HER2 Status In Patients Research Articles

Contrast-enhanced computed tomography-based radiomics nomogram for predicting HER2 status in urothelial bladder carcinoma.

To evaluate the performance of a clinical-radiomics model based on contrast-enhanced computed tomography (CE-CT) in assessing human epidermal growth factor receptor 2 (HER2) status in urothelial bladder carcinoma (UBC). From January 2022 to December 2023, 124 patients with UBC were classified into the training (n=100) and test (n=24) sets. CE-CT scans were performed on the patients. Univariate and multivariate analyses were conducted to identify independent predictors of HER2 status in patients with UBC. We employed eight machine learning algorithms to establish radiomic models. A clinical-radiomics model was developed by integrating radiomic signatures and clinical features. Receiver operating characteristic curves and decision curve analysis (DCA) were generated to evaluate and validate the predictive capabilities of the models. Among the eight classifiers, the random forest radiomics model based on CE-CT demonstrated the highest efficacy in predicting HER2 status, with area under the curve (AUC) values of 0.880 (95% CI: 0.813-0.946) and 0.814 (95% CI: 0.642-0.986) in the training and test sets, respectively. In the training set, the clinical-radiomics model achieved an AUC of 0.935, an accuracy of 0.870, a sensitivity of 0.881, and a specificity of 0.854. In the test set, the clinical-radiomics model achieved an AUC of 0.857, an accuracy of 0.760, a sensitivity of 0.643, and a specificity of 0.900. DCA analysis indicated that the clinical-radiomics model provided good clinical benefit. The radiomics nomogram demonstrates good diagnostic performance in predicting HER2 expression in patients with UBC.

A Comprehensive Model Outperformed the Single Radiomics Model in Noninvasively Predicting the HER2 Status in Patients with Breast Cancer

This study aimed to develop predictive models based on conventional magnetic resonance imaging (cMRI) and radiomics features for predicting human epidermal growth factor receptor 2 (HER2) status of breast cancer (BC) and compare their performance. A total of 287 patients with invasive BC in our hospital were retrospectively analyzed. All patients underwent preoperative breast MRI consisting of fat-suppressed T2-weighted imaging, axial dynamic contrast-enhanced MRI, and diffusion-weighted imaging sequences. From these sequences, radiomics features were derived. Three distinct models were established utilizing cMRI features, radiomics features, and a comprehensive model that amalgamated both. The predictive capabilities of these models were assessed using the receiver operating characteristic curve analysis. The comparative performance was then determined through the DeLong test and net reclassification improvement (NRI). In a randomized split, the 287 patients with BC were allotted to either training (234; 46 HER2-zero, 107 HER2-low, 81 HER2-positive) or test (53; 8 HER2-zero, 27 HER2-low, 18 HER2-positive) at an 8:2 ratio. The mean area under the curve (AUCs) for cMRI, radiomics, and comprehensive models predicting HER2 status were 0.705, 0.819, and 0.859 in training set and 0.639, 0.797, and 0.842 in test set, respectively. DeLong's test indicated that the combined model's AUC surpassed the radiomics model significantly (p<0.05). NRI analysis verified superiority of the combined model over the radiomics for BC HER2 prediction (NRI 25.0) in the test set. The comprehensive model based on the combination of cMRI and radiomics features outperformed the single radiomics model in noninvasively predicting the three-tiered HER2 status in patients with BC.

Abstract B063: Investigation of tumor heterogeneity using integrated single-cell RNA sequence data based on HER2 status in patients with breast cancer

