Oncology
The Oncology area is a research workspace for tumor cohorts and translational oncology studies. Organize samples, clinical endpoints, mutation and repertoire data; run tumor microenvironment, immune phenotype, communication, ligand activity hypothesis, mutation, and survival workflows; and interpret computed results with provenance.
Start from a research scenario
Guided workflows map common questions to data requirements, analysis steps, and documentation — pick the scenario closest to your study.
Immuno-oncology response
Research question: Why do some patients respond to anti-PD-1 / anti-PD-L1 therapy while others do not? Which immune features — infiltration, exclusion, exhaustion, repertoire diversity — best separate response groups?
Stratify checkpoint inhibitor cohorts by immune phenotype, exhaustion state, and signaling patterns to understand responder vs. non-responder biology.
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Mutation to outcomes
Research question: Which mutational features — TMB, specific driver genes, copy number alterations, or mutational signatures — associate with overall survival, progression-free survival, or treatment response in my cohort?
Connect somatic mutation landscapes to clinical endpoints — TMB, driver alterations, mutational signatures, and oncoprint summaries stratified by survival or response.
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Malignant cell CNV evidence
Research question: Which malignant populations and CNV-defined subclones are present in a tumor single-cell study, what inferred events support those calls, and how should those labels inform communication, mutation, immune, survival, or response analysis?
Use CompBio single-cell CNV handoff artifacts to review malignant tumor populations, CNV-defined subclones, inferred copy-number events, and downstream oncology features in one study context.
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TME and signaling
Research question: What cell types populate the tumor microenvironment, and which ligand-receptor signaling networks regulate immune activation, suppression, or exclusion?
Profile tumor microenvironment composition and identify dominant cell-cell communication axes across immune, stromal, and malignant populations.
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Spatial TME communication
Research question: Which tumor regions and boundaries concentrate ligand-receptor signaling across malignant, immune, stromal, endothelial, and TLS-adjacent compartments?
Use this workflow when tumor-region context matters for immuno-oncology signaling. It connects cell-cell communication results to spatial metadata such as tumor core, invasive margin, stroma, necrosis, immune-excluded regions, and TLS-adjacent neighborhoods so boundary-specific signaling can be reviewed directly.
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Integrated cohort evidence
Research question: Which completed analysis signals repeatedly support a responder, survival, mutation, copy-number, or treatment-arm hypothesis across the cohort?
Use this workflow after multiple oncology analyses have completed and you need one ranked evidence table across immune, communication, mutation, CNA, mutational signature, ligand activity, and survival outputs. It is the use case for moving from separate pipeline results to a cross-modal translational evidence review.
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Multi-omic integration
Research question: How do TME composition, immune phenotype, mutational features, and signaling patterns combine to explain treatment response and clinical outcomes across my cohort?
Connect single-cell, bulk, mutation, repertoire, and clinical data in one oncology study — from molecular profiling through survival analysis and analysis.
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Predictive biomarker discovery
Research question: Can expression features predict a clinical target (treatment response, subtype, survival group, or a continuous score) out-of-sample, and which genes carry the signal?
Train a cross-validated model that predicts a clinical outcome — responder vs non-responder, a subtype, a survival group, or a continuous score — from tumor expression features, with honest out-of-sample performance and a top-biomarker ranking.
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Platform features
Everything available in the Oncology workspace today — pipelines, explorers, exports, and provenance.
- Study containers for tumor cohorts, treatment arms, response groups, and timepoints
- Sample and clinical metadata management with patient/sample identifiers
- Tumor microenvironment composition analysis from expression-derived cell states
- Oncology communication product layer with TME sender/receiver classes, IO pathway axes, clinical metadata summaries, spatial boundary priorities, permutation-tested ligand-receptor significance (BH-FDR), and a differential-communication table across conditions (per-condition scores, log2 fold change, and condition-label permutation FDR)
- Cell-cell communication visualization and network analytics: interactive communication network (per-pathway filter, role-grouped nodes), pathway-flow Sankey, and pathway-activity heatmap with responder vs non-responder coloring, plus per-cell-type communication fingerprints (role, hub score, distinct out/in partners, dominant sending/receiving pathways), graph-centrality hub analysis — betweenness (broker), eigenvector (influence), and signaling entropy (specialist vs broadcaster) — and communication-motif detection (reciprocal feedback/cross-regulatory loops, feed-forward loops, and 3-cycles, pathway-annotated and ranked by limiting-edge strength)
- Drug-targeting overlay that annotates communication ligand/receptor genes with Open Targets tractability — flagging druggable targets by modality (small molecule, antibody, PROTAC) plus safety liabilities — so suppressive edges and motifs can be read as therapeutically actionable (research evidence from Open Targets/ChEMBL, not a treatment recommendation or clinical decision support)
- Tumor-immune phenotype classification (immune-inflamed / immune-excluded / immune-desert) from signaling axes and CD8 spatial penetration, with transparent inflamed/excluded/desert axis scores and supporting pathway evidence (CXCL9/CXCL10, IFNG vs TGFβ, MIF, CAF) — a research readout, not a diagnostic
- Spatial TME communication summaries with tumor core, invasive margin, stroma, necrosis, immune-excluded, and TLS-adjacent region context, including a communication network faceted by tissue boundary
- Ligand activity immuno-oncology hypothesis workflow for tumor/stroma/myeloid ligands, immune receiver target programs, and ligand-receptor-target chains
- Tumor-board-style IO communication and ligand activity highlights with research caveats and clinical metadata linkage
- Immuno-oncology profiling with deconvolution, immune dysfunction-and-exclusion response labels, exhaustion, bulk TME interpretation, metadata group comparisons, and repertoire metrics
- Immune dysfunction-and-exclusion response modeling with dysfunction, exclusion, myeloid/stromal suppression, IFNG, MMR/MSI-expression proxy, CD274, CD8/CTL, and therapy-context outputs
- Malignant cell detection workflow consuming CompBio single-cell CNV handoff artifacts, with malignant population summaries, CNV event evidence, mutation overlap, subclone TME features, and survival/response feature exports
- Mutation landscape analysis with TMB, SBS-6/SBS-96 signature exposure, focal/arm CNA summaries, known driver CNA annotations, oncoprints, pathway enrichment, differential mutation testing, and clinical group enrichment
- Survival analysis with Kaplan-Meier, log-rank tests, Cox regression, and longitudinal trajectories
- Cross-validated predictive biomarker modeling (classification/regression) from expression features with selectable random-forest / linear / elastic-net models, permutation testing, and top-biomarker ranking
- Integrated cohort comparison across communication, immune/TME, mutation, CNA, signature, ligand activity, and survival evidence
- Disease evidence lookup mapping mutation, expression, or biomarker gene sets against ~1.8M target-disease associations across ~24,000 diseases, with overlap enrichment (Fisher's exact, BH-FDR), integrated association scores, datasource counts, and evidence-type provenance (genetic, somatic, clinical, literature)
- Grounded analytical interpretation tied to oncology pipeline outputs and cited metrics
- Run history for pipeline status, parameters, artifacts, and reproducible outputs