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A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity.

ABSTRACT: 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. 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 machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. With cell line mixtures, we demonstrate increased sensitivity compared to previously published tools. Moreover, our patient-specific digital PCR validation approach provides a sensitive, robust orthogonal approach that could be used for clinical validation. Using DASH on 610 patients across 15 tumor types, we find that 18% of patients have HLA LOH. Moreover, we show inflated HLA LOH rates compared to genome-wide LOH and correlations between CD274 (encodes PD-L1) expression and microsatellite instability status, suggesting the HLA LOH is a key immune resistance strategy.

SUBMITTER: Pyke RM

PROVIDER: S-EPMC9005524 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity.

Pyke Rachel Marty RM Mellacheruvu Dattatreya D Dea Steven S Abbott Charles W CW McDaniel Lee L Bhave Devayani P DP Zhang Simo V SV Levy Eric E Bartha Gabor G West John J Snyder Michael P MP Chen Richard O RO Boyle Sean Michael SM

Nature communications 20220412 1

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. 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 machine learning-based algorithm to detect HLA LOH from paired tumor-normal sequencing data. With cell line mixtures, we ...[more]

PMID: 35414054

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Project description:BackgroundLoss of heterozygosity (LOH) diminishes genetic diversity within cancer genomes. A tumour arising in an individual heterozygous for a functional and a loss-of-function (LoF) allele of a gene occasionally retain only the LoF allele. This can result in deficiency of specific protein activities in cancer cells, creating unique differences between tumour cells and normal cells of the individual. Such differences may constitute vulnerabilities that can be exploited through allele-specific therapies.MethodsTo discover frequently lost genes with prevalent LoF alleles, we mined the 1000 Genomes dataset for SNVs causing protein truncation through base substitution, indels or splice site disruptions, resulting in 60 LoF variants in 60 genes. From these, the variant rs3892097 in the liver enzyme CYP2D6 was selected because it is located within a genomic region that frequently undergoes LOH in several tumor types including hepatocellular cancers. To evaluate the relationship between CYP2D6 activity and the toxicities of anticancer agents, we screened 525 compounds currently in clinical use or undergoing clinical trials using cell model systems with or without CYP2D6 activity.FindingsWe identified 12 compounds, AZD-3463, CYC-116, etoposide, everolimus, GDC-0349, lenvatinib, MK-8776, PHA-680632, talazoparib, tyrphostin 9, VX-702, and WZ-3146, using an engineered HEK293T cell model. Of these, talazoparib and MK-8776 demonstrated consistently heightened cytotoxic effects against cells with compromised CYP2D6 activity in engineered hepatocellular cancer cell models. Moreover, talazoparib displayed CYP2D6 genotype dependent effects on primary hepatocellular carcinoma organoids.InterpretationExploiting the loss of drug-metabolizing enzyme gene activity in tumor cells following loss of heterozygosity could present a promising therapeutic strategy for targeted cancer treatment.FundingThis work was funded by Barncancerfonden (T.S, PR2022-0099 and PR2020-0171, X.Z, TJ2021-0111), Cancerfonden (T.S, 211719Pj and D.G, 222449Pj), Vetenskapsrådet (T.S, 2020-02371 and D.G, 2020-04707), and the Erling Persson Foundation (T.S, 2020-0037 and T.S, 2023-0113).

Dataset Information

A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity.

Publications

A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity.

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