Personalis Presents Data from New Platform Features at the Society for Immunotherapy of Cancer (SITC) 37th Annual Meeting
Personalis, Inc. (NASDAQ: PSNL) announced it will present data from its NeXT Platform® at the Society for Immunotherapy of Cancer (SITC) Annual Meeting from November 8-12 in Boston. The presentation includes three abstracts that demonstrate advancements in next-generation sequencing (NGS) related to immuno-oncology. Key highlights include improved neoantigen prediction and the integration of immune infiltration metrics to enhance response predictions for immunotherapy treatments. The data aims to improve patient stratification and therapeutic efficacy.
- Three abstracts to be presented at the SITC Annual Meeting highlight advancements in NGS applications in immuno-oncology.
- Improved predictions of neoantigen immunogenicity could enhance the understanding of patient responses to therapies.
- Integration of composite biomarkers may lead to better patient stratification for immunotherapy.
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"To understand the complex dynamics of cancer and ultimately better predict response to therapy, we need to advance the tools we're using to characterize the tumor microenvironment. We continue to build upon the suite of technologies in our ImmunoID NeXT Platform® to enable the high-resolution study of immuno-oncology targets in an array of contexts, including improved neoantigen prediction, immune-composition profiling, and composite biomarker signatures,” said
The company will present the following posters at SITC2022, as well as with customers including the
Title: A combination of antigen presentation and T-cell recognition features improves neoantigen immunogenicity predictions (Abstract 51)
Overview: Tumor neoantigen burden outperformed tumor mutational burden in prediction of patient response to checkpoint inhibitor immunotherapy by better capturing the biological mechanism underlying response. However, immune recognition of neoantigens by T-cells requires more than antigen presentation, which has been the focus of tumor neoantigen burden thus far. To address this need, we extend the existing SHERPA® MHC-presentation framework to predict neoantigen immunogenicity. By combining antigen presentation and T-cell recognition features in a two-tiered model, we can better predict immunogenic neoantigens and make progress towards using neoantigens as biomarkers to assess checkpoint inhibitor efficacy.
Title: Sensitive prediction of immunotherapy response by integrating immune infiltration and neoantigen presentation score in late-stage melanoma (Abstract 119)
Overview: Single-modality biomarkers such as tumor mutational burden (TMB) often fail to reliably predict response to immune checkpoint blockade (ICB), likely due to incomplete characterization of the complex tumor-immune interactions that influence treatment efficacy. We previously developed the composite biomarker, neoantigen presentation score (NEOPS™), which integrates neoantigen processing and presentation potency, and showed it outperformed TMB and other single-modality biomarkers in predicting ICB response in melanoma. Here, we combined NEOPS with the assessment of tumor immune infiltration and demonstrated more accurate patient stratification for ICB response.
Title: Transcriptome augmentation provides accurate and sensitive quantification of genes associated with the tumor microenvironment (Abstract 146)
Overview: While malignant cells dictate much of the tumor biology, there is evidence that the tumor microenvironment (TME) also plays a significant role in disease progression and response to therapy. The role of the immune cells is particularly relevant in immunotherapy, and multiple transcriptome-based biomarkers have shown utility in predicting the efficacy of immune checkpoint blockade. However, little is known about the benefits of enhancing the depth and uniformity of transcriptome sequencing coverage for quantifying the TME cell type composition. Using the ImmunoID NeXT Platform®, which combines high-quality exome and transcriptome sequencing with advanced informatics to comprehensively characterize the tumor and TME from a single FFPE tumor sample, applying augmented transcriptome coverage to the assessment of TME benefited the identification of marker genes for cell type enrichment analysis without introducing bias.
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