ArticleNature communications2022
A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer human leukocyte antigen loss of heterozygosity.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- Predictors of Immune Checkpoint Blockade Response in dMMR Colorectal Cancer Using an Integrated Immune-Enhanced Multiomics Platform.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Reinvigorating COTL1Nature cell biology · 2026Article
- The digital keystone: how artificial intelligence is reshaping HLA research and clinical practice.Immunogenetics · 2026Review
- Frequent genetic alterations in myositis autoantigen genes in cancer-associated dermatomyositis.Annals of the rheumatic diseases · 2026Article
- Review
- Defects in antigen processing and presentation: mechanisms, immune evasion and implications for cancer vaccine development.Nature reviews. Immunology · 2026Review
- Immunogenicity remodeling in hepatocellular carcinoma: mechanisms and translational strategies.Therapeutic advances in medical oncology · 2026Review
- Dual-module aCAR-iCAR NK cells for solid tumors: cascade resistance mechanisms, AI-driven engineering, and precision stratification.Frontiers in immunology · 2026Review
- Article
- Article
- Genome instability and crosstalk with the immune response.Genome medicine · 2025Review
- Allele-specific HLA LOH in solid tumors: distinct patterns by tumor type and potential prognostic relevance.Journal for immunotherapy of cancer · 2025Article
- Immuno-epidemiologic mapping of human leukocyte antigen diversity across glioma patient cohorts.Neuro-oncology · 2025Article
- Pan-cancer analysis shapes the understanding of cancer biology and medicine.Cancer communications (London, England) · 2025Review
- Article
- Tumor-associated neutrophils attenuate the immunosensitivity of hepatocellular carcinoma.The Journal of experimental medicine · 2025Article
- Detecting HLA loss of heterozygosity within a standard diagnostic sequencing workflow for prognostic and therapeutic opportunities.NPJ precision oncology · 2024Article
- Current Landscape of Cancer Immunotherapy: Harnessing the Immune Arsenal to Overcome Immune Evasion.Biology · 2024Review
- Informing immunotherapy with multi-omics driven machine learning.NPJ digital medicine · 2024Review
- The genomic landscape of the immune system in lung cancer: present insights and continuing investigations.Frontiers in genetics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.