Clinical Cancer Mutant Dosage Stratification Agent with Claude Agents SDK
Calculating gene mutant dosage across large oncology cohorts is critical for predicting metastatic disease progression. Build a Claude Agents SDK workflow utilizing Llama 3.3 70B to parse clinical cancer genomic profiles and compute mutant dosage risk scores for target organ tropism.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement an automated oncology analytics workflow using Claude Agents SDK and Llama 3.3 70B to evaluate mutant allele dosage across 60,000 tumor profiles.
How work is evaluated
Evaluates mathematical accuracy of calculated mutant dosage scores and predicted metastatic organ risk.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Dosage Score Within Tolerances
Ensures calculated dosage equals vaf * copy_number.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Tropism Prediction AUC
Area under curve for predicting organ metastatic tropism. • target: 0.85 • range: 0.5-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct extended agentic workflows using Claude Agents SDK.
Leverage Llama 3.3 70B inference for quantitative allele dosage parsing.
Correlate copy-number changes and somatic mutations with metastatic organ tropism.
Export structured oncological stratification matrices.
Reference links and supporting material
Oncology patient cohort subset featuring VAF, copy number annotations, primary site descriptions, and verified metastatic outcomes.
Challenge setup
Evaluates mathematical accuracy of calculated mutant dosage scores and predicted metastatic organ risk.
Challenge input
JSON containing sample ID, gene, variant allele frequency (VAF), copy number, and tissue origin
Claude Agents SDK
Provides extended thinking framework for analytical workflows.
Llama 3.3 70B
High-capacity open model for quantitative parsing.
Evaluated output
JSON containing computed mutant dosage, risk category, and predicted metastatic site
- Ensures calculated dosage equals vaf * copy_number.
- Area under curve for predicting organ metastatic tropism. • target: 0.85 • range: 0.5-1
- Benchmark: OncoGenomics-Bench
- Tropism Prediction AUC target: 0.85
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Llama 3.3 70B · Required
- Meta · Optional
- Featuretools · Optional
Evaluation contract
- Dosage Score Within Tolerances · Weight 1
- Tropism Prediction AUC · Weight 1
Metrics
- Tropism Prediction AUC · Target 0.85
Benchmarks
- OncoGenomics-Bench
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing sample ID, gene, variant allele frequency (VAF), copy number, and tissue origin
Output: JSON containing computed mutant dosage, risk category, and predicted metastatic site
Execution harness: Python
Evaluation tools
- Claude Agents SDK · Agent Orchestration
- Llama 3.3 70B · Inference Model
Recipe freeze marker
No recipe freeze time is recorded.