Background: Cytidine nucleotide triphosphate synthase 1 (CTPS1) is a CTP synthase which play critical roles in DNA synthesis. However, its biological regulation and mechanism in triple-negative breast cancer (TNBC) has not been reported yet. Methods: The expression of CTPS1 in TNBC tissues was determined by GEO, TCGA databases and immunohistochemistry (IHC). The effect of CTPS1 on TNBC cell proliferation, migration, invasion, apoptosis and tumorigenesis were explored in vivo and in vitro. In addition, the transcription factor Y-box binding protein 1 (YBX1) was identified by bioinformatics methods, dual luciferase reporter and chromatin immunoprecipitation (CHIP) assays. Pearson correlation analysis was utilized to assess the association between YBX1 and CTPS1 expression. Results: CTPS1 expression was significantly upregulated in TNBC tissues and cell lines. Higher CTPS1 expression was correlated with a poorer disease-free survival (DFS) and overall survival (OS) in TNBC patients. Silencing of CTPS1 dramatically inhibited the proliferation, migration, invasion ability and induced apoptosis of MDA-MB-231 and HCC1937 cells. Xenograft tumor model also indicated that CTPS1 knockdown remarkably reduced tumor growth in mice. Mechanically, YBX1 could bind to the promoter of CTPS1 to promote its transcription. Furthermore, the expression of YBX1 was positively correlated with CTPS1 in TNBC tissues. Rescue experiments confirmed that the enhanced cell proliferation and invasion ability induced by YBX1 overexpression could be reversed by CTPS1 knockdown. Conclusion: Our data demonstrate that YBX1/CTPS1 axis plays an important role in the progression of TNBC. CTPS1 might be a promising prognosis biomarker and potential therapeutic target for patients with triple-negative breast cancer.

Purpose: We aimed to analysis the impact of chemotherapy and establish prediction models of prognosis in early elderly triple negative breast cancer (eTNBC) by using machine learning. Methods: We enrolled 4,696 patients in SEER Database who were 70 years or older, diagnosed with primary early TNBC(larger than 5 mm), from 2010 to 2016. The propensity-score matched method was utilized to reduce covariable imbalance. Univariable and multivariable analyses were used to compare breast cancer-specific survival(BCSS) and overall survival(OS). Nine models were developed by machine learning to predict the 5-year OS and BCSS for patients received chemotherapy. Results: Compared to matched patients in no-chemotherapy group, multivariate analysis showed a better survival in chemotherapy group. Stratified analyses by stage demonstrated that patients with stage II and stage III other than stage I could benefit from chemotherapy. Further investigation in stage II found that chemotherapy was a better prognostic indicator for patients with T2N0M0 and stage IIb, but not in T1N1M0. Patients with grade III could achieve a better survival by receiving chemotherapy, but those with grade I and II couldn’t. With 0.75 in 5-year BCSS and 0.81 in 5-year OS for AUC, the LightGBM outperformed other algorithms. Conclusion: For early eTNBC patients with stage I, T1N1M0 and grade I-II, chemotherapy couldn’t improve survival. Therefore, de-escalation therapy might be appropriate for selected patients. The LightGBM is a trustful model to predict the survival and provide precious systemic treatment for patients received chemotherapy.

Purpose: The aim of our study was to evaluate the different clinicopathological characteristics and prognostic factors for occult and non-occult breast cancer. Methods: 572 OBC cases and 117,217 non-OBC patients between 2004 and 2015 was selected from Surveillance, Epidemiology, and End Results (SEER) database. We analyzed the clinicopathological characteristics and survival outcomes between OBC and non-OBC patients. Furthermore, the propensity score matching method was utilized to reduce the influences of baseline differences in demographic and clinical characteristics on outcome differences. Univariable and multivariable analyses were used to evaluate the prognostic factors of OBC patients. Results: Compared with non-OBC patients, OBC patients in this study presented a higher proportion of older age, American Joint Committee on Cancer (AJCC) N3 stage, estrogen receptor (ER)-negative status, progesterone receptor (PR)-negative status, and human epidermal growth factor receptor-2 (HER-2)-positive status, and underwent more chemotherapy. Multivariate analysis revealed a better survival in overall patients with OBC patients according to breast cancer-specific survival (BCSS) and overall survival (OS). Propensity score analysis also achieved a similar result for OBC patients. Stratified analyses by nodal status and molecular subtypes indicated that these survival advantage were mainly presented in patients with AJCC N2/N3 stage or hormone receptor (HR)-positive tumors. In addition, nodal status, HER-2 status, and radiation status were demonstrated to be three independent prognostic factors for OBC patients. Conclusion: Patients with OBC retained exclusive clinical characteristics and were shown to have a better outcome compared with non-OBC patients, especially for those with N2/N3 stage or HR-positive tumors.

