Abstract B026: Expression levels of chaperonin containing TCP1 in cancer are among the highest in sarcomas
Bibliographic record
Abstract
Abstract Protein folding complexes are essential for oncogenesis. Of these, the multi-subunit complex, Chaperonin-Containing TCP-1 (CCT or TRiC), is upregulated across a spectrum of cancers and folds many of the oncoproteins that drive cancer growth. Using the UCSC Xena online database, which includes datasets from TCGA, TARGET, GTEx, and KidsFirst, we found significant increases in gene expression of the subunits (CCT1-8) that form the chaperonin complex in sarcomas, both adult and pediatric, as compared to other cancers like invasive breast carcinoma, lung carcinoma, glioblastoma multiforme, or prostate cancer. Most notably was the high expression of the second subunit (CCT2) in Rhabdomyosarcoma compared to other pediatric cancers. These findings were confirmed with other databases (e.g., Oncomine). Expression of CCT subunits, like CCT2, was decreased in normal tissues, such as muscle, kidney, and uterus, as compared to sarcoma tissues. Bioinformatic data was confirmed by finding detectable staining of CCT2 protein in sarcoma tissues. This was supported by data from the human protein atlas showing minimal staining for CCT2 in normal smooth and skeletal muscle. Cancer adjacent tissues from a metastatic breast cancer patient study had minimal staining for CCT2 in muscle, fat, and bone. These data show that CCT is increased in sarcomas, while decreased in normal tissues, and has potential for further study as a driver of sarcoma development and a target for therapeutic intervention. Citation Format: Amanda J. Cox, Annette Khaled. Expression levels of chaperonin containing TCP1 in cancer are among the highest in sarcomas [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 B026.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".