Scite.AI: MRD Studies in Lung Cancer

 There seem to be many fewer studies for Minimal Residual Disease monitoring (MRD) in lung cancer, compared to colorectal cancer. What are the best studies of MRD in lung cancer (circulating tumor DNA) and if possible discuss why this field lags behind MRD studies in colorectal cancer.

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SCITE.AI

Minimal Residual Disease (MRD) monitoring in lung cancer, particularly through the analysis of circulating tumor DNA (ctDNA), has garnered attention, yet it remains significantly less explored compared to colorectal cancer. This disparity can be attributed to several factors, including the complexity of lung cancer biology, the historical focus on colorectal cancer in MRD studies, and the challenges associated with ctDNA detection in lung cancer.

 Recent studies have begun to elucidate the potential of ctDNA in monitoring MRD in lung cancer. For instance, demonstrated that longitudinally undetectable MRD in localized non-small cell lung cancer (NSCLC) patients correlates with a high likelihood of remaining disease-free, suggesting a potential for identifying a "cured" population within this cohort (Zhang et al., 2022). Furthermore, highlighted the efficacy of ctDNA profiling in early detection of MRD, emphasizing its role in guiding treatment decisions (Chaudhuri et al., 2017). These findings indicate that while the prognostic value of ctDNA in lung cancer is promising, the body of research is still developing.

 In contrast, colorectal cancer has seen a more robust integration of ctDNA in MRD monitoring. Studies such as those by have established ctDNA as a reliable biomarker for recurrence in resected colorectal cancer, demonstrating its superiority over traditional imaging methods in certain contexts (Fakih et al., 2022). The comparative abundance of studies in colorectal cancer can be attributed to its well-defined treatment pathways and the established role of biomarkers like carcinoembryonic antigen (CEA) in clinical practice (Fakih et al., 2022). This has facilitated a more extensive exploration of ctDNA applications, leading to a wealth of data supporting its use in MRD monitoring.

 The lag in lung cancer research can also be linked to the inherent biological complexities of lung tumors. noted that most studies have focused on single qualitative markers in ctDNA, such as specific mutations (e.g., EGFR and KRAS), rather than adopting a comprehensive multi-marker approach that has proven effective in colorectal cancer (Szpechcinski et al., 2015). This limitation in study design may hinder the broader applicability of ctDNA as a reliable MRD marker in lung cancer.

 Moreover, the technical challenges associated with ctDNA detection in lung cancer cannot be overlooked. The sensitivity of ctDNA assays can be affected by the tumor's heterogeneity and the low levels of ctDNA present in the bloodstream, particularly in early-stage disease (Yan & Liu, 2022). This contrasts with colorectal cancer, where ctDNA levels are often higher and more consistent, facilitating easier detection and monitoring.

 In summary, while there are promising studies indicating the potential of ctDNA for MRD monitoring in lung cancer, the field lags behind colorectal cancer due to a combination of historical focus, biological complexity, and technical challenges in ctDNA detection. Continued research and the development of more sensitive and comprehensive ctDNA assays may bridge this gap in the future.

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Could you focus on 2023 and 2024 studies of circulating tumor dna to detect minimal residual disease (MRD) or recurrence, in lung cancer.

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In recent years, the application of circulating tumor DNA (ctDNA) for monitoring minimal residual disease (MRD) and recurrence in lung cancer has gained traction, with several notable studies emerging in 2023 and 2024. These studies highlight the potential of ctDNA as a non-invasive biomarker for assessing disease status post-treatment and guiding clinical decisions.

 One significant study by Kang et al. (2023) introduced a dynamic methylation-based analysis of cell-free DNA for postoperative monitoring in lung cancer patients. The study found that patients with low MRD scores were unlikely to experience relapse within four months, suggesting that such a methylation-based approach could personalize postoperative management and reduce unnecessary imaging (Kang et al., 2023). This finding underscores the utility of ctDNA in providing timely insights into patient status, potentially leading to more tailored treatment strategies.

 Another important contribution is from (Franzi, 2023), who conducted a meta-analysis on the role of liquid biopsy in non-small cell lung cancer (NSCLC). The authors noted that dynamic changes in ctDNA levels correlated with therapeutic responses, indicating that ctDNA could be effectively used for early detection of MRD and recurrence in early-stage lung cancer (Franzi, 2023). This study reinforces the idea that ctDNA monitoring can enhance the management of lung cancer by identifying patients at risk for relapse.

