Abstract TMEM-036: INTERFERON INDUCED STAT1 ASSOCIATES WITH DIFFERENTIAL CHEMOTHERAPY RESPONSE IN HIGH-GRADE SEROUS OVARIAN CANCER
Bibliographic record
Abstract
Abstract PURPOSE: High-grade serous ovarian cancer (HGSC) is the most prevalent and fatal histological subtype of ovarian cancer. Unfortunately, 70% of HGSC patients show resistance to chemotherapeutic drugs, and clinical management is challenged by a lack of accurate prognostic and predictive biomarkers of chemotherapy response. It is now established that immune cells within the tumour microenvironment (TME) significantly contribute to tumor cell death or survival following exposure to chemotherapy. We previously reported the presence of a distinct, pre-existing T-helper Type I associated immune TME, mediated by Signal Transducer and Activator of Transcription Factor 1 (STAT1), that predicted chemotherapy response in HGSC. Current work builds on our previous findings and investigates the mechanisms linking STAT1 to a pre-existing inflammatory TME and differential response to chemotherapy. EXPERIMENTAL PROCEDURES: Tumour STAT1 expression and intra-epithelial CD8+ T-cells were confirmed as prognostic and predictive biomarkers in a second independent biomarker validation study. Immunohistochemistry and a custom NanoString platform, composed of 34-target genes of STAT1 and immune phenotypic markers, were used on fresh frozen chemo-naïve HGSC tumour samples. Based on the findings from these profiling studies, a subset of samples with high and low STAT1 expression levels were subjected to further gene profiling analysis to correlate the immune transcriptomic landscape and its association with chemotherapy response. Stable silencing of the STAT1 gene was performed by lentiviral transduction of murine ovarian cancer cells (ID8) with STAT1 short hairpin RNA (shRNA) to create STAT1-knockdown (KD) cells and STAT1-non targeting (NT) control cells. Ongoing in vivo experiments will define the contribution of STAT1 in modulating; the TME, tumor progression, the recruitment of tumor infiltrating lymphocytes, and response to chemotherapy in HGSC. RESULTS: Gene expression analysis revealed significant differential expression of genes involved in the cellular Type I Interferon pathway, notably STAT1 and its target genes (such as ISG15, DDX58, IFIT1, CXCL10, CXCL11), between chemosensitive and resistant groups. Preliminary in vitro analysis has revealed proliferative and migratory advantages to STAT1-KD cells compared to the STAT1-NT cells; indicating that STAT1 plays a role in the regulation of cell survival and proliferation in the absence of the immune TME. CONCLUSION: STAT1 expression significantly associates with progression free survival and response to chemotherapy in HGSC. High levels of STAT1 and its target genes potentially contribute to CD8+ T-cell recruitment and immune mediated chemosensitivity in HGSC. Ongoing in vitro and in vivo studies will confirm the mechanisms underlying variations in the interferon induced STAT1 pathways in the TME of HGSC. Elucidating the mechanisms underlying differential STAT1 expression in HGSC primary tumours will contribute to better patient stratification for informed biomarker guided immunotherapies. Citation Format: Gillian Reid-Schachter, Peter Truesdell, Nichole Peterson, Charles Graham, Julie-Ann Francis, Andrew Craig, Madhuri Koti. INTERFERON INDUCED STAT1 ASSOCIATES WITH DIFFERENTIAL CHEMOTHERAPY RESPONSE IN HIGH-GRADE SEROUS OVARIAN CANCER [abstract]. In: Proceedings of the 11th Biennial Ovarian Cancer Research Symposium; Sep 12-13, 2016; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(11 Suppl):Abstract nr TMEM-036.
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 | 0.000 |
| 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".