Header: Via the Federal Register, FDA announces an advisory committee on September 26, 2024, to discuss PDL1. The focus will be on its use with checkpoint inhibitor therapies in gastric cancer.
This blog provides links; a Chat GPT generated summary; and a Scite.ai generated mini-review. It closes with a Chat GPT comparison of two versions of the Scite.ai output.
Links:
FDA in Fed Reg:
https://public-inspection.federalregister.gov/2024-18970.pdf
Coverage in STAT:
https://endpts.com/fda-to-host-adcomm-on-pd-l1-as-a-predictive-biomarker-for-checkpoint-inhibitors/
New Nature Cancer paper (Bergstrom) on better biomarkers for immuno-oncology, here.
AI CORNER
Chat GPT 4o:
The FDA has scheduled an important advisory committee meeting on September 26, 2024, to discuss the use of immune checkpoint inhibitors in treating unresectable or metastatic gastric and gastroesophageal junction adenocarcinoma, as well as esophageal squamous cell carcinoma.
This meeting is of particular interest to those involved in oncology and biomarker research, as it will address the role of PD-L1 expression as a predictive [drug selection] biomarker for these treatments.
Meeting Objectives
The Oncologic Drugs Advisory Committee (ODAC) will evaluate several key topics during the meeting:
- PD-L1 as a Predictive Biomarker: The committee will examine the current evidence supporting PD-L1 expression as a biomarker for predicting the efficacy of immune checkpoint inhibitors in these cancer types.
- Subpopulation Risk-Benefit Analysis: The committee will consider how differences in PD-L1 expression might affect the risk-benefit profile of these treatments across various patient subgroups.
- [For example, if high expressors have a 50% chance of response, and low expressors have a 30% chance of response, they might both be treated, but the risk/benefit profile is stronger for high expressors. BQ]
- Consideration of Approval Restrictions: The discussion will also focus on whether the FDA should limit the approval of immune checkpoint inhibitors to patients with specific PD-L1 expression levels.
Relevance to Biomarker Development
The outcomes of this meeting may have implications for the future of biomarker-driven treatment strategies, particularly in refining the use of immune checkpoint inhibitors based on PD-L1 expression. This could impact ongoing and future research efforts aimed at improving the precision and effectiveness of cancer immunotherapies.
Participation and Comments
The FDA invites public participation in this meeting. Stakeholders can submit comments electronically or in writing until September 25, 2024, and may attend the meeting either in person at the FDA's White Oak Campus or virtually. The FDA has established a public docket to collect and consider these comments.
Conclusion
This FDA advisory committee meeting will address critical regulatory questions regarding the use of PD-L1 as a biomarker in the treatment of certain cancers. The discussion and any resulting decisions could influence the development and application of more targeted cancer therapies in the future.
SCITE.AI Mini Review (Auto)
Note that below this article and citations, there is another one, focused on 2022-2023-2024.
The role of PD-L1 as a predictive biomarker for checkpoint inhibitor therapy in gastric and esophageal cancers has garnered significant attention in recent years. PD-L1 expression is often evaluated to predict patient responses to immune checkpoint inhibitors (ICIs), particularly in the context of gastric and esophageal adenocarcinomas. However, the predictive value of PD-L1 is complex and varies across studies and clinical settings.
PD-L1 expression has been recognized as a potential biomarker for selecting patients who may benefit from PD-1/PD-L1 inhibitors. In gastric cancer, studies have indicated that PD-L1 overexpression can be found in approximately 40% of cases, suggesting that a substantial subset of patients may be candidates for immunotherapy (Shaib et al., 2016). However, the predictive capability of PD-L1 alone is often described as modest. For instance, the KEYNOTE-061 trial demonstrated that pembrolizumab did not significantly improve overall survival in gastric cancer patients, even among those with high PD-L1 expression (Cheong et al., 2022). This finding underscores the need for additional biomarkers or combined approaches to enhance predictive accuracy.
Moreover, the clinical utility of PD-L1 as a standalone biomarker is limited due to variability in testing methodologies and interpretation of results. Different immunohistochemistry (IHC) assays and scoring systems have been developed, but discrepancies in cut-off thresholds and inter-reader concordance remain challenges (Alderdice et al., 2021; Lu et al., 2019). This variability can lead to inconsistent patient stratification for ICI therapy, emphasizing the necessity for more robust and standardized biomarker assessments.
