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QF-Pro Applications & Clinical Evidence

A comprehensive navigation hub for QF-Pro clinical applications and evidence

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This hub provides a comprehensive guide to QF-Pro's validated clinical applications and exploratory pipeline. Navigate to detailed evidence for each target, cancer type, and use case—organized to help researchers, clinicians, and partners understand where functional biomarker measurement has been proven and where opportunities exist.
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How to Use This Hub

This applications hub organizes QF-Pro evidence into navigable categories. Each linked term contains a dedicated QF-Pro Application section with specific evidence, clinical context, and references.

Clinically Validated applications have peer-reviewed evidence from patient outcome studies. These represent proven capabilities where QF-Pro has demonstrated superior predictive value compared to expression-based methodsLoading....

Exploratory applications have strong scientific rationale based on validated principles, but await dedicated clinical studies. These represent the expansion pipeline—targets where the same FRET-based approach should theoretically provide similar advantages.

The organizing principle throughout: functional measurement of protein interactions and activation states provides clinically meaningful information that expression-based assayLoading...s miss.

Simplified

This hub helps you find QF-Pro evidence organized by what matters to you.

Green "Validated" badges mean real patient studies have been published. QF-Pro predicted outcomes where traditional testing failed.

Blue "Exploratory" badges mean the science makes sense, but clinical studies haven't been done yet. These are future opportunities.

Click any linked term to see the full QF-Pro Application section with evidence details.

Landmark Clinical Findings

Three findings establish the foundation of QF-Pro's clinical value. Each demonstrates the same principle: functional protein state predicts patient outcomes where expression measurement fails completely.[1,2,3,4]

n=176 melanoma P=0.05 survival

iFRET-measured checkpoint interaction correlated with overall survival. Standard PD-L1 expression testing showed no prognostic value whatsoever (P=0.87). In ccRCC, iFRET detected functional engagement in 10 of 11 patients classified as "PD-L1 negative" by IHC.[3]

n=224 patients P=0.002—0.036

The foundational validation: Akt activation state (measured by amplified FRET) predicted both disease-free and overall survival in breast cancer (n=164)[1] and ccRCC (n=60).[2] IHC-based expression measurement showed no correlation in either cancer.

First-ever measurement 150-core TMA

The first time CTLA-4/CD80 interaction has been quantified at 1-10nm resolution in patient tissue. Critically, CD80 expression did not correlate with interaction state (r=-0.134, P=0.632)—proving that expression and engagement are independent variables.[4]

Simplified

Three studies established that measuring protein function beats measuring protein amount:

In 176 melanoma patients, checkpoint interaction predicted who would survive (P=0.05). Checkpoint expression predicted nothing (P=0.87). Same proteins, completely different clinical value.[3]

In 224 patients across two cancers, Akt activation predicted survival. Akt expression showed no correlation at all.[1,2]

First-ever measurement of this checkpoint pair in tissue. Expression levels didn't predict interaction—you have to measure the actual engagement.[4]

Validated Cancer Indications

QF-Pro has generated clinical evidence across five cancer types. Each indication validated the core principle in a different biological context, building confidence that functional biomarkers provide universally superior predictive information.[5,6]

PD-1/PD-L1 · n=176 · Neoadjuvant TVEC monitoring

The primary validation cohort for checkpoint interaction measurement.[3] Ongoing work with OSU/Pelotonia extends to treatment response monitoring, where complete responders showed increased iFRET efficiency post-treatment.[8]

PKB/Akt · n=164 · DFS P=0.036, OS P=0.013

The foundational 2014 study that established the functional biomarker paradigm. Tissue microarray analysis demonstrated that FRET-measured Akt activation—not expression—stratifies patient prognosis.[1]

PKB/Akt + PD-1/PD-L1 · Dual-target validation

Unique dual validation: both Akt activation (HR 0.228, P=0.002)[2] and PD-1/PD-L1 interaction[3] were validated in ccRCC. Notably, iFRET detected checkpoint engagement in patients classified as "PD-L1 negative."

