The value of studying the Indian population to identify novel genetic variants to inform mechanisms of disease and pharmacological response :: ASHG 2018

A. Das, P. Raj, V. Gopalan, Hiranjith. G.H., E. Stawiski, S. Santhosh, R. Gupta, A. Chaudhuri, R. Gupta

While Genome wide association studies can shed light on the significance of variants in susceptibility to a disease or allow to stratify patients for specific therapeutic modalities, often variants that are rare and could be of significance are not identified in these studies. This can occur due to allelic heterogeneity in a complex disease. Furthermore, spurious differences in allelic frequencies between normal and disease resulting from systematic differences in ancestry can also confound the conclusions drawn from a GWAS study. Therefore, studying population isolates where individuals with the disease and normal have a homogeneous genetic background can allow to enrich for rare alleles, and improve the accuracy of elimination of false positives, and make it possible to accurately correlate segregation of the variants to the disease traits. One such population is of the Indian subcontinent, where the ancestral populations date back to modern humans travelling out of Africa 65,000 year ago, creating a gene pool of over 1000 years starting from a few founder families, resulting in an accumulation of unique disease-causing and disease-protective alleles that were preserved and enriched within various ethnic groups in the country.

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 :: ASHG 2018

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 :: Next Gen Immuno-Oncology Congress

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 :: AACR 2018

Papia Chakraborty³, Snigdha Majumder¹, Rakshit Shah², Jisha Elias^1,2, Vasumathi Kode³, Yogesh Mistry², Coral Karunakaran¹, Priyanka Shah¹, Malini Manoharan¹, Bharti Mittal¹, Sakthivel Murugan SM¹, Lakshmi Mahadevan¹, Ravi Gupta¹, Amitabha Chaudhuri^{1,3 **} and Arati Khanna-Gupta^1**

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.