Predicting Who Will Respond to Lenvatinib in Endometrial Cancer
The key to personalized cancer therapy might lie in our own blood and tissue.
Imagine a future where, before starting a powerful cancer treatment, a doctor can know with high certainty whether it will work for you. This is the promise of predictive biomarkers—biological clues that can forecast treatment response. For patients with advanced endometrial cancer, the combination therapy of lenvatinib and pembrolizumab has emerged as a significant treatment option. However, with only a subset of patients experiencing long-term benefits, the race is on to find reliable biomarkers that can guide treatment decisions and maximize outcomes while minimizing unnecessary side effects.
Endometrial cancer, which originates in the lining of the uterus, is the most common gynecological malignancy in developed nations. While often detected early, advanced or recurrent disease has a much poorer prognosis, with five-year survival rates dropping below 20% 5 7 .
The combination of lenvatinib, a multi-kinase inhibitor that blocks tumor growth signals and blood vessels, and pembrolizumab, an immunotherapy that reactivates the immune system against cancer, has shown meaningful clinical benefits. In a pivotal clinical trial, this combination demonstrated a 38% objective response rate in previously treated patients 9 .
However, this also means that a majority of patients did not respond as favorably, underscoring a critical problem: without predictive biomarkers, clinicians cannot reliably identify which patients will benefit. This diagnostic challenge fuels extensive research into biomarkers that can serve as a treatment compass 7 .
In clinical trials, only 38% of patients responded to lenvatinib-pembrolizumab combination therapy.
Researchers are exploring biomarkers from various sources, including peripheral blood and the tumor microenvironment itself. Two categories show particular promise: systemic inflammatory markers and specific immune cell populations.
One of the most accessible biomarkers comes from a simple complete blood count. The neutrophil-to-lymphocyte ratio (NLR) measures the balance between different types of white blood cells and reflects the body's systemic inflammatory state.
A 2025 study investigated NLR in 25 patients with advanced endometrial cancer receiving lenvatinib/pembrolizumab. The findings were striking:
This simple blood marker potentially offers an effective, non-invasive way to stratify patients before beginning treatment.
| NLR Group | Progression-Free Survival | Statistical Significance |
|---|---|---|
| NLR < 5.39 | 13.5 months (median) | p = 0.023 |
| NLR ≥ 5.39 | 3.0 months (median) |
While blood-based biomarkers are convenient, the tumor microenvironment—the ecosystem surrounding cancer cells—holds crucial information. A 2024 study analyzed tumor samples from 28 patients with advanced endometrial cancer before they started lenvatinib/pembrolizumab treatment, focusing on specific immune cells 2 .
The research revealed that the composition of immune cells within tumors, particularly B lymphocytes, strongly correlates with treatment response:
These findings highlight that the immune contexture of the tumor, especially the presence of B cells, plays a critical role in determining the success of immunotargeted therapy.
| Biomarker | Responders | Non-Responders | Predictive Power |
|---|---|---|---|
| CD20+ B lymphocytes | 0.24% | 0.08% | Sensitivity: 85.71%, Specificity: 70.59% |
| CD8/CD20 lymphocyte ratio | 1.44 arbitrary units | 19.00 arbitrary units | Sensitivity: 85.71%, Specificity: 85.71% |
While the aforementioned studies identified positive biomarkers, a crucial 2024 analysis from the KEYNOTE-146 clinical trial asked a different question: Do established immunotherapy biomarkers predict response to the lenvatinib-pembrolizumab combination? This experiment represents one of the most comprehensive biomarker studies for this therapy to date 9 .
Contrary to expectations, this rigorous analysis found that none of the biomarkers showed a statistically significant association with treatment response:
| Biomarker Category | Specific Examples | Finding |
|---|---|---|
| Gene Expression Profiles | T-cell-inflamed GEP, Angiogenesis, Hypoxia | No significant association with ORR or PFS |
| Tumor Mutational Burden | TMB (≥175 or <175 mutations/exome) | Response seen across all TMB levels |
| Specific Gene Mutations | PIK3CA, PTEN, TP53 | No correlation with treatment outcome |
These negative findings are scientifically crucial. They suggest that the combination of lenvatinib and pembrolizumab may work through broader mechanisms than pembrolizumab alone, potentially overcoming the immunological limitations of "cold" tumors that would not respond to immunotherapy alone.
The authors concluded that the established biomarkers for immunotherapy lack clinical utility for this specific combination, highlighting the need to discover entirely new predictive models 9 .
To conduct the critical research highlighted above, scientists rely on specialized reagents and tools. The following table details key components of the biomarker researcher's toolkit.
| Reagent/Tool | Primary Function | Application in Research |
|---|---|---|
| Multiplex Immunofluorescence | Labels multiple protein markers on a single tissue section | Simultaneous visualization of CD8+ T-cells, CD20+ B-cells, FoxP3+ T-regs in tumor microenvironment 2 |
| RNA-sequencing | Measures gene expression levels across the entire genome | Analysis of gene signatures like T-cell-inflamed GEP or angiogenesis pathways 9 |
| Whole-Exome Sequencing | Identifies mutations across all protein-coding genes | Evaluation of tumor mutational burden and specific mutations in genes like PIK3CA and PTEN 9 |
| CustomMAP Immunoassay Panels | Quantifies multiple protein biomarkers in serum | Measurement of cytokine and angiogenic factor levels in blood samples |
| Flow Cytometry | Analyzes physical and chemical characteristics of cells | Enumeration and characterization of lymphocyte populations in peripheral blood 1 |
Blood-based biomarker showing predictive value for lenvatinib response
CD20+ B cells and CD8/CD20 ratio identified as predictors
Established immunotherapy biomarkers found ineffective for combination therapy
Composite scores, spatial transcriptomics, longitudinal monitoring
The search for reliable biomarkers continues to evolve. While simple measures like NLR show promise and complex gene signatures have so far disappointed, research is exploring other avenues:
Combining multiple biomarkers into a single score may improve predictive power. This approach has shown promise in renal cell carcinoma treated with lenvatinib-everolimus .
This emerging technology allows scientists to see which genes are being turned on and where exactly they're active within the tumor microenvironment, providing unprecedented detail.
Tracking biomarker changes during treatment rather than relying solely on baseline measurements may provide dynamic indicators of response.
Machine learning algorithms can integrate diverse data types to identify complex patterns that may predict treatment response more accurately than individual biomarkers.
The journey to precision medicine for endometrial cancer patients continues, with each study bringing us closer to the goal of prescribing the right treatment, to the right patient, at the right time.
The discovery of reliable biomarkers for lenvatinib response could: