Tumor-associated B-cells induce tumor heterogeneity and therapy resistance
Bibliographic record
Abstract
are highly effective but responses are often short-lived due to the emergence of drug-resistant tumor subpopulations. We describe here a mechanism of acquired drug resistance through the tumor microenvironment, which is mediated by human tumor-associated B cells. Human melanoma cells constitutively produce the growth factor FGF-2, which activates tumor-infiltrating B cells to produce the growth factor IGF-1. B-cell-derived IGF-1 is critical for resistance of melanomas to BRAF and MEK inhibitors due to emergence of heterogeneous subpopulations and activation of FGFR-3. Consistently, resistance of melanomas to BRAF and/or MEK inhibitors is associated with increased CD20 and IGF-1 transcript levels in tumors and IGF-1 expression in tumor-associated B cells. Furthermore, first clinical data from a pilot trial in therapy-resistant metastatic melanoma patients show anti-tumor activity through B-cell depletion by anti-CD20 antibody. Our findings establish a mechanism of acquired therapy resistance through tumor-associated B cells with important clinical implications.Resistance to BRAFV600E inhibitors often occurs in melanoma patients. Here, the authors describe a potential mechanism of acquired drug resistance mediated by tumor-associated B cells-derived IGF-1.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".