Establishment of patient-derived organoids for guiding personalized therapies in breast cancer patients.

Breast cancer has become the most commonly diagnosed cancer. The intra- and interpatient heterogeneity induced a considerable variation in treatment efficacy. There is an urgent requirement for preclinical models to anticipate the effectiveness of individualized drug responses. Patient-derived organoids (PDOs) can accurately recapitulate the architecture and biological characteristics of the origin tumor, making them a promising model that can overtake many limitations of cell lines and PDXs. However, it is still unclear whether PDOs-based drug testing can benefit breast cancer patients, particularly those with tumor recurrence or treatment resistance. Fresh tumor samples were surgically resected for organoid culture. Primary tumor samples and PDOs were subsequently subjected to H&E staining, immunohistochemical (IHC) analysis, and whole-exome sequencing (WES) to make comparisons. Drug sensitivity tests were performed to evaluate the feasibility of this model for predicting patient drug response in clinical practice. We established 75 patient-derived breast cancer organoid models. The results of H&E staining, IHC, and WES revealed that PDOs inherited the histologic and genetic characteristics of their parental tumor tissues. The PDOs successfully predicted the patient's drug response, and most cases exhibited consistency between PDOs' drug susceptibility test results and the clinical response of the matched patient. We conclude that the breast cancer organoids platform can be a potential preclinical tool used for the selection of effective drugs and guided personalized therapies for patients with advanced breast cancer.

Super-ARMS: A new method for plasma ESR1 mutation detection.

BACKGROUND: ESR1 mutation is an important mechanism of drug resistance and recurrence in hormone receptor-positive breast cancer patients during AI treatment. Patient could still benefit from treatment with fulvestrant after ESR1 mutated. OBJECTIVE: At present, there is still no suitable method to detect ESR1 mutation in plasma as clinical promotion method. We aim to improve from ARMS-PCR to get a method with higher sensitivity but no additional cost is incurred. METHODS: We designed new primers for ESR1. Then positive and negative standard sample was used for sensitivity and specificity tests. Lastly, we collected patient peripheral blood sample and analyzed the performance of Super-ARMS in plasma ctDNA samples. RESULTS: A total of 207 patients were enrolled in this study, including 142 prime breast cancer (PBC) patients and 65 metastasis breast cancer(MBC) patients. The mutation rate was as high as 27.9%(12/43) in MBC patients with AI treatment. But only 2.97%(3/101) in PBC patients with AI and 0% in both MBC or PBC patient without AI. There was no significant difference in Super-ARMS results compared with DDPCR method. CONCLUSION: Super-ARMS is a method that has sensitivity close to DDPCR and has the convenience and low price of ARMS-PCR for plasma ctDNA ESR1 mutation detection. It has obvious advantages compared with other method such NGS and DDPCR as clinical promotion method.

CircDENND2A Promotes Non-small Cell Lung Cancer Progression via Regulating MiR-34a/CCNE1 Signaling.

The mechanism regulating non-small cell lung cancers (NSCLCs) is unclear. In this study, we aimed to determine the roles of DENN domain containing 2A (circDENND2A) in the progression of NSCLC. Circular RNAs (circRNAs) are composited by "head to tail" splicing of coding or non-coding RNAs (ncRNAs), whose crucial roles in human cancers had been revealed. CircDENND2A, a new circRNA, was revealed to induce cell proliferation and migration. Our data indicated that circDENND2A was a probable oncogene in human cancers. However, the roles of circDENND2A in NSCLC remained unknown. Here, we demonstrated that circDENND2A was down-regulated in NSCLC samples. Loss-of-function assays showed circDENND2A knockdown suppressed cell growth via inducing cell cycle arrest and apoptosis and inhibited cell migration and invasion. Bioinformatics analysis and competing endogenous RNA (ceRNA) network analysis revealed that circDENND2A was involved in regulating cell cycle and tumor protein p53 (TP53) signaling via miR-34a/CCNE1 (cyclin E1). Further validation showed that circDENND2A could directly bind to miR-34a, promoting CCNE1 expression in NSCLC. In addition, rescue assays demonstrated that restoration of CCNE1 significantly impaired the suppressive effects of circDENND2A silencing in terms of NSCLC growth, migration, and invasion. We thought this study indicated that circDENND2A/miR-34a/CCNE1 may be a potential therapeutic target for NSCLC.

Concordance between treatment recommendations provided by IBM Watson for Oncology and a multidisciplinary tumor board for breast cancer in China.

OBJECTIVE: Watson for Oncology (WFO), an artificial intelligence from IBM Corporation, can provide a treatment plan by analyzing patient's disease characteristics. The present study was performed to examine the concordance between treatment recommendations proposed by WFO and the multidisciplinary tumor board at our center. The aim was to explore the feasibility of using WFO for breast cancer cases in China and to ascertain the ways to make WFO more suitable for Chinese patients with breast cancer. METHODS: Data from 302 breast cancer patients treated at the Second Affiliated Hospital of Xi'an Jiaotong University between October 2016 and February 2018 was retrieved and retrospectively analyzed by WFO. The recommendations were divided into 'recommended', 'considered' and 'not recommended' groups. Results were considered concordant when oncologists' recommendations were categorized as 'recommended' or 'for consideration' by WFO. RESULTS: The concordance rate of 200 subjects with postoperative adjuvant therapy was 77%. However, the rate was 27.5% in the remaining 102 cases with metastatic disease receiving either first-line or no treatment. Further analysis demonstrated that inconsistencies were mainly due to different choices of chemotherapy regimens. Subgroup study indicates that tumor stage, receptor status and age also had influences at the concordance rate. CONCLUSION: The results of this study suggest that WFO is a promising artificial intelligence system for the treatment of breast cancer. These findings can also serve as a reference framework for the inclusion of artificial intelligence in the ongoing medical reform in China.