Improving Cancer Screening and Monitoring: The Potential of Oncofetal Chondroitin Sulfate Proteoglycans in Liquid Biopsies
Bibliographic record
Abstract
Introduction: Cancer remains a leading cause of death worldwide, with survival rates heavily dependent on early detection. Screening tests can help detect cancer at an early, more treatable stage before symptoms appear. For example, the fecal immunochemical test (FIT) is widely adopted in many EU countries for colorectal screening (CRC). However, FIT suffers from low sensitivity for early-stage disease and poor screening compliance, and around 30% of CRC patients relapse within 5 years of disease. Furthermore, for other cancers such as bladder cancer (BC), no non-invasive, highly sensitive and specific screening tests are available. Alternative biomarkers that can identify early-stage disease and relapses are sorely needed, especially those that can easily integrate into clinical workflows. Material and method: Oncofetal chondroitin sulfate proteoglycans (ofCSPGs) are promising pan-cancer biomarkers due to their uniquely modified glycosylation patterns and involvement in tumor progression. The ofCSPGs are shed from tumor tissue into circulation, allowing for non-invasive cancer detection. We validated the expression of ofCSPGs in patient liquid biopsies and tissue using mass spectrometry and immunohistochemistry, respectively. To test their diagnostic potential, we developed two novel diagnostic assays using proprietary antibodies to detect ofCS. These assays were optimized to detect and quantify ofCSPG in both plasma and urine samples from cancer patients. Result and discussion: ofCSPGs were widely expressed in CRC and BC tumors, with minimal or no detection in matched healthy tissue, and proteomic analysis identified a panel of ofCSPGs in urine and plasma. Drawing from this, preliminary data in a clinical discovery cohort demonstrate high specificity and sensitivity for ofCSPGs in plasma, with excellent AUC scores, underscoring their potential as robust biomarkers. Notably, ofCSPGs were detectable in plasma from adenoma patients, suggesting promise for early-stage CRC detection. Furthermore, longitudinal monitoring of CRC patients undergoing surgical interventions revealed significant declines in ofCSPG levels after tumor resection, supporting their utility in assessing treatment response and disease progression. Beyond CRC plasma samples, ofCSPGs were consistently detected in urine from BC patients, with background levels in age-matched controls including hematuria and non-symptomatic individuals. Conclusion: These findings underscore the potential of ofCSPG as minimal invasive biomarkers for cancer screening, patient stratification, and disease monitoring across multiple malignancies. We are now conducting a large-scale study to assess their efficacy in early multi-cancer detection and disease monitoring. Our goal is to develop a non-invasive, highly accurate diagnostic tool to address critical unmet clinical needs and improved outcomes for CRC and BC patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".