The clinicopathological factors associated with disease progression in Luminal a breast cancer and characteristics of metastasis: A retrospective study from a single center in China

Background/Aim: This study investigated the clinicopathological factors associated with outcomes in patients with Luminal A breast cancer. Patients and Methods: Retrospective analysis of the association of clinicopathological factors and breast cancer outcome in 421 patients with newly diagnosed Luminal-A breast cancer that were enrolled from January 2008 to December 2014. Clinicopathological data were analyzed to validate the relationship with disease free survival (DFS) and overall survival (OS). Kaplan-Meier curves and log-rank tests were used to analyze the value of clinicopathological factors (tumor size, node status and lymphovascular invasion), and subsequent Cox regression analysis revealed significant prognostic factors. Results: With a median of 61 months follow up, the 5-year DFS and 5-year OS rate were 98.3% and 99.3%. Cox multivariate regression analysis showed that clinical anatomic stage, tumor size, status of lymph nodes, lymphovascular invasion and systemic treatment are strong prognostic factors for clinical outcome in patients with Luminal-A breast cancer. Of all 413 patients with stage I-III breast cancer, 14 presented with metastasis (3.4%) during the follow up. Bone (6/14, 42.9%) was the most common site of metastasis followed by liver (5/14, 35.7%) and lung (4/14, 28.6%). The median survival time after metastasis was 20.4 months. Of all the sites of distant metastasis, liver metastasis was the only factor that affected survival time after metastasis (χ2=6.263, p=0.012). Conclusion: Patients with Luminal A breast cancer have excellent outcomes. Liver metastasis is an important factor compressing the survival time after distant metastasis presents

Identification of Key Areas for Ecosystem Restoration Based on Ecological Security Pattern

Ecosystem degradation and conversion are leading to a widespread reduction in the provision of ecosystem services. It is crucial for the governance of regional land spaces to rapidly identify key areas for ecosystem restoration. Herein, we combined the InVEST Habitat Quality Model with the granularity inverse method to identify ecological sources in Jiashi county, China, based on the “source-corridor” ecological security pattern paradigm. The minimum cumulative resistance model and circuit theory were adopted to diagnose the ecological “pinch points”, barrier points, break points, and key restoration areas for land space. Our results show that: (1) the area of the ecological source and the total length of the ecological corridor were identified as 1331.13 km2 and 316.30 km, respectively; (2) there were 164 key ecological “pinch points” and 69 key ecological barrier points in Jiashi county, with areas of 15.13 km2 and 14.57 km2, respectively. Based on the above ecological security pattern, recovery strategies are put forward to improve regional ecosystem health. This study describes the best practices which can be used to guide the planning and implementation of ecosystem restoration at the local landscape scale

Identification of Key Areas for Ecosystem Restoration Based on Ecological Security Pattern

Ecosystem degradation and conversion are leading to a widespread reduction in the provision of ecosystem services. It is crucial for the governance of regional land spaces to rapidly identify key areas for ecosystem restoration. Herein, we combined the InVEST Habitat Quality Model with the granularity inverse method to identify ecological sources in Jiashi county, China, based on the “source-corridor” ecological security pattern paradigm. The minimum cumulative resistance model and circuit theory were adopted to diagnose the ecological “pinch points”, barrier points, break points, and key restoration areas for land space. Our results show that: (1) the area of the ecological source and the total length of the ecological corridor were identified as 1331.13 km2 and 316.30 km, respectively; (2) there were 164 key ecological “pinch points” and 69 key ecological barrier points in Jiashi county, with areas of 15.13 km2 and 14.57 km2, respectively. Based on the above ecological security pattern, recovery strategies are put forward to improve regional ecosystem health. This study describes the best practices which can be used to guide the planning and implementation of ecosystem restoration at the local landscape scale

A hybrid GMDH neural network and logistic regression framework for state parameter-based liquefaction evaluation

The cyclic stress or liquefaction behavior of granular materials is strongly affected by the relative density and confining pressure of the soil. In this study, the state parameter accounting for both relative density and effective stress was used to evaluate soil liquefaction potential. Based on case histories along with the cone penetration test (CPT) database, models for calculating the state parameter using a group method of data handling (GMDH) neural network were developed and recommended according to their performance. The state parameter was then used to develop a state parameter-based probabilistic liquefaction evaluation method using a logistic regression model. From a conservative point of view, the boundary curve of 20% probability of liquefaction was suggested as a deterministic criterion for state parameter-based liquefaction evaluation. Subsequently, a mapping function relating the calculated factor of safety (FS) to the probability of liquefaction (PL) was proposed based on the compiled CPT database. Based on the developed PL–FS function, a new risk criterion associated with the state parameter-based design chart was proposed. Finally, a flowchart of state-based probabilistic liquefaction evaluation and quality control for ground-improvement projects was presented for the benefit of practitioners.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

