The GP78-Spatial Aggressiveness Index (GP78-SAI): A race-independent AI-driven biomarker for precision oncology.

Abstract

e12566 Background: GP78 (AMFR) protein expression is associated with breast cancer (BC) progression and poor outcomes (PMC9310521), but traditional bulk protein quantification ignores spatial architecture within the tumor microenvironment (TME). We developed the GP78-Spatial Aggressiveness Index (GP78-SAI), and AI-integrated, spatially informed metric derived from multi-gene GP78 protein-based signatures. We hypothesized that the GP78-SAI would outperform genomics-markers, demonstrate race-independent prognostic utility, and be feasibly for use in resource-limited clinical settings. Methods: GP78-SAI was computed using spatial autocorrelation modeling across 651 breast tumor tissue microarray cores from patients with stage II-III primary invasive breast cancer treated with curative-intent surgery (diagnosed between 2005-2020) and validated using CosMx 6K plex data. Tumor cell neighborhoods were defined using k-nearest neighbors (k = 10) with row-standardized spatial weights. Ancestry-optimized multi-gene GP78 cytoplasmic signatures were derived for Black/African American (AA) compared to White/European American (EA) patient cohorts and applied across the same datasets. The resulting scoring system was designed to be computationally efficient and compatible with standard immunohistochemistry workflows. Results: GP78-SAI robustly stratified tumor aggressiveness, with significantly higher spatial clustering in high-grade (Grade 3) tumors versus low-grade (Grade 1/2) tumors. Using AA-optimized signatures, mean GP78-SAI was 0.196 in high-grade (HG) versus 0.136 in low-grade tumors (adjusted P = 0.0003); using EA-optimized signatures, mean GP78-SAI was 0.173 versus 0.126, respectively (adjusted P = 0.0064). In contrast, the single-gene AMFR marker demonstrated minimal spatial organization (mean ≈0.02) and no association with tumor grade (adjusted P = 0.77). Despite ancestry-specific signature derivation, GP78-SAI demonstrated consistent, race-independent discriminatory performance. Conclusions: GP78-SAI is a novel spatial aware AI biomarker that captures an aggressive tumor organization phenotype not detected by conventional molecular assays. By translating spatial protein architecture into a quantifiable metric, GP78-SAI improves tumor risk stratification while supporting equitable treatment prioritization. These findings support clinical integration of GP78-SAI into digital pathology workflows to advance precision oncology across diverse patient populations.