Trial requires gene mutation, patient has specific variant
Pathway match
20
Trial targets same pathway as patient's biomarker
No molecular criteria
10
General disease trial
Excluded biomarker
0
Patient's biomarker is in exclusion criteria
Clinical Eligibility (0-25 points)
Criterion
Points
Description
All criteria met
25
Disease, stage, prior treatment all match
Most criteria met
18
1-2 criteria unclear
Some criteria met
10
Several criteria unclear
Clearly ineligible
0
Fails major criterion
Evidence Strength (0-20 points)
Criterion
Points
Description
FDA-approved combination
20
T1 evidence
Phase III positive
15
T2 evidence
Phase II promising
10
T3 evidence
Phase I or no results
5
T4 evidence
Trial Phase (0-10 points)
Phase
Points
Phase III
10
Phase II
8
Phase I/II
6
Phase I
4
Geographic Feasibility (0-5 points)
Criterion
Points
Patient's city/state
5
Same country
3
International only
1
Unknown
0
Evidence Tier Classification
Tier
Symbol
Criteria
Score Impact
T1
[T1]
FDA-approved biomarker-drug, NCCN guideline
20 points
T2
[T2]
Phase III positive, clinical evidence
15 points
T3
[T3]
Phase I/II results, preclinical
10 points
T4
[T4]
Computational, mechanism inference
5 points
Recommendation Tiers
Score
Tier
Label
Action
80-100
Tier 1
Optimal Match
Strongly recommend - contact site immediately
60-79
Tier 2
Good Match
Recommend - discuss with care team
40-59
Tier 3
Possible Match
Consider - needs further eligibility review
0-39
Tier 4
Exploratory
Backup option - consider if Tier 1-3 unavailable
Molecular Match Scoring Logic
def score_molecular_match(patient_biomarkers, trial_requirements):
"""Score molecular match between patient and trial (0-40 points)."""
if not trial_requirements['required_biomarkers'] and not trial_requirements['excluded_biomarkers']:
return 10, 'No specific molecular criteria (general trial)'
patient_genes = {b['gene'].upper() for b in patient_biomarkers}
required_genes = {b['gene'].upper() for b in trial_requirements['required_biomarkers']}
excluded_genes = {b['gene'].upper() for b in trial_requirements['excluded_biomarkers']}
# Check exclusions first
excluded_match = patient_genes & excluded_genes
if excluded_match:
return 0, f'Patient biomarker(s) {excluded_match} are in exclusion criteria'
if not required_genes:
return 10, 'No specific biomarker requirements found'
# Check for exact gene match
matched_genes = patient_genes & required_genes
if matched_genes:
exact_variant_match = False
for req in trial_requirements['required_biomarkers']:
for pb in patient_biomarkers:
if pb['gene'].upper() == req['gene'].upper():
alt = pb.get('alteration', '').upper()
if alt and alt in req.get('context', '').upper():
exact_variant_match = True
break
if exact_variant_match:
return 40, f'Exact biomarker match: {matched_genes} with specific variant'
else:
return 30, f'Gene-level match: {matched_genes} (specific variant match unclear)'
return 5, 'No direct biomarker match found'
Drug-Biomarker Alignment Scoring
def score_drug_biomarker_alignment(patient_gene_symbols, drug_mechanisms):
"""Check if trial drug targets patient's biomarkers."""
patient_genes_upper = {g.upper() for g in patient_gene_symbols}
for mech in drug_mechanisms:
target_genes = {g.upper() for g in mech.get('target_genes', [])}
if patient_genes_upper & target_genes:
return True, f"Drug targets {patient_genes_upper & target_genes} via {mech.get('mechanism')}"
return False, "No direct target overlap with patient biomarkers"
