Tuesday, September 24, 2024

Brief Blog: PDL1 Sparks Fly Ahead of FDA Hearing

Update: Panel supported FDA plan for narrower labeling (votes 10-2, 11-1).   Here.

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I posted a blog a few weeks ago that FDA had a pending AdComm on PDL1 and immuno-oncology (blog here).

The meeting is now imminent and Endpoints covers briefing materials from FDA and from BMS.

Endpoints

https://endpts.com/fda-adcomm-will-re-examine-approval-of-checkpoint-inhibitors-in-two-cancers-based-on-key-biomarker/

FDA (41pp)

https://www.fda.gov/media/182138/download

BMS (50pp)

https://www.fda.gov/media/182140/download

As quoted by Endpoints, FDA writes,

The agency said that “approvals for all randomized patients may not be in the best interest of patients with tumors with low PD-L1 expression” and that giving the drugs to patients with low or no PD-L1 expression “has the potential for harm including serious immune related adverse events on top of a malignancy that can markedly affect a patient’s quality of life.”

Opposite Issues for Her2 Drugs

Note that in other quarters, we are looking at the value of Her2 drugs for patients with low and "ultra low" Her2 expression, e.g. this month in ASCO Post.

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Note that debates about PDL1 confusion and harmonization go back for years; e.g. Karim 2017 and Adam 2018.   

One response is to focus on better-defined cutoffs or reading methods for PDL1.  Entirely different approaches would attempt to make algorithms combining PDL1, TMB, MSI, or other biomarkers relevant to the checkpoint inhibitor pathways.  

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AI Corner

  • Discussion by Chat GPT 4o
  • Mini-article by Scite AI

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I asked Chat GPT 4 to read both the FDA and BMS documents and discuss.

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Summary of the Documents

FDA Document  

This document outlines the FDA's review and considerations regarding the use of immune checkpoint inhibitors (ICIs) like nivolumab (Opdivo) and pembrolizumab (Keytruda) for the first-line treatment of HER2-negative metastatic or unresectable gastric adenocarcinoma. The FDA focuses on the relevance of PD-L1 expression as a biomarker for treatment efficacy, particularly in the context of selecting patients for ICI treatment.

Key points include:

  1. PD-L1 Expression: The FDA acknowledges the predictive role of PD-L1 expression but highlights the challenges of standardizing it due to varying assays and scoring methods across trials. The document emphasizes that higher PD-L1 expression (≥10 CPS) is associated with more significant benefit, whereas patients with lower expression (<1 CPS) may not derive much benefit and could experience harm due to immune-related adverse events (AEs).
  2. Labeling: FDA approvals for nivolumab and pembrolizumab are agnostic of PD-L1 status for now, but the agency is considering whether to amend labeling to include PD-L1 expression as a requirement for patient selection.
  3. Efficacy Data: The document cites trials that show significant survival benefits for patients with higher PD-L1 expression, although these benefits are less clear for patients with low or no expression.

BMS Document  

Bristol-Myers Squibb (BMS) presents its position on nivolumab (Opdivo) combined with chemotherapy for treating gastroesophageal adenocarcinoma, emphasizing the flexibility needed in prescribing decisions. BMS advocates that existing labeling provides sufficient information for clinicians to make informed decisions, including the potential benefits for PD-L1-positive patients and those whose PD-L1 status is unknown.

Key points include:

  1. PD-L1 Cutoffs: BMS argues against setting higher PD-L1 cutoffs (such as CPS ≥10), as this would exclude a significant proportion of patients (~40-50%) who might still benefit from the treatment. Instead, BMS prefers the flexibility of the CPS ≥1 cutoff to maximize patient access.
  2. Real-World Data: BMS points to real-world data indicating that about 73% of patients receiving nivolumab are tested for PD-L1, and most test positive (CPS ≥1). This suggests that PD-L1 testing is being used effectively to inform treatment decisions.
  3. Safety: The safety profile of nivolumab plus chemotherapy is well-understood, with manageable immune-related adverse events (mostly low-grade), and no new safety concerns have emerged.

