Abstract 3423: Immunohistochemical expression and cluster analysis of mesenchymal and neural stem cell-associated proteins in pediatric undifferentiated soft tissue sarcomas
Bibliographic record
Abstract
Abstract Pediatric undifferentiated soft tissue sarcomas (USTS) are a challenging group of tumors to diagnose and accurately categorize. By definition, they do not exhibit consistent immunohistochemical profiles or harbor recurrent chromosomal rearrangements to aid in their recognition. Traditionally, they have been classified with poorly differentiated forms of embryonal rhabdomyosarcoma. More recently, however, this notion has been challenged as such tumors share characteristics with the Ewing family of tumors. Thus, the histogenesis of pediatric USTS remains unclear and optimum therapy remains a challenging decision. The aims were twofold: firstly, to determine whether differential expression of stem cell-associated proteins could be used to aid in determining the histogenesis of pediatric USTSs; and secondly, to determine whether pediatric USTS expressed a unique panel of stem cell-associated proteins and form a distinct sarcoma subcategory. Tumors included 32 Ewing sarcoma/PNETs (EWS), 22 embryonal rhabdomyosarcomas (ERMSs), 8 alveolar rhabdomyosarcomas (ARMSs), 5 synovial sarcomas (SSs), 5 malignant peripheral nerve sheath tumours (MPNSTs) and 16 USTSs. Stem cell antibodies used included a series of three mesenchymal stem cell markers (CD44, CD105 and CD166) and five neural stem cell markers (CD15, CD29, CD56, CD133 and nestin). Antibodies were applied to the sections using standard immunohistochemical techniques and the sections were scored on a six-tiered scoring system using both intensity and distribution components. The scores were then imported into Partek Genomic Suite Software, for statistical analyses, cluster analysis, and visualizations. The datasets were analyzed by comparing the expression scores from the groups using the 1-way ANOVA. Each group was compared to each individual group, as well as the remaining groups combined. The resultant Euclidean clustering divided the tumors into two major groups. EWSs and USTSs formed the majority of the first group, whereas ERMSs, ARMSs, MPNSTs and SSs formed the second group. Negativity for CD56 was strongly associated with the EWS/USTS cluster (p < 0.0001). EWSs and USTSs were further separated by CD166 staining, wherein positivity was associated with EWS and negativity with USTS (p < 0.0001). The second group included some EWS (n=5), but the vast majority (n=27) were included in the first cluster. No consistent separation of the different subgroups was seen in the second cluster of tumors. The current study demonstrates the usefulness of applying stem cell markers to pediatric sarcomas, and suggests that pediatric USTSs and EWSs are closely related and may share a common cell of origin. Furthermore, the majority of USTSs have a unique stem-cell expression profile. The findings have both biologic and diagnostic significance. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3423.
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) | 0.001 | 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 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".