Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.

No abstract

e21044 Background: Almost 70% of cases of lung cancer are diagnosed at advanced stage, albeit with sophisticated imaging modalities available. Of these, 35% are tiny nodules that are often missed at initial radiological screening, owing to limitations of resolution of human vision.Dramatic shift in therapeutic paradigm in the management of lung cancer in the last decade has ushered an era of stratified medicine. In India, one-third population inhabits rural lands;lack of awareness for molecular medicine poses challenges to treating oncologists. AI-based cancer detection and prediction of potential oncogenic driver at diagnosis, will help alleviate patient anxiety caused due to diagnostic delays, aiding in prompt initiation of optimal therapy.Here, a comprehensive deep-CNN based diagnosis of lung adenocarcinoma (LUAD) was implemented using both PET/CT and histopathology images of 100 patients. Methods: Data collection: 143 whole-slide images were downloaded from Dartmouth Lung Cancer Histology Dataset from Biomedical Informatics Research and Data Science website and PET/CT dataset-A Large-Scale CT and PET/CT Dataset for Lung Cancer Diagnosis (Lung-PET-CT-Dx) from The Cancer Imaging Archive. This comprises 251,135 images from 355 participants across 436 studies. Creation of Deep-CNN model: Residual Neural Network - 18 was used for histopathlogic classification followed by Detectron2’s region-based deep-CNN for detection of presence of tumours in lung PET/CT images. Properties such as size, morphology, attenuation, malignancy, and spiculation were also measured. The 18-layer deep network, ResNet-18, detected the histologic patterns using whole-slide histopathology images. Results: Results for 100 patients whose PET/CT images, histopathology images, and mutational status of three genes (EGFR, ALK, ROS) were readily available. The histologic patterns of tumour cells appear to be related to the mutational status of genes. The ResNet-18 CNN could predict various histologic patterns (Acinar, Lepidic, Papillary, Micropapillary, and Solid patterns). Conclusions: CNN models can help in early diagnosis and prompt treatment of lung cancer, if fairly accurate. Larger datasets are needed to improve the same. The final validation of the model is underway and will be reported in near future.[Table: see text]

No abstract