Abstract 3338: Targeted serum metabolomics for noninvasive detection of colorectal neoplasia
Bibliographic record
Abstract
Abstract Background: Using a shotgun metabolomics approach, we have previously reported distinct features in the circulating metabolome of patients with colorectal cancer (CRC) and adenoma. A clinically useful metabolomic assay requires a targeted approach that detects CRC with high sensitivity, and which also detects pre-malignant precursor lesions. Our objective was to identify a metabolomic signature for CRC, adenomatous polyps (AP), and sessile serrated adenomas (SSA), using a targeted approach. Methods: Serum from patients with all stages of CRC (N=86), AP (N=48), SSA (N=46), as well as age- and sex-matched disease-free controls (DFC; N=120) were analyzed using a targeted metabolomic assay (Biocrates MxP® Quant 500). The assay was performed on liquid chromatography-tandem mass spectrometry (LC-MS/MS), quantifying 630 metabolites across 26 biochemical classes. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to identify metabolite patterns distinguishing each neoplastic condition relative to DFCs. A small validation cohort (N=78) was additionally analyzed to test our meta-biomarkers. Results: In comparisons with DFCs, statistically significant OPLS-DA models were constructed for all three neoplastic conditions. The OPLS-DA models for CRC, AP, and SSA consisted of 39, 14, and 22 metabolites, respectively, with the most frequently represented biochemical classes being triglycerides, phosphatidylcholines, bile acids, and acylcarnitines. Based on 7-fold internal cross validation, receiver operating characteristic (ROC) analysis demonstrated an area under the curve (AUC) of 0.85, 0.89, and 0.82 for CRC, AP, and SSA, respectively. When applied to the independent validation cohort, the sensitivity and specificity of each model were: 87.0% and 87.0% for CRC; 53.3% and 93.3% for AP; and 78.6% and 57.1% for SSA. Conclusion: Metabolomic meta-biomarkers for CRC and its precursors demonstrated excellent performance based on internal validation. Importantly, these lesions were each detected with superior sensitivity compared to readily available stool-tests. A large external validation study in progress will be used to confirm these findings. Citation Format: Liam W. Fitzgerald, Dennis J. Orton, Karen A. Kopciuk, Hans J. Vogel, Robert J. Hilsden, Oliver F. Bathe. Targeted serum metabolomics for noninvasive detection of colorectal neoplasia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3338.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".