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Multiple Platforms for Single Cell Genomics to Enable Biomarker Discovery in Immunotherapy ::

Ankita Das, Kayla Lee, Derek Vargas, Gavin Washburn, Niyati Thosani, Vasumathi Kode, Nitin Mandloi, Jing Wang, Amit Chaudhuri, and Papia Chakraborty

Single cell genomic approaches can provide valuable insights into the complexity and heterogeneity of the cell types in the context of a tissue or tumor. However, challenges with the preparation of single cell suspensions, good cell viability and efficiently capturing diverse cell types in a mix via appropriate cell capture methods can override the utility of the approaches. We provide validation and application data generated from a range of single cell input types (number of starting cells, viability, and research question) and diverse commercially available platforms (the 10x Genomics Chromium and the FACS based SMART-Seq (Takara Bio). We demonstrate the utility of using the appropriate single cell genomics approach to get relevant information. Recent studies have shown that capturing additional information on cellular phenotypes or features can provide valuable information on cell identities otherwise missed and additional heterogeneity, which in turn facilitates discovery of meaningful biomarkers and understanding of molecular mechanisms of development and disease. To enable for such discovery, we have validated the sC-ATAC seq and CITE-Seq approaches and present data on the utility of those approaches and will present data to highlight the capabilities at MedGenome.

  • We present a platform agnostic and flexible approach to utilize single cell profiling of gene expression depending on the research question and the number of cells available.
  • We present single cell epigenomic profiling data on immune cells using single cell ATAC seq and show that examining chromatin accessibility can identify cellular identities and mechanisms of response to stimuli.
  • We conclude the multi-omic approaches have been validated at MedGenome and can offer it as a service.

Applications of TCR repertoire analysis for biomarker discovery and beyond ::

Ankita Das, Vasumathi Kode, Kayla Lee, Priyanka Shah, Xiaoshan “Shirley” Shi, Nitin Mandloi, Ravi Gupta, Amit Chaudhuri and Papia Chakraborty

T cell immunity provides significant therapeutic benefit to cancer patients treated with checkpoint inhibitors, however a very small fraction of patients typically respond to checkpoint inhibitors, and a smaller fraction of them have any long-term benefit. This can be attributed to the lack of prognostic and predictive biomarkers. The infiltration levels of CD8 T cell in tumors is often used as a characteristic biomarker and can be correlated with response, but recent studies have shown that examining the functional state of the T cells and other immune cell types in the tumor, and the immunogenic neoantigen burden and the TCR repertoire clonality, might give a more appropriate representation of what might be going on in the tumors and hence can be used as predictive biomarkers for response. At MedGenome we have built a suite of tools that can be utilized to: a) Study the tumor microenvironment using the OncoPeptTUME analysis pipeline, b) Predict and validate the immunogenic neoantigens using OncoPeptVAC & OncoPeptSCRN, and c) A suite of workflows to analyze the TCR repertoire from a wide range of sample types using bulk and single-cell approaches. Here we present the data highlighting the applications of TCR repertoire as a biomarker for immunotherapy and also present our capabilities of providing these solutions as a service.

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

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 ::

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).

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