MedGenome’s genomics solutions for precision medicine ::

Kushal Suryamohan, Ravi Gupta, Marco Corbo, Ramesh Menon, Megha Muraleedharan, Chaitanya E. Ramesh, Harsha Gowda, Hiranjith G.H., Vedam L. Ramprasad, Somasekar Seshagiri

  • MedGenome is a leading provider of sequencing and bioinformatics solutions.
  • Comprehensive solutions for genome, exome transcriptome and single cell sequencing.
  • State-of-the-art laboratories in the US, India, Singapore and Africa with expertise in handling diverse sample types.
  • Proprietary technologies including VarMiner, an AI-enabled variant interpretation software, and OncoPeptTUME, an algorithm to classify tumors amenable for immunotherapy.
  • Ongoing collaboration with over 5,000 hospitals and clinicians across India and South Asia on multiple genomics projects.
  • Founding member of the GenomeAsia 100K consortium to sequence and catalog novel genetic risk factors and rare variants from 100,000 South Asians.
  • Successfully launched several clinical tests, including Kardiogen, a Polygenic Risk Score test used to determine an individual’s risk of developing coronary artery disease.

VaRTK – An accurate machine learning model trained on clinically curated variants to predict the variant pathogenicity score ::

Ravi Gupta, Manju Lakshmi, Charugulla Sai Yuva Sandeep, S. G. Thenral, Sandhya Nair, Anurag Gupta, Thiramsetti Sattibabu, Amit Parhar, Tamanna Golani, Sudarshana J. Pai, Tavleen Bajwa, Sakthivel Murugan S. M., Ramprasad V. L.

Identifying and prioritizing the disease causing variants from thousands of variants that are called during a whole genome and exome sequencing analysis is a time consuming and manual task. Pathogenicity based ranking of variants greatly improves the speed of report generation, in turn increasing the diagnostic yield.

Machine learning models recognize patterns in variant data and help in informed decision making and thus reduce time in the variant prioritization process. Models trained on public disease databases have not been so successful because of the presence of noisy and unvalidated data. We have developed a supervised machine learning model trained on manually curated and reviewed disease causing variants from a cohort of ~100,000 clinical samples. The VaRTK (Variant Ranking ToolKit) model is a random forest model trained on 46,345 (10,336 disease causing and 36,009 benign variants) which cover ~10,000 genes.

Our model was able to achieve a F1 and sensitivity of 0.98 on unseen test data consisting of ~15,000 variants covering ~6,000 genes. The model performance was further evaluated on a validation cohort of 166 samples which were resolved through manual interpretation. The model ranked the manually selected pathogenic variant(s) in the Top 20 in 90.66% of cases.

MedGenome Inc. Broadens Single Cell Transcriptome and Epigenome Profiling: From Tissues to single nuclei RNA (snRNA) Sequencing and Data Analysis

Neha Verma, Kushal Suryamohan, Derek Vargas, Ekkirala Chaitanya Ramesh

Single-cell transcriptomics has revolutionized genomics and is now an integral part of therapeutics and diagnostics research. Single cell RNA sequencing (scRNA-seq) enables the analysis of gene expression at single cell resolution via droplet-based cell capture methods that rely on microfluidic instruments such as those developed by 10x genomics. While this technology is now routinely used to study a variety of research areas including the study of cellular development, the identification of cell types and states, the exploration of human disease and the development of stem cell technologies, there are several instances where it is much harder to obtain intact cells (e.g., interconnected neurons, flash frozen or cryopreserved tissues). Furthermore, the logistics and budgetary challenges of obtaining fresh tissues can often lead to significant delays for obtaining research projects. Approaches to isolate cells from such difficult sources such as flow cytometry places the cells under stress, which substantially alters gene expression. In such cases, the use of nuclei for RNA-seq avoids the difficulties involved in obtaining undamaged whole cells. To overcome this limitation, MedGenome has developed a streamlined protocol to isolate nuclei from different sources of cells (fresh and cryopreserved cells). We demonstrate that these isolated nuclei can be used for downstream applications including gene expression profiling and epigenome profiling.

