Stage I Non-Small Cell Lung Cancer (NSCLC): A 2026 Systems Oncology Treatment Stack Simulation

By Editorial Team - February 13, 2026

Executive Overview

Stage I non-small cell lung cancer (NSCLC) is often described as a “curable” disease. When detected early and surgically resected, five-year survival rates can exceed 70–90% depending on tumor size and histology.

Yet recurrence still occurs.

Why?

Standard staging (TNM classification) measures tumor anatomy — not metabolic terrain, immune resilience, or micrometastatic biology.

This flagship analysis presents a systems-based treatment stack simulation exploring how metabolic optimization and repurposed pharmacology might theoretically influence recurrence dynamics when layered onto standard of care (SOC).

This is not medical advice.

This is structured, evidence-weighted modeling for analytical discussion.

1. Clinical Foundation: What Defines Stage I NSCLC?

Stage I NSCLC is characterized by:

Tumor confined to lung
No nodal involvement
No distant metastasis

Standard management includes:

Surgical resection (lobectomy or segmentectomy)
Stereotactic body radiotherapy (SBRT) if inoperable
Imaging surveillance

Guideline frameworks from institutions such as the National Cancer Institute and the American Society of Clinical Oncology emphasize surgery as the dominant survival determinant.

And rightly so.

But anatomical removal does not address:

Circulating tumor cells
Residual micrometastases
Metabolic permissiveness
Immune microenvironment vulnerability

That is where systems modeling begins.

2. The Biological Lens: Why Think Beyond Surgery?

Cancer biology has evolved far beyond purely anatomical thinking.

The metabolic theory of cancer traces back to Otto Warburg, who observed aerobic glycolysis in tumor cells.

More recently, researchers such as Thomas Seyfried have emphasized mitochondrial dysfunction and host metabolic terrain as potential contributors to tumor progression.

In NSCLC specifically, studies demonstrate:

Elevated glucose uptake (FDG-PET avidity)
Insulin/IGF signaling involvement
Variable PD-L1 expression
Angiogenic signaling activation

Even early-stage tumors show metabolic reprogramming.

This raises a strategic question:

If recurrence requires metabolic support, could host metabolic optimization reduce recurrence probability?

That is the hypothesis we model here.

3. Why Treatment Stack Simulations?

Modern oncology evaluates therapies individually through randomized controlled trials.

But real-world biology is multi-variable.

Patients do not live in single-intervention silos.

They have:

Metabolic states
Lifestyle variables
Immune variability
Polypharmacy contexts

Treatment Stack Simulation is an analytical framework that evaluates:

Mechanistic coherence
Evidence weighting
Interaction plausibility
Risk stacking
Hypothetical recurrence impact

It does not replace trials. It synthesizes biology.

4. Simulation Design: Scenario Definition

Patient Profile Modeled

Stage I NSCLC
Surgically resected
No adjuvant chemotherapy indicated
Underlying insulin resistance phenotype (elevated TyG proxy assumption)

We assume metabolic dysfunction because it is common in modern populations.

Comparator Arms

Arm A — Standard of Care (Surgery + Surveillance)
Arm B — SOC + Metabolic Optimization
Arm C — SOC + Repurposed Drug Stack
Arm D — SOC + Integrated Immune–Metabolic Stack

Each arm is evaluated mechanistically and evidence-graded.

5. Arm A: Standard of Care (Baseline Reference)

Mechanism

Physical removal of tumor mass
Reduction of tumor burden to zero detectable disease

Strength

Highest evidence grade (A)
Robust survival data

Limitation

Does not modify systemic metabolic environment
Does not target residual micrometastases biologically
Does not alter host immune terrain

Arm A remains the dominant survival driver.

All comparisons are layered relative to this anchor.

6. Arm B: SOC + Metabolic Optimization

This arm introduces structured host-terrain modification.

Components:

Insulin resistance correction strategy
Structured exercise program
Caloric timing alignment
Vitamin D optimization

No drug stacking beyond standard care.

Mechanistic Targets

1. Insulin/IGF Axis Modulation

Hyperinsulinemia promotes:

Cellular proliferation
PI3K/Akt signaling
mTOR activation

Lower insulin exposure theoretically reduces growth signaling permissiveness.

2. Exercise as Anti-Cancer Adjunct

Exercise demonstrates:

Improved immune surveillance
Reduced systemic inflammation
Improved mitochondrial function
Reduced recurrence risk in several solid tumors

Evidence grade: High (A/B depending on tumor type).

3. Vitamin D Optimization

Observational data suggests correlation between adequate vitamin D status and improved survival in certain cancers.

Randomized data is mixed but biologically plausible.

Evidence grade: B/C.

Modeled Impact

Theoretical benefits include:

Lowered recurrence probability
Reduced angiogenic signaling
Enhanced immune vigilance
Improved metabolic flexibility

Importantly, this strategy:

Does not interfere with surgery.
Does not add polypharmacy risk.

It modifies terrain — not tumor directly.

7. Arm C: SOC + Repurposed Drug Stack

This arm introduces pharmacologic stacking.

Modeled agents:

Metformin
Ivermectin
Mebendazole

These drugs have been discussed in oncology literature with varying degrees of evidence.

