bioRxiv Science⌕ Search

Biology subjects

Chen, W.-N.

Publications and source records attributed to Chen, W.-N..

2 recordsLinked to original sources

Molecular mechanism of action of a blood brain barrier shuttle antibody

The transferrin receptor has emerged as a prime target for transcytosis of antibody shuttles from the bloodstream into the brain parenchyma to deliver therapeutic payloads that treat neurological disorders. However, how the transferrin receptor-antibody binding mode impacts avidity, degradation, pH sensitivity and delivery remains underexplored. To address this, we determined the cryo-EM structure of mouse transferrin receptor 1 bound to the model brain shuttle antibody 8D3. In combination with cell binding and localisation studies we show that 8D3 can structurally support small-scale inter-receptor cross-linking, such as in self-contained pairs, that cause avidity but do not induce receptor redistribution or degradation. The structure can also explain pH-dependent binding modes, and we show how some of these antibody variants with graded sensitivities regulate brain penetration in vivo. Overall, our study illuminates how distinct molecular features of antibody binding impact transferrin receptor behaviour and brain delivery to inform on future shuttle design.

neuroscience↗

Predicting the prognosis of non-small cell lung cancer by integrating microarray and clinical data with deep learning

AO_SCPLOWBSTRACTC_SCPLOWNon-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical application in mind, we developed a deep neural network (DNN) combining heterogeneous data sources of gene expression and clinical data to accurately predict the prognosis of NSCLC patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC markers were used to group patients into marker- and marker+ subgroups. Using a systems biology approach, prognosis relevance values (PRV) were then calculated to select eight additional novel prognostic gene markers. Gene markers along with clinical data were then used to develop an integrative DNN via bimodal learning to predict the 5-year survival rate of NSCLC patients with tremendously high accuracy (AUC: 0.8163, accuracy: 75.44%), which is superior to all other existing methods based on AUC. Using the capability of deep learning, we believe that our predicted cancer prognosis can be a promising index helping oncologists and physicians develop personalized therapy and build the foundation of precision medicine in the future.

bioinformatics↗