Haplo Donor Selection

Haplo Donor Selection Workflow

Overview

12-step pipeline: HLA report intake → clinical data collection → API-based immunogenetic analysis → Cox model DFS prediction → donor ranking → comparative outcome analysis → PDF report generation.

Workflow Steps

Step 0: Receive HLA Typing Report

Accept image (JPG/PNG) or PDF. Extract all HLA loci at 4-digit resolution: A, B, C, DRB1, DQB1, DPB1 for patient and each donor.

Step 1: Collect Patient Clinical Info

Ask sequentially (do NOT assume — ask one at a time):

1. 疾病诊断 — AML / ALL / MDS
2. 疾病状态 — see references/disease-codes.md
3. 患者年龄
4. HCT-CI 评分 — 0 / 1 / 2 / ≥3

CMV: default all positive (patient + donors). Donor ages from typing report.

Step 2: Extract HLA Typing

Parse each person's alleles from the report image/PDF. Verify: 6 loci × 2 alleles = 12 values per person.

Step 2.5: Data Cross-Validation (MANDATORY)

Use the report's built-in Matches row to verify extracted data:

• For each donor × each locus, count shared alleles with patient (0/1/2)
• Compare against the Matches number shown in the report
• If mismatch → re-read that locus from the image
• DPB1 is rightmost column — highest risk of column misalignment
• This step catches OCR/reading errors before they propagate to wrong rankings

Step 3: Haplotype Deduction & Donor Screening

For sibling donors, deduce parental haplotypes.

• 1 shared haplotype → ✅ Haploidentical
• 2 shared → MSD (flag separately)
• 0 shared → ❌ Exclude with explanation

Step 4: IPD-IMGT/HLA API Calls

Run scripts/hla_matching.py --json '{...}'

Three API endpoints:

• B-Leader: https://www.ebi.ac.uk/cgi-bin/ipd/matching/b_leader
• DPB1 TCE v3: https://www.ebi.ac.uk/cgi-bin/ipd/matching/dpb1_tce_v3
• KIR Ligand: https://www.ebi.ac.uk/cgi-bin/ipd/matching/kir_ligand

Donor IDs must be ASCII-only in URL params.

Step 5: TCE-Core Reclassification (Solomon 2024)

TCE Group 3 splits into core vs non-core. See references/tce-core.md.

Key rule: G3 non-core mismatched with G3 core → C-NPMM → nonpermissive (HR 0.72).

Step 6: DRB1/DQB1 Match Status

Compare patient vs donor alleles:

• DRB1: match / mismatch
• DQB1: match / mismatch
• Best combo: mismatch/match (HR 0.80)
• Worst combo: match/match (HR 1.32)

Step 7: Cox Model DFS Calculation

Run scripts/haplo_dfs.py --json '{...}'

Model: Fuchs et al., Blood 2022, Table 3. Baseline S₀(3yr) = 0.413. See references/cox-model.md for all coefficients.

Step 8: Donor Ranking

Sort by 3-year DFS descending.

• Ranking factors: B-Leader + DRB1/DQB1 + DPB1 TCE-Core + CMV + HCT-CI + age
• KIR: display only, does NOT affect ranking

Step 9: MSD/MMUD Comparison Table (Mehta 2024)

Always show 5-row comparison table. See references/mehta-comparison.md.

Step 10: Donor Selection Recommendation

Auto-generate recommendations for each hypothetical scenario:

• 🟢 If young MSD available → choose whom?
• 🟡 If old MSD available → choose whom?
• 🔵 If MMUD available → choose whom?
• 💡 Final conclusion

Logic depends on: Haplo group × donor age × patient age.

Step 11: Generate Final Report

Output 8-part text report:

1. Patient info
2. Donor ranking (DFS + KIR display)
3. MSD/MMUD comparison table
4. HR comparisons + age effects
5. GVHD advantage analysis
6. If-other-donor-type recommendations
7. Comprehensive conclusion
8. References (7 papers)

Step 12: Export PDF and Save

Generate PDF using scripts/report_pdf.py, filename: {patient_name}_DFS.pdf

Save location (in order of preference):

1. Google Drive — if gog CLI is configured and user provides a folder path/ID:
   • Use gog drive upload <file> --parent <folder_id>
   • Ask user for Drive folder on first use, remember for subsequent runs
2. Default local path — ~/openclaw-reports/HLA/
   • Create directory if not exists
   • Always save a local copy regardless of Drive upload

Scripts

References

Dependencies

• Python 3.x with urllib, json, math (stdlib)
• fpdf2 (for PDF generation)
• Chinese font: WenQuanYi Zen Hei (/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc)
• Internet access for IPD-IMGT/HLA API calls