 O'Sullivan et al. (2023) further explored the challenges and future directions of MRD monitoring in radically treated NSCLC. They emphasized that the variant allele frequency (VAF) of ctDNA is prognostic, particularly in early-stage patients. Those with detectable ctDNA prior to resection exhibited a higher risk of recurrence compared to those without detectable ctDNA, highlighting the potential of ctDNA as a predictive tool in clinical practice (O’Sullivan et al., 2023). This study illustrates the critical role of ctDNA in stratifying patients based on their risk of relapse, which could inform follow-up and treatment decisions.

 Desai (2024) also contributed to this field by discussing the integration of ctDNA into clinical practice for managing EGFR-mutant NSCLC. The study highlighted ctDNA's role in risk stratification and treatment decision-making, particularly for patients undergoing curative-intent treatment (Desai, 2024). This aligns with the growing recognition of ctDNA as a valuable tool for monitoring MRD and guiding therapeutic interventions.

 Moreover, Morgensztern (2024) emphasized the expanding role of ctDNA in resectable NSCLC, suggesting that its detection could facilitate personalized treatment approaches. By identifying patients at high risk for relapse, clinicians may opt for more aggressive therapies, while those at low risk could avoid overtreatment (Morgensztern, 2024). This personalized approach is crucial in improving outcomes for lung cancer patients.

 In summary, the studies from 2023 and 2024 collectively demonstrate the promising role of ctDNA in detecting MRD and predicting recurrence in lung cancer. The ability to provide real-time insights into disease status and risk stratification positions ctDNA as a vital component of modern oncological practice, particularly in the management of NSCLC.

 References:

Chaudhuri, A., Chabon, J., Lovejoy, A., Newman, A., Stehr, H., Azad, T., … & Diehn, M. (2017). Early detection of molecular residual disease in localized lung cancer by circulating tumor dna profiling. Cancer Discovery, 7(12), 1394-1403. https://doi.org/10.1158/2159-8290.cd-17-0716

Desai, A. (2024). Ctdna for the evaluation and management of egfr-mutant non-small cell lung cancer. Cancers, 16(5), 940. https://doi.org/10.3390/cancers16050940

Fakih, M., Sandhu, J., Wang, C., Kim, J., Chen, Y., Lai, L., … & Kaiser, A. (2022). Evaluation of comparative surveillance strategies of circulating tumor dna, imaging, and carcinoembryonic antigen levels in patients with resected colorectal cancer. Jama Network Open, 5(3), e221093. https://doi.org/10.1001/jamanetworkopen.2022.1093

Franzi, S. (2023). Liquid biopsy in non-small cell lung cancer: a meta-analysis of state-of-the-art and future perspectives. Frontiers in Genetics, 14. https://doi.org/10.3389/fgene.2023.1254839

Kang, G., Lizaso, A., Beck, S., Lyskjær, I., Chervova, O., Li, B., … & Wang, J. (2023). Individualized dynamic methylation-based analysis of cell-free dna in postoperative monitoring of lung cancer. BMC Medicine, 21(1). https://doi.org/10.1186/s12916-023-02954-z

Morgensztern, D. (2024). The expanding role of circulating tumor dna in resectable non–small cell lung cancer. Cancer, 130(10), 1730-1732. https://doi.org/10.1002/cncr.35268

O’Sullivan, H., Feber, A., & Popat, S. (2023). Minimal residual disease monitoring in radically treated non-small cell lung cancer: challenges and future directions. Oncotargets and Therapy, Volume 16, 249-259. https://doi.org/10.2147/ott.s322242

Szpechcinski, A., Chorostowska‐Wynimko, J., Struniawski, R., Kupis, W., Rudziński, P., Langfort, R., … & Orłowski, T. (2015). Cell-free dna levels in plasma of patients with non-small-cell lung cancer and inflammatory lung disease. British Journal of Cancer, 113(3), 476-483. https://doi.org/10.1038/bjc.2015.225

Yan, X. and Liu, C. (2022). Clinical application and prospect of mrd evaluation in lung cancer based on ctdna level: a review. Tumori Journal, 109(4), 356-362. https://doi.org/10.1177/03008916221101927

Zhang, J., Liu, S., Gao, W., Liu, S., Yan, H., Ji, L., … & Wu, Y. (2022). Longitudinal undetectable molecular residual disease defines potentially cured population in localized non–small cell lung cancer. Cancer Discovery, 12(7), 1690-1701. https://doi.org/10.1158/2159-8290.cd-21-1486

Chat GPT: CLIA Verification vs Validation.

