Abstract 681: SGI-110, a novel 5-aza-2’-deoxycytidine- containing dinucleotide, induces Cancer Germline (CG) antigen expression in Acute Myeloid Leukemia (AML) cells.
Bibliographic record
Abstract
Abstract Introduction: CG antigens are genes whose normal expression is limited to the adult testis and embryonic ovary. Aberrant expression of these genes is reported in solid cancers where they are known to be immunogenic. CG targeted cancer vaccines are in clinical development for cancers with endogenous gene expression. The CG genes NY-ESO1 and MAGEA are silent in adult tissues due to CpG island hypermethylation. Treatment of non-expressing cancer cell lines in vitro with hypmomethylating drugs (HMAs) like Decitabine (DAC) and Azacytidine (AZA), both FDA approved for patients with myeloid cancers, can induce CG gene expression. We hypothesized that SGI-110 (Astex Pharmaceuticals, Inc.), a second generation hypomethylating agent with superior pharmacokinetics, would induce CG antigen genes in AML cells in vitro. Methods: HL60 and U937 cells were cultured in vitro using standard techniques and treated with phosphate buffered saline, 0.1, 1 or 5 μM SGI-110, 0.5μM DAC or 2μM AZA. Results are pooled from a minimum of three replicates. Viability by trypan blue exclusion and DNA, RNA and protein were obtained on day 5; DNA was bisulfite converted. Methylation (LINE-1, NY-ESO1, by pyrosequencing and MAGEA by methylation specific PCR), mRNA expression (by RT-PCR), and protein expression (by Western blot) were assessed. Results: Treatment with SGI-110 produced dose-dependent cytotoxicity. Viability at 0.1, 1 and 5μM SGI-110 doses was 47, 11, 8% in HL60 cells and 91, 41, 22% in U937 cells. Viability at the 1μM SGI-110 dose was comparable to 0.5μM DAC (11 and 38%, respectively) and less than 2μM AZA (51 and 30% respectively) in both cell lines. Baseline LINE-1 elements were 80% methylated in HL60 cells and 68% methylated in U937 cells. In both lines 1μM SGI-110 produced optimal LINE-1 demethylation, comparable to DAC. At baseline both NY-ESO1 and MAGEA3/6 promoters were densely hypermethylated in both HL60 and U937 and neither expressed mRNA; treatment with AZA, DAC and 1μM SGI-110 hypomethylated both genes and induced mRNA expression. No mRNA expression of CG genes was detected at the 0.1μM SGI-110 dose. NY-ESO1 and MAGEA protein expression were induced following 1 and 5μM doses of SGI-110 and DAC in both cell lines; in U937 cells AZA induced MAGEA but not NY-ESO1. Summary: SGI-110 induces global and gene specific hypomethylation as well as mRNA and protein expression of the CG genes NY-ESO1 and MAGEA3/6 in AML cells. This protein induction in vitro is at least as potent as that observed following AZA and DAC treatment. Induction of anti-MAGE specific T-cells has been reported in myeloid cancer patients receiving AZA + Vorinostat, suggesting that induced CG specific immunity may contribute to response to HMAs (Goodyear et al. Blood. 2010;116:1908). These data suggest that the combination of HMAs such as SGI-110 with CG vaccination may be of value in myeloid malignancy. Citation Format: Elizabeth A. Griffiths, Pragya Srivastava, Golda Collamat-Lai, Smitha R. James, Adam R. Karpf. SGI-110, a novel 5-aza-2’-deoxycytidine- containing dinucleotide, induces Cancer Germline (CG) antigen expression in Acute Myeloid Leukemia (AML) cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 681. doi:10.1158/1538-7445.AM2013-681
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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 | 0.001 |
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".