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1235 Abemaciclib differentially reshapes the immunopeptidome of breast cancer subtypes

2025· article· W4415900229 on OpenAlexaff
Robin Minati, Anca Apavaloaei, Éric Bonneil, M Cahuzac, Julie Perrault, Vronique Lullier, Mathieu Courcelles, Joël Lanoix, Eralda Kina, Claude Perreault, Pierre Thibault

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2025
Typearticle
Language
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
Fundersnot available
KeywordsBreast cancerCancerHuman breastClinical trial

Abstract

fetched live from OpenAlex

Background Cyclin-dependent kinase 4/6 inhibitors (CDK4/6is) are established standard-of-care therapies for the treatment of hormone receptor-positive (HR+) metastatic breast cancer (BC), yet they offer limited clinical benefit in triple-negative breast cancer (TNBC). Despite their widespread use, the immunomodulatory effects of CDK4/6is—particularly their influence on tumor antigenicity—remain poorly understood. Emerging evidence suggests that CDK4/6is such as Abemaciclib may enhance tumor immunogenicity by modulating the tumor immune microenvironment and promoting immune recognition. However, their specific impact on the repertoire of antigens presented by tumor cells remain to be defined.Methods To investigate how CDK4/6 inhibition alters tumor antigen presentation, we treated two breast cancer cell lines—CAMA1 (HR+) and MDAMB231 (TNBC)—with either Abemaciclib or DMSO, and analyzed the MHC I-bound antigen repertoire using mass spectrometry. To maximize antigen detection, mass spectrometry data were searched against customized protein databases derived from RNA sequencing (RNA-seq) of the same samples. This proteogenomic approach enabled the identification of both canonical and non-canonical antigens, including peptides originating from non-coding regions of the genome.Results Treatment with Abemaciclib significantly increased the diversity and abundance of MHC-I-associated peptides (MAPs), with the most pronounced effects observed in the HR+ cell line, consistent with its greater clinical sensitivity to CDK4/6is. Using an established pipeline developed for tumor antigen discovery, we identified a distinct subset of novel MAPs uniquely induced by Abemaciclib. These antigens—derived predominantly from non-coding genomic regions—represent promising candidates for combinatorial immunotherapies.Mechanistic investigations integrating public transcriptomic, ATAC-seq, and ChIP-seq datasets revealed that part of the Abemaciclib-induced remodeling of the immunopeptidome is driven by Retinoblastoma protein (Rb)-dependent epigenetic remodeling. Using isogenic cell lines expressing specific Rb isoforms, we demonstrate that dephosphorylation of key Rb residues facilitates interactions with BET proteins, leading to chromatin remodeling and transcriptional activation of genes encoding induced antigens.Conclusions Our study reveals a novel role for CDK4/6 inhibition in reshaping tumor antigen presentation through Rb-driven epigenetic remodeling. Abemaciclib not only upregulates MHC-I machinery but also broadens the immunopeptidome, unveiling new antigens including those from non-coding regions that may enhance immune recognition. These findings provide a strong mechanistic rationale for combining CDK4/6is with immunotherapeutic strategies, especially in HR+ subtypes traditionally considered immunologically cold. Such combinations hold promise for expanding the repertoire of targetable tumor antigens and improving BC patient responses to cancer immunotherapy.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.009
GPT teacher head0.268
Teacher spread0.258 · 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 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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Citations0
Published2025
Admission routes1
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