Abstract 1463: Intravital discovery of miRNA drivers of human cancer cell directional invasion
Bibliographic record
Abstract
Abstract Metastatic cancer cells often use directional ECM cues such as blood vessels or collagen fibers when invading through live tissue. Oncogenic miRNAs have been implicated as key regulators of cancer progression yet the systemic discovery of miRNAs that drive directional cancer cell invasion has not been achieved. Here we describe the first in vivo quantitative whole human miRNAome screen for miRNA drivers of directional cancer cell invasion using an ex ovo avian embryo model of human cancer cell metastasis combined with high resolution intravital imaging. We identified more than twenty novel miRNAs that promote cancer cell invasion during the key rate-limiting step of cancer metastasis, the initiation and expansion of overt metastatic lesions. In silico miRNA target analysis (DIANA, Targetscan) showed significant enrichment in gene targets/pathways that control cancer cell-ECM adhesion and cancer cell contractility. These findings were further confirmed using Affymetrix gene expression analysis that showed significant changes in the expression of genes that regulate actin cytoskeleton rearrangement, cancer cell-ECM interaction and cell surface-receptor signaling. Public cancer gene expression database analysis (Oncoprint) showed that these miRNA-mRNA networks deregulated in several deadly cancers (pancreas, prostate, breast) and negatively correlate with the cancer patient survival. To elucidate potential mechanisms of these pro-metastatic miRNAs, we utilized an intravital imaging approach. In vivo 4D cancer cell tracking revealed that these pro-metastatic miRNAs are required for successful invasion into collagen-rich tissue and for attachment to the outer surface of the vascular wall. Deregulation of these miRNAs led to formation of loose contacts with the vasculature and chaotic, non-directional cancer cell invasion patterns in living tissue. Intravital second-harmonic microscopy analysis showed that inhibition of the expression of these miRNAs blocked the ability of cancer cells to rearrange the disorganized fine collagen fiber network into thicker collagen bundles that guide directional tumor cell invasion. In contrast to parental tumor cells that persistently moved along the collagen bundles, miRNA-deficient tumor cells were observed to transiently associate with multiple collagen fibers without establishing persistent collagen fiber-tumor cell protrusion contacts. Moreover, miRNA-deficient tumor cells failed to attach and protrude along preexisting perivascular collagen bundles, resulting in the complete abrogation of cancer cell/blood vessel co-option. In summary, we identified a novel panel of human miRNAs that are functionally involved in the regulation of directional invasion and metastasis. This work establishes these miRNAs as promising therapeutic targets to block the metastatic spread of lethal cancers. Citation Format: Konstantin V. Stoletov, Lian Willetts, Juan Jovel, Emma Woolner, John D. Lewis. Intravital discovery of miRNA drivers of human cancer cell directional invasion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1463. doi:10.1158/1538-7445.AM2017-1463
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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".