The Ophthatome™ Knowledgebase : A curated knowledgebase of over 500,000 ocular disease phenotypic records coupled with analyses tools to enable novel discoveries for drug development and pharmacogenomics ::

A. Das, Nagasamy S, P. Raj, B. Muthu Narayanan, J. Somasekhar, T. Chandrasekhar, D. Kumar, A. Shetty, S. Das, S. Tejwani, P. Narendra, A. Ghosh

  • Medical big data analytics has applications in clinical decision, predictive/ prognostic modelling of disease progression, disease surveillance, public health and research.
  • The electronic medical record (EMR), system is the digital storehouse of rich medical data that includes demographics, clinical (diagnosis, clinical diagnostic tests, treatment, prescription drugs, surgery, laboratory test reports) and administrative (bills, insurance claims) details of patients’ visits to hospital(s).
  • Although EMR is a repository of vast clinical data on a large patient cohort collected over many years, the data lack sufficient structure to be of any clinical value for applying deep learning methods and advanced analytics to improve disease management at an individual patient level or for the field in general.
  • Aggregated data from hospital EMRs need to be captured in a structured knowledge base to support clinical and translational research (CTR).

OncoPeptTUME™ —A novel in-silico approach to model the tumor microenvironment and predict treatment efficacy and long-term survival benefits for immunotherapy applications ::

Xiaoshan “Shirley” Shi, Vasumathi Kode, Snigdha Majumder, Priyanka Shah, Ravi Gupta, Amit Chaudhuri, and Papia Chakraborty

Somatic mutations have been found to be a rich source of potential cancer vaccines (which have shown promise in treating late stage cancers) with minimal T cell tolerance. MedGenome has built a proprietary cancer vaccine prediction platform, OncoPeptVAC using a combination of features that include TCR binding, human leukocyte antigen (HLA) binding, gene expression and proteasomal processing. Application of this platform yielded prioritized potential immunogenic peptides which had to be validated, for which a robust CD8+ T cell –dendritic cell co-culture assay was developed, to examine T cell activation in the presence of added synthetic peptides. A minigene platform was also developed to screen wild-type and mutant peptide pairs to test their immunogenicity. The analysis demonstrated that the two approaches for investigating immunogenicity of peptides – minigene approach and external addition of peptide approach – have differential utilities for testing and validating the immunogenicity of somatic mutations derived from tumors.

A minigene platform to validate novel immunogenic peptides arising from somatic mutations as therapeutic cancer vaccines ::

Papia Chakraborty3, Snigdha Majumder1, Rakshit Shah2, Jisha Elias1,2, Vasumathi Kode3, Yogesh Mistry2, Coral Karunakaran1, Priyanka Shah1, Malini Manoharan1, Bharti Mittal1, Sakthivel Murugan SM1, Lakshmi Mahadevan1, Ravi Gupta1, Amitabha Chaudhuri1,3 ** and Arati Khanna-Gupta1**

The MedGenome team has identified a germline mutation in an MMR pathway (the DNA mismatch repair pathway) gene – MLH1. Mutations in the MMR pathway genes have been known to be associated with Lynch syndrome wherein patients have a 70-80% lifetime risk of developing colorectal cancer (CRC).

The team carried out exome and RNA sequencing to identify immunogenic peptides. They also screened for immunogenic peptides using OncoPeptVAC, MedGenome’s proprietary immunogenic peptide-prediction pipeline that employs TCR-peptide interaction as a key criterion of immunogenicity. This pipeline of peptides was validated as it was shown to elicit a CD8+ T cell response in patient derived immune cells. These immunogenic peptides qualify as candidates for a personalized neoantigen-based vaccine therapy in combination with immune- checkpoint inhibitors for Lynch syndrome-tumor clearance.

A personalized cancer vaccine approach to treat Lynch syndrome ::

Priyanka, Malini, Kiran, Ravi, Rohit and Amit

Neoantigens, derived from somatic mutations are prime candidates for cancer vaccines. Currently, the available T-cell neoepitope prioritization pipelines rely primarily on two attributes – the class-I HLA-binding affinity of the mutant peptide compared to the wild-type counterpart, and the level of expression of the mutated gene in tumor cells. These approaches, however, fall short of predicting whether the HLA-bound peptide will engage T-cells by binding to T-cell receptors (TCRs). MedGenome developed a novel algorithm to circumvent this problem, that predicts the binding of HLA-peptide complexes to TCRs by analyzing the physicochemical composition of the amino acids and their positional biases in the 9-mers from crystal structures of HLA-peptide-TCR complex. Machine learning approaches were applied, and the Immune Epitope Database (IEDB) was used to select positive and negative TCR interactions. It was concluded that the inclusion of the TCR binding step to MedGenome’s T-cell neoepitope prioritization pipeline increased the accuracy of prediction, reduced false positives and selected potential neoepitopes to a manageable number for testing in cell-based assays.

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