Deep learning–based histologic classifiers enable molecular subtyping of metastatic prostate cancer
Zhijun Chen, Erolcan Sayar, Daniela Guevara, Helen Richards, Haoyue Zhang, Radhika A. Patel, Agnes C. Gawne, Lucas J. Liu, Ilsa Coleman, Ruth Dumpit, Colm Morrissey, Michael T. Schweizer, Ruben Raychaudhuri, Laura S. Graham, Evan Y. Yu, Heather H. Cheng, Chien-Kuang C. Ding, Yuzhuo Wang, Peter Choyke, Baris Turkbey, Chantal Chanel-Vos, Christina Fedorov, John R. Otilano, Troy Kane, Jyothi Manohar, Michael Sigouros, Jones T. Nauseef, Ana Molina, David Nanus, Scott T. Tagawa, Juan Miguel Mosquera, Himisha P. Beltran, Ruth Etzioni, Peter S. Nelson, Rama Soundararajan, Ana M. Aparicio, Cora N. Sternberg, Michael C. Haffner, Stephanie A. Harmon
Zhijun Chen, Erolcan Sayar, Daniela Guevara, Helen Richards, Haoyue Zhang, Radhika A. Patel, Agnes C. Gawne, Lucas J. Liu, Ilsa Coleman, Ruth Dumpit, Colm Morrissey, Michael T. Schweizer, Ruben Raychaudhuri, Laura S. Graham, Evan Y. Yu, Heather H. Cheng, Chien-Kuang C. Ding, Yuzhuo Wang, Peter Choyke, Baris Turkbey, Chantal Chanel-Vos, Christina Fedorov, John R. Otilano, Troy Kane, Jyothi Manohar, Michael Sigouros, Jones T. Nauseef, Ana Molina, David Nanus, Scott T. Tagawa, Juan Miguel Mosquera, Himisha P. Beltran, Ruth Etzioni, Peter S. Nelson, Rama Soundararajan, Ana M. Aparicio, Cora N. Sternberg, Michael C. Haffner, Stephanie A. Harmon
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Research Article Clinical Research Oncology

Deep learning–based histologic classifiers enable molecular subtyping of metastatic prostate cancer

Abstract

Metastatic prostate cancer is a clinically and molecularly heterogeneous disease. Under the selective pressure of androgen receptor–directed (AR-directed) therapies, resistant phenotypes frequently emerge, posing significant diagnostic and therapeutic challenges. Neuroendocrine prostate cancer (NEPC) is a clinically important phenotype characterized by lineage plasticity, neuroendocrine features, visceral metastases, and poor prognosis. Accurate diagnosis of NEPC remains difficult owing to its histologic and molecular complexity but has high clinical relevance. In this study, we developed a deep learning model that leverages interpretable cellular features to improve feature extraction from H&E-stained tissue sections (NEURAL-PC). By incorporating a multiple-instance learning framework, NEURAL-PC enables robust NEPC classification solely from H&E tumor images, achieving an area under the receiver operating characteristic curve of 0.921 in independent external validation. In addition to its diagnostic utility, NEURAL-PC provides prognostic information that enables further subclassification of advanced prostate cancer across diverse datasets, supporting its strong prognostic value and generalizability. Broadly, our work highlights a hybrid approach that integrates features across different domains, offering a promising strategy for developing reliable deep learning tools in pathology. Built on this framework, NEURAL-PC represents an extensively validated diagnostic and prognostic model for advanced prostate cancer.

Authors

Zhijun Chen, Erolcan Sayar, Daniela Guevara, Helen Richards, Haoyue Zhang, Radhika A. Patel, Agnes C. Gawne, Lucas J. Liu, Ilsa Coleman, Ruth Dumpit, Colm Morrissey, Michael T. Schweizer, Ruben Raychaudhuri, Laura S. Graham, Evan Y. Yu, Heather H. Cheng, Chien-Kuang C. Ding, Yuzhuo Wang, Peter Choyke, Baris Turkbey, Chantal Chanel-Vos, Christina Fedorov, John R. Otilano, Troy Kane, Jyothi Manohar, Michael Sigouros, Jones T. Nauseef, Ana Molina, David Nanus, Scott T. Tagawa, Juan Miguel Mosquera, Himisha P. Beltran, Ruth Etzioni, Peter S. Nelson, Rama Soundararajan, Ana M. Aparicio, Cora N. Sternberg, Michael C. Haffner, Stephanie A. Harmon

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Figure 3

Representative visual NEURAL-PC predictions and spatial IHC association.

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Representative visual NEURAL-PC predictions and spatial IHC association....
(A) Representative test core images from UW-RA TMA set. High-power view of H&E morphology, NEURAL-PC probability maps, and corresponding AR, SYP, and INSM1 IHC stains. Top: A true negative from AR positive (AR H-score: 160; SYP H-score: 0; INSM1 H-score: 10) with NEURAL-PC prediction < 0.01 and uniform negative/low heatmap. Middle: A mixed-phenotype true positive (AR H-score: 40; SYP H-score: 40; INSM1 H-score: 0) with NEURAL-PC prediction 0.83 and focal positivity in prediction heatmap. Bottom: A true positive from NE positive (AR H-score: 0; SYP H-score: 200; INSM1 H-score: 150) with NEURAL-PC prediction 0.93 and uniform positive prediction heatmap. (B) Representative images from PDX validation set. NE prediction heatmaps from NEURAL-PC model are shown separately, along with AR, SYP, and INSM1 IHC stains. Top: AR-positive sample from baseline/untreated PC, correctly predicted as negative by NEURAL-PC (probability score 0.04) with uniform low probability. Middle: Mixed phenotype from transitionary phase (week 24), predicted as negative by NEURAL-PC (0.154) with focal AR and NE IHC marker positivity. Bottom: NE-positive sample from post-castration (week 46) PDX model, correctly predicted as positive by NEURAL-PC (probability score 0.851) with uniform positive prediction map. (C) Representative true-positive classification from UW-MATCH cohort. NEURAL-PC probability map reflects uniformly positive predictions across the tissue sample, with overall slide classification score 0.948. Homogeneous morphology is observed across the sample (tile regions shown below). (D) Representative true-negative classification from UW-MATCH cohort, with overall NEURAL-PC classification score of 0.055. The majority of the cancer burden shows adenocarcinoma-like morphology (left and middle tiles). Notably, a small proportion of focal areas demonstrate higher tile-level probabilities for containing NEPC in the NEURAL-PC heatmap (right tile region). Digital WSIs were originally acquired at 0.25 μm/pixel (×40 magnification); scale bars are shown for each image.