Multiplexed VeraCode bead-based serological immunoassay for colorectal cancer
Bibliographic record
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer deaths in the US and Western world. Despite increased screening and advances in treatment, the mortality rate (ca. 50,000/year) and high national health-care burden for CRC are likely to remain high unless an effective non-invasive screening test for CRC is instituted for a large segment of the population. Blood-based protein biomarkers hold great promise for early disease diagnosis and personalized medicine; yet robust and reproducible multiplexing platforms and methodologies have lagged behind their genomic counterparts. Here, we report the development of a novel, multiplexed, hybrid immunoassay for CRC that is formatted on barcoded VeraCode™ micro-beads, which have until now only been used for genomic assays. The method combines a sandwich immunoassay format for detection of serum protein biomarkers with an antigen assay for autoantibody detection. The serum protein biomarkers CEA and GDF15 as well as autoantibodies to the p53 tumor associated antigen (TAA) were used to exemplify the method. This multiplex biomarker panel was configured to run on Illumina's holographically barcoded VeraCode™ micro-bead platform, which is capable of measuring hundreds of analytes simultaneously in a single well from small volumes of blood (<50 μL) using a 96-well industry standard microtiter plate. This novel use of the VeraCode™ micro-bead platform translates into a potentially low volume, high throughput, multiplexed assay for CRC, for the purposes of biomarker validation, as well as patient screening, diagnostics and prognostics. In an evaluation of a 186 patient sera training set (CRC and normal), we obtained a diagnostic sensitivity of 54% and a specificity of 98%. We anticipate that by expanding and refining the biomarkers in this initial panel, and performing more extensive clinical validations, such an assay could ultimately provide a basis for CRC population screening to complement the more invasive, expensive and low throughput (but highly sensitive and specific) colonoscopy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".