Qoppa as a New Pan-Tumor Synthetic Parameter Derived from Tumor-Associated Biomarkers for Identifying Oncology Patients at High Risk of Metastasis: A Prospective Pilot Study

<b>Background/Objective:</b> Early detection of metastatic progression remains a major challenge in precision oncology. Conventional radiological imaging cannot reliably identify micrometastatic disease. Although circulating tumor DNA is promising for minimal residual disease detection, organ-derived response biomarkers reflecting tissue adaptation to secreted factors remain unexplored. We hypothesized that integrating such biomarkers with global laboratory parameters would generate a synthetic variable with improved discrimination for de novo metastasis and mortality. <b>Methods:</b> This prospective observational pilot study enrolled 30 patients (median age 64.4 years; 56.7% female) with heterogeneous solid malignancies. Peripheral blood biomarkers responsive to tumor-secreted soluble factors (<i>n</i> = 11) were quantified using a multiplexed beads Luminex immunoassay. Global analytical parameters (<i>n</i> = 20) were derived from routine laboratory assessments. Hierarchical agglomerative clustering analysis generated two synthetic variables: Stigma (Ϛ) and Qoppa (Ϙ). Receiver operating characteristic curve analysis, Kaplan-Meier survival analysis, and Cox regression were used to evaluate the performance. <b>Results:</b> Qoppa demonstrated acceptable discriminatory performance for de novo metastasis (AUC = 0.78). For mortality prediction, performance varied by disease status (overall AUC = 0.78): superior in non-metastatic patients (AUC = 0.98) but negligible in those with baseline metastases. Kaplan-Meier analysis confirmed significant survival differences (<i>p</i> = 0.042 overall survival; <i>p</i> = 0.024 for metastasis-free survival in the non-metastatic subgroup). Differences in biomarker expression and clinical variables (stage, tumor burden, and metastatic burden) were observed between the high and low Qoppa strata. <b>Conclusions:</b> In this small heterogeneous pilot cohort, Qoppa provides a proof of concept that integrating organ-derived response biomarkers with routine laboratory parameters may capture clinically relevant signals for metastatic risk stratification in oncology patients. This composite parameter supports the generation of hypotheses for future biomarker-driven research and clinical test development. External validation in larger multicenter cohorts is required before clinical implementation.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Medicine
Published
2026-01-20
DOI
https://doi.org/10.3390/jcm15020846
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Qoppa as a New Pan-Tumor Synthetic Parameter Derived from Tumor-Associated Biomarkers for Identifying Oncology Patients at High Risk of Metastasis: A Prospective Pilot Study

Javier Diaz-Santos, O Urquízar-Rodríguez, Silvia Montoro‐García, Beatriz Berrocal-Gavilan et al.
Journal of Clinical Medicine
Cancer Genomics and Diagnostics
article

Qoppa as a New Pan-Tumor Synthetic Parameter Derived from Tumor-Associated Biomarkers for Identifying Oncology Patients at High Risk of Metastasis: A Prospective Pilot Study

Javier Diaz-Santos, O Urquízar-Rodríguez, Silvia Montoro‐García, Beatriz Berrocal-Gavilan, Alba Rodriguez-Valle
article en

Abstract

<b>Background/Objective:</b> Early detection of metastatic progression remains a major challenge in precision oncology. Conventional radiological imaging cannot reliably identify micrometastatic disease. Although circulating tumor DNA is promising for minimal residual disease detection, organ-derived response biomarkers reflecting tissue adaptation to secreted factors remain unexplored. We hypothesized that integrating such biomarkers with global laboratory parameters would generate a synthetic variable with improved discrimination for de novo metastasis and mortality. <b>Methods:</b> This prospective observational pilot study enrolled 30 patients (median age 64.4 years; 56.7% female) with heterogeneous solid malignancies. Peripheral blood biomarkers responsive to tumor-secreted soluble factors (<i>n</i> = 11) were quantified using a multiplexed beads Luminex immunoassay. Global analytical parameters (<i>n</i> = 20) were derived from routine laboratory assessments. Hierarchical agglomerative clustering analysis generated two synthetic variables: Stigma (Ϛ) and Qoppa (Ϙ). Receiver operating characteristic curve analysis, Kaplan-Meier survival analysis, and Cox regression were used to evaluate the performance. <b>Results:</b> Qoppa demonstrated acceptable discriminatory performance for de novo metastasis (AUC = 0.78). For mortality prediction, performance varied by disease status (overall AUC = 0.78): superior in non-metastatic patients (AUC = 0.98) but negligible in those with baseline metastases. Kaplan-Meier analysis confirmed significant survival differences (<i>p</i> = 0.042 overall survival; <i>p</i> = 0.024 for metastasis-free survival in the non-metastatic subgroup). Differences in biomarker expression and clinical variables (stage, tumor burden, and metastatic burden) were observed between the high and low Qoppa strata. <b>Conclusions:</b> In this small heterogeneous pilot cohort, Qoppa provides a proof of concept that integrating organ-derived response biomarkers with routine laboratory parameters may capture clinically relevant signals for metastatic risk stratification in oncology patients. This composite parameter supports the generation of hypotheses for future biomarker-driven research and clinical test development. External validation in larger multicenter cohorts is required before clinical implementation.

Journal of Clinical MedicineVol. 15(2)
Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (ES), HM Hospitales (ES), Universidad Católica de Murcia (ES)
Reduced inequalities
Openalex Percentile: Top 83%
Cancer Genomics and Diagnostics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.