SCF Discovery Phase Implementation Blueprint | Antigen-Ranking Engine for Huntington’s-Adjacent Neuro-Oncology

Engine Code: SCF-ARGEN-HTT-NO-01

Output: ranked vaccine antigen list + exclusion flags + SCF microenvironment score

Use: discovery-stage therapeutic vaccine design, not clinical decision-making

1. Core Engine Purpose

The antigen-ranking engine identifies tumor antigens most suitable for a therapeutic vaccine by integrating:

Modern neoantigen prioritization increasingly combines genomics, transcriptomics, and immunopeptidomics rather than relying on HLA-binding prediction alone.

2. Engine Architecture

Tumor/Normal DNA
      ↓
Somatic Variant Calling
      ↓
RNA Expression Confirmation
      ↓
HLA Typing + Binding Prediction
      ↓
Immunopeptidomics Confirmation
      ↓
SCF Microenvironment Scoring
      ↓
Safety / CNS Cross-Reactivity Filter
      ↓
Final Vaccine Antigen Rank

3. Input Data Requirements

FDA therapeutic cancer vaccine guidance frames cancer vaccines as IND-regulated products and emphasizes antigen/adjuvant safety, immune monitoring, and clinical trial design considerations.

4. Antigen Classes Ranked

A. Tier 1 — Personalized Neoantigens

Highest priority because they are tumor-specific.

Examples:

• Missense mutation peptides
• Frameshift peptides
• splice-junction neoepitopes
• fusion-derived peptides

B. Tier 2 — Tumor-Associated Antigens

Useful when neoantigen burden is low.

Examples:

• developmental antigens
• cancer-testis antigens
• glioma-associated antigens

C. Tier 3 — HTT-Adjacent Stress Antigens

Experimental SCF class.

Examples:

• DNA-repair-stress peptides
• repeat-instability-associated stress peptides
• neuroinflammatory tumor-state peptides
• hypoxia/redox-conditioned antigens

5. Antigen Ranking Score

Final Antigen Priority Score

APS = TS + EX + HLA + IP + CL + ME - RISK - ESC

Where:

6. Scoring Table

7. SCF Microenvironment Score

SCF-MES: Microenvironment Suitability Score

SCF-MES = Immune Access + Redox Stability + Hypoxia Penalty Correction
          + Antigen Presentation Integrity + Microglial Permission

RHENOVA is the SCF redox–hypoxia intelligence layer, using markers such as GSH:GSSG, 8-OHdG, MDA, HIF-1α, and pO₂ to map environmental variance and therapeutic suitability.

8. Quality Control Gates

Gate 1 — Variant Validity

Reject if:

• germline contamination
• low tumor allele confidence
• sequencing artifact

Gate 2 — Expression Validity

Reject if:

• no RNA expression
• expression only in normal brain tissue
• unstable transcript evidence

Gate 3 — Presentation Validity

Promote if:

• immunopeptidomics confirms peptide presentation

Downgrade if:

• only predicted binding with no MS evidence

Gate 4 — CNS Safety

Reject if:

• strong similarity to essential normal neuronal proteins
• high normal brain expression
• predicted autoimmune CNS risk

Gate 5 — Microenvironment Readiness

Flag if:

• high hypoxia
• low antigen presentation machinery
• strong immune exhaustion
• suppressive microglial terrain

9. Final Output Format

10. Discovery Phase Workflow

Phase 1 — Sample Intake

Collect matched tumor, normal tissue/blood, RNA, and plasma/CSF if available.

Phase 2 — Genomic Antigen Discovery

Call somatic variants, indels, fusions, splice variants, and noncanonical candidates.

Phase 3 — Transcriptomic Filtering

Confirm expression and classify tumor-specific versus normal-brain-associated transcripts.

Phase 4 — Immunopeptidomics Confirmation

Use mass spectrometry to identify naturally presented HLA-bound peptides. Immunopeptidomics directly detects MHC-presented peptides and strengthens neoantigen prediction beyond sequence-only approaches.

Phase 5 — SCF Microenvironment Scoring

Apply RHENOVA + immunologic terrain scoring to determine whether the antigen is likely actionable in the tumor’s local environment.

Phase 6 — Safety Filtering

Exclude antigens with unacceptable normal CNS similarity or immune-risk profiles.

Phase 7 — Vaccine Candidate Assembly

Select:

• 8–20 primary antigens
• 3–5 backup antigens
• 1–3 exploratory SCF stress antigens

11. Engine Decision Classes

12. SCF-Specific Added Value

This engine does more than rank antigens. It predicts whether the tumor terrain will permit vaccine success.

Added SCF advantages:

• avoids antigen-only selection errors
• integrates redox–hypoxia suppression
• screens for CNS autoimmune risk
• supports personalized vaccine design
• creates a traceable IND discovery rationale

13. Next Build Steps

1. Build the SCF-ARGEN scoring spreadsheet or database schema.
2. Define required assay vendors/platforms for WES/WGS, RNA-seq, HLA typing, and immunopeptidomics.
3. Generate the prototype antigen-ranking table template for Notion or clinical R&D documentation.