One Step Closer to Accurate Prediction of Antibody Delivery in Tumors
Researchers develop a non-invasive tool to measure antibody penetration of tumors
In the last decade, antibodies have become an essential part of cancer treatment. For instance, antibodies are widely used in immunotherapies, such as checkpoint blockade, and in targeted payload delivery of radio and chemotherapy.
One major challenge with antibody treatments is that to be effective, these antibodies need to penetrate and reach all the tumor tissue. Yet, we are currently unable to accurately assess how much of the antibody makes its way into the tumor, and how deeply it penetrates the tissue. Predicting antibody delivery is crucial because it affects therapy efficacy—it’s crucial for treatments to be able to reach the center of the tumor.
A study published in June in Clinical Cancer Research by researchers at the Stanford University School of Medicine sheds some light on this issue. The researchers found that tumor size is the primary determinant of antibody dissemination into a tumor. Based on these findings, they were able to separate patients with high from those with low antibody penetration using contrast-enhanced MRI images.
Tumor size defines antibody penetration
For the study, patients with head and neck cancers intravenously received a monoclonal antibody called Panitumumab labeled with a fluorescent dye before surgical removal of primary tumors and lymph node metastases. Panitumumab binds EGFR, an antigen widely expressed in head and neck cancers.
They determined the fluorescence concentration per gram of tissue and assessed localization of fluorescence in microscopic tissue slides. They reconstructed the tumor in 3D by combining scanned images of multiple tissue slides, allowing them to assess how deep in the tumor tissue the fluorescent signal reached.
Most fluorescence was found at the tumor edges, and expression was limited in the deeper, central parts of the tissue. In larger tumors, the antibodies could not penetrate the center as well as they did in smaller tumors, and this was true for both primary tumors and metastases. However, primary tumors had a more limited antibody spread than lymph node metastases. This was likely caused by the larger size of primary tumors and reduced blood flow into the deeper parts of the tissue due to disorganized, incomplete vasculature.
MRI imaging to predict antibody distribution
To develop a non-invasive diagnostic tool to measure antibody penetration, the researchers correlated antibody uptake with tumor size on contrast-enhanced MRI images. Based on the tumor volume, they were able to stratify patients into low and high antibody uptake groups with a sensitivity of 100 percent and specificity of 87.5 percent. When they looked at MRI contrast heterogeneity to determine whether it reflected the fluorescent patterns seen in the tumors, they could predict with a sensitivity of 88.9 percent and a specificity of 60 percent how well the antibody spread in each tumor. The researchers concluded that MRI images predict relatively well how much of the antibody ends up in the tumor, and how well it penetrates the tissue’s deeper parts.
Personalized dosing of antibody treatment
Currently, plasma drug concentrations are measured as a surrogate for drug levels in the tumor, and the patient’s body weight is used to determine the dose. However, these parameters are likely not representative of the actual situation in the tumor.
Based on the new findings, using contrast-enhanced MRI imaging to predict antibody penetration might be a better way to define a personalized dosing regimen for cancer patients.
Antibody distribution is not the only factor that determines the efficacy of a treatment. Another major factor is the presence of the antigen on all cells in the tumor, as an antibody can only exert its effects when it can bind its target antigen. In many cancer types, antigen expression is limited to a subset of tumor cells. Thus, it would be valuable to confirm these data in cancer types with a more heterogeneous antigen distribution.