Langworthy Diversity Supplement: Image-based models of tumor-immune dynamics in glioblastoma

ABSTRACT Glioblastoma Multiforme (GBM) is the most common of all gliomas with a median survival 14-18 months, despite aggressive treatment regimens.

Immunotherapy is emerging as a promising method to treat cancer; however, we are not able to identify early response or predict who will respond. These uncertainties pose serious challenges to being able to effectively apply immunotherapeutic approaches.

While biopsies are the most reliable way to assess the immunological landscape within the tumor, we are limited both spatially and temporally in the number of biopsies we can obtain, particularly for brain tumor patients.

The heterogeneity of the tumor-immune landscape across patients suggests that a patient-specific approach will be required to accurately assess each patient?s individual tumor-immune environment and the evolution thereof.

As part of the Parent Grant, we will use non-invasive imaging, image-guided biopsies, computational modeling, and artificial intelligence to bridge spatial and temporal scales and predict the abundance of glioma associated microglia/macrophages (GAMMs) comprising each magnetic resonance image (MRI) at the voxel level.

Linking the MRI to the biological heterogeneity using radiomics approaches provides an opportunity to individualize our understanding of the tumor-immune environment.

Specifically, for this supplement, we will use the predictive tumor-immune maps to develop an immunotherapy response metric termed GAMMs Days Gained (GDG), which is based on the existing Days Gained metric. GDG will be used to evaluate the GAMM population changes with therapy as depicted by the predictive map.

We expect that the GDG will aid in understanding who will respond based on early predictive map changes.

Additionally, the GDG metric will be compared to results from other immunotherapy response metrics, including the standard immunotherapy response assessment in neuro-oncology (iRANO) criteria.