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

Ankita Das, Jing Wang, Kayla Lee, Derek Vargas, Gavin Washburn, Niyati Thosani, Vasumathi Kode, Nitin Mandloi, Angelica Lavallee, 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. With the aim to accommodate a wide range of cellular throughput and single cell genomics assays, at MedGenome, and we have validated and integrated multiple options for single cell library preparation workflows downstream of different cell capture platforms. We have generated proof-of-concept data to show the utility of the different platforms for successfully generating libraries from different workflows. We will present data on our platform agnostic approach to generating single cell gene expression libraries, starting with a range of single cell input types using the Chromium (10X Genomics), the plate-based SMART-Seq and the iCELL8 workflows (Takara Bio). Our results from the validation studies can help to determine the number of cells needed for an approach, and the number of cells and genes per cell recovered from the different approaches, and help researchers determine the single cell approach that will work for their research needs.

Recent studies have suggested that capturing additional information on cellular phenotypes or features can also provide valuable information on cell identities otherwise missed and additional heterogeneity, which in turn facilitate discovery of meaningful biomarkers and understanding of molecular mechanisms of development and disease. To enable for such discovery, we have validated several different feature barcoding solutions, and epigenomic analyses namely the CUT&TAG, and CITE-Seq approaches. We will present data demonstrating the utility of CUT&TAG in studying chromatin accessibility and as well as understanding epigenomic regulation and mechanisms of disease.

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).

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.

A novel algorithm to identify TCR-binding somatic mutations from human cancers ::

Ashwini Patil, MS1, Ravi Gupta, PhD1, Nitin Mandloi, MS1, Kiran V. Paul, MS1, Priyanka Shah, PhD1, Malini Manoharan, PhD1, Rohit Gupta, PhD1, and Amitabha Chaudhuri, PhD1

MedGenome conducted a study where TCGA data containing 9640 tumors from 33 different cancers was analyzed using its proprietary tumor microenvironment analysis platform OncoPeptTUMETM. It was observed that observed that CD8 T-cell content of tumors varies significantly from cancer to cancer, with a large proportion of tumors containing low CD8 T-cell infiltrate. It was also investigated whether high CD8 T-cell content of a tumor has any impact on patient survival. Alterations in multiple oncogenic and tumor suppressive pathways that correlated with CD8 T-cell exclusion in a tumor were identified. This study identifies multiple pathways that can be targeted to increase the sensitivity of tumors to checkpoint blockade.

Tumor microenvironment analysis provides insights into the activity of CD8 T-cells and their impact on survival ::

Ravi Gupta, Nitin Mandloi, Kiran Paul, Ashwini Patil, Rekha Sathian, Aparna Mohan, Malini Manoharan and Amitabha Chaudhuri

MedGenome conducted a study in which TCGA data was analyzed to investigate the impact of CD8 T-cell infiltration on disease outcome. The analysis indicated that CD8 T-cell infiltration predicts favorable survival in certain cancers, whereas in other cancers it has no effect. By comparing tumors from these two groups, we show that multiple cell intrinsic and extrinsic pathways modulate the anti-tumorigenic effects of CD8 T-cells. The TCGA data was analyzed using MedGenome’s proprietary OncoPeptTUME pipeline. The pipeline applies curated gene expression signatures to dissect components of the tumor microenvironment.

Integrated genomics approach of modeling tumors to assess their sensitivity to immune-mediated elimination ::

Nitin Mandloi, Ashwini Patil, Rekha Sathian, Aparna Mohan, Malini Manoharan, Ravi Gupta and Amit Chaudhuri

With the knowledge that tumors lacking CD8 T cells are less responsive to checkpoint control blockade, MedGenome’s scientists have chosen to study a set of core pathways associated with the absence or presence of specific immune cell types in tumors, which can be modulated to alter the immune profile of these unresponsive tumors and sensitize them to checkpoint control blockade.

