Augur: Interpretable Breast Cancer Molecular Subtyping from H&E Whole-Slide Images via Attention-Based Multiple-Instance Learning with Auxiliary Mutational-Signature Supervision

Abstract

Motivation. PAM50 molecular subtype guides breast-cancer treatment but requires sequencing or multi-gene assays that are costly, slow, and often unavailable. Routine H&E histology, by contrast, is universal, and because subtype-driving alterations leave morphological traces, the image itself may carry usable signal. We ask whether H&E whole-slide images can predict molecular subtype as a low-cost triage that prioritizes which patients to send for confirmatory molecular testing, provided each prediction carries interpretable visual evidence rather than a black-box score.

Approach. We present Augur, which predicts PAM50 subtype from a whole-slide image and localizes the tissue behind each call. Because slides are gigapixel with only slide-level labels, Augur adopts the standard weakly-supervised decomposition into two stages: a tile encoder first learns histology features through multi-task pretraining, then an attention-based multiple-instance-learning (MIL) aggregator pools tiles into a slide-level prediction. Our aggregator, DualCLAM, builds on CLAM, an existing model that scores tiles with per-class attention to form a slide representation and adds an instance-clustering loss that refines tile features from slide-level labels alone. DualCLAM augments it with auxiliary mutational-signature regression heads that contribute a molecular training signal and per-signature spatial explanations.

Results. On the TCGA-BRCA cohort under 5-fold stratified cross-validation, the encoder's pretext design dominates accuracy: it adds 0.077 macro AUROC over a segmentation-only baseline, roughly 5 times the effect of any MIL-side change. The best configuration, the four-pretext encoder paired with a CNV signature-regression head, reaches macro AUROC 0.7038. Never trained on annotations, the predicted-subtype attention maps align with pathologist-delineated tumor outlines on held-out slides. The auxiliary signature regression itself, however, is weak: the COSMIC exposure vectors are sparse, high-dimensional, and unevenly covered, ill-suited to the regression objective we used and likely better cast as multilabel classification. Even so, its attention heads still surface a small, stable subset of signatures (CN17, ID14, SBS33, SBS39).

Conclusions. H&E whole-slide images can yield interpretable, spatially-grounded subtype predictions at a level useful for triage. Accuracy hinges on the tile encoder's representation, so encoder pretraining warrants the most effort, but the aggregator should be redesigned, not abandoned: its weak signature branch points to two concrete fixes, predicting only a curated subset of signatures to shrink the decoder and recasting its objective from regression to multilabel classification. By pairing each prediction with visual and molecular evidence, Augur offers a low-cost, auditable pre-screen that prioritizes confirmatory molecular testing.