Drug resistance and tumor recurrence are major challenges in cancer treatment. Cancer cells often display centrosome amplification. To maintain survival, cancer cells achieve bipolar division by clustering supernumerary centrosomes. Targeting centrosome clustering is therefore considered a promising therapeutic strategy. However, the regulatory mechanisms of centrosome clustering remain unclear. Here we report that KIFC1, a centrosome clustering regulator, is positively associated with tumor recurrence. Under DNA damaging treatments, the ATM and ATR kinases phosphorylate KIFC1 at Ser26 to selectively maintain the survival of cancer cells with amplified centrosomes via centrosome clustering, leading to drug resistance and tumor recurrence. Inhibition of KIFC1 phosphorylation represses centrosome clustering and tumor recurrence. This study identified KIFC1 as a prognostic tumor recurrence marker, and revealed that tumors can acquire therapeutic resistance and recurrence via triggering centrosome clustering under DNA damage stresses, suggesting that blocking KIFC1 phosphorylation may open a new vista for cancer therapy.

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It is difficult to predict RBC consumption accurately. This paper aims to use big data to establish a XGBoost Model to understand the trend of RBC accurately, and forecast the demand in time. XGBoost, which implements machine learning algorithms under the Gradient Boosting framework can provide a parallel tree boosting. The daily RBC usage and inventory (May 2014-September 2017) were investigated, and rules for RBC usage were analysed. All data were divided into training sets and testing sets. A XGBoost Model was established to predict the future RBC demand for durations ranging from a day to a week. In addition, the alert range was added to the predicted value to ensure RBC demand of emergency patients and surgical accidents. The gap between RBC usage and inventory was fluctuant, and had no obvious rule. The maximum residual inventory of a certain blood group was up to 700 units one day, while the minimum was nearly 0 units. Upon comparing MAE (mean absolute error):A:10.69, B:11.19, O:10.93, and AB:5.91, respectively, the XGBoost Model was found to have a predictive advantage over other state-of-the-art approaches. It showed the model could fit the trend of daily RBC usage. An alert range could manage the demand of emergency patients or surgical accidents. The model had been built to predict RBC demand, and the alert range of RBC inventory is designed to increase the safety of inventory management.