The study utilized OncoPeptTUMETM to investigate the immune landscape of tumors from RNA-seq data, using a set of proprietary immune cell type-specific gene expression signatures. Differential gene expression between sets of tumors with different CD8 levels was carried out, after which M1 and M2-specific gene expression signatures were applied on CD8 T-cell depleted tumors and those having high or low M1 or M2 macrophages were identified. Our analysis demonstrated that combining expression signatures with tumor mutanome analysis can provide a powerful tool to assess the tumor microenvironment and identify pathways that promote, or exclude infiltration/differentiation of specific immune cells.

OncoPeptTUME™ is MedGenome’s powerful tumor microenvironment analysis solution. This analysis uses extensively curated and expression-verified gene signatures to interrogate RNA sequencing data to capture the cellular landscape of tumors. Immune phenotype scores normalized to the immune content separate tumors with high and low infiltration of specific cell types. Current immune cell types captured in this version of OncoPeptTUME™ include CD8 and CD4 T-cells, T-regulatory cells, NK cells, dendritic cells, B-cells, macrophages and myeloid derived suppressor cells (MDSCs).

Differential gene expression and tumormutanome analysis reveal significantly enriched pathways associated with higher tumor burden of M1 and M2 macrophages ::

Ravi Gupta, Nitin Mandloi, Ashwini Patil, Malini Manoharan, Rekha Sathian, Kiran Paul and Amitabha Chaudhuri

In the backdrop of the remarkable success checkpoint control inhibitors have shown in treating a variety of different cancers, this study focused on deeper assessment of the tumor and its microenvironment at the genetic and phenotypic level. Data from recent clinical trials have unequivocally established that the tumor microenvironment significantly impacts the efficacy of immune-oncology drugs.

The study which made a significant comparison between the tumor microenvironments of uveal melanoma vs skin cutaneous melanoma involved a gene expression signature-based approach to qualitatively and quantitatively assess the epithelial, stromal and immune content of tumors from RNA-seq data. These signatures were then applied singly, or in combination on the TCGA RNA-seq data from 33 cancers. As part of the study, 476 skin cutaneous melanoma (SKCM) and 80 melanoma (UVM) samples from TCGA were analysed.

The idea that gene expression signatures can address a critical unmet need in the immune-oncology space, which is to create a framework for treating tumors that carry less mutation burden combined with poor T-cell infiltration is well supported by the findings we obtained through this study.

Analysis of tumor microenvironment identifies pathways predicting response to checkpoint control inhibitors: A case study comparing the immune microenvironment of uveal melanoma vs skin cutaneous melanoma ::

Ashwini Patil, Nitin Mandloi, Rekha Sathian, Aparna Mohan, Malini Manoharan, Ravi Gupta and Amit Chaudhuri

With only a few studies having analyzed the interaction between granulocytic and monocytic myeloid derived suppressor cells in human cancers, MedGenome’s scientists have chosen to investigate their immune suppressive effect on the tumor microenvironment in this study. Clinical trials in the recent past have resulted in findings that strongly indicate a definite impact that the tumor microenvironment has on the efficacy of immuno-oncology drugs.

The study utilized OncoPeptTUME to identify tumors carrying different burdens of G-MDSCs and M-MDSCs from whole tumor RNA-seq data. Proprietary gene expression signatures were used, that discriminated G- from M- MDSCs on 9345 tumors from 33 cancers available in commonly used cancer genome databases. The analysis revealed that 28 of the 33 cancers have undetectable levels of G-MDSC, but exhibited high levels of M-MDSC cells. MedGenome’s findings support the understanding that granulocytic MDSCs are more prevalent in lymphoid organs and are not usually detected at a high level in tumor tissues.

OncoPeptTUME identifies tumor intrinsic and extrinsic factors promoting infiltration of granulocytic myeloid derived suppressor cells (G-MDSCs) in human cancers

Ravi Gupta, Kiran Paul, Nitin Mandloi, Malini Manoharan and Amit Chaudhuri

A critical phase in the development of cancer is the conversion of growth-suppressive normal tissue microenvironment into a growth-promoting tumor microenvironment. MedGenome conducted a study to interrogate the tumor epithelial and the stromal compartments in a cohort of tongue and buccal cancers, using NGS sequencing. With the help of DNA and RNA-seq data, the relative abundance of epithelial, stromal and immune cells and correlated these with the mutational burden of individual tumors was estimated. It was shown that the presence of driver mutations block immune cell infiltration in both buccal and tongue cancers. By contrast, smoking and use of alcohol sharply increased immune infiltration in buccal, but not in tongue cancers, suggesting that carcinogenic impact differentially affect the composition of the tumor stroma in these cancers.

