Biomarker Dynamics and Long-Term Treatment Outcomes in Breast Cancer Patients with Residual Cancer Burden after Neoadjuvant Therapy.
Miloš HolánekIveta SelingerováPavel FabianOldrich CoufalOndrej ZapletalKatarina PetrakovaTomas KazdaRoman HrstkaAlexandr PoprachMaria ZvarikovaOndrej BilekMarek SvobodaPublished in: Diagnostics (Basel, Switzerland) (2022)
A residual cancer burden after neoadjuvant therapy (NAT) for breast cancer (BC) is associated with worse treatment outcomes compared to patients who achieved pathologic complete remission. This single-institutional retrospective study of 767 consecutive patients, including 468 patients with assessable residual cancer burden (aRCB) after NAT, with a median follow-up of 36 months, evaluated the biomarkers assessed before NAT from a biopsy and after NAT from a surgical specimen, their dynamics, and effect on long-term outcomes in specific breast cancer subtypes. The leading focus was on proliferation index Ki-67, which was significantly altered by NAT in all BC subtypes ( p < 0.001 for HER2 positive and luminal A/B HER2 negative and p = 0.001 for TNBC). Multivariable analysis showed pre-NAT and post-NAT Ki-67 as independent predictors of survival outcomes for luminal A/B HER2 negative subtype. For TNBC, post-NAT Ki-67 was significant alone, and, for HER2 positive, the only borderline association of pre-NAT Ki-67 was observed in relation to the overall survival. Steroid and HER2 receptors were re-assessed just in a portion of the patients with aRCB. The concordance of both assessments was 92.9% for ER status, 80.1% for PR, and 92.2% for HER2. In conclusion, these real-world data of a consecutive cohort confirmed the importance of biomarkers assessment in patients with aRCB, and the need to consider specific BC subtypes when interpreting their influence on prognosis.
Keyphrases
- papillary thyroid
- neoadjuvant chemotherapy
- squamous cell
- locally advanced
- rectal cancer
- risk factors
- lymph node
- newly diagnosed
- ejection fraction
- machine learning
- signaling pathway
- systemic lupus erythematosus
- mesenchymal stem cells
- childhood cancer
- deep learning
- electronic health record
- big data
- bone marrow
- disease activity
- free survival
- fine needle aspiration