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Record W2105545710 · doi:10.1158/0008-5472.can-13-3725

A Search for Novel Cancer/Testis Antigens in Lung Cancer Identifies VCX/Y Genes, Expanding the Repertoire of Potential Immunotherapeutic Targets

2014· article· en· W2105545710 on OpenAlexaff
Ayumu Taguchi, Allen D. Taylor, Jaime Rodriguez‐Canales, Müge Çeliktaş, Hui Liu, Xiaotu Ma, Qing Zhang, Chee‐Hong Wong, Alice Chin, Luc Girard, Carmen Behrens, Wan L. Lam, Stephen Lam, John D. Minna, Ignacio I. Wistuba, Adi F. Gazdar, Samir Hanash

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsCanadian Centre for Applied Research in Cancer ControlBC Cancer Agency
FundersNational Cancer Institute
KeywordsRepertoireCancerAntigenGeneBiologyLung cancerImmunologyCancer researchComputational biologyMedicineGeneticsOncology

Abstract

fetched live from OpenAlex

Cancer/testis (CT) antigens are potential immunotherapeutic targets in cancer. However, the expression of particular antigens is limited to a subset of tumors of a given type. Thus, there is a need to identify antigens with complementary expression patterns for effective therapeutic intervention. In this study, we searched for genes that were distinctly expressed at a higher level in lung tumor tissue and the testes compared with other nontumor tissues and identified members of the VCX/Y gene family as novel CT antigens. VCX3A, a member of the VCX/Y gene family, was expressed at the protein level in approximately 20% of lung adenocarcinomas and 35% of squamous cell carcinomas, but not expressed in normal lung tissues. Among CT antigens with concordant mRNA and protein expression levels, four CT antigens, XAGE1, VCX, IL13RA2, and SYCE1, were expressed, alone or in combination, in about 80% of lung adenocarcinoma tumors. The CT antigen VCX/Y gene family broadens the spectrum of CT antigens expressed in lung adenocarcinomas for clinical applications.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.190
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.385
Teacher spread0.332 · 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 teacher head, 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

Citations62
Published2014
Admission routes1
Has abstractyes

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