Biology subjects

Diao, K.

Publications and source records attributed to Diao, K..

6 recordsLinked to original sources

bioRxiv2026

TcrDesign: De novo design of epitope specific full-length T cell receptors

T cell receptors (TCRs) are essential for adaptive immune recognition. Recently computational tools have been developed to predict the interactions between TCR and epitope, and artificial intelligence models have been proposed to generate the complementarity determining region 3 (CDR3) region of {beta} chain TCR. However, de novo design of experimentally validated, functional, full-length epitope-specific TCRs remains a significant challenge. Here we developed TcrDesign, a deep learning framework based on large-scale unlabeled TCR and epitope datasets to generate epitope-specific, full-length TCRs. TcrDesign comprises two modules: TcrDesign-B for TCR-pMHC binding prediction with state-of-the-art accuracy, and TcrDesign-G for functional full-length TCR sequence generation. Pre-trained on large-scale unlabeled datasets using transformer-based architectures, TcrDesign achieves state-of-the-art performance in both TCR-epitope binding prediction and de novo TCR sequence generation. Furthermore, epitope-major histocompatibility complex (MHC) binding and functional activation of TcrDesign-generated TCRs were experimentally validated. TcrDesign provides an efficient and modular approach for designing epitope-specific full-length TCRs, with experimental validation confirming its utility.

bioinformatics↗

bioRxiv2025

Tumor hypoxia adaptation depends on FLAD1 mediated mitochondrial metabolic reprogramming

Hypoxia, a hallmark of solid tumors, promotes the malignant progression and is challenging to target. Metabolic reprogramming and the resulting metabolic vulnerabilities provide a promising strategy to target tumor hypoxia. Here we systematically compared the metabolic network differences between hypoxic and non-hypoxic cells, and developed a deep learning model, "DepFormer", to predict the dependent metabolic genes in hypoxic tumor cells. The performance of DepFormer was validated using CRISPR screening dataset. Oxidative phosphorylation was identified as the most significantly hypoxia-dependent metabolic pathway, and FLAD1 was predicted to be one of the key hypoxia-dependent metabolic genes. FLAD1 locus is amplified, and FLAD1 expression is upregulated in various tumor types, especially in hypoxic tumors. FLAD1 depletion compromises tumors adaptation to hypoxia by disrupting mitochondrial complex II activity, leading to an imbalance between succinate and fumarate, and consequent failure to adapt to hypoxia. Subsequently, we identified a drug-like inhibitor of FLAD1, which selectively inhibits the growth of tumor cells under hypoxia. Our findings reveal FLAD1 as an innovative therapeutic target for hypoxic tumors.

cancer biology↗

bioRxiv2025

Single-cell copy number alteration signature analysis reveals masked patterns and potential biomarkers for cancer

Copy number alteration (CNA) is a major type of cancer genome alteration that drives cancer progression. CNA signature analysis can reveal underlying etiology and provide biomarkers for cancer treatment, and existing CNA signature analyzes are all performed using bulk tissue samples. However CNA usually affect large proportion of genome, and the CNA profile of bulk sample does not reflect the actual CNA profiles of the individual cancer cells of the sample, especially in tumors with high heterogeneity, such as hepatocellular carcinoma (HCC). Furthermore, the evolutionary trajectory of CNA mutational processes still remain elusive. Here we build a method to comprehensively analyze the CNA signatures of HCC from single-cell and bulk sample perspective, revealing patterns and potential noise signals from the usually performed bulk tissue CNA signature analysis. Single-cell signature analysis delineated the evolutionary trajectory of HCC CNA signatures, and different CNA signatures consistently emerge in different HCC evolution stages. Single-cell CNA signatures show robust performance in patient prognosis and drug sensitivity prediction. This work not only reveals specific considerations in analyzing CNA signature derived from bulk tissue but also depicts CNA evolution process and provides potential biomarkers for the prognosis and treatment of HCC patients. HighlightSingle-cell analysis reveals CNA signatures masked in bulk tissue. Single-cell analysis delineates the evolutionary trajectory of CNA signature. Small CNAs occur early and large CNAs happens late in HCC evolution. Single-cell CNA signatures show robust performance in guiding cancer clinical treatment.

genomics↗

bioRxiv2022

The repertoire of copy number alteration signatures in human cancer

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and SNP array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes (PCAWG) WGS samples, and further validated with 10851 the cancer genome atlas (TCGA) SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution, and etiology.

bioinformatics↗

bioRxiv2022

Seq2Neo: a comprehensive pipeline for cancer neoantigen im-munogenicity prediction

Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines shows clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations can result in immunogenicity in cancer cells, and efficient tools for predicting the immunogenicity of neoepitope are still urgently needed. Here we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope features prediction from raw sequencing data, and neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions, and gene fusions are supported. Importantly a convolutional neural networks (CNN) based model has been trained to predict the immunogenicity of neoepitope. And this model shows improved performance compared with currently available tools in immunogenicity prediction in independent datasets. We anticipate that the Seq2Neo pipeline will become a useful tool in prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is an open-source software under an academic free license (AFL) v3.0 and it is freely available at https://github.com/XSLiuLab/Seq2Neo.

bioinformatics↗

bioRxiv2022

Oncogenic EFNA4 amplification promotes lung adenocarcinoma lymph node metastasis

Lymph nodes metastases are common in patients with lung cancer. And those patients are often at a higher risk for death from lung tumor than those with tumor-free lymph nodes. So-matic DNA alterations are key drivers of cancer, and copy number alterations (CNAs) are a major type of DNA alteration that promotes lung cancer progression. Here, we performed genome-wide DNA copy number analysis, and identified a novel lung cancer metastasis related gene, EFNA4. EFNA4 genome locus was significantly amplified and EFNA4 mRNA expression was significantly up-regulated in lung cancer compared with normal lung tissue, and also in lung cancer with lymph node metastases compared with lung cancer without metastasis. EFNA4 encodes Ephrin A4, which is the ligand for Eph receptors. The function of EFNA4 in human lung cancer remains largely unknown. Through cell line experiments we showed that EFNA4 overexpression contributes to lung tumor cells growth, migration and adhesion. Conversely, EFNA4 knockdown or knockout led to the growth suppression of cells and tumor xenografts in mice. Lung cancer patients with EFNA4 over-expression have poor prognosis. Together, by elucidating a new layer of the role of EFNA4 in tumor proliferation and migration, our study demonstrates a better understanding of the function of the significantly amplified and overexpressed gene EFNA4 in lung tumor metastasis and suggests EFNA4 as a potential target in metastatic lung cancer therapy. Simple SummaryLymph nodes are likely to be the first stop for lung cancer metastasis. To further investigate the mechanism of lung cancer lymph node metastasis, we performed cancer genome analysis and found that EFNA4, a member of the ephrin (EPH) family, is amplified and up-regulated in lung tumor patients, especially in patients with lymph node metastases. In vitro and in vivo experiments show that overexpression of EFNA4 promotes lung tumor cell proliferation and migration, while knockdown or knockout of EFNA4 inhibits cell proliferation and migration. Altogether, our results suggest that the DNA amplification of EFNA4 genome locus could play an oncogenic function in promoting lung cancer lymph node metastasis.

cancer biology↗