Abstract 2303: Decitabine impact on immunohistochemistry scores for tumor suppressor genes FHIT, WWOX, FUS1 and PTEN in human tumor samples
Bibliographic record
Abstract
Abstract Purpose: Since tumor suppressor gene (TSG) expression may be lost through promoter hypermethylation, we assessed whether the demethylating agent decitabine could increase TSG expression in tumor tissues of patients receiving decitabine therapeutically. Experimental Design: Patients on a phase I trial received intravenous 1-hour infusions of decitabine 2.5, 5, or 10 mg/m2/day on days 1-5 and 8-12 each 4-week cycle or 15 or 20 mg/m2/day on days 1-5 each cycle, with filgrastim added at higher doses. Tumor biopsies were done pre day 1 and on day 12 of the first cycle. For the putative TSGs FHIT, WWOX, FUS1 and PTEN, immunohistochemistry (IHC) scores (0-300) were calculated by multiplying the % tumor cells staining by the intensity (0-3+) of staining. IHC scores were correlated with methylation of the LINE-1 repetitive element (as a marker of global DNA methylation, determined by pyrosequencing) and with tumor regression. Results: Twenty-five patients had at least one pre- or post-decitabine biopsy evaluable for expression of at least 1 TSG, including 4 patients with breast cancers, 3 kidney, 3 head & neck, 4 melanomas, 3 thymic and 8 others. With negative staining pre-decitabine (score = 0), number of patients converting to positive staining post-decitabine was 1 of 1 for FHIT, 3 of 6 for WWOX, 2 of 3 for FUS1 and 1 of 10 for PTEN. In tumors with low pre-decitabine TSG scores (<150), expression was higher post-treatment in 8 of 8 cases for FHIT (p=0.014, by Wilcoxon signed rank tests for paired comparisons), 7 of 17 for WWOX (p=0.0547), 7 of 12 for FUS1 (p=0.0726), and 1 of 16 for PTEN (p=0.2034). If FHIT, WWOX and FUS1 were considered together, median pre- vs post-decitabine scores were 60 vs 100 (p=0.0002). Overall, TSG scores did not correlate with LINE-1 methylation, but if pre-decitabine scores for FHIT, FUS1 and WWOX were considered together, tumors with IHC scores = 0 for one of these genes (8 observations) had higher pre-decitabine % LINE-1 methylation than did tumors with IHC scores >0 (58 observations) (median 61.6% vs 45.4%, p=0.0481). TSG expression change did not correlate with LINE-1 methylation change, although tumors converting from negative to positive had a median decrease in LINE-1 methylation of 24%, compared to 6% in those not converting (p=0.069). Five of 15 fully evaluable patients had reductions in tumor diameter (by 0.2% to 33.4%). Of these, 3 had simultaneous increases in 3 TSGs (including the 2 patients with the greatest tumor regression) compared to 2 of 10 with tumor growth (p=0.25). Conclusions: In tumors with low TSG expression, decitabine may be associated with increased expression for the TSGs FHIT, WWOX & FUS1, but not PTEN. Further study will be needed to determine if increased TSG expression is associated with reduced DNA methylation and to determine if increased TSG expression may contribute to any decitabine therapeutic effect. Citation Format: David J. Stewart, Maria I. Nunez, Jaroslav Jelinek, David Hong, Sanjay Gupta, C. Marcelo Aldaz, Jean-Pierre Issa, Razelle Kurzrock, Ignacio I. Wistuba. Decitabine impact on immunohistochemistry scores for tumor suppressor genes FHIT, WWOX, FUS1 and PTEN in human tumor samples. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 2303. doi:10.1158/1538-7445.AM2014-2303
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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".