Abstract [Introduction] Human epidermal growth factor receptor 2 (HER2), which is characterized by ERBB2 amplification, is one of the important markers for treatment decision related to breast cancer. Many targeted therapies for HER2-positive (immunohistochemistry [IHC] scores of 3+ or 2+ with an in situ hybridization [ISH] gene amplification) or HER2-low (IHC score 1+ or 2+ with no ISH gene amplification) breast cancers have been extensively developed thus far. Meanwhile, tumoral heterogeneity is considered one of the mechanisms for drug resistance; however, it has not been fully elucidated on each HER2 status at single-cell level. Therefore, an integrated analysis of the breast cancer single-cell gene expression data of both public datasets and our cohort was used to investigate tumor heterogeneity based on HER2 status. [Methods] We collected 21 and 6 scRNA-seq samples of primary breast cancer from the public Gene Expression Omnibus (GEO) and our institution, respectively. A total of 27 samples included HER2-positive cases (pure HER2 cases: estrogen receptor [ER]-negative/HER2-positive and Luminal-HER2 cases: ER-positive/HER2-positive) and Luminal cases (ER-positive/HER2-negative). These datasets were imported into R software version 4.2.0. and transformed into Seurat objects with the package Seurat version 4.3.0. UMAP plots, which is a non-linear dimension reduction method, were used for clustering analyses. Seurat in R was used to generate UMAP, feature, and violin plots. Additionally, we performed pathway enrichment analysis with the significant gene list from each cluster between the ERBB2-high and ERBB2-low groups. [Results] Clustering analysis revealed heterogeneous distribution in each gene related to breast cancer (ESR1, PGR, ERBB2, and MKI67). One of the clusters revealed high MKI67 expression. ERBB2 expressions were diffusely distributed on each cluster of pure HER2 cases; however, the expressions were considerably higher in one of the clusters in cases of Luminal-HER2. The ERBB2 expression in Luminal cases, which included HER2-low status, was lower than that in HER2-positive cases, albeit slight expression was observed. Cell proliferation factors, including IGF, IGF1R, and EGF, were included in the ERBB2-high expression group compared with the ERBB2-low expression group for pathway analysis. Further, we examined typical gene expressions which are associated with markers related to breast cancer, cancer stem cell, and epithelial-to-mesenchymal transition in each case and revealed heterogeneous patterns across patients. [Conclusion] Heterogeneous ERBB2 expression distribution was observed in HER2-positive cases, and slight ERBB2 expression was identified in the Luminal cases. Moreover, Luminal-HER2 cases could be considered a more heterogeneous subtype compared with pure HER2 cases. Additionally, gene expressions of typical gene markers varied across patients. These results indicated that breast cancer displays heterogeneous patterns on each HER2 status not only intra-tumoral heterogeneity but also inter-patient heterogeneity. Citation Format: Sho Shiino, Momoko Tokura, Jun Nakayama, Masayuki Yoshida, Akihiko Suto, Yusuke Yamamoto. Investigation of tumor heterogeneity using integrated single-cell RNA sequence data based on HER2 status in patients with breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B063.

Changes of HER2 low expression status in primary and recurrent/metastatic breast cancer

Objective: To investigate the evolution and clinical significance of HER2 low expression status in HER2 negative patients in primary and recurrent/metastatic breast cancers. Methods: The data and archived sections of 259 breast cancer patients with recurrence/metastasis and HER2-negative primary foci were collected from January 2015 to January 2022 at the Fourth Hospital of Hebei Medical University, and the HER2 status of primary and recurrence/metastasis foci was determined by immunohistochemistry (IHC), among which IHC 2+patients were subject to fluorescence in situ hybridization (FISH). The HER2 status was classified as HER2-0 group; patients with IHC 1+, IHC 2+and no FISH amplification were classified as HER2 low expression group; and patients with IHC 3+, IHC 2+and FISH amplified were classified as HER2-positive group. The changes of HER2 status in patients with HER2 low expression in primary versus recurrent/metastatic breast cancer foci were compared, and their clinicopathologic characteristics and prognosis were analyzed. Results: The overall concordance rate between primary and recurrent/metastatic HER2 status in breast cancer was 60.6% (157/259, κ=0.178). A total of 102 patients (102/259, 39.4%) had inconsistent primary and recurrent/metastatic HER2 status; 37 patients (37/259, 14.3%) had HER2-0 at the primary foci and HER2-low expression at the recurrent/metastatic; and 56 patients (56/259, 21.6%) had HER2-low expression in the primary foci and HER2-0 in the recurrent/metastatic. The recurrent/metastatic foci became low-expressing compared with the recurrent/metastatic foci which remained HER2-0 patients, with longer overall survival time, higher ER and PR positivity, lower Ki-67 positivity index, and lower tumor histological grade; all with statistically significant differences (all P<0.05). In the primary HER2-low group, patients with recurrent/metastatic foci became HER2-0 while those with recurrent/metastatic foci remained low expression; there were no statistically significant differences in clinicopathological features and overall survival time (all P>0.05). Conclusions: Unstable HER2 status in patients with HER2-0 and low expression in primary versus recurrent/metastatic breast cancer foci, and HER2-0 in the primary foci but low HER2 expression status in recurrence/metastasis is associated with favourable prognosis, and testing HER2 status in recurrence/metastasis can provide more treatment options for such patients.

