2016
2026 Labcorp 0101 IVD NGS in LAB 4pp (cf CAP TODAY 25-Dec re FDA Elio local).pdf
2025 December CAP TODAY How 2 Labs Bring SEQ In House
2025 cap today 1201 in house NGS.pdf
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When Genomics Moves In:
How In-House NGS Is Reshaping Oncology Care
Two recent pieces — the CAP TODAY feature on health systems building their own sequencing programs and the Labcorp/PGDx case study on implementing the FDA-cleared elio™ tissue complete assay locally — describe the same transformation from different vantage points. One is bottom-up, driven by laboratory buildout and clinical demand (see CAP TODAY). The other is platform-enabled, using an engineered in-house IVD solution to consolidate testing (see Labcorp white paper). Together they show that bringing next-generation sequencing (NGS) in-house is not simply a technical choice. It is a systems-level redesign of oncology care.
The Core Misalignment: Modern Oncology vs Legacy Send-Out Testing
Both accounts begin with the same friction point: send-out comprehensive genomic profiling (CGP) no longer fits the tempo or structure of modern cancer care. Traditional workflows involve multiple handoffs — slide review, block retrieval, vendor requisition, external lab queue, and report reintegration. Each step introduces delay, and cumulatively these processes stretch turnaround times into the 9–21 day range or longer (see Labcorp white paper).
This model evolved when genomic testing was exploratory and treatment decisions were less time-sensitive. That is no longer the case. Targeted therapies, tumor-agnostic approvals, immunotherapy biomarkers, and acute leukemia management all depend on rapid molecular stratification. When genomic data arrive weeks after diagnosis, the information is clinically useful but operationally mistimed.
The problem, then, is not whether CGP works. It is that its delivery mechanism was built for a slower era of oncology.
Three Paths to the Same Destination
The CAP TODAY article describes two distinct implementation philosophies among health systems bringing NGS in-house, and the Labcorp case study illustrates a third hybrid approach.
| Implementation style | Institutional example | Core logic |
|---|---|---|
| “Headfirst” large assay first | Florida Cancer Specialists | Start with a complex pancancer panel, build capability, then expand |
| Progressive build | Sentara Health | Move from single-gene → hotspot panels → DNA/RNA CGP |
| Platform-enabled consolidation | University center using PGDx elio | Adopt an FDA-cleared, pre-engineered CGP workflow but run locally |
The first two reflect internal laboratory evolution (see CAP TODAY). The third shows how a mature IVD CGP platform can be “localized,” reducing bioinformatics burden while still shifting control back to the institution (see Labcorp white paper).
Despite different starting points, all three converge on the same endpoint: genomics becomes an in-house clinical service rather than an outsourced specialty procedure.
Turnaround Time: The Clinical Fulcrum
The most visible benefit of insourcing is speed, but the deeper impact is what speed enables.
| Testing model | Typical turnaround |
|---|---|
| Send-out CGP | 2–4+ weeks |
| In-house solid tumor NGS (community systems) | ~5–7 days |
| PGDx elio in-house workflow | ~5 days DNA-to-report |
Shorter TAT is not merely convenient. It changes care delivery:
Molecular tumor boards can meet while decisions are still pending
Therapy initiation is better aligned with molecular eligibility
Clinical trial enrollment windows are easier to meet
Acute leukemia classification can inform early management
Faster genomics effectively synchronizes molecular data with clinical decision cycles, which is a systems improvement, not just a laboratory metric.
The Quiet Revolution: Workflow Consolidation
One of the most profound effects described in both pieces is the collapse of diagnostic silos. Before in-house NGS, cytogenetics, IHC, molecular send-outs, and pathology operated as semi-independent streams. Tissue blocks might be cut repeatedly as cases moved from PD-L1 staining to FISH to external sequencing.