Differential gene expression profile of tongue and buccal cancers produce unique and shared vaccine candidates for cancer immunotherapy ::

PapManoharan Malini, Iyer Laxman, Priyanka Shah, Kiran Paul, Amit Choudhary, Ravi Gupta

Large scale sequencing of cancer genomes have revealed several mutations that remain uncharacterized, of which only few mutations are tested for activating or loss of function. MedGenome conducted a study which utilized in-silico method to characterize 4096 mutations in 190 cancer census genes spread across 33 cancer groups. Mutations were studied to identify gain and loss of function mutations. The analysis revealed 2,614 destabilizing mutations which would relate to a loss of function in 185 genes and 433 stabilizing mutations in 125 genes which could have a gain of function role. The study showed that higher number of stabilizing mutations have been identified in TP53, CDKN2A and PIK3CA and destabilizing mutations are widely distributed across several genes.

A Structural Approach to Identify Activating and Loss of Function Somatic Mutation ::

R. Gupta, N. Mandloi, M. Manoharan, Kiran V. Paul, Deborah Consiglio and Amit Chaudhuri

It is known that tumor cells employ a variety of immune-evasive mechanisms. Of particular interest is the tumor-intrinsic mechanisms that prevent T-cells from infiltrating into the tumor microenvironment. To overcome this problem MedGenome’s OncoPeptTUMETM – a proprietary genomic solution in cancer immunotherapy helps in analyzing the tumor microenvironment by addressing the mechanisms that regulate T-cell infiltration in human tumors and by understanding these pathways the possibility of T-cell infiltration can be enhanced through identification of predictive biomarkers of response to checkpoint control therapies.

OncoPeptTUME identifies pathways enriched in T-cell inflamed tumors ::

K. Kumaramangalam, S. Sharan, R. Nathan, D. Consiglio, G.H. Hiranjith, R. Gupta and A. Chaudhuri

At the conference, MedGenome introduced OncoPept- its integrated platform that combines powerful analytics to analyze exome and RNA-seq data to quantitate the epithelial, stromal and immune cell infiltration, thereby producing a holistic view of the tumor and the tumor microenvironment. It provides end to end genomic solutions to address challenges in the cancer immunotherapy space.

OncoPept is an end-to-end genomic solution to discover novel therapeutics and biomarkers in the cancer immunotherapy space ::

Viswanathan S.1, Gupta R.1, Khanna-Gupta A.1, Chaudhuri A.2

The study titled “Whole Genome Sequencing data from the Wellderley study identifies rare variants in genes associated with diabetes and cardiomyopathy” involved the analysis of publicly available whole genome sequence data (WGS) of 454 healthy elderly Caucasian individuals from the Wellderly study. Genes known to be causative/predictive of diabetes and cardiovascular diseases were analyzed, and results were examined for any rare variants in this population. A significant finding was the discovery of a rare variant of Nebulin (NEB) which was present in about 60% of the individuals in this cohort. Nebulin is a multifunctional protein that binds and stabilizes actin, and when mutated is known to be associated with certain myopathies. This variant is of considerable interest because of its possible protective role in muscle function during aging, and the subsequent insights into cardiac conditions that arise due to a loss of muscle function.

