I've been at several meetings recently where the topic turned to "defining CGP." Is it 50 genes? 500 genes? Sure, it's indels and fusions, but does it require germline comparison? If its LBx CGP, how important is WBC analysis for clonal hematopoiesis? Ought we include a transcriptome? If so, how much do we do with it? (See, "The actionable transcriptome" here.)
The February issue of Preicision Medicine Online addresses the topic directly. Browse the February 2026 issue here.
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AI CORNER
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“Comprehensive Developments: Comprehensive genomic profiling plays an increasingly central role in personalized cancer treatment,” by Chris Anderson. Precision Medicine Online, February 2026.
In this February 2026 feature, Anderson argues that comprehensive genomic profiling (CGP) has evolved from broad gene panels into a foundational clinical strategy for biology-driven oncology, integrating large DNA/RNA hybrid panels, composite biomarkers such as TMB and HRD, and increasingly tumor-informed MRD applications.
Drawing on data from Providence Health’s early, pathologist-directed 523-gene testing program, the article presents evidence that ordering CGP at diagnosis substantially increases actionable findings (67% vs. 33% with smaller panels), shifts patients toward targeted and immunotherapies, and may improve survival, while also highlighting persistent barriers in reimbursement and community adoption. For genomics stakeholders, the message is clear: CGP is less about a specific gene count and more about delivering comprehensive molecular context early enough to change therapeutic trajectories.
### DEEP DIVE
What exactly is “comprehensive genomic profiling” (CGP), and how far does it extend? From organ-based oncology to biology-driven care.
From Single Markers to Biology-Driven Oncology
Anderson opens by describing CGP as a natural progression from early single-gene tests to today’s multi-hundred gene hybrid panels. As next-generation sequencing (NGS) became cheaper and more powerful, and as actionable drivers expanded, panels grew accordingly. Modern CGP panels typically include:
DNA sequencing across hundreds of genes
RNA sequencing for fusion and splice detection
Complex biomarkers such as microsatellite instability (MSI) and tumor mutational burden (TMB)
Composite signatures like homologous recombination deficiency (HRD)
Rick Baehner (Exact Sciences) emphasizes that CGP is foundational to the shift from organ-based treatment decisions to biology-based treatment decisions. Common driver mutations—TP53, EGFR, KRAS, PIK3CA, APC—span tumor types. The logic of CGP is to match molecular profile to therapy rather than anatomical origin.
The article underscores a key principle: precision therapeutics are only as effective as precision diagnostics. Targeted drugs without robust profiling are, as Luca Quagliata (Thermo Fisher Scientific) notes, a missed opportunity.
Expanding Scope: Beyond SNVs
CGP has evolved beyond single nucleotide variants. Over the past decade, hybrid panels now capture:
Insertions/deletions (indels)
Copy number variants
Structural variants
RNA fusions and splice alterations
MSI and TMB
HRD and genomic-scar signatures
RNA sequencing is highlighted as particularly impactful. Baehner notes that adding RNA increases fusion detection by 15–20% and provides a more direct readout of tumor biology: “DNA tells you what could happen. RNA tells you what is happening.”
Ezra Cohen (Tempus) argues that combining tumor DNA, RNA, and germline testing improves diagnostic accuracy. Notably, Tempus data suggest approximately 9% of alterations are detectable only in ctDNA, supporting simultaneous tumor and blood testing.
For genomics readers, this section reinforces a critical theme: CGP is increasingly multi-analyte and multi-compartment. Tissue-only approaches may miss clinically relevant events.
Composite Biomarkers and Therapeutic Expansion
The article places special emphasis on HRD and PARP inhibitor expansion. Initially confined to ovarian cancer, PARP inhibitors are now used in breast and prostate cancer and are expanding further. HRD status—not just BRCA1/2 mutations—predicts benefit, meaning CGP must capture broader homologous recombination repair pathways (PALB2, ATM, genomic scars).
This reflects a broader evolution: integration of single genes into composite molecular signatures. The field is moving from binary mutation calls toward systems-level interpretation.
Early CGP: A Timing Paradigm Shift
The most compelling section for clinical policy readers is the Providence Health study.
Providence implemented pathologist-directed, upfront CGP at diagnosis using a 523-gene DNA/RNA hybrid panel, compared to a conventional 50-gene panel ordered later in the care pathway.
Key findings (first two years, 3,216 patients with advanced solid tumors):
67% actionable alterations detected with broad CGP vs 33% with the 50-gene panel
TMB-high identified in 22% (not captured by smaller panels)
52% received biomarker-informed therapy
Median overall survival:
25 months (CGP-guided targeted therapy)
17 months (chemotherapy)
In NSCLC:
16 months median survival (CGP) vs 7 months (small panel)
Importantly, results were available 12 days before initial oncology consultation, meaning therapeutic decisions were made with genomic data in hand.
Carlo Bifulco frames this as correcting a timing flaw in oncology: genomic testing traditionally ordered too late, after treatment trajectories are set.
For genomics policy experts, this is significant. It reframes CGP not merely as a technical enhancement, but as a workflow redesign. It also strengthens arguments for early reimbursement parity.
Barriers: Reimbursement and Community Adoption
Despite compelling data, reimbursement remains the central obstacle. In the Providence study, testing was free. Real-world adoption hinges on payer coverage.
Community oncology presents additional friction:
80% of cancer patients are treated outside academic centers.
Oncologists struggle to keep pace with rapidly evolving biomarkers.
Out-of-pocket cost concerns deter adoption.
NeoGenomics’ Warren Stone highlights peer-driven education as critical for broader uptake.
For reimbursement strategists, this section reinforces the familiar tension: clinical utility evidence is accumulating, but payer consensus lags behind guideline evolution.
CGP as Foundation for MRD
The article closes by linking early CGP to tumor-informed minimal residual disease (MRD) testing.
Broad initial profiling provides the mutation set required to design personalized ctDNA assays. Detectable ctDNA post-treatment signals high recurrence risk. This transforms CGP into the foundational “first pass” for chronic disease management rather than a one-time diagnostic.
This integration of baseline CGP and longitudinal liquid biopsy moves oncology toward:
Earlier detection of recurrence
Less invasive surveillance
Chronic disease framing of cancer care
For genomics readers tracking MRD reimbursement battles, this positioning is strategic: CGP is no longer just about initial therapy matching—it seeds future monitoring.
So What Is “CGP”?
Although the article does not provide a numeric definition (50 vs 500 genes), it implicitly defines CGP functionally:
CGP is:
Broad, multi-gene DNA profiling
Increasingly paired with RNA
Capable of composite biomarker signatures
Often integrated with germline and ctDNA
Delivered early in the care pathway
Linked to targeted therapy and immunotherapy decisions
Foundational for tumor-informed MRD
It is not simply a large panel; it is a clinical strategy centered on comprehensive molecular context.
Final Takeaway for Genomics Stakeholders
Anderson’s piece reflects where the field now stands:
Technically mature hybrid panels exist.
Clinical evidence for early use is accumulating.
MRD integration strengthens longitudinal value.
Reimbursement and community adoption remain bottlenecks.
The definition of CGP is expanding toward multiomic integration.
For those of us debating whether CGP requires RNA, germline comparison, ctDNA, HRD signatures, or composite biomarkers—the article suggests the answer is evolutionary rather than binary. CGP is becoming less about panel size and more about biological completeness at clinically actionable depth.
In that sense, the “definition” of CGP may continue to expand as oncology shifts from mutation detection to full molecular state characterization.
