Hedgehog and Notch Signaling Regulate Self-Renewal of Undifferentiated Pleomorphic Sarcomas
Bibliographic record
Abstract
Like many solid tumors, sarcomas are heterogeneous and include a small fraction of the so-called side population (SP) cells with stem-like tumor-initiating potential. Here, we report that SP cells from a soft tissue tumor of enigmatic origin termed undifferentiated pleomorphic sarcoma (also known as malignant fibrous histiocytoma or MFH sarcoma) display activation of both the Hedgehog and Notch pathways. Blockade to these pathways in murine xenograft models, this human cancer decreased the proportion of SP cells present and suppressed tumor self-renewal, as illustrated by the striking inability of xenograft tumors subjected to pathway blockade to be serially transplanted to new hosts. In contrast, conventional chemotherapies increased the proportion of SP cells present in tumor xenografts and did not affect their ability to be serially transplanted. SP cells from these tumors displayed an unexpectedly high proliferation rate which was selectively inhibited by Hedgehog and Notch blockade compared with conventional chemotherapies. Together, our findings deepen the concept that Hedgehog and Notch signaling are fundamental drivers of tumor self-renewal, acting in a small population of tumor-initiating cells present in tumors. Furthermore, our results suggest not only novel treatment strategies for deadly recurrent unresectable forms of this soft tumor subtype, but also potential insights into its etiology which has been historically controversial.
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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 | 0.000 | 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) | 0.000 | 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".