MicroRNA-mRNA networks define translatable molecular outcome phenotypes in osteosarcoma
Bibliographic record
Abstract
ABSTRACT Background There is a lack of well validated biomarkers in osteosarcoma, a rare, recalcitrant disease with variable outcome and poorly understood biologic behavior, for which treatment standards have stalled for decades. The only standard prognostic factor in osteosarcoma remains the amount of pathologic necrosis following pre-operative chemotherapy, which does not adequately capture the biologic complexity of the tumor and has not resulted in optimized patient therapeutic stratification. New, robust biomarkers are needed to understand prognosis and better reflect the underlying biologic and molecular complexity of this disease. Methods We performed microRNA sequencing in 74 frozen osteosarcoma biopsy samples, the largest single center translationally analyzed cohort to date, and separately analyzed a multi-omic dataset from a large (n = 95) NCI supported national cooperative group cohort. Molecular patterns were tested for association with outcome and used to identify novel therapeutics for further study by integrative pharmacogenomic analysis. Results MicroRNA profiles were found predict Recurrence Free Survival ( 5-microRNA profile , Median RFS 59 vs 202 months, log rank p=0.06, HR 1.87, 95% CI 0.96-3.66). The profiles were independently prognostic of RFS when controlled for metastatic disease at diagnosis and pathologic necrosis following chemotherapy in multivariate Cox proportional hazards regression ( 5-microRNA profile , HR 3.31, 95% CI 1.31–8.36, p=0.01). Strong trends for survival discrimination were observed in the independent NCI dataset, and transcriptomic analysis revealed the downstream microRNA regulatory targets are also predictive of survival (median RFS 17 vs 105 months, log rank p=0.007). Additionally, DNA methylation patterns held prognostic significance. Through machine learning based integrative pharmacogenomic analysis, the microRNA biomarkers identify novel therapeutics for further study and stratified application in osteosarcoma. Conclusions Our results support the existence of molecularly defined phenotypes in osteosarcoma associated with distinct outcome independent of clinicopathologic features. We validated candidate microRNA profiles and their associated molecular networks for prognostic value in multiple independent datasets. These networks may define previously unrecognized osteosarcoma subtypes with distinct molecular context and clinical course potentially appropriate for future application of tailored treatment strategies in different patient subgroups.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".