Abstract 4118: Rapid, non-subjective characterization of disease in preclinical cancer research using desorption electrospray ionization mass spectrometry
Bibliographic record
Abstract
Abstract Cancer tissue smears are routinely used in rapid intraoperative pathology workflows using quick staining methods to characterize cancer in surgical margin assessments or tumor pathology. Mass spectrometry (MS) is a sensitive analytic platform that can detect the presence of cancer from the pattern of cancer-specific molecules present in the mass spectrum of the tissue under examination. In particular, mass spectrometry analysis with desorption electrospray ionization (DESI-MS) is shown to have utility in research models for cancer characterization or even for grading different subclasses of disease based on tumor-specific small molecule lipid or metabolites. DESI does not require extensive tissue preparation, and the data collection and analysis can be done within a few seconds. In this work, we evaluate the combination of rapid DESI-MS detection with rapid tissue smear preparation for research use in preclinical xenograft models of breast cancer and pediatric medulloblastoma requiring only seconds of sampling, and an overall preparation and analysis time of less than one minute. Principal component analysis (PCA) was performed to evaluate the concordance between DESI-MS profiles of breast cancer from tissue slices and smears prepared on various surfaces. PCA suggested no statistical discrimination between DESI-MS profiles of tissue sections and tissue smears prepared on glass, polytetrafluoroethylene (PTFE), and porous PTFE. However, the abundances of cancer biomarker ions varied between sections and smears, with DESI-MS analysis of tissue sections yielding higher ion abundances of cancer biomarkers compared with smears. The coefficient of variance (CV) analysis suggests DESI-MS profiles from tissue smears are as reproducible as the ones from tissue sections. The limit of detection with smear samples from single pixel analysis is comparable to tissue sections that average the signal from a tissue area of 0.01 mm2. The smears prepared on the PTFE surface possessed a higher degree of homogeneity compared with the smears prepared on the glass surface. This allowed single MS scans (~1 s) from random positions across the surface of the smear to be used in rapid cancer typing with good reproducibility, providing useful pathologic information at speeds suitable for research use. Likewise, DESI-MS enabled the rapid classification of subgroups of medulloblastoma in these preclinical models. Citation Format: Michael Woolman, Alessandra Tata, Isabelle Ferry, Claudia Kuzan-Fischer, Megan Wu, Sunit Das, Michael D. Taylor, James T. Rutka, Howard J. Ginsberg, Arash Zarrine-Afsar. Rapid, non-subjective characterization of disease in preclinical cancer research using desorption electrospray ionization mass spectrometry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4118.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".