Oncology Biomarker and Genomic Stratification Automation

This workflow directly targets the operational bottleneck of manual patient screening for biomarker-driven oncology trials. By automating the ingestion and interpretation of next-generation sequencing (NGS) reports, PD-L1 IHC results, and longitudinal EHR data from systems like Epic and Cerner, it eliminates weeks of repetitive chart review. The commercial upside comes from slashing enrollment timelines, improving candidate match quality for targeted study arms, and allowing clinical staff to focus on high-value patient interactions instead of data foraging.

Implementation integrates with lab information systems (LIS) and genomic pipelines via secure APIs, using specialized agents for variant calling (e.g., using ClinVar) and tumor mutational burden calculation. The orchestration layer, built on a framework like LangGraph, manages state, executes parallel data-fetching tasks, and applies protocol-specific logic. Critical controls include an MD review gate for borderline cases, continuous data quality monitoring, and immutable audit logging to Veeva Vault for regulatory defensibility. Rollout requires phased validation against historical cohorts to tune confidence thresholds before live deployment.

This multi-agent architecture automates the high-cost bottleneck of manually reviewing genomic reports and EHRs to find patients matching complex biomarker panels for precision oncology trials. It ingests raw sequencing data from labs like Foundation Medicine, parses pathology reports from Epic or Cerner, and applies protocol-specific logic to stratify patients by TMB, PD-L1 status, and specific variants. The operational upside comes from reducing screening cycles from weeks to hours, directly accelerating enrollment into high-value therapeutic arms and improving site utilization.

Implementation integrates with lab information systems (LIS), EHRs via FHIR, and clinical trial management systems (CTMS) like Veeva. The workflow is governed by automated data quality checks, bias monitoring on screening algorithms, and a mandatory physician review gateway for all high-stakes eligibility flags. Each agent's decision rationale is logged to an immutable audit trail, generating explanation reports for regulatory inspection. Rollout requires phased validation against historical cohorts to calibrate matching precision before live deployment.

Phase 1 establishes the core data pipeline, ingesting structured genomic results from sequencing labs (e.g., Illumina DRAGEN) and pathology reports from systems like Epic Beaker. An orchestration layer, built with LangGraph, validates and harmonizes this data into a patient-genomic profile. This initial phase delivers immediate value by automating the manual aggregation of biomarker status (e.g., PD-L1, TMB) from fragmented sources, creating a single source of truth for the study team and reducing pre-screening data wrangling by 60-80%.

Phase 2 layers on the protocol-specific matching logic, where agents evaluate profiles against complex trial arms. A physician review interface is integrated for mandatory sign-off on high-stakes matches, with full audit trails. The final phase expands integration to multi-site EHRs and automates site notification, scaling the workflow. Each phase concludes with a validation gate against a gold-standard dataset, ensuring accuracy and building stakeholder confidence before proceeding, thereby de-risking the full rollout.

Operationalizing biomarker stratification demands rigorous governance from day one. The workflow must embed controls for data provenance, algorithmic bias monitoring, and physician review routing directly into its orchestration layer, typically built with frameworks like LangGraph. This ensures each patient match is traceable back to raw genomic variants and pathology reports, creating an audit trail for FDA submissions and institutional review boards while accelerating enrollment.

A phased rollout mitigates risk. Start with a single study arm and site, using the control layer to simulate outcomes and tune logic before scaling. Integrate with Epic and lab systems incrementally, routing low-confidence matches to oncologists via the EMR. This measured approach de-risks the implementation, proves ROI in cycle-time reduction, and builds clinical trust essential for expanding to multi-site, multi-protocol operations.

This workflow automates the critical bottleneck of matching cancer patients to precision oncology trials. It ingests raw sequencing outputs (FASTQ/VCF), pathology reports, and structured EHR data to identify actionable biomarkers like PD-L1 status, tumor mutational burden (TMB), and specific gene fusions. By automating this multi-source data fusion and rule evaluation, sponsors reduce patient screening from weeks to hours, accelerating enrollment into targeted study arms and improving trial economics through faster site activation and reduced screen-failure waste.

Implementation requires integrating with lab information systems (LIS), genomic pipelines (e.g., Illumina DRAGEN), and clinical platforms like Veeva or Medidata. The orchestration layer, built with frameworks like LangGraph, manages state across agents for data extraction, variant annotation, and eligibility checking. Critical controls include confidence scoring for automated calls, mandatory review gates for complex cases, and immutable audit logs for regulatory inspection. This architecture turns a fragmented, manual process into a scalable, auditable operational asset.