Abstract Venetoclax, a highly selective oral BCL-2 inhibitor, has demonstrated efficacy in patients with t(11;14) multiple myeloma (MM). Fluorescence in situ hybridization (FISH) is the mainstay methodology to determine t(11;14) status, but requires significant quantities of bone marrow tissue or aspirate and is low-throughput. Herein, we: (i) describe an artificial intelligence (AI)-based model that accurately predicts t(11;14) status from routine H&E-stained MM whole slide images (WSIs) and (ii) demonstrate that the biological signal underlying the model predictions is driven by regions containing the lymphoplasmacytic phenotype of t(11;14) positive MM cells. H&E-stained MM WSIs (n=231) with known t(11;14) status were split into training/validation/test sets (60/20/20), and CNN-based tissue and cell segmentation models were trained from expert pathologist annotations to identify high density regions of MM cells. Additive multiple instance learning (aMIL) models with 5-fold cross validation were then trained using embeddings from pathology universal transformer (PLUTO) foundation model backbone [1], to predict t(11;14) status. To interpret the biological signal underlying aMIL model predictions, nuclear features [2] for MM cells and sparse autoencoder (SAE) dimensions [3] (based on PLUTO embeddings) were extracted from the model-predicted t(11;14) positive and negative regions. Mann-Whitney U test was used to compare distribution of nuclear features between t(11;14) positive and negative regions. Activation frequency (AF) delta (AF in positive images - AF in negative images) was used to find the SAE dimensions most active in t(11;14) positive regions. Validation and held-out test sets were leveraged to evaluate the aMIL model’s performance for predicting t(11;14) status, resulting in AUROC of 0.813 and 0.849, respectively. Upon investigating the regions predicted as t(11;14) positive, analyses revealed that these contained MM cell nuclei with higher circularity, solidity and less variability in shape and size (p-values<0.001 for all comparisons) compared to those predicted as t(11;14) negative. Additionally, SAE dimensions capturing circular cells (SAE-2202) and lymphocytes (SAE-1567, SAE-1355) were significantly more active in model-predicted t(11;14) positive images compared to the predicted negative images (AF delta- SAE-2202: 0.73, SAE-1567: 0.61, SAE-1355: 0.57). t(11;14) status can be predicted with high accuracy using routine H&E WSI. Model predictions rely on biologically meaningful signals based on regions containing MM cells with lymphoplasmacytic morphology. This technology has the potential to facilitate screening for t(11;14)-directed MM clinical trials.
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