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Abstract A084: Identification of mutation-reactive T cells in patients with gastrointestinal cancers

2016· article· en· W2338745842 on OpenAlexaff
Mélissa Mathieu, Sandy Pelletier, David Laperrière, Sylvie Mader, Simon Turcotte

Bibliographic record

VenueCancer Immunology Research · 2016
Typearticle
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsInstitute for Research in Immunology and CancerCentre Hospitalier de l’Université de Montréal
Fundersnot available
Keywordsclone (Java method)Cancer researchCancerCD8BiologySomatic cellMutationGermline mutationCancer cellCell cultureMetastasisHuman leukocyte antigenExome sequencingT cellMolecular biologyAntigenImmunologyGeneGeneticsImmune system

Abstract

fetched live from OpenAlex

Abstract New immunotherapeutic advances for common epithelial cancers relies on our ability to stimulate T lymphocytes against specific tumour antigens (Ag). Next-generation sequencing now allows rapid identification of somatic cancer mutations that can lead to the expression of mutated Ag. We hypothesize that gastrointestinal cancer metastases are infiltrated by T cells recognizing tumour mutated Ag. Our aim is to set up an experimental platform to screen for and study the frequency and function of mutation-reactive T cells, while characterizing novel tumour mutated Ag. To do this, we used two cancer cell lines generated from a liver metastasis of a gastric cancer patient. The cell line A was recognized by an autologous CD8+ T cell clone infiltrating the patient's metastasis, restricted by HLA C*0701, while cell line B was not recognized by the same clone despite its expression of the HLA C*0701. The CD8+ T cell clone was not reactive to a large panel of HLA-C*0701 expressing gastrointestinal cancer cell lines, reinforcing the hypothesis that the Ag recognized was unique to the autologous gastric cancer cell line. Exome and transcriptome sequencing was performed to compare the mutated genes differentially expressed by cancer cell line A and B. A total of 27 mutated Ag were selected as candidates: 26 Ag only expressed by cell line A, and one Ag overexpressed by cell line A. To screen for reactivity of the CD8+ T cell clone to mutated Ag, 25 amino acid (aa) mini-genes containing the mutation flanked upstream and downstream by normal aa were synthesised and cloned in tandem into 3 expression plasmids. A control sequence from the MAGE-A12 gene containing an epitope restricted by HLA-C*0701was included in each tandem minigene (TMG) construct. The mRNA from these 3 TMG constructs were in vitro transcribed and electroporated into CD40-activated B cells expressing HLA C*0701, used as antigen presenting cells. Co-culture assays are ongoing, using the CD8+ T cell clones as effectors and T cells transduced with a MAGE-A12-specific TCR as control. For the detection of mutation-reactive T cells, the sensitivity of TMG expression is compared to pulsing mutated peptides on B cells. Taking advantage of the differential recognition of two cancer cell lines by an autologous CD8+ T cell clone, we have established a bioinformatics approach based on next-generation sequencing to obtain a list of candidate mutated Ag and we have designed an experimental system to assess their recognition by T cells. This platform will allow us to study the function of T cells reactive against metastatic gastrointestinal cancers and should lead to the discovery of new tumour Ag. Gaining a better understanding of T cells reactive to gastrointestinal cancers should ultimately contribute to the development of immunotherapies for these common malignancies. Citation Format: Mélissa Mathieu, Sandy Pelletier, David Laperrière, Sylvie Mader, Simon Turcotte. Identification of mutation-reactive T cells in patients with gastrointestinal cancers. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr A084.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.373
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.

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
Published2016
Admission routes1
Has abstractyes

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