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Abstract A003: High-throughput TCR sequencing demonstrates induction of long-lasting HPV16-specific T cell responses in VB10.16 vaccinated advanced cervical cancer patients

2023· article· en· W4389228010 on OpenAlexaboutno aff
Kaja C. G. Berg, Paula A. Bousquet, Milena Blaga, Thomas R. Bello, Mohammad Arabpour, Mikkel W. Pedersen, Karoline W. Schjetne

Bibliographic record

VenueCancer Immunology Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsELISPOTT-cell receptorPeripheral blood mononuclear cellT cellImmunotherapyMedicineImmunologyBiologyImmune systemGenetics

Abstract

fetched live from OpenAlex

Abstract Background: Assessment of vaccine-induced T cell responses is a primary pharmacodynamic readout in cancer vaccine clinical trials. High-throughput sequencing of T cell receptors (TCRs) is emerging as a rapid and scalable method, suitable for late-stage clinical trials, and offers sensitive and accurate quantification of the full T cell repertoire. Here we performed ELISpot and TCR sequencing on serial peripheral blood mononuclear cell (PBMC) samples from a clinical phase 2 trial investigating the therapeutic HPV16 cancer vaccine VB10.16 in combination with atezolizumab in advanced cervical cancer patients (NCT04405349). Methods: T cell responses were assessed by ex vivo IFN-g ELISpot (n=36). Immunosequencing of the TCRB locus was performed in 10 patients with available PBMCs, representing clinical response, stable disease, and progressive disease as best overall response. Immunosequencing of PBMC-derived gDNA was performed on up to five timepoints per patient, ranging from week 10 to 52. Novel and expanded clones from baseline to on-treatment timepoints were determined by a differential abundance framework, using binomial models in pair-wise comparisons, enabling longitudinal tracking of significantly expanded T cell clones. The sequences were matched and annotated to HPV16-specific TCRs present in a proprietary database of confirmed HLA class I HPV16-specific T cell clones; the database was constructed from querying the T cell repertoires of 92 healthy donors with 146 peptides derived from HPV16 E6 and E7 in MIRA assay. Results: An increased T cell response was significantly associated with disease control assessed by RECIST1.1 (n=24 patients with disease control vs n=12 patients with progressive disease, p=0.0113) and patients with a >2-fold increase measured by ex vivo IFN-g ELISpot showed a numerically improved progression-free survival (8 vs 3.7 months median PFS). The longitudinal tracking of TCRs provided a detailed insight into the dynamics of the overall TCR repertoire during treatment. Expansion of both pre-existing and newly expanded T cell clones was observed from week 10 and persisted until the end of treatment. Despite the non-exhaustive database, at least one verified HPV16-specific CD8 T cell clone was expanded in 8 out of 10 patients, supporting that the clonotypic expansion is caused by VB10.16. In 5 out of 7 patients with disease control, the breadth of HPV16-specific TCRs increased after vaccination, demonstrating induction of clinically relevant T cell responses. Conclusions: We demonstrate induction of strong and long-lasting HPV16-specific T cell responses after treatment with VB10.16 and atezolizumab in advanced cervical cancer patients. Induction of HPV16-specific T cell responses was significantly correlated with clinical efficacy. Immunosequencing and HPV16-specific annotation allowed longitudinal tracking of expanded TCRs and demonstrated a potential clinical relevance of increased repertoire diversity of HPV16-specific CD8 T cells in patients. Citation Format: Kaja C G Berg, Paula Bousquet, Milena Blaga, Thomas Bello, Mohammad Arabpour, Mikkel W Pedersen, Karoline Schjetne. High-throughput TCR sequencing demonstrates induction of long-lasting HPV16-specific T cell responses in VB10.16 vaccinated advanced cervical cancer patients [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A003.

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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 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 score0.299
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.347
Teacher spread0.297 · 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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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