The neural crest lineage as a driver of disease heterogeneity in Tuberous Sclerosis Complex and Lymphangioleiomyomatosis
Bibliographic record
Abstract
Lymphangioleiomyomatosis (LAM) is a rare neoplastic disease, best characterized by the formation of proliferative nodules that express smooth muscle and melanocytic antigens within the lung parenchyma, leading to progressive destruction of lung tissue and function. The pathological basis of LAM is associated with Tuberous Sclerosis Complex (TSC), a multi-system disorder marked by low-grade tumors in the brain, kidneys, heart, eyes, lung and skin, arising from inherited or spontaneous germ-line mutations in either of the TSC1 or TSC2 genes. LAM can develop either in a patient with TSC (TSC-LAM) or spontaneously (S-LAM), and it is clear that the majority of LAM lesions of both forms are characterized by an inactivating mutation in either TSC1 or TSC2, as in TSC. Despite this genetic commonality, there is considerable heterogeneity in the tumor spectrum of TSC and LAM patients, the basis for which is currently unknown. There is extensive clinical evidence to suggest that the cell of origin for LAM, as well as many of the TSC-associated tumors, is a neural crest cell, a highly migratory cell type with extensive multi-lineage potential. Here we explore the hypothesis that the types of tumors that develop and the tissues that are affected in TSC and LAM are dictated by the developmental timing of TSC gene mutations, which determines the identities of the affected cell types and the size of downstream populations that acquire a mutation. We further discuss the evidence to support a neural crest origin for LAM and TSC tumors, and propose approaches for generating humanized models of TSC and LAM that will allow cell of origin theories to be experimentally tested. Identifying the cell of origin and developing appropriate humanized models is necessary to truly understand LAM and TSC pathology and to establish effective and long-lasting therapeutic approaches for these patients.
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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 | 0.001 |
| 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 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".