Biology subjects

Dhanik, A.

Publications and source records attributed to Dhanik, A..

3 recordsLinked to original sources

bioRxiv2024

A novel HLA Class II presentation prediction algorithm deciphers immunogenic CD4 epitopes specific to KRAS G12C

Accurate prediction of peptide presentation by HLA molecules is important for generation of effective individualized cancer vaccines and immunotherapies. While presentation prediction algorithms for HLA class I have been successfully applied in the context of such therapies, improved prediction algorithms for class II are needed. EDGE-II is a novel algorithm based on a protein large language model that has a learned allele deconvolution network trained on existing and new immunopeptidomics data. It delivers state-of-the-art performance on prediction of peptide presentation by HLA class II and immunogenicity elicited by CD4+ T-cell epitopes. In a patient with a KRAS G12C positive tumor treated with a KRAS G12C targeting immunotherapy, EDGE-II identified KRAS G12C class II neoantigens that elicited clonally expanded CD4+ T cells with cytotoxic transcriptional profiles post-vaccination. EDGE-II could play an important role in the development of effective cancer immunotherapies by elucidating an enriched understanding of the immunopeptidome.

bioinformatics↗

bioRxiv2024

Comprehensive Longitudinal ctDNA Monitoring in Metastatic Cancer Patients Treated with an Individualized Neoantigen-directed Vaccine

PurposeCirculating-tumor DNA (ctDNA) is an emerging, minimally invasive diagnostic and prognostic biomarker for patients receiving a variety of cancer therapies. Comprehensive and robust longitudinal monitoring of ctDNA can provide an understanding of tumor burden, heterogeneity, and response or resistance to treatment. Experimental DesignctDNA of 28 metastatic cancer patients receiving an individualized neoantigen-directed immunotherapy was monitored longitudinally, up to two years, using a unique hybrid next generation sequencing assay targeting tumor-informed and tumor-naive variants. Patient-specific panels were designed targeting an average of 144 variants per patient. A tumor-naive universal panel was also designed for inclusion with patient-specific panels to monitor recurrently mutated tumor hotspots (e.g., KRAS and TP53) and genes implicated in immunotherapy resistance (B2M, TAP1/2). ResultsAnalytical characterization of the assay established linearity with a mean variant allele frequency (VAF) [≥]0.049%, and a variant-level limit of detection (LOD95) of 0.12%. Tumor-informed variants were detected in 26/28 patients, and de novo variants were observed in 25/28 patients. HLA LOH was also observed. Longitudinal ctDNA data provided key insights into patients responses to vaccine treatment. ConclusionsThe hybrid design of the ctDNA monitoring assay provides the sensitivity and specificity required for evaluating patient samples undergoing individualized therapy. It provides an improved capability to understand patient response to experimental therapies and further supports the utility of ctDNA as a cancer biomarker.

genomics↗

bioRxiv2021

Rigorous estimation of post-translational proteasomal splicing in the immunopeptidome

Recently, de novo peptide sequencing has made it possible to gain new insights into the human immunopeptidome without relying on peptide databases, while identifying peptides of unknown origin. Many recent studies have attributed post-translational proteasomal splicing as the origin of those peptides. Here, we describe a peptide source assignment workflow to rigorously assign the source of de novo sequenced peptides and find that the estimated extent of post-translational splicing in the immunopeptidome is much lower than previously reported. Our approach demonstrates that many peptides that were thought to be post-translationally spliced are likely linear peptides, and many peptides that were thought to be trans-spliced could be cis-spliced. We believe our approach furthers the understanding of post-translationally spliced peptides and thus improves the characterization of immunopeptidome which plays a critical role in the immune response to antigens in cancer, autoimmune disease, and infections.

bioinformatics↗