Abstract 4972: In vivo whole genome shRNA screen reveals novel targets to block cancer metastasis
Bibliographic record
Abstract
Abstract The formation of invasive, rapidly growing metastatic lesions is a critical step in cancer metastasis, the cause of more than 90% of cancer deaths. Development of novel therapeutic approaches that block the invasion step of metastasis is one of the highest priorities for clinical cancer research. For this reason we completed the first genome-wide in vivo shRNA screen for genes that directly contribute to invasive metastatic lesion formation. Using state of the art intravital imaging, we identified over fifty novel regulators of invasive metastatic colony formation in vivo. Interactome analysis links these genes to key cellular processes including: transcriptional regulation of gene expression, mRNA processing and cytoskeletal remodeling. The target list was then prioritized based on clinical gene expression profiles that negatively correlated with key cancer endpoints including metastasis, cancer-specific and overall survival. Pharmacological and shRNA-mediated knockdown of the high priority targets in human cancer cell lines such as prostate cancer and melanoma specifically blocked cancer cell migration and invasion in vitro and in vivo. Moreover, shRNA-mediated knockdown of these genes blocked human cancer cell metastasis in the avian embryo and mouse preclinical models of metastasis. Finally, immunohistochemical analysis on clinical prostate cancer and melanoma tissue samples showed strong correlation with disease progression and metastasis. In summary, we have identified numerous novel genes that that are functionally involved in cancer invasion and metastasis that may serve as predictive markers for disease aggressiveness and represent exciting new pharmacological targets to block cancer invasion and metastasis. Citation Format: Konstantin Stoletov, David Bond, Hon Sing Leong, Emma Woolner, Srijan Raha, Amy Robertson, Francis Wong, Andries Zijlstra, John D. Lewis. In vivo whole genome shRNA screen reveals novel targets to block cancer metastasis. [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 4972. doi:10.1158/1538-7445.AM2014-4972
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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) | 0.001 | 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) | 0.003 | 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".