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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".