GYNORYLAQ™-VLINIVAL™: Ψ-Guided Personalized Neoantigen Peptide Vaccine for High-Risk Endometrial Cancer

Overview

GYNORYLAQ-VLINIVAL is an Early Phase I, non-randomized, single-arm, open-label clinical trial enrolling 40 patients with high-risk or recurrent endometrial carcinoma. All participants receive GYNORYLAQ-TM, a personalized neoantigenic peptide vaccine generated by the GYNORYLAQ-EC™ quantum-classical engine, in combination with systemic and supportive drug regimens that are individually selected and prescribed by the treating medical oncologist, Dr Emmanouelides Christos, according to contemporary standards of care and the clinical status of each patient. Only the GYNORYLAQ-TM vaccine is considered investigational within this protocol; all concomitant drugs (including antineoplastic agents and supportive care medications) are non-investigational, chosen and adjusted at the discretion of Dr Emmanouelides Christos. The primary objectives are to evaluate the safety/tolerability of GYNORYLAQ-TM in this real-world therapeutic context and the feasibility of quantum-guided, GMP-grade personalized vaccine manufacture. Secondary and exploratory objectives characterize vaccine-induced T-cell immunity and explore correlations between quantum/physics-based scores and clinical/immunologic outcomes.

Clinical Background Endometrial carcinoma is the most common gynecologic malignancy in developed regions. While many early-stage tumours are cured with surgery ± radiotherapy, patients with high-risk, recurrent, or metastatic disease frequently experience relapse after standard therapy and have limited durable options. Particularly poor prognoses are seen in: Copy-number-high / p53-mutated and serous histologies pMMR tumours with "cold" microenvironments and modest immunotherapy responses MMRd/MSI-H tumours that initially respond to PD-1 blockade but often ultimately progress Across these molecular subtypes, tumours generate patient-specific neoantigens from point mutations, indels, frameshifts in coding microsatellites, fusions, and splice alterations. These mutant peptides can be processed and presented on HLA class I and II molecules, where they may be recognized by T cells as "non-self." This provides a biologically compelling target space for personalized neoantigen vaccination. However, conventional neoantigen pipelines are largely sequence-based, dependent on binding predictors and heuristic scoring, with limited use of structural information, energetics, or explicit uncertainty quantification. Clinically, many vaccine trials have paired neoantigen constructs with fixed chemotherapy or checkpoint regimens that do not reflect the heterogeneity of real-world oncology practice. GYNORYLAQ-VLINIVAL is designed to address both issues. It integrates a physics-aware, quantum-classical computational vaccine platform (GYNORYLAQ-EC™) with individualized clinical oncology practice: All enrolled patients receive a GYNORYLAQ-EC-selected personalized neoantigenic peptide vaccine (GYNORYLAQ-TM). Systemic and supportive drug therapy is not fixed by protocol. Instead, it is selected, initiated, and adjusted by the treating medical oncologist, Dr Emmanouelides Christos, according to tumour stage, prior treatments, organ function, comorbidities, tolerability, and contemporary guidelines. The study thus evaluates GYNORYLAQ-TM in a realistic multimodal context, layered on top of individualized best-available care rather than a single mandated backbone. All concomitant antineoplastic and supportive agents are recorded but not dictated by the protocol. GYNORYLAQ-EC™ Quantum-Classical Vaccinology GYNORYLAQ-EC™ is the computational core that maps genomic and transcriptomic data into a quantum-entangled design state and, ultimately, into a manufacturable, patient-specific peptide panel. It makes explicit and auditable the chain: Sequence → Geometry → Structure → Energy → Decision 1. Hilbert Spaces and Global Design State The engine is formalized in terms of three Hilbert spaces: Sequence space H\_"seq" Basis: {∣p\_i⟩}, where each basis vector corresponds to a candidate neoantigen peptide (typically 8-11mer class I and longer peptides