Abstract 1412: Utilizing cell surface markers to define osteosarcoma and the stages of osteoblast differentiation
Bibliographic record
Abstract
Abstract Background Osteosarcoma (OS) is the most common primary malignant bone tumor in children. Despite advances in OS treatment, survival rates have remained stagnant over the past three decades. This may in part be reflective of the complex nature of OS—although all OS’ are pleomorphic, spindle shaped cells that are capable of producing osteoid, this is often where the similarities between tumors ends. Tumor phenotypes can be used to classify OS into multiple groups including the most common form, conventional, which can be further subdivided into osteoblastic, chondroblastic, or fibroblastic OS. Although these tumors display unique phenotypes, they have similar response rates to standard treatments such as chemotherapy and surgery suggesting that they may have a common progenitor, which we seek to identify. Methods In order to determine the OS cell of origin, we first performed flow cytometry analysis using cell surface markers that are differentially expressed on mesenchymal stem cells (MSC) and osteoblasts (OB)—CD44, CD105, CD54, CD49b, CD325, and GD2. MSCs were differentiated into OBs using induction media and cells were collected for analysis at multiple time points in differentiation; day (d)0, d3, d5, d10, d15, and d20. MSCs became fully differentiated OBs by d20, as determined by alizarin red staining. Similar flow cytometry analysis was performed on four OS standard cell lines, SaOS, U2OS, HOS, and HOS-143B, and marker profiles were compared to MSC to OB differentiation. Results CD44 and CD105 expression, both of which are known MSC markers, was high in MSCs and immediately dropped off during differentiation, while decrease in CD325 expression was more gradual. CD49b expression increased over time and GD2 expression varied greatly. CD54 expression peaked at d10 and decreased as differentiation continued. Expression of these markers was varied in the OS standard cell lines. These markers can be used to sort out specific populations of cells at different time points during differentiation. Conclusions and Future Directions In this study we have identified potential markers of OB progenitor populations and compared their expression to OS standard cell lines. We will use these prospective makers to sort progenitor populations and drive their differentiation into OBs, chondroblasts, and fibroblasts in order to determine branch points in MSC differentiation. Finally, in order to assess the potential of these cells to form OS, we plan to transform candidate progenitor cells with human telomerase reverse transcriptase, simian virus 40 large T antigen, and lentivirus containing oncogenic H-Ras serially. Citation Format: Pratistha Koirala, Vincent Poon, Sajida Piperdi, Amy Park, Michael Fremed, Michael Roth, Jonathan Gill, Richard Gorlick. Utilizing cell surface markers to define osteosarcoma and the stages of osteoblast differentiation. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1412. doi:10.1158/1538-7445.AM2015-1412
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".