Individualizing Ovarian Cancer Surveillance: Discovery and Validation of Serological Personalized Biomarkers of Recurrence Using Multiplex Proteomics Technologies
Bibliographic record
Abstract
In Canada, ovarian cancer is the third most common female reproductive cancer and the leading cause of deaths among gynecological cancers. Although remission is observed in most ovarian cancer patients after first-line treatment, >80% of advanced cases see recurrence with a median survival of 12-24 months from the time of recurrence. The classical ovarian cancer biomarker, CA125, is controversial for monitoring recurrence as initiating second-line therapy sooner based on CA125 does not impact survival. Furthermore, CA125 is non-elevated at diagnosis in 10-20% of advanced ovarian cancer cases in general, leaving this population with no widely used biomarkers for surveillance. Patients being monitored with CA125 also have a 10-40% chance of CA125 being non-elevated at recurrence. With increasing selection of immunotherapies and precision medicines, novel personalized ovarian cancer biomarkers could help individualize the surveillance process. Due to tumour heterogeneity, we hypothesize that quantifying the unique array of tumour-derived serological proteins with advanced proteomics methods could identify personalized marker signatures that sensitively detect relapse. We first assessed the technical potential of two multiplex proteomics technologies for detecting proteins that may correlate to tumour burden in cancer patients. For our subsequent discovery study, we employed the proximity extension assay (PEA) to simultaneously measure 1,104 proteins in 120 longitudinal serum samples (30 ovarian cancer patients). We identified 23 candidate personalized markers (plus CA125 and FDA-approved marker HE4), in which personalized combinations was informative of recurrence in more patients (92%) compared to clinical CA125 (68%) and HE4 (32%) alone. For our ensuing validation study, we used PEAs to concurrently measure 644 proteins (includes 21 previously identified candidates plus CA125 and HE4) in 234 independent, longitudinal serum samples (39 ovarian cancer patients). The 21 candidates were each informative of recurrence in 3-35% of patients. Patient-centric analysis of all 644 proteins generated a refined panel of 33 personalized tumour markers, which includes 18 validated candidates from our discovery study. Along with HE4, the 34-marker panel offered higher sensitivity (91%) by identifying personalized marker signatures of recurrence compared to clinical CA125 (59%) and HE4 (26%) alone. Our findings show that personalized tumour markers may offer the best lens into the rich heterogeneity of ovarian tumours compared to a single marker alone. Developing a panel of personalized markers for tracking custom signatures of tumour burden in each patient may offer excellent sensitivity for detecting recurrence early and aid in prompt clinical referral to imaging and subsequent treatment interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".