OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine
Bibliographic record
Abstract
Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic targets, a better understanding of BC molecular processes is required. Here we focused on elucidating the molecular hallmarks of BC heterogeneity and the oncogenic mutations involved in precision medicine that remains poorly defined. To fill this gap, we established an OncoOmics strategy that consists of analyzing genomic alterations, signaling pathways, protein-protein interactome network, protein expression, dependency maps in cell lines and patient-derived xenografts in 230 previously prioritized genes to reveal essential genes in breast cancer. As results, the OncoOmics BC essential genes were rationally filtered to 140. mRNA up-regulation was the most prevalent genomic alteration. The most altered signaling pathways were associated with basal-like and Her2-enriched molecular subtypes. RAC1, AKT1, CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, BCL2, CTNNB1, EGFR, CDK2, GRB2, MED1 and GATA3 were essential genes in at least three OncoOmics approaches. Drugs with the highest amount of clinical trials in phases 3 and 4 were paclitaxel, docetaxel, trastuzumab, tamoxifen and doxorubicin. Lastly, we collected ~3,500 somatic and germline oncogenic variants associated with 50 essential genes, which in turn had therapeutic connectivity with 73 drugs. In conclusion, the OncoOmics strategy reveals essential genes capable of accelerating the development of targeted therapies for precision oncology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".