A PDA-Functionalized 3D Lung Scaffold Bioplatform to Construct Complicated Breast Tumor Microenvironment for Anticancer Drug Screening and Immunotherapy.

2D cell culture occupies an important place in cancer progression and drug discovery research. However, it limitedly models the "true biology" of tumors in vivo. 3D tumor culture systems can better mimic tumor characteristics for anticancer drug discovery but still maintain great challenges. Herein, polydopamine (PDA)-modified decellularized lung scaffolds are designed and can serve as a functional biosystem to study tumor progression and anticancer drug screening, as well as mimic the tumor microenvironment. PDA-modified scaffolds with strong hydrophilicity and excellent cell compatibility can promote cell growth and proliferation. After 96 h treatment with 5-FU, cisplatin, and DOX, higher survival rates in PDA-modified scaffolds are observed compared to nonmodified scaffolds and 2D systems. The E-cadhesion formation, HIF-1α-mediated senescence decrease, and tumor stemness enhancement can drive drug resistance and antitumor drug screening of breast cancer cells. Moreover, there is a higher survival rate of CD45+ /CD3+ /CD4+ /CD8+ T cells in PDA-modified scaffolds for potential cancer immunotherapy drug screening. This PDA-modified tumor bioplatform will supply some promising information for studying tumor progression, overcoming tumor resistance, and screening tumor immunotherapy drugs.

An improved cell line-derived xenograft humanized mouse model for evaluation of PD-1/PD-L1 blocker BMS202-induced immune responses in colorectal cancer.

The establishment of an in vivo mouse model mimicking human tumor-immune environments provides a promising platform for immunotherapy assessment, drug discovery and clinical decision guidance. To this end, we construct humanized NCG mice by transplanting human hCD34 + hematopoietic progenitors into non-obese diabetic (NOD) Cg- Prkdc scidIL2rg tm1Wjl /Sz (null; NCG) mice and monitoring the development of human hematopoietic and immune systems (Hu-NCG). The cell line-derived xenograft (CDX) Hu-NCG mouse models are set up to assess the outcome of immunotherapy mediated by the small molecule BMS202. As a PD-1/PD-L1 blocker, BMS202 shows satisfactory antitumour efficacy in the HCT116 and SW480 xenograft Hu-NCG mouse models. Mechanistically, BMS202 exerts antitumour efficacy by improving the tumor microenvironment and enhancing the infiltration of hCD8 + T cells and the release of hIFNγ in tumor tissue. Thus, tumor-bearing Hu-NCG mice are a suitable and important in vivo model for preclinical study, particularly in cancer immunotherapy.

AbdomenCT-1K: Is Abdominal Organ Segmentation a Solved Problem?

With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain single-center, single-phase, single-vendor, or single-disease cases, and it is unclear whether the excellent performance can generalize on diverse datasets. This paper presents a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.