We investigated the patterns of recurrence and primary endocrine resistance according to estrogen receptor (ER) alpha gene (ESR1) mutations, as assessed by digital droplet (dd) PCR, in patients with non-metastatic ER+ breast cancer. We collected 121 formalin-fixed paraffin-embedded (FFPE) surgical specimens from ER+ breast cancer patients who had relapsed after surgery. Genomic DNA was extracted from the FFPE samples and ESR1 mutations were evaluated using ddPCR. ESR1 mutations were detected in 9 (7.4%) of 121 primary breast cancer specimens. The median recurrence-free interval and overall survival were significantly lower in patients with ESR1 mutations than in those without. Of the patients treated with ET (N = 98), eight had ESR1 mutations. Of these, six (75.0%) had primary endocrine resistance and two (25.0%) had secondary endocrine resistance. By contrast, only 22 of 90 (24.4%) patients without ESR1 mutations had primary endocrine resistance. A multivariable model showed that an ESR1 mutation is a significant risk factor for primary endocrine resistance. Our findings provide clinical evidence that the presence of rare ESR1 mutant clones identified by ddPCR in primary tumors is associated with primary endocrine resistance in an adjuvant setting.

Ji JH, et al. Ann Surg Treat Res. 2017 Mar;92(3):149-155. https://doi.org/10.4174/astr.2017.92.3.149

This study aimed to develop a machine learning-based prediction model for predicting multi-gene assay (MGA) risk categories. Patients with estrogen receptor-positive (ER+)/HER2− breast cancer who had undergone Oncotype DX (ODX) or MammaPrint (MMP) were used to develop the prediction model. The development cohort consisted of a total of 2565 patients including 2039 patients tested with ODX and 526 patients tested with MMP. The MMP risk prediction model utilized a single XGBoost model, and the ODX risk prediction model utilized combined LightGBM, CatBoost, and XGBoost models through soft voting. Additionally, the ensemble (MMP + ODX) model combining MMP and ODX utilized CatBoost and XGBoost through soft voting. Ten random samples, corresponding to 10% of the modeling dataset, were extracted, and cross-validation was performed to evaluate the accuracy on each validation set. The accuracy of our predictive models was 84.8% for MMP, 87.9% for ODX, and 86.8% for the ensemble model. In the ensemble cohort, the sensitivity, specificity, and precision for predicting the low-risk category were 0.91, 0.66, and 0.92, respectively. The prediction accuracy exceeded 90% in several subgroups, with the highest prediction accuracy of 95.7% in the subgroup that met Ki-67 <20 and HG 1~2 and premenopausal status. Our machine learning-based predictive model has the potential to complement existing MGAs in ER+/HER2− breast cancer.

Nonmass enhancement extension to the nipple at breast MRI has a high positive predictive value for tumor involvement of the nipple, a known contraindication for nipple-sparing mastectomy. Background Although nonmass enhancement (NME) extension to the nipple at preoperative MRI frequently leads to sacrifice of the nipple-areolar complex (NAC), its correlation with pathologically conf...

We investigated the association between TP53 mutation and 21-gene recurrence score (RS) in ER-positive/HER2-negative breast cancer (BC) using data from 141 patients who underwent TP53 sequencing and Oncotype DX^® tests. We detected TP53 mutations in 18 (12.8%) patients. Most patients with TP53 mutation had a high 21-gene RS (≥26). The average 21-gene RS was higher in TP53 mutant tumors. Multivariate analysis showed that mutated TP53 is an independent factor for a high 21-gene RS. Mutated TP53 remained closely associated with high 21-gene RS in patients with low pathological risk (n = 103). In the ER⁺/PR⁺/HER2-negative subset (n = 356) of The Cancer Genome Atlas, the non-luminal A intrinsic subtype was more prevalent in the group with mutant TP53. mRNA levels of p53-regulated senescence gatekeeper and cell cycle-related genes were increased in BC with mutated TP53. Mutational analysis of TP53 helped identify endocrine-resistant tumors.

This study aimed to determine whether post-mastectomy radiotherapy (PMRT) is beneficial for the prognosis of patients who achieved pathologic complete response (pCR), or who had minimal residual disease, after undergoing neoadjuvant chemotherapy (NAC). Patients who underwent a total mastectomy between 2006 and 2018, after NAC, were included. Patients who did not receive PMRT were matched using 1:3 propensity score matching (PSM). Kaplan–Meier survival curves were used to compare locoregional recurrence-free survival (LRRFS) and overall survival (OS). A total of 368 patients were included after 1:3 PSM. PMRT improved the LRRFS (p = 0.016) and OS (p = 0.017) rates of patients who underwent NAC. However, PMRT did not affect the prognosis of patients with pCR (LRRFS: p = 0.999; OS: p = 0.453). In addition, PMRT had a limited effect on LRRFS and OS in patients who responded well to NAC, with a neoadjuvant response index (NRI) value of 0.7–1.0 (LRRFS: p = 0.568; OS: p = 0.875). PMRT improved the OS of patients with a large residual tumor burden, such as nodal metastases or pathologic stage II/III. The benefits of PMRT vary depending on the patients’ response to NAC, although PMRT is useful for treating patients who underwent NAC. PMRT can be omitted, not only in patients with pCR, but also in good responders with an NRI value of 0.7–1.0.