TAMAVAQ/Q‐TAMAVAQ: Fubini–study CTQW graph transport and constrained margin‐based ranking for neoantigen candidate prioritization in lower‐grade glioma and glioblastoma

Abstract Background Personalized neoantigen vaccine design for glioma requires the integration of mutation‐derived peptide evidence, molecular properties, HLA coverage and finite manufacturing capacity. TAMAVAQ/Q‐TAMAVAQ combines quantum‐geometric descriptor representation with graph transport and auditable peptide‐panel assembly. Methods A 7262‐row lower‐grade glioma/glioblastoma source ledger was reconciled to 4128 unique variant–peptide–HLA candidates. A separate Seq2Neo benchmark contained 1537 peptide–HLA records, including 65 positives defined by a fixed predictor‐derived composite endpoint. Normalized molecular descriptors were encoded by squared‐fidelity Fubini–Study distances and a weighted graph Laplacian. Continuous‐time quantum‐walk (CTQW) transport and classical heat diffusion were evaluated on identical graphs across 60 combinations of neighbourhood size, bandwidth and propagation time. Non‐compensatory biological, structural, geometric, manufacturing, provenance and validation gates were coupled to deterministic margin‐based HLA‐aware panel selection. Results The integrated ranking achieved average precision (AP) 0.552, AUROC 0.962 and NDCG@100 0.693 on the internal Seq2Neo endpoint, recovering 17, 33 and 45 positives at ranks 25, 50 and 100, respectively. At the matched reference setting, CTQW and heat diffusion yielded AP 0.481 and 0.471, NDCG@100 0.637 and 0.632, and AUROC 0.951 and 0.956, respectively. CTQW yielded higher AP in 15 of 60 parameter configurations. A 14‐candidate mechanistic register demonstrated deterministic four‐peptide selection, admissible reserves and provenance‐preserving replay. Exploratory FCS analysis supplied a separate descriptive immune‐phenotyping layer. Conclusions The integrated workflow links molecular dossier geometry, complementary graph–transport operators and explicit selection constraints to a traceable, HLA‐aware shortlist of glioma neoantigen candidates.

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Publication Details

Journal
Clinical and Translational Discovery
Published
2026-10-29
DOI
https://doi.org/10.1002/ctd2.70224
Primary Topic
vaccines and immunoinformatics approaches
Type
article
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article

TAMAVAQ/Q‐TAMAVAQ: Fubini–study CTQW graph transport and constrained margin‐based ranking for neoantigen candidate prioritization in lower‐grade glioma and glioblastoma

Ioannis Grigoriadis
3 links
Clinical and Translational Discovery
vaccines and immunoinformatics approaches
article

TAMAVAQ/Q‐TAMAVAQ: Fubini–study CTQW graph transport and constrained margin‐based ranking for neoantigen candidate prioritization in lower‐grade glioma and glioblastoma

Ioannis Grigoriadis
article en

Abstract

Abstract Background Personalized neoantigen vaccine design for glioma requires the integration of mutation‐derived peptide evidence, molecular properties, HLA coverage and finite manufacturing capacity. TAMAVAQ/Q‐TAMAVAQ combines quantum‐geometric descriptor representation with graph transport and auditable peptide‐panel assembly. Methods A 7262‐row lower‐grade glioma/glioblastoma source ledger was reconciled to 4128 unique variant–peptide–HLA candidates. A separate Seq2Neo benchmark contained 1537 peptide–HLA records, including 65 positives defined by a fixed predictor‐derived composite endpoint. Normalized molecular descriptors were encoded by squared‐fidelity Fubini–Study distances and a weighted graph Laplacian. Continuous‐time quantum‐walk (CTQW) transport and classical heat diffusion were evaluated on identical graphs across 60 combinations of neighbourhood size, bandwidth and propagation time. Non‐compensatory biological, structural, geometric, manufacturing, provenance and validation gates were coupled to deterministic margin‐based HLA‐aware panel selection. Results The integrated ranking achieved average precision (AP) 0.552, AUROC 0.962 and NDCG@100 0.693 on the internal Seq2Neo endpoint, recovering 17, 33 and 45 positives at ranks 25, 50 and 100, respectively. At the matched reference setting, CTQW and heat diffusion yielded AP 0.481 and 0.471, NDCG@100 0.637 and 0.632, and AUROC 0.951 and 0.956, respectively. CTQW yielded higher AP in 15 of 60 parameter configurations. A 14‐candidate mechanistic register demonstrated deterministic four‐peptide selection, admissible reserves and provenance‐preserving replay. Exploratory FCS analysis supplied a separate descriptive immune‐phenotyping layer. Conclusions The integrated workflow links molecular dossier geometry, complementary graph–transport operators and explicit selection constraints to a traceable, HLA‐aware shortlist of glioma neoantigen candidates.

Clinical and Translational DiscoveryVol. 6(6)
Good health and well-being
Openalex Percentile: Top 22%
vaccines and immunoinformatics approaches
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TAMAVAQ/Q‐TAMAVAQ: Fubini–study CTQW graph transport and constrained margin‐based ranking for neoantigen candidate prioritization in lower‐grade glioma and glioblastoma — Ioannis Grigoriadis · Clinical and Translational Discovery (2026) | TGRS Research Map | TGRS