Abstract A25: Dysregulation of MYC via STAT1 promotes tumor progression in serous papillary endometrial cancer
Bibliographic record
Abstract
Abstract Serous papillary endometrial cancers (SPEC) harboring several genetic alterations, such as TP53 mutation, are known highly progressive with worse prognosis compared with endometrioid counter-part, but it still remains unclear which molecular pathway is responsible for their unfavorable features. The IFN-γ; transcription factor signal transducer and activator of transcription 1 (STAT1) has been considered as a tumor suppressor with transcription-dependent and transcription-independent mechanisms, but recent studies showed STAT1 was associated with poor prognosis, and was involved in maintaining basal expression of tumor pro-survival STAT3 target genes, such as MYC, with unclear mechanism in some cancers. A previous report of the cancer genome atlas (TCGA) indicated MYC amplification in some SPECs; as MYC is known to be maintained also by STAT1, we investigate the role of STAT1-MYC axis in the malignant features of SPECs. Gene expression microarray was conducted for 63 endometrial cancer samples and SPEC cell line, SPAC-1L. Immunohistochemical staining were conducted using endometrial cancer samples under protocols approved by the Institutional Review Board to investigate SPEC-specific pathways (Kyoto cohort: n=91, Vancouver cohort: n=462). Using SPAC-1L cell line, several functional assay in vitro such as cellular proliferation, migration, invasion and apoptosis under treatment of cisplatin, doxorubicin, and paclitaxel were assessed with or without suppressing target genes. In vivo tumorigenesis was performed in mouse model and some computational analysis were done in silico. Gene expression microarray analysis revealed STAT1 pathway was commonly activated in SPECs both in our dataset and TCGA dataset as a key molecule of ‘SPEC signature’. In two independent cohorts, STAT1 expression was confirmed significantly higher in SPECs (p<0.001), and the disease specific survival of EC with high STAT1 expression was worse than those with low STAT1 expression (p<0.05). We found that STAT1 regulates MYC as well as ICAM1, PD-L1, and SMAD7, as well as the capacity for proliferation, adhesion, migration, invasion, and in vivo tumorigenecity in cells with a high SPEC signature. In contrast, suppression of STAT1 attenuated induction of these genes and inhibited xenograft tumor growth on NOD-SCID mice. Furthermore, suppressing STAT1 expression by shRNA sensitized SPEC cells to cisplatin. In STAT1 suppressed cells, apoptosis and cleaved caspase3 expression was confirmed even under low dose cisplatin treatment (p<0.05). Based on silico analysis using cBioPortal, we found amplification of MYC and up-regulated of MYC mRNA in 42% SPECs. The 227 up-regulated genes in the SPEC signature harbor the MYC binding site motif by GATHER analysis (p<0.001), and the predictive MYC activity signature score was statistically higher in SPECs than in other subtypes of endometrial cancers, while that in SPAC-1L cells was diminished with STAT1 knockdown. These results indicate that STAT1 pathway modulates the micro-environment of SPECs in deregulating MYC, as well as other target genes, to promote tumor progression. Furthermore, STAT1-MYC axis could be developed as a novel potential target for molecular targeting therapy in SPECs. Citation Format: Budiman Kharma, Tsukasa Baba, Noriomi Matsumura, Ryusuke Murakami, Ken Yamaguchi, Junzo Hamanishi, Masaki Mandai, Ikuo Konishi. Dysregulation of MYC via STAT1 promotes tumor progression in serous papillary endometrial cancer. [abstract]. In: Proceedings of the AACR Special Conference on Myc: From Biology to Therapy; Jan 7-10, 2015; La Jolla, CA. Philadelphia (PA): AACR; Mol Cancer Res 2015;13(10 Suppl):Abstract nr A25.
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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) | 0.002 | 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".