T Cell Receptor (TCR) repertoire sequencing from FFPE samples
In this white-paper we present data generated by using a modified protocol of the SMARTer® TCR Proﬁling Kit to perform TCR sequencing and analysis of tumor-infiltrating lymphocytes (TILs) from a FFPE tumor tissue block.
We accept sample both as RNA (100 ng minimum) or 5 μm unstained FFPE tumor tissue blocks.
OncoPeptVACTM accurately predicts neoantigens from tumors using a novel HLA-peptide-TCR-binding algorithm
In this whitepaper, we present OncoPeptVAC, a machine learning-based approach for neoantigen prediction. In this white-paper we show a) data to demonstrate the superior performance of the OncoPeptVAC pipe-line compared to other neoantigen prediction pipelines that use HLA-binding to predict immunogenic peptides b) features of the 9-mer peptide that favors TCR-binding over those that do not and c) validation data of the accuracy of prediction using an in-house developed dendritic-cell - T cell activation assay.
OncoPeptTUMETM — A novel in-silico approach to model the tumor microenvironment and predict treatment efficacy and long-term survival benefits for immunotherapy applications
Cancer immunotherapy is now established as a major therapeutic modality, and 70% of all cancer patients are estimated to receive some form of immunotherapy treatment as a part of their disease control by 2025. Cancer immunotherapy drugs elicit their anti-tumor immune response in a subset of the treated patients by activating CD8 T-cells and provide sustainable and long-lasting benefit in a few. Recently significant efforts have been devoted to understanding the factors that influence response to immuno-therapy or contribute to the development of resistance to therapy. While it is appreciated that many different tumor cell- intrinsic and extrinsic features, including the tumor microenvironment, driver gene mutations, host genetics, microbiome and environmental factors modulate response to immune checkpoint inhibitors , the tumor microenvironment ecosystem could be a major contributor in regulating response to immunotherapy and development of resistance [2,3].
In this whitepaper, we present key features and highlight some case studies using the Diabetome Knowledgebase. The Diabetome contains multiple data points collected over 25 years on over 300,000 Type 1 and Type 2 diabetes patients. Information available in the database includes well-characterized clinical phenotypes, biochemical investigations, pharmaceutical prescriptions, genotype mapping, complications of diabetes, pedigree charts & basic statistical tools. By utilizing this integrated solution, researchers can stratify patients into sub-groups based on parameters such as rapid deteriorates, and differential therapeutic responders (positive and negative). This will allow researchers to further study the underlying mechanisms of the identified phenotypes by correlating with their clinical data, predict risk of diabetes, and find molecular features unique to the subgroups and help in identifying ideal treatment modalities for the sub-groups. Taken together, the Diabetome is a powerful tool to facilitate accurate drug target prediction and novel discoveries. In this white paper, we highlight the a) key features of the data-sets present , b) provide an overview of the output of the filters c) highlight examples of the types of data available for complications of the disease and show data on analysis and stratification of patients based on therapy.
Ophthatome Knowledgebase: Over 500,000 Clinical Phenotype Records for Ocular Research
To enable genomic, pharmacogenomic and clinical research and discovery for ocular diseases, MedGenome has launched the OphthatomeTM Knowledgebase. This knowledgebase of ocular diseases is a comprehensive collection of clinical, phenotype and biochemical data providing researchers and clinicians with a platform to design studies that address critical unmet needs in eye disorders. The searchable interface allows end users to build complex queries to select disease cohorts based on organs affected, disease type and subtype, the age of disease onset, drug response and many other clinical and phenotypic parameters.