Cancer biomarker discovery using selected ion flow tube mass spectrometry
Bibliographic record
Abstract
Lung cancer is one of the most common types of neoplasm and is a leading cause of death in Canada. Accurate early diagnosis is key to the effective treatment of the disease, however the current means to do so, such as Computed Tomography diagnostic imaging, are costly, invasive, and not practical for routine use. The detection of volatile cancer biomarkers in breath represents an attractive non-invasive means to diagnose the disease. It is unclear, however, which, if any, breath chemicals have diagnostic utility. In this thesis I used Selected Ion Flow Tube Mass Spectrometry (SIFT-MS), a trace gas analytic method to (a) identify potential volatile cancer biomarkers in blood, and (b) investigate whether these markers are present in breath. Potential biomarkers were identified by comparing products ions formed in the reaction between hydronium (H3O+) and nitronium (NO+) precursor ions and trace gases present in the headspace of plasma obtained from patients with breast cancer, colorectal or lung cancer, and healthy controls. Using this approach product ions of interest were identified which derive from a wide range of chemical classes including aldehydes, acids, alcohols and sulphides, including some which have been identified previously by other investigators. Many of these ions could be quantified in the breath of healthy controls and therefore be suitable for quantification by breath analysis. The production rate of most of these ions was, however, poorly correlated between those formed in the reactions between nasal breath and those formed in reactions with blood headspace, even when using in samples collected and analysed simultaneously from the same participants. The lack of correlation suggests that the breath trace gases from which these product ions are formed are not dependent on the blood concentration of the same gas, but likely derive mainly from the airways. As such while my data suggest that cancer biomarkers may be found in the bloodstream, breath analysis is not a suitable means to non-invasively detect these cancer markers, in particular cancers of tissues other than those found in the airway. On the other hand, my data suggests that the detection of airway disease, including that of lung cancer, may be suitable candidates for the diagnosis and/or screening using breath analysis.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".