Immuno-oncology profiling
Immuno-oncology profiling estimates tumor-infiltrating immune composition, classifies immune phenotypes, scores T-cell exhaustion, and analyzes TCR/BCR repertoire diversity.
Algorithms are ported from CIBERSORTx, xCell, TIDE, and scirpy reference implementations.
Prerequisites
- Bulk or pseudo-bulk expression CSV (samples × genes)
- Optional TCR/BCR repertoire CSV for clonotype and diversity analysis
Launching the pipeline
Open the study Immune page, select your expression dataset (and optional repertoire dataset), and click Run immune profile.
Capabilities
| Analysis | Description |
|---|---|
| Bulk immune deconvolution | Estimate immune cell fractions from bulk RNA-seq (CIBERSORTx-style) |
| Cell-type enrichment | ssGSEA-based immune and stromal enrichment scores (xCell-style) |
| Immune phenotype | Inflamed, immune-excluded, and immune-desert classification (TIDE-style) |
| T-cell exhaustion | Co-inhibitory receptor programs, progenitor vs. terminal exhaustion states |
| Checkpoint profiling | Expression landscape of immunotherapy target genes |
| TCR/BCR repertoire | Clonotype frequency, Shannon/Simpson diversity, clonal expansion |
| IO response prediction | Immune signature scoring for responder stratification |
Outputs
The immune profile pipeline produces JSON artifacts with:
- Per-sample deconvolution fractions
- TIDE-like phenotype labels and dysfunction/exclusion scores
- Exhaustion gene program scores
- Repertoire diversity metrics and top clonotypes (when repertoire data provided)
- Immunotherapy response prediction scores
The frontend Immune page renders deconvolution bar charts, exhaustion scores, and TCR diversity summaries.
Typical use cases
- Classify tumors as inflamed vs. excluded vs. desert before IO therapy analysis
- Compare exhaustion severity between treatment arms
- Correlate repertoire diversity with response status
- Feed immune phenotype labels into Survival analysis as stratifying features