Differences Between FDA and BMS Viewpoints

  1. PD-L1 Testing and Cutoffs:

    • FDA: The FDA is leaning toward revising labeling to include PD-L1 status as a criterion for treatment, especially for patients with higher expression (≥10 CPS). It raises concerns about administering ICIs to patients with low or no PD-L1 expression due to the potential for harm without clear efficacy.
    • BMS: BMS advocates for maintaining the existing labeling, which allows nivolumab use based on a CPS ≥1 cutoff. BMS argues that higher cutoffs would exclude too many patients and that current labeling offers sufficient flexibility for clinicians to decide on treatment, even for patients with low PD-L1 expression.

  2. Risk-Benefit Analysis:

    • FDA: The FDA's focus is on ensuring that only patients who are likely to benefit from ICIs (based on PD-L1 levels) receive treatment, to avoid unnecessary exposure to immune-related adverse events.
    • BMS: BMS emphasizes the importance of broad access to nivolumab, noting that even patients with lower PD-L1 levels or unknown status may benefit. They highlight that real-world data support the efficacy of the treatment in a broader patient population.

  3. Regulatory Perspective:

    • FDA: The FDA is considering harmonizing PD-L1 cutoffs across different trials and drugs to streamline patient selection and ensure consistency in treatment. This may lead to more restrictive labeling.
    • BMS: BMS argues for keeping the flexibility in the current labeling to ensure that clinicians can make case-by-case decisions, particularly given the variability in PD-L1 testing in real-world practice.

Implications for Oncologists

For oncologists, interpreting both FDA and BMS viewpoints involves navigating complex clinical and regulatory landscapes:

  1. Patient Selection: Oncologists must weigh FDA's potential move toward more restrictive labeling based on PD-L1 expression against BMS's more flexible approach. In practice, this means that oncologists need to carefully evaluate each patient's pathology report and PD-L1 status to decide whether ICIs are appropriate.

  2. PD-L1 Testing: With differing assays and cutoffs used in clinical trials, oncologists will need to ensure that the PD-L1 testing method used is appropriate and reliable. Differences in assay performance may affect patient selection for treatment, complicating decision-making.

  3. Safety Considerations: Oncologists must also consider the risk of immune-related adverse events, particularly in patients with low or no PD-L1 expression. They may have to balance the potential for benefit against the risk of harm, especially as FDA highlights that patients with low PD-L1 expression may not see significant survival benefits.

  4. Literature and Label Interpretation: Given the evolving regulatory landscape, oncologists must stay informed about updates to drug labeling and clinical guidelines. They may need to consult both FDA guidance and manufacturer recommendations (like BMS's stance) to ensure they are making evidence-based decisions for their patients.

In conclusion, oncologists must navigate differing interpretations of trial data and regulatory guidance, considering both the potential benefits and risks of ICIs based on individual patient profiles, particularly PD-L1 expression levels.

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I asked SCITE.AI to write an automatic mini-essay based on its review of recent PubMed hits on the topic of PDL1 harmonization.

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Recent literature on the harmonization of PD-L1 testing for checkpoint inhibitor therapy reveals significant challenges and inconsistencies that impact clinical decision-making. The expression of PD-L1 is a critical biomarker for predicting the efficacy of immune checkpoint inhibitors (ICIs), particularly in cancers such as non-small cell lung cancer (NSCLC) and melanoma. However, the lack of standardized cutoffs for PD-L1 expression complicates its utility as a reliable predictive biomarker. For instance, while pembrolizumab is approved for patients with NSCLC showing PD-L1 expression greater than 50%, studies indicate that even within this group, the response rate can be less than optimal, suggesting that PD-L1 expression alone may not be sufficient to predict treatment outcomes (Maung et al., 2020; , Wang et al., 2020).

