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QF-Pro Home › QF-Pro Glossary › Clinical Applications › Patient Stratification
Clinical Applications — Step 2 of 5
1Clinical Utility
2Patient Stratification
3Melanoma
4NSCLC
5Precision Medicine
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Melanoma
Clinical Application

Patient Stratification

Identifying patients most likely to benefit from specific therapies–functional biomarkers improve prediction accuracy beyond expression-based selection.

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Definition
Patient stratification uses biomarker testing to match patients with therapies most likely to benefit them. Expression-based stratification misses patients with low-abundance but functional targets. Functional biomarkersLoading... enable stratification based on molecular engagement–identifying responders among patients deemed ineligible by expression criteria.
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The 2.13% Clinical Cutoff
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Misclassified Patients: The 280% Opportunity
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JCO Clinical Study: 188 NSCLC Patients
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Right patient, right drug
Precision medicine goal
~40% response
Even in PD-L1 'high' patients
Function predicts response
Engagement > expression
Continuous stratification
Beyond binary cutoffs

The Stratification Challenge

The promise of precision medicine is matching the right patient with the right therapy. In practice, this requires biomarkers that predict therapeutic response accurately enough to guide clinical decisions.

Current stratification tools fall short. Consider pembrolizumab in NSCLC: patients with PD-L1 TPS 50% are considered optimal candidates for first-line monotherapy. Yet only approximately 41% of these patients respond–meaning the majority of patients meeting the biomarker threshold do not benefit.

Worse still, patients classified as "PD-L1 negative" may have active checkpoint engagement that traditional IHCLoading... cannot detect. These patients are denied potentially beneficial therapy based on a measurement that fails to capture the biological reality of their tumor microenvironment.

The unmet need is clear: stratification tools that measure what proteins are doing, not merely whether they are present.

Simplified

Why We Stratify: Not all patients with the same cancer respond to the same treatment. Stratification divides patients into groups expected to have different outcomes or treatment responses.

Current Methods: Stage, grade, molecular subtypes (ER+, HER2+), mutation status (EGFR, KRAS), expression biomarkers (PD-L1).

Functional Stratification: A New Paradigm

FRETLoading...-based functional biomarkers enable stratification based on molecular engagement state rather than expression level. This fundamentally changes the stratification question.

Expression-based stratification asks: "Does this patient have enough target protein to potentially respond?"

Function-based stratification asks: "Is this patient's tumor actively dependent on the pathway we intend to block?"

A patient with high FRET efficiencyLoading... has active checkpoint-mediated immune suppression that can be relieved by checkpoint blockade. A patient with low efficiency–regardless of expression levels–may not benefit from the same therapy.

The quantitative nature of FRET efficiency enables continuous rather than binary stratification. Rather than arbitrary cutoffs that divide patients into "positive" and "negative" categories, functional measurement provides a spectrum of interaction states that correlate with clinical outcomes.

This approach extends beyond immunotherapy. HER2-HER3 dimerizationLoading... state could stratify patients for HER2-targeted therapy. PKB/Akt activationLoading... could guide PI3K pathway inhibitor selection. Any therapy targeting a protein-protein interactionLoading... is amenable to functional stratification.

Simplified

Beyond Expression: Instead of "biomarker positive/negative" based on protein levels, stratify based on functional engagement.

The Evidence: When melanoma patients were grouped by PD-1/PD-L1 engagement (not expression), the groups had significantly different survival. Function-based stratification worked; expression-based didn't.

QF-Pro Application

Clinically Validated

Functional Stratification: QF-Pro enables stratification based on molecular function rather than expression. Validated applications:
• Checkpoint engagement: High vs low PD-1/PD-L1 (P=0.05[3] survival)
• Pathway activation: High vs low Akt activation (DFS: P=0.036[1]; OS: P=0.013[1])

Click citation numbers to view full references in QF-Pro Applications & Clinical EvidenceLoading...

Simplified

Functional stratification: Instead of "biomarker positive/negative" by expression, QF-Pro stratifies by functional engagement. High vs low checkpoint engagement (P=0.05). High vs low Akt activation (P=0.013).

Expression-Based Stratification
"Is the target expressed?"
Binary cutoffs (positive/negative)
41% response in 'optimal' candidates
Misses low-expression responders
Function-Based Stratification
"Is the pathway actively engaged?"
Continuous FRET efficiency spectrum
Engagement correlates with response
Captures biology, not just abundance

Clinical Evidence: Functional Stratification

  • Metastatic melanoma: PD-L1 expression showed no correlation with overall survival (p=0.87), while PD-1/PD-L1Loading... interaction state measured by iFRETLoading... was significantly predictive (p=0.05)
  • Clear cell renal carcinoma: iFRETLoading... detected checkpoint engagement in 10 of 11 patients classified as 'PD-L1 negative' by IHCLoading...–patients who would be excluded from immunotherapy under current stratification
  • Neoadjuvant TVEC study: Complete responders showed significantly increased iFRETLoading... efficiency post-treatment, while non-responders showed decreased or unchanged values–differences not reflected by traditional PD-L1 expression or T-cell phenotyping
  • Breast cancerLoading... PKB/AktLoading...: Expression levels did not stratify patients by outcome; FRETLoading...-measured activation state showed significant prognostic stratification

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