Combining conventional ultrasound and ultrasound elastography to predict HER2 status in patients with breast cancer.

Introduction: Identifying the HER2 status of breast cancer patients is important for treatment options. Previous studies have shown that ultrasound features are closely related to the subtype of breast cancer. Methods: In this study, we used features of conventional ultrasound and ultrasound elastography to predict HER2 status. Results and Discussion: The performance of model (AUROC) with features of conventional ultrasound and ultrasound elastography is higher than that of the model with features of conventional ultrasound (0.82 vs. 0.53). The SHAP method was used to explore the interpretability of the models. Compared with HER2- tumors, HER2+ tumors usually have greater elastic modulus parameters and microcalcifications. Therefore, we concluded that the features of conventional ultrasound combined with ultrasound elastography could improve the accuracy for predicting HER2 status.

Discordance of HER2 Expression and/or Amplification on Repeat Testing.

We sought to assess discordance of HER2 status in patients with HER2-amplified/expressing solid tumors who underwent reevaluation of HER2 status. Patients with metastatic solid tumors and HER2 expression by IHC or amplification by FISH/next-generation sequencing on local testing underwent central HER2 IHC/FISH testing with either archival or fresh biopsies and were evaluated for discordance in HER2 status. 70 patients (12 cancer types) underwent central HER2 reevaluation, including 57 (81.4%) with a new biopsy. In 30 patients with HER2 3+ on local IHC, 21 (70.0%) were 3+, 5 (16.7%) were 2+, 2 (6.7%) were 1+, and 2 (6.7%) had 0 HER2 expression on central IHC. In 15 patients whose cancers were 2+ on local IHC, 2 (13.3%) were 3+, 5 (33.3%) were 2+, 7 (46.7%) were 1+, and 1 (6.7%) had 0 HER2 expression on central IHC. HER2 discordance was seen in 16 of 52 (30.8%) of patients with HER2 overexpression/amplification who underwent a new image-guided biopsy. Discordance was observed in 10 (33.3%) of 30 patients who received intervening HER2-targeted therapy and in 6 (23.8%) of 22 patients who did not. In the 8 patients who had central HER2 assessment from the same archival block used for local testing, none were discordant. Discordance of HER2 status is common in patients with tumors previously identified as HER2-expressing, especially in patients with HER2 2+ tumors. Repeat biomarker evaluation may have value when considering HER2-targeted therapies.

Prediction of HER2 status via random forest in 3257 Chinese patients with gastric cancer.

The accurate evaluation of human epidermal growth factor receptor 2 (HER2) is crucial for successful trastuzumab-based therapy in individuals with gastric cancer (GC). The present study, involving a retrospective cohort (N = 2865) from Wuhan Union Hospital and a prospective cohort (N = 392) from Renmin Hospital of Wuhan University, evaluated the benefits of clinical features using random forest and logistic regression models for the detection of HER2 status in patients with GC. Patients from the Union cohort were randomly assigned to either a training (N = 2005) or an internal validation (N = 860) group. Data processing and feature selection were done in Python, which was also used to build random forest and logistic regression models for the prediction of HER2 overexpression. The Renmin cohort (N = 392) was used as the external validation group. Ten features were closely correlated with HER2 overexpression, including age, albumin/globulin ratio, globulin, activated partial thromboplastin time, tumor stage, node stage, tumor node metastasis stage, tumor size, tumor differentiation, and neuron-specific enolase (NSE). Random forest and logistic regression had areas under the curve (AUC) of 0.9995 and 0.6653 in the training group and 0.923 and 0.667 in the internal validation group, respectively. When the two predictive models were validated using data from the Renmin cohort, random forest and logistic regression had AUCs of 0.9994 and 0.627, respectively. This is the first multicenter study to predict HER2 overexpression in individuals with GC, based on clinical variables. The random forest model significantly outperformed the logistic regression model.

42P Conflicting results of BCL2 based on HER2 status in patients with breast cancer

42P Conflicting results of BCL2 based on HER2 status in patients with breast cancer

Development and Validation of an Ultrasound-Based Radiomics Nomogram for Identifying HER2 Status in Patients with Breast Carcinoma.