Moreover, the variability in PD-L1 testing methodologies further exacerbates the issue. Different assays and scoring systems can yield divergent results, leading to uncertainty regarding patient eligibility for ICI therapies. For example, Kaufman et al. emphasize the need for functional assays to assess PD-L1 binding capabilities, which may provide a more accurate prediction of therapeutic response than mere expression levels (Kaufman et al., 2023; , Kaufman, 2023). This highlights the critical need for harmonization in PD-L1 testing protocols to ensure consistent and reliable results across different laboratories and clinical settings.

 Additionally, recent studies have pointed out the unreliability of PD-L1 testing due to biological variability. Zou et al. found that PD-L1 expression levels can fluctuate based on tumor microenvironmental factors, which may not be captured in a single biopsy (Zou et al., 2021). This variability raises questions about the timing and method of PD-L1 assessment, suggesting that a more dynamic approach may be necessary to accurately gauge a tumor's immunogenicity and potential responsiveness to ICIs (Chen et al., 2021).

 The implications of these findings are profound, as they suggest that reliance on PD-L1 expression as a sole criterion for treatment decisions may lead to suboptimal patient outcomes. For instance, the study by Kim et al. indicates that while PD-L1 expression correlates with certain clinicopathological features, it does not consistently predict therapeutic efficacy across all cancer types (Kim et al., 2020). This inconsistency underscores the necessity for integrating additional biomarkers and clinical parameters to refine patient selection for ICI therapies.

 In conclusion, while PD-L1 remains a pivotal biomarker in the context of immunotherapy, the current landscape reveals significant challenges related to its testing and interpretation. The absence of standardized cutoffs and the variability in testing methodologies necessitate a concerted effort towards harmonization to enhance the predictive power of PD-L1 as a biomarker for checkpoint inhibitor therapy.

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References:

Chen, S., Zhang, Z., Zheng, X., Tao, H., Zhang, S., Ma, J., … & Hu, Y. (2021). Response efficacy of pd-1 and pd-l1 inhibitors in clinical trials: a systematic review and meta-analysis. Frontiers in Oncology, 11. https://doi.org/10.3389/fonc.2021.562315

Kaufman, B. (2023). Functional binding of pd1 ligands predicts response to anti-pd1 treatment in patients with cancer. Science Advances, 9(21). https://doi.org/10.1126/sciadv.adg2809

Kaufman, B., Abramov, O., Yevko, A., Apple, D., Shlapobersky, M., Greenshpan, Y., … & Porgador, A. (2023). Functional binding of pd1 ligands predicts response to anti-pd1 treatment in cancer patients.. https://doi.org/10.1101/2023.02.09.527671

Kim, J., Kim, K., Kim, M., Kim, Y., Suh, J., Jeong, H., … & Choi, H. (2020). Programmed death-ligand 1 expression and its correlation with clinicopathological parameters in gallbladder cancer. Journal of Pathology and Translational Medicine, 54(2), 154-164. https://doi.org/10.4132/jptm.2019.11.13

Maung, T., Ergin, H., Javed, M., Inga, E., & Khan, S. (2020). Immune checkpoint inhibitors in lung cancer: role of biomarkers and combination therapies. Cureus. https://doi.org/10.7759/cureus.8095

Wang, M., Wang, S., Trapani, J., & Neeson, P. (2020). Challenges of pd-l1 testing in non-small cell lung cancer and beyond. Journal of Thoracic Disease, 12(8), 4541-4548. https://doi.org/10.21037/jtd-2019-itm-010

Zou, Y., Chen, Z., Han, H., Ruan, S., Jin, L., Zhang, Y., … & Jin, H. (2021). Risk signature related to immunotherapy reaction of hepatocellular carcinoma based on the immune-related genes associated with cd8+ t cell infiltration. Frontiers in Molecular Biosciences, 8. https://doi.org/10.3389/fmolb.2021.602227

SCITE.AI Prompt.  Summarize recent papers on PDL1 harmonization for making decisions to use checkpoint inhibitor drugs. Focus on 2020 and newer papers. Note papers that discuss the lack of clear cutpoints for PDL1 expression or unreliability of PDL1 testing.