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Record W4283715614 · doi:10.1172/jci151666

Reversal of viral and epigenetic HLA class I repression in Merkel cell carcinoma

2022· article· en· W4283715614 on OpenAlexafffund
Patrick C. Lee, Susan Klaeger, Phuong M. Le, Keegan Korthauer, Jingwei Cheng, Varsha Ananthapadmanabhan, Thomas C. Frost, Jonathan Stevens, Alan Y. Wong, J. Bryan Iorgulescu, Anna Tarren, Vipheaviny Chea, Isabel Carulli, Camilla K. Lemvigh, Christina B. Pedersen, Ashley K. Gartin, Siranush Sarkizova, Kyle Wright, Letitia W. Li, Jason Nomburg, Shuqiang Li, Teddy Huang, Xiaoxi Liu, Lucas Pomerance, Laura M. Doherty, Annie Apffel, Luke Wallace, Suzanna Rachimi, Kristen D. Felt, Jacquelyn O. Wolff, Elizabeth Witten, Wandi Zhang, Donna Neuberg, William J. Lane, Guanglan Zhang, Lars Rønn Olsen, Manisha Thakuria, Scott J. Rodig, Karl R. Clauser, Gabriel J. Starrett, John G. Doench, Sara J. Buhrlage, Steven A. Carr, James A. DeCaprio, Catherine J. Wu, Derin B. Keskin

Bibliographic record

VenueJournal of Clinical Investigation · 2022
Typearticle
Languageen
FieldMedicine
TopicPolyomavirus and related diseases
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Institute of General Medical SciencesNational Cancer InstituteNational Institutes of HealthProvincial Health Services AuthorityMaze TherapeuticsBeiGeneKoch Institute for Integrative Cancer Research, Massachusetts Institute of TechnologyNational Human Genome Research InstituteAgios PharmaceuticalsFoghorn TherapeuticsParker Institute for Cancer ImmunotherapyLeukemia and Lymphoma SocietyG. Harold and Leila Y. Mathers FoundationBC Children’s Hospital FoundationBC Children's HospitalHoward Hughes Medical InstituteBiogenDana-Farber/Harvard Cancer CenterChildren's Hospital FoundationConquer Cancer FoundationNational Heart, Lung, and Blood InstitutePfizerCancer Research Institute
KeywordsPsychological repressionMerkel cell carcinomaEpigeneticsHuman leukocyte antigenBiologyCarcinomaCancer researchMerkel cellVirologyImmunologyGeneticsAntigenGene

Abstract

fetched live from OpenAlex

Cancers avoid immune surveillance through an array of mechanisms, including perturbation of HLA class I antigen presentation. Merkel cell carcinoma (MCC) is an aggressive, HLA-I-low, neuroendocrine carcinoma of the skin often caused by the Merkel cell polyomavirus (MCPyV). Through the characterization of 11 newly generated MCC patient-derived cell lines, we identified transcriptional suppression of several class I antigen presentation genes. To systematically identify regulators of HLA-I loss in MCC, we performed parallel, genome-scale, gain- and loss-of-function screens in a patient-derived MCPyV-positive cell line and identified MYCL and the non-canonical Polycomb repressive complex 1.1 (PRC1.1) as HLA-I repressors. We observed physical interaction of MYCL with the MCPyV small T viral antigen, supporting a mechanism of virally mediated HLA-I suppression. We further identify the PRC1.1 component USP7 as a pharmacologic target to restore HLA-I expression in MCC.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.356
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2022
Admission routes2
Has abstractyes

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