Lung Cancer Evolution Simulator

Advanced NSCLC/SCLC oncology treatment model

For Research and Educational Purposes Only

Simulation Parameters

Disease Characteristics
NSCLC (Non-small cell) represents 85% of cases
Stage significantly impacts treatment approach
Early-Stage (I-II)

Potentially curable with surgery or SBRT. Consider adjuvant therapy for higher risk.

Locally Advanced (III)

Multimodal treatment with chemoradiation. Consider immunotherapy for stage III.

NSCLC Approach

Test for biomarkers (EGFR, ALK, ROS1, PD-L1) to guide therapy selection.

Advanced (IV)

Systemic therapy based on molecular testing. Consider local approaches for oligometastatic disease.

Patient Factors
Light Moderate Heavy
Pack-years = packs per day × years smoked (e.g., 1 pack/day for 30 years = 30 pack-years)
0-10 Light 11-40 Moderate 41+ Heavy Smoking History
Performance status is a major factor in treatment selection and prognosis
0-1 Good candidates for aggressive therapy 2 May require dose adjustments 3+ Consider palliative approaches
Tumor Composition
Treatment Strategy
Treatment Selection Guide

Treatment selection is based on cancer type, stage, biomarkers, and patient factors. Each modality has distinct resistance patterns and evolutionary pressures.

Chemotherapy

Cytotoxic agents targeting rapidly dividing cells.

  • Standard of care for most advanced NSCLC without driver mutations
  • Primary therapy for SCLC with high initial response rates
  • Promotes resistant clone selection under treatment pressure
Targeted Therapy

Precision medicines targeting specific molecular alterations.

  • First-line for NSCLC with EGFR, ALK, ROS1, BRAF, RET mutations
  • High initial response rates (60-80%)
  • Inevitable resistance through mutation or bypass pathways
Immunotherapy

Immune checkpoint inhibitors enhancing T-cell activity.

  • First-line for NSCLC with high PD-L1 expression (≥50%)
  • Durable responses in 20-40% of patients
  • Resistance through immunoediting and T-cell exhaustion
Chemo-Immunotherapy

Combination therapy leveraging synergistic effects.

  • Standard for NSCLC without driver mutations or PD-L1 < 50%
  • Chemotherapy enhances immunotherapy through tumor antigen release
  • Complex resistance patterns involving both immune escape and drug resistance
Radiation Therapy

Localized treatment using ionizing radiation.

  • Curative for early-stage NSCLC (SBRT) when surgery contraindicated
  • Combined with chemotherapy for stage III NSCLC
  • May induce abscopal effects through immune stimulation
Specific regimens available depend on the selected treatment type
The dosing strategy can significantly impact tumor evolution and resistance
Continuous

Daily uninterrupted dosing. Good for targeted therapies, maintains constant drug pressure but may accelerate resistance.

Pulsed

Standard intermittent dosing with rest periods. Allows recovery from side effects, balances efficacy and toxicity.

Metronomic

Frequent low doses without extended breaks. Targets tumor vasculature, reduces resistance, minimal side effects.

Adaptive

Response-guided dosing. Titrates treatment based on tumor burden, exploits competition between sensitive/resistant cells.

Low Medium High
Represents relative drug efficacy (e.g., standard dose vs. reduced dose)
Slow Normal Rapid
Rate at which drug is cleared from the body (affects duration of action)
Time between doses (daily = 1, weekly = 7, etc.)
1-3 days Dense scheduling 7 days Weekly 14-21 days Bi-weekly/Monthly

Running simulation...

Clinical Response Over Time
Progression-Free Survival (PFS)
Overall Survival (OS)
Tumor Burden Over Time

Tumor Cell Populations

Drug Concentration

Select a clinical scenario to automatically configure the simulator with appropriate parameters for different lung cancer cases.
Stage I NSCLC

Early-stage non-small cell lung cancer suitable for curative-intent treatment.

T1N0M0 PD-L1 5%
  • Stage: IA2 (2.3cm nodule)
  • Excellent surgical candidate
  • Low risk of recurrence
Stage II NSCLC

Localized non-small cell lung cancer with minimal nodal involvement.