 You are an expert on CLIA and clinical laboratory Policy.  What is the difference between "Verification" and "validation" in this context.  I think that verification has to do with bring up a complex IVD FDA-approved test for use, while validation has to do with validating an inhouse lab developed test (LDT).  However, don't assume I have that correct.

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You are on the right track with your understanding of verification and validation in the context of CLIA (Clinical Laboratory Improvement Amendments) and clinical laboratory policy, but let's clarify these terms in more detail.

Verification refers to the process of confirming that a laboratory can accurately perform a test according to the specifications provided by the manufacturer of an FDA-approved or cleared test (typically an in vitro diagnostic [IVD] device). When a lab brings an IVD test into use, they must verify that they can replicate the test’s performance characteristics (e.g., accuracy, precision, sensitivity) as specified in the product labeling. This step ensures that the test performs as expected in the laboratory's specific setting but does not involve altering the test methodology.

Validation, on the other hand, is a more comprehensive process used primarily for laboratory-developed tests (LDTs), which are not FDA-approved or cleared. Validation involves establishing the test’s performance characteristics from the ground up, as there are no manufacturer specifications to verify. This includes defining and confirming accuracy, precision, sensitivity, specificity, and other relevant performance characteristics for the test in question. The lab essentially proves that the test is reliable for its intended use.

In summary:

Verification: Performed for FDA-approved/cleared tests (IVDs) to ensure the lab can run the test properly.

Validation: Required for in-house LDTs, where the lab must fully establish the test's performance characteristics.

Let me know if you need more details on either process!

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Assume I am preparing to be hired for a lab director role, and I need to answer an oral question contrasting and comparing the two processes.   Provide a more detailed answer suitable for someone interviewing to be a lab director.

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Verification and validation are critical processes under CLIA, but they differ in scope, purpose, and regulatory requirements. Let’s start by defining each process:

1. Verification is required when a laboratory implements an FDA-approved or cleared test—typically an in vitro diagnostic (IVD) device. Since the test has already undergone rigorous validation by the manufacturer as part of the FDA approval process, the lab is not required to perform a full validation. Instead, the lab must verify that it can perform the test within its own environment and achieve the same performance characteristics specified by the manufacturer. The key parameters verified include:

   • Accuracy: Does the test provide results that match known standards?
   • Precision: Is the test reproducible, with minimal variation between runs?
   • Reportable Range: Does the test accurately report values across the intended range?
   • Reference Range: Do results fall within the expected normal ranges for the patient population?

   The process typically involves running controls and samples to confirm that the lab’s staff, equipment, and environment can maintain the test’s intended performance. Verification is especially focused on ensuring consistent application of a pre-established test in a new setting.

2. Validation, by contrast, applies to laboratory-developed tests (LDTs) or modified FDA-approved tests. Since LDTs are not FDA-reviewed, the lab must take full responsibility for proving that the test performs as intended. This process is more involved and includes:

   • Establishing Performance Characteristics: The lab must generate data to support accuracy, precision, sensitivity, specificity, and analytical range. 
   • Unlike verification, which confirms performance against an existing standard, validation requires building the evidence to define those standards.
   • Risk Assessment: As part of validation, the lab also conducts a thorough risk analysis, considering factors such as potential cross-reactivity, matrix effects, and robustness in various operating conditions.
   • Clinical Utility: The lab must demonstrate that the test’s results provide meaningful clinical information that can be used for patient care decisions, ensuring the test is fit for its intended clinical purpose.

Key Differences:

• Scope: Verification focuses on confirming performance characteristics established by the manufacturer, while validation involves defining and establishing these characteristics for a new or modified test.
• Regulatory Requirements: For verification, the lab is bound to follow the FDA-approved test's guidelines. 
• For validation, the lab takes on the responsibility for all aspects of test performance, requiring a higher degree of scrutiny and documentation.
• Complexity: Verification is a more straightforward process with predefined benchmarks, while validation involves more extensive, data-driven analysis to ensure the test is fit for its intended use.

Key Similarities:

• Both processes are crucial for ensuring that any test used in the clinical laboratory meets the necessary quality standards under CLIA and applicable regulatory guidelines.
• Both require careful documentation and adherence to the lab’s quality management system, including the monitoring of ongoing performance through quality control (QC) measures.

In summary, while verification is generally limited to confirming that an FDA-cleared test works as expected in the lab’s environment, validation is a more comprehensive process used when developing new tests or significantly modifying existing ones. As a lab director, it's important to oversee both processes to ensure compliance, maintain high-quality patient care, and mitigate risks associated with testing."

