Lrig1 Is an Estrogen-Regulated Growth Suppressor and Correlates with Longer Relapse-Free Survival in ERα-Positive Breast Cancer
Bibliographic record
Abstract
Lrig1 is the founding member of the Lrig family and has been implicated in the negative regulation of several oncogenic receptor tyrosine kinases including ErbB2. Lrig1 is expressed at low levels in several cancer types but is overexpressed in some prostate and colorectal tumors. Given this heterogeneity, whether Lrig1 functions to suppress or promote tumor growth remains a critical question. Previously, we found that Lrig1 was poorly expressed in ErbB2-positive breast cancer, suggesting that Lrig1 has a growth-inhibitory role in this tumor type. However, breast cancer is a complex disease, with ErbB2-positive tumors accounting for just 25% of all breast cancers. To gain a better understanding of the role of Lrig1 in breast cancer, we examined its expression in estrogen receptor α (ERα)-positive disease which accounts for the majority of breast cancers. We find that Lrig1 is expressed at significantly higher levels in ERα-positive disease than in ERα-negative disease. Our study provides a molecular rationale for Lrig1 enrichment in ERα-positive disease by showing that Lrig1 is a target of ERα. Estrogen stimulates Lrig1 accumulation and disruption of this induction enhances estrogen-dependent tumor cell growth, suggesting that Lrig1 functions as an estrogen-regulated growth suppressor. In addition, we find that Lrig1 expression correlates with prolonged relapse-free survival in ERα-positive breast cancer, identifying Lrig1 as a new prognostic marker in this setting. Finally, we show that ErbB2 activation antagonizes ERα-driven Lrig1 expression, providing a mechanistic explanation for Lrig1 loss in ErbB2-positive breast cancer. This work provides strong evidence for a growth-inhibitory role for Lrig1 in breast cancer.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".