CTLA-4/CD80 + PD-1/PD-L1 · Lung metastases

The 2022 RFA study quantified checkpoint dynamics in CRC lung metastases before and after radiofrequency ablation—demonstrating QF-Pro's utility for longitudinal treatment monitoring.[4]

CTLA-4/CD80 · 150-core commercial TMA

Antibody validation across NSCLC and SCLC tissue demonstrated technical feasibility in lung cancer—a major market for checkpoint inhibitor therapy selection.[4]

Simplified

QF-Pro has been validated in five different cancer types, each confirming that functional measurement outperforms expression testing:

Melanoma →Loading... 176 patients · PD-1/PD-L1 checkpoint validation[3]
Breast Cancer →Loading... 164 patients · Original Akt validation study[1]
Kidney Cancer (ccRCC) →Loading... Two targets validated in same cancer[2,3]
Colorectal Cancer →Loading... Treatment monitoring in metastases[4]
Lung Cancer (NSCLC) →Loading... Large tissue microarray validation[4]

Clinical Applications & Use Cases

Beyond specific targets and cancer types, QF-Pro enables distinct clinical use cases. These applications represent how the technology translates into clinical practice—from patient selection to treatment monitoring to companion diagnostic development.[5,6,7]

Patient Selection

Identify responders that expression-based testing misses. Expand therapy eligibility for "biomarker-negative" patients with functional engagement.[3]

Treatment Monitoring

Track checkpoint engagement dynamics longitudinally. Identify responders early based on functional changes rather than waiting for radiographic response.[4,8]

Platform Technology

Understand the measurement modalities: intercellular interactions (iFRET) for checkpoints, intracellular activation (aFRET) for kinases.[7]

Technical Foundations

Why FRET provides information that other methods cannot. The physics of 1-10nm resolution and why it matters clinically.[6]

Simplified

QF-Pro enables several distinct clinical use cases:

Patient Selection

Find responders that standard tests miss

StratificationLoading... · Companion DxLoading... · ICI SelectionLoading...
Treatment Monitoring

Track changes during therapy

Abscopal EffectLoading... · ValidationLoading...
Understanding the Technology

How and why QF-Pro works

iFRETLoading... · aFRETLoading... · PPI DetectionLoading... · ColocalizationLoading...

Exploratory Pipeline

These applications represent logical extensions of validated principles. The same FRET-based approach that proved superior for PD-1/PD-L1 should theoretically apply to other checkpoint pairs. Kinase activation measurement validated for Akt can extend to other oncogenic pathways.[5,6]

Each target below has scientific rationale but awaits dedicated clinical validation studies. These represent partnership and development opportunities.

Emerging Checkpoint Targets

The next generation of immune checkpoints entering clinical practice. Each follows the same intercellular interaction paradigm validated for PD-1/PD-L1 and CTLA-4/CD80.[6]

LAG-3 / MHC-II →Loading...FDA-approved target (relatlimab)
TIM-3 →Loading...T cell exhaustion marker
TIGIT / CD155 →Loading...Competing receptor paradigm
CD226 →Loading...Activating receptor (vs TIGIT)
CD155 →Loading...Dual-receptor ligand
VISTA →Loading...Awaiting receptor characterization
Oncogenes & Signaling

Receptor dimerization and kinase activation—extending the intracellular measurement approach validated for PKB/Akt to other oncogenic drivers.[1,2]

HER2 / HER3 →Loading...Dimerization drives Akt pathway
BCR-ABL →Loading...Kinase activation state
BRAF →Loading...Beyond V600E mutation status
PTEN →Loading...Functional loss via Akt readout
Immune Cell Biology

Applying validated checkpoint assays to specific immune cell populations within the tumor microenvironment.[6]

CAR-T →Loading...Engineered synapse quality
Dendritic Cells →Loading...DC-mediated suppression
Tregs →Loading...Treg checkpoint engagement
Simplified

These targets have strong scientific rationale but need clinical validation studies. They represent future opportunities.

Emerging Checkpoints

Same approach as PD-1/PD-L1, different checkpoint pairs

Oncogenes

Extending the Akt approach to other cancer drivers

Immune Cells

Checkpoint engagement on specific cell types

References & Publications

The clinical evidence presented in this hub is derived from peer-reviewed publications spanning 2014—2025. These studies establish the scientific and clinical foundation for QF-Pro's functional biomarker approach.

Foundational Clinical Validation
[1]
PKB/Akt Breast Cancer Study (2014)

Veeriah S, Montinaro C, Chen L, et al. High-Throughput Time-Resolved FRET Reveals Akt/PKB Activation as a Poor Prognostic Marker in Breast Cancer. Cancer Research. 2014;74(18):4983-4995.

Established the functional biomarker paradigm: n=164, DFS P=0.036, OS P=0.013. First demonstration that FRET-measured activation predicts outcomes where expression fails.

