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Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers

Fuente: arXiv

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Bibliographic Details
Main Authors:	Pleasure, Mara, Redekop, Ekaterina, Ilango, Dhakshina, Wang, Zichen, Ivezic, Vedrana, Flores, Kimberly, Laklouk, Israa, Makker, Jitin, Fishbein, Gregory, Sisk, Anthony, Speier, William, Arnold, Corey W.
Format:	Preprint
Published:	2026
Subjects:	Quantitative Methods Machine Learning
Online Access:	Acceder al recurso
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_version_	1866908756905820160
author	Pleasure, Mara Redekop, Ekaterina Ilango, Dhakshina Wang, Zichen Ivezic, Vedrana Flores, Kimberly Laklouk, Israa Makker, Jitin Fishbein, Gregory Sisk, Anthony Speier, William Arnold, Corey W.
author_facet	Pleasure, Mara Redekop, Ekaterina Ilango, Dhakshina Wang, Zichen Ivezic, Vedrana Flores, Kimberly Laklouk, Israa Makker, Jitin Fishbein, Gregory Sisk, Anthony Speier, William Arnold, Corey W.
contents	Current histopathological grading of prostate cancer relies primarily on glandular architecture, largely overlooking the tumor microenvironment. Here, we present PROTAS, a deep learning framework that quantifies reactive stroma (RS) in routine hematoxylin and eosin (H&E) slides and links stromal morphology to underlying biology. PROTAS-defined RS is characterized by nuclear enlargement, collagen disorganization, and transcriptomic enrichment of contractile pathways. PROTAS detects RS robustly in the external Prostate, Lung, Colorectal, and Ovarian (PLCO) dataset and, using domain-adversarial training, generalizes to diagnostic biopsies. In head-to-head comparisons, PROTAS outperforms pathologists for RS detection, and spatial RS features predict biochemical recurrence independently of established prognostic variables (c-index 0.80). By capturing subtle stromal phenotypes associated with tumor progression, PROTAS provides an interpretable, scalable biomarker to refine risk stratification.
format	Preprint
id	arxiv_https___arxiv_org_abs_2601_06360
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers Pleasure, Mara Redekop, Ekaterina Ilango, Dhakshina Wang, Zichen Ivezic, Vedrana Flores, Kimberly Laklouk, Israa Makker, Jitin Fishbein, Gregory Sisk, Anthony Speier, William Arnold, Corey W. Quantitative Methods Machine Learning Current histopathological grading of prostate cancer relies primarily on glandular architecture, largely overlooking the tumor microenvironment. Here, we present PROTAS, a deep learning framework that quantifies reactive stroma (RS) in routine hematoxylin and eosin (H&E) slides and links stromal morphology to underlying biology. PROTAS-defined RS is characterized by nuclear enlargement, collagen disorganization, and transcriptomic enrichment of contractile pathways. PROTAS detects RS robustly in the external Prostate, Lung, Colorectal, and Ovarian (PLCO) dataset and, using domain-adversarial training, generalizes to diagnostic biopsies. In head-to-head comparisons, PROTAS outperforms pathologists for RS detection, and spatial RS features predict biochemical recurrence independently of established prognostic variables (c-index 0.80). By capturing subtle stromal phenotypes associated with tumor progression, PROTAS provides an interpretable, scalable biomarker to refine risk stratification.
title	Computational Mapping of Reactive Stroma in Prostate Cancer Yields Interpretable, Prognostic Biomarkers
topic	Quantitative Methods Machine Learning
url	https://arxiv.org/abs/2601.06360