for class II / cross-presentation). HLA space H\_"HLA" Basis: {∣〖"HLA" 〗\_α⟩}, representing the patient's specific class I and II alleles. Immune/tumour space H\_"immune" Basis: {∣〖"TCR" 〗\_β⟩⊗∣〖"tumour\_state" 〗\_k⟩}, capturing an abstract TCR repertoire and coarse-grained tumour states (e.g., burden, clonality, immune infiltration). The total Hilbert space is: H=H\_"seq" ⊗H\_"HLA" ⊗H\_"immune" . A formal design-time state ∣Ψ\_s⟩∈H evolves through computational blocks (enumeration, scoring, gating, amplification) toward a final design state ∣Ψ\_T⟩ whose "support" corresponds to peptides selected for clinical manufacture. 2. Initial Superposition and Grover-Style Entangled Search From tumour and matched normal sequencing, the pipeline enumerates a set of candidate peptides {p\_i }\_(i=1)\^N ┤derived from high-confidence somatic variants, flanked appropriately for presentation. Each candidate is associated with one or more patient HLAs. In the idealized quantum picture, the engine constructs an equal superposition over candidates in sequence space: ∣Φ\_"start" ⟩=1/√N ∑\_(i=1)\^N▒∣ p\_i⟩. Each peptide-HLA pair p is also embedded as a normalized quantum state in an n-qubit Hilbert space: ∣ψ(p)⟩=U(z\_Q (p),θ)" "∣0⟩\^(⊗n), where: U(⋅) is a parametrized feature map (data-reuploading circuit with fixed entanglers), z\_Q (p) is a low-dimensional classical quantum descriptor, θ is a vector of trainable parameters. The quantum kernel and Fubini-Study distance between two candidates p and q are: K\_Q (p,q)=∣⟨ψ(p)∣ψ(q)⟩∣\^2, d\_"FS" (p,q)=arccos⁡(∣⟨ψ(p)∣ψ(q)⟩∣)∈\[0,π/2\]. d\_"FS" =0 corresponds to identical rays, d\_"FS" =π/2 to orthogonal states. K\_Q can be interpreted as the probability that ∣ψ(p)⟩ is projected onto ∣ψ(q)⟩. Peptides with low sequence identity but similar higher-order physicochemical structure may map close together on this manifold, generating large K\_Q even when classical similarity is low. To implement Grover-style search, the engine defines a marking predicate f(i) such that: f(i)=1 if peptide p\_i passes three concordant gates: Geometry: d\_"FS" to a trusted set of experimentally validated epitopes lies within a predefined window. Thermodynamics: binding affinity predicted via ΔG° and K\_d\^"eff" exceeds prespecified thresholds. Immunogenicity: calibrated probability I ̂(p\_i) is above a minimum value. The phase oracle acting on sequence space is: O ̂\_"mark" ∣p\_i⟩=(-1)\^(f(i))∣p\_i⟩. A diffusion operator reflecting about the mean of the initial superposition, D=2∣Φ\_"start" ⟩⟨Φ\_"start" ∣-I, is combined with the oracle to form the Grover iterate: G=D" " O ̂\_"mark" . After r iterations, ∣Φ\_r⟩=G\^r∣Φ\_"start" ⟩=sin⁡((2r+1)θ)" "∣"good"⟩+cos⁡((2r+1)θ)" "∣"bad"⟩, where 〖sin⁡〗\^2 θ=M/N and M is the (unknown) number of marked peptides. GYNORYLAQ-EC™ intentionally uses small r (typically 1-3) to produce a shallow, interpretable amplitude amplification of promising peptides, robust to uncertainty in M and to NISQ-era noise. 3. Unified Feature Representation and Composite Kernel For each candidate peptide-HLA pair p, GYNORYLAQ-EC™ constructs a unified feature vector: Φ(p)=\[e\_"CNN" (p)," aux"(p)," " z\_Q (p)," " ϕ\_"struct" (p)," " ϕ\_"dock" (p)\]. Components: Deep sequence/HLA embedding e\_"CNN" (p) Derived from convolutional or transformer models trained on large immunopeptidome datasets, capturing allele-specific binding motifs and sequence context. Auxiliary biological priors "aux"(p) Proteasomal cleavage likelihood, TAP transport propensity, transcript expression (TPM/FPKM), allelic copy number, clonality (clonal vs subclonal variants), and when available, ctDNA/MRD readouts. This approximates the effective antigen source strength. Quantum descriptor z\_Q (p) Low-dimensional classical feature vector parameterizing the quantum circuit. It compresses physicochemical and positional information optimized for quantum expressivity. Structural term ϕ\_"struct" (p) Summarizes pocket occupancy, peptide-HLA contact patterns, solvent accessibility, and local strain in predicted or modeled peptide-HLA complexes. Docking ensemble features ϕ\_"dock" (p) Aggregates pose energies, mean and dispersion of docking