Deep centroid: a general deep cascade classifier for biomedical omics data classification.

MOTIVATION: Classification of samples using biomedical omics data is a widely used method in biomedical research. However, these datasets often possess challenging characteristics, including high dimensionality, limited sample sizes, and inherent biases across diverse sources. These factors limit the performance of traditional machine learning models, particularly when applied to independent datasets. RESULTS: To address these challenges, we propose a novel classifier, Deep Centroid, which combines the stability of the nearest centroid classifier and the strong fitting ability of the deep cascade strategy. Deep Centroid is an ensemble learning method with a multi-layer cascade structure, consisting of feature scanning and cascade learning stages that can dynamically adjust the training scale. We apply Deep Centroid to three precision medicine applications-cancer early diagnosis, cancer prognosis, and drug sensitivity prediction-using cell-free DNA fragmentations, gene expression profiles, and DNA methylation data. Experimental results demonstrate that Deep Centroid outperforms six traditional machine learning models in all three applications, showcasing its potential in biological omics data classification. Furthermore, functional annotations reveal that the features scanned by the model exhibit biological significance, indicating its interpretability from a biological perspective. Our findings underscore the promising application of Deep Centroid in the classification of biomedical omics data, particularly in the field of precision medicine. AVAILABILITY AND IMPLEMENTATION: Deep Centroid is available at both github (github.com/xiexiexiekuan/DeepCentroid) and Figshare (https://figshare.com/articles/software/Deep_Centroid_A_General_Deep_Cascade_Classifier_for_Biomedical_Omics_Data_Classification/24993516).

A novel humanized anti-HER2 antibody conjugated with MMAE exerts potent anti-tumor activity.

Human epidermal growth factor receptor-2 (HER2) is a validated therapeutic target for breast cancer and trastuzumab (Herceptin), a humanized anti-HER2 antibody, has significant anti-cancer effects in the clinic. However, breast cancer patients often experience disease progression after prolonged Herceptin treatment. To develop a more effective therapy, we generated humanized monoclonal antibody hertuzumab and hertuzumab-drug conjugates as novel breast cancer therapies. The hertuzumab was conjugated with small molecule cytotoxic agents monomethylauristatin E (MMAE) or monomethylauristatin F (MMAF) with various linkers to generate antibody-drug conjugates (ADCs), which were evaluated for their in vitro and in vivo anti-cancer activities. Among these ADCs, hertuzumab-vc-MMAE can be effectively internalized and potently kill HER2 over-expressing tumor cells. In xenograft tumor models, hertuzumab-vc-MMAE showed a more potent anti-tumor activity than T-DM1, a FDA-approved ADC drug. More importantly, this novel ADC drug also showed superior anti-tumor activity than T-DM1 in trastuzumab- and lapatinib-resistant xenograft tumor models, suggesting its potential as an improved therapy for HER2-positive breast cancers. The novel ADC, hertuzumab-vc-MMAE, is an effective and selective agent for the treatment of HER2-positive breast tumors.