Metformin

Mechanisms:

AMPK activation
mTOR suppression
Reduced hepatic glucose output
Improved insulin sensitivity

Evidence:

Strong observational data
Mixed RCT results
Mechanistically coherent

Evidence grade: B.

Ivermectin

Proposed mechanisms:

Wnt/β-catenin modulation
Chloride channel effects
Anti-proliferative signaling
Preclinical apoptosis induction

Evidence:

Primarily preclinical
Limited human oncology trials

Evidence grade: C.

Mebendazole

Proposed mechanisms:

Microtubule destabilization
Anti-angiogenic signaling
Tumor cell mitotic arrest

Evidence:

Preclinical and small studies
Limited RCT data

Evidence grade: C.

Modeled Synergy

Potential theoretical interactions:

Metformin lowers insulin signaling
Ivermectin disrupts survival pathways
Mebendazole impairs cell division

Collectively, they stress tumor cell viability.

However:

Polypharmacy increases uncertainty.

No robust RCT demonstrates recurrence reduction in Stage I NSCLC using this stack.

Uncertainty: High.

8. Arm D: SOC + Integrated Immune–Metabolic Stack

This arm layers:

Metabolic optimization (Arm B)
Repurposed pharmacology (Arm C)
Circadian alignment
Anti-inflammatory load reduction
Immune-supportive lifestyle strategies

The aim is systemic synergy.

Theoretical Mechanism Map

Recurrence requires:

Micrometastatic survival
Angiogenic escape
Immune evasion
Growth factor signaling

Arm D attempts to address all four.

Immune Terrain

Metabolic dysfunction impairs:

T-cell function
NK cell activity
Cytotoxic response

Improved insulin sensitivity and reduced inflammation may enhance immune vigilance.

Checkpoint inhibitors are not modeled here because they are not standard in resected Stage I disease.

Circadian Considerations

Circadian disruption influences:

Hormonal regulation
Cortisol patterns
Immune modulation

Alignment may theoretically improve host resilience.

Evidence base: Emerging.

9. Comparative Systems Analysis

What Drives Recurrence Most?

Tumor biology
Surgical completeness
Host immune competence
Metabolic permissiveness

Arm A addresses #1.
Arm B addresses #3 and #4.
Arm C targets #1 and #2 biologically (indirectly).
Arm D attempts multi-domain suppression.

10. Evidence Confidence Gradient

Highest Confidence:

Surgery

Moderate Confidence:

Exercise reducing recurrence risk
Insulin sensitivity improvement
Metformin survival association

Low-to-Moderate Confidence:

Anti-helminthics as oncology agents

Lowest Confidence:

Multi-agent stacking synergy projections

Transparency is critical.

11. Risk Considerations

Arm B Risks

Minimal if medically supervised
Lifestyle adherence variability

Arm C Risks

Drug interactions
Unknown long-term oncology outcomes
Perioperative timing concerns

Arm D Risks

Complexity
Adherence burden
Polypharmacy stacking

Risk-benefit balance must always be clinician-guided.

12. Modeled Recurrence Dynamics

If recurrence risk in Stage I NSCLC is driven partially by:

Hyperinsulinemia
Chronic inflammation
Micrometastatic viability

Then metabolic optimization may provide the most plausible additive benefit relative to risk.

Aggressive repurposed drug stacking remains mechanistically intriguing but clinically unvalidated.

13. Strategic Insight

For Stage I NSCLC:

The dominant survival driver remains surgery.

The most defensible additive layer is likely:

Metabolic normalization.

The most experimental layer:

Multi-drug repurposed stacking.

The most systems-coherent hypothesis:

Integrated immune–metabolic optimization layered onto SOC.

14. What This Simulation Does — and Does Not — Claim

This article:

Does not recommend specific drugs
Does not replace oncology guidance
Does not claim improved survival
Does not validate repurposed protocols

It models biological plausibility.

Clinical trials remain the gold standard.

Institutions such as the National Cancer Institute continue to define guideline-based care.

This analysis operates in the systems-intelligence layer.

15. The Bigger Picture: Toward Metabolic Staging

Traditional TNM staging measures tumor anatomy.

But future oncology may incorporate:

Insulin resistance markers
Inflammatory burden
Mitochondrial health
Immune resilience metrics

A “metabolic staging” layer could one day complement TNM.

That remains a research frontier.

16. Final Intelligence Summary

For resected Stage I NSCLC:

Surgery remains foundational.
Exercise and metabolic optimization are biologically plausible adjuncts with relatively strong supportive data.
Metformin has mechanistic coherence but mixed clinical validation.
Ivermectin and mebendazole remain investigational in oncology.
Multi-agent stacking increases uncertainty faster than evidence.

The most rational systems approach appears to prioritize:

Host metabolic health before pharmacologic stacking.

The Future of Oncology Stack Intelligence

This flagship analysis represents the first installment in a broader Oncology Stack Simulation Series™.

Future simulations may evaluate:

Stage IV pancreatic cancer
Metastatic prostate cancer
Immunotherapy + metabolic modulation
GLP-1 agonists and cancer outcomes
AI-modeled hazard ratio projections

The goal is not advocacy.

It is structured synthesis.

Where conventional oncology publishes trials,
Systems oncology maps interactions.

That is the differentiator.