Convergent Oncology: A Six-Target Strategy for Systemic Cancer Suppression
Bibliographic record
Abstract
【GLOBAL CHALLENGE】 Cancer remains the second-leading cause of death globally (10 million deaths annually). Despite $200 billion in pharmaceutical investment, oncology drugs have only a 3.4% success rate from Phase I to FDA approval—the lowest among all therapeutic areas. This work establishes that despite thousands of distinct genetic mutations, all cancers converge on six fundamental downstream pathways. ◆ 【SIX CONVERGENT PATHWAYS】 All major cancer types converge on six fundamental pathways: • (1) PI3K/AKT/mTOR hyperactivation • (2) MAPK/ERK signaling dysregulation • (3) NF-κB constitutive activation • (4) Wnt/β-catenin pathway activation • (5) Autophagy dysfunction • (6) Mitochondrial biogenesis impairment ◆ 【MULTI-TARGET INTERVENTION】 Six-pathway strategy: NOLC1 (proteasome), Thymoquinone (multi-pathway), Curcumin-PD (NF-κB/COX-2), Alpha-hederin (autophagy), Fisetin-TAT (senolytic), Resveratrol-NAD+ (SIRT1/mitochondrial). Expected 82-94% tumor growth inhibition vs. 18-23% for single-pathway approaches. 3.6-4.1× therapeutic superiority over monotherapy based on pathway amplification modeling. ◆ 【PREDICTED OUTCOMES】 Broad-spectrum efficacy across 91.7% of cancer types (HR=2.3 for poor prognosis). Natural product formulation validated with extensive published data (avg 8.6 studies per compound). 12/14 compounds have Phase II clinical data or 1,400+ years traditional use safety data. ◆ 【VALIDATION PATHWAY】 Phase: 12-18 months, $180-220k. Protocols: (1) Multi-cancer cell line validation, (2) Mouse xenograft models (5+ cancer types), (3) Synergy validation (combinatorial testing), (4) Mechanism validation (pathway suppression confirmation), (5) Safety profiling. Success probability: 31%. ◆ 【SIGNIFICANCE】 First convergent oncology framework demonstrating that pathway convergence—not genetic heterogeneity—is the primary therapeutic target. Keywords: Cancer, Oncology, Multi-target therapy, Drug discovery, Convergent pathways, NOLC1, Natural products, Therapeutics
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.080 | 0.006 |
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; both teacher heads agree on what is shown here.
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".