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Abstract A001: Reshaping the immune microenvironment after temozolomide priming in metastatic colorectal cancer patients in ARETHUSA clinical trial

2023· article· en· W4389241535 on OpenAlexaboutno aff
Giovanni Crisafulli, Andrea Sartore‐Bianchi, Luca Lazzari, Filippo Pietrantonio, Paolo Battuello, Alice Bartolini, Federica Di Nicolantonio, Silvia Marsoni, Salvatore Siena, Alberto Bardelli

Bibliographic record

VenueCancer Immunology Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsTemozolomideImmune systemImmune checkpointImmunotherapyMSH6MedicineDNA mismatch repairColorectal cancerCancer researchOncologyTumor microenvironmentCancerImmunologyInternal medicineBiologyChemotherapy

Abstract

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Abstract Mismatch repair (MMR) proficient (MMRp) tumors, known for their weak immune response, often exhibit limited effectiveness in immunotherapy. In contrast, responsive mismatch repair deficient (MMRd) tumors, characterized by heightened immune activity, show a stronger response to immune checkpoint blockade (ICB). In the ARETHUSA clinical trial (NCT03519412), we revealed that treatment with temozolomide (TMZ) can pharmacologically deactivate the MMR machinery, as indicated by mutational signature analysis and the emergence of MMR gene mutations. We focused our investigation on a subset of initially MMRp CRC patients who experienced sustained disease stabilization with ICB after receiving TMZ priming treatment. This treatment resulted in the emergence of an inactivating MSH6 mutation and the TMZ mutational signature. Additionally, TMZ treatment induced diverse genomic changes, leading to the identification of three distinct subtypes through analysis of blood and tissue samples. Notably, we observed a dose-dependent accumulation of mutations with a specific molecular signature visible at the clonal level (subtype B2) in 10% of patients, at the subclonal level (subtype B1) in 71% of patients, and the absence of these mutations (subtype A) in 19% of patients within our cohort of 21 patients. In conjunction with previous genetic findings, in this new study we performed an analysis of T-cell receptors (TCRs) in the tumor microenvironment following TMZ treatment. Our results revealed expanded clonotypes in tumors that exhibited a clonal increase in mutations following priming treatment. Furthermore, the diversity of TCRs within the immune infiltrate confirmed the categorization of tumors into three distinct classes based on Tumor Mutational Burden (TMB) and mutational signatures. We also established a significant linear correlation (p-value 0.0048) between the diversity of T-cells in the microenvironment and the number of mutations induced by TMZ. Based on these clinically-oriented preliminary findings, we propose that increasing the number of TMZ-induced mutations could enhance the likelihood of TMZ-driven neoantigens triggering clonal expansion of specific T-cell clonotypes within the tumor. Interestingly, the subset of TMZ-treated tumors displaying an acquired MSH6 mutation, TMZ mutational signature, and increased TMB not only achieved temporary disease stabilization with ICB but also exhibited clonal expansion of T-cells in the tumor microenvironment. These clinically relevant findings suggest that the inactivation of MMR achieved through TMZ priming has the potential to reshape the immune microenvironment modulating the immune response in metastatic colorectal cancer. Citation Format: Giovanni Crisafulli, Andrea Sartore-Bianchi, Luca Lazzari, Filippo Pietrantonio, Paolo Battuello, Alice Bartolini, Federica Di Nicolantonio, Silvia Marsoni, Salvatore Siena, Alberto Bardelli. Reshaping the immune microenvironment after temozolomide priming in metastatic colorectal cancer patients in ARETHUSA clinical trial [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 A001.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.422
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.087
GPT teacher head0.410
Teacher spread0.323 · 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 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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