A study has found that analyzing both genetic and protein information in triple-negative breast cancer—a form of the disease known for being difficult to treat—can accurately predict the effectiveness of anticancer therapy. This finding is significant because it lays the groundwork for personalized treatment by enabling the prediction of individual treatment responses in advance.

New research suggests that analyzing both genetic and protein information can accurately predict the effectiveness of chemotherapy for triple-negative breast cancer, a notoriously difficult-to-treat form of the disease. This breakthrough could pave the way for personalized treatment strategies by allowing doctors to anticipate individual patient responses to therapy.

On Monday, the Yonsei Cancer Center announced that a collaborative research team, including oncologists Dr. Son Joo-hyuk and Dr. Kim Min-hwan, along with Dr. Lee Dong-ki and breast surgeon Dr. Park Se-ho, has identified key indicators of chemotherapy resistance using a comprehensive approach called genoproteomics.

Triple-negative breast cancer, characterized by the absence of estrogen, progesterone, and HER2 receptors, is known for its aggressive nature and higher risk of recurrence compared to other breast cancer types.

While preoperative chemotherapy is often the go-to treatment, predicting patient responses has been a significant challenge due to the high variability in outcomes. The limited options for targeted therapies have made this approach common, despite the difficulties in forecasting individual treatment responses.

The research team conducted a comparative analysis of tumor tissues from 50 patients who underwent surgery following preoperative chemotherapy at Yonsei Cancer Center over approximately one year, starting in September 2020. They employed mass spectrometry to precisely measure cellular protein changes, complementing their existing analytical methods.

The study revealed that triple-negative breast cancer can be classified into five distinct subtypes based on molecular characteristics, each showing markedly different responses to treatment. Tumors with heightened immune activity demonstrated better treatment outcomes, while other subtypes showed less favorable responses.

Intriguingly, the team uncovered a previously undetected mechanism at the protein level that had eluded genetic analyses. Contrary to the general belief that triple-negative breast cancer is unrelated to female hormones, the research showed that hormone-related pathways are activated at the protein level, suppressing immune responses and reducing treatment efficacy.

The researchers also identified correlations between changes in specific protein activities, increased gene expression, and chemotherapy resistance. They developed an integrated predictive model using both genetic and protein data, which outperformed traditional genetic analyses in forecasting chemotherapy responses.

Furthermore, by comparing tumor changes before and after treatment, the team discovered that increased activity of certain genes promotes chemotherapy resistance. They explored potential clinical applications of therapies targeting this resistance using organoid models derived from patient tumors.

Professors Son Joo-hyuk and Kim Min-hwan, Lecturer Lee Dong-gi of the Department of Medical Oncology at Yonsei Cancer Hospital, and Professor Park Se-ho of the Department of Breast Surgery / Provided by Yonsei Cancer Hospital

The research team emphasized that this study applies a novel analytical approach to deepen our understanding of tumor characteristics using real patient data. They expressed optimism that the newly identified indicators could lead to improved treatment outcomes in clinical settings.

This collaborative study between Yonsei University College of Medicine and the National Cancer Center has been published in the latest issue of the prestigious genetics journal <em>Genome Biology</em> (5-year impact factor 16.3).

New research suggests that analyzing both genetic and protein information can accurately p …

Unlocking Triple Negative Breast Cancer: How Gene-Protein Analysis Predicts Treatment Success