Results from a multinational study found that using artificial intelligence (AI) assistance can help pathologists more accurately classify breast cancers with low levels of HER2 expression and reduce the risk of misclassifying HER2-low and HER2-ultralow tumours as HER2-null.

This can allow more patients with HER2-low or HER2-ultralow breast cancer the option of HER2-targeted treatments that could improve their outcomes. The research will be presented at the 2025 American Society of Clinical Oncology (ASCO) Annual Meeting, taking place May 30-June 3 in Chicago.

“Roughly 65% of breast tumours once called HER2‑negative actually demonstrate some level of HER2 expression and belong to subgroups now classified as HER2-low or HER2-ultralow breast cancers. Some of these tumours could be treated with HER2-targeted drugs, but only if we detect their HER2 expression levels.

Our study provides the first multinational evidence that artificial intelligence can help close a critical diagnostic gap and open the door to new therapies like antibody-drug conjugates for a majority of patients who, until recently, had not been offered these options,” said lead study author Marina De Brot, MD, PhD, A.C. Camargo Cancer Center, São Paolo, Brazil.

It can be challenging and time-consuming for pathologists to accurately identify HER2 protein expression in HER2-low and HER2-ultralow breast cancer cases using traditional immunohistochemistry (IHC) testing that looks for HER2 proteins in cancer tissue samples. Additionally, in situ hybridisation (ISH) is a technique that uses lablled probes to identify specific nucleic acid sequences within cells or tissues.

HER2-low breast cancers have a HER2 IHC score of 1+ or 2+/ISH negative. HER2-ultralow breast cancers have an IHC score of 0 with membrane staining. An accurate diagnosis relies primarily on the accuracy of the human eye for detecting abnormalities. Even among experienced breast pathologists, about 1 in 3 HER2-ultralow breast cancers can be mistakenly labelled as HER2-null, which usually means that oncologists do not recommend patients receive HER2-targeted therapy with antibody drug conjugates.

In this study, researchers used an AI-supported digital training platform called ComPath Academy to assist pathologists with their HER2 scoring of breast cancer samples. The study included 105 pathologists from 10 different countries in Asia and South America who were tasked with performing a HER2 assessment of 20 digital breast cancer cases, both with and without AI assistance. 

Over the course of 5 sessions, the pathologists performed a total of 1,940 readings that were done during 3 separate exams. AI support was only offered during the third exam. Their readings were then compared against ground-truth IHC scores from a central reference center. Ground-truth scores are arrived at by a consensus among multiple expert pathologists who independently review and score HER2 IHC-stained tissue samples and have been established as the gold standard reference for determining HER2 breast cancer status.

“Accurate HER2 scoring is important to ensure that patients receive the best treatment for their breast cancer. This international study shows that an AI-assisted approach improved HER2 scoring, including in situations that would affect treatment decisions. These findings shed light on the promising role for AI in oncology, not as a replacement for the physician, but as a powerful tool to help us work smarter and faster to deliver high-quality, more personalized care,” said Julian Hong, MD, MS, Associate Professor and Medical Director of Radiation Oncology Informatics at the University of California, San Francisco, and an ASCO Expert in artificial intelligence.

The researchers are planning multicenter implementation studies that embed the AI tool in routine diagnostics to measure downstream clinical effects, including changes in treatment options and time-to-therapy for patients with HER2-low and HER2-ultralow breast cancers.

Source: ASCO