Abstract 3476: Profiling CD antigens signature in human hepatocellular carcinoma (HCC) by cell-based high-throughput screening flow cytometry (HTS-FC)
Bibliographic record
Abstract
Abstract Accumulating evidence supports the cancer stem cell (CSC) hypothesis, which posits that CSCs are the source of many solid tumor types including hepatocellular carcinoma (HCC). In HCC, CSCs have been identified by a variety of CD antigens, including CD133, CD326 (EpCAM), CD90, CD44, CD24, CD13, which could serve as potential therapeutic targets. However, due to the heterogeneity of HCC, these markers do not account for CSC activity in all tumors, and there is thus a need to identify novel HCC CSC markers. We have developed a cell-based high-throughput screening flow cytometry (HTS-FC) platform to robustly characterize expression profiles of CD antigens on primary human HCC cells. We titrated all commercial fluorescence conjugated CD antibodies (CD1 to CD363) into four 96-well microplates, isolated and stained HCC cells with CD45 antibody then divided and incubated these cells with different CD antibodies in microplates (1X10^5 cells/well) followed by a LIVE/DEAD® reactive dye treatment. Stained plates were fixed and subjected to BD™ LSR II flow cytometry using High Throughput Sampler Unit. Each CD antigen expression percentage was analyzed within the CD45 negative subpopulation by FlowJo software. After investigating 10 HCC tumors, we performed heat-map and cluster analysis using Multi Experiment Viewer software. Our analysis of primary human HCC cells demonstrated that previously identified index markers of HCC CSC are expressed on a highly variable fraction of tumor cells as follows: CD13 (76.10 ± 8.72%), CD24 (7.79 ± 5.33%), CD90 (5.29 ± 0.84%), EpCAM (0.50 ± 0.46%), CD44 (0.041 ± 0.019%) and CD133 (0.0051 ± 0.0034%). Based on the heat-map, we classified all CD markers into five groups: 20 CD13-like high expressions (30%∼100%), 12 median expressions (10%∼30%), 32 CD90-like low expressions (1.0%∼10%), 60 EpCAM-like rare expressions (0.03%∼1.0%) and 249 nonsense expressions. We focused on the CD90- and EpCAM-like groups (92 CD candidates) for novel potential CSC markers. Using quantitative real-time RT-PCR, we determined the mRNA level of 92 antigens in reference to the EpCAM+/CD133+ CSC subpopulations sorted from 4 human HCC cell lines (HepG2, Hep3B, PLC/PRF/5 and Huh7) and identified 20% of these 92 candidates with stem/progenitor mRNA phenotype. We then measured the mRNA level of these remaining candidates in 40 pairs of primary human HCC tumor/adjacent normal tissues and identified two novel CD antigens showing high tumor specificity. We are now purifying these cells from primary HCC tumors in order to determine whether these small populations identify cells with CSC activity in xenograft models and sphere-forming assays. These studies demonstrate that cell-based high-throughput screening flow cytometry screening can be used in combination with other cell/molecular biological techniques to identify novel populations of tumor cells with unique functional characteristics. Citation Format: Kui Chen, Laurie Ailles, John E. Dick, Anand Ghanekar. Profiling CD antigens signature in human hepatocellular carcinoma (HCC) by cell-based high-throughput screening flow cytometry (HTS-FC). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3476. doi:10.1158/1538-7445.AM2014-3476
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".