Abstract 5897: Single cell-derived analysis of desmoid tumors for studying tumor-stroma interactions
Bibliographic record
Abstract
Abstract Cancer associated fibroblasts play an important role in the maintenance and remodeling of the tumor microenvironment, providing the appropriate conditions for neoplastic cell growth and invasion. Desmoid tumors (DT), also called aggressive fibromatosis, are rare, locally invasive soft tissue tumors that consist of fibroblastic cells embedded in extracellular matrix. Identification of the stromal cells to study tumor-stroma interactions is difficult due to both populations displaying a fibroblastic phenotype, and no cell marker exists that reliably differentiates between the two populations. Majority of DT arise sporadically due to somatic activating mutations in beta-catenin (CTNNB1), a major effector molecule of canonical Wnt signaling. We established single cell derived colonies from multiple DT samples and characterized the beta-catenin mutation status of each clone by Sanger sequencing. Indeed, we were able to establish both mutant and non-mutant colonies from DT samples. Quantitative PCR for beta-catenin targets AXIN2 and LEF1 confirmed differential activity between the mutant and non-mutant colonies. The specific CTNNB1 codon mutation had no difference on beta-catenin transcriptional activity. We next performed a high throughput surface antigen screen to identify cell markers that can distinguish between the two subpopulations. Our screen found CD142 to be uniquely expressed by the mutant colonies, while the non-mutant colonies uniquely expressed Podoplanin. Quantitative PCR confirmed the differential expression of these markers. Furthermore, the CD142-positive population in heterogeneous DT samples correlated with their mutation frequency. Importantly, CD142-based cell sorting allowed the isolation of the mutant subpopulation even in samples that appeared as wild-type by Sanger sequencing. We also studied the expression of secreted factors in our mutant and non-mutant populations. We observed that CTHRC1, a ligand related to the Wnt/PCP pathway, is highly elevated exclusively in the mutant subpopulations. Recombinant CTHRC1 increased the proliferation rate of DT primary cultures, as measured by BrdU incorporation, while neutralizing antibodies against CTHRC1 decreased cell proliferation. The importance of tumor-stroma interactions cannot be studied without first identifying and characterizing the two populations. This has been especially difficult in soft tissue sarcomas where both the neoplastic and stromal cells exhibit a mesenchymal phenotype. Our study offers a novel method for identifying the mutant and non-mutant subpopulations within desmoid tumors to study how they may interact. Rapidly quantifying tumor composition will also support efforts to understand the natural progression of disease and how it responds to therapy. Citation Format: Mushriq Al-Jazrawe, Steven Xu, Qingxia Wei, Raymond Poon, Benjamin Alman. Single cell-derived analysis of desmoid tumors for studying tumor-stroma interactions [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5897. doi:10.1158/1538-7445.AM2017-5897
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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 | 0.001 | 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) | 0.004 | 0.002 |
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".