(1) Objective: To evaluate the performance of ultrasound-based radiomics in the preoperative prediction of human epidermal growth factor receptor 2-positive (HER2+) and HER2- breast carcinoma. (2) Methods: Ultrasound images from 309 patients (86 HER2+ cases and 223 HER2- cases) were retrospectively analyzed, of which 216 patients belonged to the training set and 93 patients assigned to the time-independent validation set. The region of interest of the tumors was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analyses using the intra-class correlation coefficient (ICC), Mann-Whitney U test, and the least absolute shrinkage and selection operator (LASSO) algorithm. The radiomics score (Rad-score) for each patient was calculated through a linear combination of the nonzero coefficient features. The support vector machine (SVM), K nearest neighbors (KNN), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction models based on the Rad-score. A clinical model based on significant clinical features was also established. In addition, the logistic regression method was used to integrate Rad-score and clinical features to generate the nomogram model. The leave-one-out cross validation (LOOCV) method was used to validate the reliability and stability of the model. (3) Results: Among the seven classifier models, the LR achieved the best performance in the validation set, with an area under the receiver operating characteristic curve (AUC) of 0.786, and was obtained as the Rad-score model, while the RF performed the worst. Tumor size showed a statistical difference between the HER2+ and HER2- groups (p = 0.028). The nomogram model had a slightly higher AUC than the Rad-score model (AUC, 0.788 vs. 0.786), but no statistical difference (Delong test, p = 0.919). The LOOCV method yielded a high median AUC of 0.790 in the validation set. (4) Conclusion: The Rad-score model performs best among the seven classifiers. The nomogram model based on Rad-score and tumor size has slightly better predictive performance than the Rad-score model, and it has the potential to be utilized as a routine modality for preoperatively determining HER2 status in BC patients non-invasively.

Clinical-radiomics nomogram for identifying HER2 status in patients with breast cancer: A multicenter study.

PurposeTo develop and validate a clinical-radiomics nomogram based on radiomics features and clinical risk factors for identification of human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer (BC).MethodsTwo hundred and thirty-five female patients with BC were enrolled from July 2018 to February 2022 and divided into a training group (from center I, 115 patients), internal validation group (from center I, 49 patients), and external validation group (from centers II and III, 71 patients). The preoperative MRI of all patients was obtained, and radiomics features were extracted by a free open-source software called 3D Slicer. The Least Absolute Shrinkage and Selection Operator regression model was used to identify the most useful features. The radiomics score (Rad-score) was calculated by using the radiomics signature-based formula. A clinical-radiomics nomogram combining clinical factors and Rad-score was developed through multivariate logistic regression analysis. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve and decision curve analysis (DCA).ResultsA total of 2,553 radiomics features were extracted, and 21 radiomics features were selected as the most useful radiomics features. Multivariate logistic regression analysis indicated that Rad-score, progesterone receptor (PR), and Ki-67 were independent parameters to distinguish HER2 status. The clinical-radiomics nomogram, which comprised Rad-score, PR, and Ki-67, showed a favorable classification capability, with AUC of 0.87 [95% confidence internal (CI), 0.80 to 0.93] in the training group, 0.81 (95% CI, 0.69 to 0.94) in the internal validation group, and 0.84 (95% CI, 0.75 to 0.93) in the external validation group. DCA illustrated that the nomogram was useful in clinical practice.ConclusionsThe nomogram combined with Rad-score, PR, and Ki-67 can identify the HER2 status of BC.

260P Antitumor activity of trastuzumab deruxtecan (T-DXd) in patients with metastatic breast cancer (mBC) and brain metastases (BMs) from DAISY trial

Breast cancer constitutes the second most common cause of BMs with a higher prevalence in patients with triple-negative and HER2-positive mBC. T-DXd, an antibody-drug conjugate targeting HER2, has shown promising efficacy in patients with HER2-overexpressing mBC and treated asymptomatic BMs. We aimed to analyze the efficacy of T-DXd in patients with BMs and mBC according to HER2 status in patients from DAISY trial.

Evaluation of Epidermal Growth Factor Receptor 2 Status in Gastric Cancer by CT-Based Deep Learning Radiomics Nomogram.

