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Checkpoint Inhibitors in Precision Medicine: Trials and Tribulations

December 2022

Checkpoint Inhibitors in Precision Medicine: Trials and Tribulations

The New England Journal of Medicine recently published results from a phase 2 cancer trial that would have been unthinkable 20 years ago.1 The drug dostarlimab was administered to patients with stage 2 or 3 locally advanced rectal cancer. One hundred percent of the 12 patients treated with the drug had a clinical complete response, with no evidence of tumor detected by magnetic resonance imaging, 18F-fluorodeoxyglucose–positron-emission tomography, endoscopic evaluation, digital rectal examination, or biopsy. This allowed patients to avoid both chemoradiotherapy and surgery, which can have permanent effects on fertility, sexual health, and bowel and bladder function. Furthermore, no cases of progression or recurrence were reported up to 25 months after treatment, and no adverse events of grade 3 or higher were reported.

The Rise of Checkpoint Inhibitors 

Dostarlimab is a type of immuno-oncology drug called a checkpoint inhibitor. Prior to immuno-oncology, the standard nonsurgical treatment for cancer was chemotherapy or radiotherapy, which often destroys healthy cells. Checkpoint inhibitors work by taking the brake off the immune system and enabling T cells to do what they do best: destroy harmful cells. Dostarlimab targets the programmed cell death 1 receptor (PD-1), a protein found on the surface of T cells. Binding of PD-1 to its corresponding ligand, PD-L1, on the surface of another cell stops the T cell from destroying that cell. This process is shown in the graphic below. This prevents the body from attacking healthy cells. However, cancer cells often overexpress PD-L1, leading to immune evasion. 

PD-L1 Receptor Diagram

To date, the FDA has approved six PD-1/PD-L1 drugs, as well as one for the T-cell protein CTLA-4 (Table 1).2 These drugs have improved considerably the outcome for some patients with certain aggressive cancers, such as metastatic melanoma and non-small cell lung cancer. Since approval of the first checkpoint inhibitor in 2010, the 4-year overall survival rate for patients presenting with grade 4 melanoma has increased by 31%.3  In 2021, the size of the global checkpoint inhibitor drug market was estimated at $31.4 billion.4 This is expected to grow by 18.81% between 2022 and 2030, driven by a high level of research and development as well as a global increase in cancer rates.

Table 1: Checkpoint Inhibitors Approved by the FDA for Use as Single Agents  
Drug Target Approval FDA-Approved Indications
Ipilimumab  CTLA-4  August 2010  Stage III or IV malignant melanoma  
Pembrolizumab  PD-1  September 2014  Advanced melanoma 
Nivolumab  PD-1  March 2015  Stage III-B or IV squamous NSCLC  
Durvalumab  PD-L1  February 2016  Stage III NSCLC 
Atezolizumab  PD-L1  October 2016  Stage III-B or IV nonsquamous and squamous NSCLC 
Avelumab  PD-L1 March 2017 Historically confirmed metastatic Merkel cell carcinoma 
Cemiplimab  PD-1  September 2018  Metastatic cutaneous squamous cell cancer 

NSCLC: non-small cell lung cancer.
Adapted from Shiravand et al, 2022.

A Targeted Approach

However, checkpoint inhibitors only work on 20-30% of cancers.5 In the rectal cancer study, patients were selected who had rectal tumors with a high degree of deficient mismatched repair (dMMR), about 5-10% of all rectal adenocarcinomas. dMMR has previously been shown to be a good predictor of response to treatment with a checkpoint inhibitor for certain cancers and is one of three so-called biomarkers approved by the FDA. Other FDA-approved biomarkers for checkpoint inhibitors include PD-L1 expression and tumor mutational burden. While some patients with high levels of PD-L1 tumor expression respond well to treatment with a checkpoint inhibitor, many do not, and tumors can become resistant to treatment over time.6 Treatment with a combination of a PD-1/PD-L1 inhibitor and another checkpoint inhibitor can help overcome resistance, as was shown by the recent phase 2 study of relatlimab (a LAG-3 checkpoint inhibitor) and nivolumab in patients with untreated advanced melanoma.7 In this study, an immunohistochemistry assay was used to select patients with tumors that expressed both PD-L1 and LAG-3 checkpoint proteins.

DNA

Precision medicine, where biomarkers are used to match patients with a drug that will produce the best outcome, is increasingly part of treatment with checkpoint inhibitors, and identifying new biomarkers that can be targeted is an active area of research.8 In the rectal cancer study, investigators hypothesized that patients’ gut biomes played a role in the high response rate. This was also seen in a recent paper presented at ASCO 2022 that showed that patients who responded best to treatment with a checkpoint inhibitor had high levels of a specific gut bacteria.9 Other biomarkers that have been shown to predict the efficacy of treatment with a checkpoint inhibitor include killer gene expression, T-cell infiltration, and circulating tumor DNA.

The Changing Face of Clinical Trials 

Precision medicine is changing how clinical trials with checkpoint inhibitors are designed.10 Trials are shifting from a tumor-centered approach to a patient-centered approach, where treatment is selected on the basis of a patient’s tumor biomarker.

Master protocols that include multiple substudies are rapidly increasing. These can produce better outcomes than traditional trials, which can lead to FDA approval at an earlier stage. For example, pembrolizumab received FDA approval after a series of five single-arm phase 2 trials in which patients with diverse microsatellite unstable or dMMR tumors were treated with the drug. An objective response rate was observed in 39.6% of patients, and a response duration of >6 months was observed in 78% of patients.

