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Record W3179257080 · doi:10.1158/1538-7445.am2021-2255

Abstract 2255: The impact of primary immunodeficiency informed molecular landscape on the biology and prognosis of refractory malignancies

2021· article· en· W3179257080 on OpenAlexaff
Son Tran, Luis Murguía-Favela, Aru Narendran

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsPrimary immunodeficiencyBiologyTranscriptomeCancerImmune systemGeneImmunodeficiencyComputational biologyGene expressionImmunologyGenetics

Abstract

fetched live from OpenAlex

Abstract Introduction: Cancer is one of the most common causes of death in patients with primary immunodeficiencies (PIDs). The immune surveillance concept postulates that PIDs are an important contributor to cancer growth and outcomes. Although several immunodeficiency syndromes are known to be associated with malignancies in children and adults, currently, the molecular mechanisms that link immune functions to cancers are poorly understood. Here we describe distinct molecular characteristics in adult cancers with respect to PID-associated genes that impact survival outcomes in affected patients. Methods: In this study, we integrated transcriptome data of 28 adult cancers from the public database The Cancer Genome Atlas (TCGA) with respective healthy tissues as control from the Genotype-Tissue Expression (GTEx) project. Unified datasets integrating GTEx healthy samples and TCGA cancer data were provided by the Recount2 protocol and differential expression analyses (DEA) were carried out using the integrated limma-voom and edgeR pipelines in the TCGAbiolinks R package. Gene ontology (GO) enrichment analyses were performed using the pathfindR R package. Mutational and clinical data of adult cancer patient samples in TCGA and the AACR Project GENIE were consolidated using cBioPortal and the maftools R package. PID-associated genes were curated from the latest registry by the European Society for Immunodeficiencies (ESID). Results: Of 307 PID-associated genes, 151 (32.0%) up-regulated and 88 (18.6%) down-regulated genes were identified across the 28 unified TCGA-GTEx datasets. From the identified differentially expressed gene sets, GO analysis revealed enrichment in immunological pathways related to complement and coagulation cascades (27/28 datasets), systemic lupus erythematosus (25/28), in addition to Fanconi anemia (22/28). The greatest frequency of mutations in PID-associated genes from the consolidated TCGA and GENIE datasets were KRAS (14.83%), KMT2D (10.03%), TERT (8.99%), PRKDC (7.59%) and PTEN (6.18%) across all cancer types, with a total of 259 affected PID-associated genes. Survival analyses using Cox proportional-hazards and Kaplan-Meier models will assess the clinical implications of the identified molecular alterations in affected patients. Conclusion: Our integrative approach aids in the elucidation of molecular mechanisms that bridge PIDs to tumour biology. These data may help identify important biomarkers and potential future targeted therapeutics. Citation Format: Son Tran, Luis Murguia-Favela, Aru Narendran. The impact of primary immunodeficiency informed molecular landscape on the biology and prognosis of refractory malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2255.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.052
GPT teacher head0.388
Teacher spread0.335 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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