TME and signaling
Profile tumor microenvironment composition and identify dominant cell-cell communication axes across immune, stromal, and malignant populations.
Research question
What cell types populate the tumor microenvironment, and which ligand-receptor signaling networks regulate immune activation, suppression, or exclusion?
Who this is for
- Tumor biology researchers at academic cancer centers and TME labs
- Solid tumor groups studying immune infiltration and stromal interactions
- Spatial biology teams connecting scRNA-seq findings to tissue context
Data requirements
| Data | Required | Purpose |
|---|---|---|
| Single-cell RNA-seq | Yes (for TME) | Cell-type composition, state scoring, clustering |
| Per-cell expression + metadata CSV | Yes (for communication and ligand activity) | Ligand-receptor scoring, TME sender/receiver context, and ligand-to-target hypotheses |
| CompBio single-cell CNV handoff | No | Adds cnv_malignant_status, cnv_malignant_score, and cnv_subclone_id context for malignant sender/subclone review |
| Condition labels in metadata | No | Responder/non-responder or pre/post treatment comparison |
| Spatial transcriptomics | No | Spatial TME mapping (via compbio spatial pipeline) |
| WSI slides | No | Morphology context (via Pathology area) |
Single-cell ingestion and clustering run through the Computational Biology area. Oncology adds TME annotation, communication analysis, and cohort comparison on top.
Workflow
Upload scRNA-seq (compbio) → TME composition and cell states
→ Cell-cell communication across annotated populations
→ Ligand activity IO hypotheses for receiver target programs
→ Condition comparison (optional)
→ AI interpretation
Step 1 — TME profiling
Link a compbio single-cell dataset to your oncology study. Run QC, normalization, clustering, and cell-type annotation with oncology reference signatures on the TME page.
Review compartment fractions — tumor, immune, stromal, vascular — and cell-state scores for exhaustion, activation, and regulatory suppression.
Step 2 — Cell-cell communication
With annotated per-cell expression and metadata, launch Cell-cell communication from the Communication page — set the cell-type column and your two comparison conditions (e.g. responder vs. non-responder).
Review TME context summaries, sender/receiver heatmaps, pathway-level summaries, IO pathway axes, spatial boundary priorities, and condition-comparison deltas on the Communication page.
Step 3 — Malignant CNV context (optional)
If the study has a CompBio single_cell_cnv handoff, run Oncology Malignant cell detection before or alongside communication review. Use the resulting cnv_malignant_status and cnv_subclone_id fields to interpret whether tumor subclones behave as distinct senders to T cells, myeloid cells, CAFs, endothelial cells, or TLS-adjacent populations.
Step 4 — Ligand activity IO hypotheses
Run ligand activity inference when the question is mechanistic: which tumor, stromal, myeloid, or endothelial ligands may explain a receiver immune target-gene program?
The oncology ligand activity workflow uses the shared Immunology ligand-to-target engine — a Python-native port of the NicheNet method scored against NicheNet's own regulatory-potential prior model, not an abundance heuristic — and adds IO presets for exhaustion, interferon response, antigen presentation, suppression, and cytotoxicity. Ligands are ranked by AUPR-corrected/Pearson activity with permutation significance, and a prioritization table ranks sender→receiver ligand-receptor pairs. Use response_status, timepoint, or treatment_arm as condition columns when comparing responder/non-responder or pre/post treatment biology.
Step 5 — Spatial context (optional)
When Visium or Xenium data is available, use compbio spatial pipelines for neighborhood analysis and spatial deconvolution. Pathology WSI overlays provide tissue morphology context for tumor-stroma boundary analysis.
Step 6 — Interpretation
Launch interpretation with communication run IDs for a summary of dominant signaling axes and their immunological implications.
Expected outputs
- TME composition bar charts by cell type and treatment condition
- Scored ligand-receptor interaction table with sender/receiver cell types
- Pathway-level aggregated signaling scores
- Network adjacency for interactive graph visualization
- Condition-comparison deltas highlighting axes enriched in one group
- Ligand activity rankings (AUPR-corrected, Pearson, permutation p/q-value) and ligand-receptor-target chains for IO target programs
- Prioritized sender→receiver ligand-receptor table
- Tumor-board-style research highlights with caveats
- AI narrative explaining suppressive, co-stimulatory, and cytokine signaling patterns
Typical analyses
| Analysis | Comparison | Question |
|---|---|---|
| IO resistance | Non-responder vs. responder | Which suppressive LR pairs dominate in non-responders? |
| Treatment effect | Pre- vs. post-treatment | How does signaling shift after therapy? |
| Compartment crosstalk | Tumor → T cell vs. myeloid → T cell | Which stromal populations drive T cell regulation? |
| Malignant subclone signaling | CNV subclone → immune/stromal receiver | Do inferred tumor subclones differ in immune suppression or cytokine signaling? |
| Checkpoint axis | PD-L1/PD-1 and related pairs | Where is checkpoint signaling concentrated? |
| Ligand-to-target hypotheses | Tumor/stroma/myeloid → T cell | Which ligands may explain exhaustion, IFN, or antigen-presentation targets? |
Related guides
- Tumor microenvironment
- Cell-cell communication
- Malignant cell detection
- Computational Biology Single-cell and Spatial guides