Invasive micropapillary carcinoma (IMPC) has very high rates of lymphovascular invasion and lymph node metastasis and has been reported in several organs. However, the genomic mechanisms underlying ...its metastasis are unclear. Here, we perform whole-genome sequencing of tumor cell clusters from primary IMPC and paired axillary lymph node metastases. Cell clusters in multiple lymph node foci arise from a single subclone of the primary tumor. We find evidence that the monoclonal metastatic ancestor in primary IMPC shares high frequency copy-number loss of PRDM16 and IGSF9 and the copy number gain of ALDH2. Immunohistochemistry analysis further shows that low expression of IGSF9 and PRDM16 and high expression of ALDH2 are associated with lymph node metastasis and poor survival of patients with IMPC. We expect these genomic and evolutionary profiles to contribute to the accurate diagnosis of IMPC.
Autophagy is a catabolic lysosomal degradation process essential for cellular homeostasis and cell survival. Dysfunctional autophagy has been associated with a wide range of human diseases, e.g., ...cancer and neurodegenerative diseases. A large number of small molecules that modulate autophagy have been widely used to dissect this process and some of them, e.g., chloroquine (CQ), might be ultimately applied to treat a variety of autophagy-associated human diseases. Here we found that vacuolin-1 potently and reversibly inhibited the fusion between autophagosomes and lysosomes in mammalian cells, thereby inducing the accumulation of autophagosomes. Interestingly, vacuolin-1 was less toxic but at least 10-fold more potent in inhibiting autophagy compared with CQ. Vacuolin-1 treatment also blocked the fusion between endosomes and lysosomes, resulting in a defect in general endosomal-lysosomal degradation. Treatment of cells with vacuolin-1 alkalinized lysosomal pH and decreased lysosomal Ca
2+
content. Besides marginally inhibiting vacuolar ATPase activity, vacuolin-1 treatment markedly activated RAB5A GTPase activity. Expression of a dominant negative mutant of RAB5A or RAB5A knockdown significantly inhibited vacuolin-1-induced autophagosome-lysosome fusion blockage, whereas expression of a constitutive active form of RAB5A suppressed autophagosome-lysosome fusion. These data suggest that vacuolin-1 activates RAB5A to block autophagosome-lysosome fusion. Vacuolin-1 and its analogs present a novel class of drug that can potently and reversibly modulate autophagy.
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BFBNIB, GIS, IJS, KISLJ, NUK, PNG, UL, UM, UPUK
Little is known on the tumor microenvironment (TME) response after neoadjuvant chemotherapy (NACT) in gastric cancer on the molecular level.
Here, we profiled 33,589 cell transcriptomes in 14 samples ...from 11 gastric cancer patients (4 pre-treatment samples, 4 post-treatment samples and 3 pre-post pairs) using single-cell RNA sequencing (scRNA-seq) to generate the cell atlas. The ligand-receptor-based intercellular communication networks of the single cells were also characterized before and after NACT.
Compered to pre-treatment samples, CD4+ T cells (P = 0.018) and CD8+ T cells (P = 0.010) of post-treatment samples were significantly decreased, while endothelial cells and fibroblasts were increased (P = 0.034 and P = 0.005, respectively). No significant difference observed with respect to CD4+ Tregs cells, cycling T cells, B cells, plasma cells, macrophages, monocytes, dendritic cells, and mast cells (P > 0.05). In the unsupervised nonnegative matrix factorization (NMF) analysis, we revealed that there were three transcriptional programs (NMF1, NMF2 and NMF3) shared among these samples. Compared to pre-treatment samples, signature score of NMF1 was significantly downregulated after treatment (P = 0.009), while the NMF2 signature was significantly upregulated after treatment (P = 0.013). The downregulated NMF1 and upregulated NMF2 signatures were both associated with improved overall survival outcomes based on The Cancer Genome Atlas (TCGA) database. Additionally, proangiogenic pathways were activated in tumor and endothelial cells after treatment, indicating that NACT triggers vascular remodeling by cancer cells together with stromal cells.
In conclusion, our study provided transcriptional profiles of TME between pre-treatment and post-treatment for in-depth understanding on the mechanisms of NACT in gastric cancer and empowering the development of potential optimized therapy procedures and novel drugs.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Objective
Although the incidence of gastric cancer (GC) is decreasing, GC remains one of the leading cancers in the world. Surgical resection, radiotherapy, chemotherapy, and neoadjuvant therapy have ...advanced, but patients still face the risk of recurrence and poor prognosis. This study provides new insights for assessment of prognosis and postoperative recurrence of GC patients.
Methods
We collected paired cancer and adjacent tissues of 17 patients with early primary GC for bulk transcriptome sequencing. By comparing the transcriptome information of cancer and adjacent cancer, 321 differentially expressed genes (DEGs) were identified. These DEGs were further screened and analyzed with the GC cohort of TCGA to establish a 3-gene prognostic model (
PLCL1
,
PLOD2
and
ABCA6
). At the same time, the predictive ability of this risk model is validated in multiple public data sets. Besides, the differences in immune cells proportion between the high- and low-risk groups were analyzed by the CIBERSORT algorithm with the Leukocyte signature matrix (LM22) gene signature to reveal the role of the immune microenvironment in the occurrence and development of GC.
Results
The model could divide GC samples from TCGA cohorts into two groups with significant differences in overall and disease-free survival. The excellent predictive ability of this model was also validated in multiple other public data sets. The proportion of these immune cells such as resting mast cells, T cells CD4+ memory activated and Macrophages M2 are significantly different between high and low risk group.
