Header; How to evaluate the range of impacts of comprehensive genomic profiling (CGP) in advanced cancer.
###
New at Critical Reviews in Oncology, and with several dozen up-to-date references, see van Shaik and colleagues for a systematic review of comprehensive genomic profiling in advanced. How are we appraising its value? Spoiler alert: It's messy.
The authors state it was hard to review the 83 studies collected, because of the wide differences in terminology and outcome variables.
For example, it took me a moment to realize that the point of this chart is to display 20 different ways of defining "turnaround time."
 |
| click to enlarge |
This chart catalogs factors clinicians may be considering when ordering CGP:
 |
| click to enlarge |
Thanks to the HE&G blog for highlighting.
Here's a Chat GPT 4o summary.
The article "Factors for a Broad Technology Assessment of Comprehensive Genomic Profiling (CGP) in Advanced Cancer" systematically reviews the various factors influencing the adoption of CGP in advanced cancer. While CGP offers extensive genomic insights, particularly through methods like whole genome sequencing (WGS) and whole exome sequencing (WES), its higher costs, compared to targeted molecular diagnostics, complicate reimbursement decisions.
This review shines a light on key non-clinical factors—such as feasibility, patient test journey, wider diagnostic implications, laboratory organization, and scientific spillover—that should be integrated into health technology assessments (HTA).
The study highlights that while health benefits and cost-effectiveness remain central to decision-making, empirical evidence on other crucial factors is limited. Most studies reviewed in this article focus on "test journey" and "wider implications" of CGP, such as turnaround times, actionable mutations, and matched treatments. Yet, the data’s variability and lack of uniform outcome definitions across studies pose challenges to drawing strong conclusions. Furthermore, factors like laboratory organization and scientific spillover are acknowledged but lack robust empirical evidence in current literature.
In conclusion, the study advocates for a more holistic and comparative strategy for evaluating CGP, urging the inclusion of qualitative and real-world evidence to complement clinical utility data. It recommends future research prioritize generating standardized, outcome-based evidence across broader factors to support clearer reimbursement and policy decisions.
##
I find that asking Chat GPT for 10 or 15 key bullet points can be helpful.
Cute: See why Chat GPT chose this order of bullets in a sidebar.
##
Here are 12 key takeaways from the study, focusing on its findings, frustrations, and conclusions about the utility of CGP in advanced oncology:
Here’s a re-ordered version of the 12 key takeaways, prioritized for clarity and importance:
1. Actionable Mutations Were Common, but Impact Was Unclear: Although 78.1% of studies reported finding actionable mutations, this did not consistently translate into improved patient outcomes due to barriers like access to treatments and patient health status.
2. Limited Diagnostic Impact: Only 18% of studies reported that CGP had a significant impact on refining or revising diagnoses. This low emphasis on diagnostic benefits frustrated the authors, as it could have been a clearer measure of CGP’s utility.
3. Matched Treatments Had Inconsistent Results: While 83% of studies reported patients receiving matched treatments, the percentage ranged from 1.5% to 75%, making it difficult to gauge CGP’s utility in ensuring appropriate treatments.
4. Clinical Utility Limited by Treatment Access: Even when actionable mutations were identified, logistical barriers such as lack of access to trials or insurance denials often prevented patients from receiving matched treatments, limiting the practical utility of CGP.
5. No Randomized Controlled Trials (RCTs): The lack of well-designed RCTs specifically comparing CGP to targeted profiling was a major frustration, as such studies are essential for demonstrating clear clinical utility.
6. Turnaround Time Variability: Turnaround times for CGP ranged from 11 to 116 days, with no standardization in how they were measured. This inconsistency made it hard to evaluate the efficiency of CGP in a clinical setting.
7. Lack of Standardization in Outcomes: The wide variability in outcome definitions (e.g., actionable mutations, matched treatments) across studies was a major limitation, preventing the authors from making robust conclusions about CGP’s effectiveness.
8. Difficulty in Assessing Long-Term Benefits: The authors found it challenging to assess the long-term benefits of CGP, particularly regarding potential hereditary findings, which could have generational impacts but are not often included in health technology assessments (HTAs).
9. Observational Study Design Challenges: The predominance of observational studies limited the ability to draw strong conclusions about CGP’s utility, as these designs often lack the controls necessary for definitive assessments.
10. Lack of Cost-Effectiveness Data: The absence of detailed cost-effectiveness analyses left a significant gap, making it difficult to assess CGP’s financial viability and overall value in routine oncology care.
11. Limited Empirical Evidence: Factors such as laboratory organization and scientific spillover, while important, were rarely addressed with empirical evidence, leaving critical gaps in understanding CGP’s broader value.
12. Need for Real-World Evidence and Comprehensive Assessments: The authors concluded that future research should focus on real-world evidence and more comprehensive assessment strategies to better capture CGP’s full value in advanced oncology.
In summary, while CGP shows promise, the lack of standardized outcomes, RCTs, and comprehensive data on real-world utility left the authors cautious in their conclusions about its current utility in advanced oncology.
##
For more articles on a rolling basis, see "Health Economics & Genomics" blog here.
For those with subscription access to Genomeweb, a new story about a CGP "Frankenpanel".
For the idea of more systematic, integrated assessments, see a new FDA initiative to systematize the review of drugs, which may be >300 pages long. Here.
