Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a long non-coding RNA (lncRNA), is associated with metastasis and is an independent prognostic factor for lung cancer. Recent studies have demonstrated that MALAT1 plays an important role in other malignancies. However, little is known about the role of MALAT1 in gallbladder carcinoma (GBC), which is the most common cancer of the biliary tract and has an extremely poor prognosis. In this study, we focused on the expression, biological functions and mechanism of MALAT1 in GBC and found that MALAT1 was significantly upregulated in GBC tissues compared with corresponding non-cancerous tissues. Knockdown of MALAT1 in GBC cell lines using lentivirus-mediated RNA interference significantly inhibited the proliferation and metastasis of the GBC cells both in vitro and in vivo. Furthermore, ERK/MAPK pathway was found to be inactivated in the GBC cell lines after MALAT1 knockdown. These results indicated that MALAT1 might serve as an oncogenic lncRNA ...

Our previous genomic whole-exome sequencing (WES) data identified the key ErbB pathway mutations that play an essential role in regulating the malignancy of gallbladder cancer (GBC). Herein, we tested the hypothesis that individual cellular components of the tumor microenvironment (TME) in GBC function differentially to participate in ErbB pathway mutation-dependent tumor progression. We engaged single-cell RNA-sequencing to reveal transcriptomic heterogeneity and intercellular crosstalk from 13 human GBCs and adjacent normal tissues. In addition, we performed WES analysis to reveal the genomic variations related to tumor malignancy. A variety of bulk RNA-sequencing, immunohistochemical staining, immunofluorescence staining and functional experiments were employed to study the difference between tissues with or without ErbB pathway mutations. We identified 16 cell types from a total of 114,927 cells, in which epithelial cells, M2 macrophages, and regulatory T cells were predominant in tumors with ErbB pathway mutations. Furthermore, epithelial cell subtype 1, 2 and 3 were mainly found in adenocarcinoma and subtype 4 was present in adenosquamous carcinoma. The tumors with ErbB pathway mutations harbored larger populations of epithelial cell subtype 1 and 2, and expressed higher levels of secreted midkine (MDK) than tumors without ErbB pathway mutations. Increased MDK resulted in an interaction with its receptor LRP1, which is expressed by tumor-infiltrating macrophages, and promoted immunosuppressive macrophage differentiation. Moreover, the crosstalk between macrophage-secreted CXCL10 and its receptor CXCR3 on regulatory T cells was induced in GBC with ErbB pathway mutations. Elevated MDK was correlated with poor overall survival in patients with GBC. This study has provided valuable insights into transcriptomic heterogeneity and the global cellular network in the TME, which coordinately functions to promote the progression of GBC with ErbB pathway mutations; thus, unveiling novel cellular and molecular targets for cancer therapy. We employed single-cell RNA-sequencing and functional assays to uncover the transcriptomic heterogeneity and intercellular crosstalk present in gallbladder cancer. We found that ErbB pathway mutations reduced anti-cancer immunity and led to cancer development. ErbB pathway mutations resulted in immunosuppressive macrophage differentiation and regulatory T cell activation, explaining the reduced anti-cancer immunity and worse overall survival observed in patients with these mutations.

Objectives: Patients with gallbladder carcinoma (GBC) lack effective treatment methods largely due to the inadequacy of both molecular characterisation and potential therapeutic targets. We previously uncovered a spectrum of genomic alterations and identified recurrent mutations in the ErbB pathway in GBC. Here, we aimed to study recurrent mutations of genes and pathways in a larger cohort of patients with GBC and investigate the potential mechanisms and clinical significance of these mutations. Design: We performed whole-exome sequencing (WES) in 157 patients with GBC. Functional experiments were applied in GBC cell lines to explore the oncogenic roles of ERBB2 / ERBB3 hotspot mutations, their correlation with PD-L1 expression and the underlying mechanisms. ERBB inhibitors and a PD-L1 blocker were used to evaluate the anticancer activities in co-culture systems in vitro and in vivo. Results: WES identified ERBB2 and ERBB3 mutations at a frequency of 7%–8% in the expanded cohort, and patients with ERBB2 / ERBB3 mutations exhibited poorer prognoses. A set of in vitro and in vivo experiments revealed increased proliferation/migration on ERBB2 / ERBB3 mutation. Ectopic expression of ERBB2/ERBB3 mutants upregulated PD-L1 expression in GBC cells, effectively suppressed normal T-cell-mediated cytotoxicity in vitro through activation of the PI3K/Akt signalling pathway and contributed to the growth and progression of GBC in vivo. Treatment with an ERBB2/ERBB3 inhibitor or a PD-L1 monoclonal antibody reversed these immunosuppressive effects, and combined therapy revealed promising therapeutic activities. Conclusions: ERBB2 / ERBB3 mutations may serve as useful biomarkers in identifying patients who are sensitive to ERBB2/ERBB3 inhibitors and PD-L1 monoclonal antibody treatment. Trial registration number: NCT02442414 ;Pre-results.

Gallbladder cancer (GBC) is rare, but is the most malignant type of biliary tract tumor. Unfortunately, only a small population of cancer patients is acceptable for the surgical resection, the current effective regimen; thus, the high mortality rate has been static for decades. To substantially circumvent the stagnant scenario, a number of therapeutic approaches owing to the creation of advanced technologic measures (e.g., next-generation sequencing, transcriptomics, proteomics) have been intensively innovated, which include targeted therapy, immunotherapy, and nanoparticle-based delivery systems. In the current review, we primarily focus on the targeted therapy capable of specifically inhibiting individual key molecules that govern aberrant signaling cascades in GBC. Global clinical trials of targeted therapy in GBC are updated and may offer great value for novel pathologic and therapeutic insights of this deadly disease, ultimately improving the efficacy of treatment.

Background: Liver cancer remains the leading cause of cancer death globally, and the treatment strategies are distinct for each type of malignant hepatic tumors. However, the differential diagnosis before surgery is challenging and subjective. This study aims to build an automatic diagnostic model for differentiating malignant hepatic tumors based on patients’ multimodal medical data including multi-phase contrast-enhanced computed tomography and clinical features. Methods: Our study consisted of 723 patients from two centers, who were pathologically diagnosed with HCC, ICC or metastatic liver cancer. The training set and the test set consisted of 499 and 113 patients from center 1, respectively. The external test set consisted of 111 patients from center 2. We proposed a deep learning model with the modular design of SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC), which take the advantage of deep CNN and gated RNN to effectively extract and integrate the diagnosis-related radiological and clinical features of patients. The code is publicly available at https://github.com/ruitian-olivia/STIC-model. Results: The STIC model achieved an accuracy of 86.2% and AUC of 0.893 for classifying HCC and ICC on the test set. When extended to differential diagnosis of malignant hepatic tumors, the STIC model achieved an accuracy of 72.6% on the test set, comparable with the diagnostic level of doctors’ consensus (70.8%). With the assistance of the STIC model, doctors achieved better performance than doctors’ consensus diagnosis, with an increase of 8.3% in accuracy and 26.9% in sensitivity for ICC diagnosis on average. On the external test set from center 2, the STIC model achieved an accuracy of 82.9%, which verify the model’s generalization ability. Conclusions: We incorporated deep CNN and gated RNN in the STIC model design for differentiating malignant hepatic tumors based on multi-phase CECT and clinical features. Our model can assist doctors to achieve better diagnostic performance, which is expected to serve as an AI assistance system and promote the precise treatment of liver cancer.