AI-powered intratumor heterogeneity analysis for clonal architecture reconstruction, subclonal evolution tracking, and therapy resistance prediction using multi-region and longitudinal sequencing.
The Tumor Heterogeneity Agent provides comprehensive analysis of intratumor heterogeneity (ITH) for understanding clonal architecture, tracking subclonal evolution, and predicting therapy resistance. It integrates multi-region sequencing, single-cell data, and longitudinal samples to reconstruct tumor phylogenies and identify actionable subclones.
Clonal Deconvolution: Infer clonal populations and their frequencies.
Phylogeny Reconstruction: Build tumor evolutionary trees from variants.
Subclonal Tracking: Monitor subclone dynamics over time.
Resistance Prediction: Identify pre-existing resistant subclones.
Multi-Region Integration: Combine spatial heterogeneity data.
Single-Cell ITH: Integrate scDNA-seq for ground-truth clones.
| Metric | Definition | Clinical Relevance |
|---|---|---|
| MATH Score | Mutant-allele tumor heterogeneity | ITH quantification |
| Shannon Index | Clonal diversity | Evolutionary potential |
| Clone Count | Number of distinct clones | Complexity |
| Truncal Fraction | % truncal mutations | Targetability |
| ITH Score | Composite heterogeneity | Prognosis |
Input: Multi-region/longitudinal WES/WGS, copy number, tumor purity.
Preprocessing: Variant calling, CNV calling, purity estimation.
CCF Estimation: Calculate cancer cell fraction for each mutation.
Clustering: Group mutations into clonal populations.
Phylogeny: Reconstruct evolutionary tree.
Temporal Analysis: Track clone dynamics over time.
Output: Clone structures, phylogenies, heterogeneity metrics.
User: "Analyze the clonal architecture of this multi-region lung tumor sequencing to understand heterogeneity and identify resistant subclones."
Agent Action:
python3 Skills/Oncology/Tumor_Heterogeneity_Agent/ith_analysis.py \
--multi_region_vcfs region1.vcf,region2.vcf,region3.vcf \
--cnv_segments cnv_calls.seg \
--purity 0.7,0.65,0.72 \
--sample_names Primary,Met1,Met2 \
--method pyclone-vi \
--phylogeny_method citup \
--output ith_analysis/
| Method | Approach | Best For |
|---|---|---|
| PyClone-VI | Variational inference | Large datasets |
| SciClone | Kernel density | High purity |
| EXPANDS | Probabilistic | Multi-region |
| Canopy | EM algorithm | CNV integration |
| Clonevol | Phylogeny-aware | Longitudinal |
| CITUP | Integer programming | Tree optimization |
| Input | Format | Required |
|---|---|---|
| Somatic Variants | VCF with depth | Yes |
| Copy Number | SEG file | Yes |
| Tumor Purity | Float (0-1) | Yes |
| Sample Metadata | TSV | Yes |
| Normal BAM | BAM | Recommended |
| Output | Description | Format |
|---|---|---|
| Clone Assignments | Mutation-to-clone mapping | .csv |
| Clone Frequencies | Per-sample clone fractions | .csv |
| Phylogenetic Tree | Newick and visualization | .nwk, .pdf |
| ITH Metrics | Heterogeneity scores | .json |
| Subclone Variants | Clone-specific mutations | .vcf |
| Evolution Plot | Clone dynamics over time | .png |
| Actionable Subclones | Druggable clone mutations | .csv |
| Clone Type | Definition | Implications |
|---|---|---|
| Truncal | Present in all samples | Ideal targets |
| Branch | Present in subset | Regional targets |
| Private | Single sample only | Local significance |
| Resistant | Expand under therapy | Resistance mechanism |
Clone Inference:
Resistance Prediction:
Multi-Region Integration:
| Application | ITH Insight | Clinical Action |
|---|---|---|
| Treatment Selection | Truncal vs branch targets | Prioritize truncal targets |
| Resistance Monitoring | Pre-existing resistant clones | Early combination therapy |
| Prognosis | ITH score | Risk stratification |
| Biomarker Development | Clonal biomarkers | Robust biomarker selection |
| Cancer Type | Typical ITH | Key Drivers |
|---|---|---|
| Lung (NSCLC) | High | EGFR, KRAS subclonal |
| Breast | Moderate-High | PIK3CA, ESR1 evolution |
| Colorectal | Moderate | KRAS, BRAF clonal |
| Renal | Very High | VHL truncal, diverse branches |
| Melanoma | High | BRAF/NRAS truncal |
| View Type | Shows | Best For |
|---|---|---|
| Fish Plot | Clone dynamics over time | Longitudinal |
| Tree Diagram | Branching evolution | Multi-region |
| Muller Plot | Population dynamics | Treatment response |
| Clone Map | Spatial distribution | Multi-region spatial |
| Mechanism | Detection | Intervention |
|---|---|---|
| Pre-existing resistant clone | Subclonal at baseline | Combination therapy |
| Acquired resistance | New clone emerges | Switch therapy |
| Phenotypic plasticity | Expression change | Monitor phenotype |
| Microenvironment | TME evolution | Immunotherapy |
AI Group - Biomedical AI Platform