Abstract 741: Rapid detection of necrosis in breast cancer with <i>ex vivo</i> and <i>in situ</i> mass spectrometry analysis methods
Bibliographic record
Abstract
Abstract Necrosis is a form of cell death that is often associated with highly aggressive forms of cancer, is of prognostic value in treatment planning. Mass Spectrometry (MS) is a highly sensitive analytic platform capable of providing a molecular profile of cancer on the basis of mass to charge (m/z) ratio of tissue constituent molecules. MS analysis of ex vivo tissue slices from metastatic murine xenograft tumors from LM2-4 cell line with Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) allowed direct comparisons with histology images to determine the molecular profile of necrotic tissues. The necrotic tissue is characterized by the presence of a ceramide absent from the viable cancer regions. The spatial distribution of this ion fully correlated to necrotic areas from pathology in additional independent tumor samples examined. The same ion was detected from in situ necrotic tissue using tissue aerosols generated by hand-held ablation probes coupled to evaporative ionization interface in only a few seconds of sampling. These developments further establish MS as a novel tool for rapid pathology that is highly complementary to current histology based methods widely used in characterization of cancer in both imaging mode (to provide spatial information on cancer border) and profiling mode (to provide information on cancer type and subtype); all based on unique molecular profile associated with each cancer type and subtype. Current efforts in creating cancer molecular profile libraries will facilitate translation. Citation Format: Arash Zarrine-Afsar, Bindesh Shrestha, Alessandra Tata, Michael Woolman, Manuela Ventura, Nicholas Bernards, Milan Ganguly, Howard Ginsberg, Jinzi Zheng, Emma Bluemke. Rapid detection of necrosis in breast cancer with ex vivo and in situ mass spectrometry analysis methods [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 741. doi:10.1158/1538-7445.AM2017-741
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".