the costly and time-consuming immunohistochemical staining procedure, thereby addressing diagnostic disparities in low-resource settings and low-income countries.

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.

Prevalence of low HER2 expression in breast cancer patients: An Indian experience.

e12543 Background: With the recent advent of trastuzumab deruxtecan and encouraging results of the DESTINY-Breast-04 trial, low HER2 expression has emerged as a clinically significant therapeutic biomarker. The prevalence of low HER2 expression is unexplored in Indian population. Methods: This is a single center ambispective observational study conducted over a period of 4 years (1st January 2019 to 31st January 2023) at Dr. B.R.A, I.R.C.H, All India Institute of Medical Sciences (AIIMS), New Delhi, India. Consecutive female breast cancer patients registered during the study period with available clinical-pathological details were eligible for study inclusion. The primary objective of the study was to assess the prevalence of HER2 low breast cancer. HER2 low was defined as having HER expression by immunohistochemistry (IHC) of 1+ or 2+ with no amplification of the HER2gene detected with a fluorescent in situ hybridization (FISH) assay. Patients were divided into three categories: HER-2 high (IHC3+, IHC2+/FISH+), HER-2 low (IHC 1+, IHC2+/FISH-), and HER2-0 (HER2 negative, IHC:0). Staging description of the cases were done as per anatomic TNM classification according to AJCC 7th edition. Results: A total of 2200 female breast cancer were registered during the study period, of which 1932 patients had available clinical-pathological details. Among 1932 patients, 600 patients (31%) had high HER2 expression and remaining 1332 patients had HER2 low/HER2-0 expression. Of the 1332 cases, the median age was 49 years (range 18–91 years). The stage distribution was stage I, 94 (7.06%); stage II, 409 (30.70%); stage III, 528 (39.64%); and stage IV, 301 (22.59%). Eight hundred patients (60.06%) were hormone positive, and 532 (39.94%) cases were hormone negative. The prevalence of HER2 low expression was 798 (59.9%). It was observed in 640 (80%) of hormone positive cases and 158 (29.69%) hormone-negative cases (p &lt; 0.001). Rest 534 cases were HER2 0 [160 cases (20%) were hormone positive, and 374 cases (70.31%) were hormone negative]. Conclusions: This is the first study from India to document the prevalence of low HER2 expression in female breast cancers. We observed the prevalence to be around 60 % in our population, which is more in hormone receptor-positive breast cancer. The estimated prevalence is higher than that observed in Western population, and it underscores the need to systematically categorize these patients for therapeutic decision making at presentation.

Abstract P6-04-16: ART: Automated Region segmentation of Tumor on HER2-stained breast cancer tissue

Abstract Background Computational pathology-based methods, eg, Quantitative Continuous Scoring (QCS) [Gustavson, SABCS 2020], are built to provide objective and quantitative methods to assess HER2 expression in breast cancer (BC). For accurate HER2 quantification, it is important to exclude non-invasive epithelium from analysis since HER2 overexpression could be more frequent in ductal carcinoma in situ (DCIS) and pleomorphic lobular carcinoma in situ (PLCIS) than in invasive BC [Lari, J Cancer 2011]. Generally, computational pathology-based approaches require experts to delineate the invasive BC regions of interest for analysis and exclude all benign/non-invasive epithelium. Developing a tool that delineates the invasive BC regions automatically without human intervention to avoid subjectivity of manual annotation by pathologists is ideal. We developed a novel, deep-learning–based system, called DualScaleNet, to perform Automated Region segmentation of Tumor (ART) by automatically identifying the invasive BC regions and excluding benign/non-invasive epithelium on HER2-stained digitized images. Identification and diagnosis of these regions, especially the in situ tumors are a challenge as they can mimic benign and invasive lesions causing wrong HER2 evaluation. Additional stains (eg, p63 for myoepithelial cells or laminin for basement membrane) are often required for diagnosis [Pinder, Mod Pathol 2010] but were not available for this study. Methods DualScaleNet works simultaneously on HER2-stained immunohistochemistry (IHC) image patches at 2 different resolutions. The target branch uses a higher resolution RGB image (0.5 μm/pixel) to learn accurate local details; the context branch uses a lower resolution image (4.0 μm/pixel) to incorporate more context in visual learning. The algorithm generates 4 output image layers representing probabilities of 4 classes: invasive tumor, ductal/lobular carcinoma in situ, benign epithelium, and other tissue. The final segmentation result is generated by assigning each image pixel the class with the largest probability value. The algorithm was trained using ground truth (GT) annotations generated by 5 pathologists using 6157 square field of views (FOVs), 200-500 μm in size. These FOVs were collected from 850 whole slide images (WSI), spanning 9 commercial BC sample cohorts stained with different HER2 assays and scanned by several versions of the Aperio AT2 scanner. The samples included a mixture of biopsies and resections and covered different BC histologies and HER2 staining intensities. To evaluate the reproducibility of tumor area detection by human pathologists, an interpathologist comparison in detection of invasive tumor regions was performed using 225 FOVs annotated by multiple pathologists. Results Analysis generated an average Dice/F1 Score of 81.6% among different pathologists for invasive cancer. On the same sample set (independent of the ART training set), the invasive cancer detection by the ART algorithm was on par with human pathologists, achieving a similar average Dice/F1 score of 80.7%. Conclusions Novel deep learning-based ART algorithm provides accurate segmentation of invasive cancer on HER2-stained IHC images. The performance was verified against the GT annotations provided by multiple pathologists. Since the algorithm is trained using annotations from multiple pathologists, it is not possible to generate higher accuracy with computational pathology than is achievable between independent pathologists. Importantly, the same WSI read by the ART will consistently output the exact same tumor region identification result thus removing the inherent human subjectivity and variability, while improving the turnaround time for analysis. This development serves as the necessary foundation upon which a computational pathology-based diagnostic can be built. Citation Format: Ansh Kapil, Anatoliy Shumilov, Philipp Wortmann, Sihem Khelifa, Jessica Chan, Michel Vandenberghe, Craig Barker, Mark Gustavson, Danielle Carroll, Hadassah Sade, Günter Schmidt. ART: Automated Region segmentation of Tumor on HER2-stained breast cancer tissue [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-16.

