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.
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.
Leveraging big data using a novel clinical database and analytic platform based on 323,145 individuals with and without of Diabetes ::
Hiranjith G.H., Anjana Ranjit Mohan, Praveen Raj, Jebarani Saravanan, Srinivasan Vedantham, Radha Venkatesan, Muthu Narayanan, Pradeepa Rajendra, Ranjit Unnikrishnan, Somasekhar Jayaram, Rohit Gupta, Paul George, Brijendra Kumar Srivastava, Uthra Subash Chandra Bose, Lovelena Munawar, Sam Santhosh, Mohan Viswanathan
Diabetes is a chronic disorder of glucose metabolism and is a major cause of heart disease and end-stage renal disease in world populations. It is also the single biggest cause of preventable blindness, the leading cause of non-traumatic lower extremity amputation and major cause of premature mortality. 415 million people have diabetes globally and is expected to reach 642 million by 20401. Large volumes of diabetes biomedical data are being produced every day, but it has not been used effectively. Leveraging such voluminous amount of patient data using data science approaches help to uncover hidden patterns, unknown correlations, and other insights of the disease. Integration of diverse genomic data with comprehensive electronic health records (EHRs) exhibit challenges, but essentially, they provide a feasible opportunity to better understand the underlying diseases, treatment patterns and develop an efficient and effective approach to identify biomarkers for diagnosis and improve therapy.
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.