T2N1M0 KRAS+
  • Stage: IIB
  • Surgical resection with adjuvant therapy
  • Moderate recurrence risk
EGFR+ Early NSCLC

Early-stage EGFR-mutated NSCLC suitable for surgical resection.

EGFR+ TP53+
  • Stage: II (T2bN0M0)
  • Surgery with adjuvant targeted therapy
  • Lower recurrence risk with targeted therapy
EGFR+ Advanced NSCLC

Advanced EGFR-mutated NSCLC with targetable driver mutation.

EGFR+ TP53+
  • Stage: IIIB-IV
  • High initial response
  • Early emergence of T790M resistance
ALK+ Early NSCLC

Early-stage ALK-rearranged non-small cell lung cancer, surgical candidate.

ALK+ KRAS-
  • Stage: II (T2N0M0)
  • Surgical resection followed by targeted therapy
  • Excellent disease control
ALK+ Advanced NSCLC

Advanced ALK-rearranged non-small cell lung cancer, responsive to targeted therapies.

ALK+ KRAS-
  • Stage: IV with CNS metastases
  • Dramatic initial tumor response
  • CNS-penetrant therapy required
PD-L1 High NSCLC

High PD-L1 expression NSCLC with no driver mutations, candidate for immunotherapy.

PD-L1 ≥50% TMB-High
  • Stage: III-IV
  • Immune checkpoint inhibitor response
  • High tumor mutation burden
Extensive-Stage SCLC

Rapidly progressive small cell lung cancer with high mitotic rate.

ASCL1+ RB1-
  • Stage: Extensive stage
  • High initial chemoresponsiveness
  • Early resistance pattern
Key molecular and immunologic biomarkers in lung cancer with treatment implications.
NSCLC Targetable Mutations
Biomarker Frequency Treatment Approach
EGFR 15-20% Western
40-60% Asian		 EGFR TKIs (osimertinib)
ALK 3-7% ALK inhibitors (alectinib)
ROS1 1-2% ROS1 inhibitors (entrectinib)
BRAF V600E 1-3% BRAF/MEK inhibitors
NTRK <1% TRK inhibitors (larotrectinib)
Immunotherapy Biomarkers
Biomarker Significance Treatment Impact
PD-L1 ≥50% High expression ICI monotherapy first-line
PD-L1 1-49% Intermediate Chemoimmunotherapy
TMB High ≥10 mut/Mb Better ICI response
MSI-H/dMMR Rare in NSCLC Pembrolizumab approval
SCLC Molecular Subtypes
Subtype Key Features Therapy Implications
ASCL1 (SCLC-A) Classic SCLC Chemosensitive
NEUROD1 (SCLC-N) Neural features Variable response
POU2F3 (SCLC-P) Tuft cell-like PARP inhibitor sensitive
YAP1 (SCLC-Y) Inflammatory Immunotherapy responsive
Emerging Biomarkers
Biomarker Type Clinical Application
ctDNA Liquid biopsy MRD detection, resistance
Tumor Immune Score Microenvironment Immunotherapy selection
HLA Status Immune function ICI response prediction
Microbiome Host factors Treatment response
Mathematical Verification System: Ensuring clinical reliability through redundant calculation methods.
Verification System Architecture
How Redundancy Checks Work

Critical calculations in our cancer simulation are performed using two independent mathematical methods:

  • Primary Method: Evolutionary game theory with differential equations
  • Redundant Method: Alternative analytical approach with empirical models

Results from both methods are compared to ensure consistency and reliability. If the results disagree beyond acceptable tolerance, the system flags potential calculation errors.

Tolerance Thresholds:

  • Fitness Calculations: 1% or 0.01 absolute difference
  • Tumor Volume: 1% or 0.1 cubic mm
  • Survival Probability: 5% absolute maximum variance
Verification Status

Run a simulation to see verification results.

Importance of Verification in Clinical Modeling

Mathematical redundancy in clinical simulations serves multiple critical purposes:

Reliability

Ensures predictions are mathematically sound and free from computational artifacts that could mislead clinical interpretations.

Accuracy

Confirms that complex evolutionary dynamics are being modeled correctly through independent calculation pathways.

Safety

Prevents erroneous treatment predictions by flagging inconsistencies before they affect clinical interpretation.