Abstract 2988: Quantifying the Uptake of Metal Based Cancer Therapy Drugs Using Single Cell ICP-MS
Bibliographic record
Abstract
Metallic based cancer therapy drugs have been around for several years, the most widely used being platinum-based drugs, however these come with severe side effects due to the non-specific targeting of these drugs. Recently a number of nanoparticle-based cancer therapeutics have been approved for clinical use or are currently under development. Advantages that engineered nanoparticles may offer over conventional small molecule drugs include: (i) prolonged circulation time in the body; (ii) reduction of nonspecific cellular uptake along with undesirable off-target and other side effects; and (iii) improvement in cellular interactions through specific cancer cell targeting moieties.The therapeutic effect of cancer treatment is related to the amount of drug that interacts with each individual cancer cell. Traditional drug research techniques, such as conventional inductively coupled plasma mass spectrometry (ICP-MS), have been limited to cell ensemble measurements, which require homogenization of a given cell population for quantitative analysis. All cells within this population are assumed to be similar and therefore assumed to interact with the same amount of drug, however, recent studies demonstrate that cell populations are heterogeneous, and differences exist even for cells from the same cell population and cell line. For example, gene expression measurements based on homogenized cell populations are misleading as they only provide averaged results and do not account for the small but critical changes occurring in individual cells such as size, protein levels, and expressed RNA transcripts. These variations are key aspects when answering previously unsolvable questions in cancer research, stem cell biology, immunology, developmental biology, and neurology.To overcome these limitations, PerkinElmer developed Single-Cell (SC) ICP-MS, which allows the rapid analysis of a large number of individual cells rather than a cell population as a whole or only a few cells. This allows for the quantification of the metal mass in individual cells, resulting in a histogram of the population showing not only the most frequent and mean masses of drug in the population but also the distribution throughout the population. Here we will show results for both cisplatin uptake and surface modified gold nanoparticle uptake into cancer cells. The first resulting in a wide distribution in the amount of platinum measured per cell over time with differences in resistant and non-resistant cancer cells. While the latter shows the ability of this technique to quantify the number for modified Au nanoparticles per cell as well as the number of cells containing the drug.Note: This abstract was not presented at the meeting.Citation Format: Chady Stephan, Ruth Merrifield, Stefan Wilhelm. Quantifying the Uptake of Metal Based Cancer Therapy Drugs Using Single Cell ICP-MS [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2988.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".