PurposeTo explore the role of computed tomography (CT)-based deep learning and radiomics in preoperative evaluation of epidermal growth factor receptor 2 (HER2) status in gastric cancer.Materials and methodsThe clinical data on gastric cancer patients were evaluated retrospectively, and 357 patients were chosen for this study (training cohort: 249; test cohort: 108). The preprocessed enhanced CT arterial phase images were selected for lesion segmentation, radiomics and deep learning feature extraction. We integrated deep learning features and radiomic features (Inte). Four methods were used for feature selection. We constructed models with support vector machine (SVM) or random forest (RF), respectively. The area under the receiver operating characteristics curve (AUC) was used to assess the performance of these models. We also constructed a nomogram including Inte-feature scores and clinical factors.ResultsThe radiomics-SVM model showed good classification performance (AUC, training cohort: 0.8069; test cohort: 0.7869). The AUC of the ResNet50-SVM model and the Inte-SVM model in the test cohort were 0.8955 and 0.9055. The nomogram also showed excellent discrimination achieving greater AUC (training cohort, 0.9207; test cohort, 0.9224).ConclusionCT-based deep learning radiomics nomogram can accurately and effectively assess the HER2 status in patients with gastric cancer before surgery and it is expected to assist physicians in clinical decision-making and facilitates individualized treatment planning.

Abstract 1588: Discordance of HER2 expression and/or amplification on repeat testing

Abstract Purpose: Evaluation of human epidermal growth factor receptor 2 (HER2) expression/amplification status is critical when considering HER2-targeted therapies. We sought to assess discordance of HER2 status in patients with HER2-amplified/expressing solid tumors who underwent a repeat biopsy for pre-trial screening. Methods: Consenting patients with solid tumors and documented HER2 expression by immunohistochemistry (IHC) or amplification by fluorescent in situ hybridization/next generation sequencing (FISH/NGS) on historical local testing underwent a new biopsy for central reevaluation of HER2 status. Samples were assessed for HER2 expression by IHC and ERBB2 amplification by FISH. Specimens were reviewed by a central laboratory. Patients with HER2 overexpression by IHC (3+) or ERBB2 amplification by FISH/NGS on local testing were assessed for HER2 discordance on central testing (not amplified by FISH and/or not 3+ by IHC on central reevaluation). Results: 56 patients with HER2 expression/amplification on local testing with a median age of 59.5 years and 30 (53.6%) females underwent biopsy and repeat HER2 testing. Median time between consent and treatment for those enrolled was 26 days (range 8-83 days). Twelve cancers were represented, including 12 breast (21.4%), 6 gastroesophageal (10.7%), and 6 biliary tract (10.7%). 31 patients (55.4%) received HER2-targeted therapies between local and central testing. Local assessment of HER2 status on archival tissue was performed in 36 (64.3%) patients by IHC, 27 (48.2%) by FISH, and 18 (32.1%) by NGS. There were 23 patients who were IHC 3+ on local testing and of these, 15 (65.2%) were 3+, 4 (17.4%) were 2+ (3 FISH +), 2 (8.7%) were 1+ (FISH-), and 2 (8.7%) had 0 HER2 expression on central IHC. Of 4 patients that were 2+ without amplification on local testing, 3 were 2+ and 1 was 1+ on central IHC. In 18 patients with ERBB2 amplification on local NGS, 14 (77.8%) were 3+ on central IHC and 17 (94.4%) were ERBB2 amplified by FISH. Of the 51 patients who were 3+ by IHC or amplified by FISH/NGS on local testing, 14 (27.5%) were discordant on central reevaluation. Discordance in HER2 status was found in 5/12 (41.7%) gastroesophageal, 2/9 (22.2%) breast, and 1/7 (14.3%) colorectal tumors. Discordant HER2 status was observed in 9 (30.0%) of 30 cases who received intervening HER2-targeted therapy and in 5 (23.8%) of 21 patients who did not. Of 14 patients with HER2 discordance, 7 were potentially eligible for trial enrollment based on local testing but ineligible based on central HER2 testing. Conclusions: Discordance in HER2 status on repeat biopsy is not infrequent in patients with tumors previously identified as HER2-positive/expressing. Discordance is not limited to patients with prior HER2-targeted therapy and could have therapeutic implications. Biomarker reevaluation may be warranted when considering HER2-targeted therapies but needs to be balanced with a slight delay in treatment initiation. Citation Format: Timothy D. DiPeri, Kathleen Kong, Daniel D. Karp, Jaffer A. Ajani, Shubham Pant, Sarina A. Piha-Payl, Ecaterina E. Dumbrava, Funda Meric-Bernstam. Discordance of HER2 expression and/or amplification on repeat testing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1588.

