Benzinga | Oct 26, 2021 04:33PM EDT

Personalis Says Publishes New Data Demonstrating Performance, Utility Of SHERPA For High-Accuracy Neoantigen Prediction And Cancer Diagnostic Biomarker Development

Personalis, Inc. (NASDAQ:PSNL), a leader in advanced genomics for cancer, today announced the publication of its study "Precision neoantigen discovery using large-scale immunopeptidomes and composite modeling of MHC peptide presentation," in the Immunopeptidomics Special Issue of the journal Molecular & Cellular Proteomics.

The Personalis authors created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA(tm)), a novel pan-allelic machine learning algorithm for predicting MHC-peptide binding and presentation that demonstrates significantly improved performance compared to currently available prediction tools. To improve performance and generalizability, SHERPA was trained with immunopeptidomics data from newly engineered cell lines mono-allelic for HLA combined with other publicly available datasets. In addition, SHERPA was designed to more comprehensively capture epitope binding and presentation features to further enhance the predictive power of the algorithm. Using a composite model constructed with gradient boosting decision trees, multi-allelic deconvolution, and 2.15 million peptides encompassing 167 unique human HLA alleles, SHERPA achieved a 1.44-fold improvement of positive predictive value compared to existing tools when evaluated on independent mono-allelic datasets. Since publication, Personalis has further expanded the in-house generated immunopeptidomics training data set to a total of ~70 mono-allelic cell lines, resulting in a new version of SHERPA with further enhanced performance.

"Integrating data from diverse cell lines and tissue types improved the generalizability of our models compared to other in silico methods, a critically important aspect when applying our models to patient samples. With a high degree of accuracy, SHERPA has the potential to enable higher accuracy neoantigen binding prediction for many clinical applications," said Richard Chen, MD, Personalis' CMO and SVP of R&D. "With this advancement, SHERPA is expected to facilitate the discovery of more predictive biomarkers for cancer therapy as well as empower the development of neoantigen-targeting, personalized cancer therapies. Our recently published NEOPS(tm) biomarker is one example of a SHERPA-derived composite biomarker that has shown promise in predicting immunotherapy response in cancer patients."

The publication can be found at this link.