In addition to PD-L1, other biomarkers such as microsatellite instability (MSI) and tumor mutational burden (TMB) are also critical in predicting responses to ICIs. MSI-H status, for example, has been associated with favorable responses to PD-1 inhibitors in various cancers, including gastric cancer (Chao et al., 2021). The integration of PD-L1 testing with these other biomarkers may provide a more comprehensive predictive framework, potentially improving patient outcomes.
Recent research has also explored the functional aspects of PD-L1 binding to its receptor PD-1 as a predictor of response to therapy. This approach aims to assess not just the presence of PD-L1 but its functional activity, which may correlate more closely with therapeutic efficacy (Herpe & Cutsem, 2023). Such advancements could refine patient selection and enhance the effectiveness of immunotherapy in gastric and esophageal cancers.
In conclusion, while PD-L1 serves as a valuable biomarker for predicting responses to checkpoint inhibitor therapy in gastric and esophageal cancers, its predictive power is enhanced when considered alongside other biomarkers and functional assessments. Ongoing research is essential to establish more reliable and standardized approaches for biomarker evaluation, ultimately leading to improved patient stratification and therapeutic outcomes.
References:
Alderdice, M., Craig, S., Humphries, M., Gilmore, A., Johnston, N., Bingham, V., … & McArt, D. (2021). Evolutionary genetic algorithm identifies il2rb as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer. Nar Genomics and Bioinformatics, 3(2). https://doi.org/10.1093/nargab/lqab016
Chao, J., Fuchs, C., Shitara, K., Tabernero, J., Muro, K., Cutsem, É., … & Wainberg, Z. (2021). Assessment of pembrolizumab therapy for the treatment of microsatellite instability–high gastric or gastroesophageal junction cancer among patients in the keynote-059, keynote-061, and keynote-062 clinical trials. Jama Oncology, 7(6), 895. https://doi.org/10.1001/jamaoncol.2021.0275
Cheong, J., Wang, S., Park, S., Porembka, M., Christie, A., Kim, H., … & Hwang, T. (2022). Development and validation of a prognostic and predictive 32-gene signature for gastric cancer. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-28437-y
Herpe, F. and Cutsem, É. (2023). The role of cmet in gastric cancer—a review of the literature. Cancers, 15(7), 1976. https://doi.org/10.3390/cancers15071976
Lu, S., Stein, J., Rimm, D., Wang, D., Bell, M., Johnson, D., … & Taube, J. (2019). Comparison of biomarker modalities for predicting response to pd-1/pd-l1 checkpoint blockade. Jama Oncology, 5(8), 1195. https://doi.org/10.1001/jamaoncol.2019.1549
Shaib, W., Nammour, J., Gill, H., Mody, M., & Saba, N. (2016). The future prospects of immune therapy in gastric and esophageal adenocarcinoma. Journal of Clinical Medicine, 5(11), 100. https://doi.org/10.3390/jcm5110100
PLEASE FOCUS ONLY ON 2022-2023-2024 PAPERS
The predictive value of PD-L1 biomarkers in checkpoint inhibitor therapy for gastric and esophageal cancers has been a focal point of recent research. PD-L1 expression is often used to guide treatment decisions for immune checkpoint inhibitors (ICIs), particularly in advanced gastric and esophageal cancers. However, the efficacy of PD-L1 as a standalone predictive biomarker is nuanced and varies across studies.
Recent literature indicates that PD-L1 expression correlates with the response to ICIs in gastric cancer. For instance, a study by highlights that a PD-L1 combined positive score (CPS) of ≥5 is associated with increased efficacy of PD-1 checkpoint inhibitors in advanced gastric cancer (Jang et al., 2023). This suggests that PD-L1 can serve as a useful biomarker for patient selection in this context. However, the predictive capability of PD-L1 is often described as modest. noted that while PD-L1 expression is a common criterion for therapy selection, its predictive power alone is limited, as evidenced by the KEYNOTE-061 trial, which found no significant survival benefit from pembrolizumab in patients with high PD-L1 expression (Cheong et al., 2022).