Socio Demographic Profile and HER2 Status in Patients with Colorectal Carcinoma

Background : Colorectal carcinoma (CRC) is the most frequently observed malignancy worldwide. The incidence of CRC is 3.5% in Bangladesh. Conflicting data exist about the prevalence of HER-2/neu over expression in colorectal cancer ranging from 0 to 83%. The aim of this study was to evaluate the expression of Her-2/neu protein in colorectal cancers.&#x0D; Materials and methods: This descriptive cross-sectional study was conducted in the Department of Pathology of Chittagong Medical College, Chittagong, Bangladesh from 1st January 2017 to 31st December 2017. A total of 52 cases with a histopathological diagnosis of colorectal cancers included in the study as sample size, HER2 status was evaluated by immunohistochemistry (IHC) in formalin fixed, paraffin embedded tissue.Positive expression was assigned a score from 0–3+.IHCs were analysed with grades of colon cancers.&#x0D; Results: The mean age in this study was 47.62years range from 12 to 85 years ,53.8% were female. Among 52 specimens 30 (57.7%) cases were from colon and all the cases were adenocarcinoma and rest of all (42.3%) were from rectum.33 (63.5%) cases were Grade-I, 8 (15.4%) were Grade-II, and 11 (21.2%) were Grade-III. Only 8 (15.4%) of 52 cases showed HER2 positive, 4 (50.0%) cases were from grade- Iand another 4 (50.0%) were grade- II tumours. HER2 overexpression were significantly associated with well differentiated CRC than poorly differentiated (p=0.044).&#x0D; Conclusion: HER2/neu protein expression was observed in colorectal cancer but HER-2/neu protein is less likely to be expressed in colon cancer cell lines among our patients.&#x0D; Chatt Maa Shi Hosp Med Coll J; Vol.21 (1); January 2022; Page 52-56

Quantitative parameters of magnetic resonance imaging cannot predict human epidermal growth factor receptor 2 (HER2) status in rectal cancer

PurposeTo retrospectively investigate whether magnetic resonance imaging (MRI) quantitative parameters can differentiate human epidermal growth factor receptor 2 (HER2) status in rectal cancer. Materials and methodsThis study included 89 patients with surgically confirmed rectal cancer who underwent preoperative MRI from June 2014 to May 2019. Patients were divided into three groups: HER2 negative (HER2-Neg); HER2-low expression (HER2-L); and HER2 positive (HER2-Pos). Quantitative perfusion parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) Tofts model (pharmacokinetic blood dual compartment model) were listed as follows: volume transfer constant (Ktrans), rate constant (Kep), and extracellular volume ratio (Ve). The mean, minimum, and maximum apparent diffusion coefficient (ADC) values at standard (800 s/mm2) b-values were obtained with diffusion-weighted imaging (DWI). Clinicopathologic characteristics and quantitative parameters were compared by Fisher's exact test and one-way analysis of variance (ANOVA), respectively. ResultsThe 89 patients included 52 (58.4%) with HER2-Neg, 31 (34.8%) with HER2-L, and 6 (6.8%) with HER2-Pos states. Fisher's exact test showed that clinicopathologic characteristics among the three groups were not significantly different (p = 0.281 to 1.000). Likewise, there were no associations between HER2 status and any quantitative parameters, including Ktrans (p = 0.296), Kep (p = 0.290), Ve (p = 0.184), ADCmean (p = 0.181), ADCmin (p = 0.143), or ADCmax (p = 0.058). ConclusionQuantitative perfusion parameters (Ktrans, Kep, Ve) and ADC values were not able to discriminate HER2 status in patients with rectal cancer or evaluate treatment response in real time.

The Value of Predicting Human Epidermal Growth Factor Receptor 2 Status in Adenocarcinoma of the Esophagogastric Junction on CT-Based Radiomics Nomogram.

