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Record W4405044326 · doi:10.1182/blood-2024-198639

Mis-Splicing Derived Neoantigens and Cognate T Cell Receptors in Splicing Factor Mutant Myeloid Neoplasms

2024· article· en· W4405044326 on OpenAlexaff
Won Jun Kim, Edie I. Crosse, Emma De Neef, Iñaki Etxeberría, Erich Sabio, Eric Wang, Jan Philipp Bewersdorf, Sydney X. Lu, Andrea E. Belleville, Nina Fox, Cynthia Castro, Pu Zhang, Takeshi Fujino, Jennifer Lewis, Jahan Rahman, Beatrice Zhang, Alexander M. Lewis, Robert Stanley, Susan DeWolf, Brigita Meškauskaitė Urben, Meril Takizawa, Tobias Krause, Henrik Molina, Ronan Chaligné, Priya Koppikar, Jeffrey J. Molldrem, Mathieu Gigoux, Taha Merghoub, Anthony F. Daniyan, Benjamin D. Greenbaum, Christopher A. Klebanoff, Robert K. Bradley, Omar Abdel‐Wahab

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsRNA splicingAlternative splicingReceptorSplicing factorMutantBiologyMyeloidCell biologyMyeloid cellsCancer researchExonMolecular biologyGeneticsGeneRNA

Abstract

fetched live from OpenAlex

Mutations in RNA splicing factors are the most common class of genetic alterations in MDS and are also prevalent in AML. These mutations cause recurrent splicing changes in a highly sequence-specific manner across patients and cancer types. We hypothesized that a subset of these aberrant splicing changes would generate “public” neoantigens (i.e. shared across patients) that can serve as potential targets of T cell-based immunotherapies. Here we identify a series of shared, bona fide neoantigens induced by leukemia-associated mutations in RNA splicing factors SRSF2 and ZRSR2. We validate in vitro that these neoantigens are presented on HLA class I (HLA-I) and immunogenic. We also discover T cell receptors (TCRs) reactive to identified neoantigens and demonstrate that introduction of these TCRs into primary human T cells redirects the T cells to selectively eliminate leukemic cells. To predict mis-splicing derived neoantigens, we analyzed RNA-seq datasets from five myeloid leukemia patient cohorts with SRSF2 mutations (n=107), ZRSR2 mutations (n=33), or no mutations in splicing factors (n=837). We identified mis-spliced mRNA isoforms consistently produced across SRSF2 or ZRSR2 mutant patients but minimally expressed in healthy bone marrow, PBMCs, and a panel of 14 normal tissues. We translated each tumor-specific mRNA isoform in silico, split into 8-12-mer peptides, and predicted high-affinity binders to HLA-A*02:01. In total, we selected 56 candidate mis-splicing derived neoantigens created by mutant SRSF2 and 19 by mutant ZRSR2 for further in vitro studies. We synthesized candidate peptides and validated their HLA-I binding using the T2 HLA-A2 shift assay. We then tested if the peptides are immunogenic (i.e. elicit effector CD8+ T cell responses) in PBMCs from 14 unique healthy donors. One particular peptide, derived from SRSF2 mutation-induced exon 4 skipping in CLK3 and confirmed by HLA-I immunopeptidomics, was immunogenic across multiple donors. To isolate CD8+ T cells reactive to the CLK3 neoantigen, we constructed dextramers, which are composed of ten peptide/HLA-I complexes on a dextran scaffold. We sorted CLK3 peptide-primed CD8+ T cells with dual-color CLK3 neoepitope dextramers and performed TCR-seq which identified 11 distinct TCR clonotypes from two healthy donors. Primary human T cells transduced with these TCRs demonstrated remarkably specific cytolysis of HLA-A*02:01+ leukemic cells expressing the CLK3 neoantigen as well as cells harboring SRSF2 mutations. The discovery of immunogenic neoantigens in splicing factor mutant cells begs the question of how such malignancies develop in the setting of potential immune responses to these antigens. To address this question, we synthesized a panel of DNA-barcoded dextramers against 43 SRSF2 and 12 ZRSR2 mutant-induced candidate peptides as well as one CMV and three negative control peptides. Using this dextramer panel, we isolated antigen-reactive CD8+ T cells from splicing factor mutant MDS/AML patient PBMCs and performed single-cell RNA-, TCR-, and dextramer barcode sequencing. Downstream single-cell analysis revealed that neoantigen-reactive CD8+ T cells are present in MDS and AML patient blood and are clonally expanded. However, they have a distinct gene expression profile from virus-reactive T cells with evidence of impaired T cell cytotoxic function at the time of active MDS and AML. For patients with high-risk MDS and AML, allogeneic stem cell transplantation (allo-SCT) remains the most established curative therapy and results in donor T cell-mediated graft-versus-leukemia effect. To test if donor T cells could recognize mis-splicing derived neoantigens in patient leukemia, we performed dextramer-based single-cell profiling of matched pre- and post-allo-SCT patient PBMC samples. This discovered a donor-derived TCR that is clonally expanded and cognate to a peptide derived from SRSF2 mutant-induced intron retention in RHOT2. Primary human T cells transduced with this TCR specifically recognized and lysed leukemic cells expressing the RHOT2 neoantigen. Overall, these data identify recurrent RNA mis-splicing events as sources of actionable public neoantigens in myeloid malignancies and provide proof-of-concept for genetically redirecting T cells to recognize these targets. These data have immediate therapeutic implications as the TCRs presented can be applied for transgenic TCR-T cell therapy.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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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Citations1
Published2024
Admission routes1
Has abstractyes

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