Autonomous Oncology Triage: Multimodal Variant Interpretation with Google ADK
With oncology businesses like Guardant Health and Tempus AI reporting massive revenue growth, the bottleneck in precision medicine has shifted from sequencing to data interpretation. Germany's new Precision Medicine Pilot emphasizes robust data infrastructure to handle this surge. In this challenge, you will use the Google Agent Development Kit (ADK) and the o3-mini model to build an autonomous lab agent capable of triaging genomic variants and matching them to clinical trials. Your agent must handle multimodal inputs, including raw VCF (Variant Call Format) files, pathology PDFs, and structured EHR data. Using Google ADK’s integration with Vertex AI, you will implement a 'Reasoning-and-Acting' (ReAct) loop that queries the COSMIC database and clinicaltrials.gov. The goal is to automate the extraction of 'Actionable Alterations' and flag cases where high-fidelity WGS (like PacBio HiFi) is required for resolving ambiguous structural variants in noncoding regions, as highlighted in recent Nature Genetics research.
What you are building
The core problem, expected build, and operating context for this challenge.
With oncology businesses like Guardant Health and Tempus AI reporting massive revenue growth, the bottleneck in precision medicine has shifted from sequencing to data interpretation. Germany's new Precision Medicine Pilot emphasizes robust data infrastructure to handle this surge. In this challenge, you will use the Google Agent Development Kit (ADK) and the o3-mini model to build an autonomous lab agent capable of triaging genomic variants and matching them to clinical trials. Your agent must handle multimodal inputs, including raw VCF (Variant Call Format) files, pathology PDFs, and structured EHR data. Using Google ADK’s integration with Vertex AI, you will implement a 'Reasoning-and-Acting' (ReAct) loop that queries the COSMIC database and clinicaltrials.gov. The goal is to automate the extraction of 'Actionable Alterations' and flag cases where high-fidelity WGS (like PacBio HiFi) is required for resolving ambiguous structural variants in noncoding regions, as highlighted in recent Nature Genetics research.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
False Positive Check
Ensure Benign variants are NOT flagged as 'Actionable'.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Precision at Trial Matching
Percentage of suggested trials where patient meets 100% of inclusion criteria. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master Google ADK agent initialization with Gemini and o3 model configurations
Implement multimodal tool-use to extract insights from histopathology images and genomic reports simultaneously
Design a recursive reasoning loop in o3 to evaluate 'Variant of Unknown Significance' (VUS) status
Build an autonomous tool for querying the GenomeWeb/NCBI APIs to fetch the latest research on BRCA1/2 mutations
Orchestrate a 'Human-in-the-loop' checkpoint using Vertex AI's evaluation framework for expert oncologist review
Optimize data sovereignty by implementing local-first processing for PII (Personally Identifiable Information) before cloud inference
Integrate PacBio HiFi WGS metadata into the triage logic to recommend high-resolution sequencing for complex inversions
Deploy a Streamlit-based oncology dashboard that visualizes the agent's reasoning trace and evidence links
How this agent runs
The evaluation measures the agent's ability to identify actionable variants and provide the correct clinical trial matches.
Challenge input
VCF file + Clinical History PDF
Google ADK
Provides seamless integration with multimodal Gemini models.
o3-mini
High-density reasoning for complex genomic triage logic.
Biopython
Industry standard for biological data manipulation.
Evaluated output
Trial Eligibility Report (JSON)
- Ensure Benign variants are NOT flagged as 'Actionable'.
- Percentage of suggested trials where patient meets 100% of inclusion criteria. • target: 0.95 • range: 0-1
- Benchmark: Clinvit-QA
- Precision At Trial Matching target: 0.95
- Protected reference cases support private evaluation
- Docker execution harness
- Docker sandbox
View technical recipe
Configured tools
- Modal · Optional
- Google · Required
- Google ADK · Optional
- Modal · Optional
Evaluation contract
- False Positive Check · Weight 1
- Precision at Trial Matching · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
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