Rate of reclassification of HER2-equivocal breast cancer cases to HER2-negative per the 2018 ASCO/CAP guidelines and response of HER2-equivocal cases to anti-HER2 therapy

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.

HER2 copy number determination in breast cancer using the highly sensitive droplet digital PCR method

Human epidermal growth factor receptor 2 (HER)-positive breast cancer (BC) is characterized by an aggressive clinical course. In the case of HER2 overexpression/amplification, patients benefit from HER2-targeting therapies. Standardized diagnostic HER2 assessment includes immunohistochemistry (IHC) and/or in situ hybridization (ISH). The aim of this study was to compare this "gold standard" with the Droplet Digital™ polymerase chain reaction (ddPCR), a method that allows sensitive and precise detection of copy number variations (CNV) in FFPE (formalin-fixed, paraffin-embedded) DNA samples. Partitioning of the PCR reaction into 20,000 droplets enables a precise quantitative "CN" discrimination also in heterogeneous samples. FFPE breast cancer samples (n = 170) with routinely assessed HER2 status by IHC/ISH were retrospectively analyzed using the ddPCR CNV ERBB2 assay. Comparison of HER2 status assessment by the two methods revealed concordant results in 92.9% (158/170) of the cases. Discrepant cases were verified and interpreted. For ddPCR, a cut off value of 3 HER2 copies was set to distinguish between HER2-negative and HER2-positive BC. Results obtained with the ddPCR CNV ERBB2 assay were consistent and reproducible, and serial dilutions demonstrated a high stability and sensitivity of the method. The ddPCR CNV ERBB2 assay may be a specific and convenient tool to quantify HER2 copy numbers in BC samples. In our study, this method showed high reproducibility in accuracy of HER2 assessment compared to IHC/ISH analysis.

Insights Into the Emerging Entity of HER2-Low Breast Cancer

Human epidermal growth factor receptor 2 (HER2)-low breast cancer (BC) is a subtype of BC that has been recently recognized as a separate clinical entity with distinct clinical and molecular characteristics. It is defined by a low level of HER2 protein expression, which distinguishes it from other more aggressive BC subtypes. Early studies suggest that it may have a more favorable prognosis than HER2-positive BC, as it is less likely to spread to other parts of the body and may be more responsive to standard BC treatments such as chemotherapy, radiation therapy, and hormone therapy. Given the relative new emergence of HER2-low BC, there is still much to be learned about this subtype; ongoing research is focused on identifying the underlying genetic mutations that contribute to HER2-low BC as well as developing targeted therapies that can improve outcomes for patients with this disease. This review is aimed at summarizing the current clinical knowledge on HER2-low BC, with the aim of creating a better understanding of this entity and paving the way for potential interventions and a new standard of care.

Distinct clinical and somatic mutational features of breast tumors with high-, low-, or non-expressing human epidermal growth factor receptor 2 status

BACKGROUND

HER2-low breast cancers were reported to have distinct clinicopathological characteristics from HER2-zero; however, the difference in their genetic features remains unclear. This study investigated the clinical and molecular features of breast tumors according to HER2 status.

METHODS

We analyzed the clinicopathological and genomic data of 523 Chinese women with breast cancer. Genomic data was generated by targeted next-generation sequencing (NGS) of breast tumor samples using a commercial 520 gene panel. The cohort was stratified according to HER2 status as HER2-zero (n = 90), HER2-low (n = 231), and HER2-positive (n = 202) according to their immunohistochemistry and fluorescence in situ hybridization results.

RESULTS

HER2-low breast tumors were enriched with hormone receptor-positive tumors, and who had lower Ki67 expression levels. Genes were differentially mutated across HER2 subgroups. HER2-low tumors had significantly more mutations involved in PI3K-Akt signaling than HER2-positive (p < 0.001) and HER2-zero breast tumors (p < 0.01). HER2-zero tumors had more mutations in checkpoint factors (p < 0.01), Fanconi anemia (p < 0.05), and p53 signaling and cell cycle pathway (p < 0.05) compared to HER2-low breast tumors. Compared with HER2-zero tumors, HER2-low tumors had significantly lower pathological complete response rates after neoadjuvant therapy (15.9% vs. 37.5%, p = 0.042) and proportion of relapsed/progressed patients across follow-up time points (p = 0.031), but had comparable disease-free survival (p = 0.271).

CONCLUSION

Our results demonstrate the distinct clinical and molecular features and clinical outcomes of HER2-low breast tumors.

Intratumoral Heterogeneity and Immune Response Indicators to Predict Overall Survival in a Retrospective Study of HER2-Borderline (IHC 2+) Breast Cancer Patients

Breast cancer (BC) categorized as human epidermal growth factor receptor 2 (HER2) borderline [2+ by immunohistochemistry (IHC 2+)] presents challenges for the testing, frequently obscured by intratumoral heterogeneity (ITH). This leads to difficulties in therapy decisions. We aimed to establish prognostic models of overall survival (OS) of these patients, which take into account spatial aspects of ITH and tumor microenvironment by using hexagonal tiling analytics of digital image analysis (DIA). In particular, we assessed the prognostic value of Immunogradient indicators at the tumor–stroma interface zone (IZ) as a feature of antitumor immune response. Surgical excision samples stained for estrogen receptor (ER), progesterone receptor (PR), Ki67, HER2, and CD8 from 275 patients with HER2 IHC 2+ invasive ductal BC were used in the study. DIA outputs were subsampled by HexT for ITH quantification and tumor microenvironment extraction for Immunogradient indicators. Multiple Cox regression revealed HER2 membrane completeness (HER2 MC) (HR: 0.18, p = 0.0007), its spatial entropy (HR: 0.37, p = 0.0341), and ER contrast (HR: 0.21, p = 0.0449) as independent predictors of better OS, with worse OS predicted by pT status (HR: 6.04, p = 0.0014) in the HER2 non-amplified patients. In the HER2-amplified patients, HER2 MC contrast (HR: 0.35, p = 0.0367) and CEP17 copy number (HR: 0.19, p = 0.0035) were independent predictors of better OS along with worse OS predicted by pN status (HR: 4.75, p = 0.0018). In the non-amplified tumors, three Immunogradient indicators provided the independent prognostic value: CD8 density in the tumor aspect of the IZ and CD8 center of mass were associated with better OS (HR: 0.23, p = 0.0079 and 0.14, p = 0.0014, respectively), and CD8 density variance along the tumor edge predicted worse OS (HR: 9.45, p = 0.0002). Combining these three computational indicators of the CD8 cell spatial distribution within the tumor microenvironment augmented prognostic stratification of the patients. In the HER2-amplified group, CD8 cell density in the tumor aspect of the IZ was the only independent immune response feature to predict better OS (HR: 0.22, p = 0.0047). In conclusion, we present novel prognostic models, based on computational ITH and Immunogradient indicators of the IHC biomarkers, in HER2 IHC 2+ BC patients.