Radiotherapy-Activated Cancer-Associated Fibroblasts Promote Tumor Progression through Paracrine IGF1R Activation
Bibliographic record
Abstract
Abstract Preoperative radiotherapy (RT) is a mainstay in the management of rectal cancer, a tumor characterized by desmoplastic stroma containing cancer-associated fibroblasts (CAF). Although CAFs are abundantly present, the effects of RT to CAF and its impact on cancer cells are unknown. We evaluated the damage responses of CAF to RT and investigated changes in colorectal cancer cell growth, transcriptome, metabolome, and kinome in response to paracrine signals emerging from irradiated CAF. RT to CAF induced DNA damage, p53 activation, cell-cycle arrest, and secretion of paracrine mediators, including insulin-like growth factor-1 (IGF1). Subsequently, RT-activated CAFs promoted survival of colorectal cancer cells, as well as a metabolic switch favoring glutamine consumption through IGF1 receptor (IGF1R) activation. RT followed by IGF1R neutralization in orthotopic colorectal cancer models reduced the number of mice with organ metastases. Activation of the downstream IGF1R mediator mTOR was significantly higher in matched (intrapatient) samples and in unmatched (interpatient) samples from rectal cancer patients after neoadjuvant chemoradiotherapy. Taken together, our data support the notion that paracrine IGF1/IGF1R signaling initiated by RT-activated CAF worsens colorectal cancer progression, establishing a preclinical rationale to target this activation loop to further improve clinical responses and patient survival. Significance: These findings reveal that paracrine IGF1/IGF1R signaling promotes colorectal cancer progression, establishing a preclinical rationale to target this activation loop. Cancer Res; 78(3); 659–70. ©2017 AACR.
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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) | 0.003 | 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 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".