Combined Morphological and Spectroscopic Diagnostic of HER2 Expression in Breast Cancer Tissues Based on Label-Free Surface-Enhanced Raman Scattering

Breast cancer is the most commonly diagnosed cancer type worldwide. Overexpression of human epidermal growth factor receptor 2 (HER2) is an important subtype of breast cancer and results in an increased risk of recurrence and metastasis in patients. At present, immunohistochemistry (IHC) is used to detect the expression of HER2 in breast cancer tissues as the golden standard. However, IHC has some shortcomings, such as large subjective impact, long time consumption, expensive reagents, etc. In this paper, a combined morphological and spectroscopic diagnostic method based on label-free surface-enhanced Raman scattering (SERS) for HER2 expression in breast cancer is proposed. It can not only quantitively detect HER2 expression in breast cancer tissues by spectroscopic measurements but also give morphological images reflecting the distribution of HER2 in tissues. The results show that the consistency between this method and IHC is 95% and achieves the annotation of tumor regions on tissue sections. This method is time-consuming, quantifiable, intuitive, scalable, and easy to understand. Combined with deep learning approaches, it is expected to promote the development of clinical detection and diagnosis technology for breast cancer and other cancers.

Molecular imaging of HER2 expression in breast cancer patients using a novel peptide-based tracer 99mTc-HP-Ark2: a pilot study

BackgroundDue to the temporal and spatial heterogeneity of human epidermal growth factor receptor 2 (HER2) expression in breast tumors, immunohistochemistry (IHC) cannot accurately reflect the HER2 status in real time, which may cause misguided treatment decisions. HER2-specific imaging can noninvasively determine HER2 status in primary and metastatic tumors. In this study, HER2 expression in breast cancer patients was determined in vivo by SPECT/CT of 99mTc-HP-Ark2, comparing with PET/CT of 18F-FDG lesion by lesion.MethodsA novel HER2-targeted peptide probe 99mTc-HP-Ark2 was constructed. Biodistribution and nanoScan SPECT/CT imaging were performed in mice models. The correlation between the quantified tumor uptake and HER2 expression in tumor cells was analyzed. In the pilot clinical study, a total of 34 breast cancer patients (mean age ± SD: 49 ± 10 y) suspected of having breast cancer according to mammography or ultrasonography were recruited at Peking Union Medical College Hospital, and 99mTc-HP-Ark2 SPECT/CT and 18F-FDG PET/CT were carried out with IHC and fluorescence in situ hybridization as validation.ResultsSmall animal SPECT/CT of 99mTc-HP-Ark2 clearly identified tumors with different HER2 expression. The quantified tumor uptake and tumor HER2 expression showed a significant linear correlation (r = 0.932, P < 0.01). Among the 36 primary lesions in the 34 patients, when IHC (2 +) or IHC (3 +) was used as the positive evaluation criterion, 99mTc-HP-Ark2 SPECT/CT imaging with a tumor-to-background ratio of 1.44 as the cutoff value reflected the HER2 status with sensitivity of 89.5% (17/19), specificity of 88.2% (15/17) and accuracy of 88.9% (32/36), while the 18F-FDG PET/CT showed sensitivity of 78.9% (15/19), specificity of 70.6% (12/17) and accuracy of 75.0% (27/36). In particular, 100% of IHC (3 +) tumors were all identified by 99mTc-HP-Ark2 SPECT/CT imaging.Conclusion99mTc-HP-Ark2 SPECT/CT can provide a specific, noninvasive evaluation of HER2 expression in breast cancer, showing great potential to guide HER2-targeted therapies in clinical practice.ClinicalTrials.gov Trial registration: NCT04267900. Registered 11th February 2020. Retrospectively registered, https://www.clinicaltrials.gov/ct2/results?pg=1&load=cart&id=NCT04267900.

