Abstract A24: BRM polymorphisms, part of a novel epigenetic mechanism, are predictive of cancer risk and clinic outcome in multiple cancer types
Bibliographic record
Abstract
Abstract BRM polymorphisms lie with the promoter region of the anticancer gene and SWI/SNF catalytic subunit, Brahma (SMARCA2). These polymorphisms statistically correlate with loss of BRM expression in both cell lines and primary lung tumors and function as part of an epigenetic mechanism which underlies BRM reversible silencing. Specifically, these polymorphisms function as the binding site of at least two transcription factors (MEF2D and GATA3) and two HDACs (HDAC3 and HDAC9). These proteins form a complex which drives the reversible silencing of the BRM protein. As BRM can serve as an anticancer protein, in part because BRM and SWI/SNF is required for the normal function of TP53 and RB as well as multiple DNA repair mechanisms, the silencing of BRM potentiates cancer development in mice. As such, BRM polymorphisms are predictive of cancer risk for lung cancer (n=600), breast cancer (N=300), head/neck cancer (n=400), colon cancer (N=250) and lymphoma (N=300) with an odds ratio ranging from 1.8 to 2.3. As these polymorphisms occur more frequently in African Americans, the odds ratio (cancer risk) in African Americans for lung cancer (n=250) is higher (3.5-4.5) as compared to that observed in Caucasians (2-2.3) (N=600). Similarly, BRM polymorphisms have a higher predictive value in HPV positive head/neck cancer with an odds ratio of 3.2 compared with an odds ratio of 2.0 in HPV positive head/neck cancer. This is in part due to the fact that BRM along with the HPV E2 protein regulates the expression of the transforming HPV proteins E6/E7. Unlike other polymorphisms which impart cancer risk and are fixed (cannot be changed), the fact that BRM silencing is reversible by compounds such as Flavonoids and certain NSAIDs, the cancer risk imparted by these polymorphisms can, in theory, be reversed or nullified by changes in diet, thereby making these polymorphisms unique in this respect. As BRM polymorphisms and BRM expression is also tied to differentiation and cell adhesion, these polymorphisms are also predictive of a worse clinical outcome with a hazard ratio ranging from 3 to 10 in pancreatic, head/neck, lung, colon and esophageal cancers. Thus, BRM polymorphisms represent a novel factor which is predictive of cancer risk and clinical outcome in multiple cancer types. Furthermore, these polymorphisms can potentially explain the observed health disparities (higher lung cancer occurrence despite lower rate of tobacco usage) which occur in African Americans. Citation Format: Suhdir Rai, Jennifer Wang, Kit Man Wong, Xiaoping Qiu, Geoff Liu, David Reisman. BRM polymorphisms, part of a novel epigenetic mechanism, are predictive of cancer risk and clinic outcome in multiple cancer types. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr A24.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".