Biology subjects

Li, Q.-J.

Publications and source records attributed to Li, Q.-J..

6 recordsLinked to original sources

bioRxiv2025

Amyloid-β precursor protein promotes tumor growth by establishing an immune-exclusive tumor microenvironment

During initiation and progression, cancerous tissue hijacks a series of elaborate tissue homeostatic mechanisms to avoid immune surveillance, including neuro-immune interactions. Here, we show that amyloid-{beta} precursor protein (APP) and its {beta}-cleavage product amyloid-{beta}1-42 (A{beta}1-42), well-known in the pathogenesis of Alzheimers disease (AD), are expressed in multiple cancer tissues. However, the oncogenic activity of APP is due to its E1 domain, instead of A{beta}1-42. Mechanistically, APP restricts immune cells influx into tumor microenvironment (TME) and impairs CD8+ T cell and NK cell-based immunity, by dampening type I interferon (IFN) response in TME. We also provide proof-of-concept that vaccination targeting APP is effective for cancer prevention. Our current study reveals a previously unrecognized role of APP in cancer immune surveillance, and provides a new strategy for cancer prevention and treatment by targeting APP.

cancer biology↗

bioRxiv2025

Neoadjuvant anti-4-1BB confers protection against spontaneous metastasis through low-affinity intratumor CD8+ T cells in triple-negative breast cancer

Neoadjuvant immunotherapy seeks to harness the primary tumor as a source of relevant tumor antigens to enhance systemic anti-tumor immunity through improved immunological surveillance. Despite having revolutionized the treatment of patients with high-risk early-stage triple-negative breast cancer (TNBC), a significant portion of patients remain unresponsive and succumb to metastatic recurrence post-treatment. Here, we found that optimally scheduled neoadjuvant administration of anti-4-1BB monotherapy was able to counteract metastases and prolong survival following surgical resection. Phenotypic and transcriptional profiling revealed enhanced 4-1BB expression on tumor-infiltrating intermediate (Tint), relative to progenitor (Tprog) and terminally exhausted (Tterm) T cells. Furthermore, Tint was enriched in low-affinity T cells. Treatment with anti-4-1BB drove clonal expansion of Tint, with reduced expression of tissue-retention marker CD103 in Tprog. This was accompanied by increased TCR clonotype sharing between paired tumors and pre-metastatic lungs. Further interrogation of sorted intratumor T cells confirmed enhanced T cell egress into circulation following anti-4-1BB treatment. In addition, gene signature extracted from anti-4-1BB treated Tint was consistently associated with improved clinical outcomes in BRCA patients. Combinatorial neoadjuvant anti-4-1BB and ablation of tumor-derived CXCL16 resulted in enhanced therapeutic effect. These findings illustrate the intratumor changes underpinning the efficacy of neoadjuvant anti-4-1BB, highlighting the reciprocity between local tissue-retention and distant immunologic fortification, suggesting treatment can reverse the siphoning of intratumor T cells to primary tumor, enabling redistribution to distant tissues and subsequent protection against metastases.

immunology↗

bioRxiv2024

Marker gene fishing for single-cell data with complex heterogeneity

In single-cell studies, cells can be characterized with multiple sources of heterogeneity such as cell type, developmental stage, cell cycle phase, activation state, and so on. In some studies, many nuisance sources of heterogeneity (SOH) are of no interest, but may confound the identification of the SOH of interest, and thus affect the accurate annotate the corresponding cell subpopulations. In this paper, we develop B-Lightning, a novel and robust method designed to identify marker genes and cell subpopulations correponding to a SOH (e.g., cell activation status), isolating it from other sources of heterogeneity (e.g., cell type, cell cycle phase). B-Lightning uses an iterative approach to enrich a small set of trustworthy marker genes to more reliable marker genes and boost the signals of the SOH of interest. Multiple numerical and experimental studies showed that B-Lightning outperforms existing methods in terms of sensitivity and robustness in identifying marker genes. Moreover, it increases the power to differentiate cell subpopulations of interest from other heterogeneous cohorts. B-Lightning successfully identified new senescence markers in ciliated cells from human idiopathic pulmonary fibrosis (IPF) lung tissues, new T cell memory and effector markers in the context of SARS-COV-2 infections, and their synchronized patterns which were previously neglected. This paper highlights B-Lightnings potential as a powerful tool for single-cell data analysis, particularly in complex data sets where sources of heterogeneity of interest are entangled with numerous nuisance factors.

bioinformatics↗

bioRxiv2024

Deciphering tumour microenvironment and elucidating the origin of cancer cells in ovarian clear cell carcinoma

Ovarian clear cell carcinoma (CCC) has an East Asian preponderance. It is associated with endometriosis, a benign condition where endometrial (inner lining of the uterus) tissue is found outside the uterus and on the peritoneal surface, in the abdominal or pelvic space. CCC is relatively more resistant to conventional chemotherapy compared to other ovarian cancer subtypes and is associated with a poorer prognosis. In this study, we recruited and obtained tumour tissues from seven patients across the four stages of CCC. The tumour and the tumour microenvironment (TME) from 7 CCC patients spanning clinical stages 1-4 were transcriptionally profiled using high-resolution scRNA-seq to gain insight into CCCs biological mechanisms. Firstly, we built a scRNA-seq resource for the CCC tumour microenvironment (TME). Secondly, we identified the different cell type proportions and found high levels of immune infiltration in CCC. Thirdly, since CCC is associated with endometriosis, we compared CCC with two publicly available endometriosis scRNA-seq datasets. The CCC malignant cells showed similarities with glandular secretory and ciliated epithelial cells found in endometriosis. Finally, we determined the differences in cell-cell communication between various cell types present in CCC TME and endometriosis conditions to gain insights into the transformations in CCC.

cancer biology↗

bioRxiv2023

SifiNet: A robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multiomic cellular profiles. It is conveniently available as an open-source R package.

bioinformatics↗

bioRxiv2022

Clustering Deviation Index (CDI): A robust and accurate unsupervised measure for evaluating scRNA-seq data clustering

Single-cell RNA-sequencing (scRNA-seq) technology allows us to explore cellular heterogeneity in the transcriptome. Because most scRNA-seq data analyses begin with cell clustering, its accuracy considerably impacts the validity of downstream analyses. Although many clustering methods have been developed, few tools are available to evaluate the clustering "goodness-of-fit" to the scRNA-seq data. In this paper, we propose a new Clustering Deviation Index (CDI) that measures the deviation of any clustering label set from the observed single-cell data. We conduct in silico and experimental scRNA-seq studies to show that CDI can select the optimal clustering label set. Particularly, CDI also informs the optimal tuning parameters for any given clustering method and the correct number of cluster components.

bioinformatics↗