Background: Small tyrosine kinase inhibitors (TKIs) show activity against breast cancer brain metastases (BCBM) of the human epidermal growth factor receptor 2 (HER2)‐positive subtype. This meta‐analysis aimed to objectively explore the efficacy and safety of TKIs.Methods: Electronic databases were searched for relevant clinical trials. We conducted a pairwise meta‐analysis, pooled analysis, and estimated summary survival curves to compare survival outcomes following TKIs therapy for BCBM patients using Stata version 16.0 or R x64 4.0.5.Results: Thirteen clinical trials involving 987 HER2‐positive BCBM patients were analyzed. A trend of longer progression‐free survival (PFS) was observed in the TKI‐containing arm compared to the non‐TKI‐containing arm (hazard ratio = 0.64, 95% confidence interval [CI]: 0.35–1.15, p = 0.132), although the difference is not statistically significant. Summary survival curves reported the summary median PFS and overall survival were 7.9 months and 12.3 months. Subgroup analysis revealed that TKIs combined with capecitabine (TKI + Cap) regimens resulted in improved survival outcomes. Tucatinib may be more effective in BCBM patients. The main grade 3–5 adverse events (AEs) were diarrhea (22%, 95% CI: 14%–32%), neutropenia (11%, 95% CI: 5%–18%), hepatic toxicity (7%, 95% CI: 1%–16%), and sensory neuropathy (6%, 95% CI: 2%–12%).Conclusion: TKIs therapy improved the survival outcomes of HER2‐positive BCBM patients, especially when combined with capecitabine and tolerable AEs. We also identified the clinical value of tucatinib, which appears to be the most favorable TKI drug for BCBM patients.

Aims: Currently, there are many approaches available for neoadjuvant therapy for human epidermal growth factor receptor 2 (HER2)-positive breast cancer that improve therapeutic efficacy but are also controversial. We conducted a two-step Bayesian network meta-analysis (NMA) to compare odds ratios (ORs) for pathologic complete response (PCR) and safety endpoints. Methods: The Cochrane Central Register of Controlled Trials, PubMed, Embase, and online abstracts from the American Society of Clinical Oncology and San Antonio Breast Cancer Symposium were searched comprehensively and systematically. Phase II/III randomised clinical trials for targeted therapy in at least one arm were included. Results: A total of 9779 published manuscripts were identified, and 36 studies including 10,379 patients were finally included in our analysis. The NMA of PCR showed that dual-target therapy is better than single-target therapy and combination chemotherapy is better than monochemotherapy. However, anthracycline did not bring extra benefits, whether combined with dual-target therapy or single-target therapy. On the other hand, the addition of endocrine therapy in the HER2-positive, hormone receptor (HR)-positive subgroup might have additional beneficial effects but without significant statistical difference. By performing a conjoint analysis of the PCR rate and safety endpoints, we found that ‘trastuzumab plus pertuzumab’ and ‘T-DM1 containing regimens’ were well balanced in terms of efficacy and toxicity in all target regimens. Conclusion: In summary, trastuzumab plus pertuzumab-based dual-target therapy with combination chemotherapy regimens showed the highest efficacy of all optional regimens. They also achieved the best balance between efficacy and toxicity. As our study showed that anthracycline could be replaced by carboplatin, we strongly recommended TCbHP as the preferred choice for neoadjuvant treatment of HER2-positive breast cancer. We also look forward to the potential value of T-DM1 in improving outcomes, which needs further study in future trials.

Invasive micropapillary carcinoma (IMPC) is a rare subtype of breast cancer. Its epidemiological features, treatment principles, and prognostic factors remain controversial. This study aimed to develop an improved machine learning-based model to predict the prognosis of patients with invasive micropapillary carcinoma. A total of 1123 patients diagnosed with IMPC after surgery between 1998 and 2019 were identified from the Surveillance, Epidemiology, and End Results (SEER) database for survival analysis. Univariate and multivariate analyses were performed to explore independent prognostic factors for the overall and disease-specific survival of patients with IMPC. Five machine learning algorithms were developed to predict the 5-year survival of these patients. Cox regression analysis indicated that patients aged > 65 years had a significantly worse prognosis than those younger in age, while unmarried patients had a better prognosis than married patients. Patients diagnosed between 2001 and 2005 had a significant risk reduction of mortality compared with other periods. The XGBoost model outperformed the other models with a precision of 0.818 and an area under the curve of 0.863. A machine learning model for IMPC in patients with breast cancer was developed to estimate the 5-year OS. The XGBoost model had a promising performance and can help clinicians determine the early prognosis of patients with IMPC; therefore, the model can improve clinical outcomes by influencing management strategies and patient health care decisions.