Tumor Nerve Innervation in Breast Cancer- Will it Serve as a prognostic indicator?
Bibliographic record
Abstract
Innervation in tumor microenvironment has recently been shown to regulate breast cancer (BC) progression. Specifically, the sympathetic and parasympathetic nerve signaling were shown to promote and suppress BC, respectively. A potential link between nerve activity and BC metastasis was also suggested. However, a definite quantitative association between intra-tumoral nerve density and BC metastasis has not been established using clinical samples. Identifying such an association will reveal if tumor nerve density can be used as a prognostic indicator for predicting the risk of BC metastasis. Such knowledge will also reveal if neuronal innervation is a potential therapeutic target for BC metastasis. Therefore, in this work, I examined intra-tumoral nerve densities in 36 human BC samples and determined their association with the corresponding patient’s clinical parameters, such as metastatic recurrence, overall survival, and disease progression to triple-negative breast cancer. The tumor samples were procured from the Alberta Cancer Research Biobank. The sample cohort contained 19 and 17 primary breast tumors from patients who developed and were protected from bone metastatic recurrence, respectively, within the first five years of surgical intervention. Immunostaining was performed to examine the intra-tumoral distribution of different classes of nerve fibres (axons). For example, the overall density of large calibre axons, sympathetic axons, and parasympathetic axons in tumors was examined by immunostaining for neurofilament 200, tyrosine hydroxylase, and choline acetyltransferase, respectively. The axon densities were then quantified using the Neuromath Software. Youden’s J statistics was applied to determine respective nerve density cut off values for predicting the risk of BC metastasis. The results showed an association between increased density of large calibre and parasympathetic nerves and suppression of metastasis, indicating that nerve activity, especially parasympathetic nerve activity, may suppress BC metastasis. In contrast, the results did not show a significant association between sympathetic nerve density and metastatic recurrence of BC. In addition, I found that increased tumor nerve densities in general improve the survival time of patients and suppress TNBC occurrence. Overall, albeit a small sample size, this study indicates that, among the autonomic nerves, the parasympathetic branch has a profound effect in suppressing BC metastasis and progression. Therefore, high intra-tumoral density of parasympathetic nerves may serve as a good prognostic indicator for BC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".