Precision oncology implementation

Precision oncology is the part of cancer care where molecular results actually meet a treatment decision. The data has to be in the EHR (that's clinical genomics in the EHR); it has to be interoperable (cancer data standards (mCODE)); and then it has to surface in the clinician's workflow at the moment the decision is being made - which is what this domain is about. Dr. Osterman's work in precision oncology is the implementation layer: trial matching, biomarker-driven therapy selection, molecular tumor board operations, structured staging, and the decision-support surfaces that connect a structured variant to a clinical action.

Trial matching as the first proving ground (2017-2021). Matching cancer patients to clinical trials based on molecular profile is the cleanest test of "did our genomics integration actually do anything." If the structured data is real, the trial-matching algorithm should find candidates a human screener would miss. Dr. Osterman's earliest work in this area, with mentor Dr. Mia Levy, asked exactly that question: the 2017 ASCO abstract Utility of adding clinical data to a molecular results portal for improving clinical trial prescreen was the pragmatic proof of concept, and the 2019 follow-up (Jain, Culley, Osterman & Levy, ASCO 2019) evaluated reflex trial-matching across a broader oncology population.

The harder lesson came in the Conceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow (Jain, Culley, Knoop, Micheel, Osterman & Levy, JCO Clinical Cancer Informatics, 2019). At the time most trial-matching work treated the problem as: does this patient meet the published eligibility criteria for this trial? The framework paper argued that this framing systematically underweighted the operational layer that decides whether matching actually translates into enrollment - in particular, slot availability (a trial open in the abstract may have no open enrollment slots in practice) and which specific arms of a trial are open at the patient's local site (a trial may be "open" but only for a single arm the patient doesn't qualify for, or open at a partner site the patient can't realistically travel to). The paper formalized knowledge representation of clinical trials, waitlist management, and the end-to-end enrollment workflow - and made the case that precision-oncology trial matching couldn't be solved at the eligibility-criteria layer alone.

The 2021 maturation - three companion papers. Three peer-reviewed papers landed within seven months of each other in 2021, each from the same Vanderbilt team, and together they map the precision-oncology implementation surface:

• Learnings From Precision Clinical Trial Matching for Oncology Patients Who Received NGS Testing (Jain et al., JCO Clinical Cancer Informatics, Feb 2021) - what could be matched, what couldn't, and where the structured-data gaps were.
• Framework for Implementing and Tracking a Molecular Tumor Board at a National Cancer Institute-Designated Center (Jain et al., The Oncologist, Aug 2021) - how to govern the human-review side around the structured data, with measurable cadence and accountability.
• My Cancer Genome: Coevolution of Precision Oncology and a Molecular Oncology Knowledgebase (Holt et al., JCO CCI, Sept 2021) - the knowledgebase layer that turns a raw variant into clinically actionable interpretation inside the EHR.

OKRA - alerting clinicians when the evidence changes. A targeted therapy approval, a label expansion, or a new clinical trial opening for a specific variant can dramatically change a patient's treatment options - but only if the clinician knows about it in time. OKRA (Oncology Knowledge Rapid Alerts), funded by an NCI R21, is the alerting layer that tells clinicians when a new targeted therapy becomes available for a variant a patient on their panel already carries. It depends on every preceding piece - the structured genomic data in the EHR, the curated knowledgebase, the trial-matching infrastructure - and surfaces the actionable change at the point of care.

Structured staging in Epic Hyperspace. Cancer staging - TNM, biomarker status, clinical interpretation - is the connective tissue between diagnosis and treatment selection, and historically it has lived in free-text notes that downstream applications can't parse. Dr. Osterman is PI on an ongoing project building an AI-extracted oncology staging workflow into Epic Hyperspace, with Epic as the platform collaborator. Once staging is structured at the point of care, trial-matching, mCODE submission to CMS, and outcome research all become tractable from the same data.

The through-line: precision oncology only works when the molecular data, the interoperability layer, the knowledgebase, and the workflow surfaces are all in place. Dr. Osterman has built or led all four at Vanderbilt-Ingram, and the published record is the audit trail. See also clinical genomics in the EHR for the data substrate, cancer data standards (mCODE) for the interoperability layer, and AI in oncology for where predictive models on this foundation are headed.