scores, RMSD across poses, and measures of conformational diversity. Similarity between peptide-HLA candidates p and q is encoded in a composite positive semi-definite kernel: K\_"total" (p,q)=αK\_"CNN" (p,q)+βK\_"aux" (p,q)+γK\_Q (p,q)+δK\_"struct" (p,q)+εK\_"dock" (p,q), where: K\_"CNN" ,K\_"aux" ,K\_"struct" ,K\_"dock" are PSD kernels on the respective feature blocks, K\_Q is the quantum kernel described above, α,β,γ,δ,ε≥0 adjust the relative weight of each modality. Because each component kernel is PSD, any non-negative linear combination is PSD, ensuring that K\_"total" is suitable for kernel logistic regression or related methods. A decision function is: f(p)=∑\_(i=1)\^M▒α\_i K\_"total" (p,p\_i)+b, where {p\_i } are training peptides and {α\_iⓜ,b} are learned coefficients. The immunogenicity probability is I ̂(p)=σ(f(p))=1/(1+e\^(-f(p)) ), which is then calibrated (e.g., Platt scaling, isotonic regression, or temperature scaling) so that predicted probabilities match observed frequencies as closely as possible. This calibrated I ̂(p) is used in the immunogenicity gate and in downstream analysis. 4. Quantum Geometry, Entanglement, and Regularization Beyond similarity, GYNORYLAQ-EC™ monitors the internal structure and entanglement of quantum states ∣ψ(p)⟩. For a bipartition of the n-qubit system into subsystems A and B, the reduced density matrix on A is ρ\_A (p)=Tr\_B (∣ψ(p)⟩⟨ψ(p)∣), and the von Neumann entanglement entropy is S\_A (p)=-Tr\[ρ\_A (p)log⁡ρ\_A (p)\]. A regularization term in the training objective encourages entropy values within a target range, avoiding: Trivial product states (S\_A≈0) with limited expressive power, and Excessively entangled states with high entropy that may be numerically unstable and difficult to approximate on noisy hardware. The quantum Fisher information matrix F(θ) characterizes the sensitivity of the encoded states to parameter changes. Its entries are F\_ij (θ)=R\[⟨∂\_i ψ∣∂\_j ψ⟩-⟨∂\_i ψ∣ψ⟩" "⟨ψ∣∂\_j ψ⟩\], with ∣∂\_i ψ⟩=∂∣ψ(θ)⟩/∂θ\_i. Poorly conditioned Fisher matrices (with very small eigenvalues) imply unstable parameter estimates and kernel values. Penalizing ill-conditioned F(θ) promotes well-conditioned embeddings and more robust optimization. 5. Thermodynamic Bridge Peptide-HLA binding energetics are estimated from structural docking ensembles and then converted into standard thermodynamic quantities. For each peptide-HLA pair, docking yields a set of microstates with free energies ΔG\_i\^∘ (kcal·mol-¹). The relationship between standard free energy of binding and dissociation constant is: ΔG\^∘=RTln⁡K\_d, with: R=1.987×10\^(-3) " " 〖"kcal\\cdotpmol" 〗\^(-1) 〖"\\cdotpK" 〗\^(-1), T=310" K" (37 °C), so RT≈0.616" " 〖"kcal\\cdotpmol" 〗\^(-1). An effective association constant is computed from the ensemble: K\_a\^"eff" =∑\_i▒w\_i exp⁡" ⁣" (ⓜ-(ΔG\_i\^∘)/RT),K\_d\^"eff" =1/(K\_a\^"eff" ),ΔG\_"eff" \^∘=-RTln⁡K\_a\^"eff" , where w\_i are normalized weights. Uncertainty in ΔG\_i\^∘ (e.g., dispersion across poses) is propagated to yield uncertainty bands on K\_d\^"eff" (typically reported in nM). Prespecified thresholds on ΔG\_"eff" \^∘ and K\_d\^"eff" define a unit-consistent binding gate, ensuring that retained peptides are predicted to bind with sufficient affinity under physiologic conditions. 6. Immunogenicity and Benefit-Risk Functional The calibrated classifier outputs I(p), a probability of T-cell recognition for each peptide-HLA pair. Conceptually, GYNORYLAQ-EC™ also defines a benefit-risk operator: O ̂=O ̂\_"benefit" -λO ̂\_"risk" , on H\_"immune" ⊗H\_"tumour" , where: O ̂\_"benefit" rewards states with high frequencies of vaccine-matched effector/memory TCR configurations and low tumour burden. O ̂\_"risk" penalizes states associated with excessive systemic peptide exposure, off-target similarity to critical self-proteins, or overly broad activation. For a notional evolution operator U\_"tot" (θ), ∣Ψ\_T (θ)⟩=U\_"tot" (θ)∣Ψ\_0⟩,J(θ)=⟨Ψ\_T (θ)∣O ̂∣Ψ\_T (θ)⟩. In practice, this expression summarizes a multi-objective optimization: the final GYNORYLAQ-TM panel is chosen to approximate a high-J(θ) subset while obeying manufacturing, safety, and panel-size constraints. 