Oncology Analytics quickstart
Step-by-step workflows for TMB/MSI calculation, MHC binding prediction, and neoantigen prediction.
Retired tools
hrd_score, liquid_biopsy_ctdna and tumor_fraction_estimate are retired and can no longer be run. Use an allele-specific copy-number HRD caller such as scarHRD for HRD, ichorCNA on low-pass WGS for tumor fraction, and a dedicated ctDNA/MRD pipeline for ctDNA.
You want to characterize a tumor and predict immunotherapy biomarkers; these workflows walk you from somatic variant or peptide data to clinically anchored readouts like TMB/MSI and neoantigens, each in a few steps. See Oncology Analytics on the platform for tool details.
Oncology Analytics quickstart
This guide covers three common workflows: calculating TMB/MSI, predicting MHC binding, and identifying neoantigens.
Workflow 1: Calculate TMB and MSI status
Provide somatic mutations
Or configure manually:
| Parameter | Value |
|---|---|
| Input type | VCF or mutation list |
| Genome build | GRCh38 |
| Panel size (Mb) | 1.5 (for targeted panels) |
Review TMB and MSI results
| Field | Example Value |
|---|---|
| TMB | 14.2 mut/Mb |
| TMB Status | High |
| Total Mutations | 213 |
| Coding Mutations | 186 |
| MSI Score | 32.4% |
| MSI Status | MSI-H |
| Unstable Microsatellites | 18 / 56 |
Interpret the results
| Biomarker | Threshold | Interpretation |
|---|---|---|
| TMB ≥ 10 mut/Mb | TMB-High | Checkpoint inhibitor eligible |
| TMB < 10 mut/Mb | TMB-Low | Standard of care |
| MSI-H (>= 30% unstable) | Microsatellite instable | Pembrolizumab eligible |
| MSI-L (10-30% unstable) | Low instability | Not MSI-H eligible |
| MSS (< 10% unstable) | Microsatellite stable | Not MSI eligible |
Workflow 2: Predict MHC binding
Provide peptides and HLA types
Or configure:
| Parameter | Value |
|---|---|
| Peptides | List of 8-11mer sequences |
| HLA alleles | ["HLA-A*02:01", "HLA-B*07:02"] |
| MHC class | I |
Review binding predictions
| Peptide | HLA Allele | IC50 (nM) | Percentile Rank | Classification |
|---|---|---|---|---|
| YLQPRTFLL | HLA-A*02:01 | 12.4 | 0.08 | Strong binder |
| KLPDDFTGCV | HLA-A*02:01 | 234.5 | 1.2 | Weak binder |
| SPRWYFYYL | HLA-B*07:02 | 8.1 | 0.04 | Strong binder |
Interpret binding affinity
| IC50 Range | Percentile Rank | Classification |
|---|---|---|
| < 50 nM | < 0.5% | Strong binder |
| 50-500 nM | 0.5-2% | Weak binder |
| > 500 nM | > 2% | Non-binder |
Workflow 3: Predict neoantigens
Provide somatic mutations and HLA types
Or configure:
| Parameter | Value |
|---|---|
| Mutations | VCF or mutation list |
| HLA alleles | ["HLA-A*02:01", "HLA-A*03:01", "HLA-B*07:02"] |
| Expression data | RNA-seq TPM values (optional) |
| Min binding affinity | 500 nM |
Review neoantigen candidates
| Rank | Gene | Mutation | Neoantigen Peptide | HLA Allele | IC50 (nM) | Expression (TPM) |
|---|---|---|---|---|---|---|
| 1 | KRAS | G12D | KLVVVGADGV | HLA-A*02:01 | 18.3 | 42.1 |
| 2 | TP53 | R175H | HMTEVVRHC | HLA-A*02:01 | 45.2 | 28.7 |
| 3 | BRAF | V600E | KIGDFGLATV | HLA-A*02:01 | 67.8 | 15.3 |
Prioritize candidates
Candidates are ranked by a composite score incorporating MHC binding affinity, expression level, clonality, and mutation type. Top candidates with strong binding (IC50 < 50 nM) and high expression are prioritized for vaccine design.
Next steps
- See inputs for all parameters, file formats, and HLA type specifications
- See outputs for result field descriptions and threshold interpretation
- Use Genomics for upstream variant annotation and gene information
- Use the Compound Passport for therapy selection based on biomarker results
Read next
Oncology Analytics inputs and parameters
VCF formats, HLA types, peptide sequences, and configuration options for the oncology analytics tools.
Oncology Analytics on the platform
Computational oncology tools for tumor mutational burden, microsatellite instability, and immunotherapy biomarker prediction.
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