Abstract P1-02-02: Examination of low Her2 expression in breast cancer

Abstract Introduction: Antibody-drug conjugates (ADC) are designed to effectively deliver cytotoxic agents directly to malignant cells. Trastuzumab deruxtecan (Tdx), an ADC of trastuzumab, an enzyme-cleavable linker, and a cytotoxic topoisomerase I inhibitor, has been shown to have antitumor activity in patients with breast cancer with low levels of HER2. However, current companion diagnostic tests for HER2-targetting therapies, immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH), were optimized for high (gene amplified) levels of HER2. We hypothesize that the current common assays used in clinic do not efficiently differentiate between patients whose cancers have “0” HER2 expression and “1+” HER2 expression and thus could miss patients who would benefit from treatment with this drug. Methods: Here, we evaluated two years of HER2 surveys from the College of American Pathologists’ (CAP) Proficiency Testing Surveys for HER2 expression in breast cancer from 2019 and 2020. Participating laboratories received two tissue microarrays (TMAs) of 10 breast cancer cores, each Laboratories stained for HER2 using the standard IHC assay used in their labs. The scores were returned to the CAP as part of their quality assessment program. Each survey dataset covers the scores from around 1400 labs of 20 cores each as well as supplemental questions regarding the methodology used. We summarized the relative frequency and distribution of each score given to every core. A second independent analytic dataset was selected from the archives of the Department of Pathology at Yale, from breast biopsies in 2018. The set, enriched in HER2 2+ and 3+ cases, was read by eighteen board-certified pathologists, most with over 5 years’ experience, participating in an interobserver variability study. Hematoxylin and eosin (H&amp;E) and HER2 IHC digitally scanned images of 170 independent cases were provided. Pathologists scored the cases as 0,1, 2, or 3+. Fisher’s exact test was used to compare the 0/1+ concordant cases to 2+/3+ cases. All tests were two-sided at a significant level 0.05. Statistical analysis was performed using Graphpad Prism Version 9.0.1 and the dplyr package in R Version 1.0.143. This study was approved by Yale Human Investigation IRB protocol ID 9505008219. Results: We found that 65% of the 80 cores evaluated in the CAP survey (52/80) had a concordance rate ≥90%. This high concordance was limited to scores of 0 and 3+. The lowest concordance was found between 0 versus 1+. Of the 80 cores, 56 were considered negative (HER2 score of 0 or 1). In 25% of those cores there was &amp;lt; 70% concordance (n=15; 6 in 2019 and 9 in 2020). Analytic concordance was assessed in the independent, Yale cohort where we found that of the 170 cases, 92 were read as 0 by at least one pathologist. Of these 92, 24 were concordant (26%), defined as a ≥90% agreement. In comparison, 45/170 were read as 3+ by at least one pathologist. Again,. using a 90%definition of concordance, 26 of 45 cases (58%) were concordant. Comparison of 0/1+ concordant cases versus 2+/3+ concordant cases showed a significant difference (χ2 = 12.07, p&amp;lt;0.0005). Conclusions: Assessment of laboratory performance of around 1400 CAP labs using common current HER2 assays on CAP survey specimens, there is significant discordance in the evaluation of 0 vs. 1+ cases. In a separate selected breast biopsy cohort examined by 18 breast pathologists, we showed that discordance between scores of 0 vs 1+ is significantly larger than that between 2+ and 3+. Given the efficacy of T-DXd, we believe patients may be mis-assigned for treatment or no treatment if the decision depends on performance of the standard current HER2 assays. Citation Format: Aileen I Fernandez, Matthew Liu, Andrew Bellizzi, Jane Brock, Oluwole Fadare, Krisztina Hanley, Malini Harigopal, Julie M. Jorns, M. Gabriela Kuba, Amy Ly, Mirna Podoll, Kimmie Rabe, Mary Ann Sanders, Kamaljeet Singh, Olivia L Snir, Rinda Soong, Shi Wei, Hannah Wen, Serena Wong, Esther Yoon, Lajos Pusztai, Emily Reisenbichler, David L. Rimm. Examination of low Her2 expression in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-02-02.