In esophageal cancer, the relationship between PD-L1 expression and treatment outcomes is similarly complex. The JUPITER-06 trial demonstrated that PD-L1 levels on tumor cells positively correlated with the efficacy of PD-1 blockade combined with chemotherapy (Wang et al., 2022). This finding reinforces the notion that PD-L1 can be a valuable biomarker in this setting, although the variability in expression levels and the existence of other predictive factors complicate its use.
Moreover, the integration of PD-L1 testing with other biomarkers, such as tumor mutational burden (TMB) and microsatellite instability (MSI), is gaining traction. emphasized that TMB status and PD-L1 expression are independent biomarkers that can enhance predictive accuracy for PD-1 inhibitors (Jang et al., 2023). This multi-biomarker approach may improve patient stratification and therapeutic outcomes in both gastric and esophageal cancers.
Furthermore, emerging research is exploring the functional aspects of PD-L1 binding to PD-1 as a predictor of treatment response. suggest that the functional binding of PD-L1 ligands may provide a more accurate prediction of response to anti-PD-1 treatments, highlighting the need for further investigation into the functional dynamics of PD-L1 in the tumor microenvironment (Kaufman et al., 2023; Kaufman, 2023). This perspective could lead to more refined predictive models that incorporate both expression levels and functional activity.
In summary, while PD-L1 serves as a significant biomarker for predicting responses to checkpoint inhibitor therapy in gastric and esophageal cancers, its predictive value is enhanced when considered alongside other biomarkers and functional assessments. Ongoing research is essential to establish more standardized and reliable approaches for biomarker evaluation, ultimately leading to improved patient outcomes.
References:
Alderdice, M., Craig, S., Humphries, M., Gilmore, A., Johnston, N., Bingham, V., … & McArt, D. (2021). Evolutionary genetic algorithm identifies il2rb as a potential predictive biomarker for immune-checkpoint therapy in colorectal cancer. Nar Genomics and Bioinformatics, 3(2). https://doi.org/10.1093/nargab/lqab016
Chao, J., Fuchs, C., Shitara, K., Tabernero, J., Muro, K., Cutsem, É., … & Wainberg, Z. (2021). Assessment of pembrolizumab therapy for the treatment of microsatellite instability–high gastric or gastroesophageal junction cancer among patients in the keynote-059, keynote-061, and keynote-062 clinical trials. Jama Oncology, 7(6), 895. https://doi.org/10.1001/jamaoncol.2021.0275
Cheong, J., Wang, S., Park, S., Porembka, M., Christie, A., Kim, H., … & Hwang, T. (2022). Development and validation of a prognostic and predictive 32-gene signature for gastric cancer. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-28437-y
Cheong, J., Wang, S., Park, S., Porembka, M., Christie, A., Kim, H., … & Hwang, T. (2022). Development and validation of a prognostic and predictive 32-gene signature for gastric cancer. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-28437-y
Herpe, F. and Cutsem, É. (2023). The role of cmet in gastric cancer—a review of the literature. Cancers, 15(7), 1976. https://doi.org/10.3390/cancers15071976
Jang, J., Jeon, Y., Jeong, S., Lim, S., Kang, W., Lee, J., … & Kim, S. (2023). The optimal tumor mutational burden cutoff value as a novel marker for predicting the efficacy of programmed cell death-1 checkpoint inhibitors in advanced gastric cancer. Journal of Gastric Cancer, 23(3), 476. https://doi.org/10.5230/jgc.2023.23.e29
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
Lu, S., Stein, J., Rimm, D., Wang, D., Bell, M., Johnson, D., … & Taube, J. (2019). Comparison of biomarker modalities for predicting response to pd-1/pd-l1 checkpoint blockade. Jama Oncology, 5(8), 1195. https://doi.org/10.1001/jamaoncol.2019.1549
Shaib, W., Nammour, J., Gill, H., Mody, M., & Saba, N. (2016). The future prospects of immune therapy in gastric and esophageal adenocarcinoma. Journal of Clinical Medicine, 5(11), 100. https://doi.org/10.3390/jcm5110100
Wang, Z., Cui, C., Yao, J., Zhang, Y., Li, M., Feng, J., … & Wang, F. (2022). Toripalimab plus chemotherapy in treatment-naïve, advanced esophageal squamous cell carcinoma (jupiter-06): a multi-center phase 3 trial. Cancer Cell, 40(3), 277-288.e3. https://doi.org/10.1016/j.ccell.2022.02.007
CHAT GPT 4o:
Please compare the FIRST SCITE article and the SECOND SCITE (2022 2023 2024) article.
