Accurate identification of a tumor’s site of origin is particularly critical for cancers of unknown primary (CUP), since therapy selection depends on the inferred origin. Whole-slide histopathology images (WSIs) provide rich morphological cues but are known to suffer from acquisition-driven domain shift. Genomic alteration profiles provide complementary molecular evidence about tumor lineage and biology, though they can also vary with sample processing, coverage, and variant-calling choices. Since the two modalities reflect different aspects of tumor biology and are subject to distinct sources of variation, combining them can reduce reliance on any single, potentially biased signal. In this work, we present a multimodal primary site classifier that integrates WSI representations with a compact mutation-status profile derived from a 92-gene panel. Starting from the TOAD framework for tumor origin assessment, we modified the model to: (i) focus exclusively on tumor-site classification, removing the primary-vs-metastatic discrimination task, and (ii) incorporate a binary vector describing the mutation status of 92 genes. Experiments on matched histopathology–genomics cases from TCGA demonstrate a strong interaction between modality utility and resolution domain shift: for a test set composed of digital slides with out-of-distribution microns-per-pixel (mpp), the genomics-only model is substantially more robust than the WSI-only model (Top-1 accuracy 0.51 vs. 0.27), whereas in-distribution mpp favors histopathology, and multimodal fusion yields the best performance (0.90 Top-1 accuracy).