7. Clinical Output: GYNORYLAQ-TM Panel After passing geometry, thermodynamics, and immunogenicity gates and undergoing shallow Grover-style amplification, candidate peptides are further screened through classical manufacturability and safety filters: Synthetic feasibility and solubility. Avoidance of highly problematic motifs (e.g., extreme hydrophobic or polybasic regions that compromise formulation). Proteome-wide scans for near-self matches in critical human proteins, with stringent triage of peptides at risk for dangerous cross-reactivity. This yields a patient-specific GYNORYLAQ-TM panel, typically comprising \~10-20 peptides: Multiple 8-11mer class I epitopes spanning key HLA alleles, and Selected longer peptides (e.g., 15-35mers) that support class II presentation and robust CD4⁺ T-cell help. All selected peptides are synthesized under GMP and formulated for subcutaneous or intradermal administration. Investigational Product and Concomitant Drug Use Investigational Product Name: GYNORYLAQ-TM Personalized Neoantigenic Peptide Vaccine Type: Biological (synthetic peptide mixture) Composition: Patient-specific collection of GYNORYLAQ-EC-selected neoantigenic peptides (short class I and longer helper peptides). Route of Administration: Subcutaneous or intradermal injection. Planned Early Phase I Vaccination Schedule (Non-Randomized) Priming phase: Approximately Weeks 0, 2, 4, and 8. Booster phase (optional): Additional doses around Months 6 and 12 in patients without prohibitive toxicity who appear to derive clinical benefit, at the discretion of Dr Emmanouelides Christos and according to protocol-defined criteria. Exact timing and number of doses may be refined based on emerging safety and immunogenicity data in this early-phase, 40-patient cohort. Concomitant Drugs and Clinical Management A key design principle of GYNORYLAQ-VLINIVAL is that systemic antineoplastic and supportive therapy is individualized rather than protocol-mandated: All decisions about systemic drug treatment are made by the treating medical oncologist, Dr Emmanouelides Christos. Examples include, but are not limited to: Cytotoxic chemotherapy (e.g., platinum-based regimens, liposomal anthracyclines). Hormonal therapy (e.g., progestins, aromatase inhibitors) where appropriate. Targeted agents (e.g., mTOR inhibitors, TKIs) as indicated by tumour biology and guidelines. Other immunomodulatory agents when clinically justified and permitted by the protocol. Supportive care medicines (antiemetics, analgesics, anticoagulants, growth factors, management of comorbidities, etc.) are similarly individualized. These agents are considered non-investigational background therapy. The protocol: Does not prescribe or prioritize specific drug regimens. Requires detailed documentation of concomitant therapies (drug names, doses, schedules, modifications, and reasons for change). Imposes only standard safety constraints (e.g., limits on high-dose systemic steroids during key immune monitoring windows, minimal washout periods for certain cytotoxics or biologics, exclusion of strongly immunosuppressive regimens that could invalidate immunogenicity assessment). This design: Preserves clinical autonomy, allowing Dr Emmanouelides Christos to treat each patient according to best current practice. Ensures that GYNORYLAQ-TM is evaluated in a real-world multimodal setting, reflective of clinical oncology practice rather than a narrow regimen. Enables exploratory analyses of how different background treatment classes (e.g., intensive chemotherapy vs endocrine maintenance vs minimal systemic treatment) may influence vaccine-induced immune responses and clinical outcomes. In summary, GYNORYLAQ-VLINIVAL treats GYNORYLAQ-TM as the single investigational product, layered onto individualized background therapies selected and prescribed by Dr Emmanouelides Christos. The computational pipeline GYNORYLAQ-EC™ provides a fully auditable, quantum-informed, physics-aware mechanism for peptide selection, while the trial structure evaluates feasibility, safety, and immunologic activity under conditions that closely resemble routine care for high-risk endometrial carcinoma.