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).