Predicting which patients will have pCR or residual disease (RD) provides physicians with an opportunity to improve treatment planning with more aggressive or novel treatments, while preventing overtreatment in populations expected to achieve pCR with the standard of care. In addition, only about 20% of breast cancer patients achieve pCR 11, causing unnecessary morbidity for the other 80% receiving high-toxicity treatment with limited benefit. Since pCR is correlated with prediction of 5-year disease free survival, biomarker development in this setting can establish efficacy which can then be utilized in both the neoadjuvant and adjuvant settings. Thus, NAC can allow for an early evaluation of the effectiveness of systemic therapy. Achieving pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is a surrogate marker and predictor of long-term outcomes, especially for TNBC 4, 5, 6, 7, 8, 9, 10, 11. TNBC represents 15–20% of newly diagnosed breast cancers in the United States 3. Triple-negative breast cancer (TNBC), characterized by lack of expression of the estrogen (ER), progesterone (PgR), and erb-b2 receptor tyrosine kinase 2 (HER2) receptors, is a particularly problematic form of breast cancer due to aggressive growth, high recurrence rates and poor long-term survival 1, 2. This stratification of patients may allow predicted residual disease classes to be assigned an alternative therapy. The TNBC RD patients were subdivided by our classifiers, with one class showing significantly higher levels of Ki67 expression and having significantly poorer survival rates than the other classes. However, the identification of pCR in both total and TNBC population are as low as 21.1% and 30%, respectively. For the independent cohort, the test identified 91.5% RD patients in the total population and 86.2% RD patients in the TNBC subset. The test accurately identified 70.5% (79/112) of pCR and 83.5% (340/407) of RD patients in the total population, and 75.0% (45/60) of pCR and 75.2% (88/117) of RD patients in the TNBC subset. The test performance was further validated in an independent 304-patient cohort. Two RNA classifiers of 16 genes each were sequentially applied to the total cohort, classifying patients into 3 distinct classes. Gene expression data from pretreatment biopsies of patients with all breast cancer subtypes were combined into a 519-patient cohort containing 177 TNBC patients. Results: Correlation between methods (qIHC versus RT-qPCR) was high for ER and PgR (spearman´s r = 0.We developed a test to predict which patients will achieve pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and which will have residual disease (RD). The potential of Ki-67 IHC and RT-qPCR to predict pathological complete response (pCR) was evaluated using ROC analysis and non-parametric Mann-Whitney Test. Concordance between the three methods (vIHC, qIHC and RT-qPCR) was assessed for all 3 markers. Expression of ESR1, PGR and MKI67 by RT-qPCR was performed on RNA extracted from the same formalin-fixed paraffin-embedded tissue. Methods: Ki-67 IHC visual assessment was compared to the IHC nuclear tool (AperioTM) on core biopsies from a randomized neoadjuvant clinical trial. ![]() Automatic scoring of Ki-67 with digital image analysis (qIHC) or assessment of MKI67 gene expression with RT-qPCR may improve diagnostic accuracy. This method carries significant intra- and inter-observer variability. Citation of documents: Please do not cite the URL that is displayed in your browser location input, instead use the DOI, URN or the persistent URL below, as we can guarantee their long-time accessibility.īackground: Proliferation may predict response to neoadjuvant therapy of breast cancer and is commonly assessed by manual scoring of slides stained by immunohistochemistry (IHC) for Ki-67 similar to ER and PgR.
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