Abstract P3-02-06: A phase II study of 68Ga-ABY-025 PET for non-invasive quantification of HER2 expression in breast cancer

Abstract Introduction/Aim: 68Ga-ABY-025 is a small-protein HER2-binder which has shown promising results in evaluating HER2 receptor status in earlier studies in breast cancer (BC). The aim of the current study was to investigate the role of 68Ga-ABY-025 PET imaging in relation to histopathology and treatment response. Methods: This single-center Phase II study consecutively included neoadjuvant patients with stage 2/3 BC (n=22) and recurrent metastatic BC (rMBC, n=18). All patients had at least one HER2-positive/borderline biopsy before inclusion and underwent 68Ga-ABY-025 PET at baseline followed by FDG PET/CT and biopsies for pathology analysis as part of study. FDG PET/CT was repeated after 2 cycles of treatment and glycolytic tumor burden (FDG2-FDG1)/FDG1) was calculated for up to five largest lesions in each patient. A reduction in glycolytic tumor burden &amp;gt;30% was considered good metabolic response. Uptake values (ABY-SUV) from 68Ga-ABY-025 PET in tumor lesions were compared with standard pathology analysis of tumor biopsies. Data was evaluated using regression and ROC analysis. Results: Biopsies for HER2 showed 32 positive, 3 borderline and 5 negative lesions. 68Ga-ABY-025 PET produced high-contrast images with ABY-SUV ranging from 0 to 50. Correlation of ABY-SUV and HER2 score from biopsies did not reach significance. HER2-targeted therapy in stage 2/3 BC resulted in a larger metabolic response (mean reduction of glycolytic tumor burden 70±26%) than in rMBC (mean reduction 25±61%, p&amp;lt;0.001). ABY-SUV correlated negatively with change in glycolytic tumor burden (p&amp;lt;0.0001) and ABY-SUV&amp;gt;6.0 predicted good metabolic response in soft-tissue tumors (AUC 0.82, 95% confidence interval 0.75-0.89, p&amp;lt;0.0001), corrected for neoadjuvant/recurrent setting. Using this cut-off, ABY SUV and invasive HER2 score were discordant in 12 of 40 patients (30%) - 6 with stage 2/3 BC and 6 with rMBC. Of the discordant cases, 68Ga-ABY-025 PET predicted the response correct in 7 cases (3 with stage 2/3 BC and 4 with rMBC) compared to biopsy-based pathology with correct prediction in 5 cases. Conclusion: 68Ga-ABY-025 PET is a potentially useful non-invasive indicator of in-vivo HER2 receptor status in breast cancer and might predict early response to HER2-targeted therapies. A larger multi-center study has been initiated to confirm these results. Citation Format: Ali Alhuseinalkhudhur, Henrik Lindman, Per Liss, Tora Sundin, Fredrik Y Frejd, Johan Hartman, Victor Iyer, Mark Lubberink, Irina Velikyan, Jens Sörensen. A phase II study of 68Ga-ABY-025 PET for non-invasive quantification of HER2 expression in breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-02-06.

The spectrum of HER2 expression in breast cancer: linking immunohistochemistry quantification with in situ hybridization assay.

We aimed to document the pathological characteristics of breast cancer (BC) cases with different scores of HER2 by immunohistochemistry (IHC), as well as to establish a relationship between HER2 expression and HER2 amplification by in situ hybridization (ISH). A cohort of 258 primary BC cases was evaluated for HER2 gene amplification with bright-field ISH. All HER2-negative and HER2-positive cases by IHC were concordant with the ISH classification. BC cases with score of 0 had lower average of HER2 copy number compared to cases with score of 1 + . HER2-equivocal cases by IHC had intermediate pathological characteristics between HER2-negative and HER2-positive cases. About 12% of HER2-equivocal cases were classified as ISH-positive. HER2-equivocal cases with HER2 gene amplification had proliferation index, HER2/CEP17 ratio, and average of HER2 copy number between HER2-equivocal cases without HER2 gene amplification and HER2-positive cases by IHC. Additionally, HER2-equivocal cases with HER2 amplification had score of 2 + in at least 50% of the total tumor area, with a proportion of ISH-positive cases increasing with the amount of score of 2 + present in the tumor. The quantification of score of 2 + in the tumor predicted the ISH classification with an AUC of 0.902. A logistic regression model using the same HER2 quantification and the nuclear score was able to increase the abovementioned prediction to an AUC of 0.929. As such, we were able to link HER2 quantification by IHC and morphological analysis with HER2 amplification by ISH.