Conclusion
These three genes used to build the models were validated as biomarkers for predicting tumor recurrence and survival. They may have potential significance for the treatment and diagnosis of patients in the future, and may also promote the development of targeted drugs.
W2KPE: Keyphrase Extraction with Word-Word Relation Cheng, Wen; Dong, Shichen; Wang, Wei
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2023-June-4
Conference Proceeding
Open access
This paper describes our submission to ICASSP 2023 MUG Challenge Track 4, Keyphrase Extraction, which aims to extract keyphrases most relevant to the conference theme from conference materials. We ...model the challenge as a single-class Named Entity Recognition task and developed techniques for better performance on the challenge: For the data preprocessing, we encode the split keyphrases after word segmentation. In addition, we increase the amount of input information that the model can accept at one time by fusing multiple preprocessed sentences into one segment. We replace the loss function with the multi-class focal loss to address the sparseness of keyphrases. Besides, we score each appearance of keyphrases and add an extra output layer to fit the score to rank keyphrases. Exhaustive evaluations are performed to find the best combination of the word segmentation tool, the pre-trained embedding model, and the corresponding hyperparameters. With these proposals, we scored 45.04 on the final test set.
Diffuse large B cell lymphoma (DLBCL) is one of the most common yet aggressive types of B cell lymphoma and remains incurable in 40% of patients. Herein, we profile the transcriptomes of 94,324 cells ...from 17 DLBCLs and 3 control samples using single-cell RNA sequencing. Altogether, 73 gene expression programs are identified in malignant cells, demonstrating high intra- and intertumor heterogeneity. Furthermore, 2,754 pairs of suggestive cell-cell interactions are predicted, indicating a complex and highly dynamic tumor microenvironment. Especially for B cell lymphomas, a strong costimulatory CD70-CD27 interaction is predicted between malignant and T cells. Furthermore, coinhibitory signals mediated by TIM3 or TIGIT seem to be the main driving force for T cell exhaustion. Finally, we find that chronic hepatitis B virus infection may have a significant impact on tumor cell survival and immune evasion in DLBCL. Our results provide insights into B cell lymphomagenesis and may facilitate the design of targeted immunotherapy strategies.
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•High intra- and intertumor heterogeneity in DLBCL is observed by single-cell RNA-seq•TME cells may promote DLBCL activation/survival by CD40- fand BAFF-mediated signals•Coinhibitory signals through TIM3 and TIGIT may drive T cell exhaustion in DLBCL•HBV infection likely contributes to malignant cell survival/immune evasion in DLBCL
Using single-cell RNA sequencing, Ye et al. demonstrate that genetic diversity within tumors, potential interactions between malignant and tumor-infiltrating cells, and viral infections may all contribute to the marked disease heterogeneity in DLBCL.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
The tumor microenvironment (TME) is composed of tumor cells, as well as immune and stromal cells. Cancer cells can interact with the tumor microenvironment to suppress anticancer immunity ...and host inflammatory cells can modulate that immune response to the lesion. To better understand the TME across cancer types we used single-cell RNA-seq analysis of 224,330 cells from 77 patients with breast, gastric, nasopharyngeal carcinoma (NPC), diffuse large B-cell lymphoma (DLBCL), pancreatic, colorectal and lung cancer. The tumor cells and immune cells from individual patients were analyzed simultaneously at the single-cell level to identify distinct cell clusters and subtypes. Comparison of the profile of T cells, macrophages, dendritic cells (DC), B cells, mast cells, granulocytes and cancer-associated fibroblasts (CAFs) identifies unique profiles by tumor type. Breast tumors have high numbers of macrophages, colorectal tumors are elevated for CD4+-T cells, DLBCLs are low in CAFs, gastric cancers have high levels of mast cells, lung tumors high levels of CD4+-T cells and natural killer (NK) T cells, NPC have elevated levels of B cells and DCs and pancreatic cancers high levels of CAFs and granulocytes and low levels of DC and CD8+-T cells. However, within a tumor type, there is also considerable heterogeneity. Understanding how the TME evolves and changes with therapy or predicts response to treatment and survival may provide insight into tumorigenesis and cancer progression.
Note: This abstract was not presented at the meeting.
Citation Format: Michael Dean, Guibo Li, Jianhua Jin, Yong Hou, Kui Wu, Shida Zhu, Hanlin Zhou, Ruqian Lv, Feng Lin, Si Liu, Shichen Dong, Lei Wang, Cuijuan Zhang, Yi Zhao. Analysis of the tumor microenvironment in seven cancer types by single-cell RNA-seq abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 148.
This paper describes our submission to ICASSP 2023 MUG Challenge Track 4, Keyphrase Extraction, which aims to extract keyphrases most relevant to the conference theme from conference materials. We ...model the challenge as a single-class Named Entity Recognition task and developed techniques for better performance on the challenge: For the data preprocessing, we encode the split keyphrases after word segmentation. In addition, we increase the amount of input information that the model can accept at one time by fusing multiple preprocessed sentences into one segment. We replace the loss function with the multi-class focal loss to address the sparseness of keyphrases. Besides, we score each appearance of keyphrases and add an extra output layer to fit the score to rank keyphrases. Exhaustive evaluations are performed to find the best combination of the word segmentation tool, the pre-trained embedding model, and the corresponding hyperparameters. With these proposals, we scored 45.04 on the final test set.
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