Overview
Oncology workflows connect molecular data to clinical outcomes in a single study workspace. Each use case below maps a common research question to the platform capabilities, data requirements, and suggested analysis path.
Use case index
| Use case | Research question | Key capabilities |
|---|---|---|
| Immuno-oncology response | Why do some patients respond to checkpoint blockade and others do not? | Immune deconvolution, TIDE phenotype, exhaustion scoring, cell-cell communication, repertoire analysis |
| Mutation to outcomes | Which alterations and mutational features associate with survival or response? | Mutation landscape, TMB, signatures, oncoprint, survival analysis |
| Malignant cell CNV evidence | Which malignant populations and CNV-defined subclones shape tumor biology and downstream TME signals? | CompBio CNV handoff, malignant-cell detection, CNV event evidence, mutation overlap, survival/response feature export |
| TME and signaling | What cell populations interact and which signaling axes dominate the TME? | TME composition, ligand-receptor scoring, pathway aggregation, condition comparison |
| Spatial TME communication | Which tumor regions and boundaries concentrate immuno-oncology signaling? | Spatial TME regions, boundary priorities, communication maps |
| Integrated cohort evidence | Which signals repeatedly support a responder, survival, mutation, or treatment-arm hypothesis across completed analyses? | Cohort comparison, multi-run evidence, q-values, source-run traceability |
| Multi-omic integration | How do transcriptomic, mutational, and clinical data combine in one cohort? | Full study workflow across TME, immune, mutation, survival, and interpret |
| Predictive biomarker discovery | Can expression features predict a clinical outcome out-of-sample, and which genes carry the signal? | Cross-validated random-forest modeling, permutation testing, top-biomarker ranking |
Who these use cases serve
| Audience | Typical goals |
|---|---|
| Tumor biology researchers | Cell-cell communication, spatial TME mapping, immune infiltration, cohort comparison |
| Translational oncology teams | Mutation landscape, therapy-response correlation, survival analysis, biomarker endpoints |
| Immuno-oncology researchers | Immune deconvolution, exhaustion profiling, TCR/BCR repertoire, IO response prediction |
| Computational oncology groups | Reproducible multi-omic pipelines, cohort integration, publication-ready outputs |
| Pharma and biotech programs | Multi-omic patient stratification, drug response correlation, biomarker endpoint analysis |
| Clinical trial teams | Omics-to-outcomes integration, longitudinal cohort analysis, provenance for review |
Common data requirements
Most use cases start with an oncology study containing:
- Samples with patient/sample keys, timepoints, and group labels (responder, non-responder, treatment arm)
- Clinical endpoints for survival time, event status, and response
- Molecular inputs — expression matrices, MAF files, and optional repertoire data
Single-cell and spatial data are ingested through the Computational Biology area and linked to oncology samples. Whole-slide images are ingested through Pathology when tissue morphology context is needed.
Choosing a starting point
| If your primary data is… | Start with… |
|---|---|
| Bulk RNA-seq from treated cohort | Immuno-oncology response |
| Tumor sequencing (MAF/VCF) | Mutation to outcomes |
| CompBio single-cell CNV handoff | Malignant cell CNV evidence |
| Single-cell RNA-seq | TME and signaling |
| Spatial coordinates + cell labels | Spatial TME communication |
| Multiple modalities + clinical outcomes | Multi-omic integration |
| Multiple completed oncology runs | Integrated cohort evidence |
| Expression + a labeled outcome to predict | Predictive biomarker discovery |
What these use cases are not
These workflows support exploratory and translational research. They are not clinical diagnostic or treatment decision workflows. All AI interpretation outputs include research disclaimers and cite computed metrics only.
Next steps
- Quick start — run your first pipeline
- Study workflow — how study pages fit together
- Pipelines reference — API endpoints and parameters