Polyclonal selection of immune checkpoint mutations in thyroid autoimmunity - ExomeNanoSeq
Our immune system contains multiple tolerance checkpoints to prevent the activation of self-reactive lymphocytes. How some lymphocytes escape these constraints to cause autoimmune disease remains poorly understood. A long-standing hypothesis in autoimmunity posits that somatic mutations in immune regulatory genes may enable self-reactive lymphocytes to bypass tolerance checkpoints. However, testing this hypothesis has proved challenging due to technical limitations in detecting somatic mutations in polyclonal cell populations. Here, we use deep whole-exome and targeted NanoSeq, a highly accurate single-molecule DNA sequencing protocol, to comprehensively search for driver mutations in autoimmune thyroid disease. This revealed a remarkably high number of B cell clones convergently acquiring loss-of-function somatic mutations in key immune checkpoint genes TNFRSF14 (also known as HVEM) and CD274 (encoding PD-L1), as well as less frequent driver mutations in a diversity of other immune genes. In highly inflamed biopsies, we detected tens to hundreds of independent immune-checkpoint mutant clones. Laser capture microdissection, methylation sequencing, spatial transcriptomics, immunohistochemistry and single-nucleus DNA sequencing localised these mutations to non-naive B cells and revealed frequent co-occurrence of drivers in single cells. We found widespread TNFRSF14 biallelic loss in mutant B cells, and several clones with as many as 4-6 driver mutations. Whilst each clone accounts for a small proportion of cells (typically <1%), the myriad mutant clones in each donor collectively amounted to a substantial fraction of B cells harbouring one or more driver mutations. Our results support the hypothesis that somatic mutations in autoimmune lymphocytes may allow them to escape tolerance constraints through a polyclonal cascade of somatic evolution. These findings provide new insights into the molecular basis of autoimmune disease and suggest novel diagnostic and therapeutic avenues.
- 01/04/2026
- 1 sample
- DAC: EGAC00001000000
- Technology: Illumina NovaSeq 6000
DUO:0000019 version: 2021-02-23
publication required
This data use modifier indicates that requestor agrees to make results of studies using the data available to the larger scientific community.
DUO:0000026 version: 2021-02-23
user specific restriction
This data use modifier indicates that use is limited to use by approved users.
DUO:0000028 version: 2021-02-23
institution specific restriction
This data use modifier indicates that use is limited to use within an approved institution.
DUO:0000042 version: 2021-02-23
general research use
This data use permission indicates that use is allowed for general research use for any research purpose.
Wellcome Trust Sanger Institute Cancer Genome Group Data Sharing Policy
Studies are experimental investigations of a particular phenomenon, e.g., case-control studies on a particular trait or cancer research projects reporting matching cancer normal genomes from patients.
| Study ID | Study Title | Study Type |
|---|---|---|
| EGAS00001007175 | Other | |
| EGAS00001007647 | Other |
This table displays only public information pertaining to the files in the dataset. If you wish to access this dataset, please submit a request. If you already have access to these data files, please consult the download documentation.
| ID | File Type | Size | Quality Report |
Located in
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| EGAF00009032460 | cram | 11.6 GB |
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| EGAF00009032461 | cram | 10.1 GB |
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| EGAF00009032462 | cram | 9.0 GB |
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| EGAF00009032463 | cram | 11.3 GB |
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| EGAF00009032464 | cram | 9.9 GB |
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| EGAF00009032465 | cram | 8.9 GB |
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| EGAF00009032466 | cram | 11.4 GB |
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| EGAF00009032467 | cram | 10.0 GB |
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| EGAF00009032468 | cram | 8.9 GB |
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| EGAF00009032469 | cram | 11.8 GB |
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| EGAF00009032470 | cram | 10.0 GB |
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| EGAF00009032471 | cram | 9.2 GB |
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| EGAF00009032472 | cram | 11.3 GB |
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| EGAF00009032473 | cram | 9.6 GB |
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| EGAF00009032474 | cram | 8.8 GB |
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| EGAF00009032475 | cram | 11.2 GB |
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| EGAF00009032476 | cram | 9.6 GB |
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| EGAF00009032477 | cram | 8.8 GB |
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| EGAF00009032478 | cram | 11.5 GB |
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| EGAF00009032479 | cram | 9.8 GB |
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| EGAF00009032480 | cram | 9.0 GB |
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| EGAF00009032481 | cram | 9.2 GB |
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| EGAF00009032482 | cram | 7.9 GB |
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| EGAF00009032483 | cram | 9.5 GB |
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| EGAF00009032484 | cram | 8.7 GB |
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| EGAF00009032485 | cram | 44.5 GB |
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| EGAF00009032486 | cram | 37.4 GB |
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| EGAF00009032487 | cram | 31.5 GB |
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| EGAF00009032488 | cram | 30.0 GB |
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| EGAF00009032489 | cram | 44.3 GB |
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| EGAF00009032490 | cram | 31.1 GB |
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| EGAF00009032491 | cram | 29.6 GB |
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| EGAF00009032492 | cram | 37.5 GB |
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| EGAF00009032516 | cram | 87.4 GB |
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| EGAF00009032517 | cram | 87.5 GB |
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| EGAF00009032518 | cram | 82.1 GB |
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| EGAF00009032519 | cram | 93.6 GB |
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| EGAF00009032520 | cram | 87.6 GB |
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| EGAF00009032521 | cram | 44.0 GB |
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| EGAF00009032522 | cram | 48.5 GB |
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| EGAF00009032523 | cram | 44.0 GB |
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| EGAF00009032524 | cram | 41.3 GB |
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| EGAF00009032525 | cram | 47.5 GB |
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| EGAF00009032526 | cram | 43.9 GB |
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| EGAF00009032527 | cram | 45.7 GB |
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| EGAF00009032528 | cram | 50.5 GB |
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| EGAF00009032529 | cram | 11.1 GB |
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| EGAF00009032530 | cram | 12.2 GB |
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| EGAF00009032531 | cram | 10.9 GB |
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| EGAF00009032532 | cram | 10.3 GB |
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| EGAF00009032533 | cram | 127.3 GB |
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| EGAF00009032534 | cram | 251.2 GB |
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| EGAF00009032535 | cram | 317.7 GB |
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| EGAF00009033228 | cram | 7.1 GB |
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| EGAF00009033229 | cram | 11.2 GB |
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| EGAF00009033230 | cram | 37.0 GB |
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| EGAF00009033231 | cram | 44.4 GB |
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| EGAF00009033232 | cram | 31.7 GB |
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| EGAF00009033233 | cram | 30.0 GB |
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| EGAF00009033234 | cram | 96.2 GB |
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| EGAF00009033235 | cram | 45.0 GB |
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| EGAF00009033236 | cram | 42.3 GB |
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| EGAF00009033237 | cram | 49.0 GB |
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| EGAF00009033238 | cram | 45.7 GB |
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| EGAF00009033239 | cram | 12.1 GB |
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| EGAF00009033240 | cram | 11.1 GB |
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| EGAF00009033245 | cram | 142.5 GB |
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| 67 Files (2.7 TB) | ||||
