Biology subjects

Mellacheruvu, D.

Publications and source records attributed to Mellacheruvu, D..

3 recordsLinked to original sources

bioRxiv2021

Validated machine learning algorithm with sub-clonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity

Human leukocyte antigen loss of heterozygosity (HLA LOH) allows cancer cells to escape immune recognition by deleting HLA alleles, causing the suppressed presentation of tumor neoantigens that would otherwise bind to them. Despite its importance in immunotherapy response, few methods exist to detect HLA LOH, and their accuracy is not well understood. Here, we develop DASH (Deletion of Allele-Specific HLAs), a novel machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. Through validation with cell line mixtures and patient-specific digital PCR, we demonstrate increased sensitivity compared to previously published tools and pave the way for clinical utility. Using DASH on 611 patients across 15 tumor types, we found that 18% of patients had HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (PD-L1) expression and MSI status, suggesting the HLA LOH is a key immune resistance strategy.

bioinformatics↗

bioRxiv2021

Precision neoantigen discovery using large-scale immunopeptidomes and composite modeling of MHC peptide presentation

Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anti-cancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 mono-allelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale mono-allelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC binding pocket diversity in the training data and extend allelic coverage in underprofiled populations. To improve generalizability, SHERPA systematically integrates 128 mono-allelic and 384 multi-allelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44 fold improvement of positive predictive value compared to existing tools when evaluated on independent mono-allelic datasets and a 1.15 fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications.

bioinformatics↗

bioRxiv2017

The Function of the COPII Gene Paralogs SEC23A and SEC23B Are Interchangeable In Vivo

SEC23 is a core component of the coat protein-complex II (COPII)-coated vesicle, which mediates transport of secretory proteins from the endoplasmic reticulum (ER) to the Golgi1-3. Mammals express 2 paralogs for SEC23 (SEC23A and SEC23B). Though the SEC23 gene duplication dates back >500 million years, both SEC23s are ~85% identical at the amino acid sequence level. In humans, deficiency for SEC23A or SEC23B results in cranio-lenticulo-sutural dysplasia4 or congenital dyserythropoietic anemia type II (CDAII), respectively5. The disparate human syndromes and reports of secretory cargos with apparent paralog-specific dependence6,7, suggest unique functions for the two SEC23 paralogs. Here we show indistinguishable intracellular interactomes for human SEC23A and SEC23B, complementation of yeast SEC23 by both human and murine SEC23A/B paralogs, and the rescue of lethality resulting from Sec23b disruption in zebrafish by a Sec23a-expressing transgene. Finally, we demonstrate that the Sec23a coding sequence inserted into the endogenous murine Sec23b locus fully rescues the mortality and severe pancreatic phenotype previously reported with SEC23B-deficiency in the mouse8-10. Taken together, these data indicate that the disparate phenotypes of SEC23A and SEC23B deficiency likely result from evolutionary shifts in gene expression program rather than differences in protein function, a paradigm likely applicable to other sets of paralogous genes. These findings also suggest the potential for increased expression of SEC23A as a novel therapeutic approach to the treatment of CDAII, with potential relevance to other disorders due to mutations in paralogous genes.

evolutionary biology↗