PurposeWe developed and validated a CT-based radiomics nomogram to predict HER2 status in patients with adenocarcinoma of esophagogastric junction (AEG).MethodA total of 101 patients with HER2-positive (n=46) and HER2-negative (n=55) esophagogastric junction adenocarcinoma (AEG) were retrospectively analyzed. They were then randomly divided into a training cohort (n=70) and a verification cohort (n=31). The radiomics features were obtained from the portal phase of the CT enhanced scan. We used the least absolute shrinkage and selection operator (LASSO) logistic regression method to select the best radiomics features in the training cohort, combined them linearly, and used the radiomics signature formula to calculate the radiomics score (Rad-score) of each AEG patient. A multivariable logistic regression method was applied to develop a prediction model that incorporated the radiomics signature and independent risk predictors. The prediction performance of the nomogram was evaluated using the training and validation cohorts.ResultIn the training (P<0.001) and verification groups (P<0.001), the radiomics signature combined with seven radiomics features was significantly correlated with HER2 status. The nomogram composed of CT-reported T stage and radiomics signature showed very good predictive performance for HER2 status. The area under the curve (AUC) of the training cohort was 0.946 (95% CI: 0.919–0.973), and that of the validation group was 0.903 (95% CI: 0.847–0.959). The calibration curve of the radiomics nomogram showed a good degree of calibration. Decision-curve analysis revealed that the radiomics nomogram was useful.ConclusionThe nomogram CT-based radiomics signature combined with CT-reported T stage can better predict the HER2 status of AEG before surgery. It can be used as a non-invasive prediction tool for HER2 status and is expected to guide clinical treatment decisions in clinical practice, and it can assist in the formulation of individualized treatment plans.

HER2 Expression in Circulating Tumour Cells Isolated from Metastatic Breast Cancer Patients Using a Size-Based Microfluidic Device.

Simple SummaryBreast cancer is the most prevalent type of cancer worldwide. By late 2020, there were 7.8 million women alive who had been diagnosed with breast cancer during the previous 5 years. HER2 overexpression in breast cancer is associated with poor prognosis. Existing HER2-targeting therapies significantly improved patient outcomes; still, designing these personalized treatments relies on accurate and comprehensive assessment of HER2 alterations. Frequent HER2 status determination during disease monitoring can be performed using circulating tumour cells (CTCs). Using a novel microfluidic device, we isolated and enumerated CTCs from metastatic breast cancer patients and assessed their HER2 expression in a comparative study with the current gold standard technology. CTCs isolated with our microfluidic technology showed to be a valuable biomarker, and their phenotypical analysis hinted utility to discriminate patient populations, although further validation is needed.HER2 is a prognostic and predictive biomarker in breast cancer, normally assessed in tumour biopsy and used to guide treatment choices. Circulating tumour cells (CTCs) escape the primary tumour and enter the bloodstream, exhibiting great metastatic potential and representing a real-time snapshot of the tumour burden. Liquid biopsy offers the unique opportunity for low invasive sampling in cancer patients and holds the potential to provide valuable information for the clinical management of cancer patients. This study assesses the performance of the RUBYchip™, a microfluidic system for CTC capture based on cell size and deformability, and compares it with the only FDA-approved technology for CTC enumeration, CellSearch®. After optimising device performance, 30 whole blood samples from metastatic breast cancer patients were processed with both technologies. The expression of HER2 was assessed in isolated CTCs and compared to tissue biopsy. Results show that the RUBYchipTM was able to isolate CTCs with higher efficiency than CellSearch®, up to 10 times more, averaging all samples. An accurate evaluation of different CTC subpopulations, including HER2+ CTCs, was provided. Liquid biopsy through the use of the RUBYchipTM in the clinic can overcome the limitations of histological testing and evaluate HER2 status in patients in real-time, helping to tailor treatment during disease evolution.

Dual-Energy CT-Based Nomogram for Decoding HER2 Status in Patients With Gastric Cancer.

