Simultaneous siRNA Targeting of Src and Downstream Signaling Molecules Inhibit Tumor Formation and Metastasis of a Human Model Breast Cancer Cell Line
Bibliographic record
Abstract
BACKGROUND: Src and signaling molecules downstream of Src, including signal transducer and activator of transcription 3 (Stat3) and cMyc, have been implicated in the development, maintenance and/or progression of several types of human cancers, including breast cancer. Here we report the ability of siRNA-mediated Src knock-down alone, and simultaneous knock-down of Src and Stat3 and/or cMyc to inhibit the neoplastic phenotype of a highly metastatic human model breast cancer cell line, MDA-MB-435S, a widely used model for breast cancer research. METHODOLOGY/RESULTS: Src and its downstream signaling partners were specifically targeted and knocked-down using siRNA. Changes in the growth properties of the cultured cancer cells/tumors were documented using assays that included anchorage-dependent and -independent (in soft agar) cell growth, apoptosis, and both primary and metastatic tumor growth in the mouse tumor model. siRNA-mediated Src knock-down alone, and simultaneous knock-down of Src and Stat3 and/or cMyc inhibited the neoplastic phenotype of a highly metastatic human model breast cancer cell line, MDA-MB-435S. This knock-down resulted in reduced growth in monolayer and soft agar cultures, and a reduced ability to form primary tumors in NOD/SCID mice. In addition, direct intra-tumoral injection of siRNAs targeting these signaling molecules resulted in a substantial inhibition of tumor metastases as well as of primary tumor growth. Simultaneous knock-down of Src and Stat3, and/or Myc exhibited the greatest effects resulting in substantial inhibition of primary tumor growth and metastasis. CONCLUSIONS/SIGNIFICANCE: These findings demonstrate the effectiveness of simultaneous targeting of Src and the downstream signaling partners Stat3 and/or cMyc to inhibit the growth and oncogenic properties of a human cancer cell line. This knowledge may be very useful in the development of future therapeutic approaches involving targeting of specific genes products involved in tumor growth and metastasis.
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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 | 0.000 | 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) | 0.000 | 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".