A new AI tool developed at Cedars-Sinai could change how doctors pick the most suitable presurgical therapy for breast cancer patients. The tool, called BRIDGE, was detailed in Annals of Oncology alongside early validation data. It reads genetic signals inside a tumor to identify which subtypes are present, rather than forcing the whole mass into one category.
Treatment decisions for patients with breast cancer depend on how each tumor is classified and labeled. Doctors sort masses into groups like triple-negative or HER2-positive, then select from chemotherapy, targeted therapy, or immunotherapy given before the operation.
When that approach works, a patient who would otherwise need a mastectomy might qualify for a lumpectomy instead. Stripping away stray cancer cells before surgery also substantially reduces the risk of the disease coming back later.
BRIDGE was built specifically to address one of the most persistent problems in how breast tumors are typically evaluated. Any given mass tends to harbor multiple cancer subtypes at once, yet conventional testing collapses everything into a single label. Translational Research Institute at Cedars-Sinai Deputy Director Eytan Ruppin said the team used gene expression to map each subtype separately.
The tool outputs a response score for each patient, telling clinicians which therapies are most likely to work based on a more accurate profile of their breast cancer subtypes.
Ruppin lab postdoctoral scientist and study co-corresponding author Thomas Cantore explained that the team tested BRIDGE on several hundred existing patient cases with documented outcomes. The AI-powered tool proved accurate at predicting which individuals responded to each therapy and which would not.
As BRIDGE draws on biopsy tissue already gathered during routine diagnostic workups, it adds nothing to the standard clinical process. Furthermore, working from standard samples means the system can fit into existing hospital workflows without creating extra burden for patients or staff.
Researchers then built on BRIDGE to create a related tool called BRIDGE-Slide, which eliminates the need for genetic sequencing altogether. Instead of mapping gene activity directly, it infers tumor composition from photographs of standard biopsy slides. Nishanth Ulhas Nair, a co-corresponding author, said the method reduces turnaround time and can save several thousand dollars in testing expenses.
That reduction in cost and complexity is key to making precision treatment options available at hospitals that lack the resources for genomic analysis.
Clinical trials are the next critical stage of development for both BRIDGE and BRIDGE-Slide, which are being prepared for testing in live patient settings. Cantore said biopsy slides hold far more diagnostic information than researchers typically extract, and the team plans to keep mining that resource.
The broader implication is that AI could help oncologists extract more value from material already sitting in hospital archives. More effective treatment matching at the presurgical stage could mean meaningfully better outcomes for patients facing a difficult cancer diagnosis.
This AI tool builds on other advances in the fight against cancer that are being made by numerous companies, such as Calidi Biotherapeutics Inc. (NASDAQ: CLDI) that focuses on developing next-generation immunotherapies indicated for different types of cancer.
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