The T-cell surface molecule TIGIT is an immune checkpoint molecule that inhibits T-cell responses, but its roles in cancer are little understood. In this study, we evaluated the role TIGIT checkpoint plays in the development and progression of gastric cancer. We show that the percentage of CD8 T cells that are TIGIT⁺ was increased in gastric cancer patients compared with healthy individuals. These cells showed functional exhaustion with impaired activation, proliferation, cytokine production, and metabolism, all of which were rescued by glucose. In addition, gastric cancer tissue and cell lines expressed CD155, which bound TIGIT receptors and inactivated CD8 T cells. In a T cell–gastric cancer cell coculture system, gastric cancer cells deprived CD8 T cells of glucose and impaired CD8 T-cell effector functions; these effects were neutralized by the additional glucose or by TIGIT blockade. In gastric cancer tumor cells, CD155 silencing increased T-cell metabolism and IFNγ production, whereas CD155 overexpression inhibited T-cell metabolism and IFNγ production; this inhibition was neutralized by TIGIT blockade. Targeting CD155/TIGIT enhanced CD8 T-cell reaction and improved survival in tumor-bearing mice. Combined targeting of TIGIT and PD-1 further enhanced CD8 T-cell activation and improved survival in tumor-bearing mice. Our results suggest that gastric cancer cells inhibit CD8 T-cell metabolism through CD155/TIGIT signaling, which inhibits CD8 T-cell effector functions, resulting in hyporesponsive antitumor immunity. These findings support the candidacy of CD155/TIGIT as a potential therapeutic target in gastric cancer. Cancer Res; 77(22); 6375–88. ©2017 AACR.

The success of checkpoint inhibitors in cancer treatment is associated with the infiltration of tissue-resident memory T (Trm) cells. In this study, we found that about 30% of tumor-infiltrating lymphocytes (TIL) in the tumor microenvironment of gastric adenocarcinoma were CD69+CD103+ Trm cells. Trm cells were low in patients with metastasis, and the presence of Trm cells was associated with better prognosis in patients with gastric adenocarcinoma. Trm cells expressed high PD-1, TIGIT, and CD39 and represented tumor-reactive TILs. Instead of utilizing glucose, Trm cells relied on fatty acid oxidation for cell survival. Deprivation of fatty acid resulted in Trm cell death. In a tumor cell–T-cell coculture system, gastric adenocarcinoma cells outcompeted Trm cells for lipid uptake and induced Trm cell death. Targeting PD-L1 decreased fatty acid binding protein (Fabp) 4 and Fabp5 expression in tumor cells of gastric adenocarcinoma. In contrast, the blockade of PD-L1 increased Fabp4/5 expression in Trm cells, promoting lipid uptake by Trm cells and resulting in better survival of Trm cells in vitro and in vivo. PD-L1 blockade unleashed Trm cells specifically in the patient-derived xenograft (PDX) mice. PDX mice that did not respond to PD-L1 blockade had less Trm cells than responders. Together, these data demonstrated that Trm cells represent a subset of TILs in the antitumor immune response and that metabolic reprogramming could be a promising way to prolong the longevity of Trm cells and enhance antitumor immunity in gastric adenocarcinoma.

Circulating tumor cells (CTCs) are cancer cells that break away from either a primary tumor or a metastatic site and circulate in the peripheral blood as the cellular origin of metastasis. With their role as a “tumor liquid biopsy”, CTCs provide convenient access to all disease sites, including that of the primary tumor and the site of fatal metastases. It is conceivable that detecting and analyzing CTCs will provide insightful information in assessing the disease status without the flaws and limitations encountered in performing conventional tumor biopsies. However, identifying CTCs in patient blood samples is technically challenging due to the extremely low abundance of CTCs among a large number of hematologic cells. To address this unmet need, there have been significant research endeavors, especially in the fields of chemistry, materials science, and bioengineering, devoted to developing CTC detection, isolation, and characterization technologies. Inspired by the nanoscale interactions observed in the tissue microenvironment, our research team at UCLA pioneered a unique concept of “NanoVelcro” cell-affinity substrates, in which CTC capture agent-coated nanostructured substrates were utilized to immobilize CTCs with high efficiency. The working mechanism of NanoVelcro cell-affinity substrates mimics that of Velcro: when the two fabric strips of a Velcro fastener are pressed together, tangling between the hairy surfaces on two strips leads to strong binding. Through continuous evolution, three generations (gens) of NanoVelcro CTC chips have been established to achieve different clinical utilities. The first-gen NanoVelcro chip, composed of a silicon nanowire substrate (SiNS) and an overlaid microfluidic chaotic mixer, was created for CTC enumeration. Side-by-side analytical validation studies using clinical blood samples suggested that the sensitivity of first-gen NanoVelcro chip outperforms that of FDA-approved CellSearch. In conjunction with the use of the laser microdissection (LMD) technique, second-gen NanoVelcro chips (i.e., NanoVelcro-LMD), based on polymer nanosubstrates, were developed for single-CTC isolation. The individually isolated CTCs can be subjected to single-CTC genotyping (e.g., Sanger sequencing and next-generation sequencing, NGS) to verify the CTC’s role as tumor liquid biopsy. Created by grafting of thermoresponsive polymer brushes onto SiNS, third-gen NanoVelcro chips (i.e., Thermoresponsive NanoVelcro) have demonstrated the capture and release of CTCs at 37 and 4 °C, respectively. The temperature-dependent conformational changes of polymer brushes can effectively alter the accessibility of the capture agent on SiNS, allowing for rapid CTC purification with desired viability and molecular integrity. This Account summarizes the continuous evolution of NanoVelcro CTC assays from the emergence of the original idea all the way to their applications in cancer research. We envision that NanoVelcro CTC assays will lead the way for powerful and cost-efficient diagnostic platforms for researchers to better understand underlying disease mechanisms and for physicians to monitor real-time disease progression.