OBJECTIVE. The purpose of this study was to develop and evaluate a dual-energy CT (DECT)-based nomogram for noninvasive identification of the status of human epidermal growth factor receptor 2 (HER2; also known as ERBB2) expression in gastric cancer (GC). MATERIALS AND METHODS. A total of 206 patients with histologically proven GC who underwent pretreatment DECT were retrospectively recruited and randomly allocated to a training cohort (n = 144) or a test cohort (n = 62). Information on clinical characteristics, qualitative imaging features, and quantitative DECT parameters was collected. Univariate analysis and multivariate logistic regression were implemented to screen independent predictors of HER2 status. An individualized nomogram was built, and its discrimination, calibration, and clinical usefulness were assessed. RESULTS. Tumor location, the iodine concentration of the tumor in the venous phase, and the normalized iodine concentration of the tumor in the venous phase were significant factors predictive of HER2 status (all p < .05). After these three indicators were integrated, the proposed nomogram showed a favorable diagnostic performance, with AUCs of 0.807 (95% CI, 0.718-0.897) in the training cohort and 0.815 (95% CI, 0.661-0.968) in the test cohort. The nomogram showed a preferable fitting (all p > .05 by the Hosmer-Lemeshow test) and would offer more net benefits than simple default strategies within a wide range of threshold probabilities in both cohorts. CONCLUSION. The DECT-based nomogram has great application potential in terms of detecting HER2 status in GC, and can serve as a novel substitute for invasive testing.

Correlation between family history and characteristics of breast cancer

Family history is a major risk factor for breast cancer; approximately 5–10% cases of breast cancer are associated with a family history. Herein, we investigated the link between family history and breast cancer features to elucidate the importance of family history in the diagnosis and treatment of breast cancer. Data from 10,549 patients with breast cancer were collected from 2014 to 2017. Detailed information about the family history of the patients including the degree and number of relatives affected and the types of cancer was recorded. The tumors were pathologically and clinically classified based on the stage, grade, ER, PR, HER2, Ki-67 status, and subtypes, according to standard guidelines. Data were analyzed using χ2 test and multiple logistic regression. Patients with a family history of other cancer types were significantly older at diagnosis than patients with a family history of breast/ovarian cancer (p = 0.002) and those without a family history of cancer (p < 0.001). Patients without a family history of cancer were typically diagnosed at a later stage, including high frequency in N2 (p = 0.035) and TNM stage III (p = 0.015). Compared with patients with second-/third-degree relatives, those with first-degree relatives having breast/ovarian cancer had a higher median age (54.1, p < 0.001) at diagnosis and showed more advanced disease. No significant difference was found between ER, PR, and HER2 status in patients with and without a family history of cancer. Family history of breast cancer can influence the cancer characteristics of the patients at diagnosis, especially patient age, tumor stage, and grade.

Details of human epidermal growth factor receptor 2 status in 454 cases of biliary tract cancer

Human epidermal growth factor receptor 2 (HER2)-targeted therapy has improved clinical outcomes in patients with HER2-positive breast and gastric cancers, although ineffective or recurrent cases are present. One reason for this is the heterogeneity of HER2 expression in cancer cells. The aim of this study was to investigate the clinicopathological characteristics and HER2 status of patients with biliary tract cancers (BTCs). We examined HER2 protein expression by immunohistochemistry, HER2 gene amplification by fluorescence in situ hybridization, and both HER2 protein and gene levels simultaneously by gene-protein assay. Samples were collected from 454 patients who underwent surgical resection for BTCs (110 intrahepatic cholangiocarcinomas [ICC], 67 perihilar extrahepatic cholangiocarcinomas [ECC-Bp], 119 distal extrahepatic cholangiocarcinomas [ECC-Bd], 80 gallbladder carcinomas [GBC], and 79 ampullary carcinomas [AVC]). HER2 status was assessed according to the guidelines for HER2 testing in gastroesophageal adenocarcinoma. HER2-positive status was detected in 14.5% of BTCs (3.7% of ICC, 3.0% of ECC-Bp, 18.5% of ECC-Bd, 31.3% of GBC, and 16.4% of AVC). Furthermore, HER2-positivity tended to correlate with low histological grade, tumor histology, and macroscopic features in certain tumors. HER2 heterogeneity was common and highly frequent (83%) in BTC cases. Reduced HER2 protein expression in the deeper invasive areas with simultaneous dedifferentiation was frequently observed in HER2-positive cancer cells. The findings of this study suggest that a large subgroup of HER2-positive BTC cases can be considered for HER2-targeted therapy. Moreover, the HER2 status in BTCs should be determined carefully using a sensitive approach toward larger cancer tissues.