Once CGP moves inside:
Single specimens feed integrated pipelines
Reflex testing protocols are developed (e.g., NSCLC, AML)
Tissue conservation improves because fewer recuts are needed
Reporting aligns molecular data with pathology and therapeutic context
Sentara noted that incorporating RNA fusion detection into NGS panels reduced the need for separate FISH testing, directly affecting cytogenetics workload (see CAP TODAY). The Labcorp case study similarly emphasizes the reintegration of fragmented molecular services into a consolidated unit.
This is a shift from test-by-test diagnostics to platform-based molecular pathology.
Bioinformatics: From Existential Barrier to Managed Layer
Historically, bioinformatics complexity limited adoption. The CAP TODAY experience shows that many laboratories began without deep in-house bioinformatics expertise, instead relying on vendor-supported workflows and building internal familiarity over time. Pipelines were deliberately kept manageable, and expertise grew with use.
The PGDx model represents a further step: automated, server-based analysis integrated into the assay ecosystem (see Labcorp white paper). In both cases, the trajectory is the same — bioinformatics remains critical, but it is no longer an insurmountable entry barrier. The constraint shifts from computational capacity to organizational readiness and governance.
NGS Moves Upstream: Therapy Selection → Diagnosis
A particularly important distinction raised in the CAP article concerns myeloid sequencing. In solid tumors, NGS typically follows a diagnosis and guides treatment. In myeloid neoplasms, mutations can shape the diagnosis itself (AML vs MDS, risk stratification, classification). That required rethinking reporting structures and interpretive workflows.
This illustrates a broader trend: genomics is migrating upstream in the diagnostic chain, increasingly influencing disease definition rather than simply therapy choice.
Research and Trial Integration
Both accounts highlight another institutional effect: easier linkage to clinical trials. Sentara’s in-house capability facilitated participation in NCI precision medicine trial networks (see CAP TODAY). The Labcorp case similarly emphasizes matching patients to targeted therapies and trials. In-house CGP thus becomes not only a diagnostic tool but a research infrastructure enabler.
Financial Reality: Still Complex, but Structurally Different
Reimbursement challenges persist in both narratives. Payer variation and cost coverage remain planning concerns. However, consolidating testing internally alters the financial equation:
| External model | In-house model |
|---|---|
| Per-test transactional costs | Platform-level operational control |
| Fragmented billing pathways | Integrated financial oversight |
| Limited local utilization leverage | Alignment with institutional service lines |
While reimbursement adequacy is not guaranteed, the move in-house transforms genomics from an external expense into a managed internal service line.
What This Transition Really Represents
At a higher level, these stories document a structural change:
| Historical paradigm | Emerging paradigm |
|---|---|
| Genomics as specialized send-out | Genomics as hospital core function |
| Pathology-first workflow | Molecular-pathology integrated workflow |
| Linear testing algorithms | Platform-based molecular diagnostics |
| External data ownership | Local genomic data stewardship |
The common thread is institutionalization. Precision oncology is no longer an add-on capability. It is becoming part of the hospital’s diagnostic infrastructure, much like chemistry analyzers or imaging suites once were.
Final Perspective
Bringing NGS in-house does not just accelerate testing. It reorganizes the diagnostic ecosystem, aligning molecular data with clinical timing, integrating laboratory disciplines, enabling research participation, and shifting genomics from outsourced expertise to institutional competency.
What’s changing is the basic operating model of molecular oncology.
| Older model | Emerging model |
|---|---|
| Ship specimens to distant reference labs | Generate genomic data within the health system |
| Genomics as a specialty send-out | Genomics as a routine component of care |
| Long feedback loops between diagnosis and molecular results | Molecular data available within the clinical decision window |
| Fragmented lab roles (IHC, FISH, send-out NGS) | Integrated molecular pathology workflows |
| External control of data and pipelines | Local stewardship of genomic data and interpretation |
The transition is from a world where genomic testing sits outside the care system and feeds information back in, to one where genomics is embedded inside the diagnostic and treatment workflow itself.
That shift marks the moment precision oncology stops being an external service and becomes part of hospital infrastructure.