Although Western diet and dysbiosis are the most prominent environmental factors associated with inflammatory bowel diseases (IBDs), the corresponding host factors and cellular mechanisms remain poorly defined. Here we report that the TSC1/mTOR pathway in the gut epithelium represents a metabolic and innate immune checkpoint for intestinal dysfunction and inflammation. mTOR hyperactivation triggered by Western diet or Tsc1 ablation led to epithelium necroptosis, barrier disruption, and predisposition to dextran sulfate sodium–induced colitis and inflammation-associated colon cancer. Mechanistically, our results uncovered a critical role for TSC1/mTOR in restraining the expression and activation of RIPK3 in the gut epithelium through TRIM11-mediated ubiquitination and autophagy-dependent degradation. Notably, microbiota depletion by antibiotics or gnotobiotics attenuated RIPK3 expression and activation, thereby alleviating epithelial necroptosis and colitis driven by mTOR hyperactivation. mTOR primarily impinged on RIPK3 to potentiate necroptosis induced by TNF and by microbial pathogen-associated molecular patterns (PAMPs), and hyperactive mTOR and aberrant necroptosis were intertwined in human IBDs. Together, our data reveal a previously unsuspected link between the Western diet, microbiota, and necroptosis and identify the mTOR/RIPK3/necroptosis axis as a driving force for intestinal inflammation and cancer.

Background: Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional histopathology to computational approaches, arousing a hype of deep learning methods for histopathological analysis. We aimed at exploring the potential of deep learning models in the identification of lung cancer subtypes and cancer mimics from WSIs. Methods: We initially obtained 741 WSIs from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) for the deep learning model development, optimization, and verification. Additional 318 WSIs from SYSUFH, 212 from Shenzhen People’s Hospital, and 422 from The Cancer Genome Atlas were further collected for multi-centre verification. EfficientNet-B5- and ResNet-50-based deep learning methods were developed and compared using the metrics of recall, precision, F1-score, and areas under the curve (AUCs). A threshold-based tumour-first aggregation approach was proposed and implemented for the label inferencing of WSIs with complex tissue components. Four pathologists of different levels from SYSUFH reviewed all the testing slides blindly, and the diagnosing results were used for quantitative comparisons with the best performing deep learning model. Results: We developed the first deep learning-based six-type classifier for histopathological WSI classification of lung adenocarcinoma, lung squamous cell carcinoma, small cell lung carcinoma, pulmonary tuberculosis, organizing pneumonia, and normal lung. The EfficientNet-B5-based model outperformed ResNet-50 and was selected as the backbone in the classifier. Tested on 1067 slides from four cohorts of different medical centres, AUCs of 0.970, 0.918, 0.963, and 0.978 were achieved, respectively. The classifier achieved high consistence to the ground truth and attending pathologists with high intraclass correlation coefficients over 0.873. Conclusions: Multi-cohort testing demonstrated our six-type classifier achieved consistent and comparable performance to experienced pathologists and gained advantages over other existing computational methods. The visualization of prediction heatmap improved the model interpretability intuitively. The classifier with the threshold-based tumour-first label inferencing method exhibited excellent accuracy and feasibility in classifying lung cancers and confused nonneoplastic tissues, indicating that deep learning can resolve complex multi-class tissue classification that conforms to real-world histopathological scenarios.

Organ-specific metastasis is the primary cause of cancer patient death. The distant metastasis of tumor cells to specific organs depends on both the intrinsic characteristics of the tumor cells and extrinsic factors in their microenvironment. During an intermediate stage of metastasis, circulating tumor cells (CTCs) are released into the bloodstream from primary and metastatic tumors. CTCs harboring aggressive or metastatic features can extravasate to remote sites for continuous colonizing growth, leading to further lesions. In the past decade, numerous studies demonstrated that CTCs exhibited huge clinical value including predicting distant metastasis, assessing prognosis and monitoring treatment response et al. Furthermore, increasingly numerous experiments are dedicated to identifying the key molecules on or inside CTCs and exploring how they mediate CTC-related organ-specific metastasis. Based on the above molecules, more and more inhibitors are being developed to target CTCs and being utilized to completely clean CTCs, which should provide promising prospects to administer advanced tumor. Recently, the application of various nanomaterials and microfluidic technologies in CTCs enrichment technology has assisted to improve our deep insights into the phenotypic characteristics and biological functions of CTCs as a potential therapy target, which may pave the way for us to make practical clinical strategies. In the present review, we mainly focus on the role of CTCs being involved in targeted organ metastasis, especially the latest molecular mechanism research and clinical intervention strategies related to CTCs.