Updated Austrian treatment algorithm for metastatic triple-negative breast cancer
Austrian breast cancer expert group (Austrian treatment algorithms) · 2023 · Austria
Source and licence
- Authors
- Rupert Bartsch, Gabriel Rinnerthaler, Edgar Petru et al.
- Title
- Updated Austrian treatment algorithm for metastatic triple-negative breast cancer
- Journal
- Wiener Klinische Wochenschrift, 2023
- Licence
- CC BY 4.0
- Changes
- Verbatim excerpts; formatting adapted
Full text retrieved 2026-10-27 from Europe PMC · PMC11156740
Licence statement of the original publication
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Open the original publication →Full text on Europe PMC
Recommendations
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Abstract
Approximately 15% of newly diagnosed breast cancer patients have neither hormone receptors expression nor HER2 overexpression and/or HER2/neu gene amplification. This subtype of breast cancer is known as Triple Negative Breast Cancer (TNBC), and carries a significantly elevated risk of local and distant recurrence. In comparison with other breast cancer subtypes, there is a higher rate of visceral and brain metastases. The majority of metastases of TNBC are diagnosed within three years after initial breast cancer diagnosis. While there have been major advances in hormone-receptor- positive and in human epidermal growth factor receptor 2 (HER2)-positive disease over the past two decades, only limited improvements in outcomes for patients with triple negative breast cancer (TNBC) have been observed. A group of Austrian breast cancer specialists therefore convened an expert meeting to establish a comprehensive clinical risk-benefit profile of available mTNBC therapies and discuss the role sacituzumab govitecan may play in the treatment algorithm of the triple-negative breast cancer patients.
Contents of the original publication
The section headings of the original.
- Introduction
- Patients, material and methods
- Results
- Subtypes within TNBC
- Active substances, modes of action and clinical trial landscape
- Landmark trials in metastatic TNBC
- Development of updated treatment algorithms and consensus scenarios
- Discussion and outlook
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