Abstract 124: Cellular context in epigenetics: Per-cell quantitation of miR-let-7d and its putative target in caspase-3 in reovirus-infected cancer cells
Bibliographic record
Abstract
Abstract An oncolytic virus, due to its ability to replicate and lyse cancer cells while leaving normal cells intact, is an example of targeted cancer therapy. Reovirus is intrinsically oncolytic without the need for any genetic manipulation due to its ability to target cells with an activated ras pathway. micro RNAs (miRNA) play critical role in both viral infection and oncogene activation. While significant advances in the role of miRNA in a variety of diseases including cancer have been made, the majority of the studies done on miRNA signatures have been done using homogenized tissue, typically via RT PCR. While useful, one drawback of this methodology is that it is, in essence, a population study, sampled from a heterogeneous collection of cells and tissue types. The miRNA signatures found via these methods are not able to determine whether the miR and target protein were found in the same tissue type (tumor, stroma, etc). Microscopy-based multi-analyte methods offer the benefit of visualizing miRNAs and their putative targets within the context of disease-specific molecular anatomy and on a per-cell basis.The development of simple spectral imaging systems capable of both brightfield and fluorescence multispectral imaging and morphologic image analysis packages that can be trained to recognize specific morphometric regions of interest have greatly facilitated the imaging, visualization and quantitative analysis of multicolor tissue samples. This study describes the means by which tissue sections labeled for multiple markers (proteins and microRNAs) can be analyzed and then be displayed as scatter plots, in a manner analogous to flow cytometry data, and multimarker phenotypes can be determined from threshold-based quadrant analysis.We have shown that reovirus infection of a variety of cancers, such as melanomas, head and neck squamous cell cancers, and ovarian serous carcinomas, induces increased cell death that is accentuated with taxol therapy. In situ based co-expression analysis showed that reovirus induced caspase-3 protein expression which, in turn, increased cancer cell death via apoptosis, as documented by the TUNEL assay. Reovirus plus taxol accentuated the caspase-3 expression compared to reovirus infection alone. microRNA nanostring analysis showed that miRNA-let-7-d was the most down-regulated in the cancer cells after reovirus infection. Analysis by the InForm system after co-expression analysis showed that reovirus was directly downregulating miR-let-7-d. Target Scan analysis documented that caspase-3 is directly targeted by miR-let-7d. Hence, the direct in situ co-expression testing via the InForm system is a powerful adjunct to microRNA analysis as it allows documentation of physiologic modulation of protein expression by the microRNA of interest. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 124. doi:10.1158/1538-7445.AM2011-124
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".