[2]
PKB/Akt ccRCC Study (2017)

Miles J, Golber A, Engelman R, et al. Time resolved amplified FRET identifies protein kinase B activation state as a marker for poor prognosis in clear cell renal cell carcinoma. BBA Clinical. 2017;8:97-102.

Extended Akt validation to ccRCC: n=60, HR 0.228, P=0.002. Confirmed functional biomarkers outperform expression across cancer types.

Checkpoint Interaction Studies
[3]
PD-1/PD-L1 Melanoma Study (2020)

Sanchez-Magraner L, Miles J, Baker CL, et al. High PD-1/PD-L1 Checkpoint Interaction Infers Tumor Selection and Therapeutic Sensitivity to Anti-PD-1/PD-L1 Treatment. Cancer Research. 2020;80(19):4244-4257.

Landmark checkpoint validation: n=176 melanoma, iFRET P=0.05 vs IHC P=0.87. Detected engagement in 10/11 "PD-L1 negative" ccRCC patients.

[4]
CTLA-4/CD80 & Checkpoint Dynamics (2022)

Howarth NM, Leng K, Wirtz M, et al. Determination of Interactive States of Immune Checkpoint Regulators in Lung Metastases after Radiofrequency Ablation. Cancers. 2022;14(22):5738.

First-ever CTLA-4/CD80 tissue quantification. 150-core TMA validation. Demonstrated expression does not correlate with interaction (r=-0.134, P=0.632).

Treatment Response & Reviews
[5]
Biomarker Functionality & Patient Outcomes (2021)

Miles J, Veeriah S, Sikorski K, et al. Quantification of biomarker functionality predicts patient outcomes. iScience. 2021;24(11):103249.

Meta-analysis establishing functional biomarkers as superior predictors across multiple targets and cancer types.

[6]
Quantitative Proteomics & Immune Oncology Review (2022)

Miles J, Leong HS, Astin JW. The fusion of quantitative molecular proteomics and immune oncology: a step towards precision. FEBS Letters. 2022;596:2721-2735.

Comprehensive review positioning functional biomarkers within precision oncology. Framework for CDx development.

[7]
Protein-Protein Interactions & Activation Dynamics (2022)

Miles J, Lay AJ, Leong HS. Quantification of protein-protein interactions and activation dynamics: a new path to predictive biomarkers. Expert Review of Proteomics. 2022;19(3):169-181.

Technical review of FRET-based biomarker methodology and clinical applications.

[8]
Neoadjuvant TVEC Melanoma Study (2025)

Brown JC, Miles J, Reilley MJ, et al. Toward Functional Biomarkers of Response to Neoadjuvant Oncolytic Virus in Stage II Melanoma. JCO Oncology Advances. 2025 (in press).

Treatment response monitoring: Complete responders showed increased iFRET efficiency post-treatment vs non-responders. OSU/Pelotonia collaboration.

Note: All publications are available through PubMed or journal websites. For access to full-text articles or additional technical documentation, contact HAWK Biosystems / ScientiaLux.

Simplified

All evidence in this hub comes from peer-reviewed scientific publications:

[1]
Breast Cancer Akt Study
Veeriah S et al. Cancer Research. 2014
[2]
Kidney Cancer Akt Study
Miles J et al. BBA Clinical. 2017
[3]
Melanoma PD-1/PD-L1 Study
Sanchez-Magraner L et al. Cancer Research. 2020
[4]
Checkpoint Dynamics Study
Howarth NM et al. Cancers. 2022
[5]
Biomarker Functionality Review
Miles J et al. iScience. 2021
[6]
Precision Oncology Review
Miles J et al. FEBS Letters. 2022
[7]
Technical Methodology Review
Miles J et al. Expert Rev Proteomics. 2022
[8]
TVEC Melanoma Study
Brown JC et al. JCO Oncology Advances. 2025

All studies available through PubMed. Contact HAWK Biosystems for full-text access.

Platform Capabilities

  • Validated Assays: PD-1/PD-L1, CTLA-4/CD80, LAG-3/MHC-II, TIGIT/CD155 checkpoint interactions with published outcome correlations
  • Cancer Indications: Melanoma, NSCLC, kidney cancer, colorectal cancer with specific clinical validation data for each
  • Emerging Pipeline: HER2/HER3 oncoproteins, additional checkpoint targets, T cell exhaustion markers under development

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