DataSheet_1_An efficient Agrobacterium-mediated transient transformation system and its application in gene function elucidation in Paeonia lactiflora Pall.docx

Paeonia lactiflora Pall. is known as the king of herbaceous flowers with high ornamental and precious medicinal value. However, the lack of a stable genetic transformation system has greatly affected the research of gene function in P. lactiflora. The Agrobacterium-mediated transient gene expression is a powerful tool for the characterization of gene function in plants. In this study, the seedlings of P. lactiflora were used as the transformation receptor materials, and the efficient transient transformation system with a GUS reporter gene was successfully established by Agrobacterium harboring pCAMBIA1301. To optimize the system, we investigated the effects of germination time, Agrobacterium cell density, infection time, acetosyringone (AS) concentration, co-culture time, negative pressure intensity, Tween-20 concentration and different receptor materials on the transient transformation efficiency of P. lactiflora. The results showed that the highest transient transformation efficiency (93.3%) could be obtained when seedlings in 2-3 cm bud length were subjected to 12 h infection of resuspension solution comprising 1.2 OD600Agrobacterium, 200 μM AS and 0.01% Tween-20 under 10 of negative pressure intensity followed by 3 days of co-culture in darkness condition. This method is more suitable for the study of gene function in P. lactiflora. Subsequently, stress resistance genes PlGPAT, PlDHN2 and PlHD-Zip were used to verify the effectiveness of this transformation system. These results can provide critical information for identification of key genes in non-model plants, such as P. lactiflora, and promote the development of molecular biology research for P. lactiflora.</p

Androgen Receptor: A New Marker to Predict Pathological Complete Response in HER2-Positive Breast Cancer Patients Treated with Trastuzumab Plus Pertuzumab Neoadjuvant Therapy

(1) Background: Neoadjuvant therapy is the main therapeutic strategy for human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients, and the combination of trastuzumab and pertuzumab (HP) has become a routine treatment. How to predict and screen patients who are less likely to respond to neoadjuvant therapy is the focus of research. The androgen receptor (AR) is a biomarker that is widely expressed in all breast cancer subtypes and is probably related to treatment response and prognosis. In this study, we investigated the relationship between AR expression and treatment response in HER2-positive breast cancer patients treated with HP neoadjuvant therapy. (2) Methods: We evaluated early breast cancer patients treated with HP neoadjuvant therapy from Jan. 2019 to Oct. 2020 at Peking University First Hospital Breast Cancer Center. The inclusion criteria were as follows: early HER2-positive breast cancer patients diagnosed by core needle biopsy who underwent both HP neoadjuvant therapy and surgery. We compared the clinical and pathological features between pathological complete response (pCR) and non-pCR patients. (3) Results: We included 44 patients. A total of 90.9% of patients received neoadjuvant therapy of taxanes, carboplatin, trastuzumab and pertuzumab (TCHP), and the total pCR rate was 50%. pCR was negatively related to estrogen receptor (ER) positivity (OR 0.075 [95% confidence interval (CI) 0.008–0.678], p = 0.021) and positively related to high expression levels of AR (OR 33.145 [95% CI 2.803–391.900], p = 0.005). We drew a receiver operating characteristic (ROC) curve to assess the predictive value of AR expression for pCR, and the area under the curve was 0.737 (95% CI 0.585–0.889, p = 0.007). The optimal cutoff of AR for predicting pCR was 85%. (4) Conclusion: AR is a potential marker for the prediction of pCR in HER2-positive breast cancer patients treated with HP neoadjuvant therapy

Correlation between Androgen Receptor Expression in Luminal B (HER–2 Negative) Breast Cancer and Disease Outcomes

(1) Background: Hormone receptor positive breast cancer is a subtype of breast cancer with relatively good prognosis, but luminal B (HER–2 negative) breast cancer has a higher risk of recurrence and metastasis. Patients with endocrine therapy resistance and chemotherapy insensitivity have poor prognosis. Androgen receptor (AR) is widely expressed in breast cancer, but there is no clear conclusion about its function and correlation with prognosis in luminal B breast cancer. Further research is needed to reveal the role of AR in luminal B (HER–2 negative) breast cancer. (2) Methods: Retrospectively analyzed patients with early–stage luminal B breast cancer. The correlation between AR and its associated indexes with long–term survival was determined. (3) Results: A total of 985 patients were included with 143 treated by neoadjuvant therapy. Of these, 83.5% of the patients had AR expression ≥65%. High AR expression was associated with good disease–free survival (DFS) and overall survival (OS). In the neoadjuvant population, AR/estrogen receptor (ER) > 1.06 and residual tumor Ki67 > 23% had significantly worse DFS. (4) Conclusion: Low AR ( 1.06 and residual tumor Ki67 > 23%