One type of master protocol is a platform trial, which allows evaluation of multiple hypotheses in a single protocol. Investigational arms may be added or deleted during the trial as results come in. Platform trials can be more efficient than other types of trial, with savings in both time and cost. However, the statistical analysis can also be more complicated. Multiple interim analyses may be required. The population can be very large and diverse; this can generate a large volume of complex data, which can be a challenge to analyze. Long-term follow-up assessments may also be needed.

Technical Resources International Inc. (TRI) has experience providing statistical support for oncology clinical trials, including trials with master protocols. TRI’s PhD-level statisticians are adept at determining the most suitable experimental design and analytic approach for the study objectives. We provide power calculations, statistical analysis plans, interim analysis reports, and final clinical study reports. In addition to statisticians, TRI also has a robust safety department with staff experienced in providing pharmacovigilance support for many types of clinical trial.


About the Author

Dr. Katya Vines has a PhD in Organic Chemistry from the University of Liverpool, UK, and is the writer of several scientific papers, textbooks, and blog posts. As a Clinical Research Specialist at TRI, Dr Vines provides scientific, clinical, and regulatory writing support for phase 1-3 clinical trials across oncology and infectious disease disciplines.


Footnotes

  1. Cercek, A., Lumish, M., Sinopoli, J., Weiss, J., Shia, J., Lamendola-Essel, M., El Dika, I.H., Segal, N., Shcherba, M., Sugarman, R., Stadler, Z., Yaeger, R., Smith, J.J., Rousseau, B., Argiles, G., Patel, M., Desai, A., Saltz, L.B., Widmar, M., . . . Diaz, L.A., Jr. (2022). PD-1 blockade in mismatch repair-deficient, locally advanced rectal cancer. New England Journal of Medicine, 386(25). 2363-2376. https://doi.org/10.1056/NEJMoa2201445
  2. Shiravand, Y., Khodadadi, F., Kashani, S.M.A., Hosseini-Fard, S.R., Hosseini, S., Sadeghirad, H., Ladwa, R., O'Byrne, K., & Kulasinghe, A. (2022). Immune checkpoint inhibitors in cancer therapy. Current Oncology, 29(5). 3044-3060. https://doi.org/10.3390/curroncol29050247
  3.   
  4. Dobry, A.S., Zogg, C.K., Hodi, F.S., Smith, T.R., Ott, P.A., & Iorgulescu, J.B. (2018). Management of metastatic melanoma: improved survival in a national cohort following the approvals of checkpoint blockade immunotherapies and targeted therapies.  Cancer Immunology, Immunotherapy, 67. 1833–1844. https://doi.org/10.1007/s00262-018-2241-x
  5.   
  6. Precedence Research. (2021). Immune Checkpoint Inhibitors Market – Global Industry Analysis, Size, Share, Growth, Trends, Regional Outlook, and Forecast 2022-2030. https://www.precedenceresearch.com/immune-checkpoint-inhibitors-market
  7.   
  8. Peterson, C., Denlinger, N., & Yang, Y. Recent advances and challenges in cancer immunotherapy. (2022). Cancers, 14(16). 3972. https://doi.org/10.3390/cancers14163972
  9.   
  10. Barrueto, L., Caminero, F., Cash, L., Makris, C., Lamichhane, P., & Deshmukh, R.R. (2020). Resistance to checkpoint inhibition in cancer immunotherapy. Translational Oncology, 13(3). 100738. https://doi.org/10.1016/j.tranon.2019.12.010
  11.   
  12. Tawbi, H.A., Schadendorf, D., Lipson, E.J., Ascierto, P.A., Matamala, L., Castillo Gutiérrez, E., Rutkowski, P., Gogas, H.J., Lao, C.D., De Menezes, J.J., Dalle, S., Arance, A., Grob, J.J., Srivastava, S., Abaskharoun, M., Hamilton, M., Keidel, S., Simonsen, K.L., Sobiesk, A.M., . . . Long, G.V. (2022). Relatlimab and nivolumab versus nivolumab in untreated advanced melanoma. New England Journal of Medicine, 386(1). 24-34. https://doi.org/10.1056/NEJMoa2109970
  13.   
  14. Lei, Y., Li, X., Huang, Q., Zheng, X., & Liu, M. (2021). Progress and challenges of predictive biomarkers for immune checkpoint blockade. Frontiers in Oncology, 11. 617335. https://doi.org/10.3389/fonc.2021.617335
  15.   
  16. Nassif, E.F., Chelvanambi, M., Chen, L., Wu, C.-C., Damania, A.,  Keung, E.Z.-Y., Witt, R.G., White, M.,  Ajami, N.J., Wong, M.C., Somaiah, N., Sepesi, B., Basu, S., Allison, J.P., Sharma, P., McBride, K., Fridman, W.-H.,  Wargo, J.A., Cascone, T., & Roland, C.L. (2022). Journal of Clinical Oncology 40 (16_suppl). 2511-2511. https://doi.org/10.1200/JCO.2022.40.16_suppl.2511
  17.   
  18. Fountzilas, E., Tsimberidou, A.M., Vo, H.H., & Kurzrock, R. (2022). Clinical trial design in the era of precision medicine. Genome Medicine, 14(1). 101. http://doi.org/10.1186/s13073-022-01102-1
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