New Delhi: Researchers from the University of Utah’s Huntsman Cancer Institute have made an advancement in predicting the severity of triple-negative breast cancer (TNBC), an aggressive and rare form of breast cancer known for its poor prognosis and lack of targeted treatments.

Currently, no reliable methods exist to predict TNBC recurrence following common treatments like chemotherapy and surgery. This study, published in JCO Precision Oncology, introduces a novel mechanism to accurately assess TNBC’s aggressiveness and potential for recurrence.

The researchers developed a patient-derived xenograft (PDX) model, which involves transplanting a patient’s tumor into a mouse. This model allows the tumor's growth and behavior to be monitored in a living organism. The study found that the PDX model was more effective than existing methods at predicting TNBC recurrence, offering an early and precise assessment of the cancer’s aggressiveness.

This advancement could directly impact patient care by enabling doctors to create more personalized treatment plans, particularly for patients facing recurrent TNBC. Cindy Matsen, co-author of the study and head of the Breast and Gynecologic Disease Center at Huntsman Cancer Institute, emphasized that the study provides a valuable tool to customize treatment strategies based on the individual tumor's profile.

An important practical application of the PDX model is its ability to test specific drugs, offering insights into which treatments may be most effective for each patient. This provides physicians with critical information to make more informed decisions about targeted therapies.

The study's authors highlighted that tumor growth in the PDX model often signals highly aggressive cancer, which tends to be more challenging to treat. The model's predictive capability allows doctors to identify these high-risk cases earlier, leading to more aggressive intervention strategies.

"The study's results are crucial," the authors stated, underscoring the PDX model's potential to change how recurrent TNBC is managed. By offering a more accurate prediction of cancer behavior and treatment response, this advancement could pave the way for more targeted and effective treatment approaches, improving outcomes for TNBC patients.

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