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This answer highlights your knowledge of both processes, while also demonstrating your leadership capabilities in maintaining quality and regulatory compliance in the lab.

Order of Bullets in van Shaik, CGP Value

Original blog:   https://www.discoveriesinhealthpolicy.com/2024/09/journal-club-broad-tech-assessments-of.html 

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SIDEBAR: Why the Order?

 

1. Actionable Mutations Were Common, but Impact Was Unclear: I put this first because finding actionable mutations is the core promise of CGP. But if these findings don't consistently lead to better outcomes, then the whole premise of CGP becomes questionable. This felt like the most important point to address upfront.

2. Limited Diagnostic Impact: After actionable mutations, I wanted to highlight the diagnostic value of CGP. If it's not significantly improving or refining diagnoses, it calls into question whether CGP is doing enough to justify its use. So, I placed this right after the actionable mutations discussion.

3. Matched Treatments Had Inconsistent Results: This comes next because the idea behind CGP is to guide personalized treatment. If matched treatments are inconsistent across studies, it’s a major limitation. I felt that once we’ve talked about actionable mutations and diagnostic impact, discussing treatment outcomes naturally follows.

4. Clinical Utility Limited by Treatment Access: Even if CGP finds actionable mutations, if patients can’t access the treatments due to insurance or logistical issues, then the real-world utility is limited. This is a practical barrier that felt important to address early on, right after the more theoretical points about mutations and diagnostics.

5. No Randomized Controlled Trials (RCTs): I placed this high on the list because RCTs are considered the gold standard for proving efficacy. The absence of RCTs really weakens the conclusions about CGP’s clinical utility, so it seemed important to mention early.

6. Turnaround Time Variability: Turnaround time directly affects patient care, and if it’s inconsistent or too slow, CGP’s practical value in clinical settings diminishes. I thought this was an operational issue that needed to be addressed before moving on to more abstract concerns like study design.

7. Lack of Standardization in Outcomes: This is a big frustration because it makes it hard to compare results across studies. Without standardized outcomes, we can’t draw solid conclusions about CGP’s effectiveness, so it felt like a key limitation to highlight after the discussion on practical and clinical issues.

8. Difficulty in Assessing Long-Term Benefits: I wanted to emphasize that CGP’s potential long-term benefits, like hereditary findings, are often overlooked. This is a missed opportunity, and I thought it was important to mention before moving on to methodological and cost-related issues.

9. Observational Study Design Challenges: Since observational studies are the main source of data for CGP, their limitations have to be acknowledged. But I placed this a bit lower because it’s more of a methodological issue, which feels secondary to clinical outcomes and real-world utility.

10. Lack of Cost-Effectiveness Data: Cost-effectiveness is critical when deciding whether to implement a technology like CGP, but without strong data here, it’s hard to justify its use. I placed this lower because while it’s important, clinical and operational factors seemed more pressing.

11. Limited Empirical Evidence: There’s a lack of empirical evidence for some factors, like laboratory organization and scientific spillover. This is a significant gap, but I placed it lower because it’s not as immediately impactful as the clinical and financial concerns.

12. Need for Real-World Evidence and Comprehensive Assessments: Finally, I wanted to end with a forward-looking recommendation. The need for more real-world evidence and comprehensive assessments summarizes what’s missing and where future research should go. It felt like a natural conclusion after discussing all the limitations.

This order begins with the most pressing clinical findings and gradually moves toward methodological, operational, and future research concerns, creating a logical flow that prioritizes patient-centered utility before broader or systemic issues.

OPPS Comment Radiopharm Competitive Settings

 TOPIC: Payment for diagnostic radiopharmaceuticals.   

III.V.B.c. 89 Fed Reg 59213-9.

I support the separate billing of certain costly radiopharmaceuticals.  This supports the fact that OPPS is a prospective payment system sensitive to major levels (APCs for Radiology 1, 2, 3 etc).  This use of packaging plus levels is also found in DRGs as are MS-DRGs (w/MCC, etc).

CMS does not mention in this context incentives or perverse incentives regarding location of service.  Currently, diagnostic radiopharmaceuticals in Part B non-facility settings are paid separately (e.g. $600 scan, $2000 tracer).   This provides a current incentive to use these tracers only in part of the nuclear scan places of service, creating a segmented and therefore less competitive market.  By paying for costly tracers separately in both freestanding and facility outpatient settings, there will be a more level playing field and more potential for competition.

Thank you! Your comment has been submitted to Regulations.gov for review by the the Centers For Medicare & Medicaid Services.   Comment Tracking Number: m0s-l837-lcz4