A multicenter study of the clinicopathological characteristics and a risk prediction model of early-stage breast cancer with hormone receptor-positive/human epidermal growth factor receptor 2-low expression

Abstract. Background:. In light of the significant clinical benefits of antibody-drug conjugates in clinical trials, the human epidermal growth factor receptor 2 (HER2)-low category in breast cancers has gained increasing attention. Therefore, we studied the clinicopathological characteristics of Chinese patients with hormone receptor (HR)-positive/HER2-low early-stage breast cancer and developed a recurrence risk prediction model. Methods:. Female patients with HR-positive/HER2-low early-stage breast cancer treated in 29 hospitals of the Chinese Society of Breast Surgery (CSBrS) from Jan 2015 to Dec 2016 were enrolled. Their clinicopathological data and prognostic information were collected, and machine learning methods were used to analyze the prognostic factors. Results:. In total, 25,096 patients were diagnosed with breast cancer in 29 hospitals of CSBrS from Jan 2015 to Dec 2016, and clinicopathological data for 6486 patients with HER2-low early-stage breast cancer were collected. Among them, 5629 patients (86.79%) were HR-positive. The median follow-up time was 57 months (4, 76 months); the 5-year disease-free survival (DFS) rate was 92.7%, and the 5-year overall survival (OS) rate was 97.7%. In total, 412 cases (7.31%) of metastasis were observed, and 124 (2.20%) patients died. Multivariate Cox regression analysis revealed that T stage, N stage, lymphovascular thrombosis, Ki-67 index, and prognostic stage were associated with recurrence and metastasis (P <0.05). A recurrence risk prediction model was established using the random forest method and exhibited a sensitivity of 81.1%, specificity of 71.7%, positive predictive value of 74.1%, and negative predictive value of 79.2%. Conclusion:. Most of patients with HER2-low early-stage breast cancer were HR-positive, and patients had favorable outcome; tumor N stage, lymphovascular thrombosis, Ki-67 index, and tumor prognostic stage were prognostic factors. The HR-positive/HER2-low early-stage breast cancer recurrence prediction model established based on the random forest method has a good reference value for predicting 5-year recurrence events. Registritation:. ChiCTR.org.cn, ChiCTR210004676

Multicenter study of the clinicopathological features and recurrence risk prediction model of early-stage breast cancer with low-positive human epidermal growth factor receptor 2 expression in China (Chinese Society of Breast Surgery 021)

Abstract. Background. : Breast cancer with low-positive human epidermal growth factor receptor 2 (HER2) expression has triggered further refinement of evaluation criteria for HER2 expression. We studied the clinicopathological features of early-stage breast cancer with low-positive HER2 expression in China and analyzed prognostic factors. Methods. : Clinical and pathological data and prognostic information of patients with early-stage breast cancer with low-positive HER2 expression treated by the member units of the Chinese Society of Breast Surgery and Chinese Society of Surgery of Chinese Medical Association, from January 2015 to December 2016 were collected. The prognostic factors of these patients were analyzed. Results. : Twenty-nine hospitals provided valid cases. From 2015 to 2016, a total of 25,096 cases of early-stage breast cancer were treated, 7642 (30.5%) of which had low-positive HER2 expression and were included in the study. After ineligible cases were excluded, 6486 patients were included in the study. The median follow-up time was 57 months (4–76 months). The disease-free survival rate was 92.1% at 5 years, and the overall survival rate was 97.4% at 5 years. At the follow-up, 506 (7.8%) cases of metastasis and 167 (2.6%) deaths were noted. Multivariate Cox regression analysis showed that tumor stage, lymphvascular invasion, and the Ki67 index were related to recurrence and metastasis (P < 0.05). The recurrence risk prediction model was established using a machine learning model and showed that the area under the receiving operator characteristic curve was 0.815 (95% confidence interval: 0.750–0.880). Conclusions. : Early-stage breast cancer patients with low-positive HER2 expression account for 30.5% of all patients. Tumor stage, lymphvascular invasion, and the Ki67 index are factors affecting prognosis. The recurrence prediction model for breast cancer with low-positive HER2 expression based on a machine learning model had a good clinical reference value for predicting the recurrence risk at 5 years. Trial registration. : ChiCTR.org.cn, ChiCTR2100046766