NEIM-04 LEVERAGING NOVEL NEUROIMAGING TECHNIQUES TO LINK BRAIN METASTASES AND LOCAL GENE EXPRESSION. (5th August 2022)

• Record Type: Journal Article
• Authors: Germann, Jurgen; Loh, Aaron; Dudek, Cain; Chow, Clement; Boutet, Alexandre; Lozano, Andres; Mansouri, Alireza
• Is Part Of: Neuro-oncology advances. Volume 4(2022)Supplement 1
• Page Start: i18
• Page End: i18
• Publication Date: 2022-08-05
• Subjects: 616.99481
• DOI: 10.1093/noajnl/vdac078.071
• Languages: English
• ISSNs: 2632-2498

Abstract

Abstract: Metastases are the most prevalent adult brain tumour, most commonly arising from lung, breast, or melanoma primaries. Studies have suggested that different primary tumor types may have predilection for seeding to specific brain regions. One hypothesis is that the interaction of the genomic environment within specific brain region(s) and seeding tumor cells is ideal for supporting this process. The recent availability of neuroimaging based transcriptomic atlases make it feasible to test this hypothesis. In this proof-of-concept study, we leverage the Allen atlas to evaluate whether variance in location among different tumour subtypes can be explained by normative gene expression. Manual segmentation was done on contrast-enhanced T1-weighted MRIs in 31 patients with brain metastases and known primary tumour [breast (n=7), lung (n=14), genitourinary (n=5) and melanoma (n=5)]. Segmented lesions were transformed to template brain space. First, odds-ratio maps were created for each primary tumour subtype. These maps delineate brain regions that were preferentially engaged by each subtype. Consistent with prior literature, odds-ratio maps demonstrated a preference for metastases to seed to different brain regions according to primary tumour subtype, e.g. lung - cerebellum, melanoma - frontal and temporal lobes. Next, mapping our lesions on the Allen atlas of normative gene expression, we identified significant (p<0.01) differences in the local expression of certain genes– such as LEPROT and ITPKA – related to the spatial pattern of breast, lung, genitourinary, and melanoma. This novel approach integrates imaging and transcriptomic techniques that could be used towards an improved understanding of neuro-oncologic processes. Crucially, this approach would allow investigators to leverage conventional anatomical images – acquired as part of a patient's normal clinical course and in the absence of tissue samples – to better understand cancer mechanics. This has potential ramifications for therapeutic decision-making. Large-scale prospective studies are underway.