Learning Framework Designed for Early Prediction of Breast Cancer Metastasis Consuming Genomic, Transcriptomic, and Epigenomic Profiles
Bibliographic record
Abstract
The breast cancer care would require the tools that will help to identify the patients who may develop metastasis at an early stage, when the treatment decision could be altered. Models that use single types of data (as in the case of using a single transcription factor only) can tend to overlook significant information and may not work well when applied to different hospitals. Our framework, LF-MMP, is a learning framework that integrates three types of molecular data, namely genomics (DNA changes), transcriptomics (gene activity), and epigenomics (DNA methylation) to give an early patient-level risk score of metastases. The framework normalizes and cleans every dataset, trains a compact representation of each omics layer, and lastly combines them together with an attention mechanism that allows the model to pay attention to the most informative signals. An optimized classifier transforms the fused representation into well-behaved probabilities that may be used to support clinical thresholds. We tested LF-MMP on three external populations, namely, TCGA-BRCA, METABRIC and GEO (GSE96058). The model performed better than powerful singleomic and deep multi-omic controls, and AUCs were 0.956 (TCGA-BRCA), 0.946 (METABRIC), and 0.938 (GEO). Performance was also high when trained on TCGABRCA and externally tested (AUC 0.942 on METABRIC; 0.935 on GEO). There was good calibration of the expected risks (Brier 0.085-0.098; ECE 0.021-0.028). The descriptions of the features showed familiar biology (such as TP53 and PIK3CA mutations, ESR1 and GATA3 expression, and PTEN/TWIST1 methylation). Inference and training were sufficiently quick to be used on regular GPU. The limitations of this study are as follows: the research is based on retrospective publicly available data, labels are not directly related to time-to-event but to early risk, and new environments may differ in terms of performance. Future directions will incorporate prospective, multi-centric validation; imaging and radiomics; enhancement to site differences and missing data; tracking of model calibration in real-life use
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".