Differential gene expression profile of tongue and buccal cancers produce unique and shared vaccine candidates for cancer immunotherapy ::

OncoPept(TM): A platform to interrogate tumors in search of novel cancer immunotherapy targets and biomarkers ::

R. Gupta, K. V. Paul, V. Ramprasad, R. Vijayalakshmi, A. Gupta and Amitabha Chaudhuri

MedGenome presented results from a study that examined differential gene expression, that identified unique and shared vaccine candidates that can be used in combination therapies. The study involved analysis of published genomics data from over 400 buccal and tongue cancer samples to identify potential vaccine candidates using MedGenome’s proprietary analysis platform. It examined the potential of checkpoint control inhibitors that activate host immune response to eliminate cancer cells. The results generated from analysis showed in both types of cancers, a set of tumor neo-antigens that can be used as both cancer vaccines, as well as biomarkers of response
to select patients for cancer immunotherapy, thereby serving a dual purpose.

Whole Genome Sequencing data from the Wellderley study identifies rare variants in genes associated with diabetes and cardiomyopathy ::

Ravi Gupta, Kiran Paul, Nitin Mandloi, Malini Manoharan, Kartik Kumaramangalam, Sam Santhosh and Amitabha Chaudhuri

OncoMD Cancer Analytics Platform combines tumor mutation profiles, expression signatures, copy number variations, epigenetic alterations and drug sensitivity to create a holistic view of human cancer enabling discovery of new targets for therapy and prognosis. The rich mutational data captured in OncoMD helps to identify potential T-cell neo-epitopes in different cancers and broadens our understanding of their prevalence in the context of the tumor microenvironment. This study indicates that the presence of T-cell neo-epitopes alone is not sufficient to sensitize tumors to immunotherapy drugs and requires cooperation from the tumor microenvironment. Powerful data analysis and interpretation tools such as OncoMD enable extraction of disease-relevant information from diverse genomics data to guide diagnosis and treatment decisions. Our study helps in accurate prediction of tumor response to immunotherapy treatments.

Molecular epidemiological analysis of oral cancer in the Indian population reveals region-specific differences in the spectrum of driver mutations and oncogenic pathways ::

R. Gupta, K. V. Paul, N. Mandloi, K. Kumaramangalam, D. Consiglio and A. Chaudhuri

OncoPeptTM is an integrated platform that combines powerful analytics to analyze exome and RNA-seq data with multistep prioritization to select cancer vaccine candidates as therapeutics. The platform generates neo-antigens from different types of genetic alterations such as SNVs, indels and fusion genes to generate a library of peptides that are automatically interrogated through the multiple prioritization steps. The optimized process has been validated on published datasets.

OncoMD: A powerful genomics data analysis and interpretation platform for cancer discovery research ::

Ravi Gupta, Kiran V. Paul, Vedam Ramprasad, Arati Gupta, Debbie Consiglio1 and Amitabha Chaudhuri

This study reveals the functionality of OncoPept, MedGenome’s proprietary cancer immunotherapy platform, that predicts T-cell neo-epitopes from human and mouse cancers. Multiple pre-clinical and clinical studies have demonstrated the importance of the characterization of T-cell neo epitopes and its role in the development of cancer vaccines. In this vein, MedGenome has built a platform combining exome and RNA-sequencing data and prioritizing the neo-epitopes further with advanced analytics. OncoPept combines deep sequencing to identify rare variants present in less than 10% of tumor cells to increase the repertoire of mutations that may be targeted by T-cells. OncoPept was then applied to a cohort of tongue and buccal cancers from the Indian subcontinent. The analysis revealed a set of tumor neo-antigens that can be used as both cancer vaccines in a therapeutic setting, as well as biomarkers of response to select patients for cancer immunotherapy.

OncoPeptTM interrogates the mutational landscape of tongue and buccal cancer in search of novel cancer immunotherapy targets and biomarkers ::

R. Gupta, V. Ramprasad, K. Kumaramangalam, A. Gupta, S. Santhosh and A. Chaudhuri

The poster depicts an oral cancer study to examine the molecular basis of region-specific differences by profiling four distinct oral cancer cohorts collected from the Northern, Eastern, Southern and Western regions of India. The cohorts in the study were such that it maximized the probability that all individuals shared similar environmental and lifestyle factors within a given region. In addition, samples of tongue and buccal lesions were collected to investigate factors that predisposed an individual to one cancer and not the other, even if they came from the same geographical area. The poster discusses the deep dives and analysis performed to develop a predictive score of response to immuno-oncology therapies.

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