Abstract B029: Flip-flopping of fusion-positive rhabdomyosarcoma regulating intratumoral heterogeneity for metastasis
Bibliographic record
Abstract
Abstract Of rhabdomyosarcoma (RMS), the most common pediatric soft tissue sarcoma, higher metastatic propensity of fusion-positive rhabdomyosarcoma (FPRMS), a subtype with PAX3-FOXO1 fusion gene, than fusion-negative subtype (FNRMS) suggest that the fusion gene may stimulate metastasis. Recent single-cell level research revealed that FPRMS has heterogeneity in the expression levels of the fusion gene, i.e., PAX3-FOXO1 fusion protein (P3F-FP) and corresponding phenotypes related to metastasis, e.g., higher cell motility of cells with low P3F-FP (FPLow). However, it remains unclear how the fusion gene and its heterogeneous expression regulate metastasis. Interestingly, FPRMS shows collective invasion, distinct from individual invasion of FNRMS. The collective invasion is prevalently observed in invasive tumors and has higher metastatic potentials than individual invasion. Despite its substantial commonality with well-characterized individual invasion, its features stemming from multi-cellular organization cause distinct features, potentially related to its higher metastatic potentials. Specifically, heterogeneity of a collective cell group, i.e., the leader/follower coordination, regulates collective invasion. The collective cell mass consists of “leaders,” trailblazing invasion paths, and “followers”, spreading through the paths despite their less invasiveness. At the initiation step of metastasis, the mass may enrich the subpopulation of leaders, driving collective invasion. However, it may revert the enrichment at the colonization step, suppressing migration and promoting colonization driven by followers at the secondary tumor site. This well-tuned process upon each metastasis step suggests the significance of dynamic switching between leaders/followers, regulating collective invasion and, in turn, metastasis. Here, we suggest bistability, a system with two stable states, represents the leader/follower coordination in FPRMS. The bistable system exhibits switch-like transitions between the states. i.e., “flip-flopping.” The flip-flopping enables even a small input to drive an “on/off” transition towards leaders or the other, implying the dynamic switching corresponding to each metastasis step. We discovered two distinct subpopulations with leader-like fast migrating FPLow/follower-like slow FPHigh cells, representing bistability in FPRMS. Specifically, the marginal region of FPRMS cell mass where leaders mainly reside showed higher subpopulation of fast FPLow cells. The bistability requires double-positive/negative feedback in the signaling, regulating flip-flopping. We found that YAP signaling may establish double-negative feedback with P3F-FP, modulating the flip-flopping. Thus, we suggest that the fusion gene functions as a toggle switch of bistability, and its "flip-flopping" modulation of expression by YAP signaling may designate cells as leaders or followers, regulating collective invasion and, thus, metastasis. This project will shed light on the metastasis of FPRMS, potentially leading us to suppress its metastasis. Citation Format: JinSeok Park, Anthonios Chronopolous. Flip-flopping of fusion-positive rhabdomyosarcoma regulating intratumoral heterogeneity for metastasis [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr B029.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".