The past month, I saw a nice white paper by LabCorp about "Bridging testing gaps by enabling an FDA cleared IVD tumor profiling precision oncology test within your local community setting." Find it here.
On a similar theme, in December 2025, CAP Today published a lead article on, "How two labs took on in-house sequencing." Find it here.
I asked Chat GPT 5.2 to contrast and compare the two pieces.
When Genomics Moves In: How In-House NGS Is Reshaping Oncology Care
When Genomics Moves In:
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:
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Molecular tumor boards can meet while decisions are still pending
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Therapy initiation is better aligned with molecular eligibility
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Clinical trial enrollment windows are easier to meet
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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:
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Single specimens feed integrated pipelines
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Reflex testing protocols are developed (e.g., NSCLC, AML)
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Tissue conservation improves because fewer recuts are needed
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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 Notes
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.
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Might the articles not be a little too rosy? Can a local lab, even in a med school, really compete with a multi-billion dollar company dedicated to the most cutting-edge techniques in genomics and with a huge in-house staff? And don't remarks like "payors vary and reimbursement may not cover costs" carry a pretty lethal payload? Discuss.
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Circling back:
In-House NGS:
Clinical Progress, Strategic Limits
The stories about health systems bringing next-generation sequencing in-house (see CAP TODAY) and the Labcorp/PGDx case study of local implementation of an FDA-cleared comprehensive genomic profiling assay (see Labcorp white paper) are undeniably encouraging. They show that community and academic centers can build genomic capability, shorten turnaround times, integrate testing into care, and improve coordination across pathology and oncology.
But they are written from the vantage point of successful adopters. They describe what can work operationally. They do not fully address a deeper question:
Can a hospital laboratory — even at a major academic center — truly compete with multi-billion-dollar genomics companies whose sole mission is to stay at the cutting edge?
In most respects, the honest answer is no.
The Structural Advantage of National Genomics Firms
Large oncology genomics companies are not just laboratories. They are industrial-scale R&D platforms. Their core business is continuous assay evolution, variant curation, software development, and evidence generation.
| Dimension | National Genomics Companies | Hospital / Academic Labs |
|---|---|---|
| Assay evolution | Continuous R&D cycles | Periodic upgrades, project-based |
| Panel breadth and novelty | Rapidly expanding | More static, slower refresh |
| Variant databases | Massive, multi-institutional | Local experience only |
| Bioinformatics depth | Dedicated ML, AI, cloud teams | Vendor tools + small internal teams |
| Clinical evidence production | Sponsor trials, large datasets | Limited institutional studies |
| Regulatory infrastructure | Specialized regulatory affairs | Shared compliance staff |
A hospital lab cannot replicate that scale of innovation. Nor can it match the pace at which national firms update panels, incorporate new biomarkers, and refine interpretation pipelines.
Importantly, however, that is not the competition hospitals are trying to win.
Hospitals Compete on Integration, Not Frontier Technology
In-house programs are not built to out-innovate national firms. They are built to out-perform send-out testing in clinical alignment.
| Factor | Reference Lab Model | In-House Model |
|---|---|---|
| Turnaround time | Often weeks | Typically under a week |
| Reflex testing | Limited | Embedded in workflow |
| Integration with tumor boards | Indirect | Direct and routine |
| Tissue stewardship | Multiple handoffs | Consolidated handling |
| Workflow control | External | Internal |
The advantage of in-house testing is not cutting-edge assay breadth. It is synchronization — molecular results arriving within the decision window of care, interpreted in the context of local pathology and oncology workflows.
This is a different axis of value.
The Risk That Gets Less Attention: Technological Drift
Where hospital programs are vulnerable is in assay modernization. Oncology genomics evolves quickly. New predictive biomarkers, fusion targets, resistance mutations, and therapy-linked alterations emerge continually.
For a national firm, panel updates are part of the business model. For a hospital lab, updating an assay is a major project involving validation, workflow disruption, and cost. That creates a persistent risk:
Local panels can lag behind the state of the art.
Over time, that lag can widen, especially if resources are tight. The issue is not whether the lab can run today’s test well. It is whether it can keep pace with tomorrow’s biology.
Interpretation at Scale: The Data Density Gap
Another structural difference lies in variant interpretation. Interpretation quality depends not just on expertise, but on exposure — large variant databases, continuous evidence ingestion, and AI-supported curation.
National firms operate at population scale. Hospital labs operate at institutional scale. Even excellent academic centers cannot match the volume of rare variants and longitudinal data that inform large commercial knowledge bases.
This does not make local interpretation unreliable. But it means that the informational environment is different: industrial-scale genomics companies operate with deeper data reservoirs.
The Quietly Dangerous Sentence: Reimbursement Variability
Both articles acknowledge that reimbursement “varies” and may not cover costs. This may be the most consequential issue in the entire discussion.
NGS programs carry substantial fixed and ongoing costs: instrumentation, quality systems, technical staffing, validation, informatics, and oversight. When reimbursement is inconsistent or inadequate, the economics can deteriorate rapidly.
| Financial risk | Potential impact |
|---|---|
| Underpayment | Testing performed at a loss |
| Denials and delays | Cash-flow instability |
| Coverage variability | Uncertain volume forecasting |
| Administrative burden | Higher overhead |
Large genomics companies distribute these risks across enormous test volumes and diversified contracts. A hospital laboratory cannot. As a result, in-house NGS often persists not because it is a strong profit center, but because it is strategically subsidized as part of the oncology service line.
That makes the model viable — but also fragile.
A More Realistic Long-Term Picture
The likely future is not a competition where one model displaces the other. It is a tiered ecosystem.
| Use case | Best-suited setting |
|---|---|
| Rapid reflex panels (e.g., AML, NSCLC triage) | In-house labs |
| Ultra-broad or experimental profiling | National reference labs |
| Trial screening | Both |
| Emerging areas like MRD | Mixed and evolving |
In this model, hospital labs provide time-sensitive, workflow-integrated genomics, while national firms continue to drive the technological frontier and support the most complex profiling needs.
Bottom Line
The optimistic tone of these success stories is justified at the level of clinical operations. In-house NGS can shorten turnaround times, integrate molecular data into care, and strengthen multidisciplinary oncology practice.
But structurally:
Hospital labs cannot match industrial-scale innovation velocity
Assay modernization is a continual burden
Reimbursement variability poses real financial risk
In-house genomics succeeds best when it is understood not as a technology arms race or a standalone profit center, but as core clinical infrastructure — akin to imaging or infusion services — supported by the broader oncology program.
It is, ultimately, a care delivery strategy, not a market competition. And that distinction explains both its power and its limits.
