Bo Liu

Bioengineered Escherichia coli Nissle 1917 with hematoporphyrin monomethyl ether for targeted sonodynamic therapy and immune activation in ovarian cancer

This bioengineered probiotic platform HW@EcN presents a clinically translatable approach for improving SDT efficacy and immune activation, paving the way for innovative cancer therapies.

Multi-Class Segmentation Network Based on Tumor Tissue in Endometrial Cancer Pathology Images

Endometrial cancer has the second highest incidence of malignant tumors in the female reproductive system. Accurate and efficient analysis of endometrial cancer pathology images is one of the important research components of computer-aided diagnosis. However, endometrial cancer pathology images have challenges such as smaller solid tumors, lesion areas varying in morphology, and difficulty distinguishing solid and nonsolid tumors, which would affect the accuracy of subsequent pathologic analyses. An Endometrial Cancer Multi-class Transformer Network (ECMTrans-net) is therefore proposed herein to improve the segmentation accuracy of endometrial cancer pathology images. An ECM-Attention module can sequentially infer attention maps along three separate dimensions (channel, local spatial, and global spatial) and multiply the attention maps and the input feature map for adaptive feature refinement. This approach may solve the problems of the small size of solid tumors and similar characteristics of solid tumors to nonsolid tumors and further improve the accuracy of segmentation of solid tumors. In addition, an ECM-Transformer module is proposed, which can fuse multi-class feature information and dynamically adjust the receptive field, solving the issue of complex tumor features. Experiments on the Solid Tumor Endometrial Cancer Pathological (ST-ECP) data set found that performance of the ECMTrans-net was superior to state-of-the-art image segmentation methods, and the average values of accuracy, Mean Intersection over Union, precision, and Dice coefficients were 0.952, 0.927, 0.931, and 0.901, respectively.

Abemaciclib sensitizes HPV‐negative cervical cancer to chemotherapy via specifically suppressing CDK4/6‐Rb‐E2F and mTOR pathways

ABSTRACTCervical cancer is the second most common malignancy in women, and the novel therapeutic treatment is needed. Abemaciclib is a FDA‐approved drug for breast cancer treatment. In this work, we identified that abemaciclib has potent anti‐cervical cancer activity. We demonstrate that abemaciclib is the most effective drug against human papillomavirus (HPV)‐negative cervical cancer cells compared to ribociclib and palbociclib, with its IC50 at nanomolar concentration range. This is achieved by the inhibition of proliferation and induction of apoptosis, through specifically suppressing CDK4/6‐Rb‐E2F and mTOR pathways by abemaciclib in HPV‐negative cervical cancer cells. Of note, the combination of abemaciclib with paclitaxel and cisplatin at sublethal concentration results in much greater efficacy than chemotherapy alone. In addition, we confirm the efficacy of abemaciclib and its combination with paclitaxel or cisplatin at the doses that are not toxic to mice in HPV‐negative cervical cancer xenograft mouse model. Interestingly, we show that abemaciclib and other CDK4/6 inhibitors are not effective in targeting HPV‐positive cervical cancer cells, and this is likely to be associated with the high p16 and low Rb expression in HPV‐positive cervical cancer cells. Our work is the first to provide the preclinical evidence to demonstrate the potential of abemaciclib for the treatment of HPV‐negative cervical cancer. The mechanism analysis highlights the therapeutic value of inhibiting CDK4/6 in HPV‐negative but not HPV‐positive cervical cancer.