The Use of Immunomodulation in Treating Thyroid Cancer
Bibliographic record
Abstract
Thyroid Cancer: Thyroid CancerResults of a multicenter Phase II clinical trial led by researchers at the University of Chicago Medicine Comprehensive Cancer Center show that adding an immunomodulatory agent to treatment with the targeted tyrosine kinase inhibitor (TKI) cediranib did not make a difference in outcomes for treating patients with an advanced form of thyroid cancer that develops from thyroid follicular cells called differentiated thyroid cancer (DTC) (Ann Oncol 2023; doi: 10.1016/j.annonc.2023.05.002). Most patients with DTC receive successful treatment. However, a small group develops cancer that recurs or spreads to other parts of the body, making it hard to treat with traditional methods like surgery, hormonal therapies, chemotherapy, and radioactive iodine therapy. In recent years, there has been an increased interest in treating these patients with TKIs that target vascular endothelial growth factor receptor (VEGFR) signaling in the tumor microenvironment. These potent angiogenesis mediators target the formation of new blood cells, which plays an important role in the progression of thyroid cancer. UChicago Medicine oncologists Ari Rosenberg, MD, and Everett Vokes, MD, set out to test the safety and efficacy of cediranib, a TKI that targets multiple VEGFRs. Furthermore, the researchers hypothesized that this group of patients would have an additional benefit if the drug was combined with an immunomodulatory agent known as lenalidomide. This drug is also known to block angiogenesis and has anti-tumor properties in other cancers, and early clinical trials indicated that it exhibits similar activity against DTC. Recent combination therapies consisting of traditional therapies and immunotherapies have yielded superior results in many cancer conditions. “With this scope, we set out to evaluate whether immunomodulation by lenalidomide in combination with cediranib would improve disease-free survival over cediranib alone,” said Rosenberg, Assistant Professor in the Section of Hematology and Oncology at the UChicago Medicine Comprehensive Cancer Center. Study Details In a Phase II clinical trial, 108 patients were enrolled from various hospitals in the United States and Canada. They were randomly assigned to either of the two treatment arms: 39 patients in the cediranib alone group and 69 patients in the cediranib with lenalidomide group. The cediranib alone group achieved median progression-free survival (PFS) of 14.8 months, and 44 percent of patients had a complete or partial disappearance of tumor as assessed by objective response rate (ORR). Surprisingly, the addition of lenalidomide to cediranib didn't prove to be any better than treatment with cediranib alone. “There are many recent examples in the literature demonstrating the combination of VEGF-targeted TKIs and immunotherapeutic strategies can be very effective, both preclinically and clinically, in multiple cancer conditions, like renal cell carcinoma and hepatocellular carcinoma,” Rosenberg said. “And yet, this strategy does not appear to work in this particular disease with lenalidomide.” Final analysis of the data demonstrated that cediranib is an active agent, which means that the ORR and PFS of cediranib appear similar to other approved VEGFR-targeted tyrosine kinase inhibitors in DTC, including lenvatinib, sorafenib, and vandetanib. Rosenberg added that this study identifies the importance of randomized trial design, as single-arm studies can yield false-positive results and immunomodulation has been disappointing thus far in thyroid cancer. “The most important point of this study is that the addition of lenalidomide, an immunomodulatory agent, didn't improve the progression-free survival over cediranib alone, despite what appeared to be promising single-agent activity in DTC, and should not be combined with VEGFR-targeted TKIs,” he noted. Despite the advances in the field since the time of conceptualization and the enrollment of patients from 2010-2015, the implications remain quite relevant as outcomes beyond single VEGFR-targeted TKIs in DTC have not improved, Rosenberg added. “Our study results highlight an unmet need with the current strategies of harnessing the body's immune system to treat thyroid cancer and the need to evaluate new combinations and new mechanisms, in particular new immunotherapeutic strategies that may be more effective than immunomodulatory strategies or immune checkpoint inhibitors alone,” he said.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".