Comparison of Data and Conclusions in ARTICLE ONE and ARTICLE TWO
Both ARTICLE ONE and ARTICLE TWO focus on the role of PD-L1 as a predictive biomarker for checkpoint inhibitor therapy in gastric and esophageal cancers, but they approach the topic with slight differences in emphasis and scope.
1. Predictive Value of PD-L1 as a Standalone Biomarker:
- ARTICLE ONE emphasizes that PD-L1 is recognized as a potential biomarker, but its predictive capability is often described as modest. The article points out the variability in results across different studies and clinical settings. It highlights the KEYNOTE-061 trial, which found that pembrolizumab did not significantly improve overall survival in gastric cancer patients with high PD-L1 expression, suggesting that PD-L1 alone may not be sufficient as a predictive biomarker.
- ARTICLE TWO echoes this sentiment, acknowledging that while PD-L1 expression correlates with responses to ICIs, its predictive power alone is limited. It also references the KEYNOTE-061 trial, underscoring the modest nature of PD-L1’s predictive capability in both gastric and esophageal cancers.
2. Variability and Limitations in PD-L1 Testing:
- ARTICLE ONE discusses the limitations of PD-L1 as a biomarker due to variability in testing methodologies, such as differences in immunohistochemistry (IHC) assays and scoring systems. This variability leads to challenges in consistent patient stratification for ICI therapy.
- ARTICLE TWO similarly notes the complexity of using PD-L1 as a standalone biomarker due to variability in expression levels and other predictive factors. However, it focuses more on the need to integrate PD-L1 testing with other biomarkers to improve predictive accuracy.
3. Integration with Other Biomarkers:
- ARTICLE ONE emphasizes the importance of considering other biomarkers, such as microsatellite instability (MSI) and tumor mutational burden (TMB), alongside PD-L1 to improve the predictive framework. The article suggests that a combined approach might enhance patient outcomes and addresses the need for more comprehensive assessments.
- ARTICLE TWO also stresses the value of integrating PD-L1 with other biomarkers like TMB and MSI. It highlights recent literature that supports a multi-biomarker approach, which could potentially improve patient stratification and therapeutic outcomes in gastric and esophageal cancers.
4. Functional Aspects of PD-L1:
- ARTICLE ONE touches on emerging research exploring the functional aspects of PD-L1 binding to its receptor PD-1 as a more accurate predictor of therapy response. This research could refine patient selection and enhance immunotherapy effectiveness.
- ARTICLE TWO delves deeper into this area, suggesting that the functional binding of PD-L1 ligands might offer a more precise prediction of response to PD-1 inhibitors. The article cites ongoing research that could lead to the development of more refined predictive models incorporating both expression levels and functional activity.
5. Conclusion:
- ARTICLE ONE concludes that while PD-L1 is valuable as a biomarker, its predictive power is enhanced when used alongside other biomarkers and functional assessments. The article emphasizes the need for ongoing research to establish more standardized approaches for biomarker evaluation.
- ARTICLE TWO reaches a similar conclusion, affirming the importance of PD-L1 as a significant biomarker while advocating for a more comprehensive approach that includes other biomarkers and functional assessments. The article also underscores the necessity of further research to improve biomarker reliability and patient outcomes.
Summary of Comparison:
Both articles agree on the modest predictive value of PD-L1 as a standalone biomarker and advocate for its integration with other biomarkers to enhance accuracy. They both highlight the variability in PD-L1 testing and the potential of functional assessments to improve predictive models. The articles conclude with a similar call for ongoing research to refine and standardize biomarker evaluation methods, ultimately aiming to improve patient stratification and therapeutic outcomes in gastric and esophageal cancers.
