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Abstract C011: Distinct peripheral immune signatures in early-onset colorectal cancer reveal candidate biomarkers for risk stratification

2025· article· en· W4417201305 on OpenAlexaboutno aff
María González-Sanmartin, Clara Sánchez-Menéndez, Valentina Leguizamon, Elena Mateos, Edurne Álvaro, Gonzalo Sanz, Jorge Martinez Laso, Montserrat Torres, Mayte Coiras, José Perea

Bibliographic record

VenueClinical Cancer Research · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsnot available
Fundersnot available
KeywordsImmune systemChemokineHuman leukocyte antigenPeripheral blood mononuclear cellCytokineBiomarkerImmunityColorectal cancer

Abstract

fetched live from OpenAlex

Abstract Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. While screening strategies have reduced incidence in older adults, early-onset CRC (EOCRC; <50 years) continues to rise. The Immunoscore, based on intratumoral T cell density, is a validated biomarker of recurrence and survival. However, little is known about peripheral immune alterations in EOCRC, which may provide complementary information for early detection and prognostication. We conducted an immune profiling study in age-defined cohorts of EOCRC and late-onset CRC (LOCRC) patients, matched by sex and tumor location. Peripheral blood mononuclear cells (PBMCs) and plasma were analyzed by multiparametric flow cytometry and Luminex to assess T cell polarization, innate-like lymphocytes, cytokine secretion, and metabolic activity. HLA typing was performed with HLA-ABCDRB1DQ RealAmp kit. Our analyses revealed striking differences in systemic immunity between EOCRC and LOCRC. EOCRC patients displayed a skewing toward proinflammatory Th9/Th17 and Th22 responses, accompanied by enhanced IL-13 secretion and increased NKT-like cells, whereas LOCRC was characterized by reduced effector activity and hallmarks of immune aging. In addition, metabolic profiling of PBMCs identified increased glucose uptake in EOCRC, supporting a heightened but potentially inefficient immune activation state. Plasma chemokine analyses further pointed to distinct cytokine milieus discriminating the two age groups. Notably, alleles within the B15/B17/B5 group were overrepresented in EOCRC compared to LOCRC and controls, suggesting a genetic contribution to early-onset disease. This enrichment may reflect distinct HLA-driven antigen presentation patterns that favor chronic immune activation or inefficient tumor surveillance in younger patients, thereby contributing to EOCRC pathogenesis. In conclusion, EOCRC is associated with a unique systemic immune profile that contrasts with the immunosenescence observed in LOCRC. Key alterations in Th22, CD8+ Tγδ cells, and NKT-like cells, together with enrichment of specific HLA-B allele groups that may modulate antigen presentation and anti-tumor immunity, emerge as candidate biomarkers to refine Immunoscore-based stratification. These candidates could help guide the development of more specific immunotherapeutic approaches for younger CRC patients. Overall, our results support a model in which both immune dysregulation and inherited genetic predisposition cooperate to shape EOCRC, supporting the development of risk stratification and preventive strategies based on HLA typing. Citation Format: Maria Gonzalez-Sanmartin, Clara Sanchez-Menendez, Valentina Leguizamon, Elena Mateos, Edurne Alvaro, Gonzalo Sanz, Jorge Martinez Laso, Montserrat Torres, Mayte Coiras, Jose Perea. Distinct peripheral immune signatures in early-onset colorectal cancer reveal candidate biomarkers for risk stratification [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: The Rise in Early-Onset Cancers—Knowledge Gaps and Research Opportunities; 2025 Dec 10-13; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(23_Suppl):Abstract nr C011.

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 score0.285
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.060
GPT teacher head0.433
Teacher spread0.373 · 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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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