MétaCan
Menu
Back to cohort
Record W4281486289 · doi:10.1002/hon.3033

Exome sequencing identifies <i>PD‐L2</i> as a potential predisposition gene for lymphoma

2022· letter· en· W4281486289 on OpenAlexaff
Jianming Shao, Lei Gao, Marco L. Leung, Bailey Gallinger, Cara Inglese, M. Stephen Meyn, Daniela del Gaudio, Soma Das, Zejuan Li

Bibliographic record

VenueHematological Oncology · 2022
Typeletter
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsHospital for Sick Children
FundersAmerican Cancer Society
KeywordsExome sequencingLymphomaCancer researchGermlineBiologyFollicular lymphomaGeneGeneticsMedicineImmunologyMutation

Abstract

fetched live from OpenAlex

To investigate germline predisposition in lymphoma, we performed whole-exome sequencing and discovered a novel variant (c.817-1G>T) in programmed cell death 1 ligand 2 (PD-L2) in a family with early-onset lymphomas and other cancers. The variant was present in the proband with follicular lymphoma and his son with Hodgkin's lymphoma. It was in the terminal splice acceptor site of PD-L2 and embedded in a putative enhancer of Janus kinase 2 (JAK2) and programmed cell death 1 ligand (PD-L1). We also found that gene expression of PD-L2, PD-L1, and JAK2 was significantly increased. Using 3' rapid amplification of cDNA ends (3' RACE), we detected an abnormal PD-L2 transcript in the son. Thus, the c.817-1G>T variant may result in the elevated PD-L2 expression due to the abnormal PD-L2 transcript and the elevated PD-L1 and JAK2 expression due to increased enhancer activity of PD-L1 and JAK2. The PD-L2 novel variant likely underlies the genetic etiology of the lymphomas in the family. As PD-L2 plays critical roles in tumor immunity, identification of PD-L2 as a germline predisposition gene may inform personalized immunotherapy in lymphoma patients.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.037
GPT teacher head0.310
Teacher spread0.274 · 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 designObservational
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueHematological OncologySame